Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

10.21203/rs.3.rs-24106/v1 ◽

2020 ◽

Author(s):

Sheng Chang ◽

Yang Cao

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Osteogenic Sarcoma ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

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BPI and KIR6.1 as significant hub genes for vein graft restenosis

Journal of International Medical Research ◽

10.1177/0300060520969331 ◽

2020 ◽

Vol 48 (11) ◽

pp. 030006052096933

Author(s):

Yun-peng Bai ◽

Bo-chen Yao ◽

Mei Wang ◽

Xian-kun Liu ◽

Xiao-long Zhu ◽

...

Keyword(s):

Vein Graft ◽

Area Under The Curve ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Control Group ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction

Background Vein graft restenosis (VGR), which appears to be caused by dyslipidemia following vascular transplantation, seriously affects the prognosis and long-term quality of life of patients. Methods This study analyzed the genetic data of restenosis (VGR group) and non-stenosis (control group) vessels from patients with coronary heart disease post-vascular transplantation and identified hub genes that might be responsible for its occurrence. GSE110398 was downloaded from the Gene Expression Omnibus database. A repeatability test for the GSE110398 dataset was performed using R language. This included the identification of differentially expressed genes (DEGs), enrichment analysis via Metascape software, pathway enrichment analysis, and construction of a protein–protein interaction network and a hub gene network. Results Twenty-four DEGs were identified between VGR and control groups. The four most important hub genes ( KIR6.1, PCLP1, EDNRB, and BPI) were identified, and Pearson’s correlation coefficient showed that KIR6.1 and BPI were significantly correlated with VGR. KIR6.1 could also sensitively predict VGR (0.9 < area under the curve ≤1). Conclusion BPI and KIR6.1 were differentially expressed in vessels with and without stenosis after vascular transplantation, suggesting that these genes or their encoded proteins may be involved in the occurrence of VGR.

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Analysis of differential gene expression between right-sided and left-sided colon cancer by bioinformatics analysis

10.21203/rs.3.rs-23894/v1 ◽

2020 ◽

Author(s):

Qiangwei Chi ◽

Shizuan Chen ◽

Shaotang Li

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Bioinformatics Analysis ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Abstract Background Colon cancer is a common tumor of the digestive tract worldwide. Recent researches have revealed that colon cancer exhibits distinct differences in clinical and biological characteristics depending on the location of the tumor. However, the underlying genetic and molecular mechanism of the differences between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) are not fully understood. This study aimed to identify molecular potential biomarkers and therapeutic targets for precise treatment of right-sided and left-sided colon cancer using bioinformatics analysis. Methods The gene microarray profile, named GSE44076, from the Gene Expression Omnibus (GEO) public database was downloaded and processed to then select differentially expressed genes (DEGs) on the base of two sample groups of RCC and LCC. Also, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, module analysis, validation of hub genes, and survival analysis. Results Finally, we obtained 2259 DEGs between RCC and LCC, 1300 of which were upregulated in RCC and 945 of which were upregulated in LCC. The results of GO and KEGG analysis of the DEGs indicated that the biological functions of DEGs in RCC and LCC were significantly different. CTLA4, IL10, IL2RB, IFNG, NCAM1, EGFR, MYC, SRC, CUL3, and NCBP2 were identified from the PPI networks as the hub genes of RCC and LCC. Among the hub genes, the log-rank tests for overall survival (OS) and disease free survival (DFS) were applied. Moreover, all hub genes, except CUL3, had differential expression levels of miRNA between tumor group and normal group. Conclusion These hub genes and pathways identified based on bioinformatics analysis might conduce to explain the differences between RCC and LCC, and most of the hub genes were specific to the malignant tissues. Notably, these hub genes, especially the genes associated with immunotherapy such as CTLA4, might be potential specific targets or prognostic markers for precise treatment of colon cancer.

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Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

BioMed Research International ◽

10.1155/2021/5524486 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Guangyu Gao ◽

Zhen Yao ◽

Jiaofeng Shen ◽

Yulong Liu

Keyword(s):

Drug Resistance ◽

Molecular Mechanism ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

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Quantitative proteomics analysis of FFPE tumor samples reveals the influences of novel targeting nanobubbles conjugated with NET-1 siRNA on tetraspanin protein involved in HCC

10.21203/rs.3.rs-20923/v1 ◽

2020 ◽

Author(s):

Bolin Wu ◽

Haitao Shang ◽

Xitian Liang ◽

Huajing Yang Huajing Yang ◽

Hui Jing ◽

...

Keyword(s):

Protein Interactions ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Differentially Expressed Proteins ◽

Label Free ◽

Protein Protein Interaction ◽

Study Results ◽

Ffpe Tumor

Abstract Background: Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Methods: Here, we developed a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in our previous study. Results: The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 61 proteins up-regulated and the expression of 17 proteins were significantly down-regulated. Of the differentially expressed proteins, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein-protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein-protein interactions happened after the NET-1 gene has been silenced. Conclusions: Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.

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Identification of microRNAs and genes as biomarkers of atrial fibrillation using a bioinformatics approach

Journal of International Medical Research ◽

10.1177/0300060519852235 ◽

2019 ◽

Vol 47 (8) ◽

pp. 3580-3589 ◽

Cited By ~ 2

Author(s):

Yingyuan Li ◽

Wulin Tan ◽

Fang Ye ◽

Faling Xue ◽

Shaowei Gao ◽

...

