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

ABSTRACT: 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.

SUBMITTER: Oany AR

PROVIDER: S-EPMC8147030 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNon-obstructive azoospermia (NOA) is a disease related to spermatogenic disorders. Currently, the specific etiological mechanism of NOA is unclear. This study aimed to use integrated bioinformatics to screen biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms.MethodsGSE145467 and GSE108886 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NOA tissues and matched obstructive azoospermia (OA) tissues were identified using the GEO2R tool. Common DEGs in the two datasets were screened out by the VennDiagram package. For the functional annotation of common DEGs, DAVID v.6.8 was used to perform Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In accordance with data collected from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction (PPI) network was constructed by Cytoscape. Cytohubba in Cytoscape was used to screen the hub genes. Furthermore, the hub genes were validated based on a separate dataset, GSE9210. Finally, potential micro RNAs (miRNAs) of hub genes were predicted by miRWalk 3.0.ResultsA total of 816 common DEGs, including 52 common upregulated and 764 common downregulated genes in two datasets, were screened out. Some of the more important of these pathways, including focal adhesion, PI3K-Akt signaling pathway, cell cycle, oocyte meiosis, AMP-activated protein kinase (AMPK) signaling pathway, FoxO signaling pathway, and Huntington disease, were involved in spermatogenesis. We further identified the top 20 hub genes from the PPI network, including CCNB2, DYNLL2, HMMR, NEK2, KIF15, DLGAP5, NUF2, TTK, PLK4, PTTG1, PBK, CEP55, CDKN3, CDC25C, MCM4, DNAI1, TYMS, PPP2R1B, DNAI2, and DYNLRB2, which were all downregulated genes. In addition, potential miRNAs of hub genes, including hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p, and hsa-miR-195-5p, were screened out.ConclusionsTaken together, the identification of the above hub genes, miRNAs and pathways will help us better understand the mechanisms associated with NOA, and provide potential biomarkers and therapeutic targets for NOA.

Project description:Study objectiveTo employ systems biology-based machine learning to identify biologic processes over-represented with genetic variants (gene enrichment) implicated in post-surgical pain.DesignInformed systems biology based integrative computational analyses.SettingPediatric research and teaching institution.InterventionsPubmed search (01/01/2001-10/31/2017) was performed to identify "training" genes associated with postoperative pain in humans. Candidate genes were identified and prioritized using Toppgene suite, based on functional enrichment using several gene ontology annotations, and curated gene sets associated with mouse phenotype-knockout studies.MeasurementsComputationally top-ranked candidate genes and literature-curated genes were included in pathway enrichment analyses. Hierarchical clustering was used to visualize select functional enrichment results between the two phenotypes.Main resultsLiterature review identified 38 training genes associated with postoperative pain and 31 with CPSP. We identified 2610 prioritized novel candidate genes likely associated with acute and chronic postsurgical pain, the top 10th percentile jointly enriched (p 0.05; Benjamini-Hochberg correction) several pathways, topmost being cAMP response element-binding protein and ion channel pathways. Heat maps demonstrated enrichment of inflammatory/drug metabolism processes in acute postoperative pain and immune mechanisms in CPSP.ConclusionHigh interindividual variability in pain responses immediately after surgery and risk for CPSP suggests genetic susceptibility. Lack of large homogenous sample sizes have led to underpowered genetic association studies. Systems biology can be leveraged to integrate genetic-level data with biologic processes to generate prioritized candidate gene lists and understand novel biological pathways involved in acute postoperative pain and CPSP. Such data would be key to informing future polygenic studies with targeted genome wide profiling. This study demonstrates the utility of functional annotation - based prioritization and enrichment approaches and identifies novel genes and unique/shared biological processes involved in acute and chronic postoperative pain. Results provide framework for future targeted genetic profiling of CPSP risk, to enable preventive and therapeutic approaches.

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

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