Liang Xiaojie, Cheng Zhaoxiang, Chen Xinhao, Li Jun
Department of General Surgery, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China.
Department of General Surgery, Jiangning Traditional Chinese Medicine Hospital, Nanjing, China.
Front Genet. 2022 Aug 15;13:955424. doi: 10.3389/fgene.2022.955424. eCollection 2022.
Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.
结直肠癌（CRC）是一种胃肠道恶性肿瘤，每年占全球癌症诊断和癌症相关死亡的10%。因此，迫切需要鉴定参与预测CRC预后的基因。CRC数据来自基因表达综合数据库（GEO数据库，GSE39582和GSE41258数据集）以及癌症基因组图谱（TCGA）数据库。在肿瘤组织和正常组织之间筛选出差异表达的坏死性凋亡相关基因（DENRGs）。应用单因素Cox回归分析和最小绝对收缩和选择算子（LASSO）分析，对所选DENRGs进行患者总生存期分析，并构建预后生物标志物。通过Kaplan-Meier曲线和受试者工作特征（ROC）分析评估该生物标志物的有效性。GSE39582数据集用作预后特征的外部验证。此外，通过单因素和多因素Cox回归分析，确定独立的预后因素，构建预后列线图。接下来，通过基因集富集分析（GSEA）探索由该特征调控的信号通路。使用单样本基因集富集分析（ssGSEA）算法和肿瘤免疫功能障碍与排除（TIDE）来探索高风险和低风险两组之间的免疫相关性。最后，在GSE41258数据集中检测预后基因的表达。总共筛选出27个DENRGs，并构建了基于6个DENRGs的坏死性凋亡相关预后特征，这可能有助于更好地了解CRC患者的总生存期（OS）。Kaplan-Meier曲线表明预后特征的有效性，ROC曲线显示相同结果。此外，单因素和多因素Cox回归分析显示，年龄、病理T分期和风险评分是独立的预后因素，并建立了列线图。此外，预后特征与凋亡途径的相关性最为显著。同时，24种免疫细胞在两组之间存在显著差异，如活化B细胞。此外，32种免疫检查点、TIDE评分、PD-L1评分和T细胞排除评分在两组之间存在显著差异。最后，在GSE41258数据集中，6基因预后特征在肿瘤样本和正常样本之间的表达水平存在显著差异。我们的研究建立了一个包含6个基因的特征和一个预后列线图，能够显著评估CRC患者的预后。
