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Öğe A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine(Mary Ann Liebert, Inc, 2024) Kori, Medi; Demirtas, Talip Yasir; Comertpay, Betul; Arga, Kazim Yalcin; Sinha, Raghu; Gov, EsraOvarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, SOV-module was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 x 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.Öğe Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19(Oxford Univ Press, 2021) Auwul, Md Rabiul; Rahman, Md Rezanur; Gov, Esra; Shahjaman, Md; Moni, Mohammad AliCurrent coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.Öğe Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer(Mdpi, 2022) Kori, Medi; Gov, EsraGastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.Öğe Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer(Sage Publications Ltd, 2024) Kori, Medi; Gov, Esra; Arga, Kazim Yalcin; Sinha, RaghuBackground: Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application.Objective: Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment.Design: As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach.Methods: We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs.Results: We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively.Conclusion: In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research. Biomarkers, which are routinely used in clinics, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. This research article highlights the challenges of translating cancer biomarkers into clinical practice and summarizes feasible step toward in silico pre-clinical validation using the example of lung cancer types. Accordingly, protein biomarkers proposed for lung cancer are being investigated using the in silico pre-clinical validation approach to determine whether they have cancer altering properties (i.e., oncoprotein, tumor suppressor, and cancer hallmark), are expressed in body fluids (i.e., plasma/serum, saliva, urine, and bronchoalveolar lavage) and can be targeted with FDA-approved drugs. We believe that the step of in silico pre-clinical validation is the future of biomarker research for all professionals involved in clinical, biological, epidemiological, biostatistical and health research, and that it will greatly facilitate the transition of biomarkers to the clinical phase.Öğe Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine(Mary Ann Liebert, Inc, 2021) Comertpay, Betul; Gulfidan, Gizem; Arga, Kazim Yalcin; Gov, EsraCancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 seed genes that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as systems biomarkers for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.Öğe Co-expressed functional module-related genes in ovarian cancer stem cells represent novel prognostic biomarkers in ovarian cancer(Taylor & Francis Inc, 2020) Gov, EsraOvarian cancer is the leading cause of death from gynecologic malignancies. Cancer stem cells (CSC) seem to play a crucial role in tumor metastasis, recurrence, and chemoresistance. Therefore, CSCs offer significant potential for developing therapeutic targets and to understand tumor recurrence and chemoresistance mechanisms. In the present study, our aim was the identification of the gene group in ovarian CSCs (O-CSCs) and the potential of the resultant gene group in ovarian cancer prognosis. Two different microarray data sets were analyzed by comparing gene expression levels between O-CSCs and cancer samples. The O-CSC co-expression network was reconstructed and its modules were identified. According to the analysis results, 74 mutual DEGs were identified. The O-CSC-specific co-expression network included 32 nodes and 95 edges (network density: 19%), while the co-expression network in cancer samples was reconstructed with 74 nodes and 1066 edges (network density: 39%). Understanding of the molecular mechanism and signatures of O-CSCs should provide valuable insight into chemotherapy resistance and recurrence of ovarian tumors. A highly connected 12 gene module in O-CSC samples of BAMB1, NFKB12, EZR, TNFAIP3, C1orf86, PMAIP1, GEM, KHDRBS3, FILIP1, FGFR2, TGFBR3 and PEG10, (network density: 67%) was identified. Prognostic performance of these genes was evaluated independently using six ovarian cancer datasets (n = 1933 patient samples) via survival analysis. These co-expressed genes were determined as prognostic targets in ovarian cancer. Through literature search validation, five genes (C1orf86, PMAIP1, FILIP1, NFKB12 and PEG10) suggested as novel molecular targets in ovarian cancer. The presented prognostic biomarkers here provide a resource for the understanding of tumor recurrence and chemoresistance and may facilitate critical research directions and development of new prognostic and therapeutic strategies for ovarian cancer.