Table_1_Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.docx

BackgroundUterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data.
MethodsA comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model.
ResultsOur analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort.
ConclusionThe study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation.

背景：子宫体子宫内膜癌（Uterine Corpus Endometrial Carcinoma, UCEC）是全球范围内影响女性的常见恶性肿瘤之一。鉴于其异质性特征，个体化治疗方案对于优化患者预后愈发重要。本研究结合机器学习技术与大规模基因组数据，探讨了细胞衰老基因（cellular senescence genes, CSGs）在UCEC中的预后价值。
方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中获取了579例子宫内膜癌患者的转录组与临床数据，开展全面分析。通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）及多种机器学习算法，包括高斯混合模型（Gaussian Mixture Model, GMM）、支持向量机-递归特征消除（support vector machine - recursive feature elimination, SVM-RFE）、随机森林（Random Forest）以及极限梯度提升（eXtreme Gradient Boosting, XGBoost），对503个细胞衰老基因子集进行评估，以筛选关键差异表达的细胞衰老基因。基于上述基因进一步构建预后模型。
结果：本研究分析发现，UCEC存在两种截然不同的分子亚型，二者在肿瘤微环境与生存结局上存在显著差异。利用细胞衰老基因构建的预后模型可有效将患者划分为高风险组与低风险组。高风险组患者总生存期较差，且呈现出与肿瘤进展相关的独特分子与免疫特征。至关重要的是，该预后模型展现出优异的预测性能，并在独立患者队列中得到了验证。
结论：本研究强调了细胞衰老基因在UCEC进展中的重要作用，同时凸显了机器学习在构建可靠预后模型中的有效性。研究结果表明，靶向细胞衰老有望成为UCEC个体化治疗的潜在策略，因此有待开展进一步的临床研究。