Browsing by Subject "Biomarker"

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Open Access
A novel gene list identifies tumors with a stromal-mesenchymal phenotype and worse prognosis in gastric cancer
(MDPI, 2023-06-02) Demirkol Canlı, Seçil; Üner, M.; Küçükkaraduman, Barış; Karaoğlu, D. A.; Işık, A.; Turhan, N.; Akyol, A.; Gömceli, I.; Güre, A. O. A
Background: Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors. Methods: Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively. Results: A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression (“Stromal-UP” (SU) and “Stromal-DOWN” (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival. Conclusions: A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.
Open Access
Colon cancer associated transcript-1 (CCAT1) expression in adenocarcinoma of the stomach
(Ivyspring International Publisher, 2015) Mizrahi, I.; Mazeh, H.; Grinbaum, R.; Beglaibter, N.; Wilschanski, M.; Pavlov, V.; Adileh, M.; Stojadinovic, A.; Avital, I.; Gure, A. O.; Halle, D.; Nissan, A.
Background: Long non-coding RNAs (lncRNAs) have been shown to have functional roles in cancer biology and are dys-regulated in many tumors. Colon Cancer Associated Transcript -1 (CCAT1) is a lncRNA, previously shown to be significantly up-regulated in colon cancer. The aim of this study is to determine expression levels of CCAT1 in gastric carcinoma (GC). Methods: Tissue samples were obtained from patients undergoing resection for gastric carcinoma (n=19). For each patient, tumor tissue and normal appearing gastric mucosa were taken. Normal gastric tissues obtained from morbidly obese patients, undergoing laparoscopic sleeve gastrectomy served as normal controls (n=19). A human gastric carcinoma cell line (AGS) served as positive control. RNA was extracted from all tissue samples and CCAT1 expression was analyzed using quantitative real time-PCR (qRT-PCR). Results: Low expression of CCAT1 was identified in normal gastric mucosa samples obtained from morbidly obese patients [mean Relative Quantity (RQ) = 1.95±0.4]. AGS human gastric carcinoma cell line showed an elevated level of CCAT1 expression (RQ=8.02). Expression levels of CCAT1 were approximately 10.8 fold higher in GC samples than in samples taken from the negative control group (RQ=21.1±5 vs. RQ=1.95±0.4, respectively, p<0.001). Interestingly, CCAT1 expression was significantly overexpressed in adjacent normal tissues when compared to the negative control group (RQ = 15.25±2 vs. RQ=1.95±0.4, respectively, p<0.001). Tissues obtained from recurrent GC cases showed the highest expression levels (RQ = 88.8±31; p<0.001). Expression levels increased with tumor stage (T4- 36.4±15, T3- 16.1±6, T2- 4.7±1), however this did not reach statistical significance (p=0.2). There was no difference in CCAT1 expression between intestinal and diffuse type GC (RQ=22.4±7 vs. 22.4±16, respectively, p=0.9). Within the normal gastric tissue samples, no significant difference in CCAT1 expression was observed in helicobacter pylori negative and positive patients (RQ= 2.4±0.9 vs. 0.93±0.2, respectively, p=0.13). Conclusion: CCAT1 is up-regulated in gastric cancer, and may serve as a potential bio-marker for early detection and surveillance.
Open Access
A combined ULBP2 and SEMA5A expression signature as a prognostic and predictive biomarker for colon cancer
(Ivyspring International Publisher, 2017) Demirkol, S.; Gomceli, I.; Isbilen, M.; Dayanc, B. E.; Tez, M.; Bostanci, E. B.; Turhan, N.; Akoglu, M.; Ozyerli, E.; Durdu, S.; Konu, O.; Nissan, A.; Gonen, M.; Gure, A. O.
Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico discovery of stage-independent prognostic biomarkers using two cohorts, GSE17536 and GSE17537, that include 177 and 55 colon cancer patients, respectively. This identified 2 genes, ULBP2 and SEMA5A, which when used jointly, could distinguish patients with distinct prognosis. We validated our findings using a third cohort of 48 patients ex vivo. We find that in all cohorts, a combined ULBP2/SEMA5A classification (SU-GIB) can stratify distinct prognostic sub-groups with hazard ratios that range from 2.4 to 4.5 (p=0.01) when overall- or cancer-specific survival is used as an end-measure, independent of confounding prognostic parameters. In addition, our preliminary analyses suggest SU-GIB is comparable to Oncotype DX colon(®) in predicting recurrence in two different cohorts (HR: 1.5-2; p=0.02). SU-GIB has potential as a companion diagnostic for several drugs including the PI3K/mTOR inhibitor BEZ235, which are suitable for the treatment of patients within the bad prognosis group. We show that tumors from patients with worse prognosis have low EGFR autophosphorylation rates, but high caspase 7 activity, and show upregulation of pro-inflammatory cytokines that relate to a relatively mesenchymal phenotype. Conclusions: We describe two novel genes that can be used to prognosticate colon cancer and suggest approaches by which such tumors can be treated. We also describe molecular characteristics of tumors stratified by the SU-GIB signature.
