Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Deng Z
PROVIDER: S-EPMC6885833 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Glioblastoma (GBM) is one of the most deadly brain tumors. The convenient access to The Cancer Genome Atlas (TCGA) database allows for large-scale global gene expression profiling and database mining for potential correlation between genes and overall survival of a variety of malignancies including GBM. Previous reports have shown that tumor microenvironment cells and the extent of infiltrating immune and stromal cells in tumors contribute significantly to prognosis. Immune scores and stromal scores calculated based on the ESTIMATE algorithm could facilitate the quantification of the immune and stromal components in a tumor. To better understand the effects of genes involved in immune and stromal cells on prognosis, we categorized GBM cases in the TCGA database according to their immune/stromal scores into high and low score groups, and identified differentially expressed genes whose expression was significantly associated with prognosis in GBM patients. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. Finally, we validated these genes in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Thus, we obtained a list of tumor microenvironment-related genes that predict poor outcomes in GBM patients.
| S-EPMC5940130 | biostudies-literature
Project description:Gastric cancer (GC) is the sixth most common malignancy and the third leading cause of cancer-related death worldwide. Emerging evidence suggests that tumour microenvironment cells play a vital role in the development and prognosis of GC. To investigate the possible effect of stromal scores and immune scores on the overall survival (OS) on the GC patients, we divided GC patients into 'high' and 'low' groups based on their stromal and immune scores, and found differentially expressed genes related to prognosis of GC patients. Functional enrichment analysis and GSVA further revealed that focal adhesion and ECM-receptor interaction are associated with GC patients' survival. Finally, we analysed the effects of genes commonly involved in focal adhesion and ECM-receptor interaction on GC patients' survival and validated our results in another GC cohort from GEO data sets. In conclusion, we obtained a list of tumour microenvironment-related genes that predict poor prognosis in GC patients.
| S-EPMC7576220 | biostudies-literature
Project description:Background/purposeHead and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors. The aim of this study was to elucidate the effect of tumor microenvironment-related genes on the prognosis of HNSCC and to obtain tumor microenvironment-related genes that can predict poor prognosis in HNSCC patients.Materials and methodsThe ESTIMATE algorithm was applied to the HNSCC transcriptomic data downloaded from the TCGA (The cancer genome atlas), and then the samples were divided into two groups: high and low immune scoring groups, and high and low basal scoring groups to screen for differentially expressed genes (DEGs) associated with poor patient outcomes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the potential functions of DEGs, and then to explore the potential prognostic value of individual DEGs. The results of survival analysis between DEGs and overall survival (OS) to explore tumor microenvironment-related genes relevant to the prognosis of HNSCC patients.ResultsFifty-nine tumor microenvironment-related genes were screened for association of OS with HNSCC (P < 0.05). The GO and KEGG enrichment analysis showed that the selected DEGs may mediate immune response, extracellular matrix, and immunoglobulin binding via neutrophil activation in HNSCC. Six of these DEGs, GIMAP6, SELL, TIFAB, KCNA3, P2RY8 and CCR4 were most significantly associated with OS (P < 0.001).ConclusionWe identified six tumor microenvironment-related genes that were significantly associated with poor prognosis in HNSCC. These genes may inspire researchers to discover new targets and approaches for HNSCC treatment.
| S-EPMC8025193 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent kidney malignancies. The tumor microenvironment (TME) is highly related to the oncogenesis, progress, and prognosis of ccRCC. The aim of this study was to infer the level of infiltrating stromal and immune cells and assess the prognostic value of them. The gene expression profile was obtained from TCGA and used for calculating the stromal and immune scores. Based on a cut-off value, patients were divided into low- and high-stromal/immune score groups. Survival analysis was performed to evaluate the prognostic value of stromal and immune scores. Moreover, differentially expressed genes (DEGs) that are highly related to TME were determined and applied for functional enrichment analysis and protein-protein interaction (PPI) network. The Kaplan-Meier plot demonstrated that patients with high-immune scores and stromal scores had poorer clinical outcome. In addition, a total of 89 DEGs were identified and mainly involved in 5 pathways. The top 5 degree genes were extracted from the PPI network; among them, IL10 and XCR1 were highly associated with prognosis of ccRCC. The results of the present study demonstrated that ESTIMATE algorithm-based stromal and immune scores may be a credible indicator of cancer prognosis and IL10 along with XCR1 may be a potential key regulator for the TME of ccRCC.
