Exploration of ferroptosis-related biomarkers and regulatory mechanisms in two diseases (sarcopenia and idiopathic pulmonary fibrosis) based on transcriptome and machine learning.

IF 4.3
Jun Yang, Kaihua Zhou, Xiaojian He, Ke Rong
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Abstract

Background: Sarcopenia (SP) and idiopathic pulmonary fibrosis (IPF) are debilitating diseases, but share ferroptosis-related biomarkers and their regulatory mechanisms in both remain unclear, hindering targeted research and therapy.

Methods: In this study, ferroptosis-related genes (FeRGs), as well as training sets and test sets related to SP and IPF, were obtained from public databases. After the identification of disease-related differentially expressed genes (DEGs), DEGs shared by SP and IPF were obtained through intersection (genes with consistent DEG change trends across each disease) and union analyses. These shared DEGs were then intersected with FeRGs to obtain candidate genes. Machine learning algorithms and the Wilcoxon rank-sum test were used to confirm the biomarkers, and nomograms were constructed and evaluated. Meanwhile, in-depth studies such as functional enrichment, immune infiltration, drug prediction, and molecular docking were conducted on the biomarkers.

Results: Initially, a total of 26 candidate genes were identified. After screening, DDIT4 and MGST1 were identified as ferroptosis-related biomarkers shared by the two diseases, and both were significantly upregulated in the SP and IPF groups. The area under the curve values of the constructed nomograms were 0.91 (for SP) and 0.79 (for IPF), respectively. The p-values of the Hosmer-Lemeshow test for the calibration curves (0.856 and 0.205) were both >0.05, and the DCA showed good performance. The two biomarkers showed differences in enriched pathways. A total of 5 differential immune cells shared by the two diseases were identified, and the biomarkers participated in immune regulation by targeting specific immune cells. Good binding activity (Vina Score < -5.0) was observed between Trichostatin A and both biomarkers.

Conclusion: DDIT4 and MGST1 were shared ferroptosis-related biomarkers for SP and IPF. Nomograms enabled reliable prediction, and trichostatin A was a potential targeted drug. These findings supported mechanistic research and targeted therapy for both diseases.

基于转录组和机器学习的两种疾病(肌肉减少症和特发性肺纤维化)中铁中毒相关生物标志物和调节机制的探索。
背景:骨骼肌减少症(SP)和特发性肺纤维化(IPF)是一种使人衰弱的疾病,但两者的共同生物标志物及其调控机制尚不清楚,这阻碍了有针对性的研究和治疗。方法:本研究从公共数据库中获取吸铁相关基因(FeRGs),以及SP和IPF相关的训练集和测试集。在确定疾病相关差异表达基因(DEG)后,通过交叉(在每种疾病中DEG变化趋势一致的基因)和联合分析获得SP和IPF共有的DEG。然后将这些共享的deg与ferg相交以获得候选基因。使用机器学习算法和Wilcoxon秩和检验来确认生物标志物,并构建和评估nomogram。同时,对生物标志物进行功能富集、免疫浸润、药物预测、分子对接等深入研究。结果:初步共鉴定出26个候选基因。经筛选,DDIT4和MGST1被鉴定为两种疾病共有的与铁腐病相关的生物标志物,并且在SP和IPF组中均显著上调。构建的模态图曲线下面积分别为0.91 (SP)和0.79 (IPF)。校正曲线的Hosmer-Lemeshow检验的p值(0.856和0.205)均为0.05,DCA表现出良好的性能。两种生物标志物在富集通路上表现出差异。两种疾病共有5种差异免疫细胞,生物标志物通过靶向特异性免疫细胞参与免疫调节。良好的结合活性(Vina Score )结论:DDIT4和MGST1是SP和IPF共同的铁枯相关生物标志物。诺图图可以进行可靠的预测,曲古霉素A是一种潜在的靶向药物。这些发现为这两种疾病的机制研究和靶向治疗提供了支持。
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来源期刊
Experimental gerontology
Experimental gerontology Ageing, Biochemistry, Geriatrics and Gerontology
CiteScore
6.70
自引率
0.00%
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审稿时长
66 days
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