Open Access
Default mode network connectivity is linked to cognitive functioning and CSF Aβ1-42 levels in Alzheimer's disease
(Elsevier Ireland Ltd, 2016) Celebi, O.; Uzdogan, A.; Oguz, K. K.; Has, A. C.; Dolgun A.; Cakmakli, G. Y.; Akbiyik, F.; Elibol, B.; Saka, E.
Background: Changes in the default mode network (DMN) activity are early features of Alzheimer's disease (AD) and may be linked to AD-specific Aβ pathology. Methods: Cognitive profiles; DMN connectivity alterations; and cerebrospinal fluid (CSF) amyloid beta (Aβ)1-42, total tau, phosphorylated tau 181, and α-synuclein levels were studied in 21 patients with AD and 10 controls. Results: DMN activity is altered in AD. Posterior cingulate cortex (PCC) functional connectivity with other parts of DMN was related to cognitive function scores. The reduction of connectivity of the dorsal PCC with the retrosplenial cortex on the right side was closely related to decreased CSF Aβ1-42 levels in patients with AD. Conclusions: The dorsal PCC and retrosplenial cortex may have special importance in the pathogenesis and cognitive findings of AD. © 2015 Elsevier Ireland Ltd.
Open Access
Development and validation of methods for the diagnosis of lung cancer via serological biomarkers
(2019-02) Akçay, Abbas Güven
Over 10% of all new cancer cases are lung cancer. Moreover, estimates till 2030 indicate that already increasing lung cancer incidences will keep increasing, especially in developing countries like Turkey. Lung cancer, the leading cause of cancer deaths, has two large divisions: Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC). SCLC is the most aggressive subtype of lung cancer. And although, the treatment options and median survival time is more favorable in Limited Disease (LD), high tumor growth rate and metastatic tendency of SCLC even in the early stages, makes the diagnosis troublesome. Similarly, if NSCLC is diagnosed in early stages, surgery option is open and this increases the patient survival rate. However, current methods in screening and diagnosis, such as computed tomography (CT) and positron emission tomography (PET), are all limited by false positivity rates. Additionally, biopsy methods used in histological evaluations are both invasive and prone to false negativity. Therefore, new diagnostic tools which are cheap, accurate and non-invasive are in high demand. Autologous antibodies are abundantly elicited and stably exist in patient sera years before the clinical diagnosis of disease. Several such antibodies were reported by our group and other groups in lung cancer. Therefore, new diagnostic methods incorporating autologous antibodies can be a huge step forward in early diagnosis of lung cancer. Moreover, miRNAs, with their unique hormone like features such as circulation in serum and their regulatory effects in cell, are another good candidate for the early diagnosis of lung cancer. Therefore, in this study I aimed to develop a reliable, robust and automated evaluation method to re-evaluate custom Protein Array (cPA) screenings previously performed in our lab, and to determine the autologous antibodies with highest discriminatory power between SCLC patients & healthy controls. Moreover, I aimed to develop a Quartz Crystal Microbalance with Dissipation (QCM-D) based immunoassay to be incorporated later in the validation of cPA results. Lastly, in a parallel study I aimed to identify and validate novel miRNA biomarkers NSCLC. My results indicate that cPAs can have better sensitivity and specificity than ELISA and that QCM-D can be developed as an alternative to ELISA. miRNAs identified in silico, can also be validated ex vivo. Previously, Protein Arrays (PAs) and cPAs were screened using 49 SCLC patient’s and 50 healthy serums in our laboratory, incorporating visual and manual evaluations. Sensitivity and specificity values were calculated for individual autologous-antibodies and a number of autologous-antibody panels. Moreover, validations of cPA results were carried via ELISA. However, large discrepancies between cPA and ELISA results, as well as inconsistencies among ELISA results urged me to consider re-evaluation of cPA results with a more robust way, and to focus on developing a method superior to ELISA in autologous-antibody evaluations. Therefore, I incorporated AIDA to generate numeric values out of cPA screening images and filtered low quality data with optimized cut-off values. Several Receiver Operating Characteristic (ROC) curves were plotted using evaluated data. Improved results were evident by the increased Area Under Curve (AUC) values in both individual and combined ROC curves. Moreover, I developed a QCM based immunosensor for detection of anti-SOX2 antibody to be incorporated later in validation of cPA results. Binding interaction between anti-SOX2 antibody and SOX2 protein was modelled using 1:1 Langmuir Isothermal Binding and standard curves generated in QCM. In a parallel study, I also investigated miRNAs significantly upregulated in NSCLC when compared to high risk controls. For that purpose, miRNA expression datasets were gathered from GEO. Selected 2 datasets with the same sample type were analyzed for common significantly upregulated miRNAs among these two datasets. Significantly upregulated miRNAs were subjected to logistic regression analysis with LASSO regularization (error metrics: AUC and MSE) to select best panel of miRNAs that can distinguish NSCLC patients from healthy controls in given datasets. Moreover, selected miRNAs were analyzed with qRT-PCR to validate the panel. I was able to re-evaluate cPA results by eliminating low quality data from numeric values generated via AIDA software from cPA images. I identified a panel of 4 autologous antibodies (FKBP8 – P53 – SOX2 – POLB) which resulted in 60% sensitivity at 100% specificity in discrimination of SCLC from controls. ROC of this autologous antibody panel had an AUC of 95.04%. Given panel surpassed diagnostic power of the only commercially available diagnostic kit of the same kind; EarlyCDT-Lung. Moreover, proof of concept for measurements of anti-protein antibodies were carried successfully in QCM, using anti-SOX2 antibody-SOX2 protein pair in PBS buffer as an example for it. Early results of anti-SOX2 mAb QCM indicate a linear assay range comparable to ELISA. Langmuir Isothermal Binding model revealed a strong interaction between antibody and protein in our QCM anti-SOX2 measurement experiments. Lastly, I was able to select 5 miRNAs using logistic regression and LASSO regularization that can best discriminate between NSCLC patients and high risk controls. However, validation experiments using qRT-PCR needs to be repeated as low Ct values and prominent hemolysis in serum samples prevented drawing meaningful conclusions.
