文章摘要
陈卓,马冠君,钱祥,等.多组学技术在非小细胞肺癌脾虚湿滞证中的应用研究[J].浙江中医药大学学报,2022,46(1):12-22.
多组学技术在非小细胞肺癌脾虚湿滞证中的应用研究
Application of Multi-group Technique in Non-small Cell Lung Cancer of Spleen Deficiency and Dampness Stagnation Syndrome
DOI:10.16466/j.issn1005-5509.2022.01.003
中文关键词: 非小细胞肺癌  多组学技术  肠道菌群  蛋白质组学  代谢组学  脾虚湿滞证  联合分析  信号通路
英文关键词: non-small cell lung cancer  multi-omicstechniques  intestinal flora  proteomics  metabonomics  spleen deficiency and dampness stagnation syndrome  joint analysis  signaling pathway
基金项目:国家自然科学基金面上项目(81973771);浙江省中医药科技计划项目(2019ZZ002)
作者单位
陈卓 中国科学院大学附属肿瘤医院浙江省肿瘤医院 中国科学院基础医学与肿瘤研究所 杭州 310022 
马冠君 中国科学院大学附属肿瘤医院浙江省肿瘤医院 中国科学院基础医学与肿瘤研究所 杭州 310022 
钱祥 中国科学院大学附属肿瘤医院浙江省肿瘤医院 中国科学院基础医学与肿瘤研究所 杭州 310022 
傅晓璇 中国科学院大学附属肿瘤医院浙江省肿瘤医院 中国科学院基础医学与肿瘤研究所 杭州 310022 
张爱琴 中国科学院大学附属肿瘤医院浙江省肿瘤医院 中国科学院基础医学与肿瘤研究所 杭州 310022 
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中文摘要:
      [目的] 探讨非小细胞肺癌脾虚湿滞证肠道菌群、代谢组学和蛋白质组学的基本特征。[方法] 选择非小细胞肺癌脾虚湿滞证患者13例作为患者组,健康志愿者15例作为对照组。收集两组粪便标本,运用16S核糖体DNA(16S ribosomal DNA,16S rDNA)鉴定技术检测肠道微生态,分析两组肠道菌群的多样性及差异性。采集两组血清标本,通过液相色谱-质谱联用技术对血清中的代谢物、蛋白进行检测,筛选差异明显的代谢物及蛋白,并进行富集分析通路预测。通过加权基因共表达网络分析(weighted correlation network analysis,WGCNA)软件对肠道菌群和代谢组学进行联合分析,阐明差异菌群-差异代谢物之间的关联性。通过京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)进行代谢组学和蛋白质组学数据联合分析,挖掘同一生物学进程中发生显著性变化的蛋白质和代谢物,探索肺癌关键基因和通路。[结果] 两组间存在3种显著差异菌,分别是布鲁菌、鞘氨醇单胞菌、脱硫弧菌,14种差异蛋白(上调9个、下调5个),在补体和凝血信号通路(complement and coagulation cascades)、Pertussis、磷脂酰肌醇3-激酶-蛋白激酶B(phosphatidylinositide 3-kinase-protein kinase B,PI3K-Akt)等通路上富集明显。13种差异代谢物(负离子模式下2个、正离子模式下11个),主要涉及卟啉和叶绿素代谢、不饱和脂肪酸的生物合成、缬氨酸、亮氨酸和异亮氨酸的生物合成等代谢通路。进一步通过联合分析发现,差异物质与丝裂原活化蛋白激酶(mitogen-activated protein kinase,MAPK)、PI3K-Akt、Ras信号通路、癌症信号通路、细胞因子受体相互作用(cytokine-cytokine receptor interaction)这5条信号通路相关。[结论] 非小细胞肺癌脾虚湿滞证具有特异的肠道菌群、蛋白质和代谢物,通过联合分析发现PI3K-Akt、MAPK信号通路的失调可能与肺癌的发生发展相关。
英文摘要:
      [Objectives] To explore the basic characteristics of intestinal flora, metabonomics and proteomics of non-small cell lung cancer(NSCLC) with spleen deficiency and dampness stagnation. [Methods] Thirteen NSCLC patients with spleen deficiency and dampness stagnation were selected as patients group and 15 healthy volunteers as control group. Stool specimens were collected from the two groups, and the intestinal microecology was detected by16S ribosomal DNA(16S rDNA) identification technique to analyze the diversity and difference of intestinal flora between the two groups. Serum specimens were collected from the two groups, metabolites and proteins in serum were detected by liquid chromatography-mass spectrometry to screen for metabolites and proteins with significant differences and to perform enrichment analysis for pathway prediction. Combined analysis of intestinal flora and metabolomics was performed by weighted correlation network analysis(WGCNA) software to elucidate the association between differential flora-differential metabolites. Combined analysis of metabolomics and proteomics data via the Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway was used to mine proteins and metabolites with significant changes in the same KEGG pathway and explore key genes and pathways in lung cancer. [Results] Three significantly different bacteria were present between the two groups, Brucella, Sphingomonas and Desulfovibrio; 14 differential proteins(9 up-regulated, 5 down-regulated) were analyzed and shown enrichment in the complement and coagulation cascades, Pertussis, phosphatidylinositide 3-kinase-protein kinase B(PI3K-Akt) signaling pathway. Thirteen differential metabolites (2 in negative ion mode and 11 in positive ion mode) were significantly enriched in pathways such as porphyrin and chlorophyll metabolism, unsaturated fatty acid biosynthesis, valine, leucine and isoleucine biosynthesis. Further joint analysis revealed that the differential substances were associated with five signaling pathways, mitogen-activated protein kinase(MAPK), PI3K-Akt, Ras signaling pathway, pathways in cancer, and cytokine-cytokine receptor interaction. [Conclusion] NSCLC of spleen deficiency and dampness stagnation syndrome has specific intestinal flora, proteins and metabolites, and dysregulation of the PI3K-Akt and MAPK signaling pathways may be closely associated with the development of lung cancer detected by combined analysis.
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