李奇渊 教授

2014年国家高层次青年人才

2017年厦门市高层次引进人才

2017年南强青拔人才


教育和研究经历

2017.11- 至今 厦门大学医学院,教授

2012.05—2017.10 厦门大学医学院,副教授

2010.03—2012.03 哈佛大学癌症中心,博士后

2006.07-2011.01 丹麦科技大学,系统生物学,博士

2004.08-2006.04 丹麦科技大学,生物信息学,硕士

2000.09-2003.07 厦门大学,细胞生物学,硕士

研究方向

主要研究工作是整合各种高通量数据,特别是二代测序技术,通过多层次复杂网络模型,阐述疾病不同分子水平之间的变异的相互关系,从而深入认识其发生和发展的机制。从事针对肿瘤基因组的转化医学研究,运用统计学模型从遗传多态性,体细胞变异,表观基因组学和基因表达等多组学数据中寻找与肿瘤分型,预后,靶向治疗敏感性等临床特征有关的决定因子并解释其调控机制;在此基础上建立基于多组学关联的复杂网络模型,应用人工智能方法发现新的肿瘤驱动基因。

联系方式

办公室:厦门大学医学院爱礼楼 117室

电话: 0592-2185175

邮箱: Qiyuan.li@xmu.edu.cn


学术任职

  • 2020-至今 中国优生优育协会专业委员会 妇科肿瘤防治专业 副主任委员
  • 2020-至今 中国医疗器械行业协会医疗器械物联网管理专业委员会 智慧医疗专家组 副组长
  • 2020-至今 福建省生物信息学学会 副会长
  • 2018-至今 厦门大学健康医疗大数据国家研究院 副院长
  • 2016-至今 挂职担任卫计委健康医疗大数据管理中心 主任
  • 2015-至今 挂职担任厦门大学附属第一医院儿内科 科研副主任

  • 研究基金及课题

    • 国家自然科学基金面上项目 复发性体细胞变异对肿瘤基因表达的 决定作用及其分子机制,项目编号:31371289 (2014.01-2017.12) ,主持
    • 厦门市卫计委项目,应用于孤独症早期诊断高通量测序检测平台的研发 (2015.01-2017.12) ,主持
    • 翼健(上海)信息科技有限公司,大数据指导的儿科慢性病管理和分级诊疗 (2017.7-2019.07),主持
    • 厦门市医疗保障管理局,厦门市医保定点医疗机构及定点零售药店建设发展规划 研究(2017-2020年)研究2017.12-2017.12,主持
    • 厦门市医疗保障基金管理中心, 厦门市医保定点医药机构布局规划专项业务服 务外包合同,2018.12-2020.06,主持
    • 正大天晴药业集团股份有限公司,厦门门诊统筹控费政策分析研究2020.09-2020.12, ,主持
    • 厦门市翔安区卫生健康局,翔安区医疗卫生数据综合治理及医疗保障管理系统建设(一期)技术服务2020.07-2021.07 ,主持
    • 厦门市医疗保障中心,2019年度医保基金运行风险分析与精算服务,2020.06-2020.10 ,主持
    • 中国太平洋人寿保险股份有限公司厦门分公司,长护险产品模型设计研究及模拟,2020.05-2020.09 ,主持
    • 上海覃谷生物技术有限公司,游离DNA甲基化突变阳性参考品的制备技术,2019.09-2021.07 ,主持
    • 厦门市健康医疗大数据管理中心,厦门区域业务集成平台--厦门市医疗和疾控数据资源目录,2019.05-2019.11 ,主持
    • 厦门市医疗保障基金管理中心,医保基金管理和风险与精算服务,2019.04-2019.10 ,主持

