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特发性肺纤维化的诊断生物标志物发现与中药靶向预测：基于孟德尔随机化与机器学习的多组学研究

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罗成, 叶远航, 宁博, 李家劼, 谭钧文, 王飞, 柯佳, 覃琬婷

中草药 | 数据挖掘与循证医学 2026,57(9): 3474-3494

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中草药 |数据挖掘与循证医学 2026 , 57 (9) : 3474 -3494

特发性肺纤维化的诊断生物标志物发现与中药靶向预测：基于孟德尔随机化与机器学习的多组学研究

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罗成, 叶远航, 宁博, 李家劼, 谭钧文, 王飞, 柯佳, 覃琬婷

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覃琬婷

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罗成: 罗成,博士研究生,医师,从事中医药防治呼吸病与老年病的临床研究。E-mail:1121174213@qq.com

Discovery of dagnostic bomarkers and trgeted traditional Chinese medicine prediction for idiopathic pulmonary fibrosis: A multi-omics study based on Mendelian randomization and machine learning

LUO Cheng, YE Yuanhang, NING Bo, LI Jiajie, TAN Junwen, WANG Fei, KE Jia, QIN Wanting

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doi: 10.7501/j.issn.0253-2670.2026.09.018

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目的利用生物信息学、孟德尔随机化（Mendelian randomization，MR）和机器学习分析探索特发性肺纤维化（idiopathic pulmonary fibrosis，IPF）的潜在靶点，并初步预测可能的相关中药。方法从GEO获得IPF微阵列数据集，并鉴定差异表达基因（differentially express genes，DEGs）。基于表达数量性状基因座（expression quantitative trait loci，eQTL）数据和全基因组关联研究（genome-wide association study，GWAS）数据，采用MR分析筛选与IPF相关的基因。将MR分析得出的风险基因与DEGs取交集，筛选出IPF相关的核心基因。利用功能富集分析、基因集富集分析（gene set enrichment analysis，GSEA）、免疫细胞浸润分析以及单细胞RNA测序进行评估。应用机器学习算法筛选最优诊断特征基因。使用独立的GEO队列进行差异表达验证及受试者工作特征（receiver operating characteristic，ROC）分析。此外，基于数据库挖掘与分子对接对潜在干预中药进行预测分析。结果共识别出916个与IPF相关的差异表达基因。与224个MR风险基因取交集后，得到7个关键基因：IRF7、TTC32、IFI6、ISG15（风险基因）及ZNF204P、ISOC1、CTSK（保护基因）。这些基因主要富集于干扰素-β产生、视黄酸诱导基因-1（retinoic acid-inducible gene-1，RIG-1）样受体信号通路、Toll样受体信号通路以及I型干扰素信号通路。免疫浸润分析显示，IPF患者组织中M1/M0巨噬细胞及静息肥大细胞减少，而活化肥大细胞增加。单细胞RNA测序揭示了这些基因在上皮细胞亚群中的特异性表达模式。机器学习算法确定ZNF204P和IRF7为最优诊断基因。在数据集验证中证实了这2基因在IPF中存在显著差异表达，并具有较高的诊断准确性。预测到与关键基因相关的潜在中药385味，中药四气以寒、温、平为主，五味以苦、甘、辛为主，归经以肝、肺、胃、脾、肾经为主，分类以清热药、补虚药、活血化瘀药、解表药和利水渗湿药为主。分子对接揭示了潜在靶向中药关键活性成分能够与核心基因蛋白形成稳定的互相作用。结论确定了7个与IPF相关的关键基因。机器学习筛选出ZNF204P和IRF7可作为稳健的诊断生物标志物，并具有治疗靶点潜力。并预测出栀子、柴胡、肉苁蓉、黄芪等可能是靶向IPF核心基因的潜在中药。

