Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC. Methods. Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels. Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls. Conclusion. Our immune-related signature panel may be a promising prognostic indicator for RC.

中文翻译：

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WGCNA 和 LASSO 识别基于 5 基因的评分系统以预测直肠癌患者的预后

背景。尽管越来越多的证据表明，分子特征组可能比常规临床特征更有效地预测预后，但目前的研究主要集中在结直肠癌或结肠癌上。没有报告特别关注直肠癌 (RC) 的特征组。我们目前的研究旨在为 RC 开发一种新的预后特征组。方法. 从癌症基因组图谱 (TCGA-READ) 或基因表​​达综合数据库 (GSE123390, GSE56699) 检索 RC 患者的测序（或微阵列）数据和临床病理学细节。加权基因共表达网络用于识别 RC 相关模块。进行最小绝对收缩和选择操作分析以筛选预后特征组。通过生存曲线分析评估风险评分的预后表现。基于相互作用蛋白和与肿瘤浸润免疫细胞的相关性预测预后基因的功能。人类蛋白质图谱 (HPA) 工具用于验证蛋白质表达水平。结果. 使用 TCGA 和 GSE123390 数据集通常鉴定出总共 247 个差异表达基因 (DEG)。棕色和黄色模块（包括 77 度）被确定为 RC 保留。这两个模块中的五个 DEG（ASB2、GPR15、PRPH、RNASE7 和 TCL1A）构成了最佳预后特征组。Kaplan-Meier曲线分析显示，高危组患者的预后比低危组患者差。受试者工作特征（ROC）曲线分析表明，该风险评分对不良预后具有较高的预测准确性，TCGA 和 GSE56699 数据集的 ROC 曲线下面积分别为 0.915 和 0.827。这种五 mRNA 分类器是一个独立的预后因素。其预测准确性也高于所有临床因素模型。通过整合风险评分和临床因素开发了预后列线图，显示出最高的预后能力。ASB2、PRPH 和 GPR15/TCL1A 预计通过分别与 CASQ2/PDK4/EPHA67、PTN 和 CXCL12 相互作用发挥作用。TCL1A和GPR15影响B细胞和树突状细胞的浸润水平，而PRPH的表达与巨噬细胞的丰度呈正相关。与正常对照相比，HPA 分析支持 RC 中 PRPH、RNASE7、CASQ2、EPHA6 和 PDK4 的下调。而PRPH的表达与巨噬细胞的丰度呈正相关。与正常对照相比，HPA 分析支持 RC 中 PRPH、RNASE7、CASQ2、EPHA6 和 PDK4 的下调。而PRPH的表达与巨噬细胞的丰度呈正相关。与正常对照相比，HPA 分析支持 RC 中 PRPH、RNASE7、CASQ2、EPHA6 和 PDK4 的下调。结论。我们的免疫相关特征面板可能是 RC 的一个有前途的预后指标。