Identification of a 5-Gene-Based Scoring System by WGCNA andLASSO to Predict Prognosis for Rectal Cancer Patients

Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosisprediction than routine clinical characteristics, current studies mainly focused on color ectal or colon cancers. No reportsspecifically 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 clinic opathological details of patients with RCwere 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 proteinsand the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate theprotein expression levels.Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGAand 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 forun favorable 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 no mogram was developed by integrating the risk score and clinical factors, which showed the high estprognostic 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 down regulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls.Conclusion. Our immune-relate dsignature panel may be a promising prognostic indicator for RC.