Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.

INTRODUCTION: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. MATERIAL AND METHODS: A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). RESULTS: Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65-0.75) and LGBM (AUC = 0.70, 95% CI 0.65-0.75) models achieved the highest predictive AUC values for CRC patient survival. CONCLUSIONS: This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.

Assessment of the Response of Human Umbilical Vein Endothelial Cells to Photodynamic Therapy: Highlighting the Role of Il-17 Signaling Pathway.

Introduction: Photodynamic therapy (PDT) is a method based on the application of a photosensitive agent and the administration of light irradiation on the treated samples. PDT is applied as an effective tool with minimal side effects against tumor tissues. This study aimed to assess the targets of critical genes by PDT at the cellular level of cancer to provide a new perspective on its molecular mechanism. Methods: To assess the effect of PDT, we extracted the differentially expressed genes (DEGs) from the gene expression profiles of human umbilical vein endothelial cells (HUVECs) treated with PDT from Gene Expression Omnibus (GEO) databases. The queried DEGs were evaluated via a regulatory network and gene ontology enrichment to find the critical targets. Results: Among 76 queried significant DEGs, 27 individuals were interacted by activation, inhibition, and co-expression actions. Thirty DEGs were related to the five classes of biological terms. The IL-17 signaling pathway and PTGS2, CXCL8, FOS, JUN, CXCL1, ZFP36, and FOSB were identified as the crucial targets of PDT. Conclusion: PDT as a stimulator of gene expression and an activator of gene activity overexpressed and hyper-activated many genes. It seems that PDT introduces a number of genes and pathways that can be regulated by anticancer drugs to fight against cancers.

Efficacy Evaluation of Human Skin Treatment with Photodynamic Therapy in Actinic Keratoses Patients.

Introduction: Photodynamic therapy (PDT) is a combined method of light and light-activated chemicals that are called photosensitizers (PSs). PDT is recommended as a high cure rate method with fewer side effects and a noninvasive tool to treat cancer. This study aimed to evaluate PDT efficacy as a therapeutic method against actinic keratoses in patients via protein-protein interaction (PPI) network analysis by using the gene expression profiles of Gene Expression Omnibus (GEO). Methods: Twenty-one gene expression profiles were extracted from GEO and analyzed by GEO2R to determine the significant differentially expressed genes (DEGs). The significant DEGs were included in PPI networks via Cytoscape software. The networks were analyzed by the "Network Analyzer", and the elements of the main connected components were assessed. Results: There were three main connected components for the compared sets of the gene expression profiles including the lesional region of skin before (Before set) and after (After set) PDT versus healthy (healthy set) skin and before versus after. The before-health comparison showed a partial similarity with the After-Healthy assessment. The before-after evaluation indicated that there were not considerable differences between the gene expression profile of the lesional region before and after PDT. Conclusion: In conclusion, PDT was unable to return the gene expression pattern of the actinic keratoses skin to a healthy condition completely.

Evaluating Serum Proteome in Women with Obsessive-Compulsive Disorder/Bipolar Disorder Compared to Pure Obsessive- Compulsive Disorder Subjects and Healthy Controls.

Objectives: The present study aimed to evaluate the serum proteome of women with obsessive-compulsive disorder (OCD)/bipolar disorder (BP) compared to pure OCD subjects and healthy controls. Materials & Methods: Serum proteome of women with OCD/BP, pure OCD individuals, and healthy controls were subjected to 2DE-based proteomics accompanied with MALDI-TOF-TOF mass spectrometry. Further evaluation of the identified protein spots with the significance of p<0.05 and fold≥1.5 was done by applying protein interaction mapping via Cytoscape v. 5.3.1 and its plugins. Results: The results indicate that vitamin D binding protein (GC) and haptoglobin spots (HP) significantly changed expression in OCD and OCD/BP with different expression patterns. These identified spots may contribute to OCD/BP and act as differentially recognized biomarkers comparing pure OCD and OCD/BP. Conclusion: The Findings imply that these proteins in the serum of the patients could be potential distinguishable biomarkers in clinical usage after related validation experiments. Therefore, this study provides a preliminary evaluation to understand OCD/BP proteome behavior better.

