Li Ruixin, Yao Fan, Liu Yijin, Wu Xiaodan, Su Peng, Li Tianran, Wu Nan
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China.
Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
Medicine (Baltimore). 2025 Feb 28;104(9):e41392. doi: 10.1097/MD.0000000000041392.
Immunotherapy of soft tissue sarcoma is considered an important development direction for the future. Bioinformatics analysis of genetic changes in tumors and the immune microenvironment around tumors has proven to be a mature and reliable method for predicting tumor prognosis. By mining the Cancer Genome Atlas Program database, we found immunotherapy targets of soft tissue sarcoma and analyzed their biological behavior. The data of 265 samples were downloaded to analyze the expression profile of soft tissue sarcomas. This included calculating tumor purity through the estimation of stromal and immune cells in malignant tumors using expression data, acquisition of differential genes as prognostic factors, and enrichment analysis of the differential genes. Survival analysis showed longer overall survival times for patients with higher immune scores. We obtained 83 survival-related differential genes through survival analysis, and 23 genes that could be used as independent risk factors for the prognosis of soft tissue sarcoma were obtained by multiple regression analysis of the differential genes and other recognized risk factors. Gene set enrichment analysis of the differential genes obtained immune and inflammatory gene ontology terms and signal pathways, including regulation of the T-cell apoptotic process and leukocyte transendothelial migration. After validation in an independent data set of the Gene Expression Omnibus database, 12 genes were confirmed as a result. We believe that these differential genes will be new targets for sarcoma immunotherapy and key genes for the prognosis of soft tissue sarcoma.
软组织肉瘤的免疫治疗被认为是未来一个重要的发展方向。对肿瘤及其周围免疫微环境的基因变化进行生物信息学分析已被证明是预测肿瘤预后的一种成熟且可靠的方法。通过挖掘癌症基因组图谱计划数据库，我们找到了软组织肉瘤的免疫治疗靶点并分析了它们的生物学行为。下载了265个样本的数据来分析软组织肉瘤的表达谱。这包括利用表达数据通过估计恶性肿瘤中的基质细胞和免疫细胞来计算肿瘤纯度、获取差异基因作为预后因素以及对差异基因进行富集分析。生存分析显示免疫评分较高的患者总生存时间更长。通过生存分析我们获得了83个与生存相关的差异基因，通过对差异基因与其他公认的风险因素进行多元回归分析，获得了23个可作为软组织肉瘤预后独立风险因素的基因。对差异基因进行基因集富集分析得到了免疫和炎症基因本体术语及信号通路，包括T细胞凋亡过程的调控和白细胞跨内皮迁移。在基因表达综合数据库的独立数据集中进行验证后，有12个基因得到了确认。我们认为这些差异基因将成为肉瘤免疫治疗的新靶点以及软组织肉瘤预后的关键基因。