Keyword(s):

Atrial Fibrillation ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Control Groups ◽

Protein Protein Interaction ◽

Differentially Expressed Mirnas ◽

Software Modules ◽

And Control

Objective We aimed to explore potential microRNAs (miRNAs) and target genes related to atrial fibrillation (AF). Methods Data for microarrays GSE70887 and GSE68475, both of which include AF and control groups, were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs between AF and control groups were identified within each microarray, and the intersection of these two sets was obtained. These miRNAs were mapped to target genes in the miRNet database. Functional annotation and enrichment analysis of these target genes was performed in the DAVID database. The protein-protein interaction (PPI) network from the STRING database and the miRNA-target-gene network were merged into a PPI-miRNA network using Cytoscape software. Modules of this network containing miRNAs were detected and further analyzed. Results Ten differentially expressed miRNAs and 1520 target genes were identified. Three PPI-miRNA modules were constructed, which contained miR-424, miR-15a, miR-542-3p, and miR-421 as well as their target genes, CDK1, CDK6, and CCND3. Conclusion The identified miRNAs and genes may be related to the pathogenesis of AF. Thus, they may be potential biomarkers for diagnosis and targets for treatment of AF.

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Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-200081 ◽

2020 ◽

Vol 77 (3) ◽

pp. 1255-1265

Author(s):

Hui Xu ◽

Jianping Jia

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes ◽

Data Set ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Immune Phenomena ◽

Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

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Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

10.1101/815381 ◽

2019 ◽

Cited By ~ 4

Author(s):

Daniel Palmer ◽

Fabio Fabris ◽

Aoife Doherty ◽

Alex A. Freitas ◽

João Pedro de Magalhães

Keyword(s):

Meta Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Sources Of Information ◽

Specific Expression ◽

Functional Changes ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Muscle Tissues ◽

Specific Expression Pattern

1AbstractUnderstanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing transcriptomes of these important tissues, a broad non-specific expression pattern in genes differentially expressed with age, along with altered network properties of these genes in both a PPI and co-expression network.

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R Script Approach to Infer Toxoplasma Infection Mechanisms From Microarrays and Domain-Domain Protein Interactions

Bioinformatics and Biology Insights ◽

10.1177/1177932217747256 ◽

2017 ◽

Vol 11 ◽

pp. 117793221774725 ◽

Cited By ~ 2

Author(s):

Ailan F Arenas ◽

Gladys E Salcedo ◽

Jorge E Gomez-Marin

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Host Protein ◽

Differentially Expressed ◽

Toxoplasma Infection ◽

Protein Protein Interaction ◽

Host Interaction ◽

Human Genes ◽

Domain Interactions

Pathogen-host protein-protein interaction systems examine the interactions between the protein repertoires of 2 distinct organisms. Some of these pathogen proteins interact with the host protein system and may manipulate it for their own advantages. In this work, we designed an R script by concatenating 2 functions called rowDM and rowCVmed to infer pathogen-host interaction using previously reported microarray data, including host gene enrichment analysis and the crossing of interspecific domain-domain interactions. We applied this script to the Toxoplasma-host system to describe pathogen survival mechanisms from human, mouse, and Toxoplasma Gene Expression Omnibus series. Our outcomes exhibited similar results with previously reported microarray analyses, but we found other important proteins that could contribute to toxoplasma pathogenesis. We observed that Toxoplasma ROP38 is the most differentially expressed protein among toxoplasma strains. Enrichment analysis and KEGG mapping indicated that the human retinal genes most affected by Toxoplasma infections are those related to antiapoptotic mechanisms. We suggest that proteins PIK3R1, PRKCA, PRKCG, PRKCB, HRAS, and c-JUN could be the possible substrates for differentially expressed Toxoplasma kinase ROP38. Likewise, we propose that Toxoplasma causes overexpression of apoptotic suppression human genes.

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Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

BMC Cardiovascular Disorders ◽

10.1186/s12872-020-01838-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Siwei Bi ◽

Ruiqi Liu ◽

Linfeng He ◽

Jingyi Li ◽

Jun Gu

Keyword(s):

Aortic Aneurysm ◽

Enrichment Analysis ◽

Cytosolic Calcium ◽

Gene Expression Omnibus ◽

Ion Concentration ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Thoracic Aneurysms ◽

Calcium Ion Concentration

Abstract Background Aneurysm is a severe and fatal disease. This study aims to comprehensively identify the highly conservative co-expression modules and hub genes in the abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and intracranial aneurysm (ICA) and facilitate the discovery of pathogenesis for aneurysm. Methods GSE57691, GSE122897, and GSE5180 microarray datasets were downloaded from the Gene Expression Omnibus database. We selected highly conservative modules using weighted gene co‑expression network analysis before performing the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway and Reactome enrichment analysis. The protein–protein interaction (PPI) network and the miRNA-hub genes network were constructed. Furtherly, we validated the preservation of hub genes in three other datasets. Results Two modules with 193 genes and 159 genes were identified as well preserved in AAA, TAA, and ICA. The enrichment analysis identified that these genes were involved in several biological processes such as positive regulation of cytosolic calcium ion concentration, hemostasis, and regulation of secretion by cells. Ten highly connected PPI networks were constructed, and 55 hub genes were identified. In the miRNA-hub genes network, CCR7 was the most connected gene, followed by TNF and CXCR4. The most connected miRNAs were hsa-mir-26b-5p and hsa-mir-335-5p. The hub gene module was proved to be preserved in all three datasets. Conclusions Our study highlighted and validated two highly conservative co-expression modules and miRNA-hub genes network in three kinds of aneurysms, which may promote understanding of the aneurysm and provide potential therapeutic targets and biomarkers of aneurysm.

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