Dataset Information

Identification of biomarkers related to CD8⁺ T cell infiltration with gene co-expression network in clear cell renal cell carcinoma.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is an extremely common kind of kidney cancer in adults. Immunotherapy and targeted therapy are particularly effective at treating ccRCC. In this study, weighted gene co-expression network analysis and a deconvolution algorithm that quantifies the cellular composition of immune cells were used to analyze ccRCC expression data from the Gene Expression Omnibus database, and identify modules related to CD8⁺ T cells. Ten hub genes (LCK, CD2, CD3D, CD3G, IRF1, IFNG, CCR5, CD8A, CCL5, and CXCL9) were identified by co-expression network and protein-protein interactions network analysis. Datasets obtained from The Cancer Genome Atlas were analyzed and the data revealed that the hub genes were meaningfully up-regulated in tumor tissues and correlated with promotion of tumor progression. After Kaplan-Meier analysis and Oncomine meta-analysis, CCL5 was selected as a prognostic biomarker. Finally, the experimental results show that reduced expression of CCL5 decreased cell proliferation and invasion in the ccRCC cell line. Various analyses were performed and verified, CCL5 is a potential biomarker and therapeutic target which related to CD8⁺ T cell infiltration in ccRCC.

SUBMITTER: Lin J

PROVIDER: S-EPMC7066925 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identification of biomarkers related to CD8+ T cell infiltration with gene co-expression network in clear cell renal cell carcinoma.

Lin Jiaxing J Yu Meng M Xu Xiao X Wang Yutao Y Xing Haotian H An Jun J Yang Jieping J Tang Chaozhi C Sun Dan D Zhu Yuyan Y

Aging 20200220 4

Clear cell renal cell carcinoma (ccRCC) is an extremely common kind of kidney cancer in adults. Immunotherapy and targeted therapy are particularly effective at treating ccRCC. In this study, weighted gene co-expression network analysis and a deconvolution algorithm that quantifies the cellular composition of immune cells were used to analyze ccRCC expression data from the Gene Expression Omnibus database, and identify modules related to CD8+ T cells. Ten hub genes (LCK, CD2 ...[more]

PMID: 32081834

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Project description:Background: Kidney renal clear cell carcinoma (KIRC) is an immunogenic tumor, and immune infiltrates are relevant to patients' therapeutic response and prognosis. NDUFS1, the core subunit of mitochondrial complex I, has been reported to be associated with KIRC patients' prognosis. However, the upstream regulator for NDUFS1 and their correlations with immune infiltration remain unclear. Methods: The expression of NDUFS genes in KIRC and their influences on patients' survival were investigated by UALCAN, ENCORI, Oncomine, TIMER as well as Kaplan-Meier Plotter. miRNAs regulating NDUFS1 were predicted and analyzed by TargetScan and ENCORI. The correlations between NDUFS1 expression and immune cell infiltration or gene marker sets of immune infiltrates were analyzed via TIMER. The overall survival in high/low NDUFS1 or hsa-miR-320b expressed KIRC patients with or without immune infiltrates were analyzed via Kaplan-Meier Plotter. The combined NDUFS1 expression and/or CD4+ T cell infiltration on KIRC patients' overall survival were validated by multiplexed immunofluorescence (mIF) staining in tissue microarray (TMA). Furthermore, the influences of NDUFS1 expression on the chemotaxis of CD4+ T cells to KIRC cells were performed by transwell migration assays. Results: We found that the low expression of NDUFS1 mRNA and protein in KIRC was correlated with unfavorable patients' survival and poor infiltration of CD4+ T cells. In patients with decreased CD4+ T cell infiltration whose pathological grade less than III, TMA mIF staining showed that low expression of NDUFS1 had significantly poor OS than that with high expression of NDUFS1 did. Furthermore, hsa-miR-320b, a possible negative regulator of NDUFS1, was highly expressed in KIRC. And, low NDUFS1 or high hsa-miR-320b consistently correlated to unfavorable outcomes in KIRC patients with decreased CD4+ T cell infiltration. In vitro, NDUFS1 overexpression significantly increased the chemotaxis of CD4+ T cell to KIRC cells. Conclusion: Together, NDUFS1, upregulated by decreased hsa-miR-320b expression in KIRC patients, might act as a biomarker for CD4+ T cell infiltration. And, the combination of NDUFS1 with CD4+ T cell infiltration predicts favorable prognosis in KIRC.

Project description:Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient's quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through "The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts." Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

Dataset Information

Identification of biomarkers related to CD8⁺ T cell infiltration with gene co-expression network in clear cell renal cell carcinoma.

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Identification of biomarkers related to CD8<sup>+</sup> T cell infiltration with gene co-expression network in clear cell renal cell carcinoma.

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