Integrated In Silico Analyses Identify PUF60 and SF3A3 as New Spliceosome-Related Breast Cancer RNA-Binding Proteins

Simple Summary Globally, breast cancer (BC) is the most common cancer in women. Although numerous studies have attempted to address this worldwide health problem, it has not yet been possible to understand cancer in its entirety, mainly because most of the investigations have been focused on traditional molecular traits of DNA. Thus, new characteristics of breast tumorigenesis must be tackled, such as RNA-binding proteins (RBPs), which are crucial regulators of important cellular processes. To identify novel breast cancer RNA-binding proteins, we integrated several bioinformatic resources derived from experimentation on BC patient samples and cell lines. Consequently, we identified five putative breast cancer RNA-binding proteins (PUF60, TFRC, KPNB1, NSF, and SF3A3) showing strong tumorigenic characteristics. Supplementary investigation of the molecular and cellular functions of these proteins identified PUF60 and SF3A3 as new spliceosome-related breast cancer RNA-binding proteins. Further experimentation should center on these five RBPs to identify their role in breast tumorigenesis and potentially discover new druggable targets. Abstract More women are diagnosed with breast cancer (BC) than any other type of cancer. Although large-scale efforts have completely redefined cancer, a cure remains unattainable. In that respect, new molecular functions of the cell should be investigated, such as post-transcriptional regulation. RNA-binding proteins (RBPs) are emerging as critical post-transcriptional modulators of tumorigenesis, but only a few have clear roles in BC. To recognize new putative breast cancer RNA-binding proteins, we performed integrated in silico analyses of all human RBPs (n = 1392) in three major cancer databases and identified five putative BC RBPs (PUF60, TFRC, KPNB1, NSF, and SF3A3), which showed robust oncogenic features related to their genomic alterations, immunohistochemical changes, high interconnectivity with cancer driver genes (CDGs), and tumor vulnerabilities. Interestingly, some of these RBPs have never been studied in BC, but their oncogenic functions have been described in other cancer types. Subsequent analyses revealed PUF60 and SF3A3 as central elements of a spliceosome-related cluster involving RBPs and CDGs. Further research should focus on the mechanisms by which these proteins could promote breast tumorigenesis, with the potential to reveal new therapeutic pathways along with novel drug-development strategies.

Simple Summary: Globally, breast cancer (BC) is the most common cancer in women. Although numerous studies have attempted to address this worldwide health problem, it has not yet been possible to understand cancer in its entirety, mainly because most of the investigations have been focused on traditional molecular traits of DNA. Thus, new characteristics of breast tumorigenesis must be tackled, such as RNA-binding proteins (RBPs), which are crucial regulators of important cellular processes. To identify novel breast cancer RNA-binding proteins, we integrated several bioinformatic resources derived from experimentation on BC patient samples and cell lines. Consequently, we identified five putative breast cancer RNA-binding proteins (PUF60, TFRC, KPNB1, NSF, and SF3A3) showing strong tumorigenic characteristics. Supplementary investigation of the molecular and cellular functions of these proteins identified PUF60 and SF3A3 as new spliceosome-related breast cancer RNA-binding proteins. Further experimentation should center on these five RBPs to identify their role in breast tumorigenesis and potentially discover new druggable targets.
Abstract: More women are diagnosed with breast cancer (BC) than any other type of cancer. Although large-scale efforts have completely redefined cancer, a cure remains unattainable. In that respect, new molecular functions of the cell should be investigated, such as post-transcriptional regulation. RNAbinding proteins (RBPs) are emerging as critical post-transcriptional modulators of tumorigenesis, but only a few have clear roles in BC. To recognize new putative breast cancer RNA-binding proteins, we performed integrated in silico analyses of all human RBPs (n = 1392) in three major cancer databases and identified five putative BC RBPs (PUF60, TFRC, KPNB1, NSF, and SF3A3), which showed robust oncogenic features related to their genomic alterations, immunohistochemical changes, high interconnectivity with cancer driver genes (CDGs), and tumor vulnerabilities. Interestingly, some of these RBPs have never been studied in BC, but their oncogenic functions have been described in other cancer types. Subsequent analyses revealed PUF60 and SF3A3 as central elements of a spliceosomerelated cluster involving RBPs and CDGs. Further research should focus on the mechanisms by which these proteins could promote breast tumorigenesis, with the potential to reveal new therapeutic pathways along with novel drug-development strategies.

Introduction
Breast cancer (BC) is the leading cause of cancer-associated death (15%: 626,679 cases) and the most commonly diagnosed cancer (24%: 2,088,849 cases) among women worldwide [1]. BC is characterized by a complex interaction between environmental factors and biological traits, such as gene deregulation, hormone disruption, or ethnicity [2][3][4]. Despite treatment efforts, advanced BC with distant organ metastasis is considered to be incurable [2]. Therefore, a better understanding of BC's molecular processes is still pertinent to identifying new therapeutic targets. Current oncological research generates large-scale datasets that harbor essential aspects of tumor biology. For instance, the Cancer Genome Atlas (TCGA), with over 2.5 petabytes of data, has molecularly characterized over 20,000 patient samples covering 33 cancer types [5][6][7][8][9][10]. Additionally, the Cancer Dependency Map (DepMap) project, using loss-of-function genetic screens, has identified essential genes for cancer proliferation and survival ex vivo [11][12][13]. Additionally, the Human Protein Atlas (HPA) constitutes a comprehensive resource to explore the human proteome in healthy and tumoral human tissues [14][15][16]. Although these datasets have completely redefined cancer drug development, diagnosis, and treatment, additional fundamental features of oncogenesis, tumor growth, and dissemination remain to be discovered. In this respect, post-transcriptional regulation of tumorigenesis represents an understudied trait of cancer research [17].
RNA-binding proteins (RBPs) are particularly relevant due to their implication in every post-transcriptional step of gene expression: RNA splicing, transport, stability, translation, and localization. As a result, genomic alterations of these proteins lead to dysfunctional cellular processes, but only a few have defined functions in BC [18][19][20][21][22][23][24][25]. To date, 1393 RBPs have been experimentally identified in the human RNA interactome [26]. Despite efforts to understand their role in cancer [27,28], an integrated analysis of the aforementioned databases along with other in silico approaches is still missing for BC. To shed light on this matter, we analyzed and integrated RBPs genomic alterations, protein-protein interaction (PPI) networks, immunohistochemical profiles, and loss-of-function experiments to find new putative breast cancer RNA-binding proteins.

An Overview of RNA-Binding Protein Genomic Alterations in Breast Cancer
To globally assess the potential role of RBPs in BC, we performed complementary analyses, which are depicted in Figure 1.
Then, to evaluate the potential role of RBPs in BC versus well-known BC genes, we interrogated the Breast Invasive Carcinoma (TCGA, PanCancer Atlas) and Breast Cancer (CPTAC) [5][6][7][8][9][10]35] database for genomic alterations of RBPs (n = 1392), BC genes (n = 171) [31], and non-cancer genes (n = 170) [32] (Table 1). As shown in Figure 2A, both genomic alteration frequencies of RBPs and BC genes were significantly higher than the ones observed for non-cancer genes. Interestingly, RBPs present a similar degree of genomic alterations as BC genes (Figure 2A), highlighting the putative role of RPBs in BC. Then, to evaluate the potential role of RBPs in BC versus well-known BC genes, we interrogated the Breast Invasive Carcinoma (TCGA, PanCancer Atlas) and Breast Cancer (CPTAC) [5][6][7][8][9][10]35] database for genomic alterations of RBPs (n = 1392), BC genes (n = 171) [31], and non-cancer genes (n = 170) [32] (Table 1). As shown in Figure 2A, both genomic alteration frequencies of RBPs and BC genes were significantly higher than the ones observed for non-cancer genes. Interestingly, RBPs present a similar degree of genomic alterations as BC genes (Figure 2A), highlighting the putative role of RPBs in BC. To obtain insights into how these proteins are altered in BC, we cataloged their genomic alteration types. As shown in Figure 2B and (Table S2), most genomic alterations are related to an overrepresentation of the mRNA (68.7%) or gene loci (15.4%).

Identification of Highly Altered Breast Cancer RNA-Binding Proteins
To identify breast cancer-related RNA-binding proteins, we next interrogated the Network of Cancer Genes 6.0 (NCG6) [31] for RBP having known or predicted cancer driver roles. NCG6 harbors the most recent catalog of cancer driver genes (CDG) [31]. Thus, we identified 225 RBPs, 14 implicated in BC (2 oncogenes, 4 tumor suppressors, and 8 unknown), indicating that these proteins remain poorly studied in breast carcinogenesis, and 211 related to other cancer types (21 oncogenes, 24 tumor suppressors, and 166 unknown) ( Figure 3A, Table S3). No. However, MRPL13 is an ESR2 protein interactor in MCF7 cells [43] No 34

DCAF13 574
Yes. It is overexpressed in 171 primary breast tumors [44] Yes [45] 23 YWHAZ 532 Yes. Often amplified in BC [46], leading to increased glycolysis [47]. YWHAZ is also an ESR2 protein interactor [43] Yes 492 To obtain insights into how these proteins are altered in BC, we cataloged their genomic alteration types. As shown in Figure 2B and (Table S2), most genomic alterations are related to an overrepresentation of the mRNA (68.7%) or gene loci (15.4%).

