Current Genomics最新文献

{"title":"Decoding a Million Genomes: Unveiling the Protein-coding Landscape and Its Implications for Precision Medicine.","authors":"Jinwei Zhang","doi":"10.2174/0113892029356185241216063635","DOIUrl":"10.2174/0113892029356185241216063635","url":null,"abstract":"<p><p>The study by Sun <i>et al.</i> , which sequenced exomes from 983,578 individuals, provides a comprehensive resource on protein-coding genetic variation. This commentary examines the key findings, including rare biallelic variants and loss-of-function intolerant genes, while emphasizing their implications for gene splicing, human knockouts, and disease-associated genes. Additionally, we discuss how these insights propel advancements in precision medicine and suggest future research directions, particularly in the study of non-coding DNA and regulatory RNAs at population scales.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 4","pages":"273-277"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12606665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145511773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Multi-omics Analysis of Hub Genes and miRNA Interactions in Hypertrophic Cardiomyopathy.","authors":"Huanhuan Hu, Ziheng Yu, Kongjie Lu, Hui Hu, Lang Deng","doi":"10.2174/0113892029363785250311084956","DOIUrl":"10.2174/0113892029363785250311084956","url":null,"abstract":"<p><strong>Background: </strong>Hypertrophic Cardiomyopathy (HCM) is a complex cardiac disorder marked by the thickening of the heart muscle.</p><p><strong>Methods: </strong>HCM and normal control cell lines were cultured in DMEM with 12.5% FBS and 1% penicillin-streptomycin at 37°C and 5% CO₂. Differentially expressed genes (DEGs) were identified from GSE32453, GSE53408, and GSE113439 datasets using the limma package in R. Hub genes were determined through protein-protein interaction (PPI) network and Cytoscape analysis and validated <i>via</i> Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) and Western blot analysis. Gene enrichment, miRNA predictions, drug prediction, and molecular docking analyses were conducted for functional enrichment and to explore hub gene-associated drugs.</p><p><strong>Results: </strong>To identify DEGs and hub genes implicated in HCM, we analyzed three Gene Expression Omnibus (GEO) datasets (GSE32453, GSE53404, and GSE1134439), extracting the top 1000 DEGs. Venn analysis revealed 21 common down-regulated genes. PPI analysis identified these six as key hub genes, including Iron Response Element Binding Protein 2 (IREB2), Protein Tyrosine Phosphatase, Non-Receptor Type 11 (PTPN11), IQ Motif Containing GTPase Activating Protein 1 (IQGAP1), Phosphoglucomutase 2 (PGM2), DIS3 RNA Exonuclease 3' to 5' (DIS3), Glutamine-Fructose-6-Phosphate Transaminase 1 (GFPT1) in HCM patients. Gene enrichment analysis highlighted the involvement of these genes in cellular functions such as energy metabolism and growth factor signaling, suggesting their role in the disease's progression. Validation using an additional dataset (GSE36961) confirmed significant down-regulation of all hub genes in HCM samples, supported by Receiver Operating Characteristic (ROC) curve analysis that demonstrated their diagnostic potential. Furthermore, miRNA analysis identified six up-regulated miRNAs (miR-124, miR-29b, miR-330, miR-34a, miR-375, and miR-451) that likely contribute to the dysregulation of these hub genes. Drug prediction analysis identified various potential therapeutic compounds targeting these hub genes. Molecular docking revealed favorable binding affinities, supporting the therapeutic potential of these drugs in modulating hub gene activity.</p><p><strong>Discussion: </strong>These findings demonstrate that HCM progression involves coordinated downregulation of hub genes and miRNA-mediated dysregulation of metabolic and signaling pathways. The integration of bioinformatics, validation assays, and drug docking suggests a strong translational potential for biomarker discovery and targeted therapy.</p><p><strong>Conclusion: </strong>Our findings suggest that IREB2, PTPN11, IQGAP1, PGM2, DIS3, and GFPT1 hub genes and their associated regulatory pathways may serve as biomarkers and therapeutic targets for HCM, potentially improving diagnosis and treatment strategies.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 5","pages":"400-414"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12728595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145833232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Notice to \"Genes Involved in Apoptosis Regulation: Implications for Cancer Therapy\".","authors":"Simone Fulda, Klaus-Michael Debatin","doi":"10.2174/1389202926999250910152652","DOIUrl":"https://doi.org/10.2174/1389202926999250910152652","url":null,"abstract":"<p><p>[This retracts the article DOI: 10.2174/138920205775811407.].