Hedgehog (HH) signaling plays a crucial role in cancer development. However, HH signaling–related molecular characteristics have not been comprehensively evaluated in pancreatic cancer (PC). This study dissected the characteristics of HH signaling in PC using integrated bulk and single-cell profiling. GSEA indicated that HH signaling is significantly enriched in PC tissue. Consensus clustering was utilized to classify PC samples into two HH signaling–related subtypes: HRGcluster A and HRGcluster B. In contrast with HRGcluster A, HRGcluster B has an earlier clinical stage, better outcome, less active level of HH signaling, higher infiltration level of CD8+ T cells and B cells, and a greater likelihood of benefiting from immunotherapy and gemcitabine chemotherapy. Moreover, an HH signaling–related prognostic model (including ANLN, SERPINB3, LY6D, and DCBLD2) with excellent prediction performance was established and validated. Further analysis indicated that ANLN, SERPINB3, LY6D, and DCBLD2 were significantly upregulated in PC and associated with poor prognosis. Single-cell analysis revealed that HH signaling is relatively more active in PC cells, and PC cells with DCBLD2 high expression had significantly higher HH signaling scores. In vitro assays further indicated that DCBLD2 knockdown downregulates HH signaling and inhibits the proliferation, migration, and invasion of PC cells. In conclusion, this study reveals that HH signaling characteristics in PC and DCBLD2 regulate HH signaling to drive PC progression, providing new perspectives and theories for the diagnosis and treatment of PC.
{"title":"Integrated Multiomics Unravels Hedgehog (HH) Signaling Characteristics in Pancreatic Cancer (PC) and DCBLD2 Regulates HH Signaling to Drive PC Progression","authors":"Biao Zhang, Bingqian Huang, Xinya Zhao, Bolin Zhang, Jinming Liu, Chongchan Bao, Zhizhou Wang","doi":"10.1155/humu/4806397","DOIUrl":"https://doi.org/10.1155/humu/4806397","url":null,"abstract":"<p>Hedgehog (HH) signaling plays a crucial role in cancer development. However, HH signaling–related molecular characteristics have not been comprehensively evaluated in pancreatic cancer (PC). This study dissected the characteristics of HH signaling in PC using integrated bulk and single-cell profiling. GSEA indicated that HH signaling is significantly enriched in PC tissue. Consensus clustering was utilized to classify PC samples into two HH signaling–related subtypes: HRGcluster A and HRGcluster B. In contrast with HRGcluster A, HRGcluster B has an earlier clinical stage, better outcome, less active level of HH signaling, higher infiltration level of CD8+ T cells and B cells, and a greater likelihood of benefiting from immunotherapy and gemcitabine chemotherapy. Moreover, an HH signaling–related prognostic model (including ANLN, SERPINB3, LY6D, and DCBLD2) with excellent prediction performance was established and validated. Further analysis indicated that ANLN, SERPINB3, LY6D, and DCBLD2 were significantly upregulated in PC and associated with poor prognosis. Single-cell analysis revealed that HH signaling is relatively more active in PC cells, and PC cells with DCBLD2 high expression had significantly higher HH signaling scores. In vitro assays further indicated that DCBLD2 knockdown downregulates HH signaling and inhibits the proliferation, migration, and invasion of PC cells. In conclusion, this study reveals that HH signaling characteristics in PC and DCBLD2 regulate HH signaling to drive PC progression, providing new perspectives and theories for the diagnosis and treatment of PC.</p>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/4806397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaofan Li, Yue Gan, Lan Tan, Yuxi Lin, Pengxi Zhou, Jue Wang, Bing Yang, Quan Tang
Objective
This study was aimed at clarifying the unique metabolic alterations in preterm neonates, distinct from full-term neonates, between the first 24 and 48 h postnatally.
Methods
A cohort of 60 preterm and 60 full-term neonates was analyzed. The metabolomic profiles of plasma samples were determined using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analyses were employed to discern metabolic differences. Multiple machine learning models were constructed to further select key metabolites. Spearman′s correlation analysis was performed to assess the correlation between neonatal immune cell subsets and key metabolites.
Results
The study revealed 70 specific metabolic alterations in preterm neonates during the early postnatal period. Then, 32 of these metabolites were jointly selected by the Top 5 machine learning models, which exhibited high predictive performance with an AUC > 0.9. Subsequent analyses including multivariable linear regression and ROC curve revealed 12 key metabolites significantly associated with gestational age. Correlation analyses exposed significant associations between immune cells and these metabolites. Integrated pathway analysis identified 10 key metabolic pathways involved in preterm neonates. NMR-based validation confirmed two of the 12 prioritized metabolites and six additional metabolites from the broader panel, supporting the robustness of our findings.
Conclusion
Our findings provide insights into the metabolic and immune adaptation processes in preterm neonates during the early life stage. The correlations between immune cell subsets and the key metabolites highlight a potential effect of metabolism on immune adaptation in preterm neonates. These key metabolites and pathways could serve as potential biomarkers for early diagnosis and therapeutic strategies to enhance immune function and health outcomes in preterm infants.