Targeting colon cancer via antimicrobial RT2 peptide: a system biology study.

Aim: This study aims to investigate the anticancer molecular mechanism of RT2 through protein-protein interaction (PPI) network analysis. For this aim, a bioinformatics evaluation of the proteome profile of colon cancer is carried out. Background: Antimicrobial peptides such as RT2 showed anticancer properties against various tumors. The molecular mechanism of the anticancer effect of RT2 is a challenging subject. Methods: By applying Cytoscape V.3.9.1 and integrated apps, the profile of the interaction network and related centrality is analyzed. An enrichment analysis of hub bottlenecks was also performed, and highlighted biological processes were visualized and determined. Results: Several 207 differentially expressed proteins were retrieved by PPI network analysis, and 10 hub bottlenecks were introduced. Among these differentially expressed proteins (DEPs), only AKT1 is from the queried DEPs. Key biological processes contributing to RT2 targeting mechanism include "Regulation of fibroblast proliferation", "Positive regulation of cyclin-dependent protein serine/threonine kinase activity", "positive regulation of miRNA transcription", and "fungiform papilla formation". Conclusion: In conclusion, central proteins Tp53, MYC, EGFR, AKT1, HDAC1, and SRC can be introduced as a targeted biomarker panel of bioactive peptide treatments. However, extensive research is required to establish this claim before clinical application.

Systematic Analysis of Protein-Protein and Gene-Environment Interactions to Decipher the Cognitive Mechanisms of Autism Spectrum Disorder.

Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder resulting from both genetic and environmental risk factors, is manifested by deficits in cognitive function. Elucidating the cognitive disorder-relevant biological mechanisms may open up promising therapeutic approaches. In this work, we mined ASD cognitive phenotype proteins to construct and analyze protein-protein and gene-environment interaction networks. Incorporating the protein-protein interaction (PPI), human cognition proteins, and connections of autism-cognition proteins enabled us to generate an autism-cognition network (ACN). With the topological analysis of ACN, important proteins, highly clustered modules, and 3-node motifs were identified. Moreover, the impact of environmental exposures in cognitive impairment was investigated through chemicals that target the cognition-related proteins. Functional enrichment analysis of the ACN-associated modules and chemical targets revealed biological processes involved in the cognitive deficits of ASD. Among the 17 identified hub-bottlenecks in the ACN, PSD-95 was recognized as an important protein through analyzing the module and motif interactions. PSD-95 and its interacting partners constructed a cognitive-specific module. This hub-bottleneck interacted with the 89 cognition-related 3-node motifs. The identification of gene-environment interactions indicated that most of the cognitive-related proteins interact with bisphenol A (BPA) and valproic acid (VPA). Moreover, we detected significant expression changes of 56 cognitive-specific genes using four ASD microarray datasets in the GEO database, including GSE28521, GSE26415, GSE18123 and GSE29691. Our outcomes suggest future endeavors for dissecting the PSD-95 function in ASD and evaluating the various environmental conditions to discover possible mechanisms of the different levels of cognitive impairment.

Investigation into Chronic Low-Dose Ionizing Radiation Effect on Gene Expression Profile of Human HUVECs Cells.

