Development and Validation of a Novel Gene Signature for Predicting the Prognosis by Identifying m5C Modification Subtypes of Cervical Cancer

ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment

BACKGROUND

5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment.

METHODS

Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature.

RESULTS

Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS.

CONCLUSION

Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.

Contribution of m5C RNA Modification-Related Genes to Prognosis and Immunotherapy Prediction in Patients with Ovarian Cancer

Background

5-Methylcytosine (m5C) RNA modification is closely implicated in the occurrence of a variety of cancers. Here, we established a novel prognostic signature for ovarian cancer (OC) patients based on m5C RNA modification-related genes and explored the correlation between these genes with the tumor immune microenvironment.

Methods

Methylated-RNA immunoprecipitation sequencing helped us to identify candidate genes related to m5C RNA modification at first. Based on TCGA database, we screened the differentially expressed candidate genes related to the prognosis and constructed a prognostic model using LASSO Cox regression analyses. Notably, the accuracy of the model was evaluated by Kaplan-Meier analysis and receiver operator characteristic curves. Independent prognostic risk factors were investigated by Cox proportional hazard model. Furthermore, we also analyzed the biological functions and pathways involved in the signature. Finally, the immune response of the model was visualized in great detail.

Results

Totally, 2,493 candidate genes proved to be involved in m5C modification of RNA for OC. We developed a signature with prognostic value consisting of six m5C RNA modification-related genes. Specially, samples have been split into two cohorts with low- and high-risk scores according to the model, in which the low-risk OC patients exhibited dramatically better overall survival time than those with high-risk scores. Besides, not only was this model a prognostic factor independent of other clinical characteristics but it predicted the intensity of the immune response in OC. Significantly, the accuracy and availability of the signature were verified by ICGC database.

Conclusions

Our study bridged the gap between m5C RNA modification and the prognosis of OC and was expected to provide an effective breakthrough for immunotherapy in OC patients.

Expression and prognosis analyses of the fibronectin type-III domain-containing (FNDC) protein family in human cancers: A Review

Despite advancements in early detection and treatment, cancer continues to pose a threat to human health and is the leading cause of death worldwide. According to recent research, the fibronectin type-III domain-containing (FNDC) protein family has been implicated in several different human disorders. However, little is known regarding their expression and prognostic significance in most human malignancies. We carried out a thorough cancer vs. normal expression study using the Oncomine and Tumor Immune Estimation Resource (TIMER) databases, as well as a prognostic evaluation using the Kaplan-Meier (KM) plotter and PrognoScan databases. Oncomine revealed that the mRNA expression levels of FNDC1, FNDC3A, and FNDC3B were higher in most malignancies than in normal tissues, but the mRNA expression levels of FNDC4, FNDC5, FNDC7, and FNDC8 were downregulated in most cancers when compared with normal tissues. In survival analyses based on KM Plotter and PrognoScan, all members of the FNDC family displayed significant correlations with survival outcomes in breast, gastric, and ovarian cancers. Furthermore, the whole FNDC family, except for FNDC7 and FNDC8, was found to have substantial predictive effects in lung adenocarcinoma, but not in squamous cell lung cancer. In addition, potential connections between several FNDC family members and survival results in liver and colorectal malignancies were discovered in this study. One or more members of the FNDC family demonstrated statistically significant differences in expression between cancer and normal tissues, suggesting that they could be used as prognostic biomarkers for specific cancers.

Comprehensive Analysis of m5C Methylation Regulatory Genes and Tumor Microenvironment in Prostate Cancer

Background

5-Methylcytidine (m5C) methylation is an emerging epigenetic modification in recent years, which is associated with the development and progression of various cancers. However, the prognostic value of m5C regulatory genes and the correlation between m5C methylation and the tumor microenvironment (TME) in prostate cancer remain unknown.

Methods

In the current study, the genetic and transcriptional alterations and prognostic value of m5C regulatory genes were investigated in The Cancer Genome Atlas and Gene Expression Omnibus datasets. Then, an m5C prognostic model was established by LASSO Cox regression analysis. Gene set variation analyses (GSVA), gene set enrichment analysis (GSEA), clinical relevance, and TME analyses were conducted to explain the biological functions and quantify the TME scores between high-risk and low-risk subgroups. m5C regulatory gene clusters and m5C immune subtypes were identified using consensus unsupervised clustering analysis. The Cell-type Identification By Estimating Relative Subsets of RNA Transcripts algorithm was used to calculate the contents of immune cells.

Results

TET3 was upregulated at transcriptional levels in PCa compared with normal tissues, and a high TET3 expression was associated with poor prognosis. An m5C prognostic model consisting of 3 genes (NSUN2, TET3, and YBX1) was developed and a nomogram was constructed for improving the clinical applicability of the model. Functional analysis revealed the enrichment of pathways and the biological processes associated with RNA regulation and immune function. Significant differences were also found in the expression levels of m5C regulatory genes, TME scores, and immune cell infiltration levels between different risk subgroups. We identified two distinct m5C gene clusters and found their correlation with patient prognosis and immune cell infiltration characteristics. Naive B cells, CD8+ T cells, M1 macrophages and M2 macrophages were obtained and 2 m5C immune subtypes were identified. CTLA4, NSUN6, TET1, and TET3 were differentially expressed between immune subtypes. The expression of CTLA4 was found to be correlated with the degree of immune cell infiltration.

Conclusions

Our comprehensive analysis of m5C regulatory genes in PCa demonstrated their potential roles in the prognosis, clinical features, and TME. These findings may improve our understanding of m5C regulatory genes in the tumor biology of PCa.