Dataset Information

RACK1 is a candidate gene associated with the prognosis of patients with early stage non-small cell lung cancer.

ABSTRACT:

Background

This study was conducted to identify genetic polymorphisms associated with the prognosis of patients with early stage NSCLC.

Materials and methods

We genotyped 1,969 potentially functional single nucleotide polymorphisms (SNPs) of 1,151 genes involved in carcinogenesis in 166 NSCLC patients who underwent curative surgery, using the Affymetrix custom-made GeneChip. A replication study was performed in an independent cohort of 626 patients.

Results

Fifty six SNPs which were associated with both overall survival (OS) and disease-free survival (DFS) with log-rank P values < 0.05 in discovery set were selected for validation. Among those, five SNPs (RACK1 rs1279736C>A and rs3756585T>G, C3 rs2287845T>C, PCAF rs17006625A>G, and PCM1 rs17691523C>G) were found to be significantly associated with survival in the same direction as the discovery set. In combined analysis, the rs1279736C>A and rs3756585T>G were most significantly associated with OS and DFS in multivariate analysis (P for OS = 4 × 10⁻⁵ and 7 × 10⁻⁵, respectively; and P for DFS = 0.003, both; under codominant model). In vitro promoter assay and electrophoretic mobility shift assay revealed that the rs3756585 T-to-G change increased promoter activity and transcription factor binding of RACK1.

Conclusions

We identified five SNPs, especially RACK1 rs3756585T>G, as markers for prognosis of patients with surgically resected NSCLC.

SUBMITTER: Choi YY

PROVIDER: S-EPMC4414203 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

RACK1 is a candidate gene associated with the prognosis of patients with early stage non-small cell lung cancer.

Choi Yi-Young YY Lee Shin Yup SY Lee Won Kee WK Jeon Hyo-Sung HS Lee Eung Bae EB Lee Hyun Cheol HC Choi Jin Eun JE Kang Hyo-Gyoung HG Lee Eun Jin EJ Bae Eun Young EY Yoo Seung Soo SS Lee Jaehee J Cha Seung Ick SI Kim Chang Ho CH Kim In-San IS Lee Myung Hoon MH Kim Young Tae YT Jheon Sanghoon S Park Jae Yong JY

Oncotarget 20150201 6

<h4>Background</h4>This study was conducted to identify genetic polymorphisms associated with the prognosis of patients with early stage NSCLC.<h4>Materials and methods</h4>We genotyped 1,969 potentially functional single nucleotide polymorphisms (SNPs) of 1,151 genes involved in carcinogenesis in 166 NSCLC patients who underwent curative surgery, using the Affymetrix custom-made GeneChip. A replication study was performed in an independent cohort of 626 patients.<h4>Results</h4>Fifty six SNPs w ...[more]

PMID: 25686824

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Project description:Background: The current staging system is imprecise for prognostic prediction of early-stage non-small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

Project description:BackgroundThe development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies.ResultsA new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC).ConclusionThe present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.

Dataset Information

RACK1 is a candidate gene associated with the prognosis of patients with early stage non-small cell lung cancer.

Background

Materials and methods

Results

Conclusions

Publications

RACK1 is a candidate gene associated with the prognosis of patients with early stage non-small cell lung cancer.

Similar Datasets

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