Identification of Highly Altered Breast Cancer RNA-Binding Proteins
To identify breast cancer-related RNA-binding proteins, we next interrogated the Network of Cancer Genes 6.0 (NCG6) [31] for RBP having known or predicted cancer driver roles. NCG6 harbors the most recent catalog of cancer driver genes (CDG) [31]. Thus, we identified 225 RBPs, 14 implicated in BC (2 oncogenes, 4 tumor suppressors, and 8 unknown), indicating that these proteins remain poorly studied in breast carcinogenesis, and 211 related to other cancer types (21 oncogenes, 24 tumor suppressors, and 166 unknown) ( Figure 3A, Table S3).
To categorize putative RBPs implicated in tumor progression or suppression, we analyzed RBPs' genomic alterations based on their progressor or suppressor profiles. Tumor progressors tend to be overexpressed (mRNA upregulation or genomic amplification), while suppressors are downregulated (mRNA downregulation or genomic deletion) in malignant cells [57]. Gene mutations or fusions have been observed in both tumor progressors and suppressors. On this basis, we identified highly altered breast cancer RNAbinding proteins (Tables 1 and S2). Interestingly, 30% of all human RBPs interact with the tumor suppressor ESR2 ( Figure 3B) [43]. We also found known BC progressor and To categorize putative RBPs implicated in tumor progression or suppression, we analyzed RBPs' genomic alterations based on their progressor or suppressor profiles. Tumor progressors tend to be overexpressed (mRNA upregulation or genomic amplification), while suppressors are downregulated (mRNA downregulation or genomic deletion) in malignant cells [57]. Gene mutations or fusions have been observed in both tumor progressors and suppressors. On this basis, we identified highly altered breast cancer RNA-binding proteins (Tables 1 and S2). Interestingly, 30% of all human RBPs interact with the tumor suppressor ESR2 ( Figure 3B) [43]. We also found known BC progressor and suppressor proteins, such as DAP3 [18], MTDH [19], or CCAR2 [20], which validate our strategy (Tables 1 and S2). This analysis also reveals proteins that have not been related to tumorigenesis, and yet they are highly altered in BC (e.g., TFB2M, C1ORF131, or DDX19A) (Tables 1 and S2).
To further identify important RBPs implicated in BC, we analyzed RBPs' genomic alterations by subtype (Normal, LumA, LumB, Her2, and Basal) (Table S4) or staging (Stage I to IV) ( Table S5). As shown in Figure 3C, RBP genomic alterations found in the Basal subtype samples were statistically significant compared to other subtypes (p < 0.001). Similarly, RBP genomic alterations of Stage IV samples were statistically significant compared to other stages (p < 0.001) ( Figure 3D). Individually, some RBPs reached high frequencies of genomic alterations per subtype ( Figure 3C, Table S4) or stage ( Figure 3D, Table S5). For instance, ARF1, the most altered protein in Stage IV ( Figure 3D), has been shown to promote BC metastasis [58]; PARP1 has also been demonstrated to enhance metastasis not only in BC [59] but also in other cancer types [60]. In contrast, SCAMP3 and HEATR6, which present similar degrees of genomic alterations ( Figure 3D), have not been studied in BC.

RNA-Binding Proteins Interact with Well-Known Breast Cancer Proteins
Networking analysis has proved useful in identifying RNA regulons and crucial tumoral proteins [57]. On this basis, we next explored PPIs between RBPs (n = 1392) and well-known BC proteins (n = 171) [31] using the STRING database [61]. The interactions were obtained from experiments and databases; the interaction score was 0.9. This is the highest possible confidence of an interaction to be true based on all the available evidence. Thus, we identified 113 BC proteins interacting with 398 RBPs (Table S6). By narrowing down our analysis to experimental interactions only (Figure 4), we observed Biology 2022, 11, 481 7 of 18 two main networks around SF3B1 and CDC5L proteins. According to the g:Profiler [62], proteins interacting with SF3B1 are implicated in RNA splicing (P adj = 3.783 × 10 −34 ; GO:0000377) (p-value adjusted (P adj ) for multiple testing using the Benjamin-Hochberg method), while proteins connected to CDC5L are mainly involved in chromatin binding (P adj = 1.500 × 10 −2 ; GO:0003682). We also observed proteins with both BC and RNAbinding features present in the two main networks: SF3B1, CTNNA1, RBMX, and SPEN. Additionally, 18 RBPs interact with at least 1 BC protein. Thus, we identified RBPs that may have a putative role in BC's molecular pathways through PPIs. strategy (Tables 1 and S2). This analysis also reveals proteins that have not been related to tumorigenesis, and yet they are highly altered in BC (e.g., TFB2M, C1ORF131, or DDX19A) (Tables 1 and S2).
To further identify important RBPs implicated in BC, we analyzed RBPs' genomic alterations by subtype (Normal, LumA, LumB, Her2, and Basal) (Table S4) or staging (Stage I to IV) ( Table S5). As shown in Figure 3C, RBP genomic alterations found in the Basal subtype samples were statistically significant compared to other subtypes (p < 0.001). Similarly, RBP genomic alterations of Stage IV samples were statistically significant compared to other stages (p < 0.001) ( Figure 3D). Individually, some RBPs reached high frequencies of genomic alterations per subtype ( Figure 3C, Table S4) or stage ( Figure 3D, Table S5). For instance, ARF1, the most altered protein in Stage IV ( Figure 3D), has been shown to promote BC metastasis [58]; PARP1 has also been demonstrated to enhance metastasis not only in BC [59] but also in other cancer types [60]. In contrast, SCAMP3 and HEATR6, which present similar degrees of genomic alterations ( Figure 3D), have not been studied in BC.   [5][6][7][8][9][10]35] database, are displayed. Genomic alterations per subtype and per stage were corrected by the number of patients; a Mann-Whitney U test was used to compare genomic alterations between sets. All possible comparisons between sets present significant differences (p < 0.001) except Normal vs. Lum A subtypes; ns = not significant. value adjusted (Padj) for multiple testing using the Benjamin-Hochberg method), while proteins connected to CDC5L are mainly involved in chromatin binding (Padj = 1.500 × 10 −2 ; GO:0003682). We also observed proteins with both BC and RNA-binding features present in the two main networks: SF3B1, CTNNA1, RBMX, and SPEN. Additionally, 18 RBPs interact with at least 1 BC protein. Thus, we identified RBPs that may have a putative role in BC's molecular pathways through PPIs.

Identification of Differentially Expressed RNA-Binding Proteins in Breast Tumor Tissues
The Human Protein Atlas (HPA) constitutes [14][15][16] a major effort to address protein expression in healthy and tumoral human tissues. We, therefore, identified RBPs with a different protein expression profile in tumor breast tissues. To this end, we compared immunohistochemical levels (not detected, low, medium, and high) of 1212 available RBPs between normal and cancerous breast tissues ( Figure 5A, Table S6). Most RBPs presented common immunohistochemical levels between both breast tissues: not detected (n = 130), low (n = 52), medium (n = 366), and high (n = 72) ( Figure 5A). Moderate protein expression changes, defined by one level variation (e.g., not detected to low or medium to high), were observed in 406 RBPs.
To identify RBPs with highly altered protein expression profiles in tumor tissues, we categorized RBPs with a twofold variation level as upregulated or downregulated compared with normal tissues; thus, we identified 24 upregulated and 62 downregulated RBPs ( Figure 5A, Table S6). As expected, our approach revealed well-known BC proteins, such as KPNA2 [21] or G3BP2 [22], which validate our analysis. KPNA2 is highly expressed in BC tissues (7 out of 12 tumor samples are classified as high) ( Figure 5B, Table S6). On the contrary, G3BP2 expression is reduced in tumoral breast tissues ( Figure 5B, Table S6). We also observed two RBPs that have never been studied in BC, DARS2 (overexpressed) and SUPT6H (downregulated) ( Figure 5B, Table S6). different protein expression profile in tumor breast tissues. To this end, we compared im-munohistochemical levels (not detected, low, medium, and high) of 1212 available RBPs between normal and cancerous breast tissues ( Figure 5A, Table S6). Most RBPs presented common immunohistochemical levels between both breast tissues: not detected (n = 130), low (n = 52), medium (n = 366), and high (n = 72) ( Figure 5A). Moderate protein expression changes, defined by one level variation (e.g., not detected to low or medium to high), were observed in 406 RBPs.

Unraveling Putative Breast Cancer RNA-Binding Protein
Cancer-related RBPs control hundreds of tumor mRNAs, interact with well-known cancer driver proteins, and appear to be highly altered in cancer genomic databases and tumor tissues [57]. Therefore, we reasoned that the integration of our previous analyses could narrow down the identification of a potential breast cancer RNA-binding protein.

Unraveling Putative Breast Cancer RNA-Binding Protein
Cancer-related RBPs control hundreds of tumor mRNAs, interact with well-known cancer driver proteins, and appear to be highly altered in cancer genomic databases and tumor tissues [57]. Therefore, we reasoned that the integration of our previous analyses could narrow down the identification of a potential breast cancer RNA-binding protein.

PUF60 and SF3A3 are Central Elements of a Spliceosome-Related Network Involving RNA-Binding Proteins and Cancer Driver Genes
To better understand the cellular functions of these prioritized RBPs (TFRC, KPNB1, PUF60, NSF, and SF3A3) in cancer, we next interrogated the HumanNet v2 [81,82]. This tool allowed us to integrate these five RBPs into a disease gene network. We first obtained an initial network of 2231 interactions (Table S11). To narrow down the analysis to cancerrelevant interactions, we then filtered the network by CDGs (n = 202 genes) and RBPs (n = 125 genes) and used MCODE [41] to find protein complexes within the network; the largest one and more relevant was formed by 36 nodes and 591 edges. The CORUM [42] database identified 34 of these 36 proteins as a part of the spliceosome complex where PUF60 and SF3A3 are central elements interacting with several RBPs and the cancer driver

PUF60 and SF3A3 Are Central Elements of a Spliceosome-Related Network Involving RNA-Binding Proteins and Cancer Driver Genes
To better understand the cellular functions of these prioritized RBPs (TFRC, KPNB1, PUF60, NSF, and SF3A3) in cancer, we next interrogated the HumanNet v2 [81,82]. This tool allowed us to integrate these five RBPs into a disease gene network. We first obtained an initial network of 2231 interactions (Table S11). To narrow down the analysis to cancerrelevant interactions, we then filtered the network by CDGs (n = 202 genes) and RBPs (n = 125 genes) and used MCODE [41] to find protein complexes within the network; the largest one and more relevant was formed by 36 nodes and 591 edges. The CORUM [42] database identified 34 of these 36 proteins as a part of the spliceosome complex where PUF60 and SF3A3 are central elements interacting with several RBPs and the cancer driver gene (Figure 8). were integrated into a disease gene network (filtered by RBPs and CDGs) using the HumanNet v2 [81,82]. Spliceosome-related proteins and their interactions were determined using MCODE [41] and CORUM [42].

