Effect of HVEM/CD160 Variations on the Clear Cell Renal Carcinoma Risk and Overall Survival
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ABSTRACT:
SUBMITTER: Andrzejczak A
PROVIDER: S-EPMC11241222 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundRecently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC).MethodsWe used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC.ResultThrough LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0-21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2-7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9-10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871.ConclusionsOur study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.
| S-EPMC7686748 | biostudies-literature
Project description:Background: Cuprotosis is a new form of programmed cell death induced by copper. We explored the correlation of cuprotosis with clear cell renal cell carcinoma (ccRCC) and constructed a cuprotosis-related signature to predict the prognosis of patients with ccRCC. Methods: The clinical and transcriptomic data of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA), cBioPortal, and GEO databases, and cuprotosis-related gene sets were contained in the previous study. A cuprotosis-related signature was developed based on data from TCGA and verified by data from cBioPortal and GEO databases. The immune cell infiltrates and the corresponding signature risk scores were investigated. Two independent cohorts of clinical trials were analyzed to explore the correlation of the signature risk score with immune therapy response. Results: A signature containing six cuprotosis-related genes was identified and can accurately predict the prognosis of ccRCC patients. Patients with downregulated copper-induced programmed death had a worse overall survival (hazard ratio: 1.90, 95% CI: 1.39-2.59, p < 0.001). The higher signature risk score was significantly associated with male gender (p = 0.026), higher tumor stage (p < 0.001), and higher histological grade (p < 0.001). Furthermore, the signature risk score was positively correlated with the infiltration of B cells, CD8+ T cells, NK cells, Tregs, and T cells, whereas it was negatively correlated with eosinophils, mast cells, and neutrophils. However, no correlation between cuprotosis and response to anti-PD-1 therapy was found. Conclusion: We established a cuprotosis signature, which can predict the prognosis of patients with ccRCC. Cuprotosis was significantly correlated with immune cell infiltrates in ccRCC.
| S-EPMC9562982 | biostudies-literature
Project description:Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.
| S-EPMC7642863 | biostudies-literature
Project description:CD160 is a signaling molecule that interacts with herpes virus entry mediator (HVEM) and contributes to a wide range of immune responses, including T cell inhibition, natural killer cell activation, and mucosal immunity. GPI-anchored and transmembrane isoforms of CD160 share the same ectodomain responsible for HVEM engagement, which leads to bidirectional signaling. Despite the importance of the CD160:HVEM signaling axis and its therapeutic relevance, the structural and mechanistic basis underlying CD160-HVEM engagement has not been described. We report the crystal structures of the human CD160 extracellular domain and its complex with human HVEM. CD160 adopts a unique variation of the immunoglobulin fold and exists as a monomer in solution. The CD160:HVEM assembly exhibits a 1:1 stoichiometry and a binding interface similar to that observed in the BTLA:HVEM complex. Our work reveals the chemical and physical determinants underlying CD160:HVEM recognition and initiation of associated signaling processes.
| S-EPMC7477951 | biostudies-literature
Project description: Not available
| S-EPMC10697778 | biostudies-literature
Project description:BackgroundKidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients.ResultsWe identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set.ConclusionOur findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.
| S-EPMC7470605 | biostudies-literature
Project description:BackgroundKidney cancer, especially clear cell renal cell carcinoma (ccRCC), is one of the most common cancers in the urinary system. Previous studies suggested that certain members of MUCINs could serve as independent predictors for the survival of ccRCC patients. None of them, however, is robust enough to predict prognosis accurately.ObjectiveTo analyze the correlation of MUCINs alterations and their expression levels with the prognosis of ccRCC patients and develop a prognosis-related predictor.MethodsWe applied whole-exome sequencing in samples from 22 Chinese ccRCC patients to identify genetic alterations in MUCIN genes and analyzed their genetic alterations, expression, and correlation with survival using the TCGA, GSE73731, and GSE29069 datasets.ResultGenetic alternations in MUCINs were identified in 91% and 51% of ccRCC patients in our cohort and the TCGA database, respectively. No correlation with survival was found for the genetic alterations. Using unsupervised clustering analysis of gene expression, we identified two major clusters of MUCIN expression patterns. Cluster 1 was characterized by a global overexpression of MUC1, MUC12, MUC13, MUC16, and OVGP1; and cluster 2 was characterized by a global overexpression of MUC4, MUC5B, MUC6, MUC20, EMCN, and MCAM. Patients with cluster 1 expression pattern had significantly shorter overall survival time and worse clinical features, including higher tumor grades and metastasis. Meanwhile, they had a higher level of mutation counts and more infiltrated immune cells, but lower enrichment in angiogenesis signature genes. A five-MUCINs expression signature was constructed from cluster 1, and notably, it was demonstrated to be associated with shorter overall survival. A similar worse clinical feature, lower angiogenesis but the more immune signature, was identified in samples presented with signature 1. In the validation data set GSE29069, patients with signature 1 were also associated with a trend of poor survival outcomes.ConclusionWe established a five-MUCINs expression signature as a new prognostic marker for ccRCC. The distinct tumor microenvironment feature between the two signatures may further affect ccRCC patients' clinical management.