Öğe Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis(MDPI, 2022) Mosharaf, Md. Parvez; Reza, Md. Selim; Gov, Esra; Mahumud, Rashidul Alam; Mollah, Md. Nurul HaqueNon-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low-and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Öğe Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules(Taylor & Francis Inc, 2023) Unal, Ulku; Gov, EsraThis study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.Öğe Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis(Taylor & Francis Ltd, 2022) Unal, Ulku; Comertpay, Betul; Demirtas, Talip Yasir; Gov, EsraRheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.Öğe Drug Targeting and Biomarkers in Head and Neck Cancers: Insights from Systems Biology Analyses(Mary Ann Liebert, Inc, 2018) Islam, Tania; Rahman, Md Rezanur; Gov, Esra; Turanli, Beste; Gulfidan, Gizem; Haque, Md Anwarul; Arga, Kazim YalcinThe head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, but robust biomarkers and diagnostics are still not available. This study provides in-depth insights from systems biology analyses to identify molecular biomarker signatures to inform systematic drug targeting in HNSCC. Gene expression profiles from tumors and normal tissues of 22 patients with histological confirmation of nonmetastatic HNSCC were subjected to integrative analyses with genome-scale biomolecular networks (i.e., protein-protein interaction and transcriptional and post-transcriptional regulatory networks). We aimed to discover molecular signatures at RNA and protein levels, which could serve as potential drug targets for therapeutic innovation in the future. Eleven proteins, 5 transcription factors, and 20 microRNAs (miRNAs) came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq datasets, and risk discrimination performance of the reporter biomolecules, BLNK, CCL2, E4F1, FOSL1, ISG15, MMP9, MYCN, MYH11, miR-1252, miR-29b, miR-29c, miR-3610, miR-431, and miR-523, was also evaluated. Using the transcriptome guided drug repositioning tool, geneXpharma, several candidate drugs were repurposed, including antineoplastic agents (e.g., gemcitabine and irinotecan), antidiabetics (e.g., rosiglitazone), dermatological agents (e.g., clocortolone and acitretin), and antipsychotics (e.g., risperidone), and binding affinities of the drugs to their potential targets were assessed using molecular docking analyses. The molecular signatures and repurposed drugs presented in this study warrant further attention for experimental studies since they offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of HNSCC.Öğe Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures(Mary Ann Liebert, Inc, 2022) Demirtas, Talip Yasir; Rahman, Md Rezanur; Yurtsever, Merve Capkin; Gov, EsraGastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.Öğe Genome-Wide Integrative Analysis Reveals Common Molecular Signatures in Blood and Brain of Alzheimer's Disease(Biointerface Research Applied Chemistry, 2021) Rahman, Md Rezanur; Islam, Tania; Shabjam, Md; Rana, Md Humayun Kabir; Holsinger, R. M. Damian; Quinn, Julian M. W.; Gov, EsraThe currently utilized neuroimaging and cerebrospinal fluid-based detection of Alzheimers disease (AD) suffer several limitations, including sensitivity, specificity, and cost. Therefore, the identification of AD by analyzing blood gene expression may ameliorate the early diagnosis of the AD. We aimed to identify common genes commonly deregulated in blood and brain in AD. Comprehensive analysis of blood and brain gene expression datasets of AD, eQTL, and epigenetics data was analyzed by the integrative bioinformatics approach. The integrative analysis showed nine differentially expressed genes common to blood cells and brain (CNBD1, SUCLG2-AS1, CCDC65, PDE4D, MTMR1, C3, SLC6A15, LINC01806, and FRG1JP). Analysis of SNP and cis-eQTL data showed 18 genes; namely, HSD17B1, GAS5, RPS5, VKORC1, GLE1, WDR1, RPL12, MORN1, RAD52, SDR39U1, NPHP4, MT1E, SORD, LINC00638, MCM3AP-AS1, GSDMD, RPS9, and GNL2 were observed deregulated AD blood and brain tissues. Functional gene set enrichment analysis demonstrated a significant association of these genes in neurodegeneration-associated molecular pathways. Integrative biomolecular networks revealed dysregulation of several hub transcription factors and microRNAs in AD. Moreover, hub genes were observed associated with significant histone modification. This study detected common molecular biomarkers and pathways in blood and brain tissues in AD that may be potential biomarkers and therapeutic targets in AD.