Beta hydroxybutyrate induces lung cancer cell death, mitochondrial impairment and oxidative stress in a long term glucose-restricted condition.

BACKGROUND: Metabolic plasticity gives cancer cells the ability to shift between signaling pathways to facilitate their growth and survival. This study investigates the role of glucose deprivation in the presence and absence of beta-hydroxybutyrate (BHB) in growth, death, oxidative stress and the stemness features of lung cancer cells. METHODS AND RESULTS: A549 cells were exposed to various glucose conditions, both with and without beta-hydroxybutyrate (BHB), to evaluate their effects on apoptosis, mitochondrial membrane potential, reactive oxygen species (ROS) levels using flow cytometry, and the expression of CD133, CD44, SOX-9, and ß-Catenin through Quantitative PCR. The activity of superoxide dismutase, glutathione peroxidase, and malondialdehyde was assessed using colorimetric assays. Treatment with therapeutic doses of BHB triggered apoptosis in A549 cells, particularly in cells adapted to glucose deprivation. The elevated ROS levels, combined with reduced levels of SOD and GPx, indicate that oxidative stress contributes to the cell arrest induced by BHB. Notably, BHB treatment under glucose-restricted conditions notably decreased CD133 expression, suggesting a potential inhibition of cell survival through the downregulation of CD133 levels. Additionally, the simultaneous decrease in mitochondrial membrane potential and increase in ROS levels indicate the potential for creating oxidative stress conditions to impede tumor cell growth in such environmental settings. CONCLUSION: The induced cell death, oxidative stress and mitochondria impairment beside attenuated levels of cancer stem cell markers following BHB administration emphasize on the distinctive role of metabolic plasticity of cancer cells and propose possible therapeutic approaches to control cancer cell growth through metabolic fuels.

Potential metabolic biomarkers of critical limb ischemia in people with type 2 diabetes mellitus.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a significant risk factor for the development of critical limb ischemia (CLI), the most advanced stage of peripheral arterial disease. The concurrent existence of T2DM and CLI often leads to adverse outcomes, namely limb amputation. OBJECTIVE: To identify biomarkers for improving the screening of CLI in high-risk people with T2DM. METHODS: We investigated metabolome profiles in serum samples of 113 T2DM people with CLI (n = 23, G2) and without CLI (n = 45, G0: no lower limb stenosis (LLS) and n = 45, G1: LLS < 50%), using hydrogen nuclear magnetic resonance (1H NMR) approach. Principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to analyze 1H NMR data. RESULTS: Twenty potential metabolites that could discriminate people with T2DM and CLI (G2) from non-CLI patients without LLS (G0) were determined in serum samples. The correct percent of classification for the PLS-DA model for the test set samples was 85% (n = 20) and 100% (n = 5) for G0 and G2 groups, respectively. Non-CLI patients with LLS < 50% (G1) were projected on the PCA abstract space built using 20 discriminatory metabolites. Eleven people with T2DM and LLS < 50% were prospectively followed, and their ankle-brachial index (ABI) was measured after 4 years. A promising agreement existed between the PCA model's predictions and those obtained by ABI values. CONCLUSION: The findings suggest that confirmation of blood potential metabolic biomarkers as a complement to ABI for screening of CLI in a large group of high-risk people with T2DM is needed.

In silico screening of inhibitors against human dihydrofolate reductase to identify potential anticancer compounds.

