My primary research interest lies in the discovery of gene signatures that have strong correlations with diagnosis, progress, and prognosis of diseases. It has been long established that RNA expressions of both coding and non-coding genes are subject to tight regulation and many are differentially expressed between different physio- or pathological conditions. Numerous studies have investigated the transcriptomic patterns in many diseases and discovered various RNA species as potential candidates to serve as biomarkers for disease sub-type classification, overall survival prediction, and many other purposes. I aim to identify transcriptomic signatures with a focus on the discovery of non-coding RNA signatures including micro-RNA, long non-coding RNA, and circular RNA for the purposes of disease diagnostic and prognostic prediction.
I am also interested in identifying DNA methylomic signatures for disease prediction. DNA methylation is the addition of methyl residues to the 5′ position of the cytosines ring in CpG dinucleotides. Studies show that DNA methylation is a key element in the control mechanism that governs gene expression in vertebrates; aberration in DNA methylation is associated with cancer. Numerous DNA methylome analyses have discovered methylation signatures that are of predictive value of overall survival, metastasis, and response to treatments in cancer patients. I am particularly interested in using optimized machine learning and artificial intelligence algorithms to identify methylome signatures that could reveal patients’ predispositions to drug resistance in cancer treatment, especially in cancers such as basal-like breast cancer where chemotherapy is nearly always the only treatment option. My possible findings may help with the development of diagnostic chips embedded with probes targeting methylome signature CpG sites which could be used to clinically predict patients’ response to chemotherapy and overall survival of cancer patients.
My current project focuses on investigating gene and DNA methylome signatures using cancer data obtained from the TCGA and NCBI GEO databases. I am actively seeking collaboration opportunities with research groups interested in the same area.