Construction of a Novel Immune-Related mRNA Signature to Predict the Prognosis and Immune Characteristics of Human Colorectal Cancer

Abstract

Background: Anti-cancer immunotherapeutic approaches have gained significant efficacy in multiple cancer types. However, not all patients with colorectal cancer (CRC) could benefit from immunotherapy due to tumor heterogeneity. The purpose of this study was to construct an immune-related signature for predicting the immune characteristics and prognosis of CRC.Methods: RNA-sequencing data and corresponding clinical information of patients with CRC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal (ImmPort). Then, we utilized univariate, lasso regression, and multivariate cox regression to identify prognostic IRGs and develop the immune-related signature. Subsequently, a nomogram was established based on the signature and other prognostic factors, and its predictive capacity was assessed by receiver operating characteristic (ROC) and decision curve analysis (DCA). Finally, associations between the signature and the immune characteristics of CRC were assessed.Results: In total, 472 samples downloaded from TCGA were divided into the training cohort (236 samples) and internal validation cohort (236 samples), and the GEO cohort was downloaded as an external validation cohort (122 samples). A total of 476 differently expressed IRGs were identified, 17 of which were significantly correlated to the prognosis of CRC patients. Finally, 10 IRGs were filtered out to construct the risk score signature, and patients were divided into low- and high-risk groups according to the median of risk scores in the training cohort. The high-risk score was significantly correlated with unfavorable survival outcomes and aggressive clinicopathological characteristics in CRC patients, and the results were further confirmed in the internal validation cohort, entire TCGA cohort, and external validation cohort. Immune infiltration analysis revealed that patients in the low-risk group infiltrated with high tumor-infiltrating immune cell (TIIC) abundances compared to the high-risk group. Moreover, we also found that the immune checkpoint biomarkers were significantly overexpressed in the low-risk group.Conclusion: The prognostic signature established by IRGs showed a promising clinical value for predicting the prognosis and immune characteristics of human CRC, which contribute to individualized treatment decisions.

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

“…Utilizing multiple independent datasets from diverse platforms for model construction and validation enhances the model’s generalization capability, leading to more compelling conclusions. This strategy is currently widely employed in the analysis of various diseases ( Li J. et al, 2022 ; Guan et al, 2022 ). We conducted differential gene expression analysis on patient data from the training set to identify DEGs.…”

Copper in colorectal cancer: From copper‐related mechanisms to clinical cancer therapies

“…Inhibition of FDX1 or the enzymatic processes associated with lipid acylation halts cuproptosis, highlighting the significance of biomarkers such as FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A in the study and understanding of cuproptosis. Among these, the first seven genes mediate resistance to cuproptosis, whereas the latter three genes promote cuproptosis 50 . These biomarkers are poised to become key indicators of this unique cell death mechanism 49,51 .…”

“…Moreover, cuproptosis is closely linked to the tumour immune microenvironment. Patients classified in the low‐risk group exhibiting stronger immunogenicity, higher immune scores, and a greater degree of microsatellite instability, suggesting a better response to immunotherapy 50,145,155–159 . Further investigation have highlighted the potential role of cuproptosis‐associated microRNAs and lncRNAs in CRC.…”

Construction of a novel prognostic signature based on the composition of tumor-infiltrating immune cells in clear cell renal cell carcinoma

“…With the significant advancement of high-throughput sequencing technologies and the development of computer-aided algorithms, it is possible to assess the abundance of TIICs based on transcriptome data. Several classic algorithms such as CIBERSORT ( Newman et al, 2015 ), MCPcounter ( Becht et al, 2016 ), TIMER ( Li et al, 2017 ), xCell ( Aran et al, 2017 ), and EPIC ( Racle et al, 2017 ) have been widely applied to evaluate the abundance of TIICs ( Liu et al, 2021 ; Li et al, 2022 ), estimate the status of tumors and even build immune-related diagnostic and prognostic signatures. In addition, Miao et al constructed a highly accurate algorithm, which was named ImmuCellAI, which was used to estimate the tumor-infiltrating levels of TIICs ( Miao et al, 2020 ), and expanding the scope of assessment of tumor-infiltrating levels of more T cell subsets.…”