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ARĞA, KAZIM YALÇIN

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Mühendislik Fakültesi

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ARĞA

First Name

KAZIM YALÇIN

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Now showing 1 - 10 of 38

Open Access
A pan-cancer atlas of differentially interacting hallmarks of cancer proteins
(2022-11-01) ARĞA, KAZIM YALÇIN; Kori M., Ozdemir G. E., ARĞA K. Y., Sinha R.
Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems\" biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.
Open Access
Repositioning of anti-inflammatory drugs for the treatment of cervical cancer sub-types
(2022-06-01) TURANLI, BESTE; ARĞA, KAZIM YALÇIN; Kori M., ARĞA K. Y., Mardinoglu A., TURANLI B.
Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.
Open Access
Drug Repositioning for Effective Prostate Cancer Treatment
(FRONTIERS MEDIA SA, 2018-05-15) TURANLI, BESTE; Turanli, Beste; Grotli, Morten; Boren, Jan; Nielsen, Jens; Uhlen, Mathias; Arga, Kazim Y.; Mardinoglu, Adil
Drug repositioning has gained attention from both academia and pharmaceutical companies as an auxiliary process to conventional drug discovery. Chemotherapeutic agents have notorious adverse effects that drastically reduce the life quality of cancer patients so drug repositioning is a promising strategy to identify non-cancer drugs which have anti-cancer activity as well as tolerable adverse effects for human health. There are various strategies for discovery and validation of repurposed drugs. In this review, 25 repurposed drug candidates are presented as result of different strategies, 15 of which are already under clinical investigation for treatment of prostate cancer (PCa). To date, zoledronic acid is the only repurposed, clinically used, and approved non-cancer drug for PCa. Anti-cancer activities of existing drugs presented in this review cover diverse and also known mechanisms such as inhibition of mTOR and VEGFR2 signaling, inhibition of PI3K/Akt signaling, COX and selective COX-2 inhibition, NF-kappa B inhibition, Wnt/beta - Catenin pathway inhibition, DNMT1 inhibition, and GSK-3 beta inhibition. In addition to monotherapy option, combination therapy with current anti-cancer drugs may also increase drug efficacy and reduce adverse effects. Thus, drug repositioning may become a key approach for drug discovery in terms of time- and cost-efficiency comparing to conventional drug discovery and development process.
Open Access
Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas
(MDPI, 2021-02-23) ARĞA, KAZIM YALÇIN; Caliskan, Aysegul; Gulfidan, Gizem; Sinha, Raghu; Arga, Kazim Yalcin
Although many studies have been conducted on single gene therapies in cancer patients, the reality is that tumor arises from different coordinating protein groups. Unveiling perturbations in protein interactome related to the tumor formation may contribute to the development of effective diagnosis, treatment strategies, and prognosis. In this study, considering the clinical and transcriptome data of three Renal Cell Carcinoma (RCC) subtypes (ccRCC, pRCC, and chRCC) retrieved from The Cancer Genome Atlas (TCGA) and the human protein interactome, the differential protein-protein interactions were identified in each RCC subtype. The approach enabled the identification of differentially interacting proteins (DIPs) indicating prominent changes in their interaction patterns during tumor formation. Further, diagnostic and prognostic performances were generated by taking into account DIP clusters which are specific to the relevant subtypes. Furthermore, considering the mesenchymal epithelial transition (MET) receptor tyrosine kinase (PDB ID: 3DKF) as a potential drug target specific to pRCC, twenty-one lead compounds were identified through virtual screening of ZINC molecules. In this study, we presented remarkable findings in terms of early diagnosis, prognosis, and effective treatment strategies, that deserve further experimental and clinical efforts.
Open Access
Unexpectedly lower mortality rates in COVID-19 patients with and without type 2 diabetes in Istanbul
(ELSEVIER IRELAND LTD, 2021-04) ARĞA, KAZIM YALÇIN; Satman, Ilhan; Demirci, Ibrahim; Haymana, Cem; Tasci, Ilker; Salman, Serpil; Ata, Naim; Dagdelen, Selcuk; Sahin, Ibrahim; Emral, Rifat; Cakal, Erman; Atmaca, Aysegul; Sahin, Mustafa; Celik, Osman; Demir, Tevfik; Ertugrul, Derun; Unluturk, Ugur; Arga, Kazim Yalcin; Caglayan, Murat; Sonmez, Alper
