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<p>Ovarian clear cell carcinoma (OCCC) is a clinically aggressive subtype of epithelial ovarian cancer with limited therapeutic options. Here, we present a comprehensive study integrating bioinformatic analysis, experimental validation, and prognostic evaluation to elucidate the role of Integrin alpha-5 <i>(ITGA5)</i> in OCCC and demonstrate the generalizability of this analytical framework to other diseases. Initial bioinformatic analyses of publicly available transcriptomic datasets revealed that <i>ITGA5</i> mRNA expression was significantly elevated in OCCC tumor tissues compared with normal ovarian tissues, with strong positive correlations with tumor proliferation index (Pearson <i>r</i> = 0.718, <i>P</i> &lt; 0.0001; Spearman <i>r</i> = 0.703, <i>P</i> &lt; 0.0001) and advanced FIGO stage (P for trend &lt; 0.0001). Experimental validation using patient-derived tissues confirmed marked <i>ITGA5</i> upregulation, while functional assays in OCCC cell lines showed that <i>ITGA5</i> promotes proliferation, invasion, and cell cycle progression, and suppresses apoptosis. Notably, <i>ITGA5</i> enhanced platinum resistance in ES2 and SKOV3/DDP cells, increasing IC₅₀ values and platinum resistance index (PRI), effects mediated at least in part through activation of the phosphatidylinositol 3-kinase PI3K/Akt/ mammalian target of rapamycin (mTOR) signaling pathway. Bioinformatic analyses further indicated that high <i>ITGA5</i> expression correlates with poor overall survival (hazard ratio = 2.34, 95% CI: 1.45–3.78; log-rank <i>P</i> = 0.0023) and constitutes a key predictor of advanced-stage disease in machine learning models. Collectively, our findings identify <i>ITGA5</i> as a driver of OCCC progression and chemoresistance and exemplify a versatile research framework that spans data-driven discovery, mechanistic experimentation, and integrative bioinformatic validation and can be readily applied to investigate pathogenic mechanisms and therapeutic targets in other malignancies.</p>

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Bioinformatic and experimental analysis reveals ITGA5 as a driver of ovarian clear cell carcinoma progression and platinum resistance

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Zuolian Xie,
  • Li Chen,
  • Wei Chen,
  • Xiao Chen,
  • Ling Li,
  • Liang Lin,
  • An Lin

摘要

Ovarian clear cell carcinoma (OCCC) is a clinically aggressive subtype of epithelial ovarian cancer with limited therapeutic options. Here, we present a comprehensive study integrating bioinformatic analysis, experimental validation, and prognostic evaluation to elucidate the role of Integrin alpha-5 (ITGA5) in OCCC and demonstrate the generalizability of this analytical framework to other diseases. Initial bioinformatic analyses of publicly available transcriptomic datasets revealed that ITGA5 mRNA expression was significantly elevated in OCCC tumor tissues compared with normal ovarian tissues, with strong positive correlations with tumor proliferation index (Pearson r = 0.718, P < 0.0001; Spearman r = 0.703, P < 0.0001) and advanced FIGO stage (P for trend < 0.0001). Experimental validation using patient-derived tissues confirmed marked ITGA5 upregulation, while functional assays in OCCC cell lines showed that ITGA5 promotes proliferation, invasion, and cell cycle progression, and suppresses apoptosis. Notably, ITGA5 enhanced platinum resistance in ES2 and SKOV3/DDP cells, increasing IC₅₀ values and platinum resistance index (PRI), effects mediated at least in part through activation of the phosphatidylinositol 3-kinase PI3K/Akt/ mammalian target of rapamycin (mTOR) signaling pathway. Bioinformatic analyses further indicated that high ITGA5 expression correlates with poor overall survival (hazard ratio = 2.34, 95% CI: 1.45–3.78; log-rank P = 0.0023) and constitutes a key predictor of advanced-stage disease in machine learning models. Collectively, our findings identify ITGA5 as a driver of OCCC progression and chemoresistance and exemplify a versatile research framework that spans data-driven discovery, mechanistic experimentation, and integrative bioinformatic validation and can be readily applied to investigate pathogenic mechanisms and therapeutic targets in other malignancies.