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Updated: May 26, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
Published on: September 15, 2023
A transcriptome analysis by lasso penalized Cox regression for pancreatic cancer survival
Tong Tong Wu1, Haijun Gong, Edmund M Clarke
1Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu
Abstract:
Pancreatic cancer is the fourth leading cause of cancer deaths in the United States with five-year survival rates less than 5% due to rare detection in early stages. Identification of genes that are directly correlated to pancreatic cancer survival is crucial for pancreatic cancer diagnostics and treatment. However, no existing GWAS or transcriptome studies are available for addressing this problem. We apply lasso penalized Cox regression to a transcriptome study to identify genes that are directly related to pancreatic cancer survival. This method is capable of handling the right censoring effect of survival times and the ultrahigh dimensionality of genetic data. A cyclic coordinate descent algorithm is employed to rapidly select the most relevant genes and eliminate the irrelevant ones. Twelve genes have been identified and verified to be directly correlated to pancreatic cancer survival time and can be used for the prediction of future patient's survival.
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