| S-EPMC6875323 | biostudies-literature
Project description:BACKGROUND:Clear cell renal cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma. Bioinformatics analyses were used to screen candidate genes associated with the prognosis and microenvironment of ccRCC and elucidate the underlying molecular mechanisms of action. METHODS:The gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. The ESTIMATE algorithm was used to compute the immune and stromal scores of patients. Based on the median immune/stromal scores, all patients were sorted into low- and high-immune/stromal score groups. Differentially expressed genes (DEGs) were extracted from high- versus low-immune/stromal score groups and were described using functional annotations and protein-protein interaction (PPI) network. RESULTS:Patients in the high-immune/stromal score group had poorer survival outcome. In total, 95 DEGs (48 upregulated and 47 downregulated genes) were screened from the gene expression profiles of patients with high immune and stromal scores. The genes were primarily involved in six signaling pathways. Among the 95 DEGs, 43 were markedly related to overall survival of patients. The PPI network identified the top 10 hub genes-CD19, CD79A, IL10, IGLL5, POU2AF1, CCL19, AMBP, CCL18, CCL21, and IGJ-and four modules. Enrichment analyses revealed that the genes in the most important module were involved in the B-cell receptor signaling pathway. CONCLUSION:This study mainly revealed the relationship between the ccRCC microenvironment and prognosis of patients. These results also increase the understanding of how gene expression patterns can impact the prognosis and development of ccRCC by modulating the tumor microenvironment. The results could contribute to the search for ccRCC biomarkers and therapeutic targets.
| S-EPMC7196483 | biostudies-literature
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.
| S-EPMC8184694 | biostudies-literature
Project description: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.
| S-EPMC7485698 | biostudies-literature
Project description:Breast cancer is one of the most common malignancies in women worldwide. Many studies have shown that tumor microenvironment cells, immune cells, and stromal cell infiltration have an important impact on prognosis, so it is important to identify biomarkers for achieving better treatment and prognosis.To better understand the relationship between immune and stromal cell-related genes and prognosis, we screened patients with breast cancer in The Cancer Genome Atlas (TCGA) database and divided them into high and low groups based on immune/stromal scores. We next identified differentially expressed immune-related genes that are significantly associated with the prognosis of patients with breast cancer for functional enrichment analysis and protein-protein interaction networks, respectively. Finally, we selected a separate breast cancer cohort in gene expression synthesis (GEO) for validation.Both immune scores and stromal scores are meaningful in the correlation of subtype classification. Disease-free survival of cases with the high score group of immune scores is statistically longer than the cases in the low score group. Differentially expressed immune-related genes extracted from the comparison can effectively evaluate the prognosis of patients with breast cancer and these genes are primarily involved in immune responses, extracellular matrix, and chemokine activity. At last, we obtained a series of verified tumor immune-related genes that predict the prognosis of patients with breast cancer.Combining the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression database and the TCGA database to extract the list of tumor microenvironment related genes which may help to outline the prognosis of patients with breast cancer. Some previously overlooked genes have the potential to become additional biomarkers for breast cancer. Further research on these genes can reveal a new understanding of the potential relationship between tumor microenvironment and breast cancer prognosis.
| S-EPMC7220520 | biostudies-literature
Project description:AbstractThe tumor microenvironment (TME) plays an important role in the development of breast cancer. Due to limitations in experimental conditions, the molecular mechanism of TME in breast cancer has not yet been elucidated. With the development of bioinformatics, the study of TME has become convenient and reliable.Gene expression and clinical feature data were downloaded from The Cancer Genome Atlas database and the Molecular Taxonomy of Breast Cancer International Consortium database. Immune scores and stromal scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data algorithm. The interaction of genes was examined with protein-protein interaction and co-expression analysis. The function of genes was analyzed by gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis and gene set enrichment analysis. The clinical significance of genes was assessed with Kaplan-Meier analysis and univariate/multivariate Cox regression analysis.Our results showed that the immune scores and stromal scores of breast invasive ductal carcinoma (IDC) were significantly lower than those of invasive lobular carcinoma. The immune scores were significantly related to overall survival of breast IDC patients and both the immune and stromal scores were significantly related to clinical features of these patients. According to the level of immune/stromal scores, 179 common differentially expressed genes and 5 hub genes with prognostic value were identified. In addition, the clinical significance of the hub genes was validated with data from the molecular taxonomy of breast cancer international consortium database, and gene set enrichment analysis analysis showed that these hub genes were mainly enriched in signaling pathways of the immune system and breast cancer.We identified five immune-related hub genes with prognostic value in the TME of breast IDC, which may partly determine the prognosis of breast cancer and provide some direction for development of targeted treatments in the future.
| S-EPMC8084029 | biostudies-literature
Project description:Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine-cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.
| S-EPMC8059369 | biostudies-literature