Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC remains its fatality rate about half of the infected populations globally. The major screening biomarkers and therapeutic target identification have now become a global concern. The present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from the transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4,643 differentially expressed genes. The up-regulatory genes are mainly concentrating on immune-inflammatory response and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment and they were mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC. Furthermore, the survival of the hub genes was also assessed, and among them, finally, CXCR4 has identified as one of the most potential differentially expressed gene that might play a vital role to the survival of CC patients. Thus CXCR4 could be used as a prognostic biomarker and development of a drug target for CC.

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Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

Journal of Personalized Medicine ◽

10.3390/jpm11050363 ◽

2021 ◽

Vol 11 (5) ◽

pp. 363

Author(s):

Arafat Rahman Oany ◽

Mamun Mia ◽

Tahmina Pervin ◽

Salem Ali Alyami ◽

Mohammad Ali Moni

Keyword(s):

Cervical Cancer ◽

Systems Biology ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Profile Analysis ◽

Target Identification ◽

Regulatory Genes ◽

Differentially Expressed ◽

Hub Genes ◽

Potential Biomarkers

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern. In the present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4643 differentially expressed genes. The up-regulatory genes mainly concentrate on immune-inflammatory responses, and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment, and we mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC and are verified by expression profile analysis. From our study, we have found that genes LILRB2 and CYBB play crucial roles in CC, as reported here for the first time. Furthermore, the survivability of the hub genes was also assessed, and among them, finally, CXCR4 has been identified as one of the most potential differentially expressed genes that might play a vital role in the survival of CC patients. Thus, CXCR4 could be used as a prognostic and/or diagnostic biomarker and a drug target for CC.

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Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

Current Pharmaceutical Design ◽

10.2174/1381612826666200311130133 ◽

2020 ◽

Vol 26 (29) ◽

pp. 3619-3630

Author(s):

Saumya Choudhary ◽

Dibyabhaba Pradhan ◽

Noor S. Khan ◽

Harpreet Singh ◽

George Thomas ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Gene ◽

Therapeutic Targets ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Keratinocyte Differentiation ◽

Hub Genes ◽

Lesional Skin ◽

Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

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The novel serine protease tumor-associated differentially expressed gene-14 (KLK8/Neuropsin/Ovasin) is highly overexpressed in cervical cancer

American Journal of Obstetrics and Gynecology ◽

10.1016/j.ajog.2003.07.020 ◽

2004 ◽

Vol 190 (1) ◽

pp. 60-66 ◽

Cited By ~ 22

Author(s):

Stefania Cane' ◽

Eliana Bignotti ◽

Stefania Bellone ◽

Michela Palmieri ◽

Luis De Las Casas ◽

...

Keyword(s):

Cervical Cancer ◽

Serine Protease ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

The Novel

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Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021443 ◽

2021 ◽

Vol 18 (6) ◽

pp. 8997-9015

Author(s):

Ahmed Hammad ◽

◽

Mohamed Elshaer ◽

Xiuwen Tang ◽

◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Machine Learning ◽

Roc Curve ◽

Functional Enrichment ◽

Differentially Expressed ◽

Ppi Network ◽

Hub Genes ◽

Survival Analyses ◽

Potential Biomarkers

<abstract> <p>Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.</p> </abstract>

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Network-Centric Identification of Disease Co-Occurrences: A Systems Biology Approach

Sumerianz Journal of Medical and Healthcare ◽

10.47752/sjmh.311.103.110 ◽

2020 ◽

pp. 103-110

Author(s):

Tammanna R. Sahrawat ◽

Devika Talwar

Keyword(s):