Discussion
Current oncological research generates large-scale datasets that contain undiscovered strategic features of molecular mechanisms underlying the growth and metastasis of tumors, and yet these databases are not fully exploited. Integrated in silico analyses of these data could therefore lead to the discovery of new cancer proteins.
We first revealed that RBPs are equally altered as well-known BC proteins ( Figure  2A); this was expected since many RBPs are highly altered across cancer types [28] and have been linked in silico to cancer-related cellular processes [83]. We found that most RBPs' genomic alterations in BC are mRNA upregulation (68.7%) and amplification (15.4%) ( Figure 2B). This probably will increase RBPs' cellular concentrations, leading to dysfunctional post-transcriptional processes.
To determine how many RBPs have been previously studied in BC, we analyzed the most recent catalog of CDG, NCG6 [31]. Only 14 RBPs were cataloged as BC driver genes ( Figure 3A). This indicates that RBPs have been poorly investigated in breast carcinogenesis. Thus, to identify new putative breast cancer RNA-binding proteins, we first explored their genomic alteration profiles associated with tumor progression or suppression (Ta- were integrated into a disease gene network (filtered by RBPs and CDGs) using the HumanNet v2 [81,82]. Spliceosome-related proteins and their interactions were determined using MCODE [41] and CORUM [42].

Discussion
Current oncological research generates large-scale datasets that contain undiscovered strategic features of molecular mechanisms underlying the growth and metastasis of tumors, and yet these databases are not fully exploited. Integrated in silico analyses of these data could therefore lead to the discovery of new cancer proteins.
We first revealed that RBPs are equally altered as well-known BC proteins ( Figure 2A); this was expected since many RBPs are highly altered across cancer types [28] and have been linked in silico to cancer-related cellular processes [83]. We found that most RBPs' genomic alterations in BC are mRNA upregulation (68.7%) and amplification (15.4%) ( Figure 2B). This probably will increase RBPs' cellular concentrations, leading to dysfunctional posttranscriptional processes.
To determine how many RBPs have been previously studied in BC, we analyzed the most recent catalog of CDG, NCG6 [31]. Only 14 RBPs were cataloged as BC driver genes ( Figure 3A). This indicates that RBPs have been poorly investigated in breast carcinogenesis. Thus, to identify new putative breast cancer RNA-binding proteins, we first explored their genomic alteration profiles associated with tumor progression or suppression (Tables 1 and S2). As expected, we identified well-known BC-progressor and -suppressor proteins, such as DAP3 [18], MTDH [19], or CCAR2 [20], which validate our strategy (Table 1). On the contrary, our strategy revealed RBPs that have not been associated with tumorigenesis, and yet they are highly altered in BC (e.g., TFB2M, C1ORF131, or DDX19A) ( Table 1). Interestingly, the most altered RBP in our analysis, MRPL13, has never been studied in cancer. MRPL13, along with other highly altered RBPs (Table 1), has only been shown to interact with ESR2, a tumor suppressor in breast and other cancer types [43]. This observation led us to investigate how many RBPs interact with ESR2; strikingly, we found that 30% of all RBPs interact with this receptor ( Figure 3B) [43]. ESR2 could probably exert its suppressive activity through post-transcriptional mechanisms involving several RBPs; nevertheless, more research is needed to understand this observation.
Second, to further characterize RBPs associated with BC subtypes and staging, we analyzed RBPs' genomic alterations ( Figure 3C,D). Interestingly, RBPs' genomic alterations gradually increased from the Normal to Basal subtype ( Figure 3C), i.e., from a low to high proliferation stage [2]. Concordantly, metastasized tumors (Stage IV) showed high frequencies of RBPs' genomic alterations compared to non-metastasized samples (Stage I to III) ( Figure 3D). It seems, therefore, that RBPs are acting as BC progressors rather than suppressors, which agrees with their genomic-alteration profiles ( Figure 2B). This analysis also revealed highly altered RBP per subtype or staging (Figure 3C,D; Tables S4 and S5), which could lead to the discovery of new clinical biomarkers or therapeutic targets. Indeed, SCAMP3 and HEATR6, which have not been studied in BC, presented similar degrees of genomic alterations ( Figure 3D) compared to well-known metastasis drivers, ARF1 [58] and PARP1 [59]. In hepatocellular carcinoma cells, SCAMP3 knockdown has been shown to suppress cell proliferation [84], while HEATR6 has never been associated with tumorigenesis. Thus, more research is needed to understand their role in BC.
Interaction networks are useful for identifying crucial tumoral proteins [57]. In this regard, by analyzing PPIs between RBP and well-known BC proteins, we identified SF3B1 and CDC5L at the core of two main networks ( Figure 4). While SF3B1 has been previously implicated in BC [85], CDC5L, which interacts with 14 BC proteins, has not been studied in this malignancy. However, CDC5L has been related to other cancer types, such as osteosarcoma [86] and prostate cancer [87].
We next exploited the HPA database [14][15][16] to identify differentially expressed RBPs in tumor breast tissues. We found 24 upregulated and 62 downregulated RBPs compared with normal tissues. Unsurprisingly, our analyses revealed RBPs that were already related to breast cancer. For instance, KPNA2, which has been known to enhance BC metastasis ex vivo [21], is highly expressed in BC tissues (7 out of 12 tumor samples are classified as high) ( Figure 5B, Table S6). On the contrary, G3BP2 expression is reduced in tumoral breast tissues ( Figure 5B, Table S6); accordingly, the loss of G3BP2 enhances tumor invasion and metastasis in vivo [22]. Interestingly, DARS2, which has never been related to BC, is upregulated in our analysis (10 out 12 tumor samples are classified as high) ( Figure 5B, Table S6) and has been associated with hepatocarcinogenesis [88], demonstrating its putative implication in BC. In addition, SUPT6H protein expression is diminished in breast tumoral tissues ( Figure 5B, Table S6) and has not been linked to this malignancy. Furthermore, SUPT6H knockdown is associated with DNA damage via the formation of RNA: DNA hybrids (R-loops) in HeLa cells [89], showing its possible role in breast tumorigenesis.
To identify essential RBPs for tumor survival, we next analyzed ex vivo loss-of-function screens, CERES [11] and DEMETER2 [12,13]. In toto, we identified 207 essential RBPs for tumor survival. This was expected since RBPs control every trait of RNA metabolism. However, only 59 were characterized as essential by both computational methods ( Figure 6A; Tables S8 and S9). Although CERES [11] and DEMETER2 [12,13] did not test all human RBPs, future therapeutic post-transcriptional BC research could be focused on these 59 RBPs. However, more investigation is needed to deeply understand their carcinogenic roles. We also revealed essential RBPs per BC molecular subtype ( Figure 6B) that could be analyzed to better understand subtype-related post-transcriptional processes.
In extending the scope of our previous analyses, we finally reasoned that the integration of all the databases examined could narrow down the identification of potential breast cancer RNA-binding proteins. As discussed before and depicted in Figures 2B and 3C,D, RBPs seem to act as cancer progressors rather than suppressors. Thus, we focused on RBPs with putative tumor progression profiles and distinguished 19 RBPs with tumorigenic characteristics according to our analyses ( Figure 7). As expected, most of them (13 out 19) have been described as BC tumor progressors, controlling different cellular processes such as migration, invasion, and metastasis. Interestingly, NSF, SF3A3, PRPF3, and MAGOHB have never been studied in cancer. While on the other hand, PUF60 has been associated with colon and non-small cell lung cancer [67,68], and PLEC has been shown to promote the migration and invasion of neck squamous cell carcinoma [80].
As depicted in Figure 7, we prioritized 5 RBPs according to our previous analyses. These putative BC progressor RBPs (PUF60, TFRC, KPNB1, NSF, and SF3A3) were integrated into a disease gene network to shed light on their molecular and cellular functions in cancer ( Figure 8). Thus, we obtained a very intricate network of 2231 interactions (Table S11), which emphasized the robust and complex network formed between RBP-RBP, RBP-CDG, and CDG-CDG. In addition to this complexity, some of these RBPs are also CDGs. Quattrone and Dassi already established that the RBP network is a hierarchical structure that is formed by clusters and chains that cooperate and compete on common target mRNAs controlling different cellular processes (e.g., splicing) [90]. This is also observed in our densely interconnected network, where PUF60 and SF3A3 are central elements of a spliceosome-related cluster involving RBPs and CDGs.

Conclusions
In sum, individual and integrated analysis of the aforementioned databases led us to identify RBPs that have never been studied in BC but displayed defined tumorigenic functions in other cancer types. Thus, based on their tumorigenic characteristics presented in this study and their roles in other cancer types, we identified five new putative breast cancer RBPs: PUF60, TFRC, KPNB1, NSF, and SF3A3. However, further research should focus on the mechanisms by which these proteins promote breast tumorigenesis, which holds the potential to discover new therapeutic pathways along with novel drug-development strategies.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/biology11040481/s1. Table S1: Gene sets analyzed in this study. Table S2: RNA-binding protein genomic alterations in invasive breast carcinoma (TCGA, PanCancer Atlas) and breast cancer (CPTAC). Table S3: RNA-binding proteins and their status in the Network of Cancer Genes 6.0 (NCG6). Table S4: RNA-binding protein genomic alterations in invasive breast carcinoma subtypes (TCGA, PanCancer Atlas) and breast cancer (CPTAC). Table S5: RNA-binding protein genomic alterations in invasive breast carcinoma stages (TCGA, PanCancer Atlas) and breast cancer (CPTAC). Table S6: STRING interactions between RNA-binding proteins and breast cancer proteins (experimental data and databases, interaction score: 0.9). Table S7: RBP protein level in normal and tumoral samples. Table S8: DEMETER2 score of 1290 RNA-binding proteins in 82 breast cancer cell lines. Table S9: CERES score of 1288 RNA-binding proteins in 28 breast cancer cell lines. Table S10: Subtypes of breast cancer cell lines analyzed by DEMETER2 and CERES. Table S11: Total interactions between candidate genes and RNA-binding proteins and cancer driver genes.


s2fieldsofstudy: Biology Chemistry

text:
Mitochondrial Dysfunction Pathway Alterations Offer Potential Biomarkers and Therapeutic Targets for Ovarian Cancer

The mitochondrion is a very versatile organelle that participates in some important cancer-associated biological processes, including energy metabolism, oxidative stress, mitochondrial DNA (mtDNA) mutation, cell apoptosis, mitochondria-nuclear communication, dynamics, autophagy, calcium overload, immunity, and drug resistance in ovarian cancer. Multiomics studies have found that mitochondrial dysfunction, oxidative stress, and apoptosis signaling pathways act in human ovarian cancer, which demonstrates that mitochondria play critical roles in ovarian cancer. Many molecular targeted drugs have been developed against mitochondrial dysfunction pathways in ovarian cancer, including olive leaf extract, nilotinib, salinomycin, Sambucus nigra agglutinin, tigecycline, and eupatilin. This review article focuses on the underlying biological roles of mitochondrial dysfunction in ovarian cancer progression based on omics data, potential molecular relationship between mitochondrial dysfunction and oxidative stress, and future perspectives of promising biomarkers and therapeutic targets based on the mitochondrial dysfunction pathway for ovarian cancer.