{"title":"Metabolic and Immune Adaptations in Preterm Neonates at Early Postnatal Period: Integrated Analysis of Key Metabolites and Pathways","authors":"Xiaofan Li, Yue Gan, Lan Tan, Yuxi Lin, Pengxi Zhou, Jue Wang, Bing Yang, Quan Tang","doi":"10.1155/humu/9978047","DOIUrl":"https://doi.org/10.1155/humu/9978047","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study was aimed at clarifying the unique metabolic alterations in preterm neonates, distinct from full-term neonates, between the first 24 and 48 h postnatally.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cohort of 60 preterm and 60 full-term neonates was analyzed. The metabolomic profiles of plasma samples were determined using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analyses were employed to discern metabolic differences. Multiple machine learning models were constructed to further select key metabolites. Spearman′s correlation analysis was performed to assess the correlation between neonatal immune cell subsets and key metabolites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study revealed 70 specific metabolic alterations in preterm neonates during the early postnatal period. Then, 32 of these metabolites were jointly selected by the Top 5 machine learning models, which exhibited high predictive performance with an AUC > 0.9. Subsequent analyses including multivariable linear regression and ROC curve revealed 12 key metabolites significantly associated with gestational age. Correlation analyses exposed significant associations between immune cells and these metabolites. Integrated pathway analysis identified 10 key metabolic pathways involved in preterm neonates. NMR-based validation confirmed two of the 12 prioritized metabolites and six additional metabolites from the broader panel, supporting the robustness of our findings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our findings provide insights into the metabolic and immune adaptation processes in preterm neonates during the early life stage. The correlations between immune cell subsets and the key metabolites highlight a potential effect of metabolism on immune adaptation in preterm neonates. These key metabolites and pathways could serve as potential biomarkers for early diagnosis and therapeutic strategies to enhance immune function and health outcomes in preterm infants.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/9978047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sepsis arises from a dysregulated host response to infection, leading to multiorgan inflammatory injury. Early diagnosis and treatment necessitate the identification of reliable immune biomarkers. This study investigated the relationship between aging, immunity, and sepsis by analyzing six human aging-related gene sets (656 genes). We identified 16 aging-related differentially expressed genes (DEGs) in sepsis. Among these, ATP11B, RBBP7, DOCK10, and NUP160 demonstrated the strongest connectivity with other genes and exhibited significant predictive power. Functional enrichment analysis (GO and KEGG) revealed distinct signaling pathway profiles between high-risk and low-risk sepsis groups (stratified based on risk scores). These dysregulated pathways, associated with multiple immune cells, were primarily linked to transcriptional dysregulation in cellular processes and cancer-related pathways. Experimental validation assays corroborated the roles of ATP11B and RBBP7. Collectively, our bioinformatic and experimental findings indicate that ATP11B, RBBP7, DOCK10, and NUP160 are implicated in the pathogenesis and progression of sepsis. But their potential for sepsis biomarkers still requires further verification.
{"title":"Identification of Aging-Related Hub Genes (ATP11B, RBBP7, DOCK10, and NUP160) as Potential Biomarkers and Therapeutic Targets in Sepsis","authors":"Xueyi Sun, Shaolei Geng, Zeyuan Wang, Qingjiang Chen","doi":"10.1155/humu/9789556","DOIUrl":"https://doi.org/10.1155/humu/9789556","url":null,"abstract":"<p>Sepsis arises from a dysregulated host response to infection, leading to multiorgan inflammatory injury. Early diagnosis and treatment necessitate the identification of reliable immune biomarkers. This study investigated the relationship between aging, immunity, and sepsis by analyzing six human aging-related gene sets (656 genes). We identified 16 aging-related differentially expressed genes (DEGs) in sepsis. Among these, ATP11B, RBBP7, DOCK10, and NUP160 demonstrated the strongest connectivity with other genes and exhibited significant predictive power. Functional enrichment analysis (GO and KEGG) revealed distinct signaling pathway profiles between high-risk and low-risk sepsis groups (stratified based on risk scores). These dysregulated pathways, associated with multiple immune cells, were primarily linked to transcriptional dysregulation in cellular processes and cancer-related pathways. Experimental validation assays corroborated the roles of ATP11B and RBBP7. Collectively, our bioinformatic and experimental findings indicate that ATP11B, RBBP7, DOCK10, and NUP160 are implicated in the pathogenesis and progression of sepsis. But their potential for sepsis biomarkers still requires further verification.</p>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/9789556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Zeng, Jing Huang, Juan He, Jianqin Tan, Mianmian Duan, Yanyan Song, Lin Yang
Periodontitis (PD) is a chronic inflammatory disorder marked by immune dysregulation and progressive tissue destruction. Cellular senescence and the senescence-associated secretory phenotype (SASP) have been increasingly recognized as pivotal drivers of chronic inflammation. However, their specific contributions to PD remain insufficiently clarified. In this study, integrative bioinformatic analyses were conducted across transcriptomic datasets, employing least absolute shrinkage and selection operator, support vector machine–recursive feature elimination, and eXtreme gradient boosting algorithms to identify SASP-related genes of significance. ICAM1, CXCL12, and MMP3 were found to be markedly upregulated in PD and demonstrated strong diagnostic potential through receiver operating characteristic and artificial neural network models. Functional enrichment analysis indicated their involvement in immune cell adhesion, migration, and infection-associated pathways. Immune infiltration profiling revealed disrupted immune landscapes, with ICAM1 exhibiting a negative correlation with resting mast cells. Experimental validation using real-time quantitative polymerase chain reaction and immunohistochemistry on clinical samples confirmed elevated expression of these genes at both the mRNA and protein levels. Moreover, dexamethasone was identified via molecular docking as a potential therapeutic compound targeting ICAM1 and CXCL12. Collectively, these findings advance the understanding of SASP associated with immune regulation in PD and suggest potential biomarkers and therapeutic targets for early diagnosis and intervention.
