Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.     

Transcriptome analysis of Armillaria gallica 012 m in response to auxin

Gastrodia elata is an achlorophyllous and fully mycoheterotrophic orchid which obtains carbon and other nutrients from Armillaria species in its life cycle. Many researchers suggested that plant hormones, as signing molecules, play a central role in the plant–fungi interaction. In the process of Armillaria gallica 012 m cultivation, both exogenous indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA) distinctly stimulated the growth of mycelia in solid media. The differential expression genes (DEGs) of A. gallica 012 m with IAA versus blank control (BK) and IBA versus BK were investigated. The results showed that more than 80% of DEGs of the IAA group were coincident with the DEGs of the IBA group, and more than half of upregulated DEGs and most of the downregulated DEGs of the IAA group coincided with those DEGs of the IBA group. Above research implied that A. gallica 012 m could perceive IAA and IBA, and possess similar responses and signaling pathways to IAA and IBA. The overlapping differential genes of the IAA group and IBA group were analyzed by GO term, and the results showed that several DEGs identified were related to biological processes including positive regulation of the biological process and biological process. The downregulated NmrA-like and FKBP_C genes might be benefit to the growth of mycelia. Those results can explain that exiguous IAA and IBA improved the growth of A. gallica to some extent. We speculate that IAA and IBA are signaling molecules, and regulate the expression of growth-related genes of A. gallica 012 m by the same signaling pathway.     

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。     

非吸烟女性肺癌潜在相关基因的生物信息学分析及功能预测

目的：对非吸烟女性肺癌潜在相关基因进行生物信息学分析及功能预测,探讨非吸烟女性肺癌患者的发病机制及预后标志物。方法：选择从GEO数据库下载非吸烟女性肺癌患者的基因芯片并用GEO2R软件筛选出差异表达基因（differentially expressed gene,DEG）,再利用STRING 在线分析软件对DEG 进行GO 和KEGG 分析以及蛋白互作（protein-protein interaction,PPI）网络分析,然后利用插件（M-CODE）对所有DEG进行可视化处理,筛选关键DEG,最后利用GEPIA及Kaplan-Meier plotter在线工具对关键DEG进行功能预测及预后分析。结果：共筛选出160 个DEG,其中上调54 个、下调106 个;GO分析其生物学功能主要与血管形成、单个生物细胞间黏附、GTPase活性正调控和信号转导密切相关（均P<0.05）。KEGG分析发现,可能主要与细胞黏附分子、白细胞迁移、紧密连接和胞吞作用相关（均P<0.05）。PPI 网络分析获得8 个关键DEG,分别是TIE1、PECAM1、VEGFD、ICAM2、ESAM、EMCN、ROBO4 和CLDN5。结论：TIE1、CLDN5、ICAM2、ESAM、VEGFD、ROBO4 可能是非吸烟女性肺癌发病机制的研究靶点,PECAM1、EMCN可能是预测非吸烟女性肺癌患者病情进展及预后的标志物。     

Comprehensive bioinformatics analysis of functional molecules in colorectal cancer

BackgroundColorectal cancer (CRC) is the 3rd most common cancer and the 2nd leading cause of cancer-related death. Numerous studies have found that aberrations in cellular molecules play an important role in the development of tumors. Studying and determining the interactions between these molecules can contribute to the diagnosis, treatment, and prognosis of tumors.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE151021","term_id":"151021"}}GSE151021, {"type":"entrez-geo","attrs":{"text":"GSE156720","term_id":"156720"}}GSE156720, and {"type":"entrez-geo","attrs":{"text":"GSE156719","term_id":"156719"}}GSE156719 data sets were analyzed to screen the differentially expressed messenger RNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) in CRC. Database for Annotation, Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes/Proteins software were used to examine gene enrichment and the hub genes. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and UALCAN was used to verify the expression of the hub genes. To analyze the overall survival (OS) of the hub genes, Kaplan-Meier plotter (KM plotter) was performed. Finally, the miRCancer database, TargetScan, and {"type":"entrez-geo","attrs":{"text":"GSE156719","term_id":"156719"}}GSE156719 were used to identify the targets of the identified miRNAs. To predict the lncRNA-miRNA interactions, we used DIANA-LncBase v2 and {"type":"entrez-geo","attrs":{"text":"GSE156720","term_id":"156720"}}GSE156720. Finally, the visualization protein‑protein interaction (PPI), competitive endogenous RNA (ceRNA) network was constructed using Cytoscape v3.1.ResultsBy analyzing {"type":"entrez-geo","attrs":{"text":"GSE151021","term_id":"151021"}}GSE151021 and {"type":"entrez-geo","attrs":{"text":"GSE156720","term_id":"156720"}}GSE156720, 23 upregulated mRNAs and 10 downregulated mRNAs were identified as sharing the differentially expressed genes (DEGs) between CRC and adjacent tissues. Furthermore, nucleolar protein 14 (NOP14), the sonic hedgehog (SHH) signaling molecule, phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1), the BCL2 apoptosis regulator (BCL2), and zinc finger E-box binding homeobox 2 (ZEB2) were considered hub genes. The constructed lncRNA-miRNA-mRNA network revealed 7 intersecting miRNAs (4 upregulated and 3 downregulated), 79 lncRNAs (40 upregulated and 39 downregulated), and 5 mRNAs (3 upregulated and 2 downregulated). Finally, we determined that the dysregulation of lncRNAs, such as HCG16, CASC9, SNHG16, HAND2-AS1, and NR2F1-AS1, secluded altered the expression of several miRNAs, such as hsa-miR-193a-5p, hsa-miR-485-5p, hsa-miR-17-5p, and hsa-miR-92a-3p, and affected the occurrence and development of CRC.ConclusionsWe identified a series of DElncRNAs, DEmRNAs, and DEmiRNAs in CRC that might be considered potential biomarkers in understanding the complex molecular pathways leading to CRC development.     

New genes associated with rheumatoid arthritis identified by gene expression profiling

In this study, we aimed to find new genes associated with rheumatoid arthritis (RA) so that more comprehensive genes would be used for monitoring and/or diagnosing patients. Illumina digital gene expression profiling was applied in two sample types – peripheral blood mononuclear cells (PBMCs) and synovial cells to compare the gene expression pattern between 17 patients with RA and three control groups (six osteoarthritis patients, three ankylosing spondylitis patients and 17 healthy controls). Bioinformatics was performed on pathway analysis and protein–protein interaction networks. Four novel genes from PBMCs – DHRS3, TTC38, SAP30BP and LPIN2 – were found to be associated with RA and further confirmed through quantitative real‐time polymerase chain reaction. Five new differentially expressed genes (EPYC, LIFR, GLDN, TADA3 and ZNRF3) found in synovial cells were not confirmed. Pathway analyses revealed 10 significantly enriched pathways, and a protein–protein interaction network analysis showed that four novel PBMC‐derived genes were connected to previously reported genes by four intermediate genes. Therefore, we proposed that four newly identified PBMC‐derived genes could be integrated with previously reported RA‐associated genes to monitor and/or diagnose RA.     

应用RNA-Seq技术筛选唾液腺腺样囊性癌嗜神经侵袭相关差异表达基因

目的: 应用RNA-Seq技术分析与施万细胞共培养前、后的唾液腺腺样囊性癌细胞（SACC）的基因表达水平变化,明确嗜神经侵袭（perineural invasion,PNI）相关基因。 方法: 采用腺样囊性癌与SD大鼠施万细胞共培养模型,分析共培养前、后SACC细胞基因表达变化,对差异表达基因进行聚类分析、GO功能富集分析、KEGG通路富集分析,并采用qRT-PCR对其中6个关键差异表达基因进行验证。采用edgeR软件（3.12.1）进行表达差异显著性分析,将P≤0.05、差异表达倍数的绝对值大于1作为差异显著性标准。 结果: 在腺样囊性癌单独培养组与共培养组之间发现395个差异表达基因（P≤0.05,|logFC|≥1.0）,其中135个基因上调（34.2%）,260个基因下调（65.8%）。GO功能富集分析结果表明,这些PNI相关差异表达基因参与了血管再生、细胞外基质的组成、细胞增殖、凋亡和上皮形态发生;KEGG通路富集分析结果表明,这些基因参与组氨酸代谢、肿瘤坏死因子、趋化因子等细胞因子受体相互作用等重要生物学通路。利用qRT-PCR技术对其中6个关键基因进行验证,验证结果与RNA-Seq趋势一致。 结论: 得到了SACC嗜神经侵袭PNI的相关差异基因,为阐明SACC的嗜神经侵袭机制提供了实验依据。