Evolving-pattern analysis of transient and long-term biomarkers for cancers: Hepatocellular carcinoma as a case

Cancer is a complex disease arises from combinations of changes that occur over a period of time. With the development of bioinformatics, more and more biomarkers representing changes in cancers had been identified using gene expression profiles. However, biomarkers alone are quite limited in explaining the molecular processed occurred in the due process. In this paper, we develop an evolving-pattern analysis pipeline for in-depth studies of gene expression changes during different disease stages, choosing hepatocellular carcinoma (HCC) as a case. Enrichment analyses were performed on three levels: functional terms, validated genes, and regulation factors for all the biomarkers to find out their biological characters. Our results show that biomarkers with distinct evolving patterns exhibit quite different characteristics on functional and regulation levels. For the case of HCC, transient biomarkers are mostly annotated to metabolic processes, while long-term biomarkers are mostly annotated to regulation processes, with a larger number of enriched regulation factors. Furthermore, our pipeline reveals the important roles of microRNAs in various evolving patterns, which are known to be closely related to HCC. These results confirm that evolving-pattern analysis may provide a new sight for in-depth studies of biomarkers and diseases.