{"title":"Integrative Transcriptomic and Machine Learning Analysis Identifies Key Senescence-Associated Secretory Phenotype Genes Associated With Immune Dysregulation in Periodontitis","authors":"Jing Zeng, Jing Huang, Juan He, Jianqin Tan, Mianmian Duan, Yanyan Song, Lin Yang","doi":"10.1155/humu/3637248","DOIUrl":"https://doi.org/10.1155/humu/3637248","url":null,"abstract":"<p>Periodontitis (PD) is a chronic inflammatory disorder marked by immune dysregulation and progressive tissue destruction. Cellular senescence and the senescence-associated secretory phenotype (SASP) have been increasingly recognized as pivotal drivers of chronic inflammation. However, their specific contributions to PD remain insufficiently clarified. In this study, integrative bioinformatic analyses were conducted across transcriptomic datasets, employing least absolute shrinkage and selection operator, support vector machine–recursive feature elimination, and eXtreme gradient boosting algorithms to identify SASP-related genes of significance. ICAM1, CXCL12, and MMP3 were found to be markedly upregulated in PD and demonstrated strong diagnostic potential through receiver operating characteristic and artificial neural network models. Functional enrichment analysis indicated their involvement in immune cell adhesion, migration, and infection-associated pathways. Immune infiltration profiling revealed disrupted immune landscapes, with ICAM1 exhibiting a negative correlation with resting mast cells. Experimental validation using real-time quantitative polymerase chain reaction and immunohistochemistry on clinical samples confirmed elevated expression of these genes at both the mRNA and protein levels. Moreover, dexamethasone was identified via molecular docking as a potential therapeutic compound targeting ICAM1 and CXCL12. Collectively, these findings advance the understanding of SASP associated with immune regulation in PD and suggest potential biomarkers and therapeutic targets for early diagnosis and intervention.</p>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/3637248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefan Rentas, Ramakrishnan Rajagopalan, Tolga Ayazseven, Mahdi Sarmady, Sarah E. Raible, Ian D. Krantz, Ahmad N. Abou Tayoun
Background
Genetic testing is essential to the diagnosis of nonsyndromic bilateral sensorineural hearing loss (BSNHL), where pathogenic variants in GJB2 are the most common cause. Current testing strategies often fail to provide a comprehensive diagnosis and typically require the use of multiple testing methodologies. This study evaluated the diagnostic utility of genome sequencing (GS) in a cohort with heterozygosity for GJB2 pathogenic variants and BSNHL.
Methods
A retrospective cohort of 23 individuals with BSNHL and a heterozygous pathogenic variant in GJB2 underwent targeted GJB2 resequencing and variant reinterpretation. Those without biallelic GJB2 variants upon single gene reanalysis proceeded to exome sequencing (ES) using a large virtual panel of hearing loss–associated genes. Subjects with no definitive diagnosis from ES subsequently underwent GS. Variants were interpreted using hearing loss–specific ACMG guidelines and published literature.
Results
Three individuals were diagnosed with biallelic pathogenic variants upon GJB2 single gene reanalysis. ES identified a definitive or likely diagnosis in five different hearing loss–related genes in 5/20 (25%) individuals, while two additional cases remained inconclusive due to novel or ambiguous variants in two other hearing loss–associated genes. GS of the remaining 15 cases yielded diagnoses in three individuals, including the identification of deletions in LOXHD1 and STRC, and a recently characterized 125 kb deletion overlapping CRYL1, which refines a critical upstream regulatory region associated with GJB2-related hearing loss. Overall, 11/23 (48%) individuals received a diagnosis with our stepwise testing approach, with GS providing sequencing coverage of all findings.
Conclusion
GS improves diagnostic yield in patients with BSNHL, capturing both SNVs and CNVs missed by ES and targeted testing, and supports its adoption as a comprehensive first-tier diagnostic test for nonsyndromic hearing loss.