In all species, dihydrofolate reductase (DHFR) is an essential enzyme that regulates the cellular amount of tetrahydrofolate. Human DHFR (hDHFR) activity inhibition results in tetrahydrofolate depletion and cell death. This property has made hDHFR a therapeutic target for cancer. Methotrexate is a well-known hDHFR inhibitor, but its administration has shown some light to severe adverse effects. Therefore, we aimed to find new potential hDHFR inhibitors using structure-based virtual screening, ADMET prediction, molecular docking, and molecular dynamics simulations. Here, we used the PubChem database to find all compounds with at least 90% structural similarity to known natural DHFR inhibitors. To explore their interaction pattern and estimate their binding affinities, the screened compounds (2023) were subjected to structure-based molecular docking against hDHFR. The fifteen compounds that showed higher binding affinity to the hDHFR than the reference compound (methotrexate) displayed important molecular orientation and interactions with key residues in the enzyme's active site. These compounds were subjected to Lipinski and ADMET prediction. PubChem CIDs: 46886812 and 638190 were identified as putative inhibitors. In addition, molecular dynamics simulations revealed that the binding of compounds (CIDs: 46886812 and 63819) stabilized the hDHFR structure and caused minor conformational changes. Our findings suggest that two compounds (CIDs: 46886812 and 63819) could be promising potential inhibitors of hDHFR in cancer therapy.Communicated by Ramaswamy H. Sarma.

Gut Microbiota and Their Associated Metabolites in Diabetes: A Cross Talk Between Host and Microbes-A Review.

Dysbiosis of the gut microbiota's composition and function is important in developing insulin resistance and diabetes. Diabetes has also been linked to changes in the circulating and fecal metabolites. Evidence suggests the associations between the gut microbiota and the aberrant diabetes-related metabolome. Metabolites play a crucial role in the host-microbiota interactions. Researchers have used a combination of metagenomic and metabolomic approaches to investigate the relationships between gut microbial dysbiosis and metabolic abnormalities in diabetes. We summarized current discoveries on the associations between the gut microbiota and metabolites in type 1 diabetes, type 2 diabetes, and gestational diabetes mellitus in the scoping review. According to research, the gut microbiota changes might involve in the development of diabetes through modulating the host's metabolic pathways such as immunity, energy metabolism, lipid metabolism, and amino acid metabolism. These results add to our understanding of the interplay between the host and gut microbiota metabolism.

MIL-125-based nanocarrier decorated with Palladium complex for targeted drug delivery.

The aim of this work was to provide a novel approach to designing and synthesizing a nanocomposite with significant biocompatibility, biodegradability, and stability in biological microenvironments. Hence, the porous ultra-low-density materials, metal-organic frameworks (MOFs), have been considered and the MIL-125(Ti) has been chosen due to its distinctive characteristics such as great biocompatibility and good biodegradability immobilized on the surface of the reduced graphene oxide (rGO). Based on the results, the presence of transition metal complexes next to the drug not only can reinforce the stability of the drug on the structure by preparing π-π interaction between ligands and the drug but also can enhance the efficiency of the drug by preventing the spontaneous release. The effect of utilizing transition metal complex beside drug (Doxorubicin (DOX)) on the drug loading, drug release, and antibacterial activity of prepared nanocomposites on the P. aeruginosa and S. aureus as a model bacterium has been investigated and the results revealed that this theory leads to increasing about 200% in antibacterial activity. In addition, uptake, the release of the drug, and relative cell viabilities (in vitro and in vivo) of prepared nanomaterials and biomaterials have been discussed. Based on collected data, the median size of prepared nanocomposites was 156.2 nm, and their biological stability in PBS and DMEM + 10% FBS was screened and revealed that after 2.880 min, the nanocomposite's size reached 242.3 and 516 nm respectively. The MTT results demonstrated that immobilizing PdL beside DOX leads to an increase of more than 15% in the cell viability. It is noticeable that the AST:ALT result of prepared nanocomposite was under 1.5.

EGR1 Is a Critical Gene in Response of Human Keratinocyte to Blue Light Radiation.

