感染 SARS-CoV-2 后果糖代谢异常对结直肠癌患者预后不良的影响

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-09-27 eCollection Date: 2024-09-01 DOI:10.1371/journal.pcbi.1012412
Jiaxin Jiang, Xiaona Meng, Yibo Wang, Ziqian Zhuang, Ting Du, Jing Yan
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引用次数: 0

摘要

大多数 COVID-19 患者的预后良好,但患有其他基础疾病的患者更有可能病情严重,死亡率增加。大量研究表明,癌症患者更容易感染 SARS-CoV-2,并出现严重的 COVID-19 甚至死亡。最近的转录组研究表明,SARS-CoV-2 感染者的果糖代谢发生了改变。然而,癌细胞可以利用果糖作为生长和转移的额外能量来源。此外,由于生活条件的改善,个人日常饮食习惯中果糖的摄入量明显增加。因此,我们推测,SARS-CoV-2 导致癌症患者预后不良的原因可能是果糖代谢。我们利用来自四个不同队列的 CRC 病例,通过耦合 Cox 单变量回归和 lasso 回归特征选择算法,建立并验证了一个基于 SARS-CoV-2 导致果糖代谢异常的预测模型,以确定结直肠癌的标志基因。我们还开发了一种综合预后提名图,通过将这种新型冠状病毒产生的果糖代谢异常特征与年龄和肿瘤分期相结合来改进临床实践。为了获得具有最大潜在预后价值的基因,我们进行了LASSO回归分析,在TCGA训练队列中,患者被随机分为训练集和验证集,比例为4:在TCGA训练队列中,按4:1的比例将患者随机分为训练集和验证集,通过拉索回归分析获得每个样本的最佳风险评分值,并进行进一步分析,最终选出了CLEC4A、FDFT1、CTNNB1、GPI、PMM2、PTPRD、IL7、ALDH3B1、AASS、AOC3、SEPINE1、PFKFB1、FTCD、TIMP1和GATM这15个基因。为了验证该模型的准确性,我们在外部数据集上进行了 ROC 曲线分析,结果表明该模型对患者的预后预测具有较高的预测能力。我们的研究为今后有针对性地调节结直肠癌患者的果糖代谢提供了理论基础,同时在 COVID-19 大流行的背景下优化了结直肠癌患者的饮食指导和治疗护理。
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Effect of aberrant fructose metabolism following SARS-CoV-2 infection on colorectal cancer patients' poor prognosis.

Most COVID-19 patients have a positive prognosis, but patients with additional underlying diseases are more likely to have severe illness and increased fatality rates. Numerous studies indicate that cancer patients are more prone to contract SARS-CoV-2 and develop severe COVID-19 or even dying. In the recent transcriptome investigations, it is demonstrated that the fructose metabolism is altered in patients with SARS-CoV-2 infection. However, cancer cells can use fructose as an extra source of energy for growth and metastasis. Furthermore, enhanced living conditions have resulted in a notable rise in fructose consumption in individuals' daily dietary habits. We therefore hypothesize that the poor prognosis of cancer patients caused by SARS-CoV-2 may therefore be mediated through fructose metabolism. Using CRC cases from four distinct cohorts, we built and validated a predictive model based on SARS-CoV-2 producing fructose metabolic anomalies by coupling Cox univariate regression and lasso regression feature selection algorithms to identify hallmark genes in colorectal cancer. We also developed a composite prognostic nomogram to improve clinical practice by integrating the characteristics of aberrant fructose metabolism produced by this novel coronavirus with age and tumor stage. To obtain the genes with the greatest potential prognostic values, LASSO regression analysis was performed, In the TCGA training cohort, patients were randomly separated into training and validation sets in the ratio of 4: 1, and the best risk score value for each sample was acquired by lasso regression analysis for further analysis, and the fifteen genes CLEC4A, FDFT1, CTNNB1, GPI, PMM2, PTPRD, IL7, ALDH3B1, AASS, AOC3, SEPINE1, PFKFB1, FTCD, TIMP1 and GATM were finally selected. In order to validate the model's accuracy, ROC curve analysis was performed on an external dataset, and the results indicated that the model had a high predictive power for the prognosis prediction of patients. Our study provides a theoretical foundation for the future targeted regulation of fructose metabolism in colorectal cancer patients, while simultaneously optimizing dietary guidance and therapeutic care for colorectal cancer patients in the context of the COVID-19 pandemic.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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