Dataset Information

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis.

Methods

We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups.

Results

Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69).

Conclusion

The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

SUBMITTER: Li X

PROVIDER: S-EPMC10730453 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

Li Xiaofeng X Xu Chunwei C Min Yonghua Y Zhai Zhanqiang Z Zhu Youcai Y

The clinical respiratory journal 20231210 12

<h4>Background</h4>Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis.<h4>Methods</h4>We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer ...[more]

PMID: 38071755

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Project description:Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan-Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan-Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

Project description:Background:Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods:The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results:A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Dataset Information

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

Background

Methods

Results

Conclusion

Publications

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

Similar Datasets

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