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目的对中国南方汉族青年男性肥胖患者血浆蛋白标志物进行生物信息学分析。方法根据前期研究确定的中国南方汉族青年男性肥胖患者血浆5种差异蛋白通过DAVID数据库搜索软件结合 GO数据库搜索进行注释和富集度分析，KEGG数据库进行信号转导通路分析；VISANT数据库检索蛋白质相互作用。结果 GO数据库搜索进行注释和富集度分析,差异蛋白主要参与5种生物学过程,涉及细胞分化的调节和负调节、蛋白质脱磷酸（P＜0.05）、1种细胞组分：细胞外区域部分（P＜0.05）、3种分子途径：细胞表面蛋白锚定、磷酸化蛋白活性、磷酸化蛋白酯酶活性（P＜0.05）； DAVID聚类分析得到1个显著性聚类：细胞胞外区域与信号转导及受体有关蛋白； KEGG信号转导通路分析差异蛋白主要集中在5条信号转导通路上：其中脂联素在 PPAR 信号途径、脂肪细胞因子信号传导途径、2型糖尿病信号通路三条通路上；受体型酪氨酸蛋白磷酸酶在幽门螺杆菌感染上皮细胞信号转导通路上；鞘氨醇1-磷酸磷酸酶2在鞘脂代谢通路上。结论中国南方汉族青年肥胖患者血浆蛋白标志物生物信息学推测，这些蛋白参与调控脂肪细胞分化、脂代谢和糖代谢过程，同时参与了幽门螺杆菌感染上皮细胞信号转导通路、非脂肪性肝病功能网络交叉点；以上预测，为进行下一步的分子生物学验证及功能研究提供了重要线索。[营养学报，2015，37（3）：245－249]“,”Objective This bioinformatics study was aimed to analyze plasma protein biomarkers associated with obesity in Han young males in the south China.Methods Two bioinformatics databases including DAVID and GO were utilized to annotate and analyze five differentially-expressed proteins. KEGG database was used to analyze signaling pathways and VISANT database was used to investigate the interactions among the different proteins.Results GO analysis indicated that the five differentially-expressed proteins participated in five different biological processes mainly involving in the regulation of cell differentiation and negative feedback, protein dephosphorylation (P<0.05), cellular extracellular components (P<0.05); and three molecular pathways including surface protein anchorage, protein phosphrylation and protein esterase activity. Meanwhile, KEGG analysis revealed five signaling pathways in which those proteins took part in,including PPAR signaling pathway, adipocytokine signaling pathway, type 2 diabetes mellitus, epithelial cell signaling and sphingolipid metabolism.Conclusion The bioinformatics analysis of plasma protein bomarkers of obese Han young males from the south of China demonstrates that the differentially-expressed proteins are involved in the regulation of adipocyte differentiation, lipid metabolism and glucose metabolism. Furthermore, they participate in the epithelial cell signal transduction pathways of H. pylori infection and non-fatty liver function network interaction. All these findings will provide important information for the further study.