AUTHOR=Xia Minqi , Wang Shuo , Wang Li , Mei Yingna , Tu Yi , Gao Ling 

TITLE=The role of lactate metabolism-related LncRNAs in the prognosis, mutation, and tumor microenvironment of papillary thyroid cancer

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1062317

DOI=10.3389/fendo.2023.1062317

ISSN=1664-2392

ABSTRACT=Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate metabolism-related lncRNAs (LRLs) were found to be substantially associated with prognosis. A least absolute shrinkage and selection operator analysis (Lasso) was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 - 0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and, tumor microenvironment (TME), immune escape, and tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analyses were used to confirm the differential expression levels of the six LRLs. In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and tumor microenvironment of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.