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A transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

ABSTRACT: A transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

PROVIDER: PRJNA739195 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR19331900_1.fastq.gz	Fastqsanger.gz
	SRR19331900_2.fastq.gz	Fastqsanger.gz
	SRR19331801_1.fastq.gz	Fastqsanger.gz
	SRR19331801_2.fastq.gz	Fastqsanger.gz
	SRR19331901_1.fastq.gz	Fastqsanger.gz

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A transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

Project description:Novel single cell technologies have paved the way for the characterization and improved biological understanding of clear cell renal cell carcinoma (ccRCC) where the focus has been mostly on the immunological compartments of the tumor microenvironment (TME). However, the identification of metastatic tumor cell clones, and the stromal compartments of TME of untreated ccRCC patients still remains to be elucidated. Here we perform single-cell RNA-sequencing on treatment naive ccRCC and adjacent normal kidney tissue.

2022-08-18 | GSE178481 | GEO

Epigenetic signature predicts overall survival clear cell renal cell carcinoma.

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

microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease

Project description:microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease

| PRJNA545463 | ENA

A four-gene signature predicts survival in clear-cell renal-cell carcinoma.

Project description:Clear-cell renal-cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma (RCC), accounting for about 80% of RCC. In order to find potential prognostic biomarkers in ccRCC, we presented a four-gene signature to evaluate the prognosis of ccRCC. SurvExpress and immunohistochemical (IHC) staining of tissue microarrays were used to analyze the association between the four genes and the prognosis of ccRCC. Data from TCGA dataset revealed a prognostic prompt function of the four genes (PTEN, PIK3C2A, ITPA and BCL3). Further discovery suggested that the four-gene signature predicted survival better than any of the four genes alone. Moreover, IHC staining demonstrated a consistent result with TCGA, indicating that the signature was an independent prognostic factor of survival in ccRCC. Univariate and multivariate Cox proportional hazard regression analysis were conducted to verify the association of clinicopathological variables and the four genes' expression levels with survival. The results further testified that the risk (four-gene signature) was an independent prognostic factors of both Overall Survival (OS) and Disease-free Survival (DFS) (P<0.05). In conclusion, the four-gene signature was correlated with the survival of ccRCC, and therefore, may help to provide significant clinical implications for predicting the prognosis of patients.

| S-EPMC5347726 | 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

microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease [screen]

Project description:microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease [screen]

| PRJNA545464 | ENA

microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease [validation]

Project description:microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease [validation]

| PRJNA545518 | ENA

DNA methylation status predicts survival in metastasis free clear cell renal cell carcinoma

Project description:DNA methylation status predicts survival in metastasis free clear cell renal cell carcinoma

| PRJNA453379 | ENA

LncRNA from metastatic clear cell renal cell carcinoma (ccRCC) compared with non-metastatic clear cell renal carcinoma

Project description:A total of 3 metastatic and 3 non-metastatic ccRCC tissues were collected from patients. These tissues were flash frozen in liquid nitrogen immediately after surgery and subsequently stored at -80℃. All the resected nodules or metastatic masses were identified as renal tumor by pathologic examination. No patients received anticancer treatments before surgery in this study. We analyzed gene expression microarray data from our gene chip with 3 metastatic ccRCC tissues compared with 3 non-metastatic ccRCC tissues.

2017-08-17 | GSE88948 | GEO

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma.

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

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