{"title":"Whole Genome Sequencing Improves the Identification of Pathogenic and Novel Variation in Nonsyndromic Hearing Loss","authors":"Stefan Rentas, Ramakrishnan Rajagopalan, Tolga Ayazseven, Mahdi Sarmady, Sarah E. Raible, Ian D. Krantz, Ahmad N. Abou Tayoun","doi":"10.1155/humu/6371082","DOIUrl":"https://doi.org/10.1155/humu/6371082","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Genetic testing is essential to the diagnosis of nonsyndromic bilateral sensorineural hearing loss (BSNHL), where pathogenic variants in <i>GJB2</i> are the most common cause. Current testing strategies often fail to provide a comprehensive diagnosis and typically require the use of multiple testing methodologies. This study evaluated the diagnostic utility of genome sequencing (GS) in a cohort with heterozygosity for <i>GJB2</i> pathogenic variants and BSNHL.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A retrospective cohort of 23 individuals with BSNHL and a heterozygous pathogenic variant in <i>GJB2</i> underwent targeted <i>GJB2</i> resequencing and variant reinterpretation. Those without biallelic <i>GJB2</i> variants upon single gene reanalysis proceeded to exome sequencing (ES) using a large virtual panel of hearing loss–associated genes. Subjects with no definitive diagnosis from ES subsequently underwent GS. Variants were interpreted using hearing loss–specific ACMG guidelines and published literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Three individuals were diagnosed with biallelic pathogenic variants upon <i>GJB2</i> single gene reanalysis. ES identified a definitive or likely diagnosis in five different hearing loss–related genes in 5/20 (25%) individuals, while two additional cases remained inconclusive due to novel or ambiguous variants in two other hearing loss–associated genes. GS of the remaining 15 cases yielded diagnoses in three individuals, including the identification of deletions in <i>LOXHD1</i> and <i>STRC</i>, and a recently characterized 125 kb deletion overlapping <i>CRYL1</i>, which refines a critical upstream regulatory region associated with <i>GJB2</i>-related hearing loss. Overall, 11/23 (48%) individuals received a diagnosis with our stepwise testing approach, with GS providing sequencing coverage of all findings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>GS improves diagnostic yield in patients with BSNHL, capturing both SNVs and CNVs missed by ES and targeted testing, and supports its adoption as a comprehensive first-tier diagnostic test for nonsyndromic hearing loss.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/6371082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zuzana Pakanová, Maroš Krchňák, Marek Nemčovič, Rebeka Kodríková, Nina Ondrušková, Hana Štufková, Mária Giertlová, Katarína Okáľová, Paula Stretavská, Slavomíra Martineková, Renáta Zemjarová Mezenská, Michaela Urminská, Martina Škopková, Andrea Andrésová, Miroslava Lysinová, Lenka Belujská, Anna Šalingová, Gábor Beke, Lucia Račková, Tomáš Honzík, Hana Hansíková, Peter Baráth
Background and Aims
Congenital disorders of glycosylation (CDG) are rare diseases caused by defects in protein glycosylation. We present an infant with multisystemic clinical involvement, diagnosed with COG6-CDG.
Methods
Serum and transferrin-linked N-glycans, as well as serum and apolipoprotein CIII–linked O-glycans, were analyzed by MALDI mass spectrometry. Mutation analysis was performed by next-generation sequencing. Functional studies assessed COG6 subunit expression, cooperating subunits, and retrograde transport. GlycoWorks RapiFluor-MS–based N-glycan labeling with HPLC-FLD and ESI-Orbitrap mass spectrometry enabled further comprehensive glycoprofile analysis.
Results
Aberrant glycosylation typical of combined N- and O-glycosylation defects was detected. Mutation analysis identified a novel homozygous variant in the COG6 gene: c.906_907delinsA, p.(His302GlnfsTer4), introducing a premature stop codon and producing a truncated protein of only 304 amino acids. The diagnosis of COG6-CDG was confirmed by the complete absence of the COG6 subunit, impairment of two other cooperating subunits, and delayed retrograde transport. Independent glycoprofile analyses by HPLC-FLD and ESI-Orbitrap revealed a set of potential glycobiomarkers of COG6-CDG, including underprocessed N-glycans Hex3-5HexNAc2, Hex3-5HexNAc3, Hex3-4HexNAc4, and Hex4HexNAc3-4NeuAc1.
Conclusion
This study describes a novel COG6 variant leading to complete loss of protein function and major glycosylation abnormalities. Multiomics analysis provided deeper insights into the molecular mechanisms of this rare disease and the function of the COG6 gene and demonstrated how the mutation results in significant alterations in the patient′s (glyco)phenotype.