Open Access
Development of methods for identification and characterization of autologous antibody responses in Small Cell Lung Cancer and Behcet’s Disease
(2016-08) Poyraz, Alper
Autologous antibodies are known to be elicited in Behçet’s Disease (BD) and Small Cell Lung Cancer (SCLC). SCLC consists 15-20% of all lung cancer cases. It is follows a most aggressive course and generally patients are diagnosed at later stages. The median survival of patients is 9-12 months. Diagnostic methods such as CT and PET are somewhat useful in the diagnosis of lung cancer but not so much for SCLC as the doubling time of this tumor is very rapid. Therefore, new diagnostic tools are needed for early diagnosis and to increase median survival of patients. Behçet’s Disease is autoimmune disease and the prevalence of BD in Turkey is the highest in the world. Also autologous antibodies against various antigens associated with BD have been discovered in BD. BD has vascular, oral, cutaneous and neuronal subtypes and autologous antibodies correlating with each subtype have been reported. However, for BD, there does not exist a diagnostic or prognostic test as none have been developed yet. However autoantibodies can be utilized for the diagnosis and follow-up of SCLC and BD because it is known that autoantibodies are expressed well in advance of disease symptoms. The first aim of this study was to determine a correlation between antigen expression levels in tumor tissues and the presence of autologous antibodies. The second aim of this study was to extend earlier experiments related to the characterization of autologous antibodies against known and novel antigens in SCLC and BD, utilizing high-density protein arrays (PA). The third and major aim of this study was to develop a reliable and sensitive method that could be used to evaluate protein array screening results and lastly, to validate these results by performing optimized ELISA and Western Blot experiments. Previously, PAs were screened with 50 SCLC, 50 BD and 50 healthy serums in our laboratory, and evaluated visually utilizing no automation. Sensitivity and specificity values were calculated using custom-generated antigen panels which included 180 antigens. ELISA experiments were performed to validate antigens thus discovered. However, largely discrepant PA and ELISA results, together with inconsistent ELISA results required us to optimize ELISA conditions, as well as to generate an automated PA evaluation method that would generate numeric data. We modified ELISA by altering various parameters until we were able to obtain consistent results. We also generated a reliable method by which we could produce numeric data corresponding to antibody presence as determined from PA screening results. The method is based on the calculation of pixel intensities of sero-reactive clones on the array which are converted to numeric data, and the subsequent determination of proper cut-offs by which sensitivity and specificity of antibody responses can be generated by comparing values obtained from healthy to those obtained from diseased serum. We call this the “Digital Spot Evaluation” (DSE) tool. DSE was performed utilizing Adobe Photoshop CS6 and parameters of the test were optimized using five replicate screens of a given serum. Pearson’s r correlation values of repeated experiments after optimization were close to 1. Also, when protein arrays are screened using DSE on different days by different researchers, results are highly concordant. We evaluated protein array screening data obtained for SCLC and healthy sera by DSE. In particular, antibody intensities against SOX2, p53 and POLB proteins were calculated and sensitivity/specificity values were determined. With DSE based evaluation of protein arrays, we reached 44%, 6% and 20% sensitivity at 100% specificity for SOX2, p53 and POLB proteins respectively. On the other hand if we evaluate 3 proteins together as a panel, our sensitivity increases to 56% at 100% specificity, and 66% sensitivity at 96% specificity. However, even after optimization, ELISA results showed 32%, 4% and 4% sensitivity at 100% specificity for SOX2, p53 and POLB proteins respectively, demonstrating that DSE is significantly more sensitive than ELISA. We are planning to use DSE to evaluate PA data generated from many other types of tumors in the future and to and possibly to develop a kit based on this method to be utilized for the diagnosis and follow-up of SCLC and BD.