关键词

生物信息学 / 孟德尔随机化 / 表达数量性状基因座 / 机器学习 / 特发性肺纤维化 / 中药预测

Abstract

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Objective To identify potential therapeutic targets for idiopathic pulmonary fibrosis (IPF) and predict related herbal medicines by integrating bioinformatics, Mendelian randomization (MR), and machine learning approaches. Methods IPF microarray datasets were obtained from the GEO database to identify differentially expressed genes (DEGs). Using expression quantitative trait loci (eQTL) data and genome-wide association study (GWAS) data, MR analysis was conducted to screen for genes associated with IPF. The risk genes identified from MR analysis were intersected with DEGs to filter core IPF-related genes. Subsequent evaluations included functional enrichment analysis, gene set enrichment analysis (GSEA), immune cell infiltration analysis, and single-cell RNA sequencing. Machine learning algorithms were applied to select optimal diagnostic feature genes. An independent GEO cohort was used for differential expression validation and receiver operating characteristic (ROC) analysis. Results A total of 916 IPF-associated DEGs were identified. Intersection with 224 MR risk genes yielded seven key genes: IRF7, TTC32, IFI6, and ISG15 (risk genes), along with ZNF204P, ISOC1, and CTSK (protective genes). These genes were primarily enriched in pathways related to interferon-beta production, RIG-I-like receptor signaling, Toll-like receptor signaling, and type I interferon signaling. Immune infiltration analysis revealed a decrease in M1/M0 macrophages and resting mast cells, alongside an increase in activated mast cells in IPF tissues. Single-cell RNA sequencing demonstrated specific expression patterns of these genes within epithelial cell subpopulations. Machine learning algorithms identified ZNF204P and IRF7 as the optimal diagnostic genes. Validation in the dataset confirmed their significant differential expression in IPF and high diagnostic accuracy. A total of 385 traditional Chinese medicines (TCMs) related to the key genes were predicted. The primary properties of these TCMs were cold, warm, and neutral (four natures); their main flavors were bitter, sweet, and pungent (five flavors); the principal meridian tropisms were the liver, lung, stomach, spleen, and kidney meridians; and the major classifications were heat-clearing drugs, tonifying drugs, blood-activating and stasis-resolving drugs, exterior-releasing drugs, and dampness-draining diuretics. Molecular docking simulations revealed that these chemical components could form stable interactions with the core proteins. Conclusion This study identified seven key genes associated with IPF. Machine learning screened ZNF204P and IRF7 as robust diagnostic biomarkers with therapeutic target potential. Furthermore, TCMs such as Zhizi (Gardeniae Fructus), Chaihu (Bupleuri Radix), Roucongrong (Cistanches Herba), and Huangqi (Astragali Radix) might be potential TCMs that targets core genes associated with IPF.

Key words

bioinformatics / Mendelian randomization / expression quantitative trait loci / machine learning / idiopathic pulmonary fibrosis / traditional Chinese medicine prediction

引用本文

罗成, 叶远航, 宁博, 李家劼, 谭钧文, 王飞, 柯佳, 覃琬婷. 特发性肺纤维化的诊断生物标志物发现与中药靶向预测：基于孟德尔随机化与机器学习的多组学研究. 中草药, 2026 , 57 (9) : 3474 -3494 . DOI: 10.7501/j.issn.0253-2670.2026.09.018

LUO Cheng, YE Yuanhang, NING Bo, LI Jiajie, TAN Junwen, WANG Fei, KE Jia, QIN Wanting. Discovery of dagnostic bomarkers and trgeted traditional Chinese medicine prediction for idiopathic pulmonary fibrosis: A multi-omics study based on Mendelian randomization and machine learning[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (9) : 3474 -3494 . DOI: 10.7501/j.issn.0253-2670.2026.09.018

基金

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河北省国际合作专项(19392507D);国家科技部对发展中国家科技援助项目(KY201904005)

