Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma
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ABSTRACT:
PROVIDER: PRJNA1071056 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27787800_1.fastq.gz | Fastqsanger.gz | |||
| SRR27787800_2.fastq.gz | Fastqsanger.gz | |||
| SRR27787801_1.fastq.gz | Fastqsanger.gz | |||
| SRR27787801_2.fastq.gz | Fastqsanger.gz | |||
| SRR27787802_1.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: In this study, we have undertaken the whole proteomic analysis and got a better understanding of biological processes involved in the development and progression of ccRCC. We hope promising biomarkers can be uncovered to facilitate early diagnosis, predict the prognosis and progression, more importantly, to be applied as potential therapeutic targets. Experimental design: Fresh frozen tissue samples were surgically resected from patients with local or locally advanced ccRCC. Trypsin digested proteins were analyzed using TMT-based LC-MS/MS proteomic approach, followed by bioinformatic analysis. A potential prognostic marker FMNL1, annotated with actin cytoskeleton assembly and cell migration, was chosen to be validated in TCGA_KIRC datasets(n=597), further validation sets(n=20) and expanded validation sets(n=97). Results: A total of 657 differentially expressed proteins were identified and quantified between ccRCC and adjacent normal tissues (p-value<0.05, FC>2 or<1/2), of which 186 proteins were up-regulated and 471 proteins were down-regulated. Bioinformatic analysis showed enriched metabolic biological processes and pathways including oxidative phosphorylation, valine, leucine and isoleucine degeneration, propanoate metabolism, fatty-acid degeneration, TCA cycle, glycolysis/gluconeogenesis, etc. Univariate and multivariate analysis defined FMNL1 as an independent negative prognostic marker in the TCGA datasets. High expression of FMNL1 correlated significantly with tumor stage, T stage, lymph node metastasis(P<0.05), and distant metastasis(P<0.05) both in the TCGA-KIRC datasets and expanded validation sets. And KM survival curve illustrated that patients with high FMNL1 protein level had shorter OS time in the expanded validation sets (p=0.0273) Conclusion and clinical relevance: The proteomic results uncovered sophisticated metabolic reprogramming of ccRCC and indicated that the upregulation of rate-limiting enzymes in glycolysis and mitochondrial impairment may be the cause of metabolic reprogramming in ccRCC. Moreover, FMNL1 has been identified as a diagnostic and prognostic marker, and may be implicated in the progression of ccRCC.
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Project description:Some lncRNAs can encode small nucleolar RNAs (snoRNAs), called small nucleolar RNA host genes (SNHGs), which have exerted certain predictive values for the prognosis of some cancer patients. In this study, using RNA-seq and survival data in TCGA-KIRC, we examined the expression profile of 20 SNHGs and explored their prognostic values in ccRCC. Results showed that SNHG1, GAS5, SNHG3-8, SNHG11, SNHG12, SNHG15-17, SNHG20, SNHG22 and SNHG25 were significantly upregulated in ccRCC tissues compared with adjacent normal tissues. After adjustment for confounding factors, the multivariate analysis confirmed that increased SNHG3 expression was independently associated with shorter OS, while increased SNHG15 expression was an independent predictor of shorter RFS. Using the methylation data, the methylation status of 2 CpG sites (cg07807470 and cg15161854) and 2 CpG sites (cg00953154 and cg16459265) were negatively correlated with SNHG3 and SNHG15 expression, respectively. Moreover, low methylation levels of the 4 CpG sites were significantly associated with shorter OS. Furthermore, we validated the expression patterns, methylation status and prognostic value of SNHG3 and SNHG15 using clinical ccRCC samples. Taken together, SNHG3 and SNHG15 might be valuable prognostic markers in ccRCC, and DNA hypomethylation might play an important role in elevated SNHG3 and SNHG15 transcription in ccRCC.
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Project description:Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.
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Project description:Purpose: To establish an effective prognostic model for patients with clear cell renal cell carcinoma (ccRCC). Methods: We identified four hub differentially expressed genes (DEGs) in Gene Expression Omnibus (GEO) database and verified them in the Cancer Gene Atlas (TCGA), STRING, UALCAN, TIMER, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then used TCGA and International Cancer Genome Consortium (ICGC) to identify tumor pathway molecules highly correlated with hub DEGs. And by further LASSO and Cox regression analysis, we successfully identified five genes as prognostic factors. Results: We successfully identified a risk prediction model consisting of five genes: IGF2BP3, CDKN1A, GSDMB, FABP5, RBMX. We next distributed patients into low-risk and high-risk groups using the median as a cutoff. The low-risk group obviously had better survival than those in the predicted high-risk group. The results showed discrepancies in tumor-associated immune cell infiltration between risk groups. We also combined the risk model with clinical variables to create a nomogram. Conclusion: Our model has a satisfactory predictive effect on the prognosis of ccRCC patients and may provide new ideas for future immune therapy.
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Project description:The aim of this study is to identify and verify a prognosis set that is able to distinguish patient overall survival status based on whole genome expression profiling. Using Illumina HumanWG-6 beadarray, we analyzed the expression of 47,296 transcripts in two groups of gastric cancer patients who all underwent surgical resection. Thirty-nine of 46 samples in group one were used as a training set to discover candidates for overall survival prediction by using semi-supervised method, resulting in a panel of 10 genes as prognosis markers. This 10-marker set classified each case into a low or a high risk group with significantly different survival times (P = 0.000047) and maintained independent prognostic value in multivariate analysis. These 10 candidates were then verified in a fully independent validation set comprising 33 samples (P = 0.0009). Furthermore, the prognosis was consistent with the classical TNM staging system, and showed even better prediction. Interestingly, six of ten survival markers are ribosomal proteins, suggesting a possible association between deregulation of ribosome-related expression and poor prognosis. In conclusion, by whole genome expression profiling, 10 markers, including 6 ribosomal proteins, for overall survival prognosis of gastric cancer were found and validated, and which may be parallelly used with the TNM system. We investigated the transcriptome profiles of gastric cancer patients with different prognosis. Thirty-nine patients from first batch samples were used to discover prognostic markers and another 33 samples from batch two were used for validation.
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2013-03-07 | GSE41613 | GEO
Project description:Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.
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