Scholarlink

<p>Oxaliplatin, a key chemotherapeutic agent, often induces resistance in colorectal cancer (CRC) treatment, highlighting the urgent need for reliable biomarkers to predict treatment efficacy. In this study, we aimed to identify key genes associated with oxaliplatin resistance in CRC and to evaluate their potential as prognostic biomarkers. Using CRC patient data from the TCGA dataset, we categorized patients into oxaliplatin-resistant and -sensitive groups and conducted differential expression analysis. Key feature genes were identified through univariate Cox analysis, LASSO regression, and stepwise multivariate Cox regression. The predictive value of the identified markers was validated using logistic regression, weighted gene co-expression network analysis (WGCNA), and external validation in GEO cohorts. The tumor microenvironment (TME) was assessed using the MCP-counter algorithm, and CRC cell experiments were performed to evaluate changes in drug sensitivity following oxaliplatin exposure. Based on TCGA CRC data, we constructed a prognostic index derived from a three-gene signature associated with oxaliplatin resistance. This index was significantly correlated with progression-free survival (PFS) in oxaliplatin-resistant CRC patients and showed robust prognostic performance, with AUCs of 0.848 and 0.861 in gastric cancer and pancreatic adenocarcinoma cohorts, respectively. Notably, TNFAIP2 knockout significantly reduced clonogenic ability in CRC cells following oxaliplatin treatment. Our results identify TLE4, TNFAIP2, and ARGLU1 as key contributors to oxaliplatin resistance in CRC. The oxaliplatin resistance–related gene signature (ORGSig) serves as a promising tool for predicting treatment response and prognosis in CRC patients receiving oxaliplatin-based chemotherapy. This signature also offers potential for guiding personalized therapy and overcoming drug resistance in clinical practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a prognostic model for chemotherapy response and identification of TNFAIP2 as a target in colorectal cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月5日

查看全文
  • Cheng Zhou,
  • Chuan-Hai Xu,
  • Min Xu,
  • Xin-Kun Huang,
  • Bin Zhu

摘要

Oxaliplatin, a key chemotherapeutic agent, often induces resistance in colorectal cancer (CRC) treatment, highlighting the urgent need for reliable biomarkers to predict treatment efficacy. In this study, we aimed to identify key genes associated with oxaliplatin resistance in CRC and to evaluate their potential as prognostic biomarkers. Using CRC patient data from the TCGA dataset, we categorized patients into oxaliplatin-resistant and -sensitive groups and conducted differential expression analysis. Key feature genes were identified through univariate Cox analysis, LASSO regression, and stepwise multivariate Cox regression. The predictive value of the identified markers was validated using logistic regression, weighted gene co-expression network analysis (WGCNA), and external validation in GEO cohorts. The tumor microenvironment (TME) was assessed using the MCP-counter algorithm, and CRC cell experiments were performed to evaluate changes in drug sensitivity following oxaliplatin exposure. Based on TCGA CRC data, we constructed a prognostic index derived from a three-gene signature associated with oxaliplatin resistance. This index was significantly correlated with progression-free survival (PFS) in oxaliplatin-resistant CRC patients and showed robust prognostic performance, with AUCs of 0.848 and 0.861 in gastric cancer and pancreatic adenocarcinoma cohorts, respectively. Notably, TNFAIP2 knockout significantly reduced clonogenic ability in CRC cells following oxaliplatin treatment. Our results identify TLE4, TNFAIP2, and ARGLU1 as key contributors to oxaliplatin resistance in CRC. The oxaliplatin resistance–related gene signature (ORGSig) serves as a promising tool for predicting treatment response and prognosis in CRC patients receiving oxaliplatin-based chemotherapy. This signature also offers potential for guiding personalized therapy and overcoming drug resistance in clinical practice.