Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma.

Xiaolin Yu,Xiaomei Zhang,Yanxia Zhang

doi:10.1155/2020/5310793

Abstract

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Highlights

Lung cancer has become one of the most frequently diagnosed malignant tumors with a leading death rate [1]. e major histological subtype of lung cancer is non-small-cell lung cancer (NSCLC) accounting for approximately 85% [2, 3]
Data Collection. e normalized mRNA transcriptome profiling data of Lung adenocarcinoma (LUAD) were downloaded from the Cancer Genome Atlas (TCGA) database and GSE72094 dataset from the Gene Expression Omnibus (GEO) database [13]. e TCGA cohort contained 535 LUAD samples and 59 control samples and the GSE72094 cohort contained 442 LUAD samples. e corresponding clinical features were obtained and extracted
Genes that were involved in metabolism pathways were selected as metabolic genes according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene sets downloaded from MSigDB database. e intersection of genes among these three datasets was prepared as metabolic-related genes for subsequent analyses

Summary

Introduction

Lung cancer has become one of the most frequently diagnosed malignant tumors with a leading death rate [1]. e major histological subtype of lung cancer is non-small-cell lung cancer (NSCLC) accounting for approximately 85% [2, 3]. Lung cancer has become one of the most frequently diagnosed malignant tumors with a leading death rate [1]. E major histological subtype of lung cancer is non-small-cell lung cancer (NSCLC) accounting for approximately 85% [2, 3]. Lung adenocarcinoma (LUAD) was the most common subtype of NSCLC with the 5-year survival rate of about 15% [4, 5]. E reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME) [8]. In the background of TME, the disorder of tumor metabolism could deeply influence the multiple functions of malignant cancer cells [9]. Previous reports have identified metabolic signatures for prognostic prediction based on multiomics analyses in lung cancer [10,11,12]. The TME is a complex interaction network, and the integrated research on the roles of metabolic signatures in TME is still lacking

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Discussion

Conclusion