Abstract:

Cancer biomarkers identification based on multi-omics data is of great significance for the study of molecular mechanisms of cancer, while most of the current work is based on protein-protein interaction data. Therefore, a new method based on the gene regulatory network and multi-omic data is proposed to analyze cancer molecular mechanisms and predict cancer biomarkers. Taking stomach adenocarcinoma (STAD) and esophageal carcinoma (ESCA) for example, first we integrate multi-omics data to construct cancer-specific networks for STAD and ESCA respectively. Then, analysis of weighted co-expression gene networks is carried out on the two networks, and hierarchical clustering modules are used to calculate the relationship between the first principal component of the module and all known cancer biomarkers. Furthermore, cancer-specific modules are screened out. Finally, disease-specific biological pathways are extracted, and potential cancer biomarkers are prioritized using similarity assessment methods. Experimental results show that the specific module predicted has functional characteristics, and that the Pearson correlation coefficient method is more accurate.

Key words: cancer, gene co-expression network, gene expression regulation, multi-omics data