大肠癌（CRC）的发展和进展与其复杂的肿瘤微环境（TME）密切相关。评估免疫细胞浸润（ICI）的改变模式将有助于增加对TME浸润特征的了解。已经证明羿一附子白降散（YYFZBJS）对调节CRC的免疫微环境具有积极影响。然而，其药理靶点和分子机制尚待阐明。本研究采用网络药理学分析方法，鉴定YYFZBJS在CRC TME中的靶点。利用癌症基因组图谱（TCGA）数据库中的CRC样本鉴定具有免疫炎性表型（IIP）的患者。通过对比YYFZBJS靶点、CRC疾病靶点和CRC微环境中差异表达基因的交集，确定共识基因。然后，利用最小绝对收缩和选择算法（LASSO）Cox分析法从共识基因中鉴定出预后签名。进一步使用Cytoscape软件构建YYFZBJS重要成分和预后基因靶点的独特草药-化合物-靶点网络图。此外，利用预后基因集进行Kyoto基因与基因组百科全书（KEGG）分析，探索预后基因在CRC IIP患者药物治疗中的分子机制。最后，利用TISCH2数据库进行单细胞分析，验证CRC TME中预后基因的表达。从480名CRC患者中鉴定出284名IIP患者。确定了35个共识基因作为YYFZBJS在CRC患者TME中的靶点。从共识基因中鉴定出了一个包含PIK3CG、C5AR1、PRF1、CAV1、HPGDS、PTGS2、SERPINE1、IDO1、TGFB1、CXCR2和MMP9的11个基因预后签名，训练集和测试集的受试者工作特征（ROC）曲线下面积（AUCs）值分别为0.84和0.793。在草药-化合物-靶点网络中，共有24种化合物与这11个预后基因相互作用，且在IL-17信号通路、花生四烯酸代谢和代谢通路中显著富集。预后基因的单细胞分析证实它们的异常表达与CRC TME有关。本研究有机地整合了网络药理学和生物信息学分析，从YYFZBJS的靶点中鉴定出了CRC IIP患者的预后基因。尽管这项数据挖掘工作仅限于基于免疫微环境的预后机制研究，但该方法为寻找传统中药的新治疗靶点和精确诊断肿瘤指标提供了新的视角。 版权所有©2023年Elsevier Ltd. 保留所有权利。

The development and progression of colorectal cancer (CRC) is closely associated with its complex tumor microenvironment (TME). Assessment of the modified pattern of immune cell infiltration (ICI) will help increase knowledge regarding the characteristics of TME infiltration. Yi-Yi-Fu-Zi-Bai-Jiang-San (YYFZBJS) has been shown to have positive effects on the regulation of the immune microenvironment of CRC. However, its pharmacological targets and molecular mechanisms remain to be elucidated.Network pharmacological analysis was used to identify the target of YYFZBJS in the TME of CRC. Patients with the immune-inflamed phenotype (IIP) were identified using CRC samples from The Cancer Genome Atlas (TCGA) database. Consensus genes were identified by intersecting YYFZBJS targets, CRC disease targets and differentially expressed genes in the CRC microenvironment. Then, least absolute shrinkage and selection operator (LASSO) Cox analyses were used to identify a prognostic signature from the consensus genes. Cytoscape software was further used to build a unique herb-compound-target network diagram of the important components of YYFZBJS and prognostic gene targets. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed using the prognostic gene sets to explore the molecular mechanism of the prognostic genes in drug therapy for CRC IIP patients. Finally, single-cell analysis was performed to validate the expression of the prognostic genes in the TME of CRC using the TISCH2 database.A total of 284 IIP patients were identified from 480 patients with CRC. A total of 35 consensus genes were identified as targets of YYFZBJS in the TME of CRC patients. An eleven-gene prognostic signature, including PIK3CG, C5AR1, PRF1, CAV1, HPGDS, PