Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis

Background: The risk of malignant transformation of enchondromas (EC) toward central chondrosarcoma is increased up to 35%, while the exact etiology of EC is unknown. The purpose of this research was to authenticate gene signatures during EC and reveal their potential mechanisms in occurrence and development of EC. Methods: The gene expression profiles was acquired from Gene Expression Omnibus database (no. GSE22855). The gene ontology (GO), protein–protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were utilized to identify differentially expressed genes (DEGs). Results: Finally, 242 DEGs were appraisal, containing 200 overregulated genes and 42 downregulated genes. The outcomes of GO analysis indicated that upregulated DEGs were mainly enriched in several biological processes containing response to hypoxia, calcium ion, and negative regulation extrinsic apoptotic signaling pathway. Furthermore, the upregulated DEGs were enriched in extracellular matrix (ECM)–receptor interaction, protein processing in endoplasmic reticulum and ribosome, which was analyzed by KEGG pathway. From the PPI network, the top 10 hub genes were identified, which were related to significant pathways containing ribosome, protein processing in endoplasmic reticulum, and ECM-receptor interaction. Conclusion: In conclusion, the present study may be helpful for understanding the diagnostic biomarkers of EC.

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Integrated analysis of lncRNA–miRNA–mRNA ceRNA network and the potential prognosis indicators in sarcomas

BMC Medical Genomics ◽

10.1186/s12920-021-00918-x ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Lu Gao ◽

Yu Zhao ◽

Xuelei Ma ◽

Ling Zhang

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Gene Prediction ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Cerna Network

Abstract Background Competitive endogenous RNA (ceRNA) networks have revealed a new mechanism of interaction between RNAs, and play crucial roles in multiple biological processes and development of neoplasms. They might serve as diagnostic and prognosis markers as well as therapeutic targets. Methods In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas by comparing the gene expression profiles between sarcoma and normal muscle samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed using Cytoscape software. In addition, the protein–protein interaction (PPI) network and survival analysis were performed. Results A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA–miRNA–mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients. Conclusions Overall, we constructed a ceRNA network in sarcomas, which might provide insights for further research on the molecular mechanism and potential prognosis biomarkers.

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Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

BioMed Research International ◽

10.1155/2013/267375 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 8

Author(s):

Bi-Qing Li ◽

Jin You ◽

Lei Chen ◽

Jian Zhang ◽

Ning Zhang ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Shortest Path ◽

Protein Interaction ◽

Expression Profiles ◽

Shortest Paths ◽

Gene Expression Profiles ◽

Cancer Genes ◽

Ppi Network ◽

Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

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Identification of Differential Gene Expression and Related Signaling Pathways In SARS-COV-2 Infected Human Cells

10.21203/rs.3.rs-794267/v1 ◽

2021 ◽

Author(s):

Tian-Ao Xie ◽

Hou-He Li ◽

Zu-En Lin ◽

Xiao-Ye Lin ◽

Xin Meng ◽

...

Keyword(s):

Gene Expression ◽

Vaccine Development ◽

Virus Disease ◽

Expression Profiles ◽

Biological Significance ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Human Cells ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Background: The Corona Virus Disease 2019 (COVID-19) pandemic poses a serious public health threat to the survival and health of people all over the world. We analyzed related mRNA data and gene expression profiles of human cell lines infected with SARS-CoV-2 obtained from GEO (GSE148729), using bioinformatics tools. Differentially expressed genes (DEGs) of human cells infected with SARS-CoV-2 were identified.Method: The GSE148729 datasets were downloaded from the Gene Expression Omnibus (GEO) database. To explore the Biological significance of DEGs, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were constructed by using the STRING database. The hub genes were selected using the Cytoscape Software, and a t-test was performed to validate the hub genes.Result: A total of 1241 DEGs were screened, including 1049 up-regulated genes and 192 down-regulated genes. Besides, 10 hub genes were obtained from the PPI network, among which the expression level of CXCL2, Etv7, and HIST1H2BG was found to be statistically significant.Conclusion: In conclusion, bioinformatics analysis reveals genes and cellular pathways that are significantly altered in SARS-CoV-2 infected cells. This is conducive to further guide the clinical study of SARS-CoV-2 and provides new perspectives for vaccine development.