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Luo C, Wu X H, Zhang S P, et al. Cuproptosis:A novel therapeutic mechanism in lung cancer[J]. Cancer Cell Int,2025, 25(1):231.
Denis A, Tsiri P, Guiot J, et al. A new era in the treatment of progressive fibrosing interstitial lung diseases[J].Breathe, 2025, 21(2):240259.
Wang X Y, Wu X Y, Zhou L Q, et al.Demethyleneberberine ameliorates pulmonary fibrosis by inhibiting the NLRP3 inflammasome and the epithelial–mesenchymal transition[J]. Int Immunopharmacol, 2025,161:115003.
Maher T M, Bendstrup E, Dron L, et al. Global incidence and prevalence of idiopathic pulmonary fibrosis[J]. Respir Res, 2021, 22(1):197.
Majewski S, Górska K, Lewandowska K B, et al. Realworld treatment persistence and predictive factors for discontinuation of antifibrotic therapies in patients with idiopathic pulmonary fibrosis:A post-hoc analysis of two multicenter observational cohort studies in Poland[J].Front Pharmacol, 2025, 16:1586197.
Jones N, Rahar B, Bernau K, et al. Mechanisms on how matricellular microenvironments sustain idiopathic pulmonary fibrosis[J]. Int J Mol Sci, 2025, 26(11):5393.
Divolis G, Synolaki E, Tringidou R, et al. Transcriptomic analysis reveals shared deregulated neutrophil responses in COVID-19 and idiopathic pulmonary fibrosis[J]. Respir Res, 2025, 26(1):213.
Pan H, Jing C Q. Exploring druggable targets and inflammation-mediated pathways in cancer:A Mendelian randomization analysis integrating transcriptomic and proteomic data[J]. Inflamm Res, 2025, 74(1):46.
Chen Z, Tang M Y, Wang N, et al. Genetic variation reveals the therapeutic potential of BRSK2 in idiopathic pulmonary fibrosis[J]. BMC Med, 2025, 23(1):22.
Shi Y, Chen S, Zhou Z K, et al. Causal effects between genetically determined human serum metabolite levels on the risk of idiopathic pulmonary fibrosis:A Mendelian randomization study[J]. Clin Respir J, 2025, 19(6):e70087.
Gong P, Lu Y M, Chai X, et al. Exploring the causal relationship between immune cells and idiopathic pulmonary fibrosis:A Mendelian randomization analysis[J]. J Clin Lab Anal, 2025, 39(8):e70026.
Fan Q L, Meng Y, Nie Z H, et al. The role of inflammatory factors in mediating the causal effects of type 1 diabetes mellitus on idiopathic pulmonary fibrosis:A two-step Mendelian randomization study[J]. Medicine, 2025,104(4):e41320.
吴宣諭,肖祥,陈嘉靖,等.基于“肠-肺轴”探讨肠道菌群与特发性肺纤维化的遗传因果关联及干预中药预测[J].中草药, 2024, 55(17):5921-5937.
Zhu Z H, Zhang F T, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets[J]. Nat Genet, 2016, 48(5):481-487.
Guo-Parke H, Cappa O, Linden D A, et al. IFN-mediated bronchial epithelium cellular senescence in chronic obstructive pulmonary disease[J]. Am J Respir Cell Mol Biol, 2025, 73(6):871-883.
Yin Y, Zhao S J, Li W, et al. In situ reprogramming of fibroblasts into antigen-presenting pseudo-dendritic cells via IFN-β-engineered protoplast-derived exosomes delivered by microneedle arrays to enhance adaptive immunity[J]. Theranostics, 2025, 15(17):9179-9199.
Kim J Y, Cho D W, Choi J Y, et al. CXCL11 reprograms M2-biased macrophage polarization to alleviate pulmonary fibrosis in mice[J]. Cell Biosci, 2024, 14(1):140.
Wang C G, Liu S J, Li C Y, et al. Monitoring the cascade of monocyte-derived macrophages to influenza virus infection in human alveolus chips[J]. ACS Appl Mater Interfaces, 2024, 16(44):60045-60055.
Luo Y, Zhang H K, Yu J, et al. Stem cell factor/mast cell/CCL2/monocyte/macrophage axis promotes Coxsackievirus B3 myocarditis and cardiac fibrosis by increasing Ly6Chigh monocyte influx and fibrogenic mediators production[J]. Immunology, 2022, 167(4):590-605.
Pimpalwar N, Celik S, Karbalaei Sadegh M, et al. Analysis of genetic variant associated with heart failure mortality implicates thymic stromal lymphopoietin as mediator of strain-induced myocardial fibroblast-mast cell crosstalk and fibrosis[J]. FASEB J, 2024, 38(4):e23510.
Andersson C K, Andersson-Sjöland A, Mori M, et al.Activated MCTC mast cells infiltrate diseased lung areas in cystic fibrosis and idiopathic pulmonary fibrosis[J].Respir Res, 2011, 12(1):139.
Tan C, Zhou H, Xiong Q F, et al. Cromolyn sodium reduces LPS-induced pulmonary fibrosis by inhibiting the EMT process enhanced by MC-derived IL-13[J]. Respir Res, 2025, 26(1):3.
Cardamone C, Parente R, De Feo G, et al. Mast cells as effector cells of innate immunity and regulators of adaptive immunity[J]. Immunol Lett, 2016, 178:10-14.
Galli S J, Gaudenzio N, Tsai M. Mast cells in inflammation and disease:Recent progress and ongoing concerns[J].Annu Rev Immunol, 2020, 38:49-77.
