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Identification of Methylation Driven Biomarkers for Diagnosis and Prognosis in Colorectal Cancer by Integrative Analysis of TCGA, GTEx, and GEO Database

Lichao Cao, Ying Ba, Jin Yang*, Hezi Zhang*

Background: This work investigates the use of methylation driven biomarkers for diagnosis and prognosis in Colorectal Cancer (CRC) by mining DNA methylation and gene expression data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), and the Gene Expression Omnibus (GEO).

Methods: The Differentially Expressed Genes (DEGs) and Differentially Methylated Genes (DMGs) were screened using mRNA expression and DNA methylation data from TCGA, respectively. The Methylation Driven Genes (MDGs) of CRC were further identified using the MethylMix R package. Subsequently, the MDGs were analyzed with Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms to establish diagnosis prediction models as independent indicators using mRNA expression data from TCGA and GTEx. The RF algorithm was determined to be the most suitable and used to construct the diagnostic model with the combined MDGs, which was then validated by GSE39582 from GEO. Prognostic biomarkers were used to establish the risk score model, which was generated by univariate and multivariate Cox regression analyses. Moreover, we constructed and validated a nomogram that integrated the risk score and clinical information, including age, gender, and tumor stage.

Results: 9 out of 10 MDGs performed well as independent diagnostic predictors, and STK33 and EPHX4 were also found to be associated with Overall Survival (OS). The results of the nomogram suggest that it is a better predictive model for prognosis than the risk score model.

Conclusion: Our findings suggest that the identified MDGs could be biomarkers for diagnosis and prognosis of CRC.