Background: Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs to be further elucidated.Method: CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model.Results: SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan–Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (p_merged<0.001, p_training<0.001, p_testing<0.001). TIDE score and IPS were higher in the high-risk group (p_TIDE<0.005, p_Dysfunction<0.005, p_Exclusion<0.001, p_mAb-CTLA-4 = 3e-08, p_mAb-PD-1 = 4.1e-10). The clinical samples were divided into high- and low- risk groups according to the risk score. There was a statistical difference in DFS (p=0.0108).Conclusion: This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.

Background: Tumor differentiation grade was reported to be a prognostic factor in gastric cancer (GC). Here, we identify a novel tumor differentiation grade-related genes prognostic signature and provide new biomarkers using single-cell RNA sequencing (scRNA-seq) in GC.

Neutrophils expelling neutrophil extracellular traps (NETs) during the process of NETosis play a critical role in tumor development. In this study, we aimed to identify a prognostic NETosis-related gene signature that could predict the immunetherapy response and explore the expression of these genes in hepatocellular carcinoma (HCC). Differentially expressed NETosis-related genes were identified from the GSE80489 dataset in the Gene Expression Omnibus (GEO), and a protein–protein interaction (PPI) network was constructed using Cytoscape and the STRING database. Gene Ontology (GO) enrichment analysis revealed associations with chemical stimuli and the positive regulation of cellular processes in HCC. A total of 37 differentially expressed NETosis-related genes were identified, and SNORA31, PMAIP1, GLA, CXCL1, and ADM were selected to construct a prognostic signature. In the training cohort, the low-risk group demonstrated significantly better overall survival (OS) compared to the high-risk group (P = 2.715e−06), and multivariable analysis confirmed that this signature was an independent prognostic factor for HCC (HR = 1.630, 95% CI = 1.295–2.052, P < 0.001). This signature also significantly predicted OS in validation cohorts (P = 9.704e−03) and was associated with HCC patients' OS according to the Kaplan–Meier Plotter. Moreover, it could predict the immunotherapy response in HCC patients. Finally, we investigated the expression of NETosis-related genes in single-cell subtypes and in the HepG2 cell line, providing further insights into their relevance in HCC. Overall, this NETosis-related gene signature serves as a prognostic indicator and a potential predictor for immunotherapy response in HCC.