Welcome to the Dr. Ling Cai Lab!
Bioinformatics, data integration
Inborn errors of metabolism
Cai F, Bezwada D, Cai L, Mahar R, Wu Z, Chang MC, Pachnis P, Yang C, Kelekar S, Gu W, Brooks B, Ko B, Vu HS, Mathews TP, Zacharias LG, Martin-Sandoval M, Do D, Oaxaca KC, Jin ES, Margulis V, Malloy CR, Merritt ME, DeBerardinis RJ.
Cell Metab. 2023 Oct 3;35(10):1830-1843.e5. doi: 10.1016/j.cmet.2023.07.013. Epub 2023 Aug 22. PubMed PMID: 37611583.
Kawabe N, Matsuoka K, Komeda K, Muraki N, Takaba M, Togami Y, Ito Y, Yamada M, Sunaga N, Girard L, Minna JD, Cai L, Xie Y, Tanaka I, Morise M, Sato M.
Oncol Lett. 2023 Sep;26(3):391. doi: 10.3892/ol.2023.13977. eCollection 2023 Sep. PubMed PMID: 37600329; PubMed Central PMCID: PMC10433723.
Cai L, DeBerardinis RJ, Xiao G, Minna JD, Xie Y.
iScience. 2023 Jun 16;26(6):106983. doi: 10.1016/j.isci.2023.106983. eCollection 2023 Jun 16. PubMed PMID: 37378310; PubMed Central PMCID: PMC10291506.
My work is primarily focused on the development of computational methods and user-friendly informatics tools to exploit publicly available cancer datasets. Examples from previous work includes the creation of a web portal for co-expression analysis with denoised gene expression data, the establishment of a comprehensive lung cancer patient database housing clinical and gene expression data for over 6,700 patients, the design of a web application to assess functional data consistency for cancer cell lines, and a web tool for co-mutation analysis with AACR Project GENIE data.
In collaboration with Dr. Adi Gazdaar, we studied the molecular changes during the neuroendocrine (NE) transdifferentiation of classic SCLC to variant SCLC. We discovered that cell-autonomous immune gene repression is released during this transition, a pattern also seen in pulmonary neuroendocrine cells. To facilitate further exploration, I’ve developed the SCLC Neuroendocrine Transdifferentiation Explorer, a web application that enables users to investigate transcriptomic changes associated with NE transdifferentiation in patient and preclinical model SCLC and neuroblastoma datasets. Additionally, I’ve drawn parallels between SCLC and neuroblastoma, another NE neoplasm, identifying numerous similarities in the molecular and functional changes associated with NE transdifferentiation. Lastly, I’ve devised a generalized method to compute NE scores, opening avenues for investigating NE transdifferentiation across a wider range of cancer types.
In my research, I have effectively utilized my expertise in Non-Small Cell Lung Cancer (NSCLC) data mining to generate valuable insights across multiple projects. I have developed the Lung Cancer Explorer database, a comprehensive resource that has enabled me to conduct systematic analyses investigating the relationship between tumor and normal gene expression differences and their associations with prognosis. A notable outcome of this work was the identification of KYNU as a functionally significant prognostic factor in KEAP1/STK11 co-mutated lung adenocarcinoma, discovered through a meta-analysis of bimodally-distributed prognostic genes. Furthermore, my expertise has been instrumental in supporting numerous lung cancer research collaborators in their respective projects, demonstrating the broad applicability and impact of my work.
Through a long-term collaboration with Dr. Ralph DeBerardinis, an expert in cancer metabolism research, I have developed expertise in processing and integrative analysis of data from metabolic profiling (such as metabolomics and metabolic tracing experiments). I have applied these skills to characterize the molecular mechanism of metabolic reprogramming in lung cancer. My colleagues and I investigate the association between metabolic phenotypes and orthogonal datasets that include mutations, gene expression, protein expression, therapeutic sensitivity, and imaging data. Many critical findings have been experimentally validated and mechanistically studied.
Silencing of GRHL2 induces epithelial‑to‑mesenchymal transition in lung cancer cell lines with different effects on proliferation and clonogenic growth
Dr. Ling Cai is an Assistant Professor in the Peter O’Donnell School of Public Health, the Children’s Research Institute, and a member of the Quantitative Biomedical Research Center (QBRC) and the Harold C. Simmons Cancer Center at UT Southwestern Medical Center (UTSW).
During her Ph.D. training in the Mechanism of Disease Track of the Integrative Biology Graduate Program at UTSW, she received comprehensive training in basic science, with a particular focus on cellular metabolism and disease mechanisms. This foundation has been instrumental in her research on understanding the intricate molecular mechanisms and metabolic pathways implicated in cancer development and progression. Her postdoctoral work focused on the integrated analysis of high-dimensional datasets from cancer metabolism research and developed various bioinformatics tools and databases for lung cancer research.
In her role as an Assistant Professor, Dr. Cai continues to delve into cancer metabolism and inborn errors of metabolism. Her research endeavors are centered around understanding the complex nature of tumors and the role of metabolic pathways in cancer and metabolic diseases. She is also actively involved in the creation and upkeep of data management and analysis systems specifically designed for lung cancer research.