</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 5","pages":"416"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12728589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145833281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epigenetics of Chronic Stress: Mechanisms and Health Impacts.","authors":"Kashif Abbas, Safia Habib, Mohd Mustafa, Mudassir Alam, Nazura Usmani","doi":"10.2174/0113892029402870251108055234","DOIUrl":"10.2174/0113892029402870251108055234","url":null,"abstract":"<p><p>Chronic stress is a major public health concern, contributing to a spectrum of physiological and psychological disorders, including anxiety, depression, cardiovascular diseases, and metabolic syndromes. This review examines the epigenetic mechanisms mediating the impact of chronic stress on human health, focusing on DNA methylation, histone modifications, and non-coding RNAs. These epigenetic alterations act as dynamic interfaces between environmental stressors and genomic responses, modifying gene expression in critical stress-related pathways, such as the hypothalamic-pituitary-adrenal (HPA) axis and neurotrophic factors like BDNF. The significance of this research lies in elucidating how these epigenetic changes, induced by early-life adversity, socioeconomic pressures, and environmental toxins, create lasting molecular imprints that increase susceptibility to stress-related disorders and can be transmitted across generations. Key findings include stress-induced hypermethylation of the glucocorticoid receptor gene (NR3C1), histone acetylation changes in brain regions like the hippocampus and amygdala, and the regulatory roles of microRNAs (<i>e.g</i>., miR-132) and long non-coding RNAs (<i>e.g</i>., HOTAIR). These modifications disrupt neural plasticity, emotional regulation, and cognitive function, while also contributing to physical health outcomes, including inflammation, metabolic dysregulation, and cancer. The review also explores therapeutic interventions, such as DNA methylation inhibitors (<i>e.g</i>., 5-azacytidine), histone deacetylase inhibitors (HDACIs), and lifestyle modifications like mindfulness and dietary polyphenols. By integrating molecular insights with clinical perspectives, this article underscores the critical role of epigenetic research in developing targeted interventions to mitigate the enduring effects of chronic stress, offering transformative potential for personalized medicine and public health strategies.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"544-562"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLnc-Pred: A Machine Learning Approach to Predict Long Non-Coding RNAs in Crops.","authors":"Bhavesh Kumar Choubisa, Anu Sharma, Nitesh Kumar Sharma, Mohammad Samir Farooqi, Dwijesh Chandra Mishra, K K Chaturvedi, S B Lal, Alka Arora, Girish Kumar Jha","doi":"10.2174/0113892029416615260116163601","DOIUrl":"10.2174/0113892029416615260116163601","url":null,"abstract":"<p><strong>Introduction: </strong>Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs (ncRNAs) longer than 200 nucleotides. They play key roles in plant embryogenesis, root development, reproduction, and gene silencing. Accurate identification of lncRNAs in crop species is crucial for understanding their biological functions. However, most existing computational tools are designed for human and animal lncRNAs, limiting their applicability to crop genomes. Therefore, this study aims to develop a crop-specific computational tool for the accurate classification of lncRNAs and coding RNAs in crop species, addressing the limitations of current approaches.</p><p><strong>Methods: </strong>An XGBoost classifier was trained to distinguish lncRNA and coding RNA (cRNA) sequences using sequence-intrinsic features derived from five crop species: wheat, sorghum, rice, soybean, and maize. Model performance was evaluated against benchmark tools, CPC2 and PLEKv2.</p><p><strong>Results: </strong>The trained XGBoost classifier achieved an accuracy of 95.30%, precision of 93.90%, recall of 98.40%, F1-score of 96.10%, and an area under the ROC curve (AUC-ROC) of 99.40%, outperforming existing tools. These results demonstrate the model's reliability in distinguishing lncRNAs from coding RNAs.</p><p><strong>Discussion: </strong>The trained XGBoost classifier was deployed as CLnc-Pred, a web-based application that allows users to input or upload FASTA sequences for lncRNA prediction. This framework enables efficient and accurate identification of lncRNAs in crop species.</p><p><strong>Conclusion: </strong>CLnc-Pred enhances accessibility and accuracy in crop lncRNA research and supports downstream functional and regulatory analyses. Future work will focus on expanding datasets, incorporating additional plant species, and extending the framework to support multi-class classification of diverse ncRNA types.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"726-739"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Stage Multi-View Graph Spectral Clustering for Single-Cell RNA-Seq Data.","authors":"Lianlian Zhang, Junliang Shang, Xiangzhen Kong, Feng Li, Jin-Xing Liu","doi":"10.2174/0113892029387405250921211549","DOIUrl":"10.2174/0113892029387405250921211549","url":null,"abstract":"<p><strong>Introduction: </strong>The appearance of single-cell RNA sequencing (scRNA-seq) data has brought a distinctive perspective to studying gene expression at the cell level. However, it faces challenges such as large data volume, sparsity, heterogeneity, and the curse of dimensionality. Current clustering methods still face many challenges in studying cell type distribution and have not utilized the structural relationship information between cells.