Cardiovascular Disease ◽

Systems Biology ◽

Metabolic Pathways ◽

Genetic Factors ◽

Complex Diseases ◽

Epidemiological Studies ◽

Hub Genes ◽

Network Systems ◽

The Common ◽

Potential Biomarkers

Complex diseases that occur by perturbations of molecular pathways and genetic factors result in pathophysiology of diseases. Network-centric systems biology approaches play an important role in understanding disease complexity. Diabetes, cardiovascular disease and depression are such complex diseases that have been reported to be comorbid in various epidemiological studies but there are no reports of the genetic and underlying factors which may be responsible for their reported co-occurrences. The present study was undertaken to investigate the molecular factors responsible for co-occurrence of diabetes, depression and cardiovascular disease using in-silico network systems biology approach. Genes common amongst these three diseases were retrieved from DisGeNET, a database of human diseases and their interactions were retrieved from STRING database. The resulting network containing 99 nodes (which represent genes) and 1252 edges (which represent various interactions between nodes) was analyzed using Cytoscape v: 3.7.2 and its various plug-ins i.e. ClusterONE, Cytohubba, ClueGO and Cluepedia. The hub genes identified in the present study namely IL1B, VEGFA, LEP, CAT, CXCL8, PLG, IL6, IL10, PTGS2, TLR4 and AKT1 were found to be enriched in various metabolic pathways and several mechanisms such as inflammation. These genes and their protein products may act as potential biomarkers for early detection of predisposition to diseases and potential therapeutic targets based on the common molecular underpinnings of co-occurrence of diabetes, depression and cardiovascular disease.

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Identification of Aberrantly Expressed Genes during Aging in Rat Nucleus Pulposus Cells

Stem Cells International ◽

10.1155/2019/2785207 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Shi Cheng ◽

Xiaochuan Li ◽

Linghan Lin ◽

Zhiwei Jia ◽

Yachao Zhao ◽

...

Keyword(s):

Nucleus Pulposus ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Vital Role ◽

Differentially Expressed ◽

Ppi Network ◽

Nucleus Pulposus Cells ◽

Hub Genes ◽

Software Module ◽

A Genome

Nucleus pulposus cells (NPCs) play a vital role in maintaining the homeostasis of the intervertebral disc (IVD). Previous studies have discovered that NPCs exhibited malfunction due to cellular senescence during disc aging and degeneration; this might be one of the key factors of IVD degeneration. Thus, we conducted this study in order to investigate the altered biofunction and the underlying genes and pathways of senescent NPCs. We isolated and identified NPCs from the tail discs of young (2 months) and old (24 months) SD rats and confirmed the senescent phenotype through SA-β-gal staining. CCK-8 assay, transwell assay, and cell scratch assay were adopted to detect the proliferous and migratory ability of two groups. Then, a rat Gene Chip Clariom™ S array was used to detect differentially expressed genes (DEGs). After rigorous bioinformatics analysis of the raw data, totally, 1038 differentially expressed genes with a fold change>1.5 were identified out of 23189 probes. Among them, 617 were upregulated and 421 were downregulated. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted and revealed numerous number of enriched GO terms and signaling pathways associated with senescence of NPCs. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Module analysis was conducted for the PPI network using the MCODE plugin in Cytoscape. Hub genes were identified by the CytoHubba plugin in Cytoscape. Derived 5 hub genes and most significantly up- or downregulated genes were further verified by real-time PCR. The present study investigated underlying mechanisms in the senescence of NPCs on a genome-wide scale. The illumination of molecular mechanisms of NPCs senescence may assist the development of novel biological methods to treat degenerative disc diseases.

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Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis

Bioscience Reports ◽

10.1042/bsr20194365 ◽

2020 ◽

Vol 40 (5) ◽

Cited By ~ 1

Author(s):

Xiaoling Ma ◽

Jinhui Liu ◽

Hui Wang ◽

Yi Jiang ◽

Yicong Wan ◽

...

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Functional Analysis ◽

Estrogen Receptor ◽

Differentially Expressed Genes ◽

Bioinformatics Analysis ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Venn Diagram ◽

Hub Genes

Abstract Methylation functions in the pathogenesis of cervical cancer. In the present study, we applied an integrated bioinformatics analysis to identify the aberrantly methylated and differentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identified by ‘limma’ packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein–protein interaction (PPI) information. Gene Expression Profiling Interactive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were conducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to identify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. Hypomethylated and highly expressed genes (Hypo-HGs) were significantly enriched in cell cycle and autophagy, and hypermethylated and lowly expressed genes (Hyper-LGs) in estrogen receptor pathway and Wnt/β-catenin signaling pathway. Estrogen receptor 1 (ESR1), Erythrocyte membrane protein band 4.1 like 3 (EPB41L3), Endothelin receptor B (EDNRB), Inhibitor of DNA binding 4 (ID4) and placenta-specific 8 (PLAC8) were hub genes. Kaplan–Meier method was used to evaluate survival data of each identified gene. Lower expression levels of ESR1 and EPB41L3 were correlated with a shorter survival time. GSEA results showed that ‘cell adhesion molecules’ was the most enriched item. This research inferred the candidate genes and pathways that might be used in the diagnosis, treatment, and prognosis of cervical cancer.

