Genetic Biomarkers Research Articles

Genetic and biomarker modulation of arterial stiffness change in the SardiNIA population cohort.

Arterial stiffness (AS), quantified by pulse wave velocity (PWV), arises due to impaired arterial elastic tissue and smooth muscle dysfunction. We aimed to examine the longitudinal association of genetic, lipid and inflammation biomarkers with PWV and how these associations may change with aging. We utilized genotype and four time-point biomarker data from the SardiNIA cohort [n = 6,301; mean baseline age 43.3 (SD 17.3); 58% females]. To investigate the association of PWV with genetic variants, lipid, and inflammation biomarkers, we employed linear mixed modeling, using age as the time scale. Biomarkers exhibiting significant longitudinal associations were categorized into tertiles and individuals within the second tertile or those with heterozygous alleles were excluded, leaving a cohort of 2,000 individuals. This cohort was further divided into four risk groups: low genetic and low biomarker (L-L), low genetic and high biomarker (L-H), high genetic and low biomarker (H-L), and high genetic and high biomarker risk (H-H). Subsequent analyses focused on these risk groups to assess their association to PWV with time. Using the complete dataset, we found a significant longitudinal association of total cholesterol (TC), triglycerides (TG), fibrinogen (FGN), and total white blood cell count (TWBC) with PWV, all with p < 3.33 × 10-3. After grouping, individuals with homogeneous risk alleles of SNP rs3742207 and high baseline TG levels (H-H group) exhibited a 1.39-fold higher PWV (m/s) (95% CI, 1.17-1.64, p = 1.21 × 10-4) compared to the L-L group. Similarly, individuals in the H-H group of rs3742207-TWBC combination showed 1.75 times higher PWV (95% CI, 1.48-0.2.07, p = 1.01 × 10-10) compared to the L-L group. Similar patterns were observed for groups based on SNP rs7152623-TWBC risk. Furthermore, these associations became more pronounced with increasing age (p < 3.33 × 10-3). The longitudinal association of TG and TWBC biomarkers with PWV varied by SNPs rs3742207 and rs7152623 genotype. Further studies are warranted to investigate the function of genetics, lipids, and inflammation biomarkers on PWV change.

The diversity and clinical implications of genetic variants influencing clopidogrel bioactivation and response in the Emirati population

BackgroundClopidogrel is a widely prescribed prodrug that requires activation via specific pharmacogenes to exert its anti-platelet function. Genetic variations in the genes encoding its transporter, metabolizing enzymes, and target receptor lead to variability in its activation and platelet inhibition and, consequently, its efficacy. This variability increases the risk of secondary cardiovascular events, and therefore, some variations have been utilized as genetic biomarkers when prescribing clopidogrel.MethodsOur study examined clopidogrel-related genes (CYP2C19, ABCB1, PON1, and P2Y12R) in a cohort of 298 healthy Emiratis individuals. The study used whole exome sequencing (WES) data to comprehensively analyze pertinent variations of these genes, including their minor allele frequencies, haplotype distribution, and their resulting phenotypes.ResultsOur data shows that approximately 37% (n = 119) of the cohort are likely to benefit from the use of alternative anti-platelet drugs due to their classification as intermediate or poor CYP2C19 metabolizers. Additionally, more than 50% of the studied cohort exhibited variants in ABCB1, PON1, and P2YR12 genes, potentially influencing clopidogrel’s transport, enzymatic clearance, and receptor performance.ConclusionsRecognizing these alleles and genotype frequencies may explain the clinical differences in medication response across different ethnicities and predict adverse events. Our findings underscore the need to consider genetic variations in prescribing clopidogrel, with potential implications for implementing personalized anti-platelet therapy among Emiratis based on their genetic profiles.