</p><p><strong>Methods: </strong>To avoid the insufficiency of the single characteristic space of scRNA-seq data in characterizing cell function, this paper constructs multiple view characteristic spaces and utilizes multi-view learning to characterize scRNA-seq information from distinctive perspectives comprehensively. In multi-view learning, the similarity graph is divided into weighted learning and structural learning stages. Through weighting the multi-view similarity graphs, the significance of diverse views and features is underscored. During the structural stage, the emphasis is placed on uncovering potential relationships among different views by preserving common edges in the multi-view similarity graphs. The optimization of the attribute and structure graphs was conducted separately by the alternating direction multiplier method.</p><p><strong>Results: </strong>The performance of the MVGSC was validated using eight different scales of real scRNA-seq datasets, and the experimental results showed that the proposed multi-view clustering method significantly surpasses other single-view clustering methods and multi-view clustering methods.</p><p><strong>Discussion: </strong>When the features of scRNA-seq data are complex and there are significant differences between views, the two-stage multi-view graph method can better capture the complex relationships in the data, demonstrating superior performance compared to a single framework.</p><p><strong>Conclusion: </strong>Two-stage multi-view learning can more accurately capture complex relationships in the data, thereby improving the accuracy of the model. It can also better capture consistency and complementary information in multi-view data, thereby enhancing the generalization ability of the model.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"673-689"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147971245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the Role of Sialylation-Related LncRNAs in Lung Adenocarcinoma: A New Prognostic Model.","authors":"Guixue Yang, Juncheng Yu, Xufeng Deng, Jigang Dai, Quanxing Liu","doi":"10.2174/0113892029382696251125102036","DOIUrl":"10.2174/0113892029382696251125102036","url":null,"abstract":"<p><strong>Background: </strong>Lung adenocarcinoma (LUAD) patients have a poor overall survival rate. Sialylation is closely related to lung cancer progression, and long non-coding RNAs (lncRNAs) are crucial in tumorigenesis. However, how sialylation and lncRNAs affect LUAD is unclear.</p><p><strong>Methods: </strong>This study utilized the TCGA-LUAD dataset, GSE31210, GSE131907, and a set of 109 sialylation-related genes (SRGs). Differential expression analysis was performed on TCGA-LUAD tumor and control samples to identify differentially expressed lncRNAs (DE-lncRNAs). Spearman correlation analysis with SRGs was then conducted to screen for sialylation-related lncRNAs (SRDLs). Risk-signature models were constructed using univariate and multivariate Cox regression analyses, followed by a proportional hazards (PH) test. Independent prognostic factors were identified, and a nomogram was developed to predict 1-, 3-, and 5-year survival. Gene expression in key cell populations was examined using GSE131907, and GSEA, tumor immune microenvironment assessment, and drug sensitivity analyses were performed. Finally, prognostic genes were validated using RT-qPCR.</p><p><strong>Results: </strong>A total of 139 SRDLs were identified. A four-gene risk model (LINC00115, LINC00173, LINC00968, LINC01352) was constructed. Both the risk score and T stage were independent prognostic factors. Single-cell analysis of GSE131907 identified eight cell types, with myeloid cells emerging as key contributors. The high-risk group was associated with cell-cycle-related pathways, whereas the low-risk group was enriched in neuroactive ligand-receptor interaction pathways. Immune infiltration analysis revealed multiple significant correlations, and tumor mutation burden scores differed between the two groups. RT-qPCR validated the observed gene expression patterns.</p><p><strong>Discussion: </strong>The four-lncRNA sialylation signature predicted LUAD outcomes and pointed to myeloid cells as therapeutic targets. Prospective validation and mechanistic studies are, however, needed.</p><p><strong>Conclusion: </strong>Four sialylation-associated lncRNAs were identified in LUAD, enabling the construction of an effective risk model. Myeloid cells were highlighted as key contributors, offering valuable insights for personalized treatment and prognostic intervention.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"653-672"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Blood-Based Biomarkers for Clinical Discrimination of Asymptomatic Carotid Artery Stenosis Patients with Ulcerated Plaques.","authors":"Ceyda Colakoglu Bergel, Atif Yolgosteren, Isil Ezgi Eryilmaz, Unal Egeli, Can Kosukcu, Guven Ozkaya, Basak Erdemli Gursel, Murat Bicer, Gulsah Cecener, Efe Ozoglu, Emre Sarandol, Mustafa Tok","doi":"10.2174/0113892029361154251029052509","DOIUrl":"10.2174/0113892029361154251029052509","url":null,"abstract":"<p><strong>Background: </strong>Although analysis of plaque composition using noninvasive imaging helps identify plaques prone to rupture, simultaneous assessment of factors involved in ulceration associated with plaque rupture, with imaging of carotid plaque characteristics, is essential to identify high-risk groups in asymptomatic carotid stenosis (ACS).</p><p><strong>Aims: </strong>The current study aimed to investigate blood-based biomarkers for detecting plaque ulceration in asymptomatic carotid stenosis (ACS) patients by analyzing plaque material and surface morphology using RNA sequencing (RNA-Seq).