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Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

BioMed Research International ◽

10.1155/2020/2862701 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yiting Tian ◽

Yang Xing ◽

Zheng Zhang ◽

Rui Peng ◽

Luyu Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Extracellular Matrix ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Receptor Interaction ◽

Future Research ◽

Circular Rnas ◽

Ppi Network ◽

Protein Protein Interaction

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Transcriptome profiling of digital flexor tendons after injury in a chicken model

Bioscience Reports ◽

10.1042/bsr20191547 ◽

2020 ◽

Vol 40 (6) ◽

Author(s):

Wei Feng Mao ◽

Yin Xian Yu ◽

Chen Chen ◽

Ya Fang Wu

Keyword(s):

Gene Expression ◽

Gene Ontology ◽

Expression Profiles ◽

Tendon Repair ◽

Tendon Healing ◽

Gene Expression Profiles ◽

Repair Process ◽

Receptor Interaction ◽

Protein Protein Interaction ◽

Chicken Model

Abstract Background: Modulation of tendon healing remains a challenge because of our limited understanding of the tendon repair process. Therefore, we performed the present study to provide a global perspective of the gene expression profiles of tendons after injury and identify the molecular signals driving the tendon repair process. Results: The gene expression profiles of flexor digitorum profundus tendons in a chicken model were assayed on day 3, weeks 1, 2, 4, and 6 after injury using the Affymetrix microarray system. Principal component analysis (PCA) and hierarchical cluster analysis of the differentially expressed genes showed three distinct clusters corresponding to different phases of the tendon healing period. Gene ontology (GO) analysis identified regulation of cell proliferation and cell adhesion as the most enriched biological processes. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that the cytokine–cytokine receptor interaction and extracellular matrix (ECM)–receptor interaction pathways were the most impacted. Weighted gene co-expression network analysis (WGCNA) demonstrated four distinct patterns of gene expressions during tendon healing. Cell adhesion and ECM activities were mainly associated with genes with drastic increase in expression 6 weeks after injury. The protein–protein interaction (PPI) networks were constructed to identify the key signaling pathways and hub genes involved. Conclusions: The comprehensive analysis of the biological functions and interactions of the genes differentially expressed during tendon healing provides a valuable resource to understand the molecular mechanisms underlying tendon healing and to predict regulatory targets for the genetic engineering of tendon repair. Tendon healing, Adhesion, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Weighted Gene Co-expression Network Analysis, Protein–protein Interaction

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Identify CRNDE and LINC00152 as the key lncRNAs in age-related degeneration of articular cartilage through comprehensive and integrative analysis

PeerJ ◽

10.7717/peerj.7024 ◽

2019 ◽

Vol 7 ◽

pp. e7024 ◽

Cited By ~ 1

Author(s):

Pengfei Hu ◽

Fangfang Sun ◽

Jisheng Ran ◽

Lidong Wu

Keyword(s):

Gene Expression ◽

Articular Cartilage ◽

Developmental Stages ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Receptor Interaction ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Age Related ◽

Pathway Analyses

Background Osteoarthritis (OA) is one of the most important age-related degenerative diseases, and the leading cause of disability and chronic pain in the aging population. Recent studies have identified several lncRNA-associated functions involved in the development of OA. Because age is a key risk factor for OA, we investigated the differential expression of age-related lncRNAs in each stage of OA. Methods Two gene expression profiles were downloaded from the GEO database and differentially expressed genes (DEGs) were identified across each of the different developmental stages of OA. Next, gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, a lncRNA-targeted DEG network was used to identify hub-lncRNAs. Results A total of 174 age-related DEGs were identified. GO analyses confirmed that age-related degradation was strongly associated with cell adhesion, endodermal cell differentiation and collagen fibril organization. Significantly enriched KEGG pathways associated with these DEGs included the PI3K–Akt signaling pathway, focal adhesion, and ECM–receptor interaction. Further analyses via a protein–protein interaction (PPI) network identified two hub lncRNAs, CRNDE and LINC00152, involved in the process of age-related degeneration of articular cartilage. Our findings suggest that lncRNAs may play active roles in the development of OA. Investigation of the gene expression profiles in different development stages may supply a new target for OA treatment.

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Acute Pancreatitis-Associated Genes Identified by Integrative Analysis of Gene Expression Data

10.21203/rs.3.rs-148504/v1 ◽

2021 ◽

Author(s):

Chunyan Zeng ◽

Yin Zhu ◽

Youxiang Chen

Keyword(s):

Gene Expression ◽

Acute Pancreatitis ◽

Expression Profiles ◽

Adherens Junction ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Diagnosis And Treatment ◽

Expression Data ◽

Protein Protein Interaction ◽

And Control

Abstract Background Acute pancreatitis (AP) is a common digestive disease. A better understanding of the biology and molecular pathogenesis of AP may provide the basis for the prevention and therapy of AP.Methods The gene expression datasets GSE109227, GSE3644 and GSE65146, which included gene expression profiles of pancreas samples from AP and control mice, were obtained from the Gene Expression Omnibus database. All differentially expressed genes (DEGs) were identified using GEO2R and annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Results Thirty-eight DEGs were found to overlap among the three datasets. A protein-protein interaction (PPI) network was constructed; hub proteins were identified; and functional modules were extracted. Three genes (Cdh1, Iqgap1 and Actn4), which are involved in the adherens junction pathway, were identified using PPI analysis and may provide potential biomarkers for the diagnosis and treatment of AP.Conclusions We will further study the effects of the Cdh1, Iqgap1 and Actn4 genes on the occurrence and development of acute pancreatitis, which may provide new targets for the diagnosis and treatment of AP.