Open Access
Evaluation of TAGLN as a diagnostic marker in breast cancer
(2018-08) Köseer, Ayşe Sedef
Silecing of tumor suppressor genes via CpG hypermethylation in promoter regions is one of the frequent events occurring in different types of cancers. These genes have the potential as a diagnostic or a prognostic biomarker. Liquid biopsy is a relatively less invasive technique that is used for early diagnosis, therapy response prediction, minimal residual disease detection and real-time monitoring of tumor progression. In this study, a 402 bp region (-286 bp to -80 bp for Section 1, -102 bp to +115 bp for Section 2) located in TAGLN promoter containing 22 CpGs was analyzed in breast cancer patients and healthy donors to evaluate the biomarker potential of TAGLN promoter methylation levels in breast cancer. TAGLN promoter region was significantly hypermethylated in breast cancer patients (77.3%) compared to healthy donors (68.2%). Among differentially methylated CpGs, 6 out of 22 were hypermethylated and one was hypomethylated in breast cancer patients. We also analyzed the relationship between TAGLN promoter methylation levels and the patient's clinicopathological parameters. Analyses revealed that TAGLN promoter is highly methylated in breast cancer patients over 50 years of age compared to the healthy donors in the same age group. TAGLN promoter methylation did not differ as related to various clinicopathological parameters of breast cancer patients. TAGLN promoter methylation levels diagnosed breast cancer patients with 74.45% specificity and 57.58% sensitivity. Additionally, independent of the age group breast cancer patients (131.6 ng) exhibited higher levels of total cfDNA compared to healthy donors (56.4 ng). Pre- and postmenopausal breast cancer patients possessed higher total cfDNA levels compared to pre- and postmenopausal healthy donors. Total cfDNA levels did not differ in various clinicopathological parameters of breast cancer patients; however, total cfDNA levels diagnosed breast cancer patients with 73.33% specificity and 56.72% sensitivity. In summary, breast cancer patient sera can be used to identify the tumor profile, and TAGLN promoter hypermethylation and total cfDNA levels could serve as a diagnostic biomarker in breast cancer.
Open Access
Identification and utilization of autologous anti-tumor antibodies for the diagnosis and prognosis of cancer
(2015-12) Atakan, Şükrü
Lung cancer is the leading cause of cancer related death worldwide. Current diagnostic methods have limited power and unable to extend patient life significantly. SCLC; the most aggressive subtype of lung cancer is an immunogenic cancer type and able to elicit an immune response of which autologous antibodies are a measurable component. These antibodies are elicited even when the tumor is microscobic and impossible to be diagnosed clinically by the current methods of diagnosis thus antibodies can be utilized for early diagnosis. We aimed to develop a method to identify novel autologous antibodies, identify these antibodies for SCLC, Colorectal, Gastric and Ovarian cancers and validate these antibodies for SCLC diagnosis and prognosis and investigate their utility for autoimmune disease. We have developed and optimized PA screening for novel autologous antibody discovery. We have screened PA with serum pools of cancer patients (SCLC, Colorectal, Gastric and Ovarian), BD and healthy controls since PAs have many advantages compared to other discovery methods like SEREX. We have also performed sensitivity and specificity evaluations by screening custom PAs by individual sera. Image analysis softwares developed by collaboration utilized for evaluation of the screenings. The filtered valuable clones were ordered from the PA manufacturer and HisTagged protein expression and purification was performed with these clones. Pure proteins were screened with 3 independent SCLC and 2 Healthy control cohorts by an iterative ELISA approach for validation of these antibodies as valuable biomarkers. ELISA results were also confirmed by Western blotting. Monte Carlo, SVM and PC were utilized for cut-off determination, panel formation and ROC plotting. AUC was compared for evaluation of diagnostic power. Kaplan-Meier, UCR and MCR analysis was performed for prognostic analysis of the valuable antibodies. Seperately protein expression and autologous antibody presence correlation was evaluated by comparison of IHC and ELISA. The same autologous antibody identification strategy was utilized as a collaborative support for an independent study for identification of NBD specific biomarkers.We have identified 23 distinct autologous antibody biomarkers for SCLC after evaluation of PA and custom PA screenings. For 8 of these antibodies we have completed ELISA screening for all 3 SCLC and 2 healthy control cohorts. 6 of these autologous antibodies were shown to be valuable as a panel for SCLC diagnosis both by MC and SVM. Utilization of 4 of these antibodies; SOX2, p53, POLB and C11orf20, as a panel resulted in superior AUC thus high sensitivity and specificity values (55% sensitivity, 90% specificity). PC method resulted in higher AUC even only by combination of SOX2 and p53 (82% sensitivity, 90% specificity). Although individual correlations were identified, we were unable to show a significant correlation of seropositivity with survival for any of the antibodies which is common to all cohorts. We have identified a significant correlation between SOX2 antigen expression intensity and autologous antibody presence. Mtch1 was identified as a NBD specific autologous antibody by the utilization of our autologous antibody discovery and validation methodology. We were able to identify a panel of 4 antibodies; SOX2, p53, POLB and C11orf20, which resulted in 55% sensitivity at 90% specificity for SCLC. 2 of these antibodies were identified by this study as novel biomarkers; POLB and C11orf20. The panel is capable of exceeding the diagnostic power of the only commercially available diagnostic kit; EarlyCDT-Lung. PC method is very promising since a sensitivity value of 82% was reached at 90% specificity which is a diagnostic power comparable that of low-dose CT. As a future perspective we are planning to apply PC method to all the PA data and develop a kit based on this method to be utilized for SCLC diagnosis.
