Department of Clinical Laboratory, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, China.
College of Health Management, Henan Finance University, Zhengzhou, China.
Pathol Oncol Res. 2022 Mar 14;28:1610030. doi: 10.3389/pore.2022.1610030. eCollection 2022.
Aberrant immune gene expression has been shown to have close correlations with the occurrence and progression of esophageal cancer (EC). We aimed to generate a prognostic signature based on immune-related genes (IRGs) capable of predicting prognosis, immune checkpoint gene (ICG) expressions, and half-inhibitory concentration (IC) for chemotherapy agents for EC patients. Transcriptome, clinical, and mutation data on tumorous and paratumorous tissues from EC patients were collected from The Cancer Genome Atlas (TCGA) database. Then, we performed differential analysis to identify IRGs differentially expressed in EC. Their biofunctions and related pathways were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. These gene expression profiling data were merged with survival information and subjected to univariate Cox regression to select prognostic genes, which were then included in a Lasso-Cox model for signature generation (risk score calculation). Patients were divided into the high- and low-risk groups using the median risk score as a cutoff. The accuracy of the signature in overall survival prediction was assessed, so were its performances in predicting ICG expressions and IC for chemotherapy and targeted therapy agents and immune cell landscape characterization. Fifteen prognostic IRGs were identified, seven of which were optimal for risk score calculation. As expected, high-risk patients had worse overall survival than low-risk individuals. Significant differences were found in tumor staging, immune cell infiltration degree, frequency of tumor mutations, tumor mutation burden (TMB), and immune checkpoint gene expressions between high- vs. low-risk patients. Further, high-risk patients exhibited high predicted IC for paclitaxel, cisplatin, doxorubicin, and erlotinib compared to low-risk patients. The seven-IRG-based signature can independently and accurately predict overall survival and tumor progression, characterize the tumor immune microenvironment (TIME) and estimate ICG expressions and IC for antitumor therapies. It shows the potential of guiding personalized treatment for EC patients.
异常的免疫基因表达已被证明与食管癌（EC）的发生和发展密切相关。我们旨在基于免疫相关基因（IRGs）生成一个预后标志物，用于预测 EC 患者的预后、免疫检查点基因（ICG）表达和化疗药物的半抑制浓度（IC）。我们从癌症基因组图谱（TCGA）数据库中收集了 EC 患者肿瘤和癌旁组织的转录组、临床和突变数据。然后，我们进行了差异分析，以鉴定 EC 中差异表达的 IRGs。使用基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析探索它们的生物功能和相关途径。这些基因表达谱数据与生存信息合并，并进行单变量 Cox 回归以选择预后基因，然后将这些基因纳入 Lasso-Cox 模型进行标志物生成（风险评分计算）。我们使用中位数风险评分作为截止值将患者分为高风险组和低风险组。评估该标志物在总生存率预测中的准确性，以及其在预测 ICG 表达和化疗药物及靶向治疗药物的 IC 以及免疫细胞景观特征方面的性能。确定了 15 个预后 IRGs，其中 7 个最适合风险评分计算。正如预期的那样，高风险患者的总生存率比低风险患者差。高风险与低风险患者之间在肿瘤分期、免疫细胞浸润程度、肿瘤突变频率、肿瘤突变负荷（TMB）和 ICG 表达方面存在显著差异。此外，与低风险患者相比，高风险患者对紫杉醇、顺铂、多柔比星和厄洛替尼的预测 IC 较高。基于七个 IRG 的标志物可以独立且准确地预测总生存率和肿瘤进展，描述肿瘤免疫微环境（TIME）并估计抗肿瘤治疗的 ICG 表达和 IC。它显示了指导 EC 患者个性化治疗的潜力。