Introduction
1.1. The Importance of Mitochondria in Cancer. The mitochondrion is the "power house" of a cell and provides more than 90% of adenosine triphosphates (ATPs) for cells. In recent years, as an important organelle in human body cells, mitochondria have been found to participate in the regulation of different physiological and pathological processes, for example, cell survival, proliferation, and migration, and function in tumor initiation, progression, and metastasis [1][2][3]. Mitochondria are the target and feedback center of various regulators of energy metabolism. Studies have confirmed that abnormal amino acid metabolism, fatty acid metabolism, and glucose metabolism widely occur during the progression of tumorigenesis [4]. Abnormal mitochondrial metabolism is tightly associated with the proliferation, metastasis, and survival process of cancer cells. Moreover, mitochondria are the cellular factories that produce reactive oxygen species (ROS). Accumulating evidence demonstrates that dysregulation of ROS promotes tumor malignant progression [5]. The mitochondrion, a key regulator of apoptosis, can enhance the antiapoptotic ability of cancer cells and thus cause cancer cells to rapidly proliferate [6]. Mitochondria, as the core component of intracellular calcium pool regulation, can regulate intracellular calcium homeostasis through a variety of calcium transport systems on mitochondrial membranes. Abnormalities in mitochondrial calcium homeostasis are closely related to mitochondrial dysfunction and the development of tumors [7]. Mitophagy, as selective autophagy targeting mitochondria, is one of the important regulatory mechanisms to maintain mitochondrial homeostasis [8,9]. Mitophagy is associated with various diseases, such as cancers, neurodegenerative diseases, and immune diseases [10][11][12]. Thus, mitochondria are closely involved in many processes such as tumorigenesis, proliferation, invasion, and metastasis and have become a hotspot in the field of cancer research.
1.2. The Changes of Mitochondria in Ovarian Cancer. Ovarian cancer is the most lethal gynecologic malignant tumor in women globally [13]. Currently, the pathogenesis of ovarian cancer has not been fully clarified, and the suspected causal factors include genetics, ovulation, environmental factors, and hormones [14]. The standard treatment for ovarian cancer patients is primary debulking surgery plus platinumbased adjuvant chemotherapy. Despite the initial response rates being high after first-line treatment, most ovarian cancer patients will eventually relapse and have a poor outcome with a 5-year survival rate of only up to 46%, most of which are due to late diagnosis and a high incidence of chemoresistance [15]. Therefore, the improvement of the early-stage diagnostic rate of ovarian cancer has very important clinical significance [16]. Previous studies have found that mitochondrial dysfunctions are extensively and directly implicated in ovarian cancers [13,17,18]. For example, the number of mitochondrial DNAs (mtDNAs) is significantly increased in ovarian cancers relative to controls [19]. Scanning electron microscope analysis demonstrates that the loosened mitochondria in cytoplasm of ovarian cancer cells have a moth-eaten appearance with a decrease in cristae width and cristae junction diameter [20,21]. Also, changes in mitochondrial dynamics could influence the phenotype of different subsets of ovarian cancer cells [22].

Study on Mitochondrial Dysfunctional Pathways in
Ovarian Cancer. The mitochondrion is the critical intracellular organelle that controls many important physiological and pathological processes in cancer, for example, energy metabolism, oxidative stress, mtDNA mutation, cell apoptosis, mitochondria-nuclear communication, dynamics, autophagy, calcium overload, immunity, and drug resistance [23][24][25][26][27] (Figure 1). The mitochondrion is the core of cellular energy metabolism, oxidative stress, and cell signaling. Altered energy metabolism is recognized as a hallmark of cancer [28]. Oxidative stress is closely associated with tumorigenesis and cancer development. Mitochondrial dysfunction has been proposed as a causal factor of ovarian cancer. The carcinogenesis and development of ovarian cancer are associated with multiparameter and multilevel pathological changes. Single-omics study commonly provides limited information for the complex molecular mechanisms, whereas multiomics studies have offered a new means to design targeted biomarkers for ovarian cancer therapy with the use of different genes, proteins, and metabolites in individual or in combination. This review article will assist in understanding mitochondrial dysfunction pathway alterations in ovarian cancer from different omics viewpoints.

Omics Study Status of Mitochondrial Dysfunction Pathways in Ovarian Cancer
The advancement of multiomics technologies (genomics, transcriptomics, proteomics, and metabolomics) and their relationship with individual phenotypes of patients are driving the medicine paradigm shift from a "one-size-fits-all" method to a predictive, preventive, and personalized medicine (3P medicine), which helps elucidate underlying mechanisms of human disease and provide reliable biomarkers and therapeutic targets for more detailed patient stratification and more effective personalized therapy. Here, we first review the general status of different omics in mitochondrial dysfunction pathway alterations in ovarian cancer and then summarize the research progress of integrative analysis of two or more types of omics data.

Genomic Study of Mitochondrial Dysfunction Pathways
in Ovarian Cancer. The occurrence and development of cancer are a process of accumulative alterations in a genomic sequence. Mitochondria are multifunctional organelles, which control energy production, oxidative stress, metabolic signaling, and apoptotic pathways, and hence play crucial roles in tumorigenesis. The mtDNA is a circular and double-stranded DNA with 16500 nucleotides [29], which contains 37 genes that encode 13 proteins for oxidative phosphorylation, 2 rRNAs, and 22 tRNAs for protein synthesis within mitochondria, and has a noncoding control region-the displacement loop (D-loop) [30]. The mtDNA is unique because it exists multiple copies per cell, and the copy number depends upon the cell energy demand. Apart from these proteins encoded by mtDNA genes, most proteins in the mitochondrial respiratory chain are still encoded by nuclear DNA (nDNA) and then are transported into mitochondria [31]. Numerous proteins encoded by nDNA are involved in the generation of new mitochondria (mitochondrial biogenesis), and these proteins are involved in numerous mitochondrial functions such as oxidative phosphorylation (OXPHOS), apoptosis, and mtDNA replication and gene expression. Furthermore, nDNA-encoded mtDNA genes act as regulators of mitochondrial biogenesis and participate in nuclear-mitochondrial DNA cross-talks [32]. The interplay between both nuclear and mitochondrial genomes can influence the pathophysiological processes of disease including cancer. In terms of mtDNA mutations, mutations usually occur in mtDNA genes of ovarian cancers (up to 60%), which might be the results of mitochondrial dysfunction. Apart from the D-loop region, 12S and 16S rRNA genes and cytochrome b genes are the preferred zone for mtDNA mutation in ovarian cancers [33]. Another study based on ovarian cancer samples and matched normal tissues with sequencing of mtDNA variants reveals 352 mtDNA variants [34]. Among these 352 variants, 38.3% variants are insertions and deletions, which suggests mtDNA instability in ovarian cancers, and high-frequency mutations occur at A263G, A1438G, and A8860G [34].
In terms of SNP changes, SNPs in the mtDNA and nDNA might be responsible for the pathogenesis of ovarian 2 Oxidative Medicine and Cellular Longevity cancer. For example, SNPs at various nucleotide positions, including 73A/G, 207G/A, and 523C/del, are associated with increased susceptibility to ovarian cancers [35]. Another study demonstrates that three SNPs in D-loop regions (446 C/A, 324 C/G, and 309 C/T) are useful to predict postoperational survival and that two alleles at nucleotide positions 309 and 324 are independent predictors of ovarian cancer outcome [36]. A population-based study consisting of 89 patients with primary ovarian cancer found that age-atonset in ovarian cancer women can be predicted by certain SNPs in the D-loop regions [37]. SNPs occurring at nucleotide alleles 248, 524, and 16 304 in the D-loop region are related to the age-at-onset. Patients with the minor alleles, 248 G and 16 304 T genotypes, have lower age of onset than patients with 248 A and 16 304 C, respectively. However, for the minor allele, 524C, age-at-onset of patients is higher than that of patients with a higher frequency of 524 deletions. Study finds that variants in mitochondrial biogenesis genes play a role in influencing the susceptibility of ovarian cancers. A multicenter, large-scale study of ovarian cancer demonstrates that 128 SNPs from 22 mitochondrial genetic regions along with 2839 SNPs localized at 138 nuclearencoded genes participate in steroid hormone metabolism, mitochondrial biogenesis, and OXPHOS pathways, which is associated with ovarian cancer risk [38]. For example, CO1 (cytochrome c oxidase 1) is significantly related to ovarian cancer risk, and asynonymous SNP in CO1, T6777C appears to decrease the risk of ovarian cancer. Moreover, the study of three nDNA pathways demonstrates that 25 genes and 1051 SNPs involved in mitochondrial bio-genesis are associated with ovarian cancer risk, including Nrf1 (nuclear respiratory factor 1), MTERF (mitochondrial transcription termination factor), PPARGC1A (peroxisome proliferator-activated receptor gamma, coactivator 1 alpha), CAMK2D (calcium/calmodulin-dependent protein kinase D), and ESRRA (estrogen-related receptor alpha) [38]. In terms of mtDNA content, mtDNA content in ovarian cancer cells is significantly elevated compared to the normal ovary, which meets the increased mitochondrial function need for favoring tumor growth. Quantitative analysis of the mtDNA copy number in plasma and whole blood of serious ovarian cancer patients reveals that whole-blood mtDNA copy number significantly varies between healthy controls and epithelial ovarian cancer patients [19].
Mitochondrial genetic alterations are a critical hallmark of ovarian cancer. These alterations particularly affect the electron transport chain because the OXPHOS pathway is the main pathway that is altered in ovarian cancers and other cancers. Furthermore, due to the smaller size of the mitochondrial genome, mtDNA sequencing is costeffective and highly scalable in clinical settings. Study finds that mtDNA homoplasmic mutations occur in early preneoplastic and cancerous lesions, and the association between mtDNA copy number alterations and clinicopathological features demonstrates the importance of the mtDNA copy number as prognostic biomarkers in cancers.