Introduction: Understanding the molecular mechanism of chronic low-dose ionizing radiation (LDIR) effects on the human body is the subject of many research studies. Several aspects of cell function such as cell proliferation, apoptosis, inflammation, and tumorigenesis are affected by LDIR. Detection of the main biological process that is targeted by LIDR via network analysis is the main aim of this study. Methods: GSE66720 consisting of gene expression profiles of human umbilical vein endothelial cells (HUVECs) (a suitable cell line to be investigated), including irradiated and control cells, was downloaded from Gene Expression Omnibus (GEO). The significant differentially expressed genes (DEGs) were determined and analyzed via protein-protein interaction (PPI) network analysis to find the central individuals. The main cell function which was related to the central nodes was introduced. Results: Among 64 queried DEGs, 48 genes were recognized by the STRING database. C-X-C motif chemokine ligand 8 (CXCL8), intercellular adhesion molecule 1 (ICAM1), Melanoma growth-stimulatory activity/growth-regulated protein α (CXCL1), vascular cell adhesion molecule 1 (VCAM-1), and nerve growth factor (NGF) were introduced as hub nodes. Conclusion: Findings indicate that inflammation is the main initial target of LDIR at the cellular level which is associated with alteration in the other essential functions of the irradiated cells.

Hypofractionated Radiation Versus Conventional Fractionated Radiation: A Network Analysis.

Introduction: Conventional fractionation (CF) and hypofractionation (HF) are two radiotherapy methods against cancer, which are applied in medicine. Understanding the efficacy and molecular mechanism of the two methods implies more investigations. In the present study, proteomic findings about the mentioned methods relative to the controls were analyzed via network analysis. Methods: The significant differentially expressed proteins (DEPs) of prostate cancer (PCa) cell line DU145 in response to CF and HF radiation therapy versus controls were extracted from the literature. The protein-protein interaction (PPI) networks were constructed via the STRING database via Cytoscape software. The networks were analyzed by "NetworkAnalyzer" to determine hub DEPs. Results: 126 and 63 significant DEPs were identified for treated DU145 with CF and HF radiation respectively. The PPI networks were constructed by the queried DEPs plus 100 first neighbors. ALB, CD44, THBS1, EPCAM, F2, KRT19, and MCAM were highlighted as common hubs. VTM, OCLN, HSPB1, FLNA, AHSG, and SERPINC1 appeared as the discriminator hub between the studied cells. Conclusion: 70% of the hubs were common between CF and HF conditions, and they induced radio-resistance activity in the survived cells. Six central proteins which discriminate the function of the two groups of the irradiated cells were introduced. On the basis of these findings, it seems that DU145-CF cells, relative to the DU145-UF cells, are more radio-resistant.

Network Analysis of Effect of Light-Dark Time Ratio on the Gene Expression Profile of Mouse Skin.

Introduction: Circadian rhythms refer to daily cyclic events such as activity and rest in biology. A protein-based core related to the mechanism of circadian is identified. In the present study, the gene expression profiles of mouse skin in different conditions of light-dark times were investigated via protein-protein interaction (PPI) analysis to explore the main affected genes. Methods: GSE174155 was derived from Gene Expression Omnibus (GEO) and was analyzed via GEO2R to find the significant differentially expressed genes (DEGs). The gene expression profiles of Cry-null (genotype: cryptochrome-1(-/-): crytochrome-2 (-/-)) mouse skin versus the wild-type samples in the various circadian times (CTs) were assessed. The queried DEGs plus 50 first neighbors were included in a PPI network via the STRING database by Cytoscape software. The networks were analyzed and the central nodes were evaluated. Results: Three groups of mice based on CTs were identified. 15, 15, and 14 central nodes were determined as central nodes for the analyze networks. There was not a common central node for the analyzed networks. Conclusion: It was pointed out that the light/dark time ratio had a gross effect on the gene expression profile of the skin in the mice. Results imply more investigations to suggest a standard protocol related to CT, considering human lifestyle and exploring suitable protective methods for the jobs which are fixed in the abnormal CT sets.

Obsessive-Compulsive Disorder Interactome Profile Analysis: A Perspective From Molecular Mechanism.

