Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis

BackgroundBreast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer.MethodsThe gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein–protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan–Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples.ResultsA total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan–Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted.ConclusionThese results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.     

基于RNA-seq的db/db小鼠大脑皮质的转录组学分析

目的通过对db/db和野生型(WT)小鼠大脑皮质组织全转录组学分析,探索参与调节2型糖尿病诱导的脑功能障碍的差异表达基因(DEGs)及相关通路和网络。方法取雄性野生型WT和db/db小鼠各9只,在第8和24周检测小鼠的体质量和血糖,之后收集动物大脑皮质进行全转录组测序(RNA-seq),并进行DEGs,GO、KEGG及蛋白互作网络分析。结果与WT组相比,db/db组大脑皮质发生变化的306个转录本中有178个表达上调,128个表达下调。DEGs中,43个上调(如Clcnka和Trim17),59个下调(如Arih1和Nectin-3)。蛋白互作网络图中的13个枢纽基因均下调,且大多属于线粒体编码家族。同时,db/db小鼠在多项GO富集类别中具有显著差异,如细胞过程、细胞部分等。此外,KEGG功能富集结果显示DEGs在代谢、帕金森病(PD)、阿尔茨海默病(AD)等相关通路中高度富集,且这些富集通路中的DEGs主要影响了线粒体氧化磷酸化过程。结论揭示了2型糖尿病与中枢神经系统损伤之间的关系及潜在的相关基因、通路及网络。     

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Background and objective

The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC.

Bioinformatics analysis of gene expression profiles of Inclusion body myositis

Inclusion body myositis (IBM) is a disease with a poor prognosis and limited treatment options. This study aimed at exploring gene expression profile alterations, investigating the underlying mechanisms and identifying novel targets for IBM. We analysed two microarray datasets (GSE39454 and GSE128470) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between IBM and normal samples. Gene Ontology（GO）function and Kyoto Encyclopedia of Genes and Genomes（KEGG）pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. A total of 144 upregulated DEGs and one downregulated DEG were identified. The GO enrichment analysis revealed that the immune response was the most significantly enriched term within the DEGs. The KEGG pathway analysis identified 22 significant pathways, the majority of which could be divided into the immune and infectious diseases. Following the construction of PPI networks, ten hub genes with high degrees of connectivity were picked out, namely PTPRC, IRF8, CCR5, VCAM1, HLA-DRA, TYROBP, C1QB, HLA-DRB1, CD74 and CXCL9. Our research hypothesizes that autoimmunity plays an irreplaceable role in the pathogenesis of IBM. The novel DEGs and pathways identified in this study may provide new insight into the underlying mechanisms of IBM at the molecular level.     

基于生物信息学对扩张型心肌病与慢性心力衰竭共同生物标志物的预测*

目的：通过生物信息学的方法预测扩张型心肌病（DCM）与慢性心力衰竭（CHF）发病的共同生物标志物, 为临床上2 种疾病的发病及相关性奠定理论基础。方法：从Gene Expression Omnibus（GEO）数据库下载芯片数 据GSE3585,此为DCM和正常对照组原始数据,同时下载芯片数据GSE76701,此为CHF 和对照组原始数据。 通过R软件分析获得DCM和CHF 发病的差异表达基因,并获得2 种疾病发病的共同差异表达基因,进一步对共 同差异表达基因进行GO 和KEGG富集分析,构建差异表达基因的PPI 相互作用网络图,获得扩张型心肌病和心 衰发病的共同关键基因。结果：DCM的差异表达基因有240 个,其中141 个上调基因,99 个下调基因,CHF 的 差异表达基因有654 个,其中355 个上调基因,299 个下调基因。DCM和CHF 共同的差异表达基因有36 个,其中 19 个上调基因,17 个下调基因。GO 分析显示,差异表达基因主要集中在12 种不同的生理、病理过程中,KEGG 分析获得差异表达基因参与的主要信号通路为5 条,预测7 个关键差异表达基因,分别为：CD163、KYVE1、 MRC1、VSIG4、FCER1G、S100A9、F13A1。结论：该研究初步探讨了DCM与CHF 两种疾病发病分子机制, 获得了两种疾病发病的共同差异表达基因,仍需进一步的实验研究对基因的表达和临床病理特征的相关性进行验 证。     