Introduction: Investigating the molecular mechanism of cellular response to light radiation has attracted many researchers' attention. In the present study, the critically affected gene by 7.5 min blue light radiation in human keratinocytes was investigated via network analysis. Methods: Gene expression profiles of human keratinocytes exposed to 7.5 min blue light radiation plus controls were extracted from Gene Expression Omnibus (GEO). The significant dysregulated genes plus 100 first neighbors were investigated by Cytoscape software and its applications. The central nodes of the network based on four centrality parameters were determined and discussed. Results: Among 6 significant dysregulated genes, 4 individuals were recognized by the STRING database. The network was constructed by using the 4 queried genes and 100 first neighbors. EGR1, STAT1, and ISG15 were identified as central nodes; however, the prominent role of EGR1 was highlighted. Conclusion: EGR1 appeared as a critically affected gene after blue light irradiation. It seems that this upregulated gene is responsible for protecting human keratinocytes against stress and cancer. Therefore, the application of blue light may be accompanied by antistress effects in the human body.

Effect of UV Laser Radiation on "Positive Regulation of Telomere Maintenance" in Saccharomyces cerevisiae.

Introduction: Excessive exposure to ultraviolet (UV) radiation may cause a variety of skin cancers and damage to the eye lens. The assessment of different aspects of UV damage has attracted researchers' interest. UV radiation to simple biological models such as Saccharomyces cerevisiae of yeast family could help to find out different molecular changes resulting from radiation. The assessment and network analysis of gene expression data about yeast cells radiated by the UV laser was the aim of this study. Methods: The gene expression profiles of S. cerevisiae samples in the presence of the UV laser at 30 seconds radiation and 15 minutes' post-radiation time are compared with the control profiles. The significant expressed genes interacted and the central nodes and related biological terms were identified. Results: The main connected component of the network including 427 nodes was analyzed and 11 central differentially expressed genes (DEGs) were determined. RPN11, UBI4, HSP82, and HSC82 as critical DEGs and "positive regulation of telomere maintenance" as a related biological term were introduced. Conclusion: The finding has provided a new perspective on laser application in the rejuvenation process. It seems that the laser can be used as a suitable agent against the aging process which is a limiting factor in human life.

Exploring the Reversible Effects of UV Laser Radiation on the Gene Expression Profiles of Saccharomyces cerevisiae Via Network Analysis.

Introduction: The reversibility of biological processes is an important challenge in the study of environmental pollutants and also natural and artificial radiation. There are many pieces of evidence about the reversible and irreversible effects of UV radiation on the human body. Assessment of the reversibility of UV laser effects on Saccharomyces cerevisiae was the aim of this study. Methods: Gene expression alteration in S. cerevisiae samples radiated by a 30s UV laser for 15, 30, and 60 minutes post-radiation times were investigated via network analysis to explore time-dependent reversible alteration in the gene expression profiles of the samples. Results: 19 differentially expressed genes (DEGs) were identified as targeted genes for the samples which were harvested 60 minutes after radiation; network analysis revealed no significant alteration in biological processes. Conclusion: It can be concluded that the gross effects of the UV laser on S. cerevisiae samples disappear after 60 minutes of radiation.

Assessment of Post-Radiation Time Effect on Gene Expression Profiles of Saccharomyces cerevisiae Samples After Appling a UV Laser.

Introduction: Widespread application of lasers in different fields of medicine implies more investigations into the molecular mechanism of laser effects on the human body. Network analysis of the dysregulated genes of Saccharomyces cerevisiae samples are irradiated by a UV laser and harvested 30 minutes after radiation compared with a 15-minute group is the aim of this research. Methods: The significant dysregulated genes interacted via the STRING database, and the central nodes were determined by "Networkanalyzer" application of Cytoscape software. The critical genes and the related biological terms were identified via action map analysis and gene ontology assessment. Results: The gene expression profiles of the samples with 30-minute post-radiation time were different from the samples with 15 minutes of post-radiation time. 9 potent central genes, 50% of which were similar to the nodes of the 15-minute group, were identified. The terms "positive regulation of telomere maintenance" were targeted in the two sample groups. Conclusion: In spite of large alteration in the gene expression profiles of the samples, the results indicated that the main affected biological term for the 15-minute and 30-minute groups was similar.