{"title":"Insights Into the Pathological Glycosylation Associated With COG6-CDG","authors":"Zuzana Pakanová, Maroš Krchňák, Marek Nemčovič, Rebeka Kodríková, Nina Ondrušková, Hana Štufková, Mária Giertlová, Katarína Okáľová, Paula Stretavská, Slavomíra Martineková, Renáta Zemjarová Mezenská, Michaela Urminská, Martina Škopková, Andrea Andrésová, Miroslava Lysinová, Lenka Belujská, Anna Šalingová, Gábor Beke, Lucia Račková, Tomáš Honzík, Hana Hansíková, Peter Baráth","doi":"10.1155/humu/7948771","DOIUrl":"https://doi.org/10.1155/humu/7948771","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aims</h3>\u0000 \u0000 <p>Congenital disorders of glycosylation (CDG) are rare diseases caused by defects in protein glycosylation. We present an infant with multisystemic clinical involvement, diagnosed with COG6-CDG.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Serum and transferrin-linked N-glycans, as well as serum and apolipoprotein CIII–linked O-glycans, were analyzed by MALDI mass spectrometry. Mutation analysis was performed by next-generation sequencing. Functional studies assessed COG6 subunit expression, cooperating subunits, and retrograde transport. GlycoWorks RapiFluor-MS–based N-glycan labeling with HPLC-FLD and ESI-Orbitrap mass spectrometry enabled further comprehensive glycoprofile analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Aberrant glycosylation typical of combined N- and O-glycosylation defects was detected. Mutation analysis identified a novel homozygous variant in the <i>COG6</i> gene: c.906_907delinsA, p.(His302GlnfsTer4), introducing a premature stop codon and producing a truncated protein of only 304 amino acids. The diagnosis of COG6-CDG was confirmed by the complete absence of the COG6 subunit, impairment of two other cooperating subunits, and delayed retrograde transport. Independent glycoprofile analyses by HPLC-FLD and ESI-Orbitrap revealed a set of potential glycobiomarkers of COG6-CDG, including underprocessed N-glycans Hex3-5HexNAc2, Hex3-5HexNAc3, Hex3-4HexNAc4, and Hex4HexNAc3-4NeuAc1.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study describes a novel COG6 variant leading to complete loss of protein function and major glycosylation abnormalities. Multiomics analysis provided deeper insights into the molecular mechanisms of this rare disease and the function of the <i>COG6</i> gene and demonstrated how the mutation results in significant alterations in the patient′s (glyco)phenotype.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/7948771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genetic testing is valuable to confirm molecular diagnosis in nearly 60% of cases suspected of hypertrophic cardiomyopathy (HCM). However, the interpretation of variants, especially those of uncertain significance (VUSs), remains challenging for laboratories and clinicians. In April 2024, the ClinGen Cardiomyopathy Variant Curation Expert Panel (VCEP) adapted the ACMG/AMP criteria for eight of the sarcomeric genes (MYH7, MYBPC3, TNNI3, TNNT2, TPM1, ACTC1, MYL2, and MYL3), providing a refined framework for variant interpretation in these genes. This retrospective study re-evaluated 69 VUSs identified in 84 HCM patients between 2017 and 2024, aiming to resolve uncertainty and reduce the VUS rate.
Methods
Here, two groups of curators reinterpreted variants with the most recent data using the Cardiomyopathy VCEP specifications until a consensus was reached. To streamline the process, we created a semiautomated decision support tool based on these gene-specific rules.
Results
The application of the Cardiomyopathy VCEP specifications resulted in the reclassification of 17.4% (N = 12/69, 95% CI: 10.2%–28.0%) of VUS, whereas the new data alone were not sufficient. Out of the reclassified variants, 91.7% (N = 11/12) were downgraded to benignity (involving 17 patients), and 8.3% were upgraded to pathogenicity (involving one patient), with a mean reclassification time of 68.3 months, corresponding to 5.7 years. The most applied criteria were related to population (PM2 = 55%; BA1/BS1 = 16%), bioinformatic prediction (PP3 = 45%; BP4 = 25%), and critical domains (PM1 = 21%). However, most codes suffer from a lack of evidence (segregation data, functional assays, and case-control studies). When comparing this curation with classifications in public databases, 13.3% (N = 8/60) and 16.2% (N = 11/68) of variants listed as having inconclusive significance in ClinVar and CardioClassifier were, respectively, reclassified in this study.
Conclusion
Using gene-specific ACMG/AMP criteria reduces the rate of VUS, increasing diagnostic yield, and informing clinical management in the context of HCM. Nonetheless, ongoing efforts to generate evidence and promote standardization remain essential to improve variant interpretation.
