Data-driven energy landscape reveals critical genes in cancer progression.

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2024-03-08 DOI:10.1038/s41540-024-00354-4

Juntan Liu, Chunhe Li

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Abstract

The evolution of cancer is a complex process characterized by stable states and transitions among them. Studying the dynamic evolution of cancer and revealing the mechanisms of cancer progression based on experimental data is an important topic. In this study, we aim to employ a data-driven energy landscape approach to analyze the dynamic evolution of cancer. We take Kidney renal clear cell carcinoma (KIRC) as an example. From the energy landscape, we introduce two quantitative indicators (transition probability and barrier height) to study critical shifts in KIRC cancer evolution, including cancer onset and progression, and identify critical genes involved in these transitions. Our results successfully identify crucial genes that either promote or inhibit these transition processes in KIRC. We also conduct a comprehensive biological function analysis on these genes, validating the accuracy and reliability of our predictions. This work has implications for discovering new biomarkers, drug targets, and cancer treatment strategies in KIRC.

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数据驱动的能量图谱揭示了癌症进展过程中的关键基因。

癌症的演变是一个复杂的过程，其特点是稳定状态和状态之间的转换。研究癌症的动态演化，并根据实验数据揭示癌症进展的机制是一个重要课题。在本研究中，我们旨在采用数据驱动的能量景观方法来分析癌症的动态演化。我们以肾透明细胞癌（KIRC）为例。从能量图谱中，我们引入了两个定量指标（过渡概率和屏障高度）来研究 KIRC 癌症演化过程中的关键转变，包括癌症的发生和发展，并找出参与这些转变的关键基因。我们的研究结果成功地确定了促进或抑制 KIRC 中这些转变过程的关键基因。我们还对这些基因进行了全面的生物功能分析，验证了我们预测的准确性和可靠性。这项工作对发现 KIRC 的新生物标志物、药物靶点和癌症治疗策略具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.

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