The metabolome profiling of obese and non-obese individuals: Metabolically healthy obese and unhealthy non-obese paradox.

OBJECTIVES: The molecular basis of "metabolically healthy obese" and "metabolically unhealthy non-obese" phenotypes is not fully understood. Our objective was to identify metabolite patterns differing in obese (metabolically healthy vs unhealthy (MHO vs MUHO)) and non-obese (metabolically healthy vs unhealthy (MHNO vs MUHNO)) individuals. MATERIALS AND METHODS: This case-control study was performed on 86 subjects stratified into four groups using anthropometric and clinical measurements: MHO (21), MUHO (21), MHNO (22), and MUHNO (22). Serum metabolites were profiled using nuclear magnetic resonance (NMR). Multivariate analysis was applied to uncover discriminant metabolites, and enrichment analysis was performed to identify underlying pathways. RESULTS: Significantly higher levels of glutamine, asparagine, alanine, L-glutathione reduced, 2-aminobutyrate, taurine, betaine, and choline, and lower level of D-sphingosine were observed in MHO group compared with MUHO. In comparison of MHNO and MUHNO groups, significantly lower levels of alanine, glycine, glutamine, histidine, L-glutathione reduced, and betaine, and higher levels of isoleucine, L-proline, cholic acid, and carnitine appeared in MUHNO individuals. Moreover, significantly affected pathways included amino acid metabolism, urea cycle and ammonia recycling in MUHO subjects and glutathione metabolism, amino acid metabolism, and ammonia recycling in MUHNO members. CONCLUSION: Literature review helped us to hint that altered levels of most metabolites might associate to insulin sensitivity and insulin resistance in MHO and MUHNO individuals, respectively. Besides, abnormal amino acid metabolism and ammonia recycling involved in unhealthy phenotypes (MUHO, MUHNO) might be associated with insulin resistance.

The association between SMAD7 polymorphisms and colorectal cancer susceptibility as well as clinicopathological features in the Iranian population.

AIM: Our aim was to investigate the association between two single nucleotide polymorphisms (SNPs) of SMAD7 and the risk of CRC among Iranian individuals. BACKGROUND: Genome-wide association studies (GWAS) have identified 18q21 as a risk locus for colorectal cancer (CRC), which maps to the SMAD7 gene. METHODS: This case-control study was conducted on 109 CRC cases and 109 controls in the Iranian population to evaluate the influence of two SNPs of SMAD7, rs2337106 and rs6507874, on the risk of CRC as well as on clinicopathological features. Genotype determination was performed by TaqMan assay via an ABI 7500 Real Time PCR System (Applied Biosystems) for the DNA of peripheral blood. Descriptive analysis and logistic regression model were used for statistical analyses. RESULTS: Genotyping of the SNPs in the SMAD7 gene revealed that the frequency of G allele of rs2337106 was 53.7% in controls and 56.4% in cases (p-value=0.564) while the frequency of C allele of rs6507874 was 55.5% in controls and 56.3% in cases (p-value=0.772). Further, there were no significant differences in genotype frequencies of these SNPs between CRC patients and controls. The SMAD7 genotypes were not associated with the risk of CRC or with any clinicopathological characteristics such as tumor site, tumor grade, and stage TNM in CRC patients (p-value>0.05), even after adjustment for sex, age, and smoking status. CONCLUSION: Our results provided the first evidence that SMAD7 genotypes, rs2337106 and rs6507874, could not be predisposing markers in genetic susceptibility to CRC in an Iranian population, at least in the studied population.

Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis.

