Browsing by Subject "Kidney Neoplasms"

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    Identification of genes and pathways involved in kidney renal clear cell carcinoma
    (Springer (Biomed Central Ltd.), 2014) Yang, William; Yoshigoe, Kenji; Qin, Xiang; Liu, Jun S.; Yang, Jack Y.; Niemierko, Andrzej; Deng, Youping; Liu, Yunlong; Dunker, A. Keith; Chen, Zhongxue; Wang, Liangjiang; Xu, Dong; Arabnia, Hamid R.; Tong, Weida; Yang, Mary Qu; Department of Medical and Molecular Genetics, IU School of Medicine
    BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. RESULTS: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. CONCLUSIONS: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.
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    Renal cell carcinoma in tuberous sclerosis complex
    (Ovid Technologies (Wolters Kluwer) - Lippincott Williams & Wilkins, 2014-07) Yang, Ping; Cornejo, Kristine M.; Sadow, Peter M.; Cheng, Liang; Wang, Mingsheng; Xiao, Yu; Jiang, Zhong; Oliva, Esther; Jozwiak, Sergiusz; Nussbaum, Robert L.; Feldman, Adam S.; Paul, Elahna; Thiele, Elizabeth A.; Yu, Jane J.; Henske, Elizabeth P.; Kwiatkowski, David J.; Young, Robert H.; Wu, Chin-Lee; Department of Pathology & Laboratory Medicine, IU School of Medicine
    Renal cell carcinoma (RCC) occurs in 2% to 4% of patients with tuberous sclerosis complex (TSC). Previous reports have noted a variety of histologic appearances in these cancers, but the full spectrum of morphologic and molecular features has not been fully elucidated. We encountered 46 renal epithelial neoplasms from 19 TSC patients and analyzed their clinical, pathologic, and molecular features, enabling separation of these 46 tumors into 3 groups. The largest subset of tumors (n=24) had a distinct morphologic, immunologic, and molecular profile, including prominent papillary architecture and uniformly deficient succinate dehydrogenase subunit B (SDHB) expression prompting the novel term "TSC-associated papillary RCC (PRCC)." The second group (n=15) were morphologically similar to a hybrid oncocytic/chromophobe tumor (HOCT), whereas the last 7 renal epithelial neoplasms of group 3 remained unclassifiable. The TSC-associated PRCCs had prominent papillary architecture lined by clear cells with delicate eosinophilic cytoplasmic thread-like strands that occasionally appeared more prominent and aggregated to form eosinophilic globules. All 24 (100%) of these tumors were International Society of Urological Pathology (ISUP) nucleolar grade 2 or 3 with mostly basally located nuclei. Tumor cells from 17 of 24 TSC-associated PRCCs showed strong, diffuse labeling for carbonic anhydrase IX (100%), CK7 (94%), vimentin (88%), and CD10 (83%) and were uniformly negative for SDHB, TFE3, and AMACR. Gains of chromosomes 7 and 17 were found in 2 tumors, whereas chromosome 3p deletion and TFE3 translocations were not detected. In this study, we reported a sizable cohort of renal tumors seen in TSC and were able to identify them as different morphotypes, which may help to expand the morphologic spectrum of TSC-associated RCC.