Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome
Abstract Splicing dysregulation arising from spliceosomal mutations contributes to disease progression and treatment resistance, mostly in hematologic malignancy. Whereas spliceosomal mutations are less common in solid tumors, splicing disorders are pervasive and proven to promote tumorigenesis. How...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202405626 |
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| author | Jingru Sui Dan Guo Xiao Wen Lei Zhou Yue Huang Haoyu Yu Jinyu Chen Zhaoqi Liu |
| author_facet | Jingru Sui Dan Guo Xiao Wen Lei Zhou Yue Huang Haoyu Yu Jinyu Chen Zhaoqi Liu |
| author_sort | Jingru Sui |
| collection | DOAJ |
| description | Abstract Splicing dysregulation arising from spliceosomal mutations contributes to disease progression and treatment resistance, mostly in hematologic malignancy. Whereas spliceosomal mutations are less common in solid tumors, splicing disorders are pervasive and proven to promote tumorigenesis. However, there is a lack of systematic understanding of the overall splicing dysregulation patterns and how widespread different patterns occur within or across solid tumor lineage. To address these questions, a computational method called SMNPLS (Sparse Multi‐Network Regularized Partial Least Squares) is developed to uncover the pan‐cancer splicing dysregulation landscape by extracting joint modular patterns from paired matrices of splicing factors (SFs) expressions and alternative splicing events (ASEs). Six unique patterns illustrated by ASE‐SF co‐modules are summarized, which involve 1,570 ASEs and altered expression of 170 SFs, covering 40% of TCGA solid tumors. Cross‐cancer commonalities of splicing dysregulation are observed among digestive system neoplasms, renal‐associated tumors, and urogenital tumors. By contrast, brain tumors demonstrate a distinct splicing pattern with the highest ASE‐SF correlation. In addition, some new splicing regulatory relationships are identified that are potentially oncogenic. Overall, the study characterizes the full spectrum of splicing dysregulation patterns, indicating the similarity and specificity of splicing‐derived pathogenesis across 31 human solid tumors. |
| format | Article |
| id | doaj-art-a50a403f2df84903ba8c5d99250e9c58 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-a50a403f2df84903ba8c5d99250e9c582025-01-29T09:50:19ZengWileyAdvanced Science2198-38442025-01-01124n/an/a10.1002/advs.202405626Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor TranscriptomeJingru Sui0Dan Guo1Xiao Wen2Lei Zhou3Yue Huang4Haoyu Yu5Jinyu Chen6Zhaoqi Liu7China National Center for Bioinformation Beijing 100101 ChinaChina National Center for Bioinformation Beijing 100101 ChinaChina National Center for Bioinformation Beijing 100101 ChinaChina National Center for Bioinformation Beijing 100101 ChinaChina National Center for Bioinformation Beijing 100101 ChinaSchool of Life Science Inner Mongolia University Hohhot 010021 ChinaSchool of Mathematics Statistics and Mechanics Beijing University of Technology Beijing 100124 ChinaChina National Center for Bioinformation Beijing 100101 ChinaAbstract Splicing dysregulation arising from spliceosomal mutations contributes to disease progression and treatment resistance, mostly in hematologic malignancy. Whereas spliceosomal mutations are less common in solid tumors, splicing disorders are pervasive and proven to promote tumorigenesis. However, there is a lack of systematic understanding of the overall splicing dysregulation patterns and how widespread different patterns occur within or across solid tumor lineage. To address these questions, a computational method called SMNPLS (Sparse Multi‐Network Regularized Partial Least Squares) is developed to uncover the pan‐cancer splicing dysregulation landscape by extracting joint modular patterns from paired matrices of splicing factors (SFs) expressions and alternative splicing events (ASEs). Six unique patterns illustrated by ASE‐SF co‐modules are summarized, which involve 1,570 ASEs and altered expression of 170 SFs, covering 40% of TCGA solid tumors. Cross‐cancer commonalities of splicing dysregulation are observed among digestive system neoplasms, renal‐associated tumors, and urogenital tumors. By contrast, brain tumors demonstrate a distinct splicing pattern with the highest ASE‐SF correlation. In addition, some new splicing regulatory relationships are identified that are potentially oncogenic. Overall, the study characterizes the full spectrum of splicing dysregulation patterns, indicating the similarity and specificity of splicing‐derived pathogenesis across 31 human solid tumors.https://doi.org/10.1002/advs.202405626alternative splicingmathematical modelingpan cancersplicing factor |
| spellingShingle | Jingru Sui Dan Guo Xiao Wen Lei Zhou Yue Huang Haoyu Yu Jinyu Chen Zhaoqi Liu Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome Advanced Science alternative splicing mathematical modeling pan cancer splicing factor |
| title | Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome |
| title_full | Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome |
| title_fullStr | Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome |
| title_full_unstemmed | Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome |
| title_short | Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome |
| title_sort | systematic characterization of splicing dysregulation in pan solid tumor transcriptome |
| topic | alternative splicing mathematical modeling pan cancer splicing factor |
| url | https://doi.org/10.1002/advs.202405626 |
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