Wang Miaodong, He Qin, Chen Zeshan, Qin Yijue
Department of Traditional Chinese Medicine, Jinhua Central Hospital, Jinhua, 321000, Zhejiang, People's Republic of China.
Department of Traditional Chinese Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, 6 Taoyuan Road, Qingxiu District, Nanning City, Guangxi Zhuang Autonomous Region, People's Republic of China.
Sci Rep. 2025 Jan 30;15(1):3843. doi: 10.1038/s41598-025-87444-3.
Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.
胃腺癌（STAD）是一种常见的恶性肿瘤，具有高度异质性且缺乏高度精确的治疗方案。我们从sxdyc网站下载了癌症基因组图谱（TCGA）-STAD队列中STAD患者的多组学数据，其中包括mRNA、微小RNA、长链非编码RNA、体细胞突变和DNA甲基化数据。我们使用10种聚类方法合成了STAD患者的多组学数据，通过结合10种机器学习方法构建了一个基于共识机器学习驱动的特征（CMLS）相关的预后模型，并使用C指数评估了预后模型。使用Kaplan-Meier曲线评估CMLS与STAD之间的预后关系，并通过单变量和多变量回归分析确定CMLS的独立预后价值。我们还评估了不同CMLS组的免疫特征、免疫治疗反应和药物敏感性。多组学分析结果将STAD分为三个亚型，其中CS1的生存结果最佳。总共筛选出10个枢纽基因（CES3、AHCYL2、APOD、EFEMP1、CYP1B1、ASPN、CPE、CLIP3、MAP1B和DKK1），构建的CMLS与STAD患者的预后显著相关，是STAD患者的独立预后因素。使用CMLS风险评分，将所有患者分为高CMLS组和低CMLS组。低CMLS组患者的生存期更好，免疫细胞更丰富，肿瘤突变负荷评分更高，表明免疫治疗反应性更好，可能具有“热肿瘤”表型。高CMLS组患者的预后明显较差，对免疫治疗不太敏感，但可能从化疗和靶向治疗中获益更多。在本研究中，结合10种聚类方法和10种机器学习方法对STAD的多组学进行分析，将STAD分为三个亚型，并构建了CMLS相关的预后模型特征，这对于STAD的准确管理和有效治疗具有重要意义。