Open Access
In silico validation of prognostic mRNA signature in gastric cancer and identification and validation of novel gastric tissue specific reference genes for quantitative PCR
(2021-07) Ishraq, Marzana
Gastric adenocarcinoma is a molecularly and histologically heterogeneous neoplasm with a predictively disastrous outcome if undiagnosed at early stages. Amidst global declines of gastric cancer rates, it remains the 5th most common malignancy with the 4th worst outcome of all cancers. Predictive and prognostic clinical biomarkers are scarce in gastric cancer due to its immense heterogeneity. Identifying novel biomarkers for gastric cancer is an emerging field. Previously in our lab, a 20 gene mRNA signature was developed which successfully stratified gastric cancer patients into poor and good prognosis. In this thesis, we attempted to shorten this list to a five gene signature which will successfully stratify patients into similar clusters as the 20 genes. The 5 genes being, HEYL, CALD1, ACTA2, TAGLN and TPM2. Moreover, we validated the efficacy of our 5-gene signature to stratify patients based on their prognoses in silico. The second half of the thesis focuses on identifying a set of gastric tissue specific reference genes by analyzing high-throughput gene expression data. Most commonly used reference genes are known to have varying expression in cancer tissue which often leads to an issue with reproducibility in cancer research. We aimed to design an algorithm which identifies a list of stable transcripts within a particular tissue type. We identified 3 genes EWSR1, SF1 and HNRNPK which showed stable expression throughout gastric tissue, both in normal and cancer in silico. Ex vivo validation experiments using gastric tumor and adjacent normal RNA show promise for the efficacy of our genes compared to GAPDH and B2M.
Open Access
Metabolomics approaches in experimental allergic encephalomyelitis
(Elsevier, 2018) Battini, B.; Bund, C.; Moussallieh, F. M.; Çiçek, A. Ercüment; De Sèze, J.; Namer, I. J.
A myelin basic protein (MBP)-induced experimental allergic encephalomyelitis (EAE) involves paraplegia due to a reversible thoracolumbar spinal cord impairment. The aims of this study were thus to find significant metabolic biomarkers of inflammation and identify the site of inflammation in the central nervous system (CNS) during the acute signs in of the disease using metabolomics. All the EAE samples were associated with higher levels of lactate, ascorbate, glucose and amino acids, and decreased level of N-acetyl-aspartate (NAA) compared to the control group. A decreased NAA level has been particularly shown in lumbar spinal cord in relationship with the clinical signs.
Open Access
Metabolomics approaches in pancreatic adenocarcinoma: Tumor metabolism profiling predicts clinical outcome of patients
(BioMed Central Ltd., 2017) Battini, S.; Faitot, F.; Imperiale, A.; Cicek, A. E.; Heimburger, C.; Averous, G.; Bachellier, P.; Namer, I. J.
Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis. Methods: 1H high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy using intact tissues was applied to analyze metabolites in PP tissue samples (n = 17) and intact tumor samples (n = 106), obtained from 106 patients undergoing surgical resection for PA. Results: An orthogonal partial least square-discriminant analysis (OPLS-DA) showed a clear distinction between PP and PA. Higher concentrations of myo-inositol and glycerol were shown in PP, whereas higher levels of glucose, ascorbate, ethanolamine, lactate, and taurine were revealed in PA. Among those metabolites, one of them was particularly obvious in the distinction between long-term and short-term survivors. A high ethanolamine level was associated with worse survival. The impact of neoadjuvant chemotherapy was higher on PA than on PP. Conclusions: This study shows that HRMAS NMR spectroscopy using intact tissue provides important and solid information in the characterization of PA. Metabolomics profiling can also predict long-term survival: the assessment of ethanolamine concentration can be clinically relevant as a single metabolic biomarker. This information can be obtained in 20 min, during surgery, to distinguish long-term from short-term survival. © 2017 The Author(s).
Open Access
miR-200c: a versatile watchdog in cancer progression, EMT, and drug resistance
(Springer Verlag, 2016-06) Mutlu, M.; Raza, U.; Saatci, Ö.; Eyüpoğlu, E.; Yurdusev, E.; Şahin, Ö.
