Research

Developing innovative prognostic and therapeutic strategies

Despite advancements in treatment options for cancer, a majority of cancer types continue to lack fully characterized and effective targeted therapies to improve disease diagnostics, prognoses, and patient survival outcomes. Therefore, there is an urgent need to gain a more comprehensive understanding of the molecular basis of diseases and develop novel prognostic and therapeutic strategies. We pioneered a series of pan-cancer approaches to provide clinical insights into cancer therapy. For example, we comprehensively analyzed alterations of clock genes and circadian rhythms across multiple human cancers and revealed strong interactions between clock genes and clinically actionable genes, which highlighted the clinical utility of circadian timing in cancer chronotherapy (Cell Systems, 2018). We characterized multi-omics signatures that are associated with the hypoxia status of cancer cells and correlate with drug resistance or drug sensitivity, thus contrasting the conventional view that hypoxia confers drug resistance (Nature Metabolism, 2019). We demonstrated the importance of lncRNA in modulating immune balance in favor of immunosuppression, and that the expression of LINK-A potentially modulates the protein levels of the PLC, which provided the basis for developing combinational immunotherapy (Nature Immunology, 2019). These studies shed light on future clinical considerations for the development of innovative therapies for cancer types currently lacking effective treatment options. We will further develop highly innovative prognostic and therapeutic strategies with the potential to produce a major impact on biomedical research.

Mining the multi-dimensional omics data for novel biomarkers

The human genome encodes approximately 20,000 protein-coding genes and a large number of noncoding RNAs, including long noncoding RNAs (lncRNAs), pseudogenes, small nucleolar RNAs (snoRNAs), enhancer RNAs (eRNAs), and circular RNAs (circRNAs). It is also known that human transcripts are under extensive post-transcriptional regulation, including Alternative splicing (AS), Alternative polyadenylation (APA), and RNA editing. Our previous studies characterized the genomic landscape and clinical relevance of different transcriptomic events, including pseudogene (Nature Communications, 2014), RNA editing (Cancer Cell, 2015), snoRNA (Cell Reports, 2017), APA (Journal of the National Cancer Institute, 2018), circRNA (Genome Medicine, 2019), and eRNA (Nature Communications, 2019). These results highlighted transcriptomic events as exciting themes for investigating cancer mechanisms, biomarkers, and treatments. We hypothesize that they are also involved in the initiation and progression of human diseases. We will continue to systematically dissect their potential diagnostic and prognostic utility. We expect to generate global landscapes of multiple transcriptomic events across large-scale disease samples.

Building comprehensive data resources for the research community

It is challenging to facilitate the utilization of large-scale data by the broad biomedical community. In the past several years, our group has been dedicated to developing comprehensive data resources, including TANRIC (Cancer Research, 2015), LNCediting (Nucleic Acids Research, 2016), SNORic (Cell Reports, 2017), CSCD (Nucleic Acids Research, 2017), PancanQTL (Nucleic Acids Research, 2017), Pancan-meQTL (Nucleic Acids Research, 2018), and tRic (RNA Biology, 2019). These databases provided valuable resources for the research community to use to further perform functional investigations. We hypothesized that comprehensive data resources will be of significant interest to the research community. We will further develop related data resources to accelerate the investigations in biomedical research.

Research Interests:

Bioinformatics, Next-Generation Sequencing, Cancer Genomics, Single-cell sequencing.

Resource Links:

  1. TANRIC, an integrative web server for analyzing lncRNAs in cancer

Website: https://bioinformatics.mdanderson.org/public-software/tanric/

  1. LNCediting, a database for predicting functional effects of RNA editing in lncRNAs

Website: http://bioinfo.life.hust.edu.cn/LNCediting/

  1. TSCD, a database for tissue-specific circRNAs

Website: http://gb.whu.edu.cn/TSCD/

  1. CSCD, a database for cancer-specific circRNAs

Website: http://gb.whu.edu.cn/CSCD/

  1. CircView, a visualization and exploration tool for circRNAs

Website: http://gb.whu.edu.cn/CircView/index.html

  1. SNORic, a database for snoRNAs in cancer

Website: http://bioinfo.life.hust.edu.cn/SNORic

  1. PancanQTL, a database for cis-eQTLs and trans-eQTLs in 33 cancer types

Website: http://bioinfo.life.hust.edu.cn/PancanQTL/

  1. Pancan-meQTL, a database for pan-cancer methylation quantitative trait loci

Website: http://bioinfo.life.hust.edu.cn/Pancan-meQTL/

  1. CancerSplicingQTL, a database to systematically predict the effects of SNPs on alternative mRNA splicing

Website: http://www.cancersplicingqtl-hust.com/#/

10. CircRic, a database for circRNAs across ~1000 cancer cell lines

Website: https://hanlab.uth.edu/cRic/

11.  eRic, a database for eRNAs in cancer, including TCGA cancer samples and CCLE cancer cell lines

Website: https://hanlab.uth.edu/eRic/

12. tRic, a database for tRNAs in cancer

Website: https://hanlab.uth.edu/tRic/

  1. APAatlas, a database for Alternative PolyAdenylation atlas in human tissues

Website: https://hanlab.uth.edu/apa/