{"title":"Reclassification of VUS Using ACMG/AMP Criteria Adapted for Sarcomeric Genes Related to Hypertrophic Cardiomyopathy: Resolution Rate and Considerations","authors":"Silvia Caroselli, Giulia Corona, Marco Fabiani, Martina Manzoni, Caterina Micolonghi, Camilla Savio, Aldo Germani, Stefania Bragliola, Valeria Maselli, Speranza Rubattu, Beatrice Musumeci, Giacomo Tini, Vincenzo Visco, Simona Petrucci, Valeria Novelli, Maria Piane","doi":"10.1155/humu/6500093","DOIUrl":"https://doi.org/10.1155/humu/6500093","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Genetic testing is valuable to confirm molecular diagnosis in nearly 60% of cases suspected of hypertrophic cardiomyopathy (HCM). However, the interpretation of variants, especially those of uncertain significance (VUSs), remains challenging for laboratories and clinicians. In April 2024, the ClinGen Cardiomyopathy Variant Curation Expert Panel (VCEP) adapted the ACMG/AMP criteria for eight of the sarcomeric genes (<i>MYH7</i>, <i>MYBPC3</i>, <i>TNNI3</i>, <i>TNNT2</i>, <i>TPM1</i>, <i>ACTC1</i>, <i>MYL2</i>, and <i>MYL3</i>), providing a refined framework for variant interpretation in these genes. This retrospective study re-evaluated 69 VUSs identified in 84 HCM patients between 2017 and 2024, aiming to resolve uncertainty and reduce the VUS rate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Here, two groups of curators reinterpreted variants with the most recent data using the Cardiomyopathy VCEP specifications until a consensus was reached. To streamline the process, we created a semiautomated decision support tool based on these gene-specific rules.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The application of the Cardiomyopathy VCEP specifications resulted in the reclassification of 17.4% (<i>N</i> = 12/69, 95% CI: 10.2%–28.0%) of VUS, whereas the new data alone were not sufficient. Out of the reclassified variants, 91.7% (<i>N</i> = 11/12) were downgraded to benignity (involving 17 patients), and 8.3% were upgraded to pathogenicity (involving one patient), with a mean reclassification time of 68.3 months, corresponding to 5.7 years. The most applied criteria were related to population (PM2 = 55<i>%</i>; BA1/BS1 = 16<i>%</i>), bioinformatic prediction (PP3 = 45<i>%</i>; BP4 = 25<i>%</i>), and critical domains (PM1 = 21<i>%</i>). However, most codes suffer from a lack of evidence (segregation data, functional assays, and case-control studies). When comparing this curation with classifications in public databases, 13.3% (<i>N</i> = 8/60) and 16.2% (<i>N</i> = 11/68) of variants listed as having inconclusive significance in ClinVar and CardioClassifier were, respectively, reclassified in this study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Using gene-specific ACMG/AMP criteria reduces the rate of VUS, increasing diagnostic yield, and informing clinical management in the context of HCM. Nonetheless, ongoing efforts to generate evidence and promote standardization remain essential to improve variant interpretation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/6500093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer with a poor prognosis. Palmitoylation, a posttranslational modification, plays a key role in regulating cancer progression and immune responses. However, its influence on ccRCC prognosis and immune therapy efficacy remains underexplored.
Methods
Multiple publicly available ccRCC datasets were integrated and harmonized through batch effect correction. A prognostic model based on palmitoylation-related genes was constructed using a combination of 101 machine learning algorithms. Single-cell RNA sequencing was employed to analyze cellular heterogeneity within the tumor microenvironment. Genomic profiling, including tumor mutational burden (TMB), copy number variation (CNV), and tumor stemness, was conducted to identify genomic differences between the high- and low-risk groups. Immune infiltration levels were assessed using various algorithms to compare immune profiles across patient subgroups, while immune therapy responses were predicted using multiple prediction models. Experimental validation of ZDHHC18, a key gene in the prognostic model, was performed in ccRCC cell lines (786-O and Caki-1) to evaluate its impact on cell proliferation, migration, and invasion.
Results
The palmitoylation-related prognostic model effectively stratified ccRCC patients into the high- and low-risk groups, with distinct differences in survival outcomes. Genomic analysis demonstrated higher TMB and CNV alterations in the high-risk group. Immune response predictions indicated that low-risk patients were more likely to benefit from immunotherapy. Additionally, ZDHHC18 was significantly upregulated in ccRCC tumor tissues, and its knockdown notably inhibited cell proliferation, migration, and invasion.
Conclusion
Palmitoylation-related genes, particularly ZDHHC18, serve as promising prognostic biomarkers and predictive indicators for immune therapy in ccRCC. These findings offer new insights into ccRCC biology and highlight potential therapeutic targets for improving patient outcomes.