    论文成果

    • 1. Guo J, Zhou Y, Xu C, Chen Q, Sztupinszki Z, Börcsök J, Xu C, Ye F, Tang W, Kang J et al: Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers. Cancer Res 2021.
    • 2. Kohaar I, Li Q, Chen Y, et al. Association of germline genetic variants with TMPRSS2-ERG fusion status in prostate cancer. Oncotarget 2020; 11:1321–1333
    • 3. Li W, Xu C, Guo J, et al. Cis- and Trans-Acting Expression Quantitative Trait Loci of Long Non-Coding RNA in 2,549 Cancers With Potential Clinical and Therapeutic Implications. Front. Oncol. 2020; 10
    • 4.Wu J, Ge D, Zhong T, et al. IRF4 and STAT3 activities are associated with the imbalanced differentiation of T-cells in responses to inhalable particulate matters. Respir Res 2020; 21:123
    • 5.Yi C, Li Q, Xiao J. Familial chilblain lupus due to a novel mutation in TREX1 associated with Aicardi-Goutie’res syndrome. Pediatr Rheumatol Online J 2020; 18:32
    • 6. Zhu Y, Zhong T, Ge D, et al. Multi-Factor Analysis of Single-Center Asthma Control in Xiamen, China. Front Pediatr 2019; 7:498
    • 7.Wu J, Zhong T, Zhu Y, et al. Effects of particulate matter (PM) on childhood asthma exacerbation and control in Xiamen, China. BMC Pediatr 2019; 19:194
    • 8.Ott CJ, Federation AJ, Schwartz LS, et al. Enhancer Architecture and Essential Core Regulatory Circuitry of Chronic Lymphocytic Leukemia. Cancer Cell 2018; 34:982-995.e7
    • 9.Liu K, Guo J, Liu K, et al. Integrative analysis reveals distinct subtypes with therapeutic implications in KRAS-mutant lung adenocarcinoma. EBioMedicine 2018; 36:196–208
    • 10.Guo J, Huang J, Zhou Y, et al. Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population. BMC Genomics 2018; 19:538
    • 11.Xu S, Li Q, Wu J, et al. Identification of IL2RG and CYBB mutations in two Chinese primary immunodeficiency patients by whole-exome sequencing. Immunol Invest 2018; 47:221–228
    • 12.Zhang S, Chen Q, Liu Q, et al. Hippo Signaling Suppresses Cell Ploidy and Tumorigenesis through Skp2. Cancer Cell 2017; 31:669-684.e7
    • 13. Kar SP, Adler E, Tyrer J, et al. Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci. Br J Cancer 2017; 116:524–535
    • 14. Zhang L, Li X, Li Q, et al. Rare RET Variant p.D707E in a Chinese Pedigree with Hereditary Medullary Thyroid Carcinoma. Pathobiology 2017; 84:152–160
    • 15. Guo H, Ahmed M, Zhang F, et al. Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer. Nat Genet 2016; 48:1142–1150
    • 16. Kar SP, Beesley J, Al Olama AA, et al. Genome-wide Meta-analyses of Breast, Ovarian and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by At Least Two Cancer Types. Cancer Discov 2016; 6:1052–1067
    • 17. Lawrenson K, Kar S, McCue K, et al. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast–ovarian cancer susceptibility locus. Nat Commun 2016; 7
    • 18. Hong J, Zhong T, Li H, et al. Ambient air pollution, weather changes, and outpatient visits for allergic conjunctivitis: A retrospective registry study. Sci Rep 2016; 6
    • 19. Fehringer G, Kraft P, Pharoah PD, et al. Cross-cancer genome-wide analysis of lung, ovary, breast, prostate and colorectal cancer reveals novel pleiotropic associations. Cancer Res 2016; 76:5103–5114
    • 20. Petrovics G, Li H, Stümpel T, et al. A novel genomic alteration of LSAMP associates with aggressive prostate cancer in African American men. EBioMedicine 2015; 2:1957–1964
    • 21. Spisak S, Lawrenson K, Fu Y, et al. CAUSEL: An epigenome and genome editing pipeline for establishing function of non-coding GWAS variants. Nat Med 2015; 21:1357–1363
    • 22. Han Y, Hazelett DJ, Wiklund F, et al. Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions. Hum Mol Genet 2015; 24:5603–5618
    • 23. Hoffmann TJ, Van Den Eeden SK, Sakoda LC, et al. A large multi-ethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences. Cancer Discov 2015; 5:878–891
    • 24. Lawrenson K, Iversen ES, Tyrer J, et al. Common variants at the CHEK2 gene locus and risk of epithelial ovarian cancer. Carcinogenesis 2015; 36:1341–1353
    • 25. Lawrenson K, Li Q, Kar S, et al. Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer. Nat Commun 2015; 6:8234
    • 26. Kelemen LE, Lawrenson K, Tyrer J, et al. Genome-wide significant risk associations for mucinous ovarian carcinoma. Nat Genet 2015; 47:888–897
    • 27. Li Q, Stram A, Chen C, et al. Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types. Hum Mol Genet 2014; 23:5294–5302
    • 28. Seo J-H, Li Q, Fatima A, et al. Deconvoluting complex tissues for expression quantitative trait locus-based analyses. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120363
    • 29. Li Q, Seo J-H, Stranger B, et al. Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci. Cell 2013; 152:633–641
    • 30. Li Q, Birkbak NJ, Gyorffy B,et al. Jetset: selecting the optimal microarray probe set to represent a gene. BMC bioinformatics 2011, 12:474.
    • 31. Li Q, Eklund AC, Birkbak NJ, et al.Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response. BMC bioinformatics 2011, 12:310
    • 32. Li Y, Zou L, Li Q, et al. Amplification of LAPTM4B and YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer. Nature medicine 2010, 16(2):214-218.