Diagnostic Panel of Three Genetic Biomarkers Based on Artificial Neural Network for Patients With Idiopathic Generalized Epilepsy

The aim of this study is to evaluate the utility of an artificial neural network (ANN) model in diagnosing idiopathic generalized epilepsy (IGE) and to compare the results of the diagnostic model constructed by combining the expression levels of miR‐146a, miR‐155, and miR‐132 genes using ANN, random forest (RF), and discriminant analysis (DA). qRT‐PCR is employed to determine the expression levels of the three miRNA genes. Forty‐six IGE patients and 51 healthy controls were included in the study. Three genetic biomarkers were employed to assess the discriminative power of the disease, and they were combined using ANN. Additionally, the performance of ANN was compared with RF and DA. Compared to healthy controls, the miR‐132 gene was significantly higher (p &lt; 0.001) and the miR‐155 and miR‐146a genes were significantly lower in IGE patients (p &lt; 0.001). The area under the curve (AUC) for predictions made by the ANN, RF, and DA were 0.96, 0.87, and 0.75, respectively, with accuracy rates of 0.96, 0.88, and 0.76, respectively. We demonstrate that ANN exhibits the highest accuracy, AUC, sensitivity, and specificity values among the three methods. The obtained results indicate that the combination of the three genes used as markers in IGE plays a significant role in the diagnosis of the disease. Instead of assessing biomarkers individually for the disease, combining them using machine learning methods leads to improved model performance. Additionally, not relying on a single genetic biomarker for the disease enables discrimination based on the collective impact of all biomarkers.

Генетические предикторы выживаемости и развития MACE в остром периоде инфекции, вызванной SARS-COV-2, у пациентов высокого кардиоваскулярного риска

Introduction. Despite the fact that the SARS-CoV-2 virus is highly contagious, the clinical manifestations of the disease caused by it can have significant differences: from a low-symptom course to extremely severe forms with the development of a fatal outcome. It is obvious that the observed polymorphism of the development of infection caused by SARS-CoV-2 is due, among other things, to genetic factors. These include single-nucleotide polymorphisms of genes involved in the stages of the pathogenesis of infection: from the penetration of the virus into target cells, the expression of its RNA to the response of the host organism. The study of genetic predictors that cause changes in susceptibility to the SARS-CoV-2 virus and differences in the severity of infection will help to better understand the pathogenesis of this disease and give an idea of promising areas of therapy and prevention. Aim. To study the relationship of genetic biomarkers with the severity of infection caused by SARS-CoV-2, cardiovascular complications and the risk of death in the early period of the disease. Design. A single-center retrospective study. Materials and methods. The study included 164 patients (90 men, 74 women) with infection caused by SARS-CoV-2. All patients were classified as high and very high cardiovascular risk. Concomitant somatic pathology: arterial hypertension in 164 (100%), coronary heart disease in 80 (48.9%), alimentary obesity in 100 (61%), prediabetes in 52 (31.74%), chronic kidney disease in 34 (20.7%) patients. At the first stage of the study, the relationship of genetic biomarkers with the severity of the disease was evaluated, at the second — with cardiovascular complications, and at the third — with the onset of death. Results. In the group of patients with severe disease, variant DD of ACE Alu I/D gene polymorphism, rs4646994 (p = 0.004), variant GG of IL-10 polymorphism 1082 G/A, rs1800896 (p = 0.043), allele *11 HLA-DRB1 (p = 0.015) were significantly more common. The genetic predictors of the development of cardiovascular complications were the genotype DD of the ACE gene (rs4646994) (p = 0.038), the allele *3 HLA-DRB1 (p = 0.041), the allele *13 HLA-DRB1 (p = 0.047) was less common. When searching for a correlation between the selected genetic biomarkers and mortality, only a negative contribution of the ACE Alu I/D polymorphism variant DD, rs4646994 (p = 0.015) was revealed. The survival analysis showed that in the DD variant of the ACE Alu polymorphism I/D, rs4646994, the median survival rate is lower than in the II variant (13 (9–15) days and 21 (12–n/o) days, respectively (p = 0.03)), and lower than in the ID (31 variant (14–n/ o) day (p &lt; 0.0001)). The survival rate of patients with the heterozygous variant of the polymorphism MTRR –66 A/G, rs1801394 was 11.5 (9–16) days, with variant AA — 7 (3–n/o), and with variant GG — 6 (4–14), p = 0.013. With the *16 HLA DRB1 allele, survival was only 5.5 (2–n/o) days versus 22 (17–n/o) days in patients without it, p = 0.04. Conclusion. Predictors of an increased risk of severe infection caused by SARS-CoV-2 include the DD variant of the ACE Alu I/D gene polymorphism, rs4646994, the GG variant of the IL-10 1082 G/A gene polymorphism, rs1800896 and the *11 HLA-DRB1 allele. The negative effect of the DD variant of the polymorphism of the ACE Alu I/D gene, rs4646994, the *3 HLA-DRB1 allele and the protective effect of the *13 HLA-DRB1 allele have been proven in relation to the overall risk of major cardiovascular complications. The carriage of the *16 HLA-DRB1 allele and the DD variant of the ACE Alu I/D gene polymorphism, rs4646994, correlate with an increased risk of early hospital mortality in patients. At the same time, the heterozygous variant of the MTRR –66 A/G polymorphism, rs1801394, is a protective biomarker and is associated with better survival. Key words: COVID-19, SARS-CoV-2 virus, mortality, cardiovascular complications, cardiovascular risk, genetic biomarkers

Detecting colorectal cancer using genetic and epigenetic biomarkers: screening and diagnosis.

Colorectal cancer (CRC) is one of the most frequent types of cancer, with high incidence rates and mortality globally. The extended timeframe for developing CRC allows for the potential screening and early identification of the disease. Furthermore, studies have shown that survival rates for patients with cancer are increased when diagnoses are made at earlier stages. Recent research suggests that the development of CRC, including its precancerous lesion, is influenced not only by genetic factors but also by epigenetic variables. Studies suggest epigenetics plays a significant role in cancer development, particularly CRC. While this approach is still in its early stages and faces challenges due to the variability of CRC, it shows promise as a potential method for understanding and addressing the disease. This review examined the current evidence supporting genetic and epigenetic biomarkers for screening and diagnosis. In addition, we also discussed the feasibility of translating these methodologies into clinical settings. Several markers show promising potential, including the methylation of vimentin (VIM), syndecan-2 (SDC2), and septin 9 (SEPT9). However, their application as screening and diagnostic tools, particularly for early-stage CRC, has not been fully optimized, and their effectiveness needs validation in large, multi-center patient populations. Extensive trials and further investigation are required to translate genetic and epigenetic biomarkers into practical clinical use. biomarkers, diagnostic biomarkers.

Genetic polymorphisms, biomarkers and signaling pathways associated with septic shock: from diagnosis to therapeutic targets.

Septic shock is a severe form of sepsis characterized by high global mortality rates and significant heritability. Clinicians have long been perplexed by the differential expression of genes, which poses challenges for early diagnosis and prompt treatment of septic shock. Genetic polymorphisms play crucial roles in determining susceptibility to, mortality from, and the prognosis of septic shock. Research indicates that pathogenic genes are known to cause septic shock through specific alleles, and protective genes have been shown to confer beneficial effects on affected individuals. Despite the existence of many biomarkers linked to septic shock, their clinical use remains limited. Therefore, further investigation is needed to identify specific biomarkers that can facilitate early prevention, diagnosis and risk stratification. Septic shock is closely associated with multiple signaling pathways, including the toll-like receptor 2/toll-like receptor 4, tumor necrosis factor-α, phosphatidylinositol 3-kinase/protein kinase B, mitogen-activated protein kinase, nuclear factor κB, Janus kinase/signal transducer and activator of transcription, mammalian target of rapamycin, NOD-like receptor thermal protein domain-associated protein 3 and hypoxia-induced-factor-1 pathways. Understanding the regulation of these signaling pathways may lead to the identification of therapeutic targets for the development of novel drugs to treat sepsis or septic shock. In conclusion, identifying differential gene expression during the development of septic shock allows physicians to stratify patients according to risk at an early stage. Furthermore, auxiliary examinations can assist physicians in identifying therapeutic targets within relevant signaling pathways, facilitating early diagnosis and treatment, reducing mortality and improving the prognosis of septic shock patients. Although there has been significant progress in studying the genetic polymorphisms, specific biomarkers and signaling pathways involved in septic shock, the journey toward their clinical application and widespread implementation still lies ahead.