创伤愈合及压力治疗过程中瘢痕动物模型转录组水平的变化研究

目的利用RNA测序技术(RNA-seq)研究创伤愈合及压力治疗过程中巴马小型猪瘢痕动物模型转录组水平的变化。方法通过背部取皮建立巴马小型猪瘢痕模型,取皮第60 d开始加压(3.4 kPa)治疗,在取皮第0、14、30、60和90 d分别提取瘢痕组织总RNA进行测序。将所得序列映射到猪参考基因组并进行转录组重建,寻找差异表达基因(DEGs),利用生物信息学方法进一步对所得DEGs进行GO分析和KEGG通路富集性分析,同时挑选部分基因用qRT-PCR进行验证。结果测序数据经过预处理,各组均有78%以上的读段能准确比对到参考序列。DEGs鉴定结果表明,压力治疗前后有568个基因差异表达,其中上调289个,下调279个。GO富集分析发现,各组DEGs主要与细胞外基质、组织发展和皮肤发展相关。KEGG富集分析表明,创伤愈合过程中各组DEGs主要富集于细胞外基质-受体相互作用、黏着斑和凋亡通路;压力治疗前后的DEGs除了富集于以上通路,还富集于MAPK和PI3K信号通路。qRT-PCR检测表明,6个DEGs的表达模式与RNA-Seq分析结果一致,证实RNA-seq结果的可靠性。结论 RNA-seq分析鉴定出创伤愈合及压力治疗过程中瘢痕动物模型的差异表达基因,为临床瘢痕的治疗研究提供实验依据。     

基于 TCGA 数据库筛选、 分析并验证宫颈癌生物标志物

目的 基于生物信息学筛选分析宫颈癌差异表达基因 ( differentially expressed gene, DEGs) 及差 异表达 miRNA, 并进一步对差异基因和蛋白进行验证, 以期寻找潜在的生物标志物和治疗靶点。 方法 从 肿瘤基因组图谱 (the cancer genome atlas, TCGA) 数据库获取宫颈癌相关数据, edgeR 算法筛选 DEGs 和差 异 miRNAs。 利用 Cytoscape3. 8. 2 软件构建 mRNA-miRNA 共表达网络。 利用 DAVID 软件对 DEGs 和通过 miRWalk 网站预测的差异 miRNA 的目标基因进行 GO 富集分析和 KEGG 富集分析。 利用 qPCR 和 Western 印 迹技术对 DEGs 进行进一步验证。 结果 筛选出 149 个上调的 DEGs 和 171 个下调的 DEGs, 以及 46 个上调 的差异 miRNAs 和 64 个下调的差异 miRNAs。 DEGs 和 miRNA 目标基因在细胞组成上的富集具有一致性, 都富集在胞质、 核和核质中。 但共表达网络发现 DEGs 和差异 miRNAs 之间不存在明显的调控关系。 因此, 后续实验重点放在了对 DEGs 的验证上, 对差异表达性较为显著的 TCEAL6、 CLEC3B、 LMOD1、 CNN1 进行 了验证。 qPCR 显示它们在宫颈癌中表达量均显著降低, 符合预期, 对 CNN1 进行的 Western 印迹也显示其 在宫颈癌中的低表达。 结论 TCEAL6、 CLEC3B、 LMOD1、 CNN1 在宫颈癌中均显著低表达, 有望成为宫颈 癌生物标志物。     

Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis

BackgroundThe risk of brain metastasis (BM) in HER2-positive (+) breast cancer (BC) patients is significantly higher than that in HER2-negative (-) BC patients. The high incidence and mortality rate makes it urgent to elucidate the key pathways and genes involved and identify patients who are more at risk of developing BM.Materials and methodsTo identify the target genes in HER2+BC patients with BM, we analyzed the microarray datasets (GSE43837) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to extract the differentially expressed genes (DEGs) involved in HER2+ primary BC and BC with BM. Bioinformatics methods including Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the screened DEGs. The protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Finally, GSEA analysis was performed to identify the hub genes and the important pathways.ResultsA total of 751 upregulated and 285 downregulated DEGs were identified. The GO function and KEGG pathway enrichment analyses indicated that the DEGs were all enriched in the protein binding molecular function. The top five hub nodes were screened out, included PHLPP1, UBC, ACACB, TGFB1, and ACTB. The GSEA results demonstrated that the five hub genes are mainly enriched in the ribosomal pathway.ConclusionOur study suggests that the five hub genes (PHLPP1, UBC, ACACB, TGFB1, and ACTB) are associated with HER2+BC with BM. The GSEA analysis revealed that the ribosomal pathway seems to play a very important role in the pathogenesis of HER2+BC with BM.     

Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.     

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively.Results: We downloaded the expression profiles of {"type":"entrez-geo","attrs":{"text":"GSE103236","term_id":"103236"}}GSE103236 and {"type":"entrez-geo","attrs":{"text":"GSE118897","term_id":"118897"}}GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.     

