Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis

Abstract

BackgroundColon cancer (CC) is the second most common gastrointestinal malignancy. About one in five patients have already developed distant metastases at the time of initial diagnosis, and up to half of patients develop distant metastases from initial local disease, which leads to a poor prognosis for CC patients. Necroptosis plays a key role in promoting tumor growth in different tumors. The purpose of this study was to construct a prognostic model composed of necroptosis-related genes (NRGs) in CC.MethodsThe Cancer Genome Atlas was used to obtain information on clinical features and gene expression. Gene expression differential analysis, weighted gene co-expression network analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator regression algorithm were utilized to identify prognostic NRGs. Thereafter, a risk scoring model was established based on the NRGs. Biological processes and pathways were identified by gene ontology and gene set enrichment analysis (GSEA). Further, protein-protein interaction and ceRNA networks were constructed based on mRNA-miRNA-lncRNA. Finally, the effect of necroptosis related risk score on different degrees of immune cell infiltration was evaluated.ResultsCALB1, CHST13, and SLC4A4 were identified as NRGs of prognostic significance and were used to establish a risk scoring model. The time-dependent receiver operating characteristic curve analysis revealed that the model could well predict the 1-, 3-, and 5-year overall survival (OS). Further, GSEA suggested that the NRGs may participate in biological processes, such as the WNT pathway and JAK-Stat pathway. Eight key hub genes were identified, and a ceRNA regulatory network, which comprised 1 lncRNA, 5 miRNAs and 3 mRNAs, was constructed. Immune infiltration analysis revealed that the low-risk group had significantly higher immune-related scores than the high-risk group. A nomogram of the model was constructed based on the risk score, necroptosis, and the clinicopathological features (age and TNM stage). The calibration curves implied that the model was effective at predicting the 1-, 3-, and 5-year OS of CC.ConclusionOur NRG-based prognostic model can assist in the evaluation of CC prognosis and the identification of therapeutic targets for CC.

Identification of crucial modules and genes associated with backfat tissue development by WGCNA in Ningxiang pigs

“…Carbohydrate sulfotransferase 13 ( CHST13 ), a member of the carbohydrate sulfotransferase gene family, encodes a chondroitin sulfating and chondroitin sulfate synthesizing enzyme. Previous researches suggested that CHST13 is prominently associated with several biological processes, including liver injury, cell invasion, and cancer ( Ryanto et al, 2020 ; Wang et al, 2022 ; Southekal et al, 2023 ); however, the correlation between CHST13 and adipose deposition remains largely unexplored and warrants further research.…”

Characterization of butyrate-metabolism in colorectal cancer to guide clinical treatment

“…In previous studies, ferroptosis-related gene signature was constructed in CRC, and AUC was 0.64, 0.64, and 0.71 for 1-, 3- and 5-year respectively 32 . A necroptosis-related risk score model was also identified, and AUC was 0.665, 0.712, and 0.758 for the 1-, 3-, and 5-year OS 33 .…”

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

“…Necroptosis is known as a programmed lytic cell death pathway observed in cells with deregulation based on inflammatory dysfunction ( Najafov et al, 2017 ). Escape from necroptosis is known to play an important role in the growth of various tumor types including the colon ( Yang et al, 2022 ). Solute carrier family member 4 ( SLCA4 ) is one of the genes related to necroptosis and associated with poor progression in colorectal cancer patients.…”