A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer

Abstract

Purpose: The aims of this study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. Methods: High throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. Results: A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). Conclusion: The prognostic risk score model for stage IV CRC developed in this study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

Prognosis prediction of stage IV colorectal cancer patients by mRNA transcriptional profile

“…In contrast to genetic alterations at DNA level, transcriptional alterations, and expression alterations showed a large variety of observations with little consensus. For example, some studies identified the expression of single markers for prognosis prediction, such as CD133, 27 HSF4, 28 and PLAC1, 29 etc., while other studies identified panels of genes with significant efficacy in prognosis prediction at protein expression levels, mRNA levels or miRNA levels of multiple genes 30–33 . Almost no overlapping of genes was found among the studies.…”

“…For example, some studies identified the expression of single markers for prognosis prediction, such as CD133, 27 HSF4, 28 and PLAC1, 29 etc., while other studies identified panels of genes with significant efficacy in prognosis prediction at protein expression levels, mRNA levels or miRNA levels of multiple genes. [30][31][32][33] Almost no overlapping of genes was found among the studies. The reason for the huge discrepancies in these studies may involve the large variation of gene transcription and expression profiles across different populations or individuals, the method of tissue sampling, the method for sample treatment and measurement, and the heterogeneity of CRC.…”

Comprehensive analysis of cellular senescence and immune microenvironment in papillary thyroid carcinoma

“…Some advanced cancers have been treated better with immune checkpoint inhibitors (ICIs) [ 40 ]. Boxplots, based on 47 immune checkpoints obtained from the literature [ 41 , 42 ], were used to illustrate the differential expression of immune checkpoints in high-risk and low-risk groups.…”

Integrated Analysis of Necroptosis-Related Genes for Prognosis, Immune Microenvironment Infiltration, and Drug Sensitivity in Colon Cancer

“…The R package “CIBERSORT” was used to estimate the relative proportion of twenty-two types of tumor-infiltrating immune cells (TICs) in each sample ( 28 ). To elucidate the potential role of the constructed gene signature in CC immunotherapy, we inspected the differential expression of 47 immune checkpoint genes between the different NRS-score groups ( 29 ).…”