Zepp J A, Morrisey E E. Cellular crosstalk in the development and regeneration of the respiratory system[J]. Nat Rev Mol Cell Biol, 2019, 20(9):551-566.
Liberti D C, Kremp M M, Liberti W A, et al. Alveolar epithelial cell fate is maintained in a spatially restricted manner to promote lung regeneration after acute injury[J].Cell Rep, 2021, 35(6):109092.
Gonzalez-Gonzalez F J, Chandel N S, Jain M, et al.Reactive oxygen species as signaling molecules in the development of lung fibrosis[J]. Transl Res, 2017, 190:61-68.
Liu Q, Ren Y P, Jia H M, et al. Vanadium carbide nanosheets with broad-spectrum antioxidant activity for pulmonary fibrosis therapy[J]. ACS Nano, 2023, 17(22):22527-22538.
Zmijewski J W, Thannickal V J. Autophagy in idiopathic pulmonary fibrosis:Predisposition of the aging lung to fibrosis?[J]. Am J Respir Cell Mol Biol, 2023, 68(1):3-4.
Lu Q L, Ye C W, Mao W, et al. Targeting senescent alveolar type 2 cells with a gene-editable FePt dual-atom catalyst for mitigating idiopathic pulmonary fibrosis[J]. ACS Nano, 2025, 19(25):23162-23176.
Lin H J, Qiu X K, Zhang B, et al. Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy[J]. OncoTargets Ther, 2018,11:5943-5955.
Shi J X, Levinson D F, Duan J B, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia[J]. Nature, 2009, 460(7256):753-757.
Hwang J H, Lee J, Choi W Y, et al. ZNF204P is a stemnessassociated oncogenic long non-coding RNA in hepatocellular carcinoma[J]. BMB Rep, 2022, 55(6):281-286.
Rastegari M, Sazegar H, Doosti A. Prognostic significance of CHCHD2P9 and ZNF204P in breast cancer:Exploring their expression patterns and associations with malignancy-related genes[J]. Mol Biol Rep, 2024, 51(1):707.
Zhang Z H, Trypsteen W, Blaauw M, et al. IRF7 and RNH1 are modifying factors of HIV-1 reservoirs:A genome-wide association analysis[J]. BMC Med, 2021,19(1):282.
Yang D K, Wang X W, Gao H L, et al. Downregulation of miR-155-5p facilitates enterovirus 71 replication through suppression of type I IFN response by targeting FOXO3/IRF7 pathway[J]. Cell Cycle, 2020, 19(2):179-192.
King T E Jr, Albera C, Bradford W Z, et al. Effect of interferon gamma-1b on survival in patients with idiopathic pulmonary fibrosis(INSPIRE):A multicentre,randomised, placebo-controlled trial[J]. Lancet, 2009,374(9685):222-228.
Sørensen A L, Mikkelsen S U, Knudsen T A, et al.Ruxolitinib and interferon-α2 combination therapy for patients with polycythemia vera or myelofibrosis:A phase II study[J]. Haematologica, 2020, 105(9):2262-2272.
Wu M H, Skaug B, Bi X J, et al. Interferon regulatory factor 7(IRF7)represents a link between inflammation and fibrosis in the pathogenesis of systemic sclerosis[J].Ann Rheum Dis, 2019, 78(11):1583-1591.
Ren C N, Mi T, Zhang Z X, et al. Targeting interferon regulatory factor 7 alleviates renal fibrosis by inhibiting macrophage-to-myofibroblast transition[J]. Life Sci, 2025,376:123755.
Park S J, Garcia Diaz J, Comlekoglu T, et al. Type I IFN receptor blockade alleviates liver fibrosis through macrophage-derived STAT3 signaling[J]. Front Immunol,2025, 16:1528382.
Chen S T, Jheng C Y, Lee Y C, et al. Intermittent hypoxiareoxygenation-induced miRNAs inhibit expression of IRF and interferon genes but activate NF-κB and expression of pulmonary fibrosis markers in human small airway epithelial cells[J]. Life Sci, 2025, 370:123569.
叶远航,罗成,宁博,等.基于“虚气留滞”探讨上皮间质转化对特发性肺纤维化的影响[J].国际中医中药杂志, 2024, 46(12):1543-1548.
茆春阳,杜燕,雷伟伟,等.基于“燥伤肺络”理论探析肺纤维化[J].陕西中医, 2025, 46(4):522-526.
刘学,李博洋,吴凡,等.“瘀毒”理论指导下从“肺热络瘀”论治特发性肺纤维化[J].山东中医杂志,2025, 44(2):141-144.
Yin J B, Wang Y X, Fan S S, et al. Geniposide ameliorates bleomycin-induced pulmonary fibrosis in mice by inhibiting TGF-β/Smad and p38MAPK signaling pathways[J]. PLoS One, 2024, 19(9):e0309833.
Zhang Y F, Fan L M, Wang M N. Echinacoside ameliorates bleomycin-induced idiopathic pulmonary fibrosis by regulating macrophage polarization[J]. J Mol Histol,2025, 57(1):4.
周飘,杜婧,吴程,等.黄芪注射液通过mi R-29a-3p/COL1A1信号轴干预肺纤维化的机制研究[J].中华中医药学刊, 2023, 41(3):107-110.
李妲,沈枭,吴趋荟,等.柴胡含药血清通过Smad3/Rheb轴调节HFL1细胞凋亡和肌成纤维细胞转化[J].中国实验方剂学杂志, 2023, 29(11):89-96.
邱慧萍,樊丹丹,张莉.蒲公英甾醇通过调节Wnt/β-catenin信号通路抑制转化生长因子β1对肺纤维化的改善作用[J].中国当代医药, 2024, 31(35):16-22.

2026年第57卷第9期

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doi: 10.7501/j.issn.0253-2670.2026.09.018

接收时间：2026-01-01
首发时间：2026-09-09

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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