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A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients.

ABSTRACT: Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the OS of LuADC patients. Gene co-expression network analysis revealed the biological functions of these 600 genes in normal lung and LuADCs, which were found to be enriched for cell cycle-related processes, blood vessel development, cell-matrix adhesion and metabolic processes. Finally, we implemented a multiple resampling method combined with Cox regression analysis to identify a 27-gene signature associated with OS, and then created a prognostic scoring system based on this signature. This scoring system robustly predicted OS of LuADC patients in 100 sampling test sets and was further validated in four independent LuADC cohorts. In addition, in comparison to other existing prognostic gene signatures published in the literature, our signature was significantly superior in predicting OS of LuADC patients. In summary, our multi-omics and clinical data integration study created a 27-gene prognostic risk score that can predict OS of LuADC patients independent of age, gender and clinical stage. This score could guide therapeutic selection and allow stratification in clinical trials.

SUBMITTER: Chen EG

PROVIDER: S-EPMC5805521 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients.

Chen En-Guo EG Wang Pin P Lou Haizhou H Wang Yunshan Y Yan Hong H Bi Lei L Liu Liang L Li Bin B Snijders Antoine M AM Mao Jian-Hua JH Hang Bo B

Oncotarget 20171215 6

Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the ...[more]

PMID: 29467935

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Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Project description:Background: TEA domain transcription factor 4 (TEAD4) is a member of the transcriptional enhancer factor (TEF) family of transcription factors, which is studied to be linked to the tumorigenesis and progression of various forms of cancers, including lung adenocarcinoma (LUAD). However, the specific function of this gene in the progression of LUAD remains to be explored. Method: A total of 19 genes related to the Hippo pathway were analyzed to identify the significant genes involved in LUAD progression. The TCGA-LUAD data (n = 585) from public databases were mined, and the differentially expressed genes (DEGs) in patients with the differential level of TEAD4 were identified. The univariate Cox regression, zero LASSO regression coefficients, and multivariate Cox regression were performed to identify the independent prognostic signatures. The immune microenvironment estimation in the two subgroups, including immune cell infiltration, HLA family genes, and immune checkpoint genes, was assessed. The Gene Set Enrichment Analysis (GSEA) and GO were conducted to analyze the functional enrichment of DEGs between the two risk groups. The potential drugs for the high-risk subtypes were forecasted via the mode of action (moa) module of the connectivity map (CMap) database. Results: TEAD4 was found to be significantly correlated with poor prognosis in LUAD-patients. A total of 102 DEGs in TEAD4-high vs. TEAD4-low groups were identified. Among these DEGs, four genes (CPS1, ANLN, RHOV, and KRT6A) were identified as the independent prognostic signature to conduct the Cox risk model. The immune microenvironment estimation indicated a strong relationship between the high TEAD4 expression and immunotherapeutic resistance. The GSEA and GO showed that pathways, including cell cycle regulation, were enriched in the high-risk group, while immune response-related and metabolism biological processes were enriched in the low-risk group. Several small molecular perturbagens targeting CFTR or PLA2G1B, by the mode of action (moa) modules of the glucocorticoid receptor agonist, cyclooxygenase inhibitor, and NFkB pathway inhibitor, were predicted to be suited for the high-risk subtypes based on the high TEAD4 expression. Conclusion: The current study revealed TEAD4 is an immune regulation-related predictor of prognosis and a novel therapeutic target for LUAD.

Project description:ObjectiveThe purpose of this study is to screen for microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) and to explore its prognosis and effects on the tumor microenvironment in patients with LUAD.MethodsGene expression data, miRNA expression data, and clinical data for two different databases, TCGA-LUAD and CPTAC-3 LUAD, were downloaded from the GDC database. The miRNA prognosis of LUAD was filtered by the Cox proportional hazard model and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The performance of the model was validated by time-dependent receiver operating characteristics (ROC) curves. Possible biological processes associated with the miRNAs target gene were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the prognostic model was scored by risk, divided into high- and low-risk groups by median, and the differences in the immersion level of 21 immune cells in the high- and low-risk groups were assessed. To gain a deeper understanding of the underlying mechanism behind the model, the two most important miRNAs in the model, miR-195-3p and miR-5571-5p, were selected for HPA database validation and ceRNA network construction.ResultsOf the 209 variance expressions identified in the screening analysis, 145 were upregulated and 64 were downregulated by miRNAs. The prognostic models of six miRNA genes were obtained: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293. These six genes were significantly associated with survival rates in LUAD patients. In particular, miR-1293, miR-195-3p, and miR-5571-5p are highly correlated with OS. The higher expression of miR-195-3p and miR-5571-5p, the better survival of LUAD OS is, and these two miRNA expressions contribute the most to the model. Finally, after sorting the risk scores calculated from low to high using the prognostic model, the patients with higher scores had shorter survival time and higher frequency of death, and there were significant differences in the immersion levels of 21 immune cells in the high- and low-risk groups. ceRNA network analysis found that TM9SF3 was regulated by miR-195-3p and was highly expressed in the tissues of LUAD patients, and the prognosis of the patients was poor.ConclusionsmiR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293 may be used as new biomarkers for prognosis prediction of LUAD. Our results also identified a lncRNA MEG3/miR-195-3p/RAB1A/TM9SF3 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

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A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients.

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A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients.

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