Öğe Identification of key biomolecules in rheumatoid arthritis through the reconstruction of comprehensive disease-specific biological networks(Taylor & Francis Ltd, 2020) Comertpay, Betul; Gov, EsraRheumatoid arthritis (RA) frequently seen chronic synovial inflammation causing joint destruction, chronic disability and reduced life expectancy. The pathogenesis of RA is not completely known. In this study, several gene expression data including synovial tissue and macrophages from synovial tissues were integrated with a holistic perspective and the molecular targets and signatures in RA were determined. Differentially expressed genes (DEGs) were identified from each dataset by comparing diseased and healthy samples. Afterward, the RA-specific protein-protein interaction (PPI) and the transcriptional regulatory network were reconstructed by using several biomolecule interaction data. Key biomolecules were determined through a statistical test employing the hypergeometric probability density function by using the physical interactions of transcriptional regulators and PPI. The integrative analyses of DEGs indicated that there were 110 and 494 common genes between synovial tissues and macrophages related datasets, respectively. Common DEGs of all datasets were identified as 25 genes and these core genes which might be feasible to uncover the mutual biological mechanism insights behind the RA pathogenesis were used for disease specific biological networks reconstruction. It was determined the hub proteins, novel key biomolecules (i.e. receptor, transcription factors and miRNAs) and biomolecules interactions by using the core DEGs. It was identified STAT1, RAC2 and KYNU as hub proteins, PEPD as a receptor, NR4A1, MEOX2, KLF4, IRF1 and MYB as TFs, miR-299, miR-8078, miR-146a, miR-3659 and miR-6882 as key miRNAs. It was determined that biomolecule interaction scenarios using identified key biomolecules and novel biomolecules including RAC2, PEPD, NR4A1, MEOX2, miR-299, miR-8078, miR-3659 and miR-6882 in RA. Our novel findings could be a crucial resource for the understanding of RA molecular mechanism and may be considered as drug targets and development of novel diagnostic strategies. Corresponding genes and miRNAs should be validated via experimental studies.Öğe Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm(Oae Publishing Inc, 2022) Comertpay, Betul; Gov, EsraAim: Currently, the obesity epidemic is one of the biggest problems for human health. Obesity is impacted on survival in patients with breast cancer. However, key biomarkers of obesity -related breast cancer risk are still not well known. Thus, using machine learning to identify the most appropriate features in obesity -associated breast cancer patients may improve the predictive accuracy and interpretability of regression models. Methods: In the present study, we identified 23 differentially expressed genes (DEGs) from the GSE24185 transcriptome dataset. Seed genes were identified from DEGs, the co -expression network genes and hub genes of the protein -protein interaction network. Pathway enrichment analysis was performed for DEGs. The Ridge penalty regression model was executed by using P -values of enriched pathways and seed gene pathway association score to obtain the most relevant molecular signatures. The model was performed using 10 -fold cross -validation to fit the penalized models. Results: Angiotensin II receptor type 1 (AGTR1), cyclin D1 (CCND1), glutamate ionotropic receptor AMPA type subunit 2 (GRIA2), interleukin-6 cytokine family signal transducer (IL6ST), matrix metallopeptidase 9 (MMP9), and protein kinase CAMP -dependent type II regulatory subunit beta (PRKAR2B) were considered as candidate molecular signatures of obese patients with breast cancer. In addition, RAF -independent MAPK1/3 activation, collagen degradation, bladder cancer, drug metabolism-cytochrome P450, and signaling by Hedgehog pathways in cancer were primarily associated with obesity -associated breast cancer. Conclusion: These genes may be used for risk analysis of the disease progression of obese patients with breast cancer. Corresponding genes and pathways should be validated via experimental studies.