Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

Deng, Zhenfeng; Wang, Jilong; Xu, Banghao; Jin, Zongrui; Wu, Guolin; Zeng, Jingjing; Peng, Minhao; Guo, Ya; Zhang, Wen

doi:10.1155/2019/2408348

Cited by 42 publications

(39 citation statements)

References 59 publications

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“…TCGA database mining and analysis are commonly used to predict the prognosis of patients with cancer [20]; therefore, we used the TCGA database to identify TME-related genes with a significant effect on BC prognosis, analyze the biological processes and signal transduction pathways of related DEGs, and evaluate the predictive ability of gene signatures. First, we obtained stromal, immune, and ESTIMATE scores using the ESTIMATE algorithm and then investigated the relationship between these scores and the clinical information for 1,049 samples.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Han

2020

Bioengineered

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Increasing evidence has shown that the tumor microenvironment (TME) plays an important role in tumor occurrence and development and can also affect patient prognosis. In this study, we screened key prognostic genes in the breast cancer (BC) TME by analyzing the immune and stromal scores of tumor samples to detect differentially expressed genes (DEGs) and also constructed a TME-related prognostic model. First, we obtained mRNA-Seq and related clinical information for patients with BC from The Cancer Genome Atlas (TCGA) and calculated the stromal and immune scores of tumor tissues using the ESTIMATE algorithm. Next, we performed functional enrichment analysis and generated protein–protein interaction networks from the DEGs that were highly related to the TME. Finally, Cox proportional hazards regression analysis was performed on BC datasets from TCGA, and analyses were conducted on infiltrating immune cells and the human protein atlas. Together, these analyses indicated that the KLRB1 and SIT1 genes could be used as independent prognostic factors for BC, while risk score, age, and clinical stage could be used as prognostic factors. In summary, we found that the prognosis of BC is closely related to immune regulation in the TME.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Han

2020

Bioengineered

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“…FCGR1B was overexpressed in renal cell carcinoma, predicting poor patient prognosis [32]. GPR84, a protein-coding gene of the metabolic G protein-coupled receptor family, can be regarded as a potential signature for the prognostic prediction of hepatocellular carcinoma [33]. IGSF6 was considered to be closely related to the susceptibility of inflammatory bowel disease [34].…”

Section: Discussionmentioning

confidence: 99%

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

et al. 2020

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Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

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“…The tumor microenvironment (TME) consists of tumor cells, immune cells, broblasts, extracellular matrix, chemokines, cytokines, etc. However, the immune and stromal cells in the TME are the primary nontumor components (5,6). Research on the TME demonstrates that it can downregulate the immune response to cancer therapy, which reduces the in ltration of dendritic cells and inhibits effector T cell activation (7,8).…”

Section: Introductionmentioning

confidence: 99%

Weighted Gene Co-expression Network Analysis Identifies TBC1D10C as A New Prognostic Biomarker for Breast Cancer

Qiao

Pang³

et al. 2021

Preprint

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Background: Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study aimed to find a TME-related biomarker to identify patients who might respond to immunotherapy. Method: We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA.Result: TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest, univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict survival of BRCA patients. The C-index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both p-values<0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT).Conclusion: We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.

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Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

Cited by 42 publications

References 59 publications

Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Weighted Gene Co-expression Network Analysis Identifies TBC1D10C as A New Prognostic Biomarker for Breast Cancer

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