Genetic biomarker prediction based on gender disparity in asthma throughout machine learning.

Asthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1-18% across different nations. Gender differences in asthma prevalence have attracted much attention. The aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning. The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F1 score were used to evaluate the performance of the models during the training process. After model optimization, four machine learning models were utilized to predict biomarkers of sex differences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression. In datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F1 score. Our findings reveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared to males (p < 0.01). XIST serves as a genetic biomarker for gender differences in the prevalence of asthma.

Towards a new way to classify diabetes mellitus

Precision medicine makes it possible to classify patients into groups on the basis of molecular and genetic biomarkers, as well as clinical characteristics, in order to optimize therapeutic response. For example, several types of type 2 diabetes seem to coexist with classic insulin-dependent, autoimmune type 1 diabetes : diabetes with insulinopenia (generally severe), diabetes linked to aging or obesity (less severe), and diabetes with insulin resistance, whose patients will be those with the most numerous complications, notably macrovascular. In this article, we examine the possibilities offered by this new classification of diabetes with a view to personalized medicine.

A genome-wide association study of longitudinal change in CSF tau among non-demented elderly.

This study conducted agenome-wide association study (GWAS) on longitudinal CSF t-tau and genotype data from 319 non-Hispanic Caucasians within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The aim was to identify genetic determinants influencing the rate of change in CSF t-tau, akey biomarker in AD. The GWAS identified asignificant single nucleotide polymorphism (SNP), rs17149074, within the C9orf171 (CFAP77) gene region that showed significant association with changes in CSF t-tau over time. Additionally, five other SNPs-rs10916844, rs10916846, rs9425869, rs3744474, and rs8078303-were found to potentially influence CSF t-tau variability. These findings not only enhance our understanding of t-tau's progression in AD but also suggest that these identified SNPs could serve as novel genetic biomarkers, potentially providing novel insights in the prognostic landscape of AD by refining the predictive value of CSF-tau measurements (Tab. 2, Fig. 2, Ref. 31). Text in PDF www.elis.sk Keywords: cerebrospinal fluid, t-tau, genome-wide association study, single nucleotide polymorphism, genetic factors.

Traumatic Brain Injury and Genetic Risk for Alzheimer's Disease Impact Cerebrospinal Fluid β-Amyloid Levels in Vietnam War Veterans.

Traumatic brain injuries (TBIs) may increase the risk for Alzheimer's disease (AD) and its neuropathological correlates, although the mechanisms of this relationship are unclear. The current study examined the synergistic effects of TBI and genetic risk for AD on β-amyloid (Aβ) levels among Vietnam War Veterans. We hypothesized that the combination of TBI and higher polygenic risk score (PRS) for AD would be associated with lower cerebrospinal fluid (CSF) Aβ42/40. Data were obtained from the Department of Defense Alzheimer's Disease Neuroimaging Initiative. Participants included Vietnam War Veterans without dementia who identified as White non-Hispanic/Latino and had available demographic, clinical assessment, genetic, and CSF biomarker data. Lifetime TBI history was assessed using The Ohio State University TBI Identification Method. Participants were categorized into those with and without TBI. Among those with a prior TBI, injury severity was defined as either mild or moderate/severe. CSF Aβ42/40 ratios were calculated. Genetic propensity for AD was assessed using PRSs. Hierarchical linear regression models examined the interactive effects of TBI and PRS for AD on Aβ42/40. Exploratory analyses examined the interaction between TBI severity and PRS. The final sample included 88 male Vietnam War Veterans who identified as White non-Hispanic/Latino (M age = 68.3 years), 49 of whom reported a prior TBI. There was a significant interaction between TBI and PRS, such that individuals with TBI and higher PRS for AD had lower Aβ42/40 (B = -0.45, 95% CI: -0.86 to -0.05, p = 0.03). This relationship may be stronger with increasing TBI severity (p = 0.05). Overall, TBI was associated with lower Aβ42/40, indicating greater amyloid deposition in the brain, in the context of greater polygenic risk for AD. These findings highlight who may be at increased risk for AD neuropathology following TBI.

