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Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma.

ABSTRACT: Cancer is a highly complicated biological process due to large scale heterogeneity. Identification of differentially expressed genes between normal and cancer samples is widely utilized in the discovery of prognostic factors. In this study, based on RNA sequencing data of lung adenocarcinoma, we focused on the expression differentiation during confined (with neither lymph node invasion nor distant metastasis) primary tumors and lymphnode (with only lymph node invasion but not distant metastasis) primary tumors. The result indicated that differentially expressed genes during confined-lymphnode transition were more closely related to patient's overall survival comparing with those identified from normal-cancer transition. With the aid of public curated biological network, we successfully retrieved the biggest connected module composed of 135 genes, of which the expression was significantly associated with patient's overall survival, confirmed by 9 independent microarray datasets.

SUBMITTER: An N

PROVIDER: S-EPMC5593941 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma.

An Ning N Yang Xue X

Scientific reports 20170911 1

Cancer is a highly complicated biological process due to large scale heterogeneity. Identification of differentially expressed genes between normal and cancer samples is widely utilized in the discovery of prognostic factors. In this study, based on RNA sequencing data of lung adenocarcinoma, we focused on the expression differentiation during confined (with neither lymph node invasion nor distant metastasis) primary tumors and lymphnode (with only lymph node invasion but not distant metastasis) ...[more]

PMID: 28894185

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Project description:Background:The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. Materials and Methods:We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. Results:We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). Conclusion:We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

Project description:IntroductionThe regulatory mechanisms of super enhancers (SEs) and ceRNA networks in LUAD progression are not well understood. We aimed to discover the prognostic-related ceRNA network regulated by SEs in metastatic LUAD.MethodsRNA-seq data were extracted from The Cancer Genome Atlas (TCGA) database. Differentially expressed (DE) RNAs were identified by edgeR. CeRNA network was predicted and visualized using starBase and Cytoscape. H3K27ac ChIP-seq data were derived from the Gene Expression Omnibus (GEO) database, and used for SE identification. Kaplan-Meier curve and multivariate Cox model were applied for prognostic analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network were performed for functional analysis. SEs of AC074117.1 were verified by ChIP-qPCR in A549 and H1299 cells. MTT assay was performed to analyze cell proliferation. Luciferase activity assay was carried out to validate the target targeting relationships of ceRNA network.ResultsA total of 2355 DEmRNA, 483 DElncRNA and 155 DEmiRNA were identified between metastatic LUAD and adjacent normal tissues. CeRNA network consisting of 7 DElncRNAs, 18 DEmiRNAs and 15 DEmRNAs was constructed. Among the seven DElncRNAs in ceRNA network, only AC074117.1 was regulated by SEs. SE-regulated prognostic ceRNA sub-network consisting of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p was screened and verified. The overlapping co-expressed mRNAs of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p were mainly related to cell division and Fanconi anemia pathway. Genes in the ceRNA sub-network were correlated with DNA mismatch repair markers. Functional experiments proved that AC074117.1 was highly expressed in LUAD cells. AC074117.1 silencing notably inhibited proliferation of A549 and H1299 cells. Luciferase activity assay confirmed the direct relationship in AC074117.1-hsa-let-7c-5p-FKBP3/E2F2 network.ConclusionA novel prognostic ceRNA sub-network regulated by SEs was identified in metastatic LUAD. This study provided potential therapeutic targets and prognostic markers for further study of metastatic LUAD.

Project description:BackgroundCD8+ T cells are one of the central effector cells in the immune microenvironment. CD8+ T cells play a vital role in the development and progression of lung adenocarcinoma (LUAD). This study aimed to explore the key genes related to CD8+ T-cell infiltration in LUAD and to develop a novel prognosis model based on these genes.MethodsWith the use of the LUAD dataset from The Cancer Genome Atlas (TCGA), the differentially expressed genes (DEGs) were analyzed, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Combined with the CIBERSORT algorithm, the gene module in WGCNA, which was the most significantly correlated with CD8+ T cells, was selected for the subsequent analyses. Key genes were then identified by co-expression network analysis, protein-protein interactions network analysis, and least absolute shrinkage and selection operator (Lasso)-penalized Cox regression analysis. A risk assessment model was built based on these key genes and then validated by the dataset from the Gene Expression Omnibus (GEO) database and multiple fluorescence in situ hybridization experiments of a tissue microarray.ResultsFive key genes (MZT2A, ALG3, ATIC, GPI, and GAPDH) related to prognosis and CD8+ T-cell infiltration were identified, and a risk assessment model was established based on them. We found that the risk score could well predict the prognosis of LUAD, and the risk score was negatively related to CD8+ T-cell infiltration and correlated with the advanced tumor stage. The results of the GEO database and tissue microarray were consistent with those of TCGA. Furthermore, the risk score was higher significantly in tumor tissues than in adjacent lung tissues and was correlated with the advanced tumor stage.ConclusionsThis study may provide a novel risk assessment model for prognosis prediction and a new perspective to explore the mechanism of tumor immune microenvironment related to CD8+ T-cell infiltration in LUAD.

Dataset Information

Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma.

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Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma.

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