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Blockade of integrin signaling reduces chemotherapy-induced premature senescence in collagen cultured bladder cancer cells

Abstract Background Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer. However, the critical elements in control of chemotherapy resistance remain obscure. Methods We adopted improved collagen gels and performed cytotoxicity analysis of doxorubicin (DOX) and mitomycin C (MMC) of bladder cancer cells in a 3D culture system. We then detected the expression of multidrug resistant gene ABCB1, dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1 (COUPTF1), cell proliferation marker Ki-67, and cellular senescence marker senescence-associated β-galactosidase (SA-β-Gal) in these cells. We further tested the effects of integrin blockade or protein kinase B (AKT) inhibitor on the senescent state of bladder cancer. Also, we examined the tumor growth and survival time of bladder cancer mouse models given the combination treatment of chemotherapeutic agents and integrin α2β1 ligand peptide TFA (TFA). Results Collagen gels played a repressive role in bladder cancer cell apoptosis induced by DOX and MMC. In mechanism, collagen activated the integrin β1/AKT cascade to drive bladder cancer cells into a premature senescence state via the p53/p21 pathway, thus attenuating chemotherapy-induced apoptosis. In addition, TFA had the ability to mediate the switch from senescence to apoptosis of bladder cancer cells in xenograft mice. Meanwhile, TFA combined with chemotherapeutic drugs produced a substantial suppression of tumor growth as well as an extension of survival time in vivo. Conclusions Based on our finding that integrin β1/AKT acted primarily to impart premature senescence to bladder cancer cells cultured in collagen gel, we suggest that integrin β1 might be a feasible target for bladder cancer eradication.

Introduction
Bladder cancer is among the major sources of cancer-related morbidity, accounting for ∼200 000 deaths worldwide in 2018. 1 Cytotoxic chemotherapy has been the systemic management method for bladder cancer for decades, but its efficacy remains limited. 2 The lack of response to chemotherapy is seen as a major culprit for treatment failure in bladder cancer patients, leading to disease relapse and poor overall survival. 3 Therefore, it is desirable to unravel the underlying mechanisms of drug resistance with the aim of devising potential treatment strategies.
Cancer cells could seek refuge from cytotoxic effects in a wide range of ways, among which the most straightforward method is to restrict drugs reaching the site of action. 4 One such mechanism is through the upregulation of the adenosine triphosphate (ATP)binding cassette transporters, which are able to promote drug expulsion. 5 DNA damage repair is also considered a key player in modifying the response to chemotherapy, which may be attributed to the induction of cell cycle arrest. 6 In addition, accumulating evidence suggested that the tumor microenvironment (TME) provided shelter for cancer cells from cytotoxic stress, leading to the development of acquired resistance. 7 The TME is mainly made up of stromal cells and extracellular matrix (ECM) components. It has been demonstrated that collagen, the most abundant ECM protein, exerts modulatory functions in chemotherapeutic sensitivity, which may be partially dependent on cellular adhesion. 8,9 Integrins, a large family of heterodimeric receptors, serve as cell surface adhesion molecules that connect cells to the ECM. Ligand binding to integrins modulates multiple downstream signals, including the phosphoinositide 3-kinase (PI3K)-AKT, extracellular-signal regulated kinase (ERK), and NF-κB pathways, culminating in the formation of a resistant genotype. 10 A recent study indicated that the interaction of integrin β 1 (ITGB1) with collagen I confers chemoresistance on breast cancer cells, and ITGB1 inhibition sensitized the cells to drugs. 11 Nevertheless, the role of integrins in chemoresistance development and the underlying mechanism remain a matter for further elucidation.
Our previous study reported that a novel 3D collagen I gel model could confer tumorigenic potential to bladder cancer cells and enable them to progress to cancer stem cells (CSCs). 12 Herein, we further focused on the impact of collagen on chemotherapeutic sensitivity and found that collagen culture rendered bladder cancer cells into a senescent state to evade apoptosis induced by cytotoxic reagents. In mechanism, collagen regulated the integrin β1/AKT axis to trigger premature senescence, which was dependent on the p53/p21 pathway. Additionally, we confirmed that interruption of integrin signals had an inhibitory effect on chemotherapy-induced senescence in vivo, resulting in enhanced antitumor efficacy. Taken together, our study indicated the importance of cell senescence during integrin-induced chemoresistance development, which may aid in the elaboration of therapeutic strategies for eliminating bladder cancer.

Ethical approval
The clinical experiments were carried out according to the guidance of the Declaration of Helsinki and approved by the Ethics Committee of West China Hospital, Sichuan University. All patients were informed and provided written consent to participate in the study. The animal studies were conducted in accordance with the Public Health Service Policy and complied with the World Health Organization (WHO) guidelines for the humane use and care of animals. All animal protocols were monitored by the Ethics Committee of West China Hospital, Sichuan University and conducted in accordance with the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments).

Cell culture and reagents
Human bladder cancer cell line T24 was purchased from the American Type Culture Collection and maintained in RPMI 1640 complete medium (Gibco, MA, USA) supplemented with 10% fetal calf serum (Gibco, MA, USA), at 37 • C in a 5% CO 2 atmosphere. 3D collagen culture of cancer cells was performed as previously described. Briefly, collagen I was diluted to 0.6 mg/ml with RPMI 1640 complete medium supplemented with 10% fetal calf serum. Cancer cells (1 × 10 4 ) were added into the 295 μl collagen solution containing 25 μl of 10× PBS and 20 μl of 1 N NaOH. After 2 h of 37 • C incubation, the solid clotty collagen (containing tumor cells) was added to RPMI 1640 complete medium supplemented with 10% fetal calf serum for further use. Collagen I and type I collagenase were purchased from Sigma (USA). Integrin α2β1 ligand peptide TFA (TFA) and AKT inhibitor Miransertib (Mir) were purchased from MedChemExpress (USA). Chemotherapeutic doxorubicin (DOX) and mitomycin C (MMC) were purchased from Sangon (China).

Primary tumor cell culture and patients' information
Human bladder tumor tissues were collected from the West China Hospital, Sichuan University. A total of 20 tumor tissues (paraffin sections) were divided into recurrent (R) and non-recurrent (NR) groups according to a follow-up visit after chemotherapy. For establishment of primary bladder cancer cell lines, 15 primary tumor tissues (BP 1-15) were collected, and digested to be seeded in 3D Matrigel containing growth factors (#354234, Coring, USA). Two samples (BP5 and BP7) succeeded in forming colonies, which were collected and digested to seed in Matrigel again. After being cultured in Matrigel for 2 weeks, BP5 and BP7 were digested and cultured in a dish for further analysis. Both BP5 and BP7 survived and displayed proliferative characteristics in the dish for 4 months.

Cell apoptosis analysis
Cell apoptosis was determined using the FITC-Annexin V and PE-PI apoptosis detection kit (Becton, Dickinson and Company, USA). Briefly, pre-treated tumor cells were harvested and stained with FITC-Annexin V and PE-PI viability staining solution for 15 min at room temperature. Subsequently, apoptosis was analyzed by flow cytometry on a C6 flow cytometer (Becton, Dickinson and Company, USA).

Cell cycle assay
Cell cycle analysis was performed using the PE-PI cell cycle analysis kit (Becton, Dickinson and Company, USA). In brief, pre-treated cancer cells were harvested and fixed with cold ethanol. Subsequently, samples were incubated with propidium iodide (PI) staining solution for 10 min according to the manufacturer's protocol. The samples were detected by flow cytometry (Becton, Dickinson and Company, USA).

Immunofluorescence and immunohistochemistry
Tumor tissues were fixed in 10% formalin solution for at least 48 h. Subsequently, samples were processed, embedded in paraffin, and sectioned at 5 μm for immunofluorescence and immunohistochemical staining. Sections were then dewaxed, rehydrated, quenched of endogenous peroxidase, blocked, and incubated with the primary antibody anti-collagen I (1:100; Abcam, ab270993, UK) at 4 • C overnight, followed by signal amplification using horseradish peroxidase-linked secondary antibodies (Thermo Fisher, USA). The intensity of collagen I expression was determined by Image-Pro Plus 6.0 software (USA).