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Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer

PeerJ ◽

10.7717/peerj.10468 ◽

2020 ◽

Vol 8 ◽

pp. e10468

Author(s):

Kai Zhang ◽

Kuikui Jiang ◽

Ruoxi Hong ◽

Fei Xu ◽

Wen Xia ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Target Genes ◽

Expression Profiles ◽

Tamoxifen Resistance ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Hub Genes ◽

Mcf 7

Background Tamoxifen resistance in breast cancer is an unsolved problem in clinical practice. The aim of this study was to determine the potential mechanisms of tamoxifen resistance through bioinformatics analysis. Methods Gene expression profiles of tamoxifen-resistant MCF-7/TR and MCF-7 cells were acquired from the Gene Expression Omnibus dataset GSE26459, and differentially expressed genes (DEGs) were detected with R software. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using Database for Annotation, Visualization and Integrated Discovery. A protein–protein interaction (PPI) network was generated, and we analyzed hub genes in the network with the Search Tool for the Retrieval of Interacting Genes database. Finally, we used siRNAs to silence the target genes and conducted the MTS assay. Results We identified 865 DEGs, 399 of which were upregulated. GO analysis indicated that most genes are related to telomere organization, extracellular exosomes, and binding-related items for protein heterodimerization. PPI network construction revealed that the top 10 hub genes—ACLY, HSPD1, PFAS, GART, TXN, HSPH1, HSPE1, IRAS, TRAP1, and ATIC—might be associated with tamoxifen resistance. Consistently, RT-qPCR analysis indicated that the expression of these 10 genes was increased in MCF-7/TR cells comparing with MCF-7 cells. Four hub genes (TXN, HSPD1, HSPH1 and ATIC) were related to overall survival in patients who accepted tamoxifen. In addition, knockdown of HSPH1 by siRNA may lead to reduced growth of MCF-7/TR cell with a trend close to significance (P = 0.07), indicating that upregulation of HSPH1 may play a role in tamoxifen resistance. Conclusion This study revealed a number of critical hub genes that might serve as therapeutic targets in breast cancer resistant to tamoxifen and provided potential directions for uncovering the mechanisms of tamoxifen resistance.

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Microarray Data of Lacrimal Gland Implicates Dysregulated Protein Processing in Endoplasmic Reticulum in Graves’ Ophthalmopathy

Frontiers in Endocrinology ◽

10.3389/fendo.2020.571151 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenling Tu ◽

Jia Yao ◽

Zhanjun Mei ◽

Xue Jiang ◽

Yuhong Shi

Keyword(s):

Gene Expression ◽

Endoplasmic Reticulum ◽

Gene Ontology ◽

Lacrimal Gland ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Protein Processing ◽

Potential Mechanism ◽

Hub Genes ◽

Graves Ophthalmopathy

Graves’ ophthalmopathy (GO) has become one of the most common orbital diseases. Although some evidences announced the potential mechanism of pathological changes in extraocular muscle and orbital adipose tissue, little is known about that in lacrimal enlargement of GO patients. Thus, gene expression profiles of lacrimal gland derived from GO patients and normal controls were investigated using the microarray datasets of GSE105149 and GSE58331. The raw data and annotation files of GSE105149 and GSE58331 were downloaded from Gene Expression Omnibus (GEO) database. Bioinformatics including differentially expressed genes (DEGs), Gene Ontology, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway, protein-protein interaction (PPI) network construction, hub gene identification, and gene set variation analysis (GSVA) were successively performed. A total of 173 overlapping DEGs in GSE105149 and GSE58331 were screened out, including 20 up-regulated and 153 down-regulated genes. Gene Ontology, KEGG and GSVA analyses of these DEGs showed that the most significant mechanism was closely associated with endoplasmic reticulum (ER). Moreover, we identified 40 module genes and 13 hub genes which were also enriched in the ER-associated terms and pathways. Among the hub genes, five genes including HSP90AA1, HSP90B1, DNAJC10, HSPA5, and CANX may be involved in the dysfunction of protein processing in ER. Taken together, our observations revealed a dysregulated gene network which is essential for protein processing in ER in GO patients. These findings provided a potential mechanism in the progression of lacrimal enlargement in GO patients, as a new insight into GO pathogenesis.

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