利什曼原虫感染树突状细胞早期基因表达差异分析及关键基因筛选

目的:分析利什曼原虫感染树突状细胞(DCs)早期的基因表达与信号通路变化,探究DCs感染后应答,寻找利什曼原虫感染后基于DCs的免疫治疗方法。方法:GEO数据库下载利什曼原虫感染前后DCs基因芯片数据,RStudio软件筛选差异表达基因(DEGs),STRING构建DEGs蛋白质相互作用网络(PPI),Cytoscape筛选差异表达蛋白质的核心模块,RStudio软件对DEGs进行GO和KEGG富集分析。结果:共筛选出DEGs 129个,其中IL12B与CXCL10差异最为显著,GO分析共富集23个过程,主要涉及病毒感染过程相关细胞反应及Ⅰ-IFN相关免疫反应;KEGG分析共富集3条信号通路,分别为甲型流感、麻疹及DNA复制信号通路。结论:利什曼原虫感染DCs前后Ⅰ-IFN信号通路和TLR4/NF-κB信号通路激活,影响IL12表达,提示Ⅰ-IFN/IL12信号通路与TLR4/NF-κB/IL12信号通路可作为利什曼原虫感染治疗的靶点,CXCL10也有望成为潜在的治疗靶点;利什曼原虫感染后,出现类似病毒感染现象,推测抗病毒免疫疗法可能在对抗利什曼原虫感染中具有一定疗效。     

Role of miR-452-5p in the tumorigenesis of prostate cancer: A study based on the Cancer Genome Atl(TCGA), Gene Expression Omnibus (GEO), and bioinformatics analysis

Background

MiR-452-5p has been reported to be down-regulated in prostate cancer, affecting the development of this type of cancer. However, the molecular mechanism of miR-452-5p in prostate cancer remains unclear. Therefore, we investigated the network of target genes of miR-452-5p in prostate cancer using bioinformatics analyses.

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer(CRC)is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients.In this study,mRNA microarray datasets GSE113513,GSE21510,GSE44076,and GSE32323 were obtained from the Gene Expression Omnibus(GEO)and analyzed with bioinformatics to identify hub genes in CRC development.Differentially expressed genes(DEGs)were analyzed using the GEO2 R tool.Gene ontology(GO)and KEGG analyses were performed through the DAVID database.STRING database and Cytoscape software were used to construct a protein-protein interaction(PPI)network and identify key modules and hub genes.Survival analyses of the DEGs were performed on GEPIA database.The Connectivity Map database was used to screen potential drugs.A total of 865 DEGs were identified,including 374 upregulated and 491 downregulated genes.These DEGs were mainly associated with metabolic pathways,pathways in cancer,cell cycle and so on.The PPI network was identified with 863 nodes and 5817 edges.Survival analysis revealed that HMMR,PAICS,ETFDH,and SCG2 were significantly associated with overall survival of CRC patients.And blebbistatin and sulconazole were identified as candidate drugs.In conclusion,our study found four hub genes involved in CRC,which may provide novel potential biomarkers for CRC prognosis,and two potential candidate drugs for CRC.     

轴丝动力蛋白中链基因1在肺腺癌中低表达且抑制肺腺癌细胞侵袭能力

目的 探讨轴丝动力蛋白中链基因1(DNAI1)在肺腺癌(LUAD)中的表达情况以及对肺腺癌侵袭能力的影响.方法 微阵列基因芯片筛选肺腺癌组织(3例)与癌旁组织(3例)的差异表达基因;聚类热图(heatmap)、火山图(volcano plot)展示筛选后mRNA的表达和分布情况;利用DAVID数据库进行基因本体论(GO...     

CCl4诱导的小鼠纤维化肝组织微小RNA差异表达谱及初步功能分析

 目的：应用微小RNA(miRNA)芯片技术研究miRNAs在四氯化碳（CCl4）诱导的小鼠纤维化肝脏中的差异表达谱,并基于基因本体论（gene ontology,GO)分析及信号转导通路分析发现差异miRNAs的主要功能。方法：实验分为正常组及模型组,皮下注射 CCl4复制小鼠肝纤维化模型;应用Agilent 小鼠 miRNA 寡核苷酸基因芯片检测各组肝脏miRNA表达谱。用随机方差模型t检验筛选2组间的差异miRNAs,并预测其靶基因。对靶基因进行GO分析及信号转导通路分析发现差异miRNAs发挥的主要功能。结果：正常组与模型组间共筛选出39个差异miRNAs,其中模型组较正常组上调的23个,下调的16个。GO分析及信号转导通路分析结果提示差异miRNAs可能调控的靶基因及其参与的生物学功能包括细胞的增殖与活化、细胞凋亡、细胞周期、细胞黏附、细胞迁移、炎症反应、转化生长因子β（TGF-β）/Smads信号转导通路、Wnt受体信号转导通路、蛋白代谢过程的调控等。GO分析发现关键的上调miRNA包括mmu-miR-322、mmu-miR-15b、mmu-miR-195、mmu-miR-200b、mmu-miR-214等,关键的下调miRNA包括mmu-miR-16、mmu-miR-130a、mmu-miR-101b、mmu-miR-30a和mmu-miR-30e等。对显著性GO与显著性信号通路所属的靶基因取交集,对网络中miRNA在网络中的调控地位进行评价,结果发现关键的上调miRNAs包括mmu-miR-200b、mmu-miR-322、mmu-miR-106b、mmu-miR-23a、mmu-miR-15b等,关键的下调miRNAs包括mmu-miR-16、mmu-miR-30e、mmu-miR-30c、mmu-miR-30a、mmu-miR-130a等。结论：纤维化肝组织miRNAs表达较正常肝组织发生明显变化;肝纤维化形成的各个环节,包括细胞的增殖与活化、细胞黏附、细胞凋亡、细胞迁移与分化、物质代谢、TGF-β信号通路等都可能受miRNAs的调控。     

Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation

HCC (hepatocellular carcinoma) is a highly aggressive malignancy that cause a mass of deaths world widely. We chose gene expression datasets of GSE27635 and GSE28248 from GEO database to find out key genes and their interaction network during the progression and metastasis of HCC. GEO2R online tool was used to screen differentially expressed genes (DEGs) between tumor and peri-tumor tissues based on these two datasets. The identified differentially expressed genes were prepared for further analysis such as GO function, KEGG pathway, PPI network analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Retrieval of Interacting Genes (STRING). Two modules were constructed by MOCDE plugin in Cytoscape and 21 genes were selected as hub genes during this analysis. The expression heatmap and GO function of hub genes were performed using R pheatmap package and BiNGO plugin in Cytoscape respectively. Six hub genes including CDC25 A, CDK1, HMMR, MYBL2, TOP2A were recollected for survival analysis and their expression was validated using Kaplan Meier-plotter and GEPIA website. We also investigated the DEGs between metastasis and non-metastasis tissues and two genes (NQO1 and PTHLH) are highly associated with the metastasis in HCC. Further verification using woundhealing and transwell assay confirmed their ability to mediate cell migration and invasion. In summary, our results obtained by bioinformatic analysis and experimental validation revealed the dominant genes and their interaction networks that are associated with the progression and metastasis of HCC and might serve as potential targets for HCC therapy and diagnosis.     

Bioinformatics analysis of gene expression profiles to diagnose crucial and novel genes in glioblastoma multiform

Therefore, the current study aimed to diagnose the genes associated in the pathogenesis of GBM. The differentially expressed genes (DEGs) were diagnosed using the limma software package. The ToppFun was used to perform pathway and Gene Ontology (GO) enrichment analysis of the DEGs. Protein-protein interaction (PPI) networks, extracted modules, miRNA-target genes regulatory network and miRNA-target genes regulatory network were used to obtain insight into the actions of DEGs. Survival analysis for DEGs carried out. A total of 701 DEGs, including 413 upregulated and 288 downregulated genes, were diagnosed between U1118MG cell line (PK 11195 treated with 1?h exposure) and U1118MG cell line (PK 11195 treated with 24?h exposure). The up-regulated genes were enriched in superpathway of pyrimidine deoxyribonucleotides de novo biosynthesis, cell cycle, cell cycle process and chromosome. The down-regulated genes were enriched in folate transformations I, biosynthesis of amino acids, cellular amino acid metabolic process and vacuolar membrane. The current study screened the genes in PPI network, extracted modules, miRNA-target genes regulatory network and miRNA-target genes regulatory network with higher degrees as hub genes, which included MYC, TERF2IP, CDK1, EEF1G, TXNIP, SLC1A5, RGS4 and IER5L Survival suggested that low expressed NR4A2, SLC7?A5, CYR61 and ID1 in patients with GBM was linked with a positive prognosis for overall survival. In conclusion, the current study could improve our understanding of the molecular mechanisms in the progression of GBM, and these crucial as well as new molecular markers might be used as therapeutic targets for GBM.     

胰岛淀粉样多肽对阿尔茨海默病小鼠脑组织中LncRNA和mRNA表达谱的影响

目的:探讨胰岛淀粉样多肽（IAPP）对阿尔茨海默病（AD）小鼠脑组织中长链非编码RNA（LncRNA）和信使RNA（mRNA）表达谱的影响。方法:选取7月龄雄性APP/PS1转基因AD模型小鼠10只,体质量20～30 g。将AD模型小鼠按数字表法随机分为IAPP干预组和对照组,每组5只。IAPP干预组小鼠腹腔内注射0....