Dataset Information

Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.

ABSTRACT: Breast carcinoma (BRCA) is the most common carcinoma among women worldwide. Despite the great progress achieved in early detection and treatment, morbidity and mortality rates remain high. In the present study, we make a systematic analysis of BRCA using TCGA database by applying CIBERSORT and ESTIMATE computational methods, uncovered CD3D as a prognostic biomarker by intersection analysis of univariate COX and protein-protein interaction (PPI). It revealed that high CD3D expression was strongly associated with poor survival of BRCA, based on The Cancer Genome Atlas (TCGA) database and online websites. Gene Set Enrichment Analysis (GSEA) revealed that the high CD3D expression group was mainly enriched for the immune-related pathways and the low CD3D expression group was mainly enriched for metabolic-related activities. Based on CIBERSORT analysis, the difference test and correlation test suggested that CD3D had a strong correlation with T cells, particularly CD8 + T cells, which indicated that CD3D up-regulation may increase T cell immune infiltration in the TME and induce antitumor immunity by activating T lymphocytes. Furthermore, the correlation analysis showed that CD3D expression had a strongly positive correlation with immune checkpoints, which indicating that the underlying mechanism involves CD3D mediated regulation of T cell functions in BRCA, and single cell RNA-seq analysis revealed that CD3D correlate with CD8 + T cells and it is itself highly expressed in CD8 + T cells. In summary, we identified a prognostic biomarker CD3D in BRCA, which was associated with lymphocyte infiltration, immune checkpoints and could be developed for innovative therapeutics of BRCA.

SUBMITTER: Zhu Z

PROVIDER: S-EPMC7791551 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.

Zhu Zhipeng Z Ye Weipeng W Wu Xiaofang X Lin Sihao S Xu Jiuhua J Li Lulu L Li Jiayi J Wang Haibin H Huang Zhengjie Z

Bioscience reports 20210101 1

Breast carcinoma (BRCA) is the most common carcinoma among women worldwide. Despite the great progress achieved in early detection and treatment, morbidity and mortality rates remain high. In the present study, we make a systematic analysis of BRCA using TCGA database by applying CIBERSORT and ESTIMATE computational methods, uncovered CD3D as a prognostic biomarker by intersection analysis of univariate COX and protein-protein interaction (PPI). It revealed that high CD3D expression was strongly ...[more]

PMID: 33350431

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Project description:Background: Colorectal cancer (CRC) is one of the most common malignant tumors and has high morbidity and mortality rates. Previous studies have shown that TSPEAR mutations are involved in the development and progression of gastric cancer and liver cancer. However, the role of TSPEAR in CRC is still unclear. Methods: In The Cancer Genome Atlas (TCGA) database, 590 CRC patients with complete survival information were analyzed. We assessed TSPEAR expression in a pan-cancer dataset from the TCGA database. Cox regression analysis was performed to evaluate factors associated with prognosis. Enrichment analysis via the R package "clusterProfiler" was used to explore the potential function of TSPEAR. The single-sample GSEA (ssGSEA) method from the R package "GSVA" and the TIMER database were used to investigate the association between the immune infiltration level and TSPEAR expression in CRC. The R package "maftools" was used to explore the association between tumour mutation burden (TMB) and TSPEAR expression in CRC. CCK-8 assays and cell invasion assays were used to detect the effect of TSPEAR and TGIF2 on the biological behavior of CRC cells. Results: Pan-cancer analysis revealed that TSPEAR was upregulated in CRC tissues compared to normal tissues and that high TSPEAR expression was associated with poorer overall survival (OS) (p=0.0053). The expression of TSPEAR increased with increasing TNM stage, T stage, N stage, and M stage. The nomogram constructed with TSPEAR, age, and TNM stage showed better predictive value than TSPEAR, age, or TNM stage alone. Immune cell infiltration analysis revealed that high expression of TSPEAR was associated with lower immune cell infiltration. Tumor mutation burden (TMB) analysis indicated that high expression of TSPEAR was associated with lower TMB (p=0.005), and high TMB was associated with shorter OS (p=0.02). CCK-8 assays and cell invasion assays indicated that in vitro knockdown of TSPEAR inhibited the proliferation, migration, and invasion of CRC cells. In addition, TSPEAR expression may be regulated by the upstream transcription factor TGIF2. Conclusion: TSPEAR expression was higher in CRC tissues than in normal tissues. Its upregulation was significantly associated with a poor prognosis. Additionally, TSPEAR plays a significant role in tumor immunity and the biological behavior of CRC cells. Thus, TSPEAR may become a promising prognostic biomarker and therapeutic target for CRC patients.

Project description:AbstractGrowing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the "edgeR" package of the R software. Functional annotation and pathway enrichment were performed using "ggplots2" and "clusterProfiler" packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients' prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients' prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.

Dataset Information

Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.

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Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.

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