Background PI4K2A has been found to have a tumor‐promoting role in various solid tumors and be involved in various biological procedures. In this article, we aim to investigate the prognostic values of PI4K2A and provide new insights in colon adenocarcinoma (COAD). Methods The Cancer Genome Atlas (TCGA) database, Human Protein Atlas online database, and UALCAN database were used to analyze the expression of PI4K2A in COAD and the survival of patients. Univariate and multifactorial Cox regression analyses were used to assess the prognosis of PI4K2A on COAD. GSEA was used to explore PI4K2A‐related signaling pathways. In addition, the effect of PI4K2A on immune checkpoint inhibitors (ICIs) treatment was investigated by constructing a TIDE model and predicting the association between PI4K2A and anticancer drug sensitivity through the CellMiner database. Results In the TCGA database, PI4K2A was highly expressed in COAD and the similar results were verified by qRT‐PCR. Survival analysis, utilizing Kaplan–Meier curves, revealed that COAD patients with high PI4K2A expression had a worse prognosis. In addition, PI4K2A expression was discovered to have been associated with T‐stage, N‐stage, and pathological stage by logistic analysis. Next, we utilized univariate and multifactorial Cox regression analyses to identify PI4K2A as an independent predictor. Additionally, GSEA analysis indicates that PI4K2A is enriched in MAPK signaling pathway, Toll‐like receptor signaling pathway, etc. In COAD, PI4K2A was remarkably associated with the tumor immune microenvironment. In addition, by constructing a TIDE model, we discovered that COAD patients in the PI4K2A low‐expression cohort were better treated with ICI. Finally, analysis of the CellMiner database predicted that PI4K2A was adversely correlated with the sensitivity of various anticancer drugs. Conclusions Our study suggests that PI4K2A may be a potential predictor of poor prognosis in COAD and a potential biomarker for early diagnosis, prognosis, and treatment.

Background The glycosyltransferase CHSY3 is a CHSY family member, yet its importance in the context of gastric cancer development remains incompletely understood. The present study was thus developed to explore the mechanistic importance of CHSY3 as a regulator of gastric cancer. Methods Expression of CHSY3 was verified by TCGA, GEO and HPA databases. Kaplan–Meier curve, ROC, univariate cox, multivariate cox, and nomogram models were used to verify the prognostic impact and predictive value of CHSY3. KEGG and GO methods were used to identify signaling pathways associated with CHSY3. TIDE and IPS scores were used to assess the immunotherapeutic value of CHSY3. WGCNA, Cytoscape constructs PPI networks and random forest models to identify key Hub genes. Finally, qRT-PCR and immunohistochemical staining were performed to verify CHSY3 expression in clinical specimens. The ability of CHSY3 to regulate tumor was further assessed by CCK-8 assay and cloning assay, EDU assay, migration assay, invasion assay, and xenograft tumor model analysis. Results The expression of CHSY3 was discovered to be abnormally upregulated in GC tissues through TCGA, GEO, and HPA databases, and the expression of CHSY3 was associated with poor prognosis in GC patients. Correlation analysis and Cox regression analysis revealed higher CHSY3 expression in higher T staging, an independent prognostic factor for GC. Moreover, elevated expression of CHSY3 was found to reduce the benefit of immunotherapy as assessed by the TIDE score and IPS score. Then, utilizing WGCNA, the PPI network constructed by Cytoscape, and random forest model, the Hub genes of COL5A2, POSTN, COL1A1, and FN1 associated with immunity were screened. Finally, the expression of CHSY3 in GC tissues was verified by qRT-PCR and immunohistochemical staining. Moreover, the expression of CHSY3 was further demonstrated by in vivo and in vitro experiments to promote the proliferation, migration, and invasive ability of GC. Conclusions The results of this study suggest that CHSY3 is an important regulator of gastric cancer progression, highlighting its promise as a therapeutic target for gastric cancer.

N7-methyladenosine (m7G) modifications have been the subject of growing research interest with respect to their relationship with the progression and treatment of various cancers. This analysis was designed to examine the association between m7G-related gene expression and colorectal cancer (CRC) patient outcomes. Initial training analyses were performed using the TCGA dataset, with the GSE28722 dataset then being used to validate these results. Univariate Cox analyses were initially conducted to screen out prognostic m7G-related genes, after which a LASSO approach was used to construct an m7G risk score (MRS) model. Kaplan–Meier curves, ROC curves, and Cox analyses were subsequently used to validate the prognostic utility of this model in CRC patients. The R maftools package was further employed to assess mutational characteristics in CRC patients in different MRS subgroups, while the ESTIMATE, CIBERSORT, and ssGSEA tools were used to conduct immune infiltration analyses. A WGCNA was then performed to identify key immune-associated hub genes. The EIF4E3, GEMIN5, and NCBP2 genes were used to establish the MRS model. Patients with high MRS scores exhibited worse overall survival than patients with low scores. In Cox analyses, MRS scores were independently associated with CRC patient prognosis. Patients with low MRS scores exhibited a higher tumor mutational burden and higher levels of microsatellite instability. In immune infiltration analyses, higher immune checkpoint expression and greater immune cell infiltration were also observed in patients with low MRS scores. WGCNA analyses further identified 25 CD8+ T cell infiltration-associated genes. These findings suggest that MRS values represent a useful biomarker capable of differentiating among CRC patients with different immunological features and prognostic outcomes, offering an opportunity to better determine which patients are likely to benefit from immune checkpoint inhibitor treatment.