Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-09-26 DOI:10.1007/s13258-024-01569-9

Suhyun Hwangbo, Sungyoung Lee, Md Mozaffar Hosain, Taewan Goo, Seungyeoun Lee, Inyoung Kim, Taesung Park

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引用次数: 0

Abstract

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.

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基于核的分层结构组件模型用于生存表型的通路分析

背景：高通量测序，尤其是 RNA 测序（RNA-seq），推动了差异基因表达分析的发展，揭示了涉及各种生物条件的通路。传统的基于通路的方法通常独立考虑通路，忽略了通路之间的相关性，也忽略了通路之间大量重叠的生物标记物。此外，大多数基于通路的方法都假定生物标记物对相关表型具有线性影响：本研究旨在开发 HisCoM-KernelS 模型，通过考虑基因与生存结果之间复杂的非线性关系，同时考虑通路间的相关性，来识别与生存表型相关的通路：我们将 HisCoM-KernelS 模型应用于 TCGA 胰腺导管腺癌（PDAC）RNA-seq 数据集，该数据集由来自 148 个 PDAC 样本、映射到 186 个 KEGG 通路的 4498 个蛋白编码基因组成。利用核机器回归建立了通路对生存结果影响的模型，并纳入了分层基因通路结构。使用交替最小二乘法估计模型参数，并通过置换检验评估通路的显著性：结果：HisCoM-KernelS发现了几条与胰腺癌生存显著相关的通路，其中包括那些已被先前研究证实的通路。与其他四种基于通路的方法（HisCoM-PAGE、Global Test、GSEA 和 CoxKM）相比，HisCoM-KernelS（尤其是高斯核）在检测率和重要通路数量方面表现出更好的平衡：结论：HisCoM-KernelS 成功地将基于通路的分析扩展到了生存结果，捕捉到了复杂的非线性基因效应和通路间的相关性。它在 TCGA PDAC 数据集上的应用强调了它在识别生物相关通路方面的实用性，为高通量测序数据中的生存表型研究提供了一个强大的工具。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

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.