{"title":"Integrated Genomic and Functional Characterization of Palmitoylation in Clear Cell Renal Cell Carcinoma","authors":"Dong Zhang, Ke Zhang, Minghao Deng, Jiakang Ma, Jian Zhu, Shuijie Shen, Jianjun Xie, Chao Chen","doi":"10.1155/humu/4647115","DOIUrl":"https://doi.org/10.1155/humu/4647115","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer with a poor prognosis. Palmitoylation, a posttranslational modification, plays a key role in regulating cancer progression and immune responses. However, its influence on ccRCC prognosis and immune therapy efficacy remains underexplored.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Multiple publicly available ccRCC datasets were integrated and harmonized through batch effect correction. A prognostic model based on palmitoylation-related genes was constructed using a combination of 101 machine learning algorithms. Single-cell RNA sequencing was employed to analyze cellular heterogeneity within the tumor microenvironment. Genomic profiling, including tumor mutational burden (TMB), copy number variation (CNV), and tumor stemness, was conducted to identify genomic differences between the high- and low-risk groups. Immune infiltration levels were assessed using various algorithms to compare immune profiles across patient subgroups, while immune therapy responses were predicted using multiple prediction models. Experimental validation of ZDHHC18, a key gene in the prognostic model, was performed in ccRCC cell lines (786-O and Caki-1) to evaluate its impact on cell proliferation, migration, and invasion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The palmitoylation-related prognostic model effectively stratified ccRCC patients into the high- and low-risk groups, with distinct differences in survival outcomes. Genomic analysis demonstrated higher TMB and CNV alterations in the high-risk group. Immune response predictions indicated that low-risk patients were more likely to benefit from immunotherapy. Additionally, ZDHHC18 was significantly upregulated in ccRCC tumor tissues, and its knockdown notably inhibited cell proliferation, migration, and invasion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Palmitoylation-related genes, particularly ZDHHC18, serve as promising prognostic biomarkers and predictive indicators for immune therapy in ccRCC. These findings offer new insights into ccRCC biology and highlight potential therapeutic targets for improving patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/4647115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhipeng Jin, Kun Fang, Xue Zhang, Mengying Song, Hong Jiang, Yefu Liu
Background
Cancers are characterized by high heterogeneity. This study seeks to identify the factors driving hepatocellular carcinoma (HCC) heterogeneity to aid in prognostic stratification and inform personalized treatment approaches.
Methods
We used a computational pipeline to integrate multiomics data from HCC patients, applying 10 clustering algorithms. These results were combined with a machine learning framework to identify high-resolution molecular subtypes (MSs) and to create a robust molecular subtype–related risk score (MSRRS). Subsequent integrated bioinformatics algorithms further analyzed the heterogeneity of HCC at the level of molecular pathways, therapeutic response, and tumor microenvironment, thereby assessing potential clinical value.
Results
Through multiomics clustering, we identified two heterogeneous MSs associated with prognosis, with MS2 exhibiting a more favorable prognostic outcome. Subsequently, we applied bootstrap resampling-based univariate Cox regression and Boruta algorithm to screen for more clinically relevant genes from the marker genes of each MS. Next, we benchmarked seven survival-related machine learning algorithms for overall survival (OS) using nested cross-validation. The hyperparameter-tuned Ridge survival model outperforms other tuned models and was therefore used to develop a robust MSRRS. MSRRS demonstrated superior performance in predicting patient OS in multiple independent HCC cohorts. Downstream analysis suggested that MSRRS has the potential to guide individualized targeted therapy, chemotherapy, and immunotherapy for HCC and to assess the tumor microenvironment. Pathway enrichment analysis identified the cell cycle as a crucial driver of heterogeneity differences between the two subtypes. Finally, we confirmed that KIF2C may be the most central MSRRS gene and demonstrated by in vitro experiments that KIF2C could promote G2/M transition in HCC cells by targeting CDK1/CCNB1/PLK1 signaling.
Conclusion
The novel MSs and robust MSRRS we identified effectively exposed the heterogeneity of HCC and have the potential to predict prognosis and guide individualized precision therapy.
{"title":"Consensus Integration of Multiomics Data With Machine Learning Algorithms Reveals Heterogeneous Molecular Subtypes and Enables Personalized Treatment Strategies for Hepatocellular Carcinoma","authors":"Zhipeng Jin, Kun Fang, Xue Zhang, Mengying Song, Hong Jiang, Yefu Liu","doi":"10.1155/humu/9967779","DOIUrl":"https://doi.org/10.1155/humu/9967779","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Cancers are characterized by high heterogeneity. This study seeks to identify the factors driving hepatocellular carcinoma (HCC) heterogeneity to aid in prognostic stratification and inform personalized treatment approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used a computational pipeline to integrate multiomics data from HCC patients, applying 10 clustering algorithms. These results were combined with a machine learning framework to identify high-resolution molecular subtypes (MSs) and to create a robust molecular subtype–related risk score (MSRRS). Subsequent integrated bioinformatics algorithms further analyzed the heterogeneity of HCC at the level of molecular pathways, therapeutic response, and tumor microenvironment, thereby assessing potential clinical value.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Through multiomics clustering, we identified two heterogeneous MSs associated with prognosis, with MS2 exhibiting a more favorable prognostic outcome. Subsequently, we applied bootstrap resampling-based univariate Cox regression and Boruta algorithm to screen for more clinically relevant genes from the marker genes of each MS. Next, we benchmarked seven survival-related machine learning algorithms for overall survival (OS) using nested cross-validation. The hyperparameter-tuned Ridge survival model outperforms other tuned models and was therefore used to develop a robust MSRRS. MSRRS demonstrated superior performance in predicting patient OS in multiple independent HCC cohorts. Downstream analysis suggested that MSRRS has the potential to guide individualized targeted therapy, chemotherapy, and immunotherapy for HCC and to assess the tumor microenvironment. Pathway enrichment analysis identified the cell cycle as a crucial driver of heterogeneity differences between the two subtypes. Finally, we confirmed that KIF2C may be the most central MSRRS gene and demonstrated by in vitro experiments that KIF2C could promote G2/M transition in HCC cells by targeting CDK1/CCNB1/PLK1 signaling.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The novel MSs and robust MSRRS we identified effectively exposed the heterogeneity of HCC and have the potential to predict prognosis and guide individualized precision therapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/9967779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Liang, Weiyi Fu, Fan Wu, Tong Liu, Liyan Sun, Jin Qiu, Yuan Yao, Runjun He, Zhihui Deng, Yanli Ji
Background
The antigen of H blood group system, synthesized by the fucosyltransferase encoded by FUT1 and FUT2 genes, is crucial in transfusion medicine. Traditional sequencing struggles with haplotype determination, necessitating advanced technologies like long-read sequencing.