Gestational diabetes mellitus: Impacts on fetal neurodevelopment, gut dysbiosis, and the promise of precision medicine.

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting approximately 16.5% of pregnancies worldwide and causing significant health concerns. GDM is a serious pregnancy complication caused by chronic insulin resistance in the mother and has been associated with the development of neurodevelopmental disorders in offspring. Emerging data support the notion that GDM affects both the maternal and fetal microbiome, altering the composition and function of the gut microbiota, resulting in dysbiosis. The observed dysregulation of microbial presence in GDM pregnancies has been connected to fetal neurodevelopmental problems. Several reviews have focused on the intricate development of maternal dysbiosis affecting the fetal microbiome. Omics data have been instrumental in deciphering the underlying relationship among GDM, gut dysbiosis, and fetal neurodevelopment, paving the way for precision medicine. Microbiome-associated omics analyses help elucidate how dysbiosis contributes to metabolic disturbances and inflammation, linking microbial changes to adverse pregnancy outcomes such as those seen in GDM. Integrating omics data across these different layers-genomics, transcriptomics, proteomics, metabolomics, and microbiomics-offers a comprehensive view of the molecular landscape underlying GDM. This review outlines the affected pathways and proposes future developments and possible personalized therapeutic interventions by integrating omics data on the maternal microbiome, genetics, lifestyle factors, and other relevant biomarkers aimed at identifying women at high risk of developing GDM. For example, machine learning tools have emerged with powerful capabilities to extract meaningful insights from large datasets.

Mycobacterium tuberculosis: immune response, biomarkers, and therapeutic intervention.

Although tuberculosis (TB) is an infectious disease, the progression of the disease following Mycobacterium tuberculosis (MTB) infection is closely associated with the host's immune response. In this review, a comprehensive analysis of TB prevention, diagnosis, and treatment was conducted from an immunological perspective. First, we delved into the host's immune response mechanisms against MTB infection as well as the immune evasion mechanisms of the bacteria. Addressing the challenges currently faced in TB diagnosis and treatment, we also emphasized the importance of protein, genetic, and immunological biomarkers, aiming to provide new insights for early and personalized diagnosis and treatment of TB. Building upon this foundation, we further discussed intervention strategies involving chemical and immunological treatments for the increasingly critical issue of drug-resistant TB and other forms of TB. Finally, we summarized TB prevention, diagnosis, and treatment challenges and put forward future perspectives. Overall, these findings provide valuable insights into the immunological aspects of TB and offer new directions toward achieving the WHO's goal of eradicating TB by 2035.

Uromodulin in Kidney Health and Disease: Genetic Variants, Biomarker Applications, and Clinical Implications

Uromodulin, also known as Tamm-Horsfall protein, is the most abundant protein in normal human urine and plays a crucial role in kidney function. This review explores the multifaceted role of uromodulin in renal physiology and pathophysiology, focusing on its genetic basis and utility as a biomarker. We systematically reviewed literature published between 2000 and 2024, analyzing studies on uromodulin's biomarker potential and genetic associations with kidney diseases. Our findings reveal that uromodulin is a promising biomarker for various renal conditions, including chronic kidney disease, acute kidney injury, and urinary tract infections. Urinary uromodulin levels correlate with kidney function and can predict disease progression and outcomes. Additionally, we examined genetic variants in the UMOD gene and their associations with kidney diseases. Several Single Nucleotide Polymorphisms (SNPs) in UMOD have been linked to an increased risk of chronic kidney disease, kidney stones, and hypertension. The interplay between genetic variants and uromodulin's biomarker capabilities offers potential for personalized medicine approaches in nephrology. However, standardization of uromodulin measurement techniques and more extensive prospective studies are needed to establish its clinical utility fully. This review synthesizes current knowledge on uromodulin's dual role as a genetically influenced protein and a renal biomarker, providing insights for future research directions and potential clinical applications in kidney disease management.

