A costimulatory molecule-related signature in regard to evaluation of prognosis and immune features for clear cell renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Hua X
PROVIDER: S-EPMC8449780 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Immune status affects the initiation and progression of clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma. In this study, we identified an immune-related, five-gene signature that improves survival prediction in ccRCC. Patients were classified as high- and low-risk based on the signature risk score. Survival analysis showed differential prognosis, while principal component analysis revealed distinctly different immune phenotypes between the two risk groups. High-risk patients tended to have advanced stage, higher grade disease, and poorer prognoses. Functional enrichment analysis showed that the signature genes were mainly involved in the cytokine-cytokine receptor interaction pathway. Moreover, we found that tumors from high-risk patients had higher relative abundance of T follicular helper cells, regulatory T cells, and M0 macrophages, and higher expression of PD-1, CTLA-4, LAG3, and CD47 than low-risk patients. This suggests our gene signature may not only serve as an indicator of tumor immune status, but may be a promising tool to select high-risk patients who may benefit from immune checkpoint inhibitor therapy. Multivariate Cox regression analysis showed that the signature remained an independent prognostic factor after adjusting for clinicopathological variables, while prognostic accuracy was further improved after integrating clinical parameters into the analysis.
| S-EPMC7041771 | biostudies-literature
Project description:Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC. Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated. Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC. Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.
| S-EPMC8416991 | biostudies-literature
Project description:Collagen is the main component of the extracellular matrix (ECM) and might play an important role in tumor microenvironments. However, the relationship between collagen and clear cell renal cell cancer (ccRCC) is still not fully clarified. Hence, we aimed to establish a collagen-related signature to predict the prognosis and estimate the tumor immune microenvironment in ccRCC patients. Patients with a high risk score were often correlated with unfavorable overall survival (OS) and an immunosuppressive microenvironment. In addition, the collagen-related genetic signature was highly correlated with clinical pathological features and can be considered as an independent prognostic factor in ccRCC patients. Moreover, GSEA results show that patients with a high risk grade tend to be associated with epithelial-mesenchymal junctions (EMT) and immune responses. In this study, we developed a collagen-related gene signature, which might possess the potential to predict the prognosis and immune microenvironment of ccRCC patients and function as an independent prognostic factor in ccRCC.
| S-EPMC8707639 | biostudies-literature
Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.
| S-EPMC9627172 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important.MethodsPrognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration.ResultsAmong 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation).ConclusionWe established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.
| S-EPMC8290606 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal malignancy, although newly developing targeted therapy and immunotherapy have been showing promising effects in clinical treatment, the effective biomarkers for immune response prediction are still lacking. The study is to construct a gene signature according to ccRCC immune cells infiltration landscape, thus aiding clinical prediction of patients response to immunotherapy.MethodsFirstly, ccRCC transcriptome expression profiles from Gene Expression Omnibus (GEO) database as well as immune related genes information from IMMPORT database were combine applied to identify the differently expressed meanwhile immune related candidate genes in ccRCC comparing to normal control samples. Then, based on protein-protein interaction network (PPI) and following module analysis of the candidate genes, a hub gene cluster was further identified for survival analysis. Further, LASSO analysis was applied to construct a signature which was in succession assessed with Kaplan-Meier survival, Cox regression and ROC curve analysis. Moreover, ccRCC patients were divided as high and low-risk groups based on the gene signature followed by the difference estimation of immune treatment response and exploration of related immune cells infiltration by TIDE and Cibersort analysis respectively among the two groups of patients.ResultsBased on GEO and IMMPORT databases, a total of 269 differently expressed meanwhile immune related genes in ccRCC were identified, further PPI network and module analysis of the 269 genes highlighted a 46 genes cluster. Next step, Kaplan-Meier and Cox regression analysis of the 46 genes identified 4 genes that were supported to be independent prognosis indicators, and a gene signature was constructed based on the 4 genes. Furthermore, after assessing its prognosis indicating ability by both Kaplan-Meier and Cox regression analysis, immune relation of the signature was evaluated including its association with environment immune score, Immune checkpoint inhibitors expression as well as immune cells infiltration. Together, immune predicting ability of the signature was preliminary explored.ConclusionsBased on ccRCC genes expression profiles and multiple bioinformatic analysis, a 4 genes containing signature was constructed and the immune regulation of the signature was preliminary explored. Although more detailed experiments and clinical trials are needed before potential clinical use of the signature, the results shall provide meaningful insight into further ccRCC immune researches.
| S-EPMC10337188 | biostudies-literature
Project description:BackgroundRecent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).MethodsIn the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.ResultsA 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.ConclusionWe established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.
| S-EPMC9758795 | biostudies-literature
Project description:PURPOSE:Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis. MATERIALS AND METHODS:An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves. RESULTS:This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2). CONCLUSION:The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.
| S-EPMC6813151 | biostudies-literature
Project description:Renal cell carcinoma (RCC) metastasis portends a poor prognosis and cannot be reliably predicted. Early determination of the metastatic potential of RCC may help guide proper treatment. We analyzed microRNA (miRNA) expression in clear cell RCC (ccRCC) for the purpose of developing a miRNA expression signature to determine the risk of metastasis and prognosis. We used the microarray technology to profile miRNA expression of 78 benign kidney and ccRCC samples. Using 28 localized and metastatic ccRCC specimens as the training cohort and the univariate logistic regression and risk score methods, we developed a miRNA signature model in which the expression levels of miR-10b, miR-139-5p, miR-130b and miR-199b-5p were used to determine the status of ccRCC metastasis. We validated the signature in an independent 40-sample testing cohort of different stages of primary ccRCCs using the microarray data. Within the testing cohort patients who had at least 5 years follow-up if no metastasis developed, the signature showed a high sensitivity and specificity. The risk status was proven to be associated with the cancer-specific survival. Using the most stably expressed miRNA among benign and tumorous kidney tissue as the internal reference for normalization, we successfully converted his signature to be a quantitative PCR (qPCR)-based assay, which showed the same high sensitivity and specificity. The 4-miRNA is associated with ccRCC metastasis and prognosis. The signature is ready for and will benefit from further large clinical cohort validation and has the potential for clinical application.
| S-EPMC3356334 | biostudies-literature
Project description:To explore the role of metastasis-related long noncoding RNA (lncRNA) signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) patients. Firstly, metastasis-associated genes were identified to establish a metastasis-related lncRNA signature by statistical analysis. Secondly, the ccRCC patients were grouped into high-risk or low-risk group according to the established signature, and the different pathways between the 2 groups were identified by gene set enrichment analysis (GSEA). Finally, investigations involving PCR, transwell migration and invasion assay were carried out to further confirm our findings. The metastasis-related lncRNA signature was successfully constructed according to 7-metastasis-related genes (ADAM12, CD44, IL6, TFPI2, TGF-β1, THBS2, TIMP3). The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated. Most of the values of the area under the time-dependent receiver-operating characteristic (ROC) were greater than 0.70. The nomogram constructed by integrating clinical data and risk scores confirmed that the risk score calculated from our signature was a good prognosis predictor. GSEA analysis showed that some tumor-related pathways were enriched in the high-risk group, while metabolism-related pathways were enriched in the low-risk group. In carcinoma tissues, the SSR3-6, WISP1-2 were highly expressed, but the expression of UBAC2-6 was low there. Knocking down SSR3-6 decreased the ability of migration and invasion in ccRCC cells. In conclusion, we successfully constructed a metastasis-related lncRNA signature, which could accurately predict the survival and prognosis of ccRCC patients.
| S-EPMC8209488 | biostudies-literature