Bioinformatics identification and validation of m6A/m1A/m5C/m7G/ac4 C-modified genes in oral squamous cell carcinoma.

BACKGROUND

RNA modifications, including m6A, m1A, m5C, m7G, and ac4C, may play a role in the occurrence and development of cancer, such as proliferation. However, the effects of RNA modification-related genes (RRGs) in the development of oral squamous cell carcinoma (OSCC) have not been fully elucidated. The present study aimed to evaluate the effects and mechanisms of RRGs on OSCC development progression.

METHODS

RNA-seq transcriptome data, along with clinical and prognostic information, were extracted for 328 patients with OSCC from the TCGA database. A total of 49 RRGs were analyzed for differential expression. We then performed Lasso analysis, as well as univariate and multivariate Cox regression analyses, followed by Kaplan-Meier survival analysis to identify relevant prognostic genes and establish a risk-prognosis model. Patients were categorized into high-risk and low-risk groups, and gene set enrichment analysis (GSEA) was conducted to analyze differences in gene signatures between these two groups, using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. RT-PCR was employed to validate the expression levels of differentially expressed genes in OSCC samples. The four most significantly differentially expressed genes were selected for further functional analysis, and small interfering RNA (siRNA) vectors targeting these genes were transfected into OSCC CAL27 cells. The Cell Counting Kit-8 (CCK-8) assay was used to evaluate cell proliferation. Additionally, a subcutaneous CAL27 xenograft model transfected with short hairpin RNA (shRNA), combined with Ki-67 immunohistochemical (IHC) staining and TUNEL assay, was used to investigate their underlying molecular mechanisms in vivo.

RESULTS

Among the 49 RRGs, four genes (IGF2BP2, HNRNPC, NAT10, and TRMT61B) were found to be associated with the development of OSCC. Based on various methodological validations, a risk score model was constructed using these four genes. The high-risk and low-risk groups of OSCC patients exhibited significantly different survival outcomes and clinicopathological characteristics. Patients in the low-risk group had longer overall survival (OS) and lower mortality rates compared to those in the high-risk group. The nomogram and decision curve analysis (DCA) demonstrated that our risk model accurately and reliably predicted the impact of risk factors on OS at 1-, 3-, and 5-year. Additionally, risk scores correlated with the infiltration of several immune cells, particularly CD8 T cells and B cells, which showed significant negative correlations. Furthermore, the results of the CCK-8 assay indicated that inhibition of NAT10 and IGF2BP2 expression using siRNA inhibited the proliferation of OSCC cell lines in vitro. Meanwhile, inhibition of NAT10 and IGF2BP2 expression using shRNA influenced proliferation of tumorigenicity in vivo.

CONCLUSION

In this study, we established a risk model and nomogram based on four RRGs, which can be used for risk stratification and predicting survival outcomes in patients with OSCC. This provides a reliable reference for individualized therapy in OSCC patients.