Генетические биомаркеры ответа на болезнь-модифицирующую терапию рассеянного склероза

This review examines pharmacogenetic markers of the response of patients with multiple sclerosis (MS) to disease-modifying therapy (DMT). Evaluation of the results of pharmacogenetic studies already conducted indicates that in such a complex disease as MS, in order to effectively identify patients who are responders to BMT and have a low risk of developing adverse events, it will be necessary to simultaneously evaluate multiple genetic biomarkers. Research to identify relevant biomarkers is underway and the results will need to be extended to all BMT technologies. It should be understood, however, that extensive testing in large groups of patients will be required before they can be used in clinical practice.

Advances in periodontal biomarkers.

Due to technologic advancements, periodontology has witnessed a boost in biomarker research over the past three decades. Indeed, with the aid of omics, our understanding of the healthy periodontium, pathogenesis of periodontal diseases, and healing after periodontal treatment has improved significantly. Yet, the traditional methods, periodontal probing and radiographies, remain the most common methods to diagnose periodontal disease and monitor treatment. Although these approaches can produce reliable diagnostic outcomes, they generally detect disease only after significant tissue degradation thus making treatment outcome highly uncertain. Accordingly, laboratories worldwide have collaborated with clinicians to design accurate, rapid and cost-effective biomarkers for periodontal disease diagnosis. Despite these efforts, biomarkers that can be widely used in early disease diagnosis and for treatment outcome prediction are far from daily use. The aim of this chapter is to give a general overview on periodontal health and diseases, and review recent advancements in periodontal biomarker research. A second aim will discuss the strengths and limitations of translating periodontal biomarker research to clinical practice. Genetic biomarkers of periodontitis are not discussed as the available confirmatory data is scarce.

Biomarkers in macular hole surgery

A macular hole is a condition that affects the central part of the retina, the macula. It is characterized by the formation of a hole or tear in the tissue, which can lead to blurred or distorted vision. Early detection and treatment of macular holes are critical to prevent permanent vision loss. Biomarkers are measurable indicators of biological processes that can aid in the diagnosis and management of macular holes.One of the most commonly used biomarkers for macular holes is optical coherence tomography (OCT). OCT uses light waves to create high‐resolution images of the retina, allowing for the detection and monitoring of macular holes. OCT can also be used to measure the size and depth of the hole, which can help guide treatment decisions.Another biomarker for macular holes is the presence of cytokines and growth factors in the vitreous, the gel‐like substance that fills the eye. Cytokines and growth factors are signalling molecules that play a role in the inflammatory response and tissue repair. In patients with macular holes, the levels of certain cytokines and growth factors are altered, indicating the presence of inflammation and tissue damage.Recent studies have also identified genetic biomarkers associated with the development and progression of macular holes. For example, mutations in the FZD4 gene have been linked to an increased risk of macular holes, while variations in the CX3CR1 gene have been associated with a more severe form of the condition.Biomarkers can also be used to monitor the response to treatment in patients with macular holes. For example, changes in cytokine and growth factor levels following treatment can indicate whether the treatment is effective in reducing inflammation and promoting tissue repair. Similarly, changes in OCT measurements can indicate whether the hole is closing and vision is improving.Overall, biomarkers play an important role in the diagnosis, management, and monitoring of macular holes. By providing objective measures of disease progression and response to treatment, biomarkers can improve outcomes for patients with this condition. However, more research is needed to fully understand the role of biomarkers in macular holes and to identify new biomarkers that may aid in the diagnosis and treatment of this condition.

Association of the Single Nucleotide Polymorphisms in the Renin-Angiotensin-Aldosterone System with Hypertension in the Uzbek Population.

