Dataset Information

Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma.

ABSTRACT: BACKGROUND:Immune infiltration may predict survival and have clinical significance in lung cancer. However, immune signatures derived from immune profiling based on bulk tumor transcriptomes have not been systematically established in lung adenocarcinoma. We aimed to construct an immune cell infiltrating score, using a new algorithm for evaluating immune infiltration, to improve the prognostic model of lung adenocarcinoma. METHODS:Public datasets of lung adenocarcinoma from the Gene Expression Omnibus and The Cancer Genome Atlas were adopted as the training and validation cohorts. Fractions of different immune cell subtypes in each sample were estimated using the CIBERSORT algorithm. The immune infiltrating score was further developed by a least absolute shrinkage and selection operator regression model. The prognostic value and clinical relationship of the model was then further explored. RESULTS:An immune infiltrating score model was established on the basis of the immune cells in the training cohort. A high score was associated with significantly worse survival in patients with lung adenocarcinoma (P?

SUBMITTER: Yang X

PROVIDER: S-EPMC6615164 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma.

Yang Xiaodong X Shi Yu Y Li Ming M Lu Tao T Xi Junjie J Lin Zongwu Z Jiang Wei W Guo Weigang W Zhan Cheng C Wang Qun Q

Journal of translational medicine 20190708 1

<h4>Background</h4>Immune infiltration may predict survival and have clinical significance in lung cancer. However, immune signatures derived from immune profiling based on bulk tumor transcriptomes have not been systematically established in lung adenocarcinoma. We aimed to construct an immune cell infiltrating score, using a new algorithm for evaluating immune infiltration, to improve the prognostic model of lung adenocarcinoma.<h4>Methods</h4>Public datasets of lung adenocarcinoma from the Ge ...[more]

PMID: 31286969

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Project description:AimThis study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms.MethodsThe data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358).ResultsA total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells.ConclusionThe proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.MethodsIn this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.ResultsWe identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.ConclusionOur identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Project description:BackgroundLung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression.MethodsThe transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used.ResultsThe six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms.ConclusionsOur study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.

Project description:Background: Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Because of its low early diagnosis rate, poor late prognosis, and high mortality, it is of great significance to find biomarkers for diagnosis and prognosis. Methods: Five hundred and twelve LUADs from The Cancer Genome Atlas were used for differential expression analysis and short time-series expression miner (STEM) analysis to identify the LUAD-development characteristic genes. Survival analysis was used to identify the LUAD-unfavorable genes and LUAD-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate Cox regression analysis were used to explore the diagnostic and prognostic ability of the two GSVA score systems. Two independent data sets from Gene Expression Omnibus (GEO) were used for verifying the results. Functional enrichment analysis was used to explore the potential biological functions of LUAD-unfavorable genes. Results: With the development of LUAD, 185 differentially expressed genes (DEGs) were gradually upregulated, of which 84 genes were associated with LUAD survival and named as LUAD-unfavorable gene set. While 237 DEGs were gradually downregulated, of which 39 genes were associated with LUAD survival and named as LUAD-favorable gene set. ROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated both of LUAD-unfavorable GSVA score and LUAD-favorable GSVA score were a biomarker of LUAD. Moreover, both of these two GSVA score systems were an independent factor for LUAD prognosis. The LUAD-unfavorable genes were significantly involved in p53 signaling pathway, Oocyte meiosis, and Cell cycle. Conclusion: We identified and validated two LUAD-development characteristic gene sets that not only have diagnostic value but also prognostic value. It may provide new insight for further research on LUAD.

Project description:BackgroundLung cancer is the most frequently diagnosed carcinoma and the leading cause of cancer-related mortality. Although molecular targeted therapy and immunotherapy have made great progress, the overall survival (OS) is still poor due to a lack of accurate and available prognostic biomarkers. Therefore, in this study we aimed to establish a multiple-gene panel predicting OS for lung adenocarcinoma.MethodsWe obtained the mRNA expression and clinical data of lung adenocarcinoma (LUAD) from TCGA database for further integrated bioinformatic analysis. Lasso regression and Cox regression were performed to establish a prognosis model based on a multi-gene panel. A nomogram based on this model was constructed. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. The prognosis value of the multi-gene panel was further validated in TCGA-LUAD patients with EGFR, KRAS and TP53 mutation and a dataset from GEO. Gene set enrichment analysis (GSEA) was performed to explore potential biological mechanisms of a novel prognostic gene signature.ResultsA four-gene panel (including DKK1, GNG7, LDHA, MELTF) was established for LUAD prognostic indicator. The ROC curve revealed good predicted performance in both test cohort (AUC = 0.740) and validation cohort (AUC = 0.752). Each patient was calculated a risk score according to the model based on the four-gene panel. The results showed that the risk score was an independent prognostic factor, and the high-risk group had a worse OS compared with the low-risk group. The nomogram based on this model showed good prediction performance. The four-gene panel was still good predictors for OS in LUAD patients with TP53 and KRAS mutations. GSEA revealed that the four genes may be significantly related to the metabolism of genetic material, especially the regulation of cell cycle pathway.ConclusionOur study proposed a novel four-gene panel to predict the OS of LUAD, which may contribute to predicting prognosis accurately and making the clinical decisions of individual therapy for LUAD patients.

Project description:BackgroundThe immunosuppressive tumor microenvironment produced by cancer cells is a key mechanisms of cancer immune escape. In this study, we investigated the relationship between the metabolic patterns and tumor immune environment in the TME of lung adenocarcinoma (LUAD) with the p53 mutation.MethodsThe clinical data of 495 LUAD patients was obtained from The Cancer Genome Atlas as transcriptomic and somatic mutation data. Using differential analysis, survival analysis, and a LASSO regression model based on metabolic unigenes from KEGG pathways, a tumor metabolic model was constructed to predict the prognosis of LUAD patients. Subsequently, nomogram, receiver operating characteristic, and decision curve analyses were conducted to assess the predictive ability of the model. In addition, the ESTIMATE and CIBERSORT algorithms were used to detect tumor purity and estimate the fractions of 22 immune cell types in each patient, respectively. We found a correlation between the composition of immune cells and the tumor metabolic model. The results were validated using an independent GSE72094 dataset with 442 patients, as well as an immunohistochemistry assay, RT-qPCR, and western blot.ResultsThe tumor metabolic model reassigned the risk score of every patient, and a tumor metabolic risk score (TMRS) was generated to show the predictive ability for patient prognoses (hazard ratio =0.39; 95% confidence interval: 0.18-0.85). Using a combination of TMRS and clinical features, a nomogram was produced with a predictive accuracy of 0.72. Further analysis showed that CD4 memory resting T cells and M1 macrophages may by correlated with the TMRS, which corresponded to immunoediting in p53 mutant patients. Additionally, the similar expression of ALDH3A1 and MGAT5B were also verified by wetlab experiments.ConclusionsBased on the identified tumor metabolism-immune landscape, we were able to predict a metabolism risk score for patient prognosis and identify a correlation with two types of infiltrating lymphocytes in the TME of p53-mutated LUAD. This landscape provides insights that will help identify the molecular mechanisms of immune-editing tumor metabolism.

Dataset Information

Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma.

Publications

Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma.

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