MicroRNAs (miRNAs) are 20–22-nucleotide small endogenous non-coding RNAs which regulate gene expression at post-transcriptional level. In the last two decades, identification of almost 2600 miRNAs in human and their potential to be modulated opened a new avenue to target almost all hallmarks of cancer. miRNAs have been classified as tumor suppressors or oncogenes depending on the phenotype they induce, the targets they modulate, and the tissue where they function. miR-200c, an illustrious tumor suppressor, is one of the highly studied miRNAs in terms of development, stemness, proliferation, epithelial-mesenchymal transition (EMT), therapy resistance, and metastasis. In this review, we first focus on the regulation of miR-200c expression and its role in regulating EMT in a ZEB1/E-cadherin axis-dependent and ZEB1/E-cadherin axis-independent manner. We then describe the role of miR-200c in therapy resistance in terms of multidrug resistance, chemoresistance, targeted therapy resistance, and radiotherapy resistance in various cancer types. We highlight the importance of miR-200c at the intersection of EMT and chemoresistance. Furthermore, we show how miR-200c coordinates several important signaling cascades such as TGF-β signaling, PI3K/Akt signaling, Notch signaling, VEGF signaling, and NF-κB signaling. Finally, we discuss miR-200c as a potential prognostic/diagnostic biomarker in several diseases, but mainly focusing on cancer and its potential application in future therapeutics.
Open Access
Nanocarbon-assisted biosensor for diagnosis of exhaled biomarkers of lung cancer: DFT approach
(Sami Publishing Company, 2021-03) Mirzaei, M.; Gülseren, Oğuz; Rafienia, M.; Zare, A.
Density functional theory (DFT) calculations were performed to investigate a nanocarbon-assisted biosensor for diagnosis of exhaled biomarkers of lung cancer. To this aim, an oxidized model of C20 fullerene (OC) was chosen as the surface for adsorbing each of five remarkable volatile organic compounds (VOC) biomarkers including hydrogen cyanide, methanol, methyl cyanide, isoprene, and 1-propanol designated by B1-B5. Geometries of the models were first optimized to achieve the minimum energy structures to be involved in further optimization of B@OC bi-molecular complexes. The relaxation of B counterparts at the surface of OC provided insightful information for capability of the investigated system for possible diagnosis of such biomarkers. In this case, B1 was placed at the highest rank of adsorption to make the strongest B1@OC complex among others whereas the weakest complex was seen for B4@OC complex. The achievement was very much important for differential detection of each of VOC biomarkers by the investigated OC nanocarbon. Moreover, the recorded infrared spectra indicated that the complexes could be very well recognized in complex forms and also among other complexes. As a final remark, such proposed nanocarbon-assisted biosensor could work for diagnosis of remarkable VOC biomarkers of lung cancer.
Open Access
Prediction of prognosis and chemosensitivity in breast cancer
(2020-09) Akbar, Muhammad Waqas
Breast cancer (BC) is responsible for the highest mortality and morbidity out of all the cancers in women which is primarily due to both inter and intra-tumoral molecular heterogeneity. This heterogeneity arises from stemness, epithelial to mesenchymal transition and the type of treatment given to patients. These three biological processes are highly related with each other. Traditional therapy when given to breast cancer patients generally results in the transition of epithelial cells to mesenchymal phenotype. Because treatment targets primarily non-stem cells, it can leave stem cells alive which can later result in a relapse of cancer. In this study we aimed to identify such markers that could classify breast cancer patients into stem/mesenchymal or non-stem/epithelial like phenotypes, to determine how generalized the above stated hypotheses are. We developed a gene list of 15 genes we term as CSC/non-CSC gene list (CNCL) which classifies tumors into stemness and/or EMT based phenotypes and can also classify tumor cells based on their relative sensitivity to treatment with traditional therapeutics such as paclitaxel and doxorubicin. When classified into stem/mesenchymal (CS/M) and non-stem/epithelial (NS/E) phenotypes, we showed that Lapatinib and Midostaurin have a specific growth inhibitory effects on NS/E cells, and CS/M cells, respectively. Surprisingly the CNCL showed prognostic significance only for patients who were treated with paclitaxel in neoadjuvant setting, while it could not prognosticate most other BC cohorts. We argue that this is due to the dynamic plasticity of these tumors, as studied within the third aim of this thesis. Secondly, we aimed to identify chemotherapy biomarkers for paclitaxel, cisplatin and doxorubicin to stratify patients in groups that will or will not benefit from these drugs. Using biomarkers, we selected for this purpose, we performed linear regression analysis using breast cancer cell lines to generate cytotoxicity prediction models which can predict IC50 values for these drugs, based on the expression of two genes in each model. Two models were selected for doxorubicin and cisplatin, and three models were selected for paclitaxel. All models were validated both in silico and in vitro. Thirdly, we aimed to evaluate breast cancer plasticity that occurs upon treatment or when a tumor metastasizes. We noted that some breast tumors not only switch their clinical subtype but also change their molecular subtype upon treatment or metastasis. As breast cancer patient treatment in the routine practice is routed based on breast cancer subtype, it is very important to identify the subtype switches which can be critical for changes in treatment decisions. Additionally, we also identified metastatic biomarkers using large number of cohorts. Lastly, as CNCL genes did not show any prognostic importance in terms of both overall survival and metastasis free survival, we checked if the same is true for melanoma. We used Melanin A (MLANA) and Inhibin (INHBA) genes as the markers for invasive/proliferative, stem/non-stem and mesenchymal/epithelial phenotypes. High INHBA expression, which is epithelial, proliferative and non-stem phenotype biomarker, was associated with poor survival and high MLANA expression, which is mesenchymal, invasive and stem phenotype marker, was associated with good prognosis in melanoma patients. Therefore, these findings in melanoma supported our results in breast cancer.
