Comprehensive analysis of a new prognosis signature based on histone deacetylases in clear cell renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Cheng F
PROVIDER: S-EPMC8446567 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor and the most common subtype of RCC. Ferroptosis is a novel form of regulated cell death, and ferroptosis-related genes (FRGs) have been associated with the prognosis of patients with certain cancers. However, the detailed prognostic correlation between FRGs and ccRCC has not yet been elucidated. To address this, the current study used The Cancer Genome Atlas (TCGA) dataset to explore 64 FRGs and determine their prognostic value in ccRCC. Results showed that 52 out of the 64 genes displayed significantly different expression levels in tumor tissue, and 35 out of the 52 differentially expressed genes (DEGs) were associated with overall survival. Subsequently, a four-gene prognostic signature (CD44, DPP4, NCOA4 and SLC7A11) was constructed and could successfully distinguish ccRCC patients with different prognosis in TCGA train and test sets. Furthermore, clinical ccRCC samples from our medical center were used to verify the application value of the new prognostic signature through immunohistochemistry and quantitative real-time polymerase chain reaction (qRT-PCR). Biological functional analysis implied that immune-related functions and pathways were enriched in the TCGA cohort and the immune status scores were significantly different between high- and low-risk sets. These results suggest that the four ferroptosis-related regulatory genes can act as reliable prognostic biomarkers of ccRCC, and might be exploited as potential targets of therapeutic strategies.
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Project description:ObjectivesTo investigate the role of DNA methylation regulators in the prognosis of clear cell renal cell carcinoma (ccRCC) and to construct a DNA methylation regulator-based signature for predicting patient outcome.MethodsData from the TCGA dataset were downloaded and analyzed to identify differentially expressed DNA methylation regulators and their interaction as well as correlation. Consensus clustering was used to establish groups of ccRCC with distinct clinical outcomes. A prognostic signature based on two sets of DNA methylation regulators was established and validated in an independent cohort.ResultsOur analysis revealed that the expression levels of DNMT3B, MBD1, SMUG1, DNMT1, DNMT3A, TDG, TET3, MBD2, UHRF2, MBD3, UHRF1, and TET2 were significantly upregulated in ccRCC samples, while UNG, ZBTB4, TET1, ZBTB38, and MECP2 were markedly downregulated. UHRF1 was identified as a hub gene in the DNA methylation regulator interaction network. Significant differences were found regarding overall survival, gender, tumor status, and grade between ccRCC patients in the two risk groups. The prognostic signature, based on two sets of DNA methylation regulators, was an independent prognostic indicator, and these findings were validated in an external, independent cohort.ConclusionsThe study provides evidence that DNA methylation regulators play a significant role in the prognosis of ccRCC and the developed DNA methylation regulator-based signature could effectively predict patient outcome.
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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.
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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.
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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.
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Project description:Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.
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Project description:Clear cell renal cell carcinoma (ccRCC) usually affects multiple organs (e.g., bone and brain), and patient prognosis is usually poor. Although it is known that CD8+ T cell infiltration can potentially alleviate ccRCC progression, few studies have concentrated on the correlation between CD8+ T cell infiltration and ccRCC prognosis. In this study, ten genes expressed by infiltrated CD8+ T cells (i.e., AMD1, CCSER2, CIB1, DRAP1, HMGB2, HMGN1, NPIPB5, PTP4A2, RORA, and SAP18) were suggested as potential ccRCC prognostic biomarkers, by using next-generation sequencing (i.e. bulk sequencing and single-cell sequencing) of ccRCC, papillary renal cell carcinoma (papRCC), and control kidney biopsies. Specifically, we identified four genes (i.e., CCSER2, DRAP1, NPIPB5, and SAP18) as potential novel prognostic biomarkers for ccRCC. It is noteworthy that SAP18 derived from CD8+ T cells negatively correlates to Atg7+ neutrophils in ccRCC, compared with papRCC, indicating a potential decreased neutrophil metabolic function in autophagy and fatty acids. This study elucidated the protective role of infiltrated CD8+ T cells in ccRCC and identified ten candidate genes related to an improved prognosis in patients with ccRCC.
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Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion in the kidney. This study aims to establish an aging and senescence-related mRNA model for risk assessment and prognosis prediction in ccRCC patients. Methods: ccRCC data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. By applying univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, a new prognostic model based on aging and senescence-related genes (ASRGs) was established. Depending on the prognostic model, high- and low-risk groups were identified for further study. The reliability of the prediction was evaluated in the validation cohort. Pan-cancer analysis was conducted to explore the role of GNRH1 in tumors. Results: A novel prognostic model was established based on eight ASRGs. This model was an independent risk factor and significantly correlated with the prognosis and clinicopathological features of ccRCC patients. The high- and low-risk groups exhibited distinct modes in the principal component analysis and different patterns in immune infiltration. Moreover, the nomogram combining risk score and other clinical factors showed excellent predictive ability, with AUC values for predicting 1-, 3-, and 5-year overall survival in the TCGA cohort equal to 0.88, 0.82, and 0.81, respectively. Conclusion: The model and nomogram based on the eight ASRGs had a significant value for survival prediction and risk assessment for ccRCC patients, providing new insights into the roles of aging and senescence in ccRCC.
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Project description:This study aims to explore the role of abnormal fatty acid metabolism (FAM) in ccRCC and construct a novel fatty acid metabolism-related gene signature (FAMGS) for prognosis. Three independent ccRCC cohorts, including The Cancer Genome Atlas, E-MTAB-1980 and our clinical cohort (including RNA-seq, microarray and RT-qPCR data), were applied as training and two independent validation cohorts. Firstly, FAM levels were found to be significantly decreased in ccRCC and correlated with degrees of malignancy, confirming the pivotal role of FAM in ccRCC. Applying the least absolute shrinkage and selection operator cox regression, we established a novel FAMGS for overall survival (OS). The FAMGS divided patients into low or high-risk groups in the training cohort and were successfully validated in both the EMTAB and our clinical validation cohorts. Additionally, the FAMGS serves as an independent risk factor for OS of ccRCC. Results of the immune cell abundance identifier (ImmuCellAI) algorithm and gene set variation analysis (GSVA) revealed that patients in the high-risk group have comprehensively impaired metabolism, including lipids, amino acids and tricarboxylic acid cycle-related pathways and a more immunosuppressive tumor microenvironment. In conclusion, our study constructed and validated a novel FAMGS, which may improve the risk stratification optimization and personalized management of ccRCC.
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Project description:Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes. Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM-related differentially expressed genes were screened via the "limma" package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the "survminer" package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m6A)-related genes, and sensitivity to chemotherapy was assessed. Results: A novel consensus AMM-related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m6A modification, and sensitivity to chemotherapy drugs. Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.
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