Dataset Information

Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes.

ABSTRACT: Background: The incidence of lung adenocarcinoma (LUAD) increased substantially in recent years. A systematic investigation of the metabolic genomics pattern is critical to improve the treatment and prognosis of LUAD. This study aimed to analyze the relationship between tumor microenvironment (TME) and metabolism-related genes of LUAD. Methods: The data was extracted from TCGA and GEO datasets. The metabolism-related gene expression profile and the corresponding clinical data of LUAD patients were then integrated. The survival-related genes were screened out using univariate COX regression and lasso regression analysis. The latent properties and molecular mechanisms of these LUAD-specific metabolism-related genes were investigated by computational biology. Results: A novel prognostic model was established based on 8 metabolism-related genes, including TYMS, ALDH2, PKM, GNPNAT1, LDHA, ENTPD2, NT5E, and MAOB. The immune infiltration of LUAD was also analyzed using CIBERSORT algorithms and TIMER database. In addition, the high- and low-risk groups exhibited distinct layout modes in the principal component analysis. Conclusions: In summary, our studies identified clinically significant metabolism-related genes, which were potential signature for LUAD diagnosis, monitoring, and prognosis.

SUBMITTER: Zhang J

PROVIDER: S-EPMC7646164 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes.

Zhang Jianguo J Zhang Jianzhong J Yuan Cheng C Luo Yuan Y Li Yangyi Y Dai Panpan P Sun Wenjie W Zhang Nannan N Ren Jiangbo J Zhang Junhong J Gong Yan Y Xie Conghua C

Journal of Cancer 20201018 24

<b>Background:</b> The incidence of lung adenocarcinoma (LUAD) increased substantially in recent years. A systematic investigation of the metabolic genomics pattern is critical to improve the treatment and prognosis of LUAD. This study aimed to analyze the relationship between tumor microenvironment (TME) and metabolism-related genes of LUAD. <b>Methods:</b> The data was extracted from TCGA and GEO datasets. The metabolism-related gene expression profile and the corresponding clinical data of LU ...[more]

PMID: 33193873

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Project description:Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

Project description:BackgroundTUBA1C is a microtubule component that is involved in a variety of cancers. Our main objective was to investigate TUBA1C expression, its prognostic value, its potential biological functions, and its impact on the immune system of patients with lung adenocarcinoma (LUAD).MethodsThe Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA) and Immunohistochemistry Analysis were used to analyze TUBA1C expression, its clinicopathology, overall survival (OS), and disease-free survival (DFS) in LUAD patients. We also determined the correlation between TUBA1C and tumor-infiltrating immune cells (TIICs) by using CIBERSORT and GEPIA databases. To determine the expression of TUBA1C in LUAD, we analyzed a collection of immune infiltration levels and cumulative survival of LUAD tissues in TIMER database. By using UALCAN, STRING, and GeneMANIA databases, we investigated the protein-coding genes related to TUBA1C and its co-expression genes in LUAD tissues. Gene set enrichment analysis (GSEA) was performed by using the TCGA dataset.ResultsThe mRNA and the protein expression of TUBA1C were found to be up-regulated in LUAD tissues. The univariate analysis indicated that an increased expression of TUBA1C was significantly correlated to the following parameters: age, stage, and lymph node metastasis. An over-expression of TUBA1C was associated with a poor prognosis of LUAD. In TIMER and CIBERSORT databases, we found that TUBA1C is correlated with 13 types of TIICs: activated B cell, activated CD4 T cell, central memory CD4 T cell, effector memory CD8 T cell, eosinophils, immature B cell, gamma-delta T cell, immature dendritic cell, mast cell, memory B cell, natural killer T cell, regulatory T cell, and type 2T helper cell. By performing GSEA, we found that TUBA1C is closely correlated to cell cycle, p53 signaling pathway, glycolysis, and gluconeogenesis.ConclusionsOur findings indicate that TUBA1C is associated with TIICs in tumor microenvironment. Therefore, it serves as a novel prognostic biomarker and a target for future treatment methods of LUAD.

Dataset Information

Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes.

Publications

Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes.

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