INTRODUCTION: Obsessive-Compulsive Disorder (OCD) is one of the complex neuropsychiatric conditions. This disorder disables individuals in many different aspects of their personal and social life. Interactome analysis may provide a better understanding of this disorder's molecular origin and its underlying mechanisms. METHODS: In this study, the OCD-associated genes were extracted from the literature. The criterion for gene selection was to choose genes with at least one significant report. Furthermore, by applying Cytoscape and its plugins, protein-protein interaction network, and gene ontology of the 31 candidate genes related to OCD from genetic association studies is examined. The cross-validation method was used for network centrality assessment. RESULTS: A scale-free network, including 1940 nodes and 3269 edges for 31 genes, was constructed. According to the network centrality evaluation, ESR1, TNFα, DRD2, DRD4, HTR1B, HTR2A, and CDH2 showed the highest values and can be considered hub-bottlenecks elements. It is also confirmed by the number of 123 cross-validation tests that the frequency of these essential genes remains unaltered against the initial seed genes' changes with the accuracy of 0.962. Besides, enrichment analysis identified four highlighted biological processes related to the 31 candidate genes. The top biological processes are determined as dopamine transport, learning, memory, and monoamine transport. CONCLUSION: Among 31 initial genes, 7 were introduced as crucial elements for onset and development in OCD and can be suggested for further investigations. Furthermore, the complex molecular origin of OCD requires high-throughput screening for diagnosis and treatment goals. The findings are a possible valuable source to establish molecular-based diagnostic tools for OCD.

Introducing physical exercise as a potential strategy in liver cancer prevention and development.

AIM: This study aimed to investigate the anticancer properties of physical activity by network analysis in trained rats. BACKGROUND: Much evidence supports the benefits of physical activity, most of which are related to metabolism regulation and body health. Deeper investigation deals with other features of physical activity, such as its anticancer properties. METHODS: Protein-protein interaction network analysis was applied to investigate the proteome profile of livers of rats subjected to physical activity through bioinformatics. Twelve differentially expressed proteins were searched and analyzed by Cytoscape 3.7.2 and its plug-ins. The network was analyzed to identify hub-bottleneck nodes. An action map was constructed for the central proteins. RESULTS: Among the queried proteins, Eno1 and Pgm1 were only assigned as hubs by Network Analzyer. Gpi, Pkm, Aldoa, and Aldoart2 were identified as central nodes among the first neighbors of network elements. Furthermore, the glycolytic, carbohydrate catabolic, and glucose metabolic processes are key elements that could be imperative in the mechanism of exercise in liver function. The anticancer properties of the central nodes were highlighted. CONCLUSION: The network findings indicate the anticancer properties of physical activity, which has also been supported by previous investigations.

Platelet and Haemostasis are the Main Targets in Severe Cases of COVID-19 Infection; a System Biology Study.

INTRODUCTION: Many proteomics-based and bioinformatics-based efforts are made to detect the molecular mechanism of COVID-19 infection. Identification of the main protein targets and pathways of severe cases of COVID-19 infection is the aim of this study. METHODS: Published differentially expressed proteins were screened and the significant proteins were investigated via protein-protein interaction network using Cytoscape software V. 3.7.2 and STRING database. The studied proteins were assessed via action map analysis to determine the relationship between individual proteins using CluePedia. The related biological terms were investigated using ClueGO and the terms were clustered and discussed. RESULTS: Among the 35 queried proteins, six of them (FGA, FGB, FGG, and FGl1 plus TLN1 and THBS1) were identified as critical proteins. A total of 38 biological terms, clustered in 4 groups, were introduced as the affected terms. "Platelet degranulation" and "hereditary factor I deficiency disease" were introduced as the main class of the terms disturbed by COVID-19 virus. CONCLUSION: It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients' condition by examining the introduced critical differentially expressed proteins (DEPs).

Bioinformatics Investigation and Contribution of Other Chromosomes Besides Chromosome 21 in the Risk of Down Syndrome Development.

