Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Abstract

Background: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of cooccurring genes that are the most influential based on an influence maximization algorithm. Results: The sets of co-occurring genes that are the most influential that we discover include RARA-CRBP1, CASP3-BCL2, BCL2-CASP3-CRBP1, RARA-CASP3-CRBP1, FOXJ1-RASSF3-ESR1, FOXJ1-RASSF1A-ESR1, FOXJ1-RASS F1A-TNFAIP8-ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

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