Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients’ survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.

中文翻译：

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基于免疫相关基因的小细胞肺癌预后风险模型构建

小细胞肺癌（SCLC）是一种高度侵袭性和致命的恶性肿瘤。现阶段研究提示免疫相关基因的表达与小细胞肺癌的预后有关。因此，探索有效的免疫相关分子标志物对判断预后和治疗SCLC具有重要意义。我们的研究从 Gene Expression Omnibus (GEO) 获得 SCLC 数据集，并对其中的 mRNA 进行差异表达分析。将差异表达的 mRNA (DEmRNA) 与免疫相关基因相交以产生免疫相关的差异表达基因 (DEG)。基因本体论 (GO) 和京都基因和基因组百科全书 (KEGG) 富集分析揭示了这些 DEG 的功能。此后，我们通过 K-means 聚类对免疫相关 DEG 的 3 种亚型进行了分类。Kaplan-Meier 曲线分析了 3 种亚型对 SCLC 患者生存的影响。单样本基因集富集分析 (ssGSEA) 和 ESTIMATE 验证了不同免疫基因亚型的激活存在显着差异。最后，通过单变量-Lasso-多Cox回归分析构建了免疫相关7基因评估模型。风险评分、Kaplan-Meier 曲线、受试者工作特征 (ROC) 曲线和独立预后分析验证了免疫相关 7 基因评估模型的预后价值。正如 GSEA 所建议的那样，高风险组和低风险组之间的细胞因子相关通路存在显着差异。随着分析的深入，我们发现在不同组中，人白细胞抗原 (HLA) 蛋白和 CD274、CD152 和 T 淋巴细胞表面表达的共刺激分子的表达存在统计学上的显着差异。总之，我们从免疫相关基因入手，构建了小细胞肺癌的预后模型，可以有效评估临床结局，为小细胞肺癌患者的治疗和预后提供指导。