INTRODUCTION: Down syndrome as a genetic disorder is a popular research topic in molecular studies. One way to study Down syndrome is via bioinformatics. METHODS: In this study, a gene expression profile from a microarray study was selected for more investigation. RESULTS: The study of Down syndrome patients shows specific genes with differential expression and network centrality properties. These genes are introduced as RHOA, FGF2, FYN, and CD44, and their level of expression is high in these patients. CONCLUSION: This study suggests that besides chromosomes 21, there are additional contributing chromosomes to the risk of Down syndrome development. Besides, these genes could be used for clinical studies after more analysis.

Investigating the Effects of Ibuprofen on the Gene Expression Profile in Hippocampus of Mice Model of Alzheimer's Disease through Bioinformatics Analysis.

Non-steroidal anti-inflammatory drugs (NSAIDs) are identified as effective in many diseases. One of which is neurodegenerative diseases including Alzheimer disease (AD). In this study gross alteration of gene expression in AD mice by ibuprofen treatment is investigated via Protein-protein interaction network (PPI) analysis. Expression profiling of microarray dataset GSE67306 was retrieved from GEO database and analyzed via GEO2R tool. PPI analysis was performed via Cytoscape 3.7.0. and its plug-ins including Network Analyzer, Gene MANIA, and CluePedia. Numbers of 10 central genes including Htr1a, Sstr2, Drd2, Htr1b, Penk, Pomc, Oprm1, Npy, Sst, and Chrm2 were identified as potential biomarkers. However, the role of Penk gene was highlighted. The finding indicates that ibuprofen changes gene expression level of several genes that are involved in AD.

Introducing APOA1 as a key protein in COVID-19 infection: a bioinformatics approach.

AIM: Introducing possible diagnostic and therapeutic biomarker candidates via the identification of chief dysregulated proteins in COVID-19 patients is the aim of this study. BACKGROUND: Molecular studies, especially proteomics, can be considered as suitable approaches for discovering the hidden aspect of the disease. METHODS: Differentially expressed proteins (DEPs) of three patients with demonstrated severe condition (S-COVID-19) were compared to healthy cases by a proteomics study. Cytoscape software and STRING database were used to construct the protein-protein interaction (PPI) network. The central DEPs were identified through topological analysis of the network. ClueGO+CluePedia were applied to find the biological processes related to the central nodes. MCODE molecular complex detection (MCODE) was used to discover protein complexes. RESULTS: A total of 242 DEPs from among 256 query ones were included in the network. Centrality analysis of the network assigned 16 hub-bottlenecks, nine of which were presented in the highest-scored protein complex. Ten protein complexes were determined. APOA1 was identified as the protein complex seed, and APP, EGF, and C3 were the top hub-bottlenecks of the network. The results specify that up-regulation of C3 and down-regulation of APOA1 in urine play a role in the stiffness in respiration and, accordingly, the severity of COVID-19. Moreover, dysregulation of APP and APOA1 could both contribute to the possible adverse effects of COVID-19 on the nervous system. CONCLUSION: The introduced central proteins of the S-COVID-19 interaction network, particularly APOA1, can be considered as diagnostic and therapeutic targets related to the coronavirus disease after being approved with complementary studies.

Assessment of the Microbiome Role in Skin Protection Against UV Irradiation Via Network Analysis.

Introduction: Diverse microbiotas which have some contributions to gene expression reside in human skin. To identify the protective role of the skin microbiome against UV exposure, proteinprotein interaction (PPI) network analysis is used to assessment gene expression alteration. Methods: A microarray dataset, GEO accession number GSE117359, was considered in this respect. Differential expressed genes (DEGs) in the germ-free (GF) and specific pathogen-free (SPF) groups are analyzed by GEO2R. The top significant DEGs were assigned for network analysis via Cytoscape 3.7.2 and its applications. Results: A total of 28 genes were identified as significant DEGs and the centrality analysis of the network indicated that only one of the seven hub-bottlenecks was from queried genes. The gene ontology analysis of Il6, Cxcl2, Cxcl1, TNF, Il10, Cxcl10, and Mmp9 showed that the crucial genes were highly enriched in the immune system. Conclusion: The skin microbiome plays a significant role in the protection of skin against UV irradiation and the role of TNF and IL6 is prominent in this regard.