Transcriptomics Study of Mitochondrial Dysfunction
Pathways in Ovarian Cancer. Since the advent of the postgenome era, transcriptomics has been extensively used in all   Oxidative Medicine and Cellular Longevity fields of medicine due to its high accuracy and throughput [39]. A transcriptome is a bridge to link the genome with the proteome [40]. Gene regulation is a complex network system at multiple levels, and regulation at the transcriptional level is the primary means for all organisms to control their specific gene expressions. The human transcriptome is composed of a considerable number of protein-coding mRNAs as well as noncoding RNAs [41].
In terms of mRNAs, one study based on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases demonstrates the positive correlations between ovarian cancer biomarkers (such as CD44) and glycolytic biomarkers (such as hexokinase 2 (HK2), lactate dehydrogenase A (LDHA), and ENO1) [42]. LDHA is a HIF1α-targeted glycolytic gene, which is upregulated in ovarian cancer cells [43]. Altered energy metabolism is regarded as a hallmark of cancer. Enhanced glycolysis is a feature of cancer cells and tissues, which is commonly named as the Warburg effect [44]. Mitochondria occupy a central stage in cellular altered energy metabolism. Mitochondrial dysfunction is closely related to reprogramming of energy metabolism, which causes tumorigenesis and tumor progression. The tumor microenvironment (TME) plays crucial roles in tumor initiation and promotion of ovarian cancers, which might be the therapeutic targets. Previous studies also show that glycolysis-related gene expressions are lower in CD8+ Treg cells that coculture with SKOV3, compared to CD8+ T cells cultured alone [45]. Moreover, differentially expressed genes (DEGs) profiles are studied in peripheral CD4+ T cells from ovarian cancer patients (n = 5) and controls (n = 5), which identifies 5,175 DEGs in peripheral CD4+ T cells and finds that the metabolic pathway is the most significantly enriched pathway, and eight glycolysis-related DEGs (GLUT1, HIF1α, mTOR, PKM2, ENO1, GPI, PDK1, and LDHα) are significantly increased in CD4+ T cells from ovarian cancer peripheral blood [46].
Long noncoding RNAs (lncRNAs) and miRNAs are critical players in regulating large-scale protein-coding genes in tumor-associated pathways [47]. A study finds 39 differentially expressed miRNAs between ovarian cancer and controls, including 29 downregulated and 10 upregulated miRNAs [48]. In addition, miR-450a is the most significantly downregulated miRNAs in ovarian cancer, which restricts the metastatic capacity of ovarian cancer cells via targeting several mitochondrial mRNAs, such as translocase of inner mitochondrial membrane domain-containing protein 1 (TIMMDC1), aconitase 2 (ACO2), and ATP synthase F1 β subunit (ATP5B) [49]. TIMMDC1, a mitochondrial inner membrane protein, is confirmed to be associated with mitochondrial complex I [50]. ACO2 participates in the tricarboxylic acid cycle (TCA) by converting citrate to isocitrate within the TCA pathway [51]. ATP5B is one of the subunits of mitochondrial ATP synthase, which is termed as complex V to function as a mitochondrial proton pump [52]. The miR-450a can downregulate ATP5B and TIMMDC1 to impair mitochondrial functions. Moreover, miR-450a affects mitochondrial functions and the activities of ACO2, subunit 2 of NADH dehydrogenase (ND2), and TIMMDC in TCA pathways to decrease OXPHOS levels and then further increase the consumption of glucose. Therefore, many miR-NAs can modulate mitochondrial functions by regulating miRNA-targeting specific genes.
Additionally, lncRNAs have acted as important regulators in several biological processes, such as gene expression, transcription, and posttranscriptional modifications [53]. lncRNA SNHG3 has been discovered to associate with ovarian cancer survival through analysis of the TCGA database. Moreover, lncRNA SNHG3 is involved in the energy metabolism process through the regulation of EIF4AIII and miRNAs, which could bind to PKM (pyruvate kinase) in glycolysis, PDHB (pyruvate dehydrogenase E1 component subunit β) and IDH2 (isocitrate dehydrogenase 1) in TCA cycle, and ubiquinol-cytochrome c reductase hinge protein (UQCRH) in OXPHOS pathways [54]. It clearly demonstrates that lncRNA SNHG3 is closely associated with cellular energy metabolism and controls a series of important metabolic pathways in ovarian cancers.
These findings demonstrate miRNA and lncRNA function as a critical layer of the regulation at the transcriptional level and also indicate the functional diversity and complexity of energy metabolism in the mitochondrial dysfunction pathway system.

Proteomics Study of Mitochondrial Dysfunction
Pathways in Ovarian Cancer. Proteomics is an effective methodology that can analyze the entire proteins in cells, tissues, and organisms, including protein subtypes, posttranslational modifications, protein interactions, and protein structures [55]. As an important component of phenotype, the proteome is the ultimate executor of genome function. Proteomics can more directly and accurately reflect the state of cell activity to determine the mechanism of tumor development.
Mitochondrial proteomics is extensively used to identify and quantify proteins in a mitochondrial proteome due to its fast speed and high sensitivity. For example, a total of 5,115 mitochondrial expressed proteins (mtEPs) are identified in mitochondrial samples prepared from human ovarian cancer tissues with iTRAQ (isobaric tags for relative and absolute quantification) [56]. In addition, integrative analysis of mtEPs and transcriptomics data with corresponding clinical characteristics extracted from the TCGA database find that 262 mtEPs are significantly related to the overall survival of ovarian cancer patients. Subsequently, further analysis of those mtEPs identifies several important signaling pathways and candidate biomarkers in ovarian cancers, which might prompt more investigations for new biomarkers [52].
Moreover, Zhan et al. employ iTRAQ-based quantitative proteomics and identify 1,198 mitochondrial differentially expressed proteins (mtDEPs) in human ovarian cancer tissues compared to the controls [57]. Among them, a range of enzymes are enriched in energy metabolism pathways, such as PKM, PFKM, PDHB, CS (citrate synthase), IDH2, isocitrate dehydrogenase 3b (IDH3B), isocitrate dehydrogenase 3a (IDH3A), oxoglutarate dehydrogenase-like (OGDHL), NADH dehydrogenase subunit 5 (ND5), ND2, UQCRH, and CYB. Many of those enzymes could participate 4 Oxidative Medicine and Cellular Longevity in controlling cancer progression. For example, PDHB is the enzyme that catalyzes the conversion of pyruvate to acetyl-CoA, attenuates the production of lactate, and contributes to OXPHOS. PDHB is expressed at low levels in ovarian cancers [58]. CS is overexpressed in ovarian cancers and cell lines compared to controls. In addition, CS can promote cancer cell proliferation, invasion, and migration and constitute a potential therapeutic target in ovarian cancers [59]. Mitochondrial proteomics analysis has found differentially expressed proteins (DEPs) that mainly function in energy metabolism, which may underlie the potential mechanisms of mitochondrial dysfunction [60]. Posttranslational modification (PTM) is usually defined as the enzymatic modification of proteins following protein biosynthesis [61], such as palmitoylation, phosphorylation, ubiquitination, glycosylation, nitration, acetylation, decarboxylation, and methylation [62]. Phosphorylation is one of the common PTMs, which often results in the altered function of a protein.
With the progress of mass spectrometry-(MS-) based proteomics, Li et al. constructed a quantitative mitochondrial phosphoprotein profile of human ovarian cancer [63]. In his study, 124 phosphorylation sites located within 67 mitochondrial phosphoproteins (mtPPs), including 84 phosphorylation sites within 48 mitochondrial differentially phosphorylated proteins (mtDPPs), are identified in ovarian cancer mitochondria compared to controls.

Metabolomics Study of Mitochondrial Dysfunction
Pathways in Ovarian Cancer. In the past 20 years, metabolo-mics has been continuously developed and served as an essential role in the exploration of pathogenic mechanisms contributing to carcinogenesis, the discovery of novel diagnostic biomarkers, and therapeutic targets. The reprogramming of energy metabolism has been listed as one of the hallmarks of cancer [68]. It is well known that cancer cells undergo metabolic reprogramming to achieve rapid and explosive proliferation [69]. Cancer often causes significant metabolic alterations to sustain its growth, while metabolomics makes early diagnosis of cancer possible.
The metabolic profile of ovarian cancer tissue is significantly different from normal ovarian tissue. One study finds that the differences in metabolite levels are analyzed between serous papillary ovarian cancer (n = 44) and healthy controls (n = 44) with an ambient ionization technique for MS [70]. These results show that differential metabolites include histamine, purine nucleotide, glycine, serine, and sarcosine. Among them, alanine, serine, cysteine, threonine, and glycine are overexpressed [70]. Another study also finds that metabolic profiling reveals metabolic alterations among ovarian cancers and normal ovaries. Those diversely expressed metabolites include carnitine, acetylcarnitine, butyrylcarnitine, phenylpyruvate, and phenyllactate [71]. Carnitine is a transport protein which transports fatty acids into the mitochondria for β-oxidation [72].
Mitochondrial dynamics, also known as mitochondrial morphological alteration, mainly refers to fusion and fission [73][74][75]. Abnormal mitochondrial dynamics can lead to mitochondrial dysfunction, promote apoptosis, and affect various organ systems, including kidneys, nerves, and cardiovascular system [76][77][78]. Furthermore, mitochondrial dynamics may also be an important factor inducing metabolic reprogramming in tumors [79]. Mitochondrial elongation factor 2 (MIEF2), located in the mitochondrial outer membrane, serves a regulatory role in mitochondrial fission and is upregulated in ovarian cancer [80]. A metabolomics analysis finds that MIEF2 is overexpressed in ovarian cancer cells to cause an elevated concentration of glyceraldehyde 3phosphate (GA3P), G6P (glucose 6-phosphate), 3PG (3phosphoglycerate), F6P (fructose 6-phosphate), and lactate in the glycolytic pathway and a reduced concentration of α-ketoglutarate, aconitate, citrate, malate, and fumarate metabolized from the TCA cycle [81]. These results clearly demonstrate that alerted mitochondrial dynamics MIEF2 has a prooncogenic role in ovarian cancers by the shift from OXPHOS to glycolysis.
Consequently, metabolomics is an effective clinical strategy to identify specific biomarkers for 3PM practice in ovarian cancers.

