Dataset Information

Prognostic values of GPNMB identified by mining TCGA database and STAD microenvironment.

ABSTRACT: The survival rate of stomach adenocarcinoma patients with immune and stromal scores and different clinicopathological features obtained from the TCGA datasets was systematically compared. A list of genes that are correlated with stomach adenocarcinoma microenvironment were extracted using the TCGA database to predict the prognosis and survival. In addition, the differentially expressed genes were extracted by comparing the immune and stromal scores of the groups. The protein-protein interaction network, and functional and pathway enrichment analyses of differentially expressed genes were performed. A total of 8 hub genes were selected from the differentially expressed genes to predict the overall survival and disease-free survival rates. GPNMB was selected from the hub genes based on the survival and prognosis analyses. A nomogram was built by including the potential risk factors based on multivariate Cox analysis. Cell function experiments and xenograft tumors were conducted in vivo to further verify the role of GPNMB in tumor progression. The predicted microRNA, miR-30b-3p, might act as upstream negative regulator and binding to 3' UTR of GPNMB, confirming by fluorescent enzyme reporter gene experiment. In summary, immune-related scores are crucial factors in the malignant progression of stomach adenocarcinoma and GPNMB acts as a potentially useful prognostic factor for stratification and in developing the treatment strategy.

SUBMITTER: Yao K

PROVIDER: S-EPMC7485698 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Prognostic values of GPNMB identified by mining TCGA database and STAD microenvironment.

Yao Kunhou K Wei Lunshou L Zhang Junjie J Wang Chenyu C Wang Chaoyang C Qin Changjiang C Li Song S

Aging 20200821 16

The survival rate of stomach adenocarcinoma patients with immune and stromal scores and different clinicopathological features obtained from the TCGA datasets was systematically compared. A list of genes that are correlated with stomach adenocarcinoma microenvironment were extracted using the TCGA database to predict the prognosis and survival. In addition, the differentially expressed genes were extracted by comparing the immune and stromal scores of the groups. The protein-protein interaction ...[more]

PMID: 32833670

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Project description:BackgroundTo identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.MethodsWe downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.ResultsGene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.ConclusionsWe identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Project description:Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

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Prognostic values of GPNMB identified by mining TCGA database and STAD microenvironment.

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Prognostic values of GPNMB identified by mining TCGA database and STAD microenvironment.

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