In vivo experiments
Nude mice (6-8 weeks old) were purchased from Huafukang (Beijing, China) and housed in a specific pathogen-free facility. For cell senescence and apoptosis analysis in vivo, 5 × 10 6 T24 cells were subcutaneously injected into nude mice. On day 20, mice were treated with PBS, MMC (5 mg/kg), or DOX (5 mg/kg) by tail vein injection (n = 3 in each group). On day 23, mice were sacrificed for tumor tissue collection. p21 and p53 expression were determined by western blotting. Part of the tumor tissues were digested for cell apoptosis and senescence analysis. For tumor volume and survival analysis, 5 × 10 6 T24 cells were subcutaneously injected into nude mice. On day 20, mice were treated with PBS, MMC (5 mg/kg), TFA (2.5 mg/kg), or a combination twice a week by tail vein injection (n = 6 in each group). Tumor volume and sur-vival were recorded every day. The formula for the calculation of tumor volume is: tumor volume = length × width 2 /2.

Statistical analysis
All data are presented as the mean ± SEM. Statistical significance was analyzed using GraphPad 6.0 software (La Jolla, CA, USA). Statistical significance between groups was calculated by Student's t test for two groups or by one-way ANOVA for more than two groups. Bonferroni analysis was further used for post hoc testing. Survival rates were determined by Kaplan-Meier survival analysis. * P < 0.05; * * P < 0.01; ns, no significant difference. Each experiment was performed at least three times, independently.

Collagen reduced chemotherapy-induced cell apoptosis and correlated with tumor recurrence in patients
Our previous work has demonstrated that collagen I served as the major element in ECM and was tightly correlated with the tumorigenic potential of tumor initiating cells (TICs). 12 Stemming from this evidence, we next sought to investigate the clinical relevance of the role of collagen in regulating chemoresistance during bladder cancer progression. To do this, 15 tumor tissues were collected from bladder cancer patients, and tumor cells were digested and seeded in 3D matrix gels (containing growth factors) for primary cancer cells culture. Two primary bladder cancer cell lines (bladder patient #5, BP5 and bladder patient #7, BP7) succeeded in surviving in the dish culture and exhibited proliferative characteristics for 4 months (Fig. 1A). Subsequently, bladder cancer cell line T24 and primary BP5/BP7 cells were seeded in our improved collagen gels as described. To verify whether collagen was able to promote chemoresistance, MMC and DOX were added into the culture medium of dish/collagen cultured bladder cancer cells (T24, BP5 and BP7), and cell apoptosis was assessed by Annexin V/PI assay after 48 h. As expected, analysis by flow cytometry showed that collagen cultured bladder cancer cells (T24, BP5 and BP7) were significantly resistant to DOX (Fig. 1B) when compared to the dish cultured group. Notably, enhanced resistance to MMC was observed in collagen cultured cells (Fig. 1B), suggesting that in vitro 3D collagen culture reduced the cytotoxicity of chemotherapy in bladder cancer cells. Seeking to further assess the clinical relationship between collagen I and chemoresistance development, we acquired information and tumor specimens of 20 bladder cancer patients, and then divided these patients into two groups, including NR and R tumor groups, according to a follow-up visit after standard surgery combing adjuvant chemotherapy. Here, the expression of collagen I associated genes (COL1A1 and COL1A2) in the two groups was analyzed by quantified PCR. Of note, we observed that the R group displayed elevated expression of COL1A1 and COL1A2 when compared to the NR group (Fig. 1C). Consistently, the protein expression of collagen I in the R group was higher than in the NR tumors (Fig. 1D). These data suggested that elevated collagen I expression might be the causative reason for therapeutic resistance and tumor relapse. Additionally, we assessed the overall survival of bladder cancer patients with high/low COL1A1/COL1A2 expression by analyzing the Cancer Genome Atlas Program (TCGA) databases. Notably, patients with low levels of COL1A1/COL1A2 expression had a remarkably longer survival time than those with high levels ( Fig. 1E and F). Together, these results suggested that collagen reduced chemotherapy-induced cell apoptosis and correlated with tumor recurrence and poor prognosis in bladder cancer patients.

Chemotherapy-induced cellular senescence to escape apoptosis in 3D collagen cultured TICs
Motivated by prior results implying that collagen reduced cell cytotoxicity induced by chemotherapeutic agents, we next sought to elucidate the mechanism underlying collagen-induced chemoresistance. Development of chemoresistance is bound up with diverse biological processes, including upregulation of multidrug resistant protein P-glycoprotein and arrest of cell cycle (cell dormancy and senescence). 13 We first examined the expression of multidrug resistant gene ABCB1 in dish-and collagen/MMCcultured T24 cells by quantified PCR, respectively. However, no significant alterations in gene expression were detected for ABCB1 in bladder cancer cells ( Fig. 2A). Similarly, we did not find upregulation of multidrug resistant protein P-glycoprotein at the protein level in collagen/MMC-cultured cancer cells when compared to the dish group (Fig. 2B). These results implied that collagen reduced chemotherapy-induced cell apoptosis in a multidrug resistant proteins-independent manner. Subsequently, we wondered whether collagen played a role in regulating the cell cycle to help tumor cells escape from apoptosis. Thus, we assessed the cell cycle of dish-and collagen-cultured bladder cancer cells using AV-PI staining, respectively. Intriguingly, no significant difference was observed in cell cycle between dish-and collagen-cultured T24 cells, while MMC-treated T24 cells (collagen culture) were arrested in the G0/G1 phase (Fig. 2C), suggesting that chemotherapy combining collagen culture induced cell cycle arrest, thus affecting cell apoptosis. As previously reported, a senescence or dormancy-like state could enable tumor cells to be arrested in the G0/G1 state, which would help tumor cells to escape the apoptosis state during chemotherapy. Thus, to assess whether collagen-cultured bladder cancer cells entered a dormancy-like state, an immunofluorescence assay was performed to examine dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1 (COUPTF1) and cell proliferation marker Ki-67 expression in collagen/MMC-treated T24 cells. Unfortunately, no significant difference was detected in COUPTF1 expression among groups (dish, collagen, MMC, or collagen combining MMC, Fig. 2D). This suggested to us that T24 cells might progress to a premature senescence-like state, instead of dormancy, to escape apoptosis. To test our hypothesis, an SA-β-Gal (a senescence marker) assay was conducted to evaluate cell senescence in T24 cells. Remarkably, MMC treatment enhanced SA-β-Gal expression in T24 cells (cultured in collagen), whereas the expression of SA-β-Gal was unchanged in mono MMC or collagen cultured group (Fig. 2E). Similarly, an elevated expression level of SA-β-Gal was found in DOX-treated T24 (collagen culture, Fig. 2F) and BP5/BP7 (Fig. 2G) cells. Based on these findings, it was suggested that collagen culture promoted the entry of tumor cells into a senescent state in the presence of chemotherapeutic agents. Collectively, these results suggested that collagen culture enabled bladder cancer cells to progress to a senescent state to escape apoptosis induced by chemotherapeutic MMC and DOX.

