Abstract
BACKGROUND: Colorectal cancer (CRC) is a major global public health issue, with over 1.8 million new cases and 881,000 deaths in 2018. This study aims to explore the application of integrating microbiome and transcriptome analysis in the diagnosis of colorectal adenocarcinoma (COAD), focusing on its potential in identifying cancer biomarkers and predicting disease progression. METHODS: By utilizing COAD transcriptome and microbiome data from TCGA, diversity and differential expression analyses were conducted to identify microbiome composition differences between primary tumors (PT) and solid tissue normal (STN) samples, and the role of mRNA in prognosis. Mediation analysis was used to identify interactions between microbiome, transcriptome, in COAD tumors. The Multi-Omics Graph convolutional NET works (MOGONET) framework was employed to combine these data for COAD tumor prediction. RESULTS: Significant changes in microbiome composition and specific mRNA expression patterns were closely related to COAD development. It was observed that the Simplex virus genus can mediate the abundance of the BRAF transcripts, thereby affecting the risk of COAD. The MOGONET model demonstrated high accuracy in predicting COAD tumors, achieving 0.977 accuracy, 0.988 F1 score, and 1.0 AUROC. CONCLUSION: Integrating microbiome and transcriptome analysis shows significant potential in COAD diagnosis and prognostic assessment. These findings provide important insights for further clinical applications and cancer treatment strategies.