Differentiation of H. pylori-negative and positive gastric cancer via regulatory network analysis.

AIM: To understand the molecular difference between H.pylori negative and positive gastric cancer, a regulatory network analysis is investigated. BACKGROUND: Helicobacter pylori as the one of the most leading causes of gastric cancer is yet to be studied in terms of its molecular pathogenicity. METHODS: Cytoscape version of 3.7.2 with its applications was employed to conduct this study via corresponding algorithms. RESULTS: A total of 161 microRNAs were identified differentially expressed in the comparison of two groups of gastric cancer including negative and positive with H.pylori infection. CluePedia explored the regulatory network and found down-regulation dominant while considering the linked hub genes. CONCLUSION: It can be concluded that the presented microRNAs and target genes could have associations with H.pylori carcinogenesis in gastric cancer through dysregulation of some vital biological processes. These microRNAs and target genes include hsa-miR-943, hsa-miR-935, hsa-miR-367, hsa-miR-363, hsa-miR-25, and hsa-miR-196b and ADRA1A, KCNA4, SOD1, and SESN3, respectively. However, verification analysis in this regard is required to establish these relationships.

The Effect of Low-Power Laser Therapy on the TGF/ß Signaling Pathway in Chronic Kidney Disease: A Review.

Objective: The purpose of this study is to investigate the effects of low-power lasers on kidney disease by investigating several studies. Methods: A number of articles from 1998 to 2019 were chosen from the sources of PubMed, Scopus, and only the articles studying the effect of low-power lasers on kidney disease were investigated. Results: After reviewing the literature, 21 articles examining only the effects of low-power lasers on kidney disease were found. The results of these studies showed that the parameter of the lowpower laser would result in different outcomes. So, a low-power laser with various parameters can be effective in the treatment of kidney diseases such as acute kidney disease, diabetes, glomerulonephritis, nephrectomy, metabolic syndrome, and kidney fibrosis. Most studies have shown that low-power lasers can affect TGFß1 signaling which is the most important signaling in the treatment of renal fibrosis. Conclusion: Lasers can be effective in reducing or enhancing inflammatory responses, reducing fibrosis factors, and decreasing reactive oxygen species (ROS) levels in kidney disease and glomerular cell proliferation.

Highlighted role of "IL17 signaling pathway" in gastroesophageal reflux disease.

AIM: The aim of this study is to assess the molecular profile of gastroesophageal reflux disease (GERD) via Protein-protein interaction (PPI) network analysis and gene ontology (GO) investigation. BACKGROUND: GERD which affects the life of about 30% of people is associated with high costs in the human papulation. Several risk factors such as smoking, eating habits, BMI, and dysfunction of lower esophageal sphincter have been reported to contribute to the onset and progression of GERD. The roles of some types of interleukins and inflammatory factors as molecular features of GERD are investigated. METHODS: Genes related to GERD were analyzed by Cytoscape v.3.7.2 and the corresponding plug-ins. ClueGO and CluePedia assessed the gene ontology and action type properties for the central nodes. RESULTS: The results indicated that there are 12 hub-bottlenecks almost all of which except ALB are dispersed in the network clusters 1 and 2. Il17 signaling pathway among 7 identified biochemical pathways was also detected as a most related annotation for these central genes. CONCLUSION: Numbers of 11 critical genes and one pathway (IL17 signaling pathway) were highlighted as the deregulate genes and pathway in GERD. Common molecular features of GERD and cancer appeared.

Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis.

BACKGROUND: The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study. MATERIALS AND METHODS: Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined. RESULTS: Pathway analysis revealed 18 disturbed pathways related to the CVD. glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound. CONCLUSION: It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches.