Fig. 1: Global RNA m5C hypermethylation and its functional implications in cervical cancer.
A Schematic overview of the study design. Tumor and normal cervical tissues were analyzed using RNA bisulfite sequencing (RNA BS-seq), bulk RNA-seq, and spatial transcriptomics (ST), while single-cell RNA-seq (scRNA-seq) was performed exclusively on tumor samples. The multi-omics data were integrated to construct the MORGAN model for target identification, followed by experimental validation. Created with BioRender. B Comparison of m5C density, number of m5C sites, number of m5C-modified genes, and percentage of m5C-modified genes between control (n = 8) and cancer (n = 14) samples. Data are presented as box plots showing the median and min to max range. Data were analyzed using a two-tailed unpaired t test. *P < 0.05. C Violin plots show the distribution and median of methylation rates across samples. The global per-site m5C methylation rate (methRate) was significantly elevated in cancer samples compared to controls (***P < 0.001, Wilcoxon rank-sum test). D Genomic distribution of m5C sites in control and cancer samples. Pie charts show the distribution of m5C sites across different transcript regions, including 5’UTR, 3’UTR, introns, and CDS. E Functional enrichment analysis of genes with significantly increased m5C methylation in cervical cancer. The Dot plot shows selected enriched terms and pathways grouped into four categories: Epigenetics and chromatin remodeling, Regulation of cell proliferation and survival, Tumor invasion and metastasis, and Tumor microenvironment and immune evasion. Dot size indicates the number of genes involved, and color represents the significance level. F A PPI network was constructed based on genes with significantly elevated m5C levels in cancer samples. Three major functional modules were identified using the MCODE algorithm.