Multiomics Study of Mitochondrial Dysfunction
Pathways in Ovarian Cancer. The occurrence and development of ovarian cancer is a multifaceted and complex process. Carcinogens induce a wide range of molecular changes at different levels of genome, transcriptome, proteome, and metabolome, and these molecules interact with each other to play critical functions in different cancers [82]. The multiomics approach is an effective strategy to comprehensively study and understand cancer pathogenesis.  (Figure 2), which finds that several energy metabolismrelated enzymes are upregulated, including PFKP and PKM in aerobic glycolysis; CS, PDHB, IDH2, and OGDHL in the TCA pathway; and UQCRH in the OXPHOS pathway. In addition, integrative analysis of proteomics and transcriptomics data in ovarian cancers finds that lncRNA SNHG3 is involved in energy metabolism in cancer cells by targeting TCA and OXPHOS pathways [54].
An integrative analysis of proteomics and transcriptomics plus clinical characteristics has demonstrated that lncRNA SNHG3 is able to regulate miRNAs and EIF4AIII that specifically targets the key molecules (PKM, IDH2, PDHB, and UQCRH) in glycolysis, TCA, and OXPHOS pathways. A seven-hub-molecule-signature model (RRAS2, HIST1H2BK, ALB, RPL23A, HIBCH, RPS20, and EIF3E) from those 1198 mtDEPs is established to predict the survival time of ovarian cancer. It clearly demonstrates that mitochondria-based multiomics is an important approach to identifying effective energy metabolism-specific biomarkers and prognostic models for ovarian cancer.

Interaction of Mitochondrial Dysfunctional
Pathway with Oxidative Stress Pathway in Ovarian Cancer

Association of Mitochondrial Dysfunction with Oxidative
Stress. Oxidative stress occurs when imbalance occurs between ROS production and antioxidant defenses, which results in oxidation and damage to lipids, proteins, and DNAs. ROS is a collective term for hydrogen peroxide, superoxide anion, and hydroxyl radical [83]. Mitochondrial respiration is the major endogenous source of ROS and pro-duces~90% ROS [84]. Mitochondria are significantly associated with ROS production, apoptosis, and aging and are susceptible to oxidative stress [85]. Generally, normal cells obtain ROS homoeostasis at a low level of ROS [86]. The surplus ROS initiates lipid peroxidation and causes oxidative damage to mitochondrial membrane systems resulting in depletion of its fluidity and increases Ca 2+ influx [87]. The dramatic increase in Ca 2+ influx accelerates the transformation of xanthine dehydrogenase and xanthine oxidase, increases the levels of oxygen free radicals, and activates phospholipase A2 and phospholipase C to degrade a large number of mitochondrial membrane phospholipids, which results in a doubling of arachidonic acid content in the mitochondrial membrane and decreases the function of the electron transport chain, and the damage of the respiratory chain impairs the function of mitochondria to synthesize ATP and impairs OXPHOS, which in turn leads to Ca 2+ influx and oxygen free radical generation, and forms a "vicious cycle" resulting in mitochondrial respiratory dysfunction and energy metabolism failure [88]. Simultaneously, the increase in Ca 2+ can aggravate the damage of peroxidation to mitochondria. Decreased membrane poten-tial and membrane fluidity also reduce mitochondrial matrix volume changes and ATP production and cause ion transport dysfunction, intracellular calcium overload, mitochondrial swelling, vacuolization, cristae breakage, and ultimately mitochondrial dysfunction and cell death [89]. Generally, ROS is regarded as a double-edged sword since low levels of ROS can promote tumor cell proliferation, infiltration, metastasis, and chemoradiotherapy resistance [90], whereas excessive levels of ROS over the cytotoxic threshold induce oxidative stress, cause oxidative damage, and initiate apoptotic signaling in cancer cells [91]. Therefore, some anticancer agents can specifically target cancer cells by causing an excessive accumulation of ROS and stimulating ROSdependent cell death pathways [92].

Different Omics Reveal Mitochondrial Dysfunction and
Oxidative Stress Pathways. Continuous progress and innovation in medicine lie in the rapid development of omics technology. Different omics approaches exhibit diverse perspectives to investigate mitochondrial dysfunction and oxidative stress pathways. A total of 1198 mtDEPs in ovarian cancers relative to controls is identified by iTRAQ-based quantitative proteomics and is further analyzed with Ingenuity Pathway Analysis (IPA) to construct pathway networks and significant signaling pathways [17]. Pathway network analysis reveals the most significant pathways, including mitochondrial dysfunction, antigen presentation, EIF2 signaling, GP6 signaling, glutathione-mediated detoxification, and NRFmediated oxidative stress response pathways. Recently, metabolic reprogramming has gained significant attention and interest [93]. Cancer cells are able to reprogram their energy metabolism and meet the energy demand. However, current studies on mitochondrial function in cancer energy metabolism have not come to a consistent conclusion. Those mtDEPs enriched in mitochondrial dysfunction pathways are associated with aberrant energy metabolism, including ATP synthase mitochondrial F1 complex assembly factor 1 (ATPAF1), apoptosis-inducing factor mitochondria associated 1 (AIFM1), aconitase 1 (ACO1), copper chaperone for cytochrome c oxidase (COX17), BCL2 apoptosis regulator (BCL2), ATP synthase mitochondrial F1 complex assembly factor 2 (ATPAF2), cytochrome c oxidase subunit 6C (COX6C), cytochrome c oxidase subunit 4I2 (COX4I2), cytochrome c oxidase subunit 4I1 (COX4I1), cytochrome c, somatic (CYCS), cytochrome c oxidase subunit 7A2 like (COX7A2L), cytochrome c oxidase subunit 7A2 (COX7A2), HtrA serine peptidase 2 (HTRA2), glutathione peroxidase 7 (GPX7), thioredoxin 2 (TXN2), monoamine oxidase B (MAOB), and UQCRH. Oxidative stress is a common pathophysiological process in ovarian cancers, which has been proven by numerous multiomics studies in ovarian cancers. Nrf2 (nuclear respiratory factor 2) is an oxidative stress sensor, which plays a core role in oxidative stress response. Nrf2 is modulated by a variety of factors such as ERK5, ERK1/2, Keap1, p38 MAPK, JNK1/2, PI3K/AKT, PKC, and ER stress. Oxidative stress promotes Nrf2 to quickly translocate from cytoplasm to nucleus. Nrf2 induces expressions of many antioxidant genes, ubiquitination/proteasomal degradation 6 Oxidative Medicine and Cellular Longevity proteins, chaperone/stress response genes, phase III detoxifying genes, and phase I and II metabolizing enzymes after Nrf2 binds to antioxidant response element. The mtDEPs enriched in Nrf2-mediated oxidative stress response pathway include actin beta (ACTB), actin alpha cardiac muscle 1 (ACTC1), DnaJ heat shock protein family member B1

Combination Effects of Mitochondrial Dysfunction and
Oxidative Stress in Ovarian Cancer. It is well known that mitochondria are the primary power factories of eukaryotic cells and the primary intracellular source of ROS production. A proteomic study clearly demonstrates that mitochondrial dysfunction and Nrf2-mediated oxidative stress response alterations function in ovarian cancers [17]. Nrf2-mediated oxidative stress response is significantly involved in tumorigenesis and progression of ovarian cancers [94]. Nrf2 is a critical regulator of antioxidant response and is primarily located in the cytoplasm under basal conditions [95]. When the excessive productions of ROS/RNS induce oxidative stress, Nrf2 subsequently translocates to the nucleus and activates the antioxidant response to protect cells against oxidative stress damages [96][97][98][99]. Many studies find that ovarian cancer manifests a prooxidant status and is characterized by increased expressions of prooxidant enzymes and decreased expressions of antioxidant enzymes [71,[100][101][102]. For example, ovarian cancer cells A2780 and OVCAR-3 exhibit higher ROS levels than   [101]. Mitochondrial dysfunction could lead to elevated levels of ROS in cancer cells, trigger DNA damage through complex biochemical reactions, modulate cancer signaling pathways, and activate oncogenic signals, which promote cancer progression, cancer proliferation, survival, angiogenesis, apoptosis, metastasis, and inflammatory response ( Figure 3).

Mitochondrial Dysfunction Pathway-Based Biomarkers and Therapeutic Targets in Ovarian Cancer
The altered molecules in mitochondrial dysfunction pathways are an important resource to identify specific mitochondria-related biomarkers. These important biomarkers have been identified in ovarian cancer, including superoxide dismutase 2 (SOD2) [103], Sab [104], cytoplasmic estrogen receptor β2 (cERβ2) [105], mimitin [106], TNF receptor-associated protein 1 (TRAP1) [107], mitochondrial ribosomal protein S12 (MRPS12) [108], MIEF2 [81], dynamin-related protein 1 (Drp1) [109], and calcium/ calmodulin-dependent protein kinase I (CaMKI) [109]. Current investigations have recognized that the development of cancer is an extremely complex biological process that involves multiple mechanisms and factors, and clinical practice should broadly focus on the comprehensive consideration of multiple factors instead of a one-factor model [110]. An integrated biomarker model based on multiomics has been proposed. Generally, cancer biomarkers can be divided into two main types; type one is used for prediction, diagnosis, and prognosis, and type two is used for exploring underlying mechanisms and therapeutic targets. However, a one-factor model occupies a leading position in published literature. The development of various omics has promoted one to shift from a traditional one-factor model to a multiparameter systematic strategy in the perspective of 3P medicine [82,110].
In total, 5115 mtEPs in ovarian cancer are identified through iTRAQ-based quantitative mitochondrial proteomics. Further, integrative analysis of these mtEPs and transcriptomics data with clinical information finds that 262 mtEPs are closely linked to the prognosis of ovarian cancers. A total of 63 mtEPs is proposed to serve as candidate biomarkers for ovarian cancer [111]. Those findings are the first reference atlas of human ovarian cancer mitochondrial proteome [56] and are helpful for serving the goal of establishing a human mitochondrial proteomic database and provide an effective clue to discover useful biomarkers for clinical practice [18]. In addition, 102 hub molecules are obtained with a molecular complex detection analysis of 1198 mtDEPs. A seven-hub-molecule-signature model (HIBCH, HIST1H2BK, ALB, EIF3E, RPS20, RRAS2, and RPL23A) from a multivariate regression analysis can predict the survival risks of ovarian cancer [17]. Those mtEPs, mtDEPs, and pattern biomarkers might contribute to the 3PM practice of ovarian cancers after they are further validated with clinical practice.

Future Perspective
The present review has comprehensively discussed the roles and contributions of integrative omics/multiomics in clarifi-cation of mitochondrial dysfunction pathways of ovarian cancers, establishment of multiomics-based signaling pathway networks, pattern biomarkers, and therapeutic targets for effective 3P medicine of ovarian cancers. Moreover, we would like to highlight and discuss several future directions for research and practice in ovarian cancers.