Öğe Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis(Mdpi, 2019) Rahman, Md Rezanur; Islam, Tania; Gov, Esra; Turanli, Beste; Gulfidan, Gizem; Shahjaman, Md; Banu, Nilufa AkhterBackground and objectives: Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. Materials and Methods: We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein-protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. Results: A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan-Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. Conclusions: This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.Öğe Identifying the function of methylated genes in Alzheimer's disease to determine epigenetic signatures: a comprehensive bioinformatics analysis(Cambridge University Press, 2021) Rahman, Md Rezanur; Islam, Tania; Gov, Esra; Quinn, Julian M.W.; Moni, Mohammad AliGene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer's disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene methylation profiling. We identified methylated differentially expressed genes (MDEGs). Protein-protein interaction (PPI) of the MDEGs showed 18 hypermethylated low-expressed genes (Hyper-LGs) involved in cell signaling and metabolism; also 10 hypomethylated highly expressed (Hypo-HGs) were involved in regulation of transcription and development. Molecular pathways enriched in Hyper-LGs included neuroactive ligand-receptor interaction pathways. Hypo-HGs were notably enriched in pathways including hippo signaling. PPI analysis also identified both Hyper-LGs and Hypo-HGs, as hub proteins. Our analysis of AD datasets identified Hyper-LGs, Hypo-HGs, and transcription factors linked to these genes. These pathways, which may participate in Alzheimer's disease development, may be affected by treatments that influence gene methylation patterns. ©Öğe Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches(Elsevier Sci Ltd, 2023) Comertpay, Betul; Gov, EsraRheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.Öğe Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer(Sage Publications Ltd, 2022) Erkin, Ozgur Cem; Comertpay, Betul; Gov, EsraDifferential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach. we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein-protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1. EZH2. and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary. high mRNA expressions of CBL. FYN, LRKK2. and SOCS2 were associated with a significantly better prognosis. Furthermore. our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat. TGX-221, PHA-793887, AG-879. and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.Öğe Mapping the Molecular Basis and Markers of Papillary Thyroid Carcinoma Progression and Metastasis Using Global Transcriptome and microRNA Profiling(Mary Ann Liebert, Inc, 2020) Akyay, Ozlem Zeynep; Gov, Esra; Kenar, Halime; Arga, Kazim Yalcin; Selek, Alev; Tarkun, Ilhan; Canturk, ZeynepPapillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). In a subgroup of patients with PTC, the disease progresses to an invasive stage or in some cases to distant organ metastasis. At present, there is an unmet clinical and diagnostic need for early identification of patients with PTC who are at risk of disease progression or metastasis. In this study, we report several molecular leads and potential biomarker candidates of PTC metastasis for further translational research. The study design was based on comparisons of PTC in three different groups using cross-sectional sampling: Group 1, PTC localized to the thyroid (n = 20); Group 2, PTC with extrathyroidal progression (n = 22); and Group 3, PTC with distant organ metastasis (n = 20). Global transcriptome and microRNAs (miRNA) analyses were conducted using an initial screening set comprising nine formalin-fixed paraffin-embedded PTC samples obtained from three independent patients per study group. The findings were subsequently validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the abovementioned independent patient sample set (n = 62). Comparative analyses of differentially expressed miRNAs showed that miR-193-3p, miR-182-5p, and miR-3607-3p were novel miRNAs associated with PTC metastasis. These potential miRNA biomarkers were associated with TC metastasis and miRNA-target gene associations, which may provide important clinicopathological information on metastasis. Our findings provide new molecular leads for further translational biomarker research, which could facilitate the identification of patients at risk of PTC disease progression or metastasis.Öğe Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer(Mary Ann Liebert, Inc, 2017) Gov, Esra; Kori, Medi; Arga, Kazim YalcinOvarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomoleculesgenes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolitesby integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.
