This site is under development. For the current active site, go to bcbhub.csic.es.

Orcid Publication

Detalle de: Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients

Title Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients
Abstract

Small cell lung cancer (SCLC) prognosis is the poorest of all types of lung cancer. Its clinical management remains heterogeneous and therefore, the capability to predict survival would be of great clinical value. Metabolic health (MH) status and lipid metabolism are two relevant factors in cancer prevention and prognosis. Nevertheless, their contributions in SCLC outcome have not yet been analyzed. We analyzed MH status and a transcriptomic panel of lipid metabolism genes in SCLC patients, and we developed a predictive genetic risk score (GRS). MH and two lipid metabolism genes, racemase and perilipin 1, are biomarkers of SCLC survival (HR = 1.99 (CI95%: 1.11–3.61) p = 0.02, HR = 0.36 (CI95%: 0.19–0.67), p = 0.03 and HR = 0.21 (CI95%: 0.09–0.47), respectively). Importantly, a lipid GRS of these genes predict better survival (c-index = 0.691). Finally, in a Cox multivariate regression model, MH, lipid GRS and smoking history are the main predictors of SCLC survival (c-index = 0.702). Our results indicate that the control of MH, lipid gene expression and environmental factors associated with lifestyle is crucial for increased SCLC survival. Here, we propose for the first time, a metabolic precision approach for SCLC patients.

Apa Reference Fernández, L.P., Merino, M., Colmenarejo, G., Moreno Rubio, J., González Pessolani, T., Reglero, G., Casado, E., Sereno, M. & Ramírez de Molina, A. (2021). Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients. Cancers, 13(5), 1112. 10.3390/cancers13051112
Doi 10.3390/cancers13051112
Publication Date 5 Mar 2021
Publication Type Article
Researcher Gonzalo Colmenarejo
Ontology Transcriptomics, Expression Profiling, Statistical Methods for Biology, Multivariate statistics, Clinical data
Research Groups
Tools

Suggest changes

Publications are synced automatically from ORCID, so they can't be edited here directly. Spotted something wrong? Let us know below and we'll take care of it.

← Return to Home