Noncoding ribonucleic acids (ncRNAs) are involved in various functions in the formation and progression of different tumors. However, the association between N6-methyladenosine-related ncRNAs (m6A-related ncRNAs) and gastric cancer (GC) prognosis remains elusive. As such, this research was aimed at identifying m6A-related ncRNAs (lncRNAs and miRNAs) in GC and developing prognostic models of relevant m6A-related ncRNAs and identifying potential biomarkers regulated by m6A. In this study, the m6A2Target database, Starbase database, and The Cancer Genome Atlas (TCGA) were used to screen m6A-related ncRNAs. And then, we performed integrated bioinformatics analyses to determine prognosis-associated ncRNAs and to develop the m6A-related ncRNA prognostic signature (m6A-NPS) for GC patients. Finally, five m6A-related ncRNAs (including lnc-ARHGAP12, lnc-HYPM-1, lnc-WDR7-11, LINC02266, and lnc-PRIM2-7) were identified to establish m6A-NPS. The predictive power of m6A-NPS was better in the receiver operating characteristic (ROC) curve analysis of the training set (area under the curve (AUC), >0.6). The m6A-NPS could be utilized to classify patients into high- and low-risk cohorts, and the Kaplan-Meier analysis indicated that participants in the high-risk cohort had a poorer prognosis. The entire TCGA dataset substantiated the predictive value of m6A-NPS. Significant differences in TCGA molecular GC subtypes were observed between high- and low-risk cohorts. The ROC curve analysis indicated that m6A-NPS had better predictive power than other clinical characteristics of GC prognosis. Uni- and multivariate regression analyses indicated m6A-NPS as an independent prognostic factor. Furthermore, the m6A status between the low-risk cohort and high-risk cohort was significantly different. Differential genes between them were enriched in multiple tumor-associated signaling pathways. In summary, five m6A-related ncRNA signatures that could forecast the overall survival of patients with GC were identified.

Endometrial cancer (UCEC) is very common in gynecological diseases and ranks second in the death cause of gynecological cancer in developed countries. The connection between the overall survival of UCEC patients and immune invasion of the tumor microenvironment is positive. The PARVG gene has not been given notice in cancer, and its mechanism is unknown. The research utilized TCGA data to test the function of PARVG in UCEC. The manifestation of PARVG in UCEC was studied by GEPIA. By assessing the survival module, the authors learned the impact of PARVG on the survival of people with UCEC and then obtained UCEC information from TCGA. This study uses logistic regression to prove the possible relationship between PARVG expression and clinical information. From the research of Cox regression, clinicopathological characteristics of people with TCGA were connected with overall survival. Furthermore, the “correlation” module of GEPIA and CIBERSORT was used to study the association between cancer immune invasion and PARVG. Using univariate logistic regression analysis with PARVG expression as a categorical variable (median expression value of 2.5), the result suggested that raised PARVG expression was considerably connected with tumor status, pathological stage, and lymph nodes. Multiple factor studies have shown that upregulation of PARVG, distant metastasis, and negative pathological stage are absolute elements of excellent prognosis. In addition, CIBERSORT analysis was utilized to determine that raised PARVG expression has a positive connection with immune infiltration by T cells, mast cells, neutrophils, and B cells. This is recognized in GEPIA’s “correlation” module. The above outcomes show us that the raised expression of PARVG is associated with a good prognosis and it raises the proportion of immune cells (such as T cells, mast cells, neutrophils, and B cells) in UCEC. These outcomes tell us that PARVG can be utilized as a possible biomarker to evaluate UCEC’s immune infiltration levels and prognosis.

Cumulative studies have suggested that dysregulation of m6A regulators and immunity is highly linked to the prognosis of patients with cancer. However, the potential contribution of m6A modification patterns to the tumor microenvironment (TME) and the therapeutic efficacy of immunotherapy for colorectal cancer (CRC) remain elusive. A comprehensive analysis of the m6A modification profiles of 458 patients with CRC was performed by clustering 21 genes encoding m6A methylation regulators and linking the m6A modification pattern with TME characteristics. Using principal component analysis (PCA), a risk model was constructed to quantify individual m6A modification patterns in patients with CRC. The results indicated that the expression profiles and genetic mutations of 21 genes encoding m6A methylation regulators in CRC were characterized by a high degree of heterogeneity. Three m6A clusters had significant differences in prognosis, m6A modification patterns, and TME characteristics. Furthermore, a risk model, termed m6Ascore, was developed by PCA to quality m6A methylation patterns at an individual level. The m6Ascore could stratify patients into high- and low-m6Ascore groups. Further analyses demonstrated that the m6Ascore had a good predictive performance for overall survival and clinical efficacy of immunotherapy in patients with CRC. Finally, the predictive value of the model was validated by external cohorts. In conclusion, the comprehensive characterization of m6A methylation modification patterns might contribute to our understanding of the TME in CRC and the development of personalized antitumor immunotherapy in the future.