Suhyun Hwangbo, Sungyoung Lee, Md Mozaffar Hosain, Taewan Goo, Seungyeoun Lee, Inyoung Kim, Taesung Park
{"title":"基于核的分层结构组件模型用于生存表型的通路分析","authors":"Suhyun Hwangbo, Sungyoung Lee, Md Mozaffar Hosain, Taewan Goo, Seungyeoun Lee, Inyoung Kim, Taesung Park","doi":"10.1007/s13258-024-01569-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.</p><p><strong>Objective: </strong>This study aims to develop the HisCoM-KernelS model to identify survival phenotype-related pathways by accommodating complex, nonlinear relationships between genes and survival outcomes, while accounting for inter-pathway correlations.</p><p><strong>Methods: </strong>We applied HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset, comprising 4,498 protein-coding genes mapped to 186 KEGG pathways from 148 PDAC samples. Kernel machine regression was used to model pathway effects on survival outcomes, incorporating hierarchical gene-pathway structures. Model parameters were estimated using the alternating least squares algorithm, and the significance of pathways was assessed through a permutation test.</p><p><strong>Results: </strong>HisCoM-KernelS identified several pathways significantly associated with pancreatic cancer survival, including those corroborated by previous studies. HisCoM-KernelS, especially with the Gaussian kernel, showed a better balance of detection rate and number of significant pathways compared to four other existing pathway-based methods: HisCoM-PAGE, Global Test, GSEA, and CoxKM.</p><p><strong>Conclusion: </strong>HisCoM-KernelS successfully extends pathway-based analysis to survival outcomes, capturing complex nonlinear gene effects and inter-pathway correlations. Its application to the TCGA PDAC dataset emphasizes its utility in identifying biologically relevant pathways, offering a robust tool for survival phenotype research in high-throughput sequencing data.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.\",\"authors\":\"Suhyun Hwangbo, Sungyoung Lee, Md Mozaffar Hosain, Taewan Goo, Seungyeoun Lee, Inyoung Kim, Taesung Park\",\"doi\":\"10.1007/s13258-024-01569-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.</p><p><strong>Objective: </strong>This study aims to develop the HisCoM-KernelS model to identify survival phenotype-related pathways by accommodating complex, nonlinear relationships between genes and survival outcomes, while accounting for inter-pathway correlations.</p><p><strong>Methods: </strong>We applied HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset, comprising 4,498 protein-coding genes mapped to 186 KEGG pathways from 148 PDAC samples. Kernel machine regression was used to model pathway effects on survival outcomes, incorporating hierarchical gene-pathway structures. Model parameters were estimated using the alternating least squares algorithm, and the significance of pathways was assessed through a permutation test.</p><p><strong>Results: </strong>HisCoM-KernelS identified several pathways significantly associated with pancreatic cancer survival, including those corroborated by previous studies. HisCoM-KernelS, especially with the Gaussian kernel, showed a better balance of detection rate and number of significant pathways compared to four other existing pathway-based methods: HisCoM-PAGE, Global Test, GSEA, and CoxKM.</p><p><strong>Conclusion: </strong>HisCoM-KernelS successfully extends pathway-based analysis to survival outcomes, capturing complex nonlinear gene effects and inter-pathway correlations. Its application to the TCGA PDAC dataset emphasizes its utility in identifying biologically relevant pathways, offering a robust tool for survival phenotype research in high-throughput sequencing data.</p>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1007/s13258-024-01569-9\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s13258-024-01569-9","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.
Background: High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.
Objective: This study aims to develop the HisCoM-KernelS model to identify survival phenotype-related pathways by accommodating complex, nonlinear relationships between genes and survival outcomes, while accounting for inter-pathway correlations.
Methods: We applied HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset, comprising 4,498 protein-coding genes mapped to 186 KEGG pathways from 148 PDAC samples. Kernel machine regression was used to model pathway effects on survival outcomes, incorporating hierarchical gene-pathway structures. Model parameters were estimated using the alternating least squares algorithm, and the significance of pathways was assessed through a permutation test.
Results: HisCoM-KernelS identified several pathways significantly associated with pancreatic cancer survival, including those corroborated by previous studies. HisCoM-KernelS, especially with the Gaussian kernel, showed a better balance of detection rate and number of significant pathways compared to four other existing pathway-based methods: HisCoM-PAGE, Global Test, GSEA, and CoxKM.
Conclusion: HisCoM-KernelS successfully extends pathway-based analysis to survival outcomes, capturing complex nonlinear gene effects and inter-pathway correlations. Its application to the TCGA PDAC dataset emphasizes its utility in identifying biologically relevant pathways, offering a robust tool for survival phenotype research in high-throughput sequencing data.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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