PURPOSE: Type 1 diabetes (T1D) and multiple sclerosis (MS) are classified as T cell-mediated autoimmune diseases. Although convergent evidence proposed common genetic architecture for autoimmune diseases, it remains a challenge to identify them. This study aimed to determine common gene signature and pathways in T1D and MS via systems biology approach. METHODS: Gene expression profiles of peripheral blood mononuclear cells (PBMCs) and pancreatic-ß cells in T1D as well as PBMCs and cerebrospinal fluid (CSF) in MS were analyzed in our previous published data, and differential expressed genes were integrated with protein-protein interactions data to construct Query-Query PPI (QQPPI) networks. In this study, QQPPI networks were further analyzed to investigate more central genes, functional modules and complexes shared in T1D and MS progression. Lastly, the interaction of common genes with drugs was also explored. RESULTS: Several cytokines such as IL-23A, IL-32, IL-34, and IL-37 tend to be differentially expressed in both diseases. In addition, PSMA1, MYC, SRPK1, YBX1, HNRNPM, NF-κB2, IKBKE, RAC1, FN1, ARRB2, ESR1, HSP90AB1, and PPP1CA were common high central genes in QQPPI networks corresponding to each disease. Proteasome, spliceosome, immune responses, apoptosis, cellular communication/signaling transduction mechanism, interaction with environment, and activity of intercellular mediators were shared biological processes in T1D and MS. Finally, azathioprine, melatonin, resveratrol, and geldanamycin identified as prioritized drugs for the treatment of patients with T1D and MS. CONCLUSIONS: This study represented novel key genes and pathways shared between T1D and MS, which may facilitate the identification of potential therapeutic targets in these diseases.

Centrality Analysis of Protein-Protein Interaction Networks and Molecular Docking Prioritize Potential Drug-Targets in Type 1 Diabetes.

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic ß- cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic ß-cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic ß-cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2 and SRC) QQPPI networks. Thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol-ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.

Epithelial-to-mesenchymal transition in thyroid cancer: a comprehensive review.

The Metastatic progression of solid tumors, such as thyroid cancer is a complex process which involves various factors. Current understanding on the role of epithelial-mesenchymal transition (EMT) in thyroid carcinomas suggests that EMT is implicated in the progression from follicular thyroid cancer (FTC) and papillary thyroid cancer (PTC) to poorly differentiated thyroid carcinoma (PDTC) and anaplastic thyroid cancer (ATC). According to the literature, the initiation of the EMT program in thyroid epithelial cells elevates the number of stem cells, which contribute to recurrent and metastatic diseases. The EMT process is orchestrated by a complex network of transcription factors, growth factors, signaling cascades, epigenetic modulations, and the tumor milieu. These factors have been shown to be dysregulated in thyroid carcinomas. Therefore, molecular interferences restoring the expression of tumor suppressors, or thwarting overexpressed oncogenes is a hopeful therapeutic method to improve the treatment of progressive diseases. In this review, we summarize the recent findings on EMT in thyroid cancer focusing on the main role-players and regulators of this process in thyroid tumors.

Identification of new key genes for type 1 diabetes through construction and analysis of protein-protein interaction networks based on blood and pancreatic islet transcriptomes.

BACKGROUND: Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic ß-cells are destroyed by infiltrating immune cells. Bilateral cooperation of pancreatic ß-cells and immune cells has been proposed in the progression of T1D, but as yet no systems study has investigated this possibility. The aims of the study were to elucidate the underlying molecular mechanisms and identify key genes associated with T1D risk using a network biology approach. METHODS: Interactome (protein-protein interaction [PPI]) and transcriptome data were integrated to construct networks of differentially expressed genes in peripheral blood mononuclear cells (PBMCs) and pancreatic ß-cells. Centrality, modularity, and clique analyses of networks were used to get more meaningful biological information. RESULTS: Analysis of genes expression profiles revealed several cytokines and chemokines in ß-cells and their receptors in PBMCs, which is supports the dialogue between these two tissues in terms of PPIs. Functional modules and complexes analysis unraveled most significant biological pathways such as immune response, apoptosis, spliceosome, proteasome, and pathways of protein synthesis in the tissues. Finally, Y-box binding protein 1 (YBX1), SRSF protein kinase 1 (SRPK1), proteasome subunit alpha1/ 3, (PSMA1/3), X-ray repair cross complementing 6 (XRCC6), Cbl proto-oncogene (CBL), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), phospholipase C gamma 1 (PLCG1), SHC adaptor protein1 (SHC1) and ubiquitin conjugating enzyme E2 N (UBE2N) were identified as key markers that were hub-bottleneck genes involved in functional modules and complexes. CONCLUSIONS: This study provide new insights into network biomarkers that may be considered potential therapeutic targets.

