Establishment and Validation of a Prognostic Signature for Lung Adenocarcinoma Based on Metabolism-related Genes

Abstract

Background: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes(MRGs).Methods: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify MRGs that related to overall survival(OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD.The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.Results: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs (ALDOA, CAT, ENTPD2, GNPNAT1, LDHA, and TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The signature was found to be associated with various clinicopathological features. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cells and firstly found that ENTPD2 might played an important role on lung adenocarcinoma cell lines clone formation and migration.Conclusions: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis and prognosis in patients with LUAD.

GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer

“…8 GNPNAT1 along with other metabolism-related genes correlate with the poor prognosis of LUAD patients, and an independent prognostic model was established based on these genes. 19,20 GNPNAT1 overexpression potentially correlated with DNA copy amplification, low DNA methylation and loss of miRNA. 10 Here, we showed that the upregulation of some TFs was also responsible for increased GNPNAT1 expression.…”

A Novel Signature Derived from Metabolism-Related Genes GPT and SMS to Predict Prognosis of Laryngeal Squamous Cell Carcinoma

“…Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were applied to construct a prognosis signature. The risk score formula of the prognosis signature was as follows: Risk score = coef * Exp (geneA) + coef * Exp (geneB) + coe * Expi (genei) [20,21]. 90 patients were divided into high and low-risk group according to the median risk score (1.244).…”