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Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

10.21203/rs.3.rs-349764/v1 ◽

2021 ◽

Author(s):

Mingyi Yang ◽

Yani Su ◽

Yao Ma ◽

Yirixiati Aihaiti ◽

Peng Xu

Keyword(s):

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Hub Gene ◽

Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

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Bioinformatics analysis of the potential biomarkers for acute respiratory distress syndrome

Bioscience Reports ◽

10.1042/bsr20192436 ◽

2020 ◽

Vol 40 (9) ◽

Author(s):

Lin Liao ◽

Pinhu Liao

Keyword(s):

Gene Expression ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Distress Syndrome ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Potential Biomarker ◽

Potential Biomarkers

Abstract Background: Acute respiratory distress syndrome (ARDS) is caused by uncontrolled inflammation, and the activation of alveolar macrophages (AM) is involved in pathophysiologic procedures. The present study aimed to identify key AM genes and pathways and try to provide potential targets for prognosis and early intervention in ARDS. Methods: The mRNA expression profile of GSE89953 was obtained from the Gene Expression Omnibus database. The LIMMA package in R software was used to identify differentially expressed genes (DEGs), and the clusterProfiler package was used for functional enrichment and pathway analyses. A protein–protein interaction network of DEGs was constructed to identify hub genes via the STRING database and Cytoscape software. Hub gene expression was validated using differentially expressed proteins (DEPs) obtained from the ProteomeXchange datasets to screen potential biomarkers. Results: A total of 166 DEGs (101 up-regulated and 65 down-regulated) were identified. The up-regulated DEGs were mainly enriched in regulation of the ERK1 and ERK2 cascade, response to interferon-gamma, cell chemotaxis, and migration in biological processes. In the KEGG pathway analysis, up-regulated DEGs were mainly involved in rheumatoid arthritis, cytokine–cytokine receptor interactions, phagosome, and the chemokine signaling pathway. The 12 hub genes identified included GZMA, MPO, PRF1, CXCL8, ELANE, GZMB, SELL, APOE, SPP1, JUN, CD247, and CCL2. Conclusion: SPP1 was consistently differentially expressed in both DEGs and DEPs. SPP1 could be a potential biomarker for ARDS.

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Identification of the Differentially Expressed Genes Involved in the Synergistic Neurotoxicity of an HIV Protease Inhibitor and Methamphetamine

Current HIV Research ◽

10.2174/1570162x17666190924200354 ◽

2019 ◽

Vol 17 (4) ◽

pp. 290-303

Author(s):

Sangsang Li ◽

Yanfei Li ◽

Bingpeng Deng ◽

Jie Yan ◽

Yong Wang

Keyword(s):

Protease Inhibitor ◽

Growth Inhibition ◽

Quantitative Polymerase Chain Reaction ◽

Differentially Expressed Gene ◽

Antiretroviral Drugs ◽

Differentially Expressed ◽

Hiv Protease ◽

Hiv Protease Inhibitor ◽

Dopaminergic Cells ◽

Immunodeficiency Syndrome

Background: The abuse of psychostimulants such as methamphetamine (METH) is common in human immunodeficiency virus (HIV)-infected individuals. Acquired immunodeficiency syndrome (AIDS) patients taking METH and antiretroviral drugs could suffer severe neurologic damage and cognitive impairment. Objective: To reveal the underlying neuropathologic mechanisms of an HIV protease inhibitor (PI) combined with METH, growth-inhibition tests of dopaminergic cells and RNA sequencing were performed. Methods: A combination of METH and PI caused more growth inhibition of dopaminergic cells than METH alone or a PI alone. Furthermore, we identified differentially expressed gene (DEG) patterns in the METH vs. untreated cells (1161 genes), PI vs. untreated cells (16 genes), METH-PI vs. PI (3959 genes), and METH-PI vs. METH groups (14 genes). Results: The DEGs in the METH-PI co-treatment group were verified in the brains of a mouse model using quantitative polymerase chain reaction and were involved mostly in the regulatory functions of cell proliferation and inflammation. Conclusion: Such identification of key regulatory genes could facilitate the study of their neuroprotective potential in the users of METH and PIs.

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