Project description:We investigated the expression profiles of microRNAs (miRNA) in 26 renal cell carcinoma (RCC) FFPE tissues (3 chRCC, 5 papRCC and 18 ccRCC), 4 urothelial cell carcinomas of the upper urinary tract (UT-UCs) and 20 normal kidneys by using the miRCURY LNA™ microRNA Array, 6th gen (Exiqon, Woburn MA), containing capture probes that target all miRNAs for all species registered in the miRBASE version 16.0. Real-time PCR (qRT-PCR) using appropriate endogenous controls was performed in order to validate the microarray results of the 27 most differentially expressed (DE) miRNAs. We identified 434 miRNAs that were significantly deregulated in all tumours compared to the normal kidney tissues. Overall, 126 miRNAs (29%) showed increased expression and 303 miRNAs (69.8%) had decreased expression in RCCs and UT-UCs vs. the normal kidneys. Specifically, 374, 420, 421 and 409 miRNAs were 90% consistently down-regulated in ccRCC, papRCC, chRCC and UT-UC, respectively. Unsupervised two-way hierarchical clustering (HCL) with Euclidian distance accurately discriminated between RCC and UT-UC. Apart from one chRCC sample that was clustered with ccRCCs, HCL also managed to successfully classify the 3 RCC subtypes among them. Furthermore, it showed that ccRCC is more closely related to papRCC and that both are distinct from chRCC or UT-UC. Ninety-four miRNAs were co-upregulated among ccRCC, papRCC and chRCC; and 11, 44 and 24 miRNAs were specifically up-regulated in each one of the three RCC subtypes (ccRCC, chRCC and papRCC), respectively. On the other hand, 222 miRNAs were co-down-regulated in the three RCC subtypes, whereas 16, 18 and 5 miRNAs were specifically down-regulated in ccRCC, chRCC and papRCC, respectively. When the DE miRNAs in each RCC subtype were combined with those in UT-UC, we identified 89 and 206 miRNAs, respectively, that were up- and down-regulated in all tumor types. miRNA profiling can distinguish between RCC and UT-UC as well as among distinct RCC subtypes. Our data validicate that miRNA expression tends to be down-regulated in RCC versus the normal kidney tissue.

Project description:Clear cell renal cell carcinoma (ccRCC) initiated from the renal epithelium is the most prevalent histological type of adult kidney cancers. Dissecting intratumoral heterogeneity (ITH) of ccRCC has leveraged to extend our knowledge on how primary tumors harboring driver mutations evolve and spread to other sites. The cellular fractions within and across the primary (pRCC) and metastatic RCC (mRCC) are heterogeneous in both their genetic and biological features determining the variability in clinical aggressiveness and sensitivity to the therapy. To achieve sustainable therapeutic benefit with targeted agents in mRCC, the effective target should focus on signaling pathways that are related to driver mutations occurred early in the clonal evolution of the disease and thus should be common to primary tumor and metastatic sites. Considering that extensive genetic heterogeneity may result in drug response variability among patients and treatment resistance, the tailored strategies for metastatic RCC is urgently needed. Here, we analyze single-cell RNA-seq (scRNA-seq) data from a matched primary RCC (pRCC) and lung metastasis (mRCC) to dissect ITH at the highest resolution to date with the objective of discovering the better therapeutic regimen. In order to identify successful clonal propagation from patient to PDX samples and understand pathogenesis from primary to metastatic RCC, we performed whole-exome sequencing (WES, n=4) and matched aCGH (n=4) on bulk tumor samples. And we utilized single-cell RNA sequencing (scRNA-seq) to model and dissect functional heterogeneity acroass primary and metastatic RCC tumors. We checked whether of capturing live one cell, not more cells, in microfluidics by fluorescent microscopic observation. To construct RNA sequencing libraries, we performed further quality controls including adequate quantities and qualities of amplified transcriptomes respectively from single cells. Tumor cells from the parental mRCC (n=34), PDX-mRCC (n=36) and PDX-pRCC (n=46) were finally analyzed in this study after filtering out poor quality cells.

Dataset Information

Single-cell RNA-seq identification of the cellular molecular characteristics of sporadic bilateral clear cell renal cell carcinoma

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