This research aims to identify the association between the nine polymorphic variants (rs4961, rs699, rs4762, rs5186, rs1403543, rs1799998, rs5443, rs2070744, rs1799983) and the occurrence of hypertension and its clinical manifestations in the Uzbek population. The study included 227 individuals, comprising 179 patients with hypertension and 48 controls. Clinical parameters such as age, weight, blood glucose, triglycerides, total cholesterol, low-density lipoprotein and high-density lipoprotein, blood urea nitrogen, creatinine, pulse wave velocity, left ventricular mass, and microalbuminuria levels were identified. We assessed the distribution of allele frequencies of these polymorphic variants in the Uzbek population to establish their association with cardiovascular diseases and their clinical manifestations. Genetic analysis of the polymorphic variants demonstrated a significant association of the AGT 521 C>T variant with arterial hypertension [P ≤ 0.01; Odds Ratio (OR) = 2.91]. The NOS3 -786 T>C variant correlated with left ventricular hypertrophy (P ≤ 0.05; OR = 0.35) and increased pulse wave velocity (P ≤ 0.01; OR = 0.21). The correlations of the AGTR2 1675 G>A variant with left ventricular hypertrophy (P ≤ 0.01; OR = 1.59) and increased pulse wave velocity (P ≤ 0.01; OR = 0.33) were identified. The AGT 704 T>C variant showed a significant association with increased pulse wave velocity (P ≤ 0.05; OR = 2.73). Four of the nine studied polymorphic variants were associated with clinical manifestations of hypertension in the Uzbek population. These variants can be used as genetic biomarkers to identify the risks of developing cardiovascular diseases and hypertension in the Uzbek population.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

New genetic biomarkers predicting 5-aminosalicylate-induced adverse events in patients with inflammatory bowel diseases.

Notably, 5-aminosalicylates (5-ASA) are vital in treating inflammatory bowel diseases (IBD). The adverse events of 5-ASA rarely occur but they could be fatal. We aimed to discover new genetic biomarkers predicting 5-ASA-induced adverse events in patients with IBD. This was a retrospective observational study. We performed a genome-wide association study on patients with IBD in South Korea. We defined subset 1 as 39 all adverse events and 272 controls; subset 2 as 20 severe adverse events and 291 controls (mild adverse events and control); subset 3 as 20 severe adverse events and 272 controls; and subset 4 as 19 mild adverse events and 272 controls. Logistic regression analysis was performed and commonly found associated genes were determined as candidate single-nucleotide polymorphisms predicting 5-ASA adverse events. Patients with Crohn's disease (CD) were significantly negatively associated with the development of adverse events compared to patients with ulcerative colitis (UC) (5.3% versus 22.9%). However, sex and age at diagnosis were unassociated with the adverse events of 5-ASA. rs13898676 [odds ratio (OR), 20.33; 95% confidence interval (CI), 5.69-72.67; p = 3.57 × e-6], rs12681590 (OR, 7.35; 95% CI, 2.85-19.00; p = 3.78 × e-5), rs10967320 (OR, 4.51; 95% CI, 2.18-9.31; p = 4.72 × e-5), and rs78726924 (OR, 3.54; 95% CI, 1.69-7.40; p = 7.96 × e-5) were genetic biomarkers predicting 5-ASA-induced severe adverse events in patients with IBD. The adverse events of 5-ASA were more common in patients with UC than those with CD in our study. We found that novel rs13898676 nearby WSB2 was the most significant genetic locus contributing to 5-ASA's adverse event risk.

Interplay of machine learning and bioinformatics approaches to identify genetic biomarkers that affect survival of patients with glioblastoma

Glioblastoma, also known as grade IV astrocytoma, is an aggressive and quickly developing brain tumor whose median survival period is believed to be between 12 and 18 months. Patients with glioblastoma are at high risk of developing comorbidities like leukemia, atherosclerosis, autism, sudden cardiac death, and pancreatic neoplasms. Identification of influential biomarker genes is crucial to diagnose and design therapeutic targets for cancer. To do this, we considered The Cancer Genome Atlas (TCGA) dataset to identify the significant genes of glioblastoma. Therefore, we pre-processed the dataset and applied the Kruskal-Wallis test and Bonferroni correction methods to select significant biomarker genes. A total of 26 significant dysregulated genes have been identified from 16261 genes, of which 19 are up-regulated and 7 are down-regulated genes. We performed analysis of functional and ontological pathways, protein-protein interactions (PPI), and protein-drug interactions (PDI) to predict the functions of these influential genes. Comorbidities validation was performed using gold benchmark databases. Furthermore, the Cox proportional hazard model and the product-limit (PL) estimator were used to examine the influence of clinical and genetic variables that play an important role in the survival of glioblastoma patients. This study provides the basis of identifying cancer-influencing genes and understanding the impact of glioblastoma on the progression of comorbidities.