Dataset Information

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside.

ABSTRACT:

Background

Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).

Methods

The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets-GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram.

Results

In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873).

Conclusions

We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.

SUBMITTER: Wang X

PROVIDER: S-EPMC7867791 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside.

Wang Xiaofei X Xiao Zengtuan Z Gong Jialin J Liu Zuo Z Zhang Mengzhe M Zhang Zhenfa Z

Translational lung cancer research 20210101 1

<h4>Background</h4>Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).<h4>Methods</h4>The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets-GSE10072, GSE32863 and GSE43458; the corresponding fra ...[more]

PMID: 33569302

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Project description:BackgroundPrognostic indicators in lung adenocarcinoma (LUAD) have been seeking under database analysis, and remarkable advance is on the way.MethodsThis study calculated the scores of stromal and immune components of the tumor microenvironment (TME) in 551 LUAD samples using the ESTIMATE algorithm on The Cancer Genome Atlas (TCGA) database. R package ''limma'' was used to selected differentially expressed genes (DEG). We have analyzed the DEGs by means of Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments. The protein-protein network, univariate Cox analysis, and Lasso regression analysis were performed to selected survival-related genes. Gene Set Enrichment Analysis (GSEA) represented the enriched pathway of CC chemokine receptor 2 (CCR2). The ratios of immune cells in the TME of each LUAD sample were obtained using the R package "limma" and CIBERSORT algorithm in R 4.0.2.ResultsThe ImmuneScore was positively correlated with prognosis regarding survival rate, T classification of TNM stages, and clinicopathological staging characteristics. GO and KEGG enrichments showed DEGs were associated with immune-related activities. Three genes of LUAD were selected from the PPI network and Cox proportional hazards regression analysis. CCR2 was the most survival correlated gene by Lasso regression analysis. GSEA results showed that C2 kegg gene sets in the CCR2 high-expression group were mainly enriched in the B cell or T cell receptor signaling pathway and natural killer cell-mediated cytotoxicity. Correlation of CCR2 expression with prognosis was conducted, implicating a positive correlation with the prognosis of survival rate and M classification, negative correlation with the prognosis of T and N classifications. The correlation between CCR2 and tumor-infiltrating immune cells (TICs) was analyzed, and 14 kinds of TICs were found closely correlated with CCR2 expression through difference analysis.ConclusionTherefore, CCR2 has prognostic value as an immune indicator in LUAD.

Project description:The existence of tumor heterogeneity and complex carcinogenic mechanisms in lung adenocarcinoma (LUAD) make the most commonly used TNM staging system unable to well-interpret the prognosis of patients. Using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA) database, we constructed an immune signature based on a multivariate Cox analysis (stepwise model). We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients according to the pRRophetic algorithm. Gene-set variation analysis (GSVA) was used to reveal pathway enrichment between groups. Moreover, immune microenvironment landscape was described by single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT and systematically correlated with genomic of these patients. A prognostic nomogram combining the immune signature and TNM stage to predict the prognosis was developed by multivariate Cox regression. The novel signature with four immune-related genes (MAL, MS4A1, OAS1, and WFDC2) had good robustness, which can accurately distinguish between high- and low-risk patients. Compared with low-risk patients, high-risk patients with a worse prognosis (5-year OS: 46.5 vs. 59.4%, p = 0.002) could benefit more from immunotherapy and the application of common chemotherapeutic agents such as cisplatin and paclitaxel (Wilcoxon test, all p < 0.05). There were significant differences in tumor immune microenvironment and metabolic pathways between the two groups. Additionally, the constructed nomogram had reliable predictive performance with the C-index of 0.725 (95% CI = 0.668-0.781) in the development set (n = 500), 0.793 (95% CI = 0.728-0.858) in the internal validation set (n = 250) and 0.679 (95% CI = 0.644-0.714) in the external validation set (n = 442). The corresponding calibration curves also showed good consistency. To sum up, we developed an immune-related gene signature and comprehensively evaluated LUAD immune landscape and metabolic pathways. Effective differentiation of high- and low-risk patients and accurate construction of nomogram would be helpful to the development of individualized treatment strategies.

Dataset Information

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside.

Background

Methods

Results

Conclusions

Publications

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside.

Similar Datasets

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