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis.

BACKGROUND: The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein-protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. METHODS: Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. RESULTS: The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. DISCUSSION: This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.

Protein-protein interaction networks (PPI) and complex diseases.

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.

Polymorphism of SMAD7 gene (rs2337104) and risk of colorectal cancer in an Iranian population: a case-control study.

AIM: The purpose of this study was to evaluate the influence of intronic polymorphism of the SMAD7 (Mothers Against Decantaplegic Homolog 7) gene (rs2337104) on the risk of colorectal cancer (CRC) and clinicopathological features in an Iranian population. BACKGROUND: SMAD7 has been identified as an antagonist of transforming growth factor beta (TGF-b)-mediating fibrosis, carcinogenesis, and inflammation. Regarding to the recent genome-wide scan, a risk locus for colorectal cancer at 18q21 has been found, which maps to the SMAD7 gene. PATIENTS AND METHODS: This case-control study was performed on 109 CRC patients and 109 healthy controls recruited in Taleghani Hospital. The genotyping of all samples were done by TaqMan assay via an ABI 7500 Real Time PCR System (Applied Biosystems) with DNA from peripheral blood. The association of this polymorphism with the risk of CRC and clinicopathological features was investigated. RESULTS: Our results indicated that there were no significant association between genotypic and allelic frequencies of SMAD7 polymorphism (rs2337104) and CRC risk in our population. Although the T allele is the most frequent one in this population and its frequency was 86.7% in patients compared with 91.7% in controls (OR=1.705, 95% CI= 0.916-3.172). Also, the SMAD7 genotypes were not associated with any clinicopathological characteristics in CRC patients (P>0.05). CONCLUSION: For the first time, this study results revealed that this SMAD7 polymorphism couldn't be a potential risk factor for CRC or a prognostic biomarker for prediction of clinicopathological features in an Iranian population. A large-scale case-control study is needed to validate our results.

Lack of influence of the SMAD7 gene rs2337107 polymorphism on risk of colorectal cancer in an Iranian population.

SMAD7 has been identified as a functional candidate gene for colorectal cancer (CRC). SMAD7 protein is a known antagonist of the transforming growth factor beta (TGF-ß) signaling pathway which is involved in tumorigenesis. Polymorphisms in SMAD7 may thus alter cancer risk. The aim of this study was to investigate the influence of a SMAD7 gene polymorphism (rs2337107) on risk of CRC and clinicopathological features in an Iranian population. In total, 210 subjects including 105 patients with colorectal cancer and 105 healthy controls were recruited in our study. All samples were genotyped by TaqMan assay via an ABI 7500 Real Time PCR System (Applied Biosystems) with DNA from peripheral blood. The polymorphism was statistically analyzed to investigate the relationship with the risk of colorectal cancer and clinicopathological properties. Logistic regression analysis revealed that there was no significant association between rs2337107 and the risk of colorectal cancer. In addition, no significant association between genotypes and clinicopathological features was observed (p value>0.05). Although there was not any association between genotypes and disorder, CT was the most common genotype in this population. This genotype prevalence was also higher in the patients with well grade (54.9%) and colon (72.0%) tumors. Our results provide the first evidence that this polymorphism is not a potential contributor to the risk of colorectal cancer and clinicopathological features in an Iranian population, and suggests the need of a large-scale case-control study to validate our results.