Integrin-AKT axis induced premature senescence via p53/p21 pathway
Given the crucial role of collagen/chemotherapy in regulating the cell cycle, we next sought to elucidate the molecular pathway driving cell senescence. As reported in our previous study, biomaterial 3D collagen gels reverted differentiated tumor cells back into CSCs through the integrin α2β1-AKT cascade. Importantly, a compelling report provided evidence that the integrin-Src-AKT axis could mediate cellular senescence by counteracting apoptosis in irradiated tumor cells. 14 Thus, we put forward the hypothesis that collagen induces upregulation of integrin β1, which leads to activation of the AKT signaling pathway. Subsequently, activated AKT blocks caspase activation and p21 cleavage induced by chemotherapy, eventually resulting in p53/p21 signaling activation and cell senescence, instead of apoptosis, in bladder cancer cells (Fig. 3A). To confirm this, western blotting was performed to examine the integrin β1-AKT axis in T24 cells with four process modes (dish, collagen, MMC, MMC and collagen combination). As excepted, western blotting analysis revealed that both integrin β1 and AKT were notably upregulated in collagen-cultured cells (Fig. 3B), indicating that collagen mediated integrin-AKT axis activation. p53 is a critical tumor suppressor gene, and activating p53 and its downstream targets to induce apoptosis is a traditional apoptosis pathway in tumor cells during chemotherapy. p21 is an inhibitor of cyclin-dependent kinase, which has been reported to serve as a downstream molecule of p53 to promote cell senescence. 15 Thus, we further examined p53/p21 expression in T24 cells (dish, collagen, MMC, MMC and collagen combination). As shown by western blotting, MMC treatment upregulated p53 expression, and collagen culture mediated p21 activation in MMCtreated T24 cells (Fig. 3C). These results implied that MMC mediated p53 apoptosis pathway activation, and collagen culture might counteract cell apoptosis and mediate p21-associated cell senescence. Subsequently, in an attempt to clarify whether the activation of p53/p21 signaling was affected by the integrin-AKT axis, T24 cells were seeded into collagen gels and treated with MMC, followed by TFA and AKT inhibitor Mir treatment. We found that blockade of integrin or AKT induced strongly repressed expression of p21, while the protein level of p53 was unchanged (Fig. 3D). Consistently, SA-β-Gal and cell apoptosis analysis suggested that TFA and Mir treatment suppressed cell senescence (Fig. 3E) and increased cell apoptosis (Fig. 3F) in T24 cells (collagen and MMC treatment). These results suggest that collagen promoted integrin-AKT axis activation to induce premature senescence via the p53/p21 pathway to escape MMC-induced cell apoptosis. To further verify our results, expression of integrin β1-AKT and p53/p21 signaling was evaluated in DOX-treated BP5 and BP7 cells. Similar results were observed in DOX-treated BP5/BP7 cells ( Fig. 3G and H). Together, these results suggest that collagen regulated the integrin-AKT axis to induce premature senescence via the p53/p21 pathway.

Blockade of integrin-ATK signaling suppressed chemotherapy-induced senescence in vivo
Stemming from our in vitro results, we next became interested in assessing the influence of senescence on chemotherapy in vivo.
To do this, we first checked the expression of collagen-integrin β1-ATK expression in T24-bearing mice in vivo. In fact, collagen I, the dominant component in the ECM, has been detected in a wide range of tumor tissues. Consistent with previous reports, a higher expression of collagen I in subcutaneous T24 tumor tissues was found when compared to dish culture (Fig. 4A). Meanwhile, activation of integrin β1-AKT signaling was observed in T24 tumor tissues (Fig. 4B), which was in line with the elevated expression of collagen I in vivo. Next, we treated T24-bearing mice with DOX and MMC by tail vein injection. After 3 days, mice were sacrificed for tumor tissue collection and p53/p21 expression analysis. Accordingly, the protein levels of p53 and p21 were both  much higher in MMC-or DOX-treated tumor tissues when compared to the PBS group (Fig. 4C). Those results were in line with the hypothesis that activation of integrin enhanced cell senescence through p53/p21 signaling in the presence of chemotherapeutic agents. Subsequently, we established a subcutaneous T24-bearing mouse model, followed by MMC and TFA treatment (day 20), for cell senescence and apoptosis assay. At 48 h later, reduced SA-β-Gal-positive cells (Fig. 4D) and enhanced cell apoptosis (Fig. 4E) were found in mice treated with MMC/TFA combination, when compared to the MMC group. Additionally, TFA and MMC combination efficiently halted tumor growth (Fig. 4F) and extended the survival time of tumor-bearing mice (Fig. 4G). These results supported the notion that blockade of integrin/ATK signaling suppressed chemotherapy-induced senescence in vivo, resulting in an improved tumor suppressive effect.

Discussion
Development of chemoresistance is intimately linked with the microenvironment which comprises both neoplastic cells and stromal components. The bidirectional communications between tumor cells and the stroma are thought to initiate events that contribute to drug resistance. 16 A large body of information exists regarding the role of collagen, the main constituent of the ECM, in modulating the response of tumors to diverse treatments. Moon et al. reported that ovarian cancer cells within a collagenbased hydrogel were less vulnerable to the induction of apoptosis by antitumor agents, resulting in a higher level of drug resistance. 17 It was also demonstrated that collagen mediated tumor initiating cell-like characteristics and decreased sensitivity to 5fluorine and paclitaxel in gastric cancer. 18 In a study of lung carcinoma, chemo-resistant patients exhibited enhanced expression of COL1A1, which might serve as an independent prognostic factor. 19 As for bladder cancer, we established an improved 3D culture system and found that collagen gels had an inhibitory effect on cell apoptosis induced by MMC and DOX. Clinical data further revealed elevated collagen I expression in patients with recurrent bladder cancer. Furthermore, analysis of the TCGA database correlated a survival benefit with low levels of COL1A1/COL1A2 in bladder cancer patients. The above results highlighted that collagen promoted drug resistance in bladder cancer and was closely associated with tumor relapse.
Based on the crucial role of collagen, we sought to elucidate the specific mechanism whereby collagen suppressed chemotherapymediated cell apoptosis. Evidence is accumulating that apoptosis is not the only way in which tumor cells respond to drug stimuli. Tumor cells failing to undergo apoptosis may enter into a terminally arrested state termed premature senescence. 20 A panel of markers participate in defining a cell as being senescent, including SA-β-gal reactivity, altered expression of mediators of cell cycle checkpoints, as well as lack of the cell-cycleassociated Ki67 protein. 21 Despite losing the ability to proliferate and migrate, senescent cells remain viable over an extended period of time and secrete a plethora of proteins known as the senescence-associated secretory phenotype (SASP), which influences the TME beneficial to tumor progression. Indeed, studies have identified premature senescence as a key player in modulating the responses to chemotherapy. 22,23 Research on breast cancer demonstrated that drug treatment triggered the generation of senescent cells from which multidrug-resistant colonies with aggressive stem-like phenotype emerged. 24 In vivo experiments also confirmed that after DOX administration, breast cancer developed characteristics of senescence, with the secretion of cytokines that led to the phosphorylation of signal transducer and activator of transcription 3 (STAT3), a substantial contributor to chemoresistance. 25 In addition, colorectal mouse models suggested that late disease recurrence was associated with the senescent state in response to drug stimuli, and interruption of this state augmented the apoptotic response of colorectal cancer cells to SN38, hinting at the reversibility of chemotherapy-induced senescence. 26 In the current study, we first ruled out the influence of multidrug resistant protein and found that collagen played a role in regulating the cell cycle to help bladder cancer cells evade apoptosis. We further demonstrated that cellular senescence, rather than dormancy, was responsible for cell cycle arrest in the presence of collagen, finally resulting in low sensitivity to chemotherapy. Meanwhile, MMC or DOX treatment caused an elevation in SA-β-Gal levels in bladder cancer cells under this 3D culture condition, which provided proof of the involvement of the senescent state in chemoresistance development. Compared to previous work, we initially addressed the relationship between chemotherapy and cellular senescence in a 3D culture model, underscoring that drug administration in conjunction with collagen culture enabled bladder cancer cells to enter a premature senescence-like state, thus impairing the curative effect of cytotoxic agents. Also, our study offered new explanations for collagen-mediated chemoresistance in bladder cancer and added further insights into the role of collagen in tumor development.
The above findings prompted us to unveil the specific molecular pathways driving cellular senescence. Senescence activation is executed by intracellular and extracellular signals and depends heavily on the engagement of the ECM. 27,28 Integrins, transmembrane receptors interacting with the matrix elements, are involved in the control of complicated cellular behaviors, including adhesion, proliferation, migration, survival, and cell fate transitions. Upon ligand binding, integrins recruit intracellular proteins and activate downstream signals, such as Ras-ERK, PI3K/AKT, and Yes1-associated transcriptional regulator (YAP)/tafazzin (TAZ) pathways. 29 Importantly, compelling findings have established the role of integrins in cellular senescence. β1-Integrin activation was proved to be essential for tenascin-C-induced senescence of fibroblasts, which secreted soluble factors driving the malignant transformation of epithelial cells. 30 In addition, when exposed to ionizing radiation, lung carcinoma cells might undergo a shift from cell apoptosis to premature senescence via the integrin α6β4-Src-AKT signaling pathway. 14 Consistently, our in vitro and in vivo experiments confirmed that collagen enhanced the activation of the integrin-AKT axis to mediate a state of premature senescence in bladder cancer, leading to low reactivity to cytotoxic agents. We further identified the p53/p21 pathway, a core regulator of the cell cycle, as the downstream molecules of integrin signals. Notably, inhibitors and monoclonal antibodies of integrins have been demonstrated to dampen tumor progression and disrupt angiogenesis in mouse models. 31 More importantly, integrintargeted drugs (e.g. cilengitide, etaracizumab, CNTO 95) are being extensively investigated in cancer clinical trials with promising results. 32 Both clinical and experimental data has suggested the tumor suppressive effects of cilengitide (an RGD pentapeptide ανβ3 and ανβ5 integrin inhibitor) in clinical glioblastoma, 33 and E7820 (an integrin α2β1 inhibitor) in combination with erlotinib for non-small cell lung cancer treatment. 34 Here, our study determined the efficacy of TFA in combination with MMC, which hindered bladder cancer growth and prolonged the survival time of tumor-bearing mice. On the basis of these results, our study illustrated the following points. (1) The expression of collagen was correlated with tumor recurrence and could be potentially translated into a prognostic marker for bladder cancer. (2) Bladder cancer cells cultured in collagen gels were skewed toward premature senescence rather than cellular apoptosis in response to chemotherapeutic agents. (3) Induction of the senescent state was dependent on the integrin-AKT axis. (4) The suppression of tumor growth could be achieved by MMC (or other chemotherapeutic agents) in cooperation with an integrin α2β1 ligand peptide, which opened up possibilities for targeting integrins in oncological treatment.
In brief, our study indicated that chemotherapy triggered premature senescence to evade apoptosis in bladder cancer cells within collagen gels. We provided evidence for the integrin-AKT axis being a regulator of senescence via the p53/p21 pathway. Targeting integrin signals may provide a feasible strategy to improve the clinical treatment for bladder cancer.


Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer

Objectives To investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents. Methods Human immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR. Results We found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays. Conclusions Briefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.

Introduction
The World Health Organization's International Agency for Research on Cancer (IARC) released the latest data on the global cancer burden in 2020. New cases of breast cancer (BC) increased rapidly to 2.26 million, accounting for 11.7% of all new cancer patients, officially overtaking lung cancer (2.2 million cases) as the world's leading cancer for the first time (1). Effective prevention, diagnosis, treatment, and whole-process management strategies for BC have become important mediums for improving the global cancer burden.
The efficacy of local and systemic treatment of BC has improved greatly in recent years, and avoid the underutilization of medical resources has become a focus (2). The clinical decision of BC is mainly dependent on the abnormal expression of estrogen, progesterone endocrine receptors (ER, PR) and HER2. Immunotherapy has been shown to have salvage implications in many cancers. Antibodies against the immunomodulators PD-L1/PD-1 and CTLA4 have had staged clinical success, but more in-depth studies of immunotherapy are still needed. Proper functioning of the immune system requires constant modulation to ensure protection against foreign factors and the tolerance of autoantigens (3).
Immune editing is a dynamic process that regulates tumor evolution through the immune system, including elimination, homeostasis and escape (4). Immune escape is key to the persistence of most solid tumors, and it is a key obstacle to the success of cancer (immune) treatment (5). The mechanisms of immune escape include a decrease in immune analysis, downregulation of co-stimulatory molecules, and overexpression of co-inhibitory molecules, causing a decrease in CD8+ T cell activity and weakening the body's anti-tumor immunity (6). Immune escape is a necessary condition for the formation and development of breast tumors and a key step in the transition from pre-invasive lesions to aggressive tumors (7). Current work is focused on developing combination therapies to convert non-responders into responders, deepen existing responses, and overcome acquired immunotherapy resistance (8). The response elicited by immunotherapy is expected to clearly target and destroy tumor cells while preserving normal cells. Immunotherapeutic approaches include the use of antibodies to neutralize or block immune checkpoints, induction of proliferation and/or increased activity of tumor infiltrating lymphocytes (CTL), and it regulating the tumor microenvironment (TME) (9). Effective anti-tumor strategies must focus on targeting multiple immune pathways to fully activate endogenous tumor immunity (10,11). In advanced BC patients, immunotherapy combined with targeted therapy is undergoing basic and clinical trials to confirm biomarkers in the tumor and TME, as well as to identify the pharmacokinetics and pharmacodynamics of drug combinations, and to optimize drug dosing in order to find the optimal combination of treatments for individual patients (12). BC is extremely heterogeneous at the clinical and molecular level. Because of the use of various histological techniques (genomics, transcriptomics or proteomics, etc.), we have gained deep understanding of the complexity of the development of BC (13). Multiomic is an emerging analytical approach that combines next-generation DNA and RNA sequencing with protein characterization to provide features such as protein expression levels, post-translational modifications, and protein-protein interactions (14).
Keith A. Lawson et al. have found a key set of genes and pathways, which make tumor cells dodge CTL-mediated killing. They found 182 immune escape related genes by the genome wide CRISPR screen on a set of mouse BC cell lines cultured in the presence of CTL, and disrupting these genes alone increased the sensitivity and resistance of cancer cells to CTL killing (15). There are no comprehensive studies on the genes encoding immune escape in BC, and endocrine therapy plus CDK4/6 inhibitors have become the standard of care for estrogen receptor-positive (ER+) BC. Although immune checkpoint inhibitors (ICIs) have shown promising antitumor activity in a variety of cancer types, only limited success has been achieved in patients with metastatic breast cancer (mBC), particularly the ER+ subtype, with typically exhibiting a lower tumor mutational load (TMB) compared to other subtypes and are therefore considered immunostasis. This may lead to missed treatment opportunities and overdosing (16,17).
Targeted therapies have been greatly developed in BC, such as Toremifene and Pertuzumab (18). Vemurafenib is often used in BRAF-mutated melanoma and is gradually being explored in BC. A xenograft model of vemurafenib-treated MDA-MB-231 showed growth-inhibitory activity associated with inhibition of tumor angiogenesis (19). Magdalena Pircher et al. reported the first successful control of multiple lung metastases from triplenegative BC with vemurafenib (20). The specific mechanism by which vemurafenib inhibits breast cancer needs to be further explored.
The main objective of this study is to screen for immune escape related genes, and to identify hub genes that are associated with prognosis and efficacy of BC, and to provide new targets for the treatment of BC.

Differentially expressed protein analysis and enrichment analysis
The "t-test" of "GraphPad Prism 8" were used for differential expression analysis of normal and tumor samples, and the "survival" R package for survival analysis. The cBioportal database (http://www.cbioportal.org/) was used for further differentially expressed proteins analysis (22). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differential expression of protein-coding genes were performed using "clusterprofiler" package. Category, Count, and -log10pvalue were used by GO analysis to obtain important metabolic pathways.

Cox regression analysis to establish a risk model
First, the gene sequencing data and patient survival data were combined, and univariate Cox analysis was performed using the "surviving" and "forestplot" R packages, and critical genes associated with prognosis were further explored using the "glmnet" R package. Multivariate cox analysis of candidate genes was performed with the "surv" package, and it constructed a prognostic risk assessment model.
Then, risk prediction and risk score calculation were performed using a prediction model. Based on the risk scores, patients were divided into high-and low-risk groups.

Measure the risk model
The "Hiplot" plotting tool was used to show the distribution of survival situation of low-and high-risk patients. The "pheatmap" R package was used to observe the difference in expression levels between high-and low-risk groups. Kaplan-Meier Survival analysis was used to assess the survival rates of BC patients in both groups. The validity of candidate genes in predicting the prognosis of BC patients was measured by plotting ROC curves using the "pROC" R package, which based on survival status, survival time and candidate gene expression in BC patients. The TCIA database (https://tcia.at/ home) was used to analysis the enrichment of immune cells in the high-and low-risk groups (23). The prognostic model was externally validated by GSE20685 and GES42568 datasets.

Screening of core prognostic factors and construction of nomogram
The "T test" of "GraphPad Prism 8" was used for differential expression analysis of normal and tumor samples, and "survival" package was used to obtain survive-related hub genes, and P<0.05 was regarded statistically significant. The nomogram was used to predict the effect of each differentially expressed protein coding genes on 1-, 3-, and 5-year overall survival. The nomogram is composed of central prognostic factors, whose point scale is assigned to each variable. We used a horizontal line to determine the points for each variable and calculated the total points for each patient by adding points for all variables by normalizing the distribution from 0 to 100. Then we established the performance of the calibration curve for the visual nomogram. We compared the predictions in the calibration curves with the observed results. The best prediction was when the slope was close to 1.

Immunoassay database
The "XY" of "GraphPad Prism 8" performed correlation analysis of central protein-encoding genes with immune checkpoints PD-1, PD-L1 and CTLA4 and showed the relationship between them using Hiplot's chord charts. The Gene Set Cancer Analysis (GSCA) database (http://bioinfo.life. hust.edu.cn/GSCA/) was used to analyze the expression of central protein-encoding genes and associated immune infiltration (24). The TIMER database (https://cistrome. shinyapps.io/timer/) was used to capture the immune cells and the correlation of tumor purity and quantity of gene expression, and represented by the heat map of the Hiplot (25).

Drug sensitivity analysis database
Used the CELLMINER database (https://discover.nci.nih. gov/cellminer/) download the processed data "Processed Data Set" (26), and the data was processed by the "readxl" package. There were some "NA" missing values in the drug sensitivity data, and the "impute. KNN ()" function was used to evaluate and complement the missing values. Pearson correlation coefficients between each gene expression and different drugs were calculated and the two groups of drugs with the maximum and minimum correlation for each protein-coding gene were found based on the correlation coefficients. Correlation analysis was performed using "XY" of "GraphPad Prism 8" for visualization and validation purposes. The GES97681 dataset was used to verify whether Vemurafenib affects the expression of B-cell lymphoma 2-associated protein A1 (BCL2A1) and to find the pathway of drug effects through the literature, integrating the genes and pathways in the KEGG data (https://www.genome.jp/ kegg/) to discover the effects of drugs on genes.

MTT assay
For the MTT assay, MCF-7, SK-BR-3 and MDA-MB-231 cells were detached by trypsinization and counted in a haemocytometer. Cells were seeded in 96-well plates at a density of 4000 cells/well in 100 ml medium per well. Twentyfour hours after incubation in the CO2 incubator, adherent cells were treated with increasing concentrations of drugs: vemurafenib (0, 20, 40, and 60 mM) in fresh 1640 and DMEM medium. For measuring the concentration of vemurafenib that resulted in 50% control growth inhibition (IC50), at 48 hours following drug treatment, 10 ml MTT (3-(4,5-Dimethylthiazol-2yl)-2,5-diphenyltetrazolium bromide) (Sigma-Aldrich) solution (5 mg/ml in PBS) was added to each well after 48h of drug treatment. In addition, cell viability was measured by adding MTT at 0, 24, 48, 72h. Then incubation was continued for additional 4h in the CO2 incubator. The blue formazan product, formed by reduction in live attached cells, was dissolved by adding 100 ml of 100% DMSO per well. The plates were gently swirled at room temperature for 10 minutes to dissolve the precipitate. Absorbance was monitored at 490 nm using a microplate reader (CYTATION 3; Agilent Technologies, Inc.). The effect of vemurafenib on cell viability was assessed as the percent of cell viability compared with vehicle-treated control cells, which were arbitrarily assigned 100% viability. Doseresponse curves and IC50 values were obtained using GraphPad Prism software. At least 3 dose-response experiments were performed for each compound, and the mean IC50 ± SD was calculated.

Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)
Cells were seeded at the density described above and incubated for 24 hours before the addition of vemurafenib. The compounds were added to the final concentrations of 0, 20, 40, and 60 mM. After 48 hours of treatment, Cell pellets were collected for experiments. Total RNA was isolated from MCF-7, SK-BR-3 and MDA-MB-231 by using Trizol reagent (Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Total RNA (1 mg) from each group of treated cells was converted to cDNA using a FastKing RT reagent kit (Tiangen, Inc.). The RT reaction was performed at 42°C for 15 min and 95°C for 3 min. qPCR was performed with a SYBR Green Real Time PCR kit (Thermo Fisher Scientific, Inc.) on CFX96 Touch Real Time PCR System (BioRad Laboratories, Inc.) under the following conditions: 95°C for 1 min, then 40 cycles of 95°C for 5 sec and 60°C for 15 sec. The gene expression level was calculated as 2( −△△Ct ) method, and the △Ct means the Ct of target gene minus the Ct of reference gene. The primers used for real-time PCR were BCL2A1 (forward) 5'-AAATTGC CCCGGATGTGGAT-3' and (reverse) 5'-ACAAAGC CATTTTCCCAGCCT-3'; GAPDH (forward) 5'-CTGGGC TACACTGAGCACC-3' and (reverse) 5'-AAGTGGTCGT TGAGGGCAATG-3'.

Statistical analysis
Our research mainly used "GraphPad Prism 8" and "R language" for data difference analysis, visualization, etc. From the "GraphPad Prism 8", "T test" was used to compare the differences between two data sets, "one-way ANOVA" was used to compare differences between three or more groups of data, and "XY" was used for correlation analysis. P<0.05 was considered statistically significant, indicating significant differences between the data were used. R is the language and operating environment for statistical analysis and mapping. we used the " Pheatmap" , " Survival" , " GGPUBR" and "clusterprofiler " package fort expression analysis, survival analysis and enrichment analysis for differentially expressed genes and proteins. All experiment data are presented as the mean ± standard deviation (SD), further analyzing using GraphPad Prism 9.0. Differences in the results of two groups were evaluated using either two-tailed Student's t test or one-way ANOVA followed by post-hoc Dunnett's test. The differences with P< 0.05 were considered statistically significant.

Results
Fifteen immune escape encoding genes significantly associated with the survival and prognosis of BC Venn plot was used to identify the overlap of 2483 human immune associated genes from Immport database with 182 immune escape associated genes from Keith A. Lawson's study ( Figure 1A), yielded 31 crossover genes ( Figure 1B). We compared the expression levels of immune escape encoding genes between BC patients and healthy individuals, found that 18 genes were significantly up-regulated and 12 genes were significantly down-regulated in BC patients, and ERAP1 was not significantly different in BC patients and healthy individuals ( Figure 1C). BC patients were divided into high and low groups according to "res.cut" expression levels, and Kaplan-Meier survival analysis was performed for BC immune escape gene expression levels. Among them, 15 genes showed significant differences in survival and expression levels in BC patients ( Figure 1D), and the survival curves of the other 16 genes were also shown ( Figure S1A).

Immune escape in BC is mainly influenced by PI3K-Akt signaling pathway
We identified 181 differentially expressed proteins regulated by BC immune escape encoding genes through the cBioPortal database (Table S1). The volcanic plots (Figure 2A) show proteins with significant changes (P<0.05) and were used for further analysis. To determine which pathway differential proteins were mainly enriched, we performed GO and KEGG enrichment analysis using "clusterprofiler" R package, which showed that these differentially expressed proteins play important roles in protein serine/threonine kinase activity, ubiquitin-like protein ligase binding, membrane raft, cell substrate connection, gland development andreproductive structure development. The PI3K-Akt signaling pathway is considered to be the most prominent downstream signaling pathway for immune escape related genes in BC ( Figures 2B, C). The major signaling pathway affected by genes associated with immune escape. (A) Volcano mapping to identify differential expressed proteins affected by immune escape related genes and analyzed by reverse phase protein array (RPPA) in cBioPortal. The Y-axis is the value that expresses the change in the horizontal fold, based on the logarithmic ratio (average of the changing expression/average of the constant expression). -log10 (p value)>1.30 is considered a significant difference. (B) The bubble diagram of GO enrichment analysis of immune escape gene was analyzed through clusterProfiler. Ten proteins have been shown to be important in biological processes, cell components, and molecular functions. (C) The downstream pathways related to immune escape gene changes were analyzed by KEGG pathway in clusterProfiler.
in BC, and 31 of them were identified to be associated with survival ( Figure 3A). To evaluate differentially expressed protein-coding genes meaningfully associated with BC prognosis and to obtain a better-fitting model, we used LASSO to downscale the high-dimensional information by adding a constraint to the absolute value of the coefficients and further screening by 10-fold cross-validation. The optimal l value (lmin=0.025) was obtained from the minimum local likelihood deviation, and 16 genes were found to be significantly associated with prognosis ( Figures 3B, C). The previous one looks at each feature individually to see if it is related to survival, while multivariate cox regression is to test whether multiple features are related to survival at the same time. Multivariate Cox analysis recognized 11 excellent prognostic gene models, namely EIF4EBP1, BCL2A1, NDRG1, MRE11A, ERRFI1, CLDN7, PIK3CA, CASP9, BRD4, PDCD4, and G6PD ( Figure 3D There were more deaths in the high-risk group than the low-risk group, while fewer patients survived longer than 5 years than in the low-risk group ( Figure 4A). The heatmap shows the risk of prognostic genes with a multivariate outcome P value less than 0.05. As the risk score increased, the expression levels of MRE11A, PIK3CA, EIF4EBP1 and NDRG1 increased, while the expression levels of BCL2A1 and BRD4 decreased ( Figure 4C). Kaplan-Meier survival curve assessed the difference in survival between high-and low-risk patients in the risk model, which showed that survival decreased more significantly over time in the highrisk group than in the low-risk group, and the mean prognosis was worse in the high-risk group than in the low-risk group (P<0.0001) ( Figure 4E). The prognostic value of Kaplan-Meier survival curves was determined by the P-ROC curve, and the area under the curve (AUC) was 0.672, indicating the median reliability of the kaplan-Meier survival curve ( Figure 4G). The AUC of the survival assessment model was 0.68 for 2 years, 0.71 for 5 years, 0.75 for 8 years, and 0.77 for 10 years ( Figure 4I). The applicability of the prognostic model was externally validated using the GSE20685 dataset ( Figures 4B, D, F, H, J) and GES42568 dataset ( Figure S2), and the results further confirmed the reliability of the risk model. We imported the high-and low-risk groups into the TCIA database and found that the high-risk groups had a lower percentage of immune cells clustering ( Figure S2).

Nomogram validates the predictive value of 5 core prognostic factors
Seven of the 11 prognostic indicators were statistically significant, when Kaplan-Meier survival analysis was performed on them according to expression level, six of them were significantly different ( Figure 5A). Analysis of their expression levels in tumor patients and normal subjects identified five core prognostic markers, which were EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4 ( Figure 5B). We plotted the expression levels of five central prognostic factors based on 1069 TCGA-BC patients. By testing these parameters to obtain the corresponding assigned scores for their expression, the individual gene scores were summed to find the total score, which can predict the survival of BC patients for 1, 3, and 5 years ( Figure 5C). A calibration curve was plotted to verify the accuracy of the prediction of the line graph. In the calibration diagram, the predicted result (blue line) was very close to the actual result (black line), with a c-index of 0.695, indicating that the prediction quality of Nomogram was very high ( Figure 5D). In conclusion, five core prognostic markers can accurately predict the prognosis of BC patients.

BCL2A1 is significantly associated with BC immunity
Correlation analysis of 5 core prognostic factors and immune checkpoints (PD-1, PD-L1 and CTLA4) showed that BCL2A1 had the highest correlation with immune checkpoints, while other factors with no significant difference ( Figure 5E). Correlation analysis of the 5 central protein-encoding genes with 26 immune cells showed that BCL2A1 had the highest correlation with immune cells ( Figure 5F). The correlation of 5 central protein-coding genes with tumor purity and 6 kinds of immune cells (B cells, CD4+ T cells, CD8+ T cells, Neutrphils, Macrophages and Dendritic cells) was further analyzed. BCL2A1 was negatively correlated with tumor purity and positively correlated with immune cells ( Figure 5G).

Analysis of the expression of five core prognostic factors in different BC subtypes
We analyzed the expression levels of five core prognostic factors in two sets of BC classification data from public databases TCGA ( Figure 6A) and GSE96058 ( Figure 6B). The expression levels of EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4 in all BC patients are consistent. The expression levels of EIF4EBP1 and BCL2A1 in BC patients are significantly higher than those in Vemurafenib impacts the expression of BCL2A1 by affecting PI3K-AKT signaling pathway and inhibit BC cell growth Analysis of the potential correlation between the expression of 5 core protein coding genes and drug sensitivity in different human cancer cell lines from CellMiner database, which showed that the expression of BCL2A1 was positively correlated with the drug sensitivity of Vemurafenib ( Figure 7A). The results of the correlation between other drugs and gene expression are placed in the Supplementary Table (Table S2). We used RNA sequencing data from 78 BC patients in GSE97681, 24 without and 54 with Vemurafenib, the expression levels of BCL2A1 were analyzed and were found to be significantly reduced with the drug ( Figure 7B). A review of the literature and the KEGG database Verification of prognostic risk model based on TCGA database (Data were collected from low risk groups (n = 564) and high risk groups (n = 504)) and GSE20685 (Data were collected from low risk groups (n = 171) and high risk groups (n = 156)). To explore the effect of vemurafenib on BC cells, the concentration of vemurafenib that resulted in 50% control growth inhibition (IC50) of MCF7, MDA-MB-231 and SKBR3 cells was assessed by MTT ( Figure 7E). The IC50 of MCF-7 is 42 mM, MDA-MB-231 is 34 mM and SKBR3 is 51 mM. Next, the inhibitory activity of different concentrations of vemurafenib (0, 20, 40, 60 mM) on MCF7, SKBR3 and MDA-MB-231 cells at 0, 24, 48, and 72 h were further studied by MTT ( Figure 7F). The results showed that vemurafenib inhibited cell proliferation in a dosedependent manner, and the inhibitory effect increased with time.
To further explore the mechanism by which vemurafenib inhibit the growth of MDA-MB-231, MCF7 and SKBR3 cells, the expression level of PI3K/AKT signaling pathway and BCL2A1 in vemurafenib treated cells was evaluated by western blot (Figures 8A-C). The expression level of PI3K, AKT, p-AKT and BCL2A1 were reduced in vemurafenib treated cells compared to without vemurafenib. This suggested that the drug inhibits the PI3K/AKT signaling pathway and BCL2A1. In addition, apoptosis-associated genes expression was detected. BCL2A1 downregulation was confirmed using RT-qPCR ( Figures 8D-F), and the results indicated that vemurafenib promoted cell apoptosis of MDA-MB-231 and MCF-7.

