{"title":"U1小核rna的多样性及其突变诊断方法","authors":"Takuma Nakashima, Tsubasa Miyauchi, Ryota Takeuchi, Yuriko Sugihara, Yusuke Funakoshi, Fumiharu Ohka, Sachi Maeda, Junko Hirato, Takako Yoshioka, Hajime Okita, Yoshitaka Narita, Yonehiro Kanemura, Yasuhiro Kojima, Yuko Watanabe, Ryuta Saito, Hiromichi Suzuki","doi":"10.1111/cas.70110","DOIUrl":null,"url":null,"abstract":"<p><p>U1 small nuclear RNA (snRNA) mutations are recurrent non-coding alterations found in various malignancies, yet their identification has proven challenging due to their repetitive nature. We characterized the complex interindividual diversity and genomic architecture of U1 snRNA loci using sequencing data and a pangenome reference. Our analysis uncovered copy number variations and the diversity of single-nucleotide variants in regions not predicted to have significant functional impact. Compared to traditional linear reference-based analyses for mutations, the pangenome graph demonstrated the best accuracy, successfully identifying previously undetectable mutations. This underscores the utility of pangenome graph references for cancer genome research, particularly in repetitive and highly diverse genomic regions. Additionally, we developed mutation detection methods employing targeted capture sequencing, rapid quantitative polymerase chain reaction, and a machine learning approach based on splicing patterns, all exhibiting high precision in identifying U1 snRNA mutations. Our findings elucidate the structural complexity of U1 snRNA loci and establish robust methodologies for precise mutation detection in these regions.</p>","PeriodicalId":48943,"journal":{"name":"Cancer Science","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diversity of U1 Small Nuclear RNAs and Diagnostic Methods for Their Mutations.\",\"authors\":\"Takuma Nakashima, Tsubasa Miyauchi, Ryota Takeuchi, Yuriko Sugihara, Yusuke Funakoshi, Fumiharu Ohka, Sachi Maeda, Junko Hirato, Takako Yoshioka, Hajime Okita, Yoshitaka Narita, Yonehiro Kanemura, Yasuhiro Kojima, Yuko Watanabe, Ryuta Saito, Hiromichi Suzuki\",\"doi\":\"10.1111/cas.70110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>U1 small nuclear RNA (snRNA) mutations are recurrent non-coding alterations found in various malignancies, yet their identification has proven challenging due to their repetitive nature. We characterized the complex interindividual diversity and genomic architecture of U1 snRNA loci using sequencing data and a pangenome reference. Our analysis uncovered copy number variations and the diversity of single-nucleotide variants in regions not predicted to have significant functional impact. Compared to traditional linear reference-based analyses for mutations, the pangenome graph demonstrated the best accuracy, successfully identifying previously undetectable mutations. This underscores the utility of pangenome graph references for cancer genome research, particularly in repetitive and highly diverse genomic regions. Additionally, we developed mutation detection methods employing targeted capture sequencing, rapid quantitative polymerase chain reaction, and a machine learning approach based on splicing patterns, all exhibiting high precision in identifying U1 snRNA mutations. Our findings elucidate the structural complexity of U1 snRNA loci and establish robust methodologies for precise mutation detection in these regions.</p>\",\"PeriodicalId\":48943,\"journal\":{\"name\":\"Cancer Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Science\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/cas.70110\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/cas.70110","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Diversity of U1 Small Nuclear RNAs and Diagnostic Methods for Their Mutations.
U1 small nuclear RNA (snRNA) mutations are recurrent non-coding alterations found in various malignancies, yet their identification has proven challenging due to their repetitive nature. We characterized the complex interindividual diversity and genomic architecture of U1 snRNA loci using sequencing data and a pangenome reference. Our analysis uncovered copy number variations and the diversity of single-nucleotide variants in regions not predicted to have significant functional impact. Compared to traditional linear reference-based analyses for mutations, the pangenome graph demonstrated the best accuracy, successfully identifying previously undetectable mutations. This underscores the utility of pangenome graph references for cancer genome research, particularly in repetitive and highly diverse genomic regions. Additionally, we developed mutation detection methods employing targeted capture sequencing, rapid quantitative polymerase chain reaction, and a machine learning approach based on splicing patterns, all exhibiting high precision in identifying U1 snRNA mutations. Our findings elucidate the structural complexity of U1 snRNA loci and establish robust methodologies for precise mutation detection in these regions.
期刊介绍:
Cancer Science (formerly Japanese Journal of Cancer Research) is a monthly publication of the Japanese Cancer Association. First published in 1907, the Journal continues to publish original articles, editorials, and letters to the editor, describing original research in the fields of basic, translational and clinical cancer research. The Journal also accepts reports and case reports.
Cancer Science aims to present highly significant and timely findings that have a significant clinical impact on oncologists or that may alter the disease concept of a tumor. The Journal will not publish case reports that describe a rare tumor or condition without new findings to be added to previous reports; combination of different tumors without new suggestive findings for oncological research; remarkable effect of already known treatments without suggestive data to explain the exceptional result. Review articles may also be published.