Open Access
Prediction of prognosis and chemosensitivity in gastrointestinal cancers
(2017-12) Demirkol, Seçil
Colon and gastric cancers are the third and fifth cancer types with the poorest survival. Surgery is considered the primary treatment option, which can be curative. However the decision as to whether chemotherapy administration after surgery is needed, is critical for especially stage 2 colon cancer; since a group of patients who receive chemotherapy do not have significantly improved clinical outcome. For this purpose, clinical risk factors are currently evaluated to determine patients with a high risk of progression. However this is not standardized by guidelines yet. Therefore, in this thesis my first aim was to ex vivo validate two novel independent mRNA based biomarkers, ULBP2 and SEMA5A, which have been previously identified in our lab, and to generate a prognostic signature which could stratify colon cancer patients with differential prognostic profiles using the best stratification method. I showed that a 3-group prognostic signature, SU-GIB, based on expression of these two genes is associated with cancer-specific, disease-free, and overall survival independent of clinical confounding factors in colon cancer. I performed in silico analysis in order to understand the biological details of the prognostic distinction, and revealed that patients with poorer prognosis show higher expression of pro-inflammatory cytokines and a more mesenchymal profile. Patients with better clinical outcome exhibit a more epithelial profile with higher levels of phosphorylated EGFR and Shc proteins. Analysis of high-throughput drug cytotoxicity databases also showed that colon cancer cell lines with „Bad‟ SU-GIB signature are more sensitive to a dual PI3K-MTOR inhibitor, BEZ235. In this thesis, my second goal was to define prognostic and molecular sub-groups for gastric cancer and characterize the specific biology of each sub-group. Utilizing an unsupervised approach on publicly available microarray data, I identified 3 biological groups, which harbor differential characteristics related to ECM involvement, EMT, proliferation and cell cycle. Moreover I identified distinct prognostic groups which are related to this molecular classification which can further stratify patients with known pathologic subtypes (diffuse and intestinal). I found that EMT was an important parameter for molecular and prognostic classifications for both types of cancers. I therefore, studied high-throughput drug cytotoxicity databases in order to identify selective compounds with selective growth inhibition on epithelial or mesenchymal cancer cells. I identified EGFR inhibitors as being significantly more effective on epithelial cancer cells regardless of the tissue type. My future plans include the large scale validation of SU-GIB, ex vivo validation of the gastric prognostic signature, and in vitro studies that would demonstrate effectivity of EGFR inhibitors on epithelial cancer cells and combination of EGFR inhibitors with MET inducers.
Open Access
Resveratrol affects histone 3 lysine 27 methylation of vessels and blood biomarkers in DOCA salt-induced hypertension
(Kluwer Academic Publishers, 2015) Han, S.; Uludag, M.O.; Usanmaz, S.E.; Ayaloglu-Butun F.; Akcali, K.C.; Demirel-Yilmaz, E.
Hypertension is a risk factor for the cardiovascular diseases. Although, several drugs are used to treat hypertension, the success of the antihypertensive therapy is limited. Resveratrol decreases blood pressure in animal models of hypertension. This study researched the mechanisms behind the effects of resveratrol on hypertension. Hypertension was induced by using the deoxycorticosterone acetate (DOCA)-induced (15 mg/kg twice per week, subcutaneously) salt-sensitive hypertension model of Wistar rats. Hypertension caused a decrease in endothelium-dependent relaxations of the isolated thoracic aorta. Resveratrol treatment (50 mg/l in drinking water) prevented DOCA salt-induced hypertension, but did not improve endothelial dysfunction. Plasma nitric oxide (NO), asymmetric dimethylarginine (ADMA), total antioxidant capacity (TAC) and hydrogen sulfide (H2S) levels were not changed by DOCA salt application. However, treatment of resveratrol significantly decreased ADMA and increased TAC and H2S levels. NO level in circulation was not significantly changed by resveratrol. DOCA salt application and resveratrol treatment also caused an alteration in the epigenetic modification of vessels. Staining pattern of histone 3 lysine 27 methylation (H3K27me3) in the aorta and renal artery sections was changed. These results show that preventive effect of resveratrol on DOCA salt-induced hypertension might due to its action on the production of some blood biomarkers and the epigenetic modification of vessels that would focus upon new aspect of hypertension prevention and treatment. © 2014, Springer Science+Business Media Dordrecht.
