Xu Luo, Xinpeng Zhang, Dongqing Su, Honghao Li, Min Zou, Yuqiang Xiong, Lei Yang
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In this study, based on sparse somatic mutation data from 4581 NSCLC patients from the Memorial Sloan Kettering Cancer Center (MSKCC) database, we systematically evaluate the metabolic pathway activity in NSCLC patients through the application of network propagation algorithm and computational biology algorithms. Based on these metabolic pathways associated with prognosis, as recognized through univariate Cox regression analysis, NSCLC patients are stratified using the deep clustering algorithm to explore the optimal classification strategy, thereby establishing biologically meaningful metabolic subtypes of NSCLC patients. The precise NSCLC metabolic subtypes obtained from the network propagation algorithm and deep clustering algorithm are systematically evaluated and validated for survival benefits of immunotherapy. 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引用次数: 0
摘要
作为一种常见的下呼吸道恶性肿瘤,非小细胞肺癌(NSCLC)以其高发病率和高死亡率为特征,是全球肿瘤学的一大挑战。最近的研究强调了体细胞突变在非小细胞肺癌的发生和发展中的关键作用。基于体细胞突变数据的非小细胞肺癌患者分层可以促进识别可能对个性化治疗策略有反应的患者。然而,由于这些数据的稀疏性,使用体细胞突变数据对NSCLC患者进行分层是具有挑战性的。本研究基于美国Memorial Sloan Kettering Cancer Center (MSKCC)数据库中4581例NSCLC患者的稀疏体细胞突变数据,应用网络传播算法和计算生物学算法,系统评估NSCLC患者代谢通路活性。基于这些与预后相关的代谢途径,通过单变量Cox回归分析,采用深度聚类算法对NSCLC患者进行分层,探索最佳分类策略,从而建立具有生物学意义的NSCLC患者代谢亚型。通过网络传播算法和深度聚类算法获得的精确NSCLC代谢亚型被系统地评估和验证免疫治疗的生存效益。我们的研究标志着开发一种基于体细胞突变谱、采用深度聚类算法对非小细胞肺癌患者进行分类的通用方法的进展。本研究的实施将有助于从肿瘤微环境角度深化对NSCLC患者代谢亚型的分析,为制定更精准的个性化治疗方案提供有力依据。
Deep Clustering-Based Metabolic Stratification of Non-Small Cell Lung Cancer Patients Through Integration of Somatic Mutation Profile and Network Propagation Algorithm.
As a common malignancy of the lower respiratory tract, non-small cell lung cancer (NSCLC) represents a major oncological challenge globally, characterized by high incidence and mortality rates. Recent research highlights the critical involvement of somatic mutations in the onset and development of NSCLC. Stratification of NSCLC patients based on somatic mutation data could facilitate the identification of patients likely to respond to personalized therapeutic strategies. However, stratification of NSCLC patients using somatic mutation data is challenging due to the sparseness of this data. In this study, based on sparse somatic mutation data from 4581 NSCLC patients from the Memorial Sloan Kettering Cancer Center (MSKCC) database, we systematically evaluate the metabolic pathway activity in NSCLC patients through the application of network propagation algorithm and computational biology algorithms. Based on these metabolic pathways associated with prognosis, as recognized through univariate Cox regression analysis, NSCLC patients are stratified using the deep clustering algorithm to explore the optimal classification strategy, thereby establishing biologically meaningful metabolic subtypes of NSCLC patients. The precise NSCLC metabolic subtypes obtained from the network propagation algorithm and deep clustering algorithm are systematically evaluated and validated for survival benefits of immunotherapy. Our research marks progress towards developing a universal approach for classifying NSCLC patients based solely on somatic mutation profiles, employing deep clustering algorithm. The implementation of our research will help to deepen the analysis of NSCLC patients' metabolic subtypes from the perspective of tumor microenvironment, providing a strong basis for the formulation of more precise personalized treatment plans.
期刊介绍:
Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology.
The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer.
The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.