Discussion
BC is a malignant tumor that seriously endangers women's health, and is occasionally seen in men. Most of the early symptoms of BC patients are not obvious and can be easily ignored, and the failure to seek timely medical attention leads to poor prognosis of BC. Personalized treatment and other new therapies can help patients choose the most appropriate treatment among various therapies. Establishing an accurate patient prognosis prediction system to provide patients with a more optimal treatment approach in molecularly targeted therapy is essential for personalized treatment (27). Molecular prognostic markers can change with tumor progression, and monitoring these markers can dynamically reflect patient prognosis. In addition, some molecular prognostic markers are involved in tumor progression and may be potential targets for tumor therapy and diagnostic indicators for early tumors (28). Molecular prognostic markers may be heterogeneous across patients, so that a group of molecular markers is superior to individual markers in terms of prognosis.
A large number of cancer and normal breast tissues were analyzed and screened in TCGA, GTEx, and GEO databases for broad applicability. 182 mouse immune escape related genes were obtained from Keith A. Lawson et al. and intersected with 2483 human immune escape related genes downloaded from the IMMPORT database, and 31 reliable immune escape related genes were identified. Fifteen of the immune escape related genes were significantly associated with survival and expression, and 181 differentially expressed proteins of the 15 hub genes were identified through the cBioportal database. Gene enrichment analysis showed that differentially expressed proteins are involved in many important biological processes, such as protein serine/threonine kinase activity, ubiquitin-like protein ligase binding, membrane raft, cell substrate connection, gland development and reproductive structure development. In addition, they are associated with important tumor-related pathways such as the PI3K-AKT signaling pathway, Hepatitis B, and Human papillomavirus infection pathway. The PI3K-AKT signaling pathway mainly inhibits cell apoptosis and promotes cell proliferation, indicating that immune escape genes play an important role in the occurrence and development of tumors. In recent years, an increasing number of studies had found that PI3K/Akt signaling pathway is closely related to the occurrence and development of lung cancer, BC, colorectal cancer, prostate cancer, ovarian cancer, liver cancer and lymphoma (29).
To find the most representative survival-related differentially expressed proteins, univariate Cox analysis, lasso regression analysis and multivariate Cox analysis were used to analyze the genes encoding these differentially expressed proteins. The prognostic model consisted of EIF4EBP1, BCL2A1, NDRG1, MRE11A, ERRFI1, CLDN7, PIK3CA, CASP9, BRD4, PDCD4 and G6PD. According to these 11 genetic characteristics, BC patients can be divided into high-and low-risk group, the survival rate of high-risk group is significantly lower than that of low-risk group. Differential expression analysis and survival analysis were performed on the prognostic models, and five core prognostic models were summarized, including EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4. EIF4EBP1, NDRG1 and BRD4 are associated with poor prognosis of BC, while BCL2A1 and ERRFI1 are associated with good prognosis of BC. The nomogram found that the five prognostic factors had good prediction accuracy at 1, 3 and 5 years of BC. Similarly, HPA database (Human Protein Atlas) was used to test the performance of the prognostic model based on the characteristics of these five protein-coding genes, and the results showed that they performed well in predicting the prognosis of BC. EIF4EBP1 (also known as 4EBP1) gene encodes a translation repressor protein, which competitively binds to eukaryotic translation initiation factor 4E (EIF4E), and inhibits the assembly of the EIF4E complex, thereby suppressing capdependent translation (30). In BC and cervical cancer, EIF4EBP1 is considered to be a major factor in the signaling pathway related to prognosis and malignancy, which was not changed by the presence of other upstream carcinogens (31,32), it is consistent with our findings. The Bromodomain and extra-Terminal Domain (BET) protein BRD4 was a transcriptional and epigenetic regulator that plays a key role in embryonic and cancer development. Aberrant degradation of the BRD4 protein in cancer leads to resistance to BET inhibitors, so BRD4 is emerging as a promising anticancer therapeutic target (33).
Recent evidence suggests that BRD4 has additional nontranscriptional functions in cancer, affecting processes such as DNA damage repair, checkpoint activation, or telomere homeostasis (34). ERBB receptor feedback inhibitor 1(ERRFI1) was known as a tumor suppressor, and initiates cell growth by directly inhibiting the epidermal growth factor receptor and its downstream pathway. In addition, ERFFI1 inhibits another receptor family (Erbb), whose activation leads to cell survival, proliferation, migration, and invasion (35). A dual mechanism by which ERRFI1 regulates AKT has been identified. ERRF1 inhibits growth and enhances response to chemotherapy in cells expressing high levels of EGFR. This was partly mediated by ERRFI1-dependent direct inhibition of the negative regulation of AKT signaling by EGFR. In cells expressing low levels of EGFR, ERRFI1 positively regulates AKT by interfering with the interaction of the inactivated phosphatase PHLPP with AKT, thereby promoting cell growth and chemotherapy desensitization (36). N-myc Downstream regulated gene-1 (NDRG1) was a potent inhibitor of metastasis regulated by hypoxia, metal ions including iron, free radicals nitric oxide (NO), and various stress stimuli. This intriguing molecule had shown multiple functions in cancer, regulating a plethora of oncogenes through cellular signaling and inhibiting epithelial mesenchymal transition (EMT), cell migration and angiogenesis (37).
BCL2A1 is a member of the Bcl-2-associated protein family, as an anti-apoptotic protein associated with resistance to chemotherapeutic drugs and targeted drugs (38). The Bcl-2 protein family plays a key role in regulating the internal pathways apoptosis by inhibiting cytochrome C and releasing activated proteases (39). We found that BCL2A1 expression was higher in BC than in normal breast tissue, and the expression of BCL2A1 was upregulated as the malignancy of the molecular subtype of BC increased. We also found patients with high expression BC showed a better survival advantage, which may result from the high correlation of BCL2A1 with immune cells and immune checkpoints (CYLA4, PD-1, PD-L1). However, the tissue clump-based transcriptome sequencing data are indistinguishable between cancer cells and their surrounding machinery cells, suggesting that if BCL2A1 expression is increased in the machinery cells surrounding tumor cells, this could also lead to an increase in overall BCL2A1 expression levels. As there is no effective drugs to treat cancers that high expression BCL2A1 at present (40), the discovery of its target drug could provided value to guide clinical treatment. We found a significant correlation between BCL2A1 and vemurafenib by drug sensitivity analysis. We also analyzed the data of GSE97681 and found that the expression level of BCL2A1was significantly lower in the group using vemurafenib compared to the nondrug group.
BCL2A1 is present downstream of PI3K/AKT and is regulated by it, affecting survival. Vemurafenib (PLX4032) is a novel small molecule BRAF inhibitor that has been approved by the US Food and Drug Administration for the treatment of patients with melanoma (41). One study found that applying Vemurafenib to a xenograft model of BC cells (MDA-MB-231) revealed that Vemurafenib treatment resulted in downregulation of PI3K-AKT signaling (19), which may be responsible for the downregulation of BCL2A1 expression after Vemurafenib. We found that vemurafenib had an inhibitory effect on the cell growth of MDA-MB-231, SKBR3 and MCF-7 cells by MTT assay. Further, we detected the level of PI3K/AKT signaling pathway in vemurafenib-treated cells by Western Blot and found that Vemurafenib significantly inhibited PI3K/AKT signaling pathway and the expression of BCL2A1. RT-qPCR confirmed the vemurafenib down-regulation of the expression of BCL2A1 too, and the results showed that vemurafenib inhibited the survival of MDA-MB-231, SKBR3 and MCF-7 cells.
In general, our study established an 11-gene prognostic model for predicting the efficacy of immunotherapy for BC, and further identified the 5 most core prognostic factors of the prognostic model, which plays a certain role in personalized treatment and accurate prognostic prediction for BC patients. In addition, this study also provides targeting reference drugs for the core prognostic genes to provide a reference for clinical practice, and the accuracy of drug targeting genes was verified by basic experiments and elucidates in detail that Vemurafenib suppresses BC by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1. However, further experimental studies and larger scale clinical trials are needed to further determine the applicability and accuracy of the 5 core factors that constitute prognostic markers for BC development and progression and the potential mechanisms of related drugs.

Conclusion
In summary, our 11-gene expression prediction model based on multiple data sets is more economical and clinically feasible than whole-gene sequencing. We also plotted histograms of five core prognostic factor prediction models, which can individually assess the prognosis of different patients by detecting the expression of genes. We also elucidate in detail that Vemurafenib suppresses BC by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1. Our findings will provide therapeutic targets for individualized treatment of BC and find its potential drugs, which will certainly be more beneficial for selecting effective treatments.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author/s.

Author contributions
ZX, QW, and JS contributed to the concept, design, and review of the study. YD and DZ organize the database, LYa and DZ carry out statistical analysis. LYa, LZ conducted experiments, DL, YD and AS wrote the first manuscripts. All authors were involved in revising, reading, and approving the version of the submitted manuscript.

Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ fonc.2022.906197/full#supplementary-material FIGURE S1 The remaining Kaplan -Meier survival curve in Figure 1D and Figure 5A.


s2fieldsofstudy:
Biology
Agricultural And Food Sciences

text:
Detection of rifampicin resistance in sputum samples by PCR-ELISA

Introduction Tuberculosis caused by Mycobacterium tuberculosis shows resistance to anti-tuberculosis drugs like Rifampicin and Isoniazid generating a public health problem. Using molecular techniques, such as PCR-ELISA, is an alternative that will detect resistance to RIF easily and economically. Objective Optimize a PCR-ELISA that can detect resistance to RIF in sputum samples. Method A PCR-ELISA was standardized to amplify a 255bp of rpoB gene that encodes resistance to RIF. Different parameters were optimized: hybridization temperatures, and probe concentration, among others. The technique’s analytical sensitivity, specificity, and concordance against GenotypeMTBDRplus v2 were evaluated using 20 samples in a pilot assay. Results A PCR-ELISA was optimized for the detection of resistance mutations; the analytical sensitivity of the PCR-ELISA was 4ng of PCR product for the H445D and H445Y probes while for the S450L probe the sensitivity was 0.4 ng of PCR product respectively. The technique has a specificity of 100%. Two mutations, S450W and L452P, were not detected by our system. In the pilot assay, a “good agreement” (k=0.737) was obtained between both techniques. Conclusions A PCR-ELISA was standardized for the detection of the 3 more frequent mutations in Peru associated with resistance to RIF simultaneously, the most frequent being S450L.

