Browsing by Author "Wang, Xusheng"

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    Genetic modulation of protein expression in rat brain
    (Elsevier, 2025-02-21) Li, Ling; Wu, Zhiping; Guarracino, Andrea; Villani, Flavia; Kong, Dehui; Mancieri, Ariana; Zhang, Aijun; Saba, Laura; Chen, Hao; Brozka, Hana; Vales, Karel; Senko, Anna N.; Kempermann, Gerd; Stuchlik, Ales; Pravenec, Michal; Lechner, Joseph; Prins, Pjotr; Mathur, Ramkumar; Lu, Lu; Yang, Kai; Peng, Junmin; Williams, Robert W.; Wang, Xusheng; Pediatrics, School of Medicine
    Genetic variations in protein expression are implicated in a broad spectrum of common diseases and complex traits but remain less explored compared to mRNA and classical phenotypes. This study systematically analyzed brain proteomes in a rat family using tandem mass tag (TMT)-based quantitative mass spectrometry. We quantified 8,119 proteins across two parental strains (SHR/Olalpcv and BN-Lx/Cub) and 29 HXB/BXH recombinant inbred (RI) strains, identifying 597 proteins with differential expression and 464 proteins linked to cis-acting quantitative trait loci (pQTLs). Proteogenomics identified 95 variant peptides, and sex-specific analyses revealed both shared and distinct cis-pQTLs. We improved the ability to pinpoint candidate genes underlying pQTLs by utilizing the rat pangenome and explored the connections between pQTLs in rats and human disorders. Collectively, this study highlights the value of large proteo-genetic datasets in elucidating protein modulation in the brain and its links to complex central nervous system (CNS) traits.
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    Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival
    (Oxford, 2018-03) Cheng, Jun; Mo, Xiaokui; Wang, Xusheng; Parwani, Anil; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers.
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    Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
    (AACR, 2017-11) Cheng, Jun; Zhang, Jie; Han, Yatong; Wang, Xusheng; Ye, Xiufen; Meng, Yuebo; Parwani, Anil; Han, Zhi; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers.