Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Differences in potential key genes and pathways between primary and radiation-associated angiosarcoma of the breast

BackgroundAngiosarcoma of the breast is a high-grade malignant soft tissue tumor, it can be divided into primary and radiation-associated angiosarcoma(secondary). However, the differences between primary and secondary angiosarcomas in terms of pathogenesis, clinical behavior, early diagnosis biomarkers, genetic abnormalities, and therapeutic targets remain to be fully elucidated. At the same time, due to its rarity, most of current information relating to angiosarcoma is provided by case reports. Therefore, exploring the mechanisms of primary and secondary breast angiosarcoma have important value for the discovery of new biomarkers and research into potential therapeutic targets.MethodsThe differentially expressed genes (DEGs) between 36 cases of primary angiosarcoma and 54 cases of secondary angiosarcoma were screened. Then, the DEGs were used to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, a protein-protein interaction (PPI) network was constructed using the STRING database.ResultsA total of 18 DEGs were identified, of which 13 were upregulated and 5 were downregulated in secondary breast angiosarcoma. The GO enrichment analysis showed that the DEGs were most enriched in metabolism, energy pathways, and protein metabolism in biological processes. The enriched signaling pathways of DEGs were the transforming growth factor-β (TGF-β), Wnt, Hippo and PI3K-Akt signaling pathways. Then, the PPI network was conducted and hub genes were identified and they were involved in thyroid hormone, Hippo and other signaling pathways.ConclusionThis study lay the foundation for the discovery of effective and reliable molecular biomarkers and essential therapeutic targets for these malignancies.     

Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the most common subtype of head and neck cancer; however, its pathogenesis and potential therapeutic targets remain largely unknown. In the present study, we analyzed three gene expression profiles and screened differentially expressed genes (DEGs) between HNSCC and normal tissues. The DEGs were subjected to gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), protein–protein interaction (PPI), and survival analyses, while the connectivity map (CMap) database was used to predict candidate small molecules that may reverse the biological state of HNSCC. Finally, we measured the expression of the most relevant core gene in vitro and examined the effect of the top predicted potential drug against the proliferation of HNSCC cell lines. Among the 208 DEGs and ten hub genes identified, CDK1 and CDC45 were associated with unfavorable HNSCC prognosis, and three potential small molecule drugs for treating HNSCC were identified. Increased CDK1 expression was confirmed in HNSCC cells, and menadione, the top predicted potential drug, exerted significant inhibitory effects against HNSCC cell proliferation and markedly reversed CDK1 expression. Together, the findings of the present study suggest that the ten hub genes and pathways identified may be closely related to HNSCC pathogenesis. In particular, CDK1 and CDC45 overexpression could be reliable biomarkers for predicting unfavorable prognosis in patients with HNSCC, while the new candidate small molecules identified by CMap analysis provide new avenues for the development of potential drugs to treat HNSCC.     

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Prognostic value of metabolic genes in lung adenocarcinoma via integrative analyses

BackgroundLung adenocarcinoma (LUAD) is the most common malignant lung tumor. Metabolic pathway reprogramming is an important hallmark of physiologic changes in cancers. However, the mechanisms through which these metabolic genes and pathways function in LUAD as well as their prognostic values have not been fully established.MethodsFour publicly available datasets from GEO and TCGA were used to identify differentially expressed genes (DEGs) in LUAD, which were then subjected to GO and KEGG pathway enrichment analysis. Associations between metabolic gene expressions with overall survival, tumor stage, TP53 mutation status, and infiltrated immune cells were investigated. Protein-protein interactions were evaluated using GeneMANIA and Metascape.ResultsBy integrating four public datasets, 247 DEGs were identified in LUAD. These DEGs were significantly enriched in regulation of chromosome segregation, centromeric region, and histone kinase activity GO terms, as well as in cell cycle, p53 signaling pathway, metabolic pathways, and other KEGG pathways. Elevated expressions of ten metabolic genes in LUAD were significantly associated with poor survival outcomes. These metabolic genes were highly expressed in more advanced tumor stage and TP53 mutated patients. Moreover, expression levels were significantly correlated with tumor-infiltrating immune cells. PPI interaction analysis revealed that the top 20 genes interacting with each metabolic gene were significantly enriched in DNA replication, response to radiation, and central carbon metabolism in cancer.ConclusionThis study elucidates on molecular changes in metabolic genes in LUAD, which may inform the development of genetically oriented diagnostic approaches and effective treatment options.     

紫外辐照恢复早期耐辐射奇球菌的转录组学分析（英文）

目的 耐辐射奇球菌是一种对紫外线、电离、干燥和化学试剂具有较强抗性的极端微生物。然而，该菌在紫外辐照后恢复早期的分子响应还不完全清楚。本文的目的是揭示耐辐射奇球菌在这一阶段的转录组响应。方法 本研究采用RNA-seq技术，测定了正常和紫外辐照培养条件下耐辐射奇球菌的转录组。为确定关键的差异表达基因及其调控关系，进行了功能富集分析。选取部分关键差异表达基因，进行实时定量PCR实验验证。利用以往研究中的转录组数据，寻找紫外辐照、电离辐射和干燥胁迫条件下公共的差异表达基因。构建了蛋白质-蛋白质相互作用网络；对蛋白质互作网络中的枢纽基因和主要模块进行了鉴定；对这些枢纽基因和模块进行了功能富集分析。结果 紫外辐照后的恢复早期，上调基因数量是下调基因数量的2倍以上，且多数与应激反应和DNA修复有关。恢复早期的修复途径主要有单链退火（SSA）途径（涉及基因：ddr A-D）、非同源端连接（NHEJ）途径（涉及基因：lig B、ppr A）和核苷酸切除修复（NER）途径（涉及基因：uvr A-C），前两种途径为同源重组（HR）做准备，而NER途径去除紫外线照射带来的嘧啶二聚体。通过比较紫外辐照、电离辐...     

Investigation of hub genes and immune status in heart transplant rejection using endomyocardial biopsies

T cell‒mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.     

Revealing the link between macrophage in microenvironment of osteosarcoma and poor prognosis by utilizing the Integrated analysis

Objectives:Osteosarcoma (OS) is the most common type of primary malignant bone tumor, The effect of tumor microenvironment components on OS oncogenesis remains unknown.Methods:To investigate the function of immune cells in osteosarcoma, we provided a text-based GMT (Gene Matrix Transposed) file in which each line defines one of lm22 with their markers. We used STRING to draw DEG’s PPI network and selected hub genes and modules. Then, survival analysis was conducted to hub genes. We identified 10,390 common genes, and identified 218 DEGs based on the combined t-value and Z scores.Results:The KEGG and GSEA enrichment analysis showed that macrophages are significantly activated in osteosarcoma. PPI network analysis revealed that hub gene CD163 molecule. We found that the expression of CD163 was negatively associated with the OS of osteosarcoma patients. These results suggest that macrophages are a risk factor in patients with osteosarcoma.Conclusions:This study has systematically validated results of the studies carried out previously and filled up the gap in the field of OS on large-scaled meta-analysis. In addition, for the hub gene (CD163) and the macrophage cell capable of being used as a novel biomarker in promoting early diagnosis and development of therapeutic approaches.     

肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

Construction of miRNA-mRNA network for the identification of key biological markers and their associated pathways in IgA nephropathy by employing the integrated bioinformatics analysis

BackgroundAbout half-century ago, Immunoglobulin A nephropathy (IgAN) was discovered as a complicated disease with frequent clinical symptoms. Until now, exact mechanism underlying the pathogenesis of IgAN is poorly known. Therefore, current study was aimed to understand the molecular mechanism of IgAN by identifying the key miRNAs and their targeted hub genes. The key miRNAs might contribute to the diagnosis and therapy of IgAN, and could turn out to be a new star in the field of IgAN.MethodsThe microarray datasets were downloaded from Gene Expresssion Omnibus (GEO) database and analyzed using R package (LIMMA) in order to obtain differential expressed genes (DEGs). Then, the hub genes were identified using cytoHubba plugin of cytoscpae tool and other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, and miRNA-hub gene network construction was also performed.ResultsA total of 348 DEGs were identified, of which 107 were upregulated genes and 241 were downregulated genes. Subsequently, the 12 overlapped genes were predicted from cytoHubba, and considered as hub genes. Moreover, a network among miRNA-hub genes was created to explore the correlation between the hub genes and their targeted miRNAs. Network construction ultimately lead to the identification of nine gene named FN1, EGR1, FOS, JUN, SERPINE1, MMP2, ATF3, MYC, and IL1B and one novel key miRNA namely, has-miR-144-3p as biomarker for diagnosis and therapy of IgAN.ConclusionThis study updates the information and yield a new perspective in context of understanding the pathogenesis and development of IgAN. In future, key miRNAs might be capable of improving the personalized detection and therapies for IgAN. In vivo and in vitro investigation of miRNAs and pathway interaction is essential to delineate the specific roles of the novel miRNAs, which may help to further reveal the mechanisms underlying IgAN.     

Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles

Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Potential mechanisms underlying the promoting effects of 3D collagen scaffold culture on stemness and drug resistance of glioma cells

Background3D collagen scaffold culture is a good tool to study glioma metastasis and recurrence in vitro.MethodsThe effect of 3D collagen culture on the colony formation, the sphere formation, and drug sensitivity of glioma cells was observed by soft-agar colony formation assays, sphere formation assays, and CCK-8 assays, respectively. 3D-glioma-drug genes were identified by previous results and online databases. Gene enrichment and PPI analyses were performed by R software and Metacsape. Hub 3D-glioma-drug genes were screened by STRING and Cytoscape. TCGA and CGGA databases and R software were used to analyze the distribution of hub genes in glioma and their effects on the prognosis. Western Blot was used to verify the effect of 3D collagen culture on the expression of hub genes. miRNAs targeting hub genes were predicted by ENCORI.Results3D collagen scaffold culture promoted colony formation, sphere formation, and drug resistance of glioma cells. There were 77 3D-glioma-drug genes screened, and the pathways enriched in the protein interaction network mainly included responses to stressors, DNA damage and repair, and drug metabolism. Hub 3D-glioma-drug genes were AKT1, ATM, CASP3, CCND1, EGFR, PARP1, and TP53. These genes and predicted miRNAs were expressed differentially in glioma samples and partially affected the prognosis of patients with glioma. These findings suggested these hub genes and miRNAs may play a key role in the effects generated by the 3D culture model and become new markers for glioma diagnosis and treatment.