Dataset Information

Two-gene signature improves the discriminatory power of IASLC/ATS/ERS classification to predict the survival of patients with early-stage lung adenocarcinoma.

ABSTRACT: BACKGROUND:In this study, we investigated the contribution of a gene expression-based signature (composed of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR) to survival prediction for early-stage lung adenocarcinoma categorized by the new International Association for the Study of Lung Cancer (IASLC)/the American Thoracic Society (ATS)/the European Respiratory Society (ERS) classification. We also aimed to verify whether gene signature improves the risk discrimination of IASLC/ATS/ERS classification in early-stage lung adenocarcinoma. PATIENTS AND METHODS:Total RNA was extracted from 93 patients with pathologically confirmed TNM stage Ia and Ib lung adenocarcinoma. The mRNA expression levels of ten genes in the signature (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, and SH3BGR) were detected using real-time polymerase chain reaction. Each patient was categorized according to the new IASLC/ATS/ERS classification by accessing hematoxylin-eosin-stained slides. The corresponding Kaplan-Meier survival analysis by the log-rank statistic, multivariate Cox proportional hazards modeling, and c-index calculation were conducted using the programming language R (Version 2.15.1) with the "risksetROC" package. RESULTS:The multivariate analysis demonstrated that the risk factor of the ten-gene expression signature can significantly improve the discriminatory value of TNM staging in survival prediction, but not the value of the IASLC/ATS/ERS classification. Further analysis suggested that only BRCA1 and ERBB3 in the signature were independent risk factors after adjusting for the IASLC/ATS/ERS classification by Cox regression. A new algorithm of the two-gene expression signature containing BRCA1 and ERBB3 was generated. Adding the two-gene signature into the IASLC/ATS/ERS classification model further improved the discriminatory c-statistic from 0.728 to 0.756. CONCLUSION:The two-gene signature composed of BRCA1 and ERBB3 was an independent risk factor of the IASLC/ATS/ERS classification, which can be used to improve the discriminatory power of prognosis prediction of the IASLC/ATS/ERS classification in early-stage lung adenocarcinoma. The two-gene signature combination with the IASLC/ATS/ERS classification might contribute to better patient stratification for adjuvant chemoradiotherapy or targeted therapy after the surgery.

SUBMITTER: Sun Y

PROVIDER: S-EPMC4966694 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Two-gene signature improves the discriminatory power of IASLC/ATS/ERS classification to predict the survival of patients with early-stage lung adenocarcinoma.

Sun Yifeng Y Hou Likun L Yang Yu Y Xie Huikang H Yang Yang Y Li Zhigang Z Zhao Heng H Gao Wen W Su Bo B

OncoTargets and therapy 20160725

<h4>Background</h4>In this study, we investigated the contribution of a gene expression-based signature (composed of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR) to survival prediction for early-stage lung adenocarcinoma categorized by the new International Association for the Study of Lung Cancer (IASLC)/the American Thoracic Society (ATS)/the European Respiratory Society (ERS) classification. We also aimed to verify whether gene signature improves the risk discrimination o ...[more]

PMID: 27524912

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Project description:The present study aimed to investigate the association between epidermal growth factor receptor (EGFR)/Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations, anaplastic lymphoma receptor tyrosine kinase (ALK) rearrangements and the morphological characteristics of lung adenocarcinoma (LAC), according to the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) classification in a large group of patients with primary LAC. A total of 200 patients with invasive LAC who had undergone complete resections at the Beijing Chest Hospital (Beijing, China) were randomly selected. The morphology of the samples was reassessed in 5% increments by two pathologists, according to the IASLC/ATS/ERS scheme. EGFR and KRAS mutations were tested by direct DNA sequencing. ALK rearrangements were screened by immunohistochemistry on a Benchmark XT stainer. The data revealed that EGFR and KRAS mutations, and ALK rearrangements were identified in 46.0% (92/200), 9.0% (18/200) and 11.5% (23/200) of the patients, respectively. The EGFR/KRAS mutations and ALK rearrangements were mostly exclusive. However, 1 patient exhibited the coexistence of the EGFR (at exon 20) and KRAS (codon 12) mutations, and another patient exhibited the coexistence of the EGFR mutation (at exon 21) and the ALK gene fusion. EGFR mutations were indicated to be closely associated with the acinar predominant (43/77; 55.8%; P=0.030) and papillary predominant (26/49; 53.1%; P=0.006) subtypes. KRAS mutations were more commonly associated with the solid predominant subtype (9/52; 17.3%; P=0.023) and invasive mucinous LAC (5/10; 50.0%; P=0.004), and less commonly associated with the acinar predominant subtype (1/77; 1.3%; P=0.002). ALK rearrangements more commonly occurred in the solid predominant subtype compared with other subtypes (13/52; 25%; P=0.002), and less commonly occurred in the papillary predominant subtype (1/49; 2.0%; P=0.004). Tumors harboring ALK rearrangements were characterized by signet-ring cell (7/9; 77.8%; P<0.0001) and cribriform (7/12; 58.3%; P<0.0001) patterns. The association between the mutation status and histological subtype in LAC was distinct. The predominant subtype according to the IASLC/ATS/ERS classification provided important information for gene mutations and integrated clinical findings to improve the treatment of LAC patients.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

Dataset Information

Two-gene signature improves the discriminatory power of IASLC/ATS/ERS classification to predict the survival of patients with early-stage lung adenocarcinoma.

Publications

Two-gene signature improves the discriminatory power of IASLC/ATS/ERS classification to predict the survival of patients with early-stage lung adenocarcinoma.

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