Expanded Multiomics Study of Mitochondrial
Dysfunction Pathway in Ovarian Cancer. As the rapid development of omics technologies is evolved in, the research scope is not only limited to genomics, transcriptomics, proteomics, and metabolomics but also expanded to other multiomics approaches such as lipidomics, glycomics, and radiomics. The main objective of lipidomics is to identify and quantify cellular lipid molecular species and their function in biological progress [173,174]. The lipid molecules in mitochondria exert diverse biological functions in the activity of the electron transport chain, the production of ROS, and mitochondrial dynamics [175,176]. With these features, mitochondrial lipidome has tremendous potential to considerably enrich our understanding of the mitochondrial dysfunction pathway.
As the main executive substance of life function, the functional study of protein has been favored. One of the main reasons why proteins are versatile is PTMs. This is because after individual amino acid residues in a protein are covalently modified, their physical and chemical properties, conformation, etc., are also changed, which can significantly affect the functions of the protein. PTM is a process that occurs after the completion of protein translation and covalently binds various types of chemical small molecule groups or small proteins to specific amino acid residues in the substrate. At present, there are as many as hundreds of known PTMs, and PTMs occur in almost all proteins, and the same protein also has a variety of PTMs at the same time, which can not only exponentially expand the number of proteoforms but also endow more complexity with human life processes. A variety of PTMs has been identified, including glycosylation, acetylation, methylation, phosphorylation, and ubiquitination [177]. Protein glycosylation, which is a common form of PTMs, refers to the addition of a multitude of glycans to peptides [178]. This modification occurs primarily to alter the structural conformation and regulate protein interactions, activity, localization, and stability [179]. Abnormal glycosylation has been related to a variety of human diseases, such as cancer and neurodegenerative, and cardiovascular diseases [180]. One MS-based glycoproteomic characterization of 119 TCGA high-grade serous ovarian cancer (HGSOC) tissues classifies HGSOC into 3 major tumor clusters and 5 groups of glycopeptides. Different tumor subtypes show different glycopeptide combination preferences, and intact glycopeptide (IGP) 3/4/1 glycopeptides are highly expressed in subtypes 1/2/3, respectively. IGP1 mainly contains fucose glycans, IGP3 contains fucose and sialic acid residue glycans, and IGP4 mainly contains high mannose glycans [181]. In addition, acetylation, another PTM, occurs mainly on chromatin histones and maintains a dynamic balance under the action of histone acetyltransferases with deacetyltransferases. Protein acetylation is 9 Oxidative Medicine and Cellular Longevity FDA-approved antibiotics Inhibit mitochondrial OXPHOS and/or biogenesis Ovarian cancer stem cells [115] IMT1B Targeting the human mitochondrial RNA polymerase and affecting the biogenesis of OXPHOS A2780 cells [116] Ivermectin Targeted lncRNA-EIF4A3-mRNA pathways A2780 and TOV-21G cells [117] Metabolites from invasive Caulerpa species Selectively inhibiting respiratory complex II activity OV2008 and C13 cells [118] Metformin-loaded PLGA-PEG nanoparticles OXPHOS SKOV-3 ovarian cells [119] Tigecycline Inhibiting translation by mitochondrial ribosome SW626 and SKOV-3 cells [120] Oxidative stress

OVCAR-3 cells [138]
Chaetomugilin J Enhancing apoptosis through inhibiting Pink1/Parkin-mediated mitophagy A2780 cells [139] Danusertib Danu induced mitochondria-dependent apoptosis and autophagy in a dose-and time-dependent manner C13 and A2780cp cells [140] Epothilones The induction of apoptosis via mitochondrial pathway OV-90 cells [141] Exosomal microRNAs Inhibitory effects on cells by blocking the cell cycle and activating mitochondriamediated apoptosis signaling A2780 and SKOV-3 cells [142] Flex-Het Promoting mitochondrial-mediated apoptosis A2780 and OVCAR-3 cells [143] Gedunin isolated Triggered severe ROS generation leading to DNA damage and cell cycle arrest in G2/M phase thus inhibiting cell proliferation. ROS upregulation also led to mitochondrial stress and membrane depolarization, which eventually resulted in mitochondria-mediated apoptosis following cytochrome C release, caspase 9 and 3 activation, and PARP cleavage PA-1 and OVCAR-3 cells [144] Gentiopicroside Loss of MMP and induction of apoptosis SKOV-3 cells [145] Glycyrrhetinic acid rhodamine B benzyl amide 35 Triggered apoptosis A2780 cells [146] Hedyotis diffusa willd Induced through the mitochondriaassociated apoptotic pathway A2780 cells [147] Jaceosidin Induction of apoptosis involving cytochrome c release from mitochondria to cytosol
Protein phosphorylation is the most common PTM in eukaryotes. Phosphorylation strongly influences many cellular events, such as transcriptional activity, protein subcellular localization, protein stability, and enzymatic activity [184]. Recently, one study indicates that phosphorylation at residue Calcium overload Gentisyl alcohol Loss of mitochondrial membrane potential with calcium dysregulation ES2 and OV90 cells [170] Methiothepin Epolarization of the mitochondrial membrane and increased mitochondrionspecific Ca2+ levels and decreased ATP production and oxidative phosphorylation ES2 and OV90 cells [171] Autophagy BMI1 inhibitor (PTC-209) Targeting PINK1-PARK2-dependent mitochondrial pathway OVCAR4 and CP20 cells [172] 12 Oxidative Medicine and Cellular Longevity S616 in mitochondrial Drp1 triggers autophagy for chemoresistance and regrowth in the surviving cancer cells [185]. Ubiquitination refers to that ubiquitin molecules covalently bind to target proteins under the action of E1 activating enzyme, E2 coupling enzyme, and E3 ligase. The ubiquitinlabeled target proteins are recognized and degraded by 26s proteasome, which can remove wrong proteins and regulate the cell cycle and immune function of the body. Parkin mediates ubiquitination of mitochondrial outer membrane proteins including TOMM20, mitofusin, mitochondrial Rho (miro), and VDAC, forming a recognition site for adaptor proteins, and promoting the selective autophagy of damaged mitochondria [186]. Future studies are needed to further expand mul-tiomics study and integrate data to obtain a multiomics understanding of the mitochondrial dysfunction pathway in ovarian cancer.

Development of Mitochondrial Dysfunctional Pathway-Based Biomarker and Therapeutic Targets in Ovarian
Cancer for 3P Medicine Practice in Ovarian Cancer. The complex molecular interaction networks constructed from mitochondrial dysfunctional pathways based on multiomics data could provide new perspectives to elucidate molecular mechanisms, discover novel therapeutic targets, and identify effective and reliable pattern biomarkers for 3PM practice in ovarian cancers [62]. Some mitochondrial dysfunctional  13 Oxidative Medicine and Cellular Longevity pathway-based pattern biomarkers and multiple therapeutic targets have been discovered. However, those studies are not satisfactory enough in ovarian cancers due to the following factors: (i) Omics studies of mitochondrial protein PTMs are still in the exploratory stage. To date, no studies on mitochondrial protein PTMs in ovarian cancers have been reported, with the exception of mitochondrial phosphoproteome. (ii) Metabolites have not been completely investigated. (iii) The existing database limits the ability to discover tumor heterogeneity. Most of the existing databases contain different information of high-grade serous ovarian cancer (HGSOC), lacking other histological types of ovarian cancers. (iv) The large-sample size studies are needed to verify mitochondrial dysfunctional pathway-based pattern biomarkers in clinical practice. As a consequence, it is necessary to widen the scope of omics research to maximize the coverage of genome, transcriptome, proteome, and metabolome.

Phenome-Centered Mitochondrial Dysfunctional
Pathway Study in Ovarian Cancer. With the advancement of high-throughput technologies, the core of multiomics is shifting from genomics to phenomics ( Figure 5). The term "phenomics," first presented by Dr. Steven A. Garan in 1996, refers to the discipline of systematically studying all phenotypes of a certain organism or cell under the complex conditions. The phenome is a range of measurable features, including physical, chemical, and radiographic features of individuals and populations, which are derived from the complex interactions of genes, epigenetics, proteins, and metabolites. Proteomics and metabolomics are the two major components of phenomics. Protein is one of the important structural and functional units of life activities. Proteomics essentially refers to the protein features at the large-scale level, including protein expression, PTMs, and protein-protein interactions, from which there is a global understanding of the initiation and progression of diseases on the protein level. Metabolomics, with the goal of identification and quantification of all endogenous and exogenous metabolites in tissues, cells, and biofluids, could reflect changes in biological phenotypes more directly and immediately. Phenome-centered multiomics study will be a longterm trend for future cancer studies.

Conclusions
Mitochondrial dysfunctional pathway alterations are the important hallmarks of ovarian cancer. Great progress has been achieved to understand mitochondrial dysfunction pathway alterations in ovarian cancer. However, molecular mechanisms and their regulation of mitochondrial dysfunctional pathways in ovarian cancer remain unknown. The published evidence demonstrates that mitochondrial dysfunction is crosslinked with various complex biological processes, including energy metabolism, oxidative stress, cell apoptosis, cell cycle, mitophagy, and immunity process. This review provides the evidence base for the comprehensive understanding of underlying mechanisms between mitochondrial dysfunction and tumorigenesis in ovarian cancer. It holds the promise to develop new candidate biomarkers and therapeutic targets based on mitochondrial dysfunction pathways to effectively treat ovarian cancer in the context of 3P medicine.

Data Availability
All data are present in the table and figures in this article; they can be available from the journal website.

Conflicts of Interest
The authors have declared that no competing interests exist.

Authors' Contributions
L.S. collected and analyzed references, prepared the figures and table, and wrote the manuscript. X.Z. conceived the concept, designed the manuscript, and critically revised the manuscript and was responsible for its financial support and corresponding works.


Crab spiders (Araneae, Thomisidae) of Jinggang Mountain National Nature Reserve, Jiangxi Province, China

Abstract A list of 34 thomisid species belonging to 21 genera collected in Jangxi Province of China is provided. Five new species are described: Angaeusxieluae Liu, sp. nov. (♂♀), Lysitelessubspirellus Liu, sp. nov. (♀), Oxytatemucunica Liu, sp. nov. (♀), Phartalingxiufengica Liu, sp. nov. (♀), Stephanopisxiangzhouica Liu, sp. nov. (♀). A new combination is proposed: Ebelingiaforcipata (Song & Zhu, 1993) comb. nov. (ex. Ebrechtella Dahl, 1907). Previously unknown females of E.forcipata (Song & Zhu, 1993), Oxytatebicornis Liu, Liu & Xu, 2017, and Xysticuslesserti Schenkel, 1963 are described for the first time. Stephanopis O Pickard-Cambridge, 1869, a genus previously known from Australasia and South America, is recorded from the Asian mainland for the first time.

Introduction
Thomisidae Sundevall, 1833, commonly known as crab spiders, is the seventh largest spider family with a global distribution, comprising 2154 extant species belonging to 171 genera (WSC 2022). More than half of the Thomisidae species are known from a single sex: 752 of these were described from females and 337 from males (WSC 2022). More than fifty species were described from juveniles (WSC 2022).
The family has never been globally revised, but regional revisions have been made in, e.g., Canada (Dondale and Redner 1978), Japan (Ono 2009), and China (Song et al. 1999), etc. In the past ten years, many efforts were made to review or re-assign species described or recorded from China. Although there are several recent publications dealing with revisions, re-assignments of species, and descriptions of unknown sexes of Chinese crab spiders, there are still many species requiring study.
Thomisids in China are relatively well studied due to the revisions by Song et al. (1999) and Tang and Li (2010a, b). Currently, 306 species belonging to 51 genera are known from this country (Li 2020;WSC 2022). Level of knowledge is uneven in different provinces. Numbers of species known per province varies from north (Heilongjiang, n = 10) to south (Hainan, n = 47). One of the poorly studied provinces is Jiangxi, with only eight known species (Li and Lin 2016). To fill this gap, we studied material collected from the Jinggang Mountain National Nature Reserve in Jiangxi Province of China.
The aims of the present paper are (1) to report findings of 34 species belonging to 21 genera, (2) to provide detailed descriptions of five new species, (3) to provide descriptions of previously unknown females of three species, (4) to propose a new combination, and (5) to provide the first record of the genus Stephanopis from Asian mainland.

Materials and methods
More than 300 adult specimens belonging to 34 species from 21 genera were collected from Jinggang Reserve. Specimens were examined using a Zeiss Stereo Discovery V12 stereomicroscope with a Zoom Microscope System. Both male palps and female copulatory organs were detached and examined in 75% ethanol, using a Zeiss Axio Scope A1 compound microscope with a KUY NICE CCD. For SEM photographs, specimens were dried under natural conditions and photographed with a ZEISS EVO LS15 scanning electron microscope. The epigynes were cleared in pancreatin. Specimens including detached male palps and epigynes were stored in 80% ethanol after examination. All the specimens treated in this work are deposited in the Animal Specimen Museum, Life Science of College, Jinggangshan University (ASM-JGSU).
Measurements were taken with the AxioVision software (SE64 Rel. 4.8.3) and are given in millimetres. Terminology of the male and female copulatory organs follows Benjamin (2011), Ramírez (2014), and Machado et al. (2019). Promarginal and retromarginal teeth on the chelicerae are given as the first, second, third, etc., and measured from the base of the fang to the distal groove.
Leg measurements are given as total length (femur, patella, tibia, metatarsus, tarsus). The abbreviations used in the text are:

Eyes ALE
anterior lateral eye; AME anterior median eye; MOA median ocular area; PLE posterior lateral eye; PME posterior median eye.
Distribution. Known only from the type locality in Jiangxi Province of China (Fig. 17).

Genus Ebelingia Lehtinen, 2004
Comments. This genus includes only two species, both from East Asia (WSC 2022).

Female. Habitus as in
Distribution. China: Jiangxi and Fujian Provinces (Song et al. 1993; present data). Note. The left leg I was lost when we reviewed the holotype after photography.
Comments. This species clearly belongs to Ebelingia due to the markedly bifurcated retrolateral tibial apophysis, short embolus, broad anterior hood, and kidneyshaped spermathecae.

Genus Lysiteles Simon, 1895
Comments. This genus includes 60 species, mainly distributed in eastern Asia. Half of them are recorded from China, but there are still 13 species known only from females in China, and three from males. Most of them (ten species) are recorded from Yunnan Province. No species were recorded from Jiangxi Province. Etymology. The specific name is derived from that of a similar species, L. spirellus Tang, Yin, Peng, Ubick & Griswold, 2008;adjective. Diagnosis. The new species is similar to L. auriculatus Tang, Yin, Peng, Ubick &Griswold, 2008 andL. spirellus Tang, Yin, Peng, Ubick &Griswold, 2008 in having coiling spermathecae (SP), but differs from them by the carapace lacking pale median band (vs. present), abdomen with three pairs of large, touching, dark brown markings (Fig. 7A) (vs. relatively narrowed and widely separated markings). The new species can be distinguished from L. auriculatus by copulatory ducts (CD) located at posteromedian part of epigyne (vs. located anteriorly) (cf. Fig. 7C and Tang et al. 2008: fig. 2b, d). Finally, L. subspirellus sp. nov. differs from L. spirellus by spermathecae forming a tight coil (vs. loose coil) (cf. Fig. 7D and Tang et al. 2008: fig. 16d, f ).
Male Distribution. Known only from the type locality in Jiangxi Province of China (Fig. 17).

Genus Oxytate L. Koch, 1878
Comments. This genus includes 28 species distributed mainly in Asia and Africa. Half of them have been recorded from China, mainly in Yunnan and Guangxi provinces (Li and Lin 2016), but two species are known only from females and three from males.
Comments. Some females were collected in the type locality and others raised from juveniles. Newly collected females have general appearance and leg spination similar to the holotype male. Based on this, we consider them conspecific. This judgment will be confirmed or rejected in future when both sexes are collected together, simultaneously.
Distribution. Known only from Jiangxi Province, China (Fig. 17). Etymology. The specific name is derived from the type locality, Mucun Town. Diagnosis. Female of the new species is similar to those of O. bhutanica Ono, 2001 and O. bicornis in having vulva with M-shaped pattern formed by copulatory ducts (CD) and spermathecae (SP), but differ from both species by the copulatory openings oriented anteriorly (vs. outwards or inwards) (cf. Fig. 10C vs. Fig. 9C and Ono 2001: fig. 4). Additionally, O. mucunica sp. nov. differs from O. bicornis by the copulatory ducts being as wide as spermathecae (vs. copulatory ducts 2 × thinner) (cf. Fig. 10D and Fig. 8D).

Male. Unknown.
Notes. While many juveniles were collected and reared in the lab, only one female reached maturity.
Comments. The new species could potentially be a synonym of one of the species known only from males and occurring in China: O. clavulata Tang, Yin & Peng, 2008 (Yunnan) or O. placentiformis Wang, Chen & Zhang, 2012 (Guangxi).
Distribution. Known only from the type locality in Jiangxi Province of China (Fig. 17).

Genus Pharta Thorell, 1891
Comments. This genus includes nine species, the majority of which are distributed in Southeast Asia (WSC 2022). Four species are known to occur in China, all in Yunnan and Guizhou provinces (Li and Lin 2016). Among them, only P. tengchong (Tang, Griswold & Yin, 2009) is known from the female. Etymology. The specific name is derived from the type locality, Lingxiufeng Scenic Spot; adjective.

Pharta lingxiufengica
Diagnosis. The female of P. lingxiufengica is similar to that of P. tangi Wang, Mi & Peng, 2016 in having well-developed transverse ridge of copulatory openings (TrR) (= atrial intermediate margin of Wang et al. 2016) and invisible copulatory openings (CO). The new species can be easily differentiated from P. tangi by the convex transverse ridge of copulatory openings (vs. concave) (cf. Fig. 11C and Wang et al. 2016: fig. 2C).

Male. unknown.
Comments. There is only one species in the region known from the male only, P. koponeni Benjamin, 2014 (Thailand); however, it is unlikely to be conspecific with our female because of differences in colouration, and the long distance between type localities.
Distribution. Known only from the type locality in Jiangxi Province of China (Fig. 17).

Genus Stephanopis O. Pickard-Cambridge, 1869
Comments. This genus includes 23 species (WSC 2022). Most of them are distributed in South America, Oceania, Australia, and Papua New Guinea (WSC 2022). The recent phylogenetic analyses based on a matrix of 117 morphological characters scored for 77 terminal taxa have revealed a large variation in morphology among Stephanopis species (Machado and Teixeira 2021); these taxonomic revisions have greatly contributed to a better understanding of the group. Here we report the first species from the Asian continent as well as from the entire Oriental zoogeographical realm.  Xiangzhou Village, Fengshuping Group, 26°36'10.8"N, 114°15'28.8"E, 412 m, 5.VIII.2015, leg. K. Liu et al. leg. Etymology. The specific name refers to the type locality, Xiangzhou Village. Diagnosis. The new species is similar to S. nigra O. Pickard-Cambridge, 1869 by having slit-like copulatory openings (CO), but differs by lacking lateral sclerotised margins of copulatory openings (vs. lateral margins sclerotised), touching membranous sacs (vs. separated) and slightly separated spermathecae (vs. touching) (cf. Fig. 13 and Machado et al. 2019: fig. 37C, D).
Male. unknown. Distribution. Known only from the type locality in Jiangxi Province of China (Fig. 17). Comments. Xysticus is one of the most diverse genera in Thomisidae with 293 named species (WSC 2022). Fifty-nine species are known from China. Of these, 19 are known only from females, and five from males. Most species are distributed in northern China and only one species, Xysticus lesserti Schenkel, 1963, is known from Guizhou Province (Li and Lin 2016). Schenkel, 1963 Figs 14-16 Xysticus lesserti Schenkel, 1963: 219, fig. 124a- (Fig. 14A, B). Carapace reddish brown with dark mottling, medially with broad pale yellow stripe extending from PME to posterior edge. Chelicerae, endites, and labium reddish yellow. Sternum yellow, with numerous dark brown spots. Legs: I and II darker than III and IV; femora I-IV dorsally with reddish brown stripes, other segments yellowish to dark brown, with dark brown spots. Abdomen with grey oval patten, sub-medially with two indistinct transverse whitish stripes. Epigyne (Fig. 14C, D). Epigyne approximately as long as wide. Anteriorly with two atria (At) separated by the septum (Se). Copulatory openings (CO) located in the anterior part of atria, hidden by sclerotised anterior margins of atria. Spermathecae (SP) kidney-shaped, with several constrictions. Fertilisation duct (FD) short, as long as width of the spermatheca in its posterior part, directed laterally.