Transcriptomics analysis and experimental assay to screen prognostic genes of N6-methyladenosine RNA methylation related to cuproptosis in colorectal cancer.

Abstract

Cuproptosis and N6-methyladenosine (m6A) RNA methylation have been identified as significant factors in cancer. Consequently, we seek to uncover m6A RNA methylation-related prognostic genes that are correlated with cuproptosis-related genes (CRGs) in colorectal cancer (CRC) and develop an effective prognostic model. Based on public datasets, differential expression analysis, correlation analysis, and machine learning were conducted to identify prognostic genes. Subsequently, a prognostic model was constructed and validated. Furthermore, independent predictive markers were found through the use of clinical characteristics and risk score, to establish a prognostic nomogram. Prognostic gene expression in CRC and normal samples was evaluated using reverse transcription-quantitative PCR (RT-qPCR). According to the prognostic genes, potential drugs for CRC were predicted using artificial intelligence drug design (AIDD) and computer-aided drug design (CADD). A prognostic model was developed utilizing 7 m6A RNA methylation-related prognostic genes that were significantly correlated with CRGs (MMP1, TIMP1, SNAI1, CDC25C, XRN2, COX5A, and FBXL5) in CRC. Survival analysis uncovered a notable contrast in survival outcomes between the high- and low-risk groups. The nomogram constructed based on age, stage, T stage, and risk scores demonstrated good predictive ability. RT-qPCR results showed that MMP1, TIMP1, SNAI1, and CDC25C were upregulated in CRC samples, while COX5A was downregulated, consistent with the bioinformatics analysis results. Furthermore, 186 candidate drugs targeting COX5A were identified, 153 of which showed better predicted binding affinity than the positive-control ligand 3,5‑diiodo‑L‑thyronine. Our analysis sets the groundwork for a more comprehensive comprehension of the fundamental molecular pathways involved in CRC.