» Articles » PMID: 32194805

Development and Validation of a 4-gene Combination for the Prognostication in Lung Adenocarcinoma Patients

Overview
Journal J Cancer
Specialty Oncology
Date 2020 Mar 21
PMID 32194805
Citations 6
Authors
Affiliations
Soon will be listed here.
Abstract

To identify a multi-gene prognostic factor in patients with lung adenocarcinoma (LUAD). Prognosis-related genes were screened in the TCGA-LUAD cohort. Then, patients in this cohort were randomly separated into training set and test set. Least absolute shrinkage and selection operator (LASSO) regression was performed to the penalized the Cox proportional hazards regression (CPH) model on the training set, and a prognostication combination based on the result of LASSO analysis was developed. By performing Kaplan-Meier curve analysis, univariate and multivariable CPH model on the overall survival (OS) as well as recurrence free survival (RFS), the prognostication performance of the multigene combination were evaluated. Moreover, we constructed a nomogram and performed decision curve analysis to evaluate the clinical application of the multigene combination. We obtained 99 prognosis-related genes and screened out a 4-gene combination (including CIDEC, ZFP3, DKK1, and USP4) according to the LASSO analysis. The results of survival analyses suggested that patients in the 4-gene combination low-risk group had better OS and RFS than those in the 4-gene combination high-risk group. The 4-gene mentioned was demonstrated to be independent prognostic factor of patients with LUAD in the training set(OS, HR=11.962, P<0.001; RFS, HR=9.281, P<0.001) and test set (OS, HR=5.377, P=0.003; RFS, HR=2.949, P=0.104). More importantly, its prognosis performance was well in the validation set (OS, HR=0.955, P=0.002; RFS, HR=1.042, P<0.001). We introduced a 4-gene combination which could predict the survival of LUAD patients and might be an independent prognostic factor in LUAD.

Citing Articles

m6A-Related Genes Contribute to Poor Prognosis of Hepatocellular Carcinoma.

Zou Y, Jiang G, Xie Y, Li H Comput Math Methods Med. 2022; 2022:2427987.

PMID: 36339682 PMC: 9629938. DOI: 10.1155/2022/2427987.


Recent Advances on the Role of ATGL in Cancer.

Zhang R, Meng J, Yang S, Liu W, Shi L, Zeng J Front Oncol. 2022; 12:944025.

PMID: 35912266 PMC: 9326118. DOI: 10.3389/fonc.2022.944025.


A Novel Stool Methylation Test for the Non-Invasive Screening of Gastric and Colorectal Cancer.

Ma L, Gong J, Zhao M, Kong X, Gao P, Jiang Y Front Oncol. 2022; 12:860701.

PMID: 35419280 PMC: 8995552. DOI: 10.3389/fonc.2022.860701.


Signature identification of relapse-related overall survival of early lung adenocarcinoma after radical surgery.

Han P, Yue J, Kong K, Hu S, Cao P, Deng Y PeerJ. 2021; 9:e11923.

PMID: 34430085 PMC: 8349519. DOI: 10.7717/peerj.11923.


Development and validation of a three-long noncoding RNA signature for predicting prognosis of patients with gastric cancer.

Zhang J, Piao H, Wang Y, Lou M, Guo S, Zhao Y World J Gastroenterol. 2020; 26(44):6929-6944.

PMID: 33311941 PMC: 7701940. DOI: 10.3748/wjg.v26.i44.6929.


References
1.
Huang D, Sherman B, Lempicki R . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57. DOI: 10.1038/nprot.2008.211. View

2.
Gao H, Li L, Xiao M, Guo Y, Shen Y, Cheng L . Elevated expression is an independent unfavorable prognostic indicator of survival in head and neck squamous cell carcinoma. Cancer Manag Res. 2018; 10:5083-5089. PMC: 6215925. DOI: 10.2147/CMAR.S177043. View

3.
Zhou L, Yu M, Arshad M, Wang W, Lu Y, Gong J . Coordination Among Lipid Droplets, Peroxisomes, and Mitochondria Regulates Energy Expenditure Through the CIDE-ATGL-PPARα Pathway in Adipocytes. Diabetes. 2018; 67(10):1935-1948. DOI: 10.2337/db17-1452. View

4.
Rojas A, Mardones R, Pritzker K, van Wijnen A, Galindo M, Las Heras F . Dickkopf-1 reduces hypertrophic changes in human chondrocytes derived from bone marrow stem cells. Gene. 2018; 687:228-237. DOI: 10.1016/j.gene.2018.11.037. View

5.
Vickers A, Elkin E . Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006; 26(6):565-74. PMC: 2577036. DOI: 10.1177/0272989X06295361. View