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A new immune signature for survival prediction and immune checkpoint molecules in lung adenocarcinoma.

ABSTRACT: BACKGROUND:Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer. The prognostic signature could be reliable to stratify LUAD patients according to risk, which helps the management of the systematic treatments. In this study, a systematic and reliable immune signature was performed to estimate the prognostic stratification in LUAD. METHODS:The profiles of immune-related genes for patients with LUAD were used as one TCGA training set: n?=?494, other validation set 1: n?=?226 and validation set 2: n?=?398. Univariate Cox survival analysis was used to identify the candidate immune-related genes from each cohort. Then, the immune signature was developed and validated in the training and validation sets. RESULTS:In this study, functional analysis showed that immune-related genes involved in immune regulation and MAPK signaling pathway. A prognostic signature based on 10 immune-related genes was established in the training set and patients were divided into high-risk and low-risk groups. Our 10 immune-related gene signature was significantly related to worse survival, especially during early-stage tumors. Further stratification analyses revealed that this 10 immune-related gene signature was still an effective tool for predicting prognosis in smoking or nonsmoking patients, patients with KRAS mutation or KRAS wild-type, and patients with EGFR mutation or EGFR wild-type. Our signature was negatively correlated with B cell, CD4+ T cell, CD8+ T cell, neutrophil, dendritic cell (DC), and macrophage immune infiltration, and immune checkpoint molecules PD-1 and CTLA-4 (P?

SUBMITTER: Guo D

PROVIDER: S-EPMC7060601 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A new immune signature for survival prediction and immune checkpoint molecules in lung adenocarcinoma.

Guo Dina D Wang Mian M Shen Zhihong Z Zhu Jiaona J

Journal of translational medicine 20200306 1

<h4>Background</h4>Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer. The prognostic signature could be reliable to stratify LUAD patients according to risk, which helps the management of the systematic treatments. In this study, a systematic and reliable immune signature was performed to estimate the prognostic stratification in LUAD.<h4>Methods</h4>The profiles of immune-related genes for patients with LUAD were used as one TCGA training set: n = 494, other validation set ...[more]

PMID: 32143735

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Project description:BACKGROUND Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma. MATERIAL AND METHODS Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Lasso regression analysis was conducted to reduce the overfitting of model. Nomogram integrated the signature and clinicopathological characteristics was established for prognosis prediction of LUAD. The GSE30219, GSE41271, and GSE42127 datasets were analyzed for external validation. LUAD patients were separated into low-risk and high-risk subgroups based on the optimum cutoff threshold of calculated risk score. The predictive value of the signature was evaluated using Kaplan-Meier survival analysis, Harrell's C-index, and receiver operating characteristic (ROC) curve analysis and calibration curve. Clinical- and immune-correlation of the signature was further performed. Gene Set Enrichment Analysis (GSEA) was performed for functional exploration. RESULTS An immune-related signature containing 7 genes was identified. The signature exhibited reliable performance in the prediction of overall survival for LUAD with the C-index being 0.72. The areas under the curve (AUCs) of the model in 1-year risk prediction were 0.781, 0.797, 0.659, and 0.822 for TCGA-LUAD, GSE30219, GSE41271, and GSE42127 datasets, respectively. In all datasets, the signature proved to an independent risk factor for LUAD. Correlation analyses and GSEA further revealed the close relationship between the predictive biomarker and tumor immunity. CONCLUSIONS A Cox proportional hazard model consisting of 7 genes was identified for prognostic prediction of LUAD. The signature was highly correlated with immunity and deserves further exploration.

Project description:We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Project description:BackgroundAlternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.MethodsWe analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan-Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.ResultsA total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.ConclusionTaken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

Dataset Information

A new immune signature for survival prediction and immune checkpoint molecules in lung adenocarcinoma.

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A new immune signature for survival prediction and immune checkpoint molecules in lung adenocarcinoma.

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