Bernard Tawfik, Kendal Jacobson, Ursa Brown-Glaberman, Mikaela Kosich, M Lee Horn, Jacklyn Nemunaitis, Zoneddy Dayao, V Shane Pankratz, Andrew L Sussman, Dolores Guest
Developing Questions to Assess and Measure Patients' Perceived Survival Benefit from Adjuvant Endocrine Therapy in Breast Cancer: A Mixed Methods Pilot Study
Research square
major depressive disorder mdd interstitial cystitis ic two highly debilitating conditions often coexist reciprocal effect significantly exacerbating patients suffering however molecular underpinnings linking disorders remain poorly understood transcriptomic data geo datasets including mdd ic patients systematically analyzed develop validate model following removal batch effect differentially expressed genes degs respective disease control groups identified shared degs conditions underwent functional enrichment analyses additionally immune infiltration analysis quantified ssgsea diagnostic model mdd constructed exploring 113 combinations 12 machine learning algorithms 10fold crossvalidation training sets following external validation test sets finally enrichr platform utilized identify potential drugs mdd totally 21 key genes closely associated mdd ic identified predominantly involved immune processes based enrichment analyses immune infiltration analysis revealed distinct profiles immune cell infiltration mdd ic compared healthy controls genes robust 11gene abcd2 atp8b4 tnnt1 akr1c3 slc26a8 s100a12 ptx3 fam3b itga2b olfm4 bcl7a diagnostic signature constructed exhibited superior performance existing mdd diagnostic models training testing cohorts additionally epigallocatechin gallate 10 drugs emerged potential targets mdd work developed diagnostic model mdd employing combination bioinformatic techniques machine learning methods focusing shared genes mdd ic