| S-EPMC8419780 | biostudies-literature
Project description:This study attempts to evaluate the prognostic role of PHYH for overall survival (OS) in clear cell renal cell carcinoma (ccRCC) by means of publicly available data from The Cancer Genome Atlas (TCGA). Clinical pathologic features and PHYH expression were downloaded from the TCGA database and relationships between them were analyzed by univariate and multivariate Cox regression analyses. Gene Set Enrichment Analysis (GSEA) and gene-gene interactions were also performed between tissues with different PHYH expression levels. PHYH expression levels were significantly lower in patient with ccRCC compared with normal tissues (p = 1.156e-19). Kaplan-Meier survival analysis showed that high expression of PHYH had a better prognosis than low expression (p = 9e-05). Moreover, PHYH expression was also significantly associated with high grade (G2-4, p = 0.025), high stage (StageIII & IV, p = 5.604e-05), and high level of stage_T (T3-4, p = 4.373e-05). Univariate and multivariate Cox regression analyses indicated that PHYH could be acted as an independent prognostic factor (p < 0.05). Nomogram including clinical pathologic features and PHYH expression were also provided. GSEA revealed that butanoate metabolism, histidine metabolism, propanoate metabolism, pyruvate metabolism, tryptophan metabolism, PPAR signalling pathway, and renin-angiotensin system were differentially enriched in PHYH high-expression phenotype. ICGC database was utilized to verify the expression level and survival benefit of PHYH (both p < 0.05). We suspect that elevated PHYH expression may be served as a potential prognostic molecular marker of better survival in ccRCC. Besides, alpha-oxidation was closely regulated by PHYH, and PPAR signalling, pyruvate metabolism, butanoate metabolism, and RAS might be the key pathways regulated by PHYH in CCRC.
| S-EPMC7816304 | biostudies-literature
Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and lethal type of kidney cancer. Although differential expression of cyclin-dependent kinase-like 2 (CDKL2) has been reported to be associated with tumor progression in other cancers, its prognostic value, and potential mechanism in patients with ccRCC still remain unknown. Methods: Gene expression analysis was conducted using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, and International Cancer Genome Consortium databases. Further, clinicopathologic analysis; Kaplan-Meier survival analysis; weighted gene co-expression network analysis; gene set enrichment analysis; gene ontology enrichment; methylation; and immune infiltration analyses were performed using TCGA-kidney renal clear cell carcinoma profiles. CDKL2 translational levels were analyzed using The Human Protein Atlas database. Results: CDKL2 expression was decreased in ccRCC samples retrieved from the four databases. Gender, survival status, histologic grade, clinical stage, TNM classification, and tumor status were closely related to CDKL2 expression. In addition, CDKL2 downregulation was an independent prognostic factor for poor prognosis in multivariate analysis. Enrichment analyses using multiple tests revealed that CDKL2 is not just closely related to immune response but this association is highly correlated as well. Further, we found that CDKL2 expression was significantly correlated with the infiltration levels of T cell CD4 memory resting; monocytes; macrophages M0, M1, and M2; dendritic cells resting; mast cells resting; plasma cells; T cell CD8; and T cell regulatory. Conclusion: This is the first report to study the expression of CDKL2 in ccRCC, wherein we suggest that decreased CDKL2 expression is closely correlated with poor prognosis in ccRCC. We consider that CDKL2 is a novel and potential prognostic biomarker associated with immune infiltrates in ccRCC.
| S-EPMC8793634 | biostudies-literature
Project description:The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS > 5 years (149 patients) and those with <1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74-0.82), 0.75 (0.70-0.80), and 0.70 (0.60-0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.
| S-EPMC7494988 | biostudies-literature