AUTHOR=Zhao Fangchao , Li Yishuai , Dong Zefang , Zhang Dengfeng , Guo Pengfei , Li Zhirong , Li Shujun 

TITLE=Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.845293

DOI=10.3389/fcell.2022.845293

ISSN=2296-634X

ABSTRACT=Lactate, once considered as an endpoint or a waste metabolite of glycolysis, has emerged as a major regulator of cancer development, maintenance, and progression.  However, studies about lactate metabolism-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear. In this work, univariate Cox regression analysis was performed in the TCGA cohort, and 9 lncRNAs were discovered to be substantially linked with prognosis. LASSO regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. Six prognostic-associated lactate metabolism-related lncRNAs were identified as independent prognostic factors for ESCC patients used to construct the prognostic risk score model subsequently. To evaluate the predictive ability of the risk model, we divided the ESCC patients into two groups based on the median value of risk scores: low-risk group and high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariable analysis, the prognostic risk score model was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3- and 5-year survival rates of patients. Significant correlation was observed between the lncRNA-based prognostic model and immune-cell infiltration, role in the tumor microenvironment (TME), tumor somatic mutational, and chemotherapeutic drug sensitivity. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 lactate metabolism-related lncRNAs. In conclusion, our study constructed a prognostic model based on lactate metabolism-related lncRNAs that could predict the prognosis and immunotherapy response of ESCC patients.