</p><p><strong>Materials and methods: </strong>After RNA isolation, RNA-Seq and analyzing were performed using the Ion AmpliSeq Transcriptome Human Gene Expression Panel and IPA. Expression levels were analyzed to validate IPA results using RT-qPCR and ELISA.</p><p><strong>Results & discussion: </strong>Based on IPA results, the genes that can be used in this discrimination were determined to be involved in the pathways as follows: (i) actin skeletal system, (ii) integrin, and (iii) ABRA signaling. Then, the determined fold changes were validated in RT-qPCR. Based on results, <i>CHI3L2, IL-6</i>, and <i>CCL24</i> gene expression levels were determined to be -2.91-, -1.3-, and -3.66- fold downregulated, and <i>FHL5</i> gene expression levels were determined to be 1,61- fold upregulated (<i>p</i><0.05). After validation in blood material, only the <i>FHL5</i> expression level was determined to be upregulated in both plaque and blood materials.</p><p><strong>Conclusion: </strong>This is the first study to report that blood <i>FHL5</i> levels may help discriminate high-risk groups of asymptomatic carotid stenosis (ACS) patients with ulcerated plaques, based on plaque surface morphology and biological characteristics. Thus, this study provides the first evidence supporting the biomarker potential of <i>FHL5</i> gene expression levels in the blood of ACS patients with ulcerated plaques.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"599-608"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming Cancer Care: Genomic Profiling, AI, and Next-Generation Drug Delivery Systems.","authors":"Kaushal Aggarwal, Priya Jindal, AkashVikal, Preeti Patel, Balak Das Kurmi","doi":"10.2174/0113892029406752251110100414","DOIUrl":"10.2174/0113892029406752251110100414","url":null,"abstract":"<p><p>Cancer genetics plays a revolutionary role in the field of oncology by providing novel insights into tumor evolution, heterogeneity, and treatment targets. Recent advances in sequencing technology, such as whole-genome and transcriptome sequencing, have enabled the precise molecular profiling of cancers, leading to the development of targeted and individualized treatment approaches. Liquid biopsy has emerged as a non-invasive approach for collecting samples, utilizing blood or other body fluids to identify tumor cells, molecular changes, and metabolites. Liquid biopsy helps to detect various biological markers, including ctDNA, CTCs, and exosomes. Immunogenomics has revolutionized cancer management through the use of immune checkpoint inhibitors and personalized neoantigen vaccines. Furthermore, the integration of machine learning facilitates the examination of extensive genomic datasets, enabling the detection of patterns, the prediction of treatment responses, and the identification of novel therapeutic targets. Innovative drug delivery technologies, such as nanoparticle-based and CRISPR-mediated genome editing, provide promise for more effective and less harmful treatments. This review discusses recent advancements in cancer genomics, including liquid biopsy, tumor heterogeneity, immunogenomics, and the function of machine learning in the analysis of genomic data. By understanding ethical and social issues, successful implementation can be possible. The combination of genomics, immunogenomics, and liquid biopsy holds promise for novel, personalized cancer treatments, indicating a new era in cancer drug development.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"563-580"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the Genetics of Snoring: Insights into Family Patterns.","authors":"Sunil Kumar Kadiri, Prashant Tiwari","doi":"10.2174/0113892029378464260218054551","DOIUrl":"10.2174/0113892029378464260218054551","url":null,"abstract":"<p><p>Snoring is an everyday sleep phenomenon that impacts the overall health and quality of life. This review article describes the complex interplay of genetic and familial factors in snoring behavior. The discussion begins by reviewing the anatomical and physiological basis of snoring, with particular consideration of how genetic factors may affect the shaping of the upper airway. The article explores several studies proving that snoring can be inherited, exposing family case studies that reveal the patterns of recurrence. This review focuses on the detection of certain gene-related markers of snoring and the role of exogenous factors, such as obesity and habits of living, that may intensify genetic predisposition. The analysis illustrates the importance of assessing the genetic risk and the potential of genetic counseling in familial snoring. The prospective lines of investigation to further understand the genetic basis of snoring and the implications for tailored therapeutic approaches have been underlined. This contribution aims to clarify the genetic underpinnings of snoring and to facilitate further research into its hereditary potential by synthesising current findings for interested readers. Genetic research has shown that snoring is associated with heritable traits related to the structure of the airway, muscle tone, and the distribution of fat. Technologies such as next-generation sequencing and CRISPR enable the identification of genetic variants for targeted therapeutic intervention. Genetic-based treatments are more effective, and this has led to a paradigm change in sleep care with personalized approaches to snoring management.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 6","pages":"495-512"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}