Open Access
Transcript variants of CELF2 gene as unique prognostic indicators in breast cancer
(2022-08) Azizolli, Shila
Breast cancer (BC) is the most common malignant tumor in women around the world. Aside from finding a cure for this disease, it is also critical to identify prognostic biomarkers that can help clinicians intervene with the appropriate treatment and prevent BC progression. Current biomarker identification methods rely primarily on multi-gene prognostic signature models. However, due to the tumors' high heterogeneity, the accuracy of these multi-gene signatures is questionable. As a result, our main objective was to conduct a comprehensive analysis to identify a reliable prognostic biomarker in BC. Previously, a group of eight carnitine metabolites and SAH were linked to a poor prognosis in BC (Dr. Waqas Akbar, Unpublished Data). We discovered the genes associated with these metabolites using correlation analysis, and then we identified CELF2 as a good prognostic biomarker in BC. We validated CELF2's prognostic role in RNA-Seq and Microarray datasets in-silico. We demonstrate that the CELF2 1554569_a_at probeset is more consistent in its association with a favorable prognosis direction than the 202157_s_at probeset. When compared to the other probeset, CELF2 – 202157_s_at is expressed at higher levels and in a broader range of tissues. We were unable to find a clear significant association between CELF2 expression and prognosis during the in-vitro immunohistochemistry validation experiments. We hypothesized that this could be because our polyclonal anti-CELF2 antibody also recognized the less consistent 202157_s_at CELF2 probeset. We discovered that there are probeset-specific CELF2 transcript variants that are associated with different prognosis while testing this hypothesis. We created a Risk Score model by combining the expression levels of good and less-favorable CELF2 prognostic transcripts to improve prognosis prediction accuracy. The model successfully stratified the patients and predicted a higher overall survival in the Low-Risk group versus the High-Risk group. Overall, our findings suggest that each unique transcript variant of a gene can be associated with different prognosis directions. Therefore, we propose that studying prognostic associations at a gene transcript level could be a rich resource for the development of more robust biomarkers and therapeutics in cancer in the future.
Open Access
Unmasking of epigenetically silenced genes and identification of transgelin as a potential methylation biomarker in breast cancer
(2015-11) Sayar, Nilüfer
Tumor suppressor genes (TSG) are frequently silenced in cancer by epigenetic mechanisms, including promoter DNA hypermethylation and repressive chromatin formation by means of histone deacetylation. 5-aza-2'-deoxycytidine (AZA) and Trichostatin A (TSA) are DNA methyl-transferase and histone deacetylase inhibitors, respectively, and are used as anti-cancer agents for induction of epigenetically suppressed genes. In this study, in an attempt to unmask epigenetically suppressed potential TSGs in breast cancer, two breast carcinoma cells and one non-tumorigenic breast cell line were treated with AZA and TSA, either separately or in combination, and with DMSO as control. Afterwards, highthroughput expression profiling revealed significantly affected genes and pathways in response to epigenetic induction in each cell line. Analysis of 32 candidate genes highlighted Transgelin (TAGLN) as a putative TSG that is frequently downregulated by promoter DNA hypermethylation in breast cancer cell lines, and in 61.9% of normal-paired breast tumors according to bisulfite sequencing; and in 63.02% of unpaired breast tumor tissues as determined by bioinformatics analyses of public microarray data. Both relapse-free and overall survivals of patients were more favorable with lower TAGLN methylation. Moreover, TAGLN promoter methylation levels diagnosed tumors tissues with 83.14% sensitivity and 100% specificity. qRTPCR and IHC experiments demonstrated that, TAGLN was persistently downregulated in breast cancer cell lines in comparison to non-tumorigenic cells; and in three independent sets of breast tumor tissues, compared to normal tissues. Furthermore, TAGLN expression was associated with good prognostic factors. Functional analyses in breast cancer cells revealed negative effect of transgelin on colony formation abilities of cells, and analyses with epithelial-to-mesenchymal (EMT) markers implicate an association of transgelin with EMT status of breast cancer. In short, TAGLN downregulation by promoter DNA hypermethylation in breast cancer could serve as a growth advantage to the breast cancer cells, while it could be used as a diagnostic or prognostic biomarker for breast cancer.
Open Access
The Yin and Yang of exosome isolation methods: conventional practice, microfluidics, and commercial kits
(Elsevier Inc, 2022-01) Shirejini, Saeedreza Zeibi; İnci, Fatih
Exosomes are a subset of extracellular vesicles released from various cells, and they can be found in different bodily fluids. Exosomes have been utilized as biomarkers to diagnose many diseases and to monitor therapy efficiency as they represent the status and origin of the cell, which they are released from. Considering that they co-exist in bodily fluids with other types of particles, their isolation still remains challenging since conventional methods are time-consuming, user-dependent, and result in low isolation yield. This review summarizes the conventional strategies and microfluidic-based methods for exosome isolation along with their strengths and limitations. In particular, microfluidic devices emerge as a promising approach to tackle the existing limitations of conventional methods, and they provide unique features, such as operating with minute volume of samples and rapid process, in order to isolate exosomes with the high yield and the high purity, which make them unprecedented tools for molecular biology and clinical applications in exosome research. This review further elaborates on the existing microfluidic-based exosome isolation methods and denotes their benefits and drawbacks. Herein, we also introduce various commercially available platforms and kits for exosome isolation along with their working principles.