Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma (original) (raw)
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Cancers, 2021
The Identification of reliable Biomarkers able to predict the outcome after nephrectomy of patients with clear cell renal cell carcinoma (ccRCC) is an unmet need. The gene expression analysis in tumor tissues represents a promising tool for better stratification of ccRCC subtypes and patients’ evaluation. Methods: In our study we retrospectively analyzed using Next-Generation expression analysis (NanoString), the expression of a gene panel in tumor tissue from 46 consecutive patients treated with nephrectomy for non-metastatic ccRCC at two Italian Oncological Centres. Significant differences in expression levels of selected genes was sought. Additionally, we performed a univariate and a multivariate analysis on overall survival according to Cox regression model. Results: A 17-gene expression signature of patients with a recurrence-free survival (RFS) < 1 year (unfavorable genomic signature (UGS)) and of patients with a RFS > 5 years (favorable genomic signature (FGS)) was iden...
Gene expression profile of renal cell carcinoma clear cell type
International braz j urol : official journal of the Brazilian Society of Urology
The determination of prognosis in patients with renal cell carcinoma (RCC) is based, classically, on stage and histopathological aspects. The metastatic disease develops in one third of patients after surgery, even in localized tumors. There are few options for treating those patients, and even the new target designed drugs have shown low rates of success in controlling disease progression. Few studies used high throughput genomic analysis in renal cell carcinoma for determination of prognosis. This study is focused on the identification of gene expression signatures in tissues of low-risk, high-risk and metastatic RCC clear cell type (RCC-CCT). We analyzed the expression of approximately 55,000 distinct transcripts using the Whole Genome microarray platform hybridized with RNA extracted from 19 patients submitted to surgery to treat RCC-CCT with different clinical outcomes. They were divided into three groups (1) low risk, characterized by pT1, Fuhrman grade 1 or 2, no microvascula...
Oncotarget, 2017
Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widesp...
Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma
PLoS Medicine, 2006
Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival.
Proceedings of the National Academy of Sciences, 2003
To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.
Proceedings of the National Academy of Sciences, 2001
To better understand the molecular mechanisms that underlie the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC), we studied the gene expression profiles of 29 ccRCC tumors obtained from patients with diverse clinical outcomes by using 21,632 cDNA microarrays. We identified gene expression alterations that were both common to most of the ccRCC studied and unique to clinical subsets. There was a significant distinction in gene expression profile between patients with a relatively nonaggressive form of the disease [100% survival after 5 years with the majority (15/17 or 88%) having no clinical evidence of metastasis] versus patients with a relatively aggressive form of the disease (average survival time 25.4 months with a 0% 5-year survival rate). Approximately 40 genes most accurately make this distinction, some of which have previously been implicated in tumorigenesis and metastasis. To test the robustness and potential clinical usefulness of this molecular d...
2016
PURPOSE: Evaluation of 12 ccRCC publicly-available ccRCC gene expression datasets showed that previously proposed discrete molecular subtypes are unstable. To reflect the continuous nature of gene expression observed, we developed a quantitative score (Continuous Linear Enhanced Assessment of Renal cell carcinoma, or CLEAR) using expression analysis founded on pathologic parameters. MATERIALS AND METHODS: 265 ccRCC gene expression profiles were used to develop the CLEAR score, representing a genetic correlate of the continuum of morphological tumor grade. A signature derivation method based on correlation of CLEAR score with gene expression ranking was used to derive an 18-transcript signature. External validation was conducted in multiple public expression datasets. Results: As a measure of intertumoral gene expression heterogeneity, the CLEAR score demonstrated both superior prognostic estimates (94% vs 83% adequacy index in TCGA dataset) and inverse correlation with anti-angiogen...
Clinical Cancer Research
Current diagnosis of renal cancer consists of histopathologic examination of tissue sections and classification into tumor stages and grades of malignancy. Until recently, molecular differences between tumor types were largely unknown. To examine such differences, we did gene expression measurements of 112 renal cell carcinoma and normal kidney samples on renal cell carcinoma–specific cDNA microarrays containing 4,207 genes and expressed sequence tags. The gene expression patterns showed deregulation of complete biological pathways in the tumors. Many of the molecular changes corresponded well to the histopathologic tumor types, and a set of 80 genes was sufficient to classify tumors with a very low error rate. Distinct gene expression signatures were associated with chromosomal abnormalities of tumor cells, metastasis formation, and patient survival. The data highlight the benefit of microarrays to detect novel tumor classes and to identify genes that are associated with patient va...
Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer
Scientific reports, 2017
In this study, an attempt has been made to identify expression-based gene biomarkers that can discriminate early and late stage of clear cell renal cell carcinoma (ccRCC) patients. We have analyzed the gene expression of 523 samples to identify genes that are differentially expressed in the early and late stage of ccRCC. First, a threshold-based method has been developed, which attained a maximum accuracy of 71.12% with ROC 0.67 using single gene NR3C2. To improve the performance of threshold-based method, we combined two or more genes and achieved maximum accuracy of 70.19% with ROC of 0.74 using eight genes on the validation dataset. These eight genes include four underexpressed (NR3C2, ENAM, DNASE1L3, FRMPD2) and four overexpressed (PLEKHA9, MAP6D1, SMPD4, C11orf73) genes in the late stage of ccRCC. Second, models were developed using state-of-art techniques and achieved maximum accuracy of 72.64% and 0.81 ROC using 64 genes on validation dataset. Similar accuracy was obtained on...