» Articles » PMID: 23357462

Network-based Approach Identified Cell Cycle Genes As Predictor of Overall Survival in Lung Adenocarcinoma Patients

Overview
Journal Lung Cancer
Specialty Oncology
Date 2013 Jan 30
PMID 23357462
Citations 27
Authors
Affiliations
Soon will be listed here.
Abstract

Lung adenocarcinoma is the most common type of primary lung cancer. The purpose of this study was to delineate gene expression patterns for survival prediction in lung adenocarcinoma. Gene expression profiles of 82 (discovery set) and 442 (validation set 1) lung adenocarcinoma tumor tissues were analyzed using a systems biology-based network approach. We also examined the expression profiles of 78 adjacent normal lung tissues from 82 patients. We found a significant correlation of an expression module with overall survival (adjusted hazard ratio or HR=1.71; 95% CI=1.06-2.74 in discovery set; adjusted HR=1.26; 95% CI=1.08-1.49 in validation set 1). This expression module contained genes enriched in the biological process of the cell cycle. Interestingly, the cell cycle gene module and overall survival association were also significant in normal lung tissues (adjusted HR=1.91; 95% CI, 1.32-2.75). From these survival-related modules, we further defined three hub genes (UBE2C, TPX2, and MELK) whose expression-based risk indices were more strongly associated with poor 5-year survival (HR=3.85, 95% CI=1.34-11.05 in discovery set; HR=1.72, 95% CI=1.21-2.46 in validation set 1; and HR=3.35, 95% CI=1.08-10.04 in normal lung set). The 3-gene prognostic result was further validated using 92 adenocarcinoma tumor samples (validation set 2); patients with a high-risk gene signature have a 1.52-fold increased risk (95% CI, 1.02-2.24) of death than patients with a low-risk gene signature. These results suggest that a network-based approach may facilitate discovery of key genes that are closely linked to survival in patients with lung adenocarcinoma.

Citing Articles

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers.

Zolotovskaia M, Kovalenko M, Pugacheva P, Tkachev V, Simonov A, Sorokin M Proteomes. 2023; 11(3).

PMID: 37755705 PMC: 10535530. DOI: 10.3390/proteomes11030026.


Molecular Radiobiology in Non-Small Cell Lung Cancer: Prognostic and Predictive Response Factors.

Peinado-Serrano J, Carnero A Cancers (Basel). 2022; 14(9).

PMID: 35565331 PMC: 9101029. DOI: 10.3390/cancers14092202.


A Six-Gene Prognostic and Predictive Radiotherapy-Based Signature for Early and Locally Advanced Stages in Non-Small-Cell Lung Cancer.

Peinado-Serrano J, Quintanal-Villalonga A, Munoz-Galvan S, Verdugo-Sivianes E, Mateos J, Ortiz-Gordillo M Cancers (Basel). 2022; 14(9).

PMID: 35565183 PMC: 9099638. DOI: 10.3390/cancers14092054.


Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.

Zhang S, Zeng X, Lin S, Liang M, Huang H J Clin Lab Anal. 2021; 36(2):e24190.

PMID: 34951053 PMC: 8841135. DOI: 10.1002/jcla.24190.


Prediction of Genes Involved in Lung Cancer with a Systems Biology Approach Based on Comprehensive Gene Information.

Parvin S, Sedighian H, Sohrabi E, Mahboobi M, Rezaei M, Ghasemi D Biochem Genet. 2021; 60(4):1253-1273.

PMID: 34855070 DOI: 10.1007/s10528-021-10163-7.


References
1.
Borczuk A, Toonkel R, Powell C . Genomics of lung cancer. Proc Am Thorac Soc. 2009; 6(2):152-8. PMC: 2674225. DOI: 10.1513/pats.200807-076LC. View

2.
Shedden K, Taylor J, Enkemann S, Tsao M, Yeatman T, Gerald W . Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008; 14(8):822-7. PMC: 2667337. DOI: 10.1038/nm.1790. View

3.
Ballman K, Grill D, Oberg A, Therneau T . Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics. 2004; 20(16):2778-86. DOI: 10.1093/bioinformatics/bth327. View

4.
Beer D, Kardia S, Huang C, Giordano T, Levin A, Misek D . Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002; 8(8):816-24. DOI: 10.1038/nm733. View

5.
Miller J, Oldham M, Geschwind D . A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008; 28(6):1410-20. PMC: 2902235. DOI: 10.1523/JNEUROSCI.4098-07.2008. View