Dataset Information

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma.

ABSTRACT:

Background

Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs).

Methods

Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature.

Results

We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures.

Conclusions

Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.

SUBMITTER: Li T

PROVIDER: S-EPMC8856807 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma.

Li Tianqi T Chen Jiquan J Liu Jun J Chen Qingjie Q Nie Wei W Xu Mi-Die MD

Journal of oncology 20220211

<h4>Background</h4>Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs).<h4>Methods</h4>Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve, nomo ...[more]

PMID: 35186082

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Project description:AimLung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.MethodsTCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.ResultsA 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.ConclusionIn this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Project description:PurposeThe dysregulation of copper metabolism is closely related to the occurrence and progression of cancer. This study aims to investigate the prognostic value of copper metabolism-related genes (CMRGs) in lung adenocarcinoma (LUAD) and its characterization in the tumor microenvironment (TME).MethodsThe differentially expressed CMRGs were identified in The Cancer Genome Atlas (TCGA) of LUAD. The least absolute shrinkage and selection operator regression (LASSO) and multivariate Cox regression analysis were used to establish the copper metabolism-related gene signature (CMRGs), which was also validated in Gene Expression Omnibus (GEO) database (GSE72094). The expression of key genes was verified by quantitative real-time PCR (qRT-PCR). Then, the CMRGS was used to develop a nomogram to predict the 1-year, 3-year, and 5-year overall survival (OS). In addition, differences in tumor mutation burden (TMB), biological characteristics and immune cell infiltration between high-risk and low-risk groups were systematically analyzed. Immunophenoscore (IPS) and an anti-PD-L1 immunotherapy cohort (IMvigor210) were used to verify whether CMRGS can predict the response to immunotherapy in LUAD.Results34 differentially expressed CMRGs were identified in the TCGA dataset, 11 of which were associated with OS. The CMRGS composed of 3 key genes (LOXL2, SLC31A2 and SOD3) had showed good clinical value and stratification ability in the prognostic assessment of LUAD patients. The results of qRT-PCR confirmed the expression of key CMRGs in LUAD and normal tissues. Then, all LUAD patients were divided into low-risk and high-risk groups based on median risk score. Those in the low-risk group had a significantly longer OS than those in the high-risk group (P<0.0001). The area under curve (AUC) values of the nomogram at 1, 3, and 5 years were 0.734, 0.735, and 0.720, respectively. Calibration curves comparing predicted and actual OS were close to ideal model, indicating a good consistency between prediction and actual observation. Functional enrichment analysis showed that the low-risk group was enriched in a large number of immune pathways. The results of immune infiltration analysis also confirmed that there were a variety of immune cell infiltration in the low-risk group. In addition, multiple immune checkpoints were highly expressed in the low-risk group and may benefit better from immunotherapy.ConclusionCMRGS is a promising biomarker to assess the prognosis of LUAD patients and may be serve as a guidance on immunotherapy.

Dataset Information

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma.

Similar Datasets

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