Browsing by Author "Luo, Mei"

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    Characterization of intratumor microbiome in cancer immunotherapy
    (Elsevier, 2023-07-12) Zhang, Zhao; Gao, Qian; Ren, Xiangmei; Luo, Mei; Liu, Yuan; Liu, Peilin; Liu, Yun; Ye, Youqiong; Che, Xiang; Liu, Hong; Han, Leng; Biostatistics and Health Data Science, School of Medicine
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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.