INTRODUCTION
44 Tuberculosis (TB) is caused by the bacillus Mycobacterium tuberculosis (MTb), and is 45 transmitted when sick people expulse bacteria into the air (e.g., by coughing) (1) The disease 46 typically affects the lungs (pulmonary TB) but can affect other organs of the body. 47 TB is a communicable, curable, and preventable disease; approximately 85% of people who 48 develop the disease can be successfully treated (2). However, social determinants such as 49 poverty, malnutrition, HIV infection, smoking, and diabetes aggravate this disease (2).  Drug-resistant TB remains a public health problem of great concern due to simultaneous 57 resistance to isoniazid (INH) and rifampicin (RIF) the two most effective first-line drugs for TB 58 treatment, which is called Multidrug-resistant TB (MDR-TB). Extensive drug-resistant (XDR-TB) 59 tuberculosis which defined as a case of MDR-TB that is resistant to either fluoroquinolone and 60 one of the second-line injectable drugs (capreomycin, kanamycin, or amikacin) (4) also 61 contribute to worsening the problem. RIF-resistant TB (RR-TB), MDR-TB, and XDR-TB require 62 longer treatment with second-line drugs causing more side effects when patients are treated 63 (2). 64 Peru leads MDR-TB incidence cases in the Americas region unlike Brazil, which has a higher 65 incidence of TB and HIV-TB cases (3). methods and that can mainly be implemented in primary care hospitals or less complex health 114 centers. In this sense, this work aimed to optimize and evaluate, in the laboratory, a PCR-ELISA 115 for the detection of H445D, H445Y, and S450L mutations conferring RIF resistance in sputum 116 samples from tuberculosis patients.  120 The following strains were used for this work: ATCC N° 27294 (H37Rv) which was used as a 121 control sensitive to all drugs, ATCC N° 35838 used as a control for resistance to RIF with the 122 S450L mutation, and 2 strains (RIF-8853 and RIF-4469) of Mycobacterium tuberculosis which 123 were used as controls for the H445D and H445Y mutations, from the National Reference  The sputum samples were provided by the LRNM and had the following characteristics: a 131 bacilloscopy (BK+) ranging from 1+ to 3+ and a storage time in refrigeration (2 -8°C) between 132 6 and 30 days since they were collected. The sensitive sputum was used for the CUT-OFF  The aqueous phase was transferred to another sterile 1.5 ml tube taking care not to drag the 143 interphase, 500 μL of ice-cold 100% Isopropanol (-20°C) was added, mixed by inversion 20 144 times, and then centrifuged for 15 min at 14000 RPM and the supernatant was removed. To 145 the precipitate, 1 mL of ice-cold 70% ethanol was added, mixed by inversion 20 times, and 146 centrifuged for 5 minutes at 14000 RPM, the supernatant was discarded, and the tubes were 147 dried at room temperature. Finally, the DNA was resuspended in 200 µL of TE buffer and stored 148 at -20°C until use.    To determine the analytical sensitivity of the PCR-ELISA, 2 dilutions of PCR product were 182 performed: 1/10 and 1/100, starting from a concentration of 4ng for the 3 resistant probes.

183
To determine the specificity of the PCR-ELISA, DNA from 7 Non-tuberculosis mycobacteria  Samples that showed "inferred resistance" by GenotypeMTBDRplus v2 were also sequenced 202 to identify mutations conferring resistance to RIF.  The CUT-OFF was determined using SPSSv25 software which allowed the calculation of the 209 cut-off limit of the average of the absorbances of the 15 sensitive and H37Rv samples plus 3 210 times standard deviation (SD) for the H445Y and H445D probes and 2 times the SD for the 211 S450L probe. A value above the CUT-OFF was considered resistant (presence of the mutation) 212 and below was considered sensitive (absence of the mutation).
Once the cut-off values were obtained, the pilot assay was performed in blind (without knowing 214 the resistance profile) using 20 sputum samples, whose resistance was previously determined 215 by GenotypeMTBDRplus v2. DNA was extracted from these samples and PCR-ELISA was 216 performed using the 3 probes simultaneously. The PCR-ELISA results were compared with the 217 results obtained against GenotypeMTBDRplus v2 after revealing the blind.  (Table 2). 235 The PCR profile was established: Initial denaturation at 95°C for 10 min (1 cycle), denaturation 236 at 95°C for 30 sec, annealing at 60°C for 45 sec and extension at 72°C for 30 sec (35 cycles).

237
Final extension at 72°C for 5 min (1 cycle). PCR products were stored at -20°C until use.

238
Standardization of all PCR-ELISA conditions allowed to establish the following parameters: 239 hybridization temperature at 50°C, SSPE hybridization buffer at low stringency (3X), 240 streptavidin concentration at 10 μg/mL, probe concentration at 20 pmol/mL, PCR product 241 concentration at 4ng and conjugate concentration at 150mU (1/1000) ( Table 2). The analytical sensitivity of the PCR-ELISA was 4ng of PCR product for the H445D and H445Y 245 probes while for the S450L probe the sensitivity was 0.4 ng of PCR product (Table S1).  For the samples that did amplify, the PCR-ELISA gave negative results, determining that there 253 was no hybridization in the NTMs nor the negative control (Table S2) (Table S3). Only for the H445D probe, it was decided to  (Table 3).

292
The PCR-ELISA system presented a sensitivity of 86.7% which corresponded to a successful 293 concordance of 13 out of 15 samples. The value of Cohen's Kappa index was 0.737, which 294 denotes a "good agreement" between GenotypeMDRTBplus v2 and PCR-ELISA (Table 4).

298
This work presents the standardization and pilot evaluation of the PCR-ELISA technique for the 299 detection of RIF resistance using biotinylated probes for which several parameters were 300 optimized including DNA extraction.

301
In the present work, sputum samples and strains from which DNA was extracted by the CTAB-302 lyz method were used; this method proved to be adequate for direct extraction of DNA from 303 sputum compared to other extraction protocols (29-31).    The analytical sensitivity of PCR-ELISA for the H445D and H445Y mutations was 4 ng of PCR 328 product (Table S1), while for the S450L mutation a sensitivity of 0.4ng was detected because 329 the latter tends to have high absorbance values, this behavior coincides with that reported by  However, for the NTMs that did amplify when the ELISA was performed, they did not hybridize, 341 therefore, the PCR-ELISA has 100% specificity (Table S2)   BK between (1 to 2 +) and with a storage time in refrigeration between 6 to 20 days (Table 3), 356 despite having a higher concentration of DNA there was no amplification of PCR product, 357 probably because prolonged refrigeration times cause lysis in cells and MTB bacilli causing the 358 DNA concentration detected during quantification to be more human DNA than bacillus DNA in 359 these samples. Therefore, the PCR-ELISA analysis was performed on 17 samples (Table 3).  , Table S4.A), and for sample 13, the nucleotide change T → C was found at position 1356 which generated 369 the variation of the amino acid Leucine for Proline (L452P , Table S4.B).

370
On the other hand, the samples (M-9 and M-14) were discordant in the detection of resistance. 371 these samples had many days of refrigeration (approx. 24 days) and a bacilloscopy lower than 372 3+, this situation caused a low concentration of extracted DNA (<1ng/µL) which generated a 373 faint amplification, poor hybridization, and therefore a false sensitive result in the PCR-ELISA 374 (Table 3). 375 The concordance (kappa index "k") between GenotypeMTBDRplus v2 and the PCR-ELISA 376 technique was calculated. A value k = 0.737 was found (Table 4), which corresponded to a 377 "good agreement" between both techniques.