A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

Abstract

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

“…For instance, Tan et al constructed a 9 IRG panel in triple-negative breast cancer (TNBC), and the related nomogram could assess the axillary lymph node (ALN) status preoperatively with better predictive ability than the IRG model alone 23 . Feng et al revealed that 14 IRGs were correlated with the OS of patients with colorectal cancer (CRC) after chemotherapy, and the constructed model could predict immune status and chemotherapy sensitivity 24 . Consistent with these studies, our risk model was an independent prognostic indicator with higher prognostic efficiency in evaluating OS of BC than other independent clinicopathological variables, including age, clinical stage, and TNM stage.…”

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

“…Some of the most highly up-regulated gene products in our analysis included MMP7, PPBP, and CXCL5. Prior work by others has identified the MMP7 ( Gao et al, 2019 ; Huang et al, 2021 ; Vočka et al, 2019 ), PPBP ( Chen et al, 2022 ; Kothalawala & Győrffy, 2023 ; Feng et al, 2022 ), and CXCL5 gene products to be extremely useful in the mechanisms and diagnosis of colorectal cancer ( Chen et al, 2019 ; Zhang et al, 2021 ; Novillo et al, 2020 ; Zhang et al, 2020 ). We also identified multiple down-regulated genes in colorectal cancer that are supported by recent studies on the gene products of the OTOP2 ( Qu et al, 2019 ; Yang & Sakharkar, 2022 ), OTOP3 ( Yang & Sakharkar, 2022 ), SPIB ( Zhao et al, 2021 ; Liu et al, 2019 ), and INSL5 genes ( Yang et al, 2021c ; Sun et al, 2019 ).…”