Methods
We employed long-read sequencing to analyze FUT1 and FUT2 haplotypes in 154 individuals, including 138 multi-ethnic donors (46 Chinese Han, 49 Uyghur, and 43 Indian) and 16 para-Bombay cases.
Results
We successfully reconstructed 8.5 kb FUT1 and 10.5 kb FUT2 haplotypes, identifying 44 and 82 single-nucleotide variants (SNVs), respectively. These SNVs allowed for the classification into four FUT1 and three FUT2 SNV patterns, reflecting ethnic diversity. Notably, pattern C for both FUT1 and FUT2 showed strong linkage disequilibrium (R2 = 0.84), consistent with their close genomic proximity, and was not observed in the Han populations. The prevalence of H-negative phenotype-related FUT2 alleles was markedly lower in Han (1.09%) compared to Uyghur (26.53%) and Indian (50.00%), explaining the rarity of the Bombay phenotype in Han. Furthermore, we found that a single allele named by ISBT can encompass multiple distinct haplotype sequences (spanning various patterns), revealing a level of diversity not captured by traditional SNV-based classifications.
Conclusions
Our findings highlight the unique genetic patterns within FUT1/FUT2, characterized by specific SNVs, suggesting a need for more nuanced approaches to accurately capture genetic variation, evolutionary relationships, and clinical implications. This study establishes long-read sequencing as pivotal for blood group genetics, revealing population-specific evolutionary dynamics that clarify ethnic divergence in antigen expression and disease susceptibility.
{"title":"Comparative Analysis of FUT1 and FUT2 Haplotype Diversity in Multi-Ethnic Populations Via Long-Read Sequencing","authors":"Shuang Liang, Weiyi Fu, Fan Wu, Tong Liu, Liyan Sun, Jin Qiu, Yuan Yao, Runjun He, Zhihui Deng, Yanli Ji","doi":"10.1155/humu/5597086","DOIUrl":"https://doi.org/10.1155/humu/5597086","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The antigen of H blood group system, synthesized by the fucosyltransferase encoded by <i>FUT1</i> and <i>FUT2</i> genes, is crucial in transfusion medicine. Traditional sequencing struggles with haplotype determination, necessitating advanced technologies like long-read sequencing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We employed long-read sequencing to analyze <i>FUT1</i> and <i>FUT2</i> haplotypes in 154 individuals, including 138 multi-ethnic donors (46 Chinese Han, 49 Uyghur, and 43 Indian) and 16 para-Bombay cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We successfully reconstructed 8.5 kb <i>FUT1</i> and 10.5 kb <i>FUT2</i> haplotypes, identifying 44 and 82 single-nucleotide variants (SNVs), respectively. These SNVs allowed for the classification into four <i>FUT1</i> and three <i>FUT2</i> SNV patterns, reflecting ethnic diversity. Notably, pattern C for both <i>FUT1</i> and <i>FUT2</i> showed strong linkage disequilibrium (<i>R</i><sup>2</sup> = 0.84), consistent with their close genomic proximity, and was not observed in the Han populations. The prevalence of H-negative phenotype-related <i>FUT2</i> alleles was markedly lower in Han (1.09%) compared to Uyghur (26.53%) and Indian (50.00%), explaining the rarity of the Bombay phenotype in Han. Furthermore, we found that a single allele named by ISBT can encompass multiple distinct haplotype sequences (spanning various patterns), revealing a level of diversity not captured by traditional SNV-based classifications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings highlight the unique genetic patterns within <i>FUT1/FUT2</i>, characterized by specific SNVs, suggesting a need for more nuanced approaches to accurately capture genetic variation, evolutionary relationships, and clinical implications. This study establishes long-read sequencing as pivotal for blood group genetics, revealing population-specific evolutionary dynamics that clarify ethnic divergence in antigen expression and disease susceptibility.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/5597086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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