Aims: Type 2 diabetes mellitus (T2DM) is a risk factor for severe COVID-19. Our aim was to compare the clinical outcomes of patients with and without T2DM during the first hit of COVID-19 in Istanbul.& nbsp; Methods: A retrospective population-based study was conducted including all consecutive adult symptomatic COVID-19 cases. Patients were confirmed with rt-PCR; treated and monitored in accordance with standard protocols. The primary endpoints were hospitalization and 30-day mortality.& nbsp; Results: Of the 93,571 patients, 22.6% had T2DM, with older age and higher BMI. Propensity Score matched evaluation resulted in significantly higher rates of hospitalization (1.5-fold), 30-day mortality (1.6-fold), and pneumonia (1.4-fold). They revealed more severe laboratory deviations, comorbidities, and frequent drug usage than the Non-DM group. In T2DM age, pneumonia, hypertension, obesity, and insulin-based therapies were associated with an increased likelihood of hospitalization; whereas age, male gender, lymphopenia, obesity, and insulin treatment were considerably associated with higher odds of death.& nbsp; Conclusions: COVID-19 patients with T2DM had worse clinical outcomes with higher hospitalization and 30-day mortality rates than those without diabetes. Compared to most territories of the world, COVID-19 mortality was much lower in Istanbul, which may be associated with accessible healthcare provision and the younger structure of the population.& nbsp; (C)& nbsp;2021 Elsevier B.V. All rights reserved.
Open Access
Editorial: Omics integration and network medicine to decipher human complex diseases
(2023-01-01) ARĞA, KAZIM YALÇIN; Zanfardino M., Babbi G., ARĞA K. Y., Pane K.
Open Access
Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis
(MDPI, 2019-01-17) TURANLI, BESTE; Rahman, Md Rezanur; Islam, Tania; Gov, Esra; Turanli, Beste; Gulfidan, Gizem; Shahjaman, Md; Banu, Nilufa Akhter; Mollah, Md Nurul Haque; Arga, Kazim Yalcin; Moni, Mohammad Ali
Background 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.
Open Access
Systems-level biomarkers identification and drug repositioning in colorectal cancer
(2021-07-15) TURANLI, BESTE; Beklen, Hande; Yildirim, Esra; Kori, Medi; Turanli, Beste; Arga, Kazim Yalcin
Open Access
RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective
(TAYLOR & FRANCIS INC, 2017-07-04) ARĞA, KAZIM YALÇIN; Gov, Esra; Kori, Medi; Arga, Kazim Yalcin
Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer.Abbreviations: CAF: cancer-associated fibroblasts
Open Access
Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19
(ELSEVIER, 2020-11) ARĞA, KAZIM YALÇIN; Islam, Tania; Rahman, Md Rezanur; Aydin, Busra; Beklen, Hande; Arga, Kazim Yalcin; Shahjaman, Md
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease, more commonly COVID-19 has emerged as a world health pandemic. There are couples of treatment methods for COVID-19, however, well-established drugs and vaccines are urgently needed to treat the COVID-19. The new drug discovery is a tremendous challenge; repurposing of existing drugs could shorten the time and expense compared with de novo drug development. In this study, we aimed to decode molecular signatures and pathways of the host cells in response to SARS-CoV-2 and the rapid identification of repurposable drugs using bioinformatics and network biology strategies. We have analyzed available transcriptomic RNA-seq COVID-19 data to identify differentially expressed genes (DEGs). We detected 177 DEGs specific for COVID-19 where 122 were upregulated and 55 were downregulated compared to control (FDR<0.05 and logFC >= 1). The DEGs were significantly involved in the immune and inflammatory response. The pathway analysis revealed the DEGs were found in influenza A, measles, cytokine signaling in the immune system, interleukin-4, interleukin 13, interleukin 17 signaling, and TNF signaling pathways. Protein-protein interaction analysis showed 10 hub genes (BIRC3, ICAM1, IRAK2, MAP3K8, S100A8, SOCS3, STAT5A, TNF, TNFAIP3, TNIP1). The regulatory network analysis showed significant transcription factors (TFs) that target DEGs, namely FOXC1, GATA2, YY1, FOXL1, NFKB1. Finally, drug repositioning analysis was performed with these 10 hub genes and showed that in silico validated three drugs with molecular docking. The transcriptomics signatures, molecular pathways, and regulatory biomolecules shed light on candidate biomarkers and drug targets which have potential roles to manage COVID-19. ICAM1 and TNFAIP3 were the key hubs that have demonstrated good binding affinities with repurposed drug candidates. Dabrafenib, radicicol, and AT-7519 were the top-scored repurposed drugs that showed efficient docking results when they tested with hub genes. The identified drugs should be further evaluated in molecular level wet-lab experiments in prior to clinical studies in the treatment of COVID-19.