Each year, cancer is responsible for the deaths of millions worldwide. Personalized medcine-based clinical diagnostics are moving toward more accurate, minimally-invasive approaches with cost-effective next generation sequencing (NGS) technologies. My most significant research involves identification of genetic mutations associated with key biological pathways important in drug response, potentially leading to biomarker discoveries. I also develop bioinformatics software to identify non-invasive biomarkers for personalized medicine utilising integrative large-scale NGS data.
The expression of genes differs between normal and cancer tissues. Therefore, the expression signature of cancer cells can provide important information for cancer diagnosis, and also has potential to be useful for prognosis. The inappropriate gene expression in human cancers is caused by aberrant gene regulation involving multi-step changes in the genome. Genetically defective tumour suppressor genes (TSGs) and hyperactive oncogenes (OCGs) heavily contribute to dysregulation of cell proliferation and apoptosis in oncogenesis. Recent genome-wide studies also demonstrated that non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), can function as TSGs or OCGs and influence gene expression. However, there is little known about cancer gene regulation at a 'systems level', i.e. how transcription factors (TFs), miRNAs, and lncRNAs function together in complex to orchestrate the expression of large sets of cancer genes for uncontrolled cell proliferation. My research goal is to comprehensively characterize the structure and function of cancer gene regulatory networks to identify the key regulatory molecules for cancer diagnosis.
The ongoing projects include:
- 1.Characterisation of competitive regulatory mechanisms of tumor suppressor and oncogeenes in cancers
- 2.Prioritisation of candidate miRNAs/circRNA for non-invasive cancer diagnosis
- 3.Identification of neuro-peptidomic biomarkers for cancers
- 4.Pan-cancer analysis based data integration on oncogenic processes such as epithelial mesenchymal transition, cancer metastasis and cell senescence