Browsing by Author "Chen, Chengxuan"

Now showing 1 - 4 of 4
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    Comprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains
    (American Association for the Advancement of Science, 2025) Chen, Chengxuan; Zhang, Zhao; Liu, Yuan; Hong, Wei; Karahan, Hande; Wang, Jun; Li, Wenbo; Diao, Lixia; Yu, Meichen; Saykin, Andrew J.; Nho, Kwangsik; Kim, Jungsu; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Alzheimer's disease (AD) is the leading dementia among the elderly with complex origins. Despite extensive investigation into the AD-associated protein-coding genes, the involvement of noncoding RNAs (ncRNAs) and posttranscriptional modification (PTM) in AD pathogenesis remains unclear. Here, we comprehensively characterized the landscape of ncRNAs and PTM events in 1460 samples across six brain regions sourced from the Mount Sinai/JJ Peters VA Medical Center Brain Bank Study and Mayo cohorts, encompassing 33,321 long ncRNAs, 92,897 enhancer RNAs, 53,763 alternative polyadenylation events, and 900,221 A-to-I RNA editing events. We additionally identified 25,351 aberrantly expressed ncRNAs and altered PTM events associated with AD traits and further identified the corresponding protein-coding genes to construct regulatory networks. Furthermore, we developed a user-friendly data portal, ADatlas, facilitating users in exploring our results. Our study aims to establish a comprehensive data platform for ncRNAs and PTMs in AD to advance related research.
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    Deciphering genetic regulation at single-cell resolution in gastric cancer
    (Elsevier, 2025) Chen, Chengxuan; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Understanding cell-type-specific genetic regulation in gastric cancer is essential for uncovering disease susceptibility. By performing single-cell eQTL mapping in gastric tissues, Bian et al.1 identified previously uncharacterized regulatory genetic mechanisms, risk genes, and co-localization signals associated with gastric cancer susceptibility, providing insights into its pathogenesis and potential therapeutic approaches.
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    PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers
    (Oxford University Press, 2024) Chen, Chengxuan; Liu, Yuan; Luo, Mei; Yang, Jingwen; Chen, Yamei; Wang, Runhao; Zhou, Joseph; Zang, Yong; Diao, Lixia; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
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    The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers
    (American Association for Cancer Research, 2023) Chen, Chengxuan; Liu, Yuan; Li, Qiang; Zhang, Zhao; Luo, Mei; Liu, Yaoming; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in cancer patients could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas (TCGA) and protein expression data from The Cancer Proteome Atlas (TCPA), we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTL were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.