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Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma.

ABSTRACT: Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A prognostic immune-related gene signature was built to predict the survival of early-stage LUAD patients. Several machine learning methods (support vector machine, naive Bayes, random forest, and neural network-based deep learning) were applied to train the classifiers to identify the immune clusters in early-stage LUAD based on the gene signature. The four classifiers exhibited a robust effect in identifying the immune clusters. A random forest regression model identified that TP53 was the most important gene mutation associated with the immune-related signature. Furthermore, a decision tree and a nomogram were constructed based on the immune-related gene signature and clinicopathological traits to improve risk stratification and quantify risk assessment for individual patients. Five external test cohorts were applied to validate the accuracy of the immune-related signature. Our study might contribute to the development of immunotherapy and the personalized treatment of early-stage LUAD. KEY MESSAGES: Immune landscape correlates with the clinical outcome of early-stage adenocarcinoma (LUAD). Machine learning methods identifies a prognostic gene signature to predict the survival and prognosis of early-stage LUAD. TP53 gene mutation status correlates with the immune landscape in early-stage LUAD.

SUBMITTER: Bao X

PROVIDER: S-EPMC7297823 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma.

Bao Xuanwen X Shi Run R Zhao Tianyu T Wang Yanfang Y

Journal of molecular medicine (Berlin, Germany) 20200425 6

Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A pro ...[more]

PMID: 32333046

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Project description:BackgroundThe incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.MethodsThe public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan-Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.ResultsA prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29-8.11; P = 6.16E-06), GSE31210 (HR = 11.91, 95% CI 4.15-34.19; P = 4.10E-06), GSE50081 (HR = 3.63, 95% CI 1.90-6.95; P = 9.95E-05), the combined data set (HR = 3.15, 95% CI 1.98-5.02; P = 1.26E-06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49-3.13; P = 4.54E-05) and GSE72094 (HR = 2.95, 95% CI 1.86-4.70; P = 4.79E-06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.ConclusionsOur study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

Project description:BackgroundSuppressive tumor microenvironment is closely related to the progression and poor prognosis of lung adenocarcinoma (LUAD). Novel individual and universal immune-related biomarkers to predict the prognosis and immune landscape of LUAD patients are urgently needed. Two-gene pairing patterns could integrate and utilize various gene expression data.MethodsThe RNA-seq and relevant clinicopathological data of the LUAD project from the TCGA and well-known immune-related genes list from the ImmPort database were obtained. Co-expression analysis followed by an analysis of variance was performed to identify differentially expressed immune-related lncRNA (irlncRNA) (DEirlncRNA) between tumor and normal tissues. Two arbitrary DEirlncRNAs (DEirlncRNAs pair) in a tumor sample underwent pairwise comparison to generate a score (0 or 1). Next, Univariate analysis, Lasso regression and Multivariate analysis were used to screen survival-related DEirlncRNAs pairs and construct a prognostic model. The Acak information standard (AIC) values of the receiver operating characteristic (ROC) curve for 3 years are calculated to determine the cut-off point for high- or low-risk score. Finally, we evaluated the relationship between the risk score and overall survival, clinicopathological features, immune landscape, and chemotherapy efficacy.ResultsData of 54 normal and 497 tumor samples of LUAD were enrolled. After a strict screening process, 15 survival-independent-related DEirlncRNA pairs were integrated to construct a prognostic model. The AUC value of the 3-year ROC curve was 0.828. Kaplan-Meier analysis showed that patients with low risk lived longer than patients with high risk (p <0.001). Univariate and Multivariate Cox analysis suggested that the risk score was an independent factor of survival. The risk score was negatively associated with most tumor-infiltrating immune cells, immune score, and microenvironment scores. The low-risk group was correlated with increased expression of ICOS. The high-risk group had a connection with lower half inhibitory centration (IC50) of most chemotherapy drugs (e.g., etoposide, paclitaxel, vinorelbine, gemcitabine, and docetaxel) and targeted medicine-erlotinib, but with higher IC50 of methotrexate.ConclusionThe established irlncRNA pairs-based model is a promising prognostic signature for LUAD patients. Furthermore, the prognostic signature has great potential in the evaluation of tumor immune landscape and guiding individualized treatment regimens.

Project description:PurposeTP53 mutation, one of the most frequent mutations in early-stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early-stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early-stage LUAD and formulate a TP53-associated immune prognostic model (IPM) that can estimate prognosis in early-stage LUAD patients.Materials and methodsImmune-associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT ) and TP53 wild-type (TP53WT ) early-stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune-associated DEGs. We constructed and validated an IPM based on the TCGA and a meta-GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.ResultsTP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early-stage LUAD. Sixty-seven immune-associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune-associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta-GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43-3.06, p < 0.001). Increased expressions of PD-L1, CTLA-4, and TIGIT were revealed in the high-risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.ConclusionThe IPM based on TP53 status is a reliable and robust immune signature to identify early-stage LUAD patients with high risk of unfavorable survival.

Project description:BackgroundCuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD.MethodsRNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm.ResultsWe constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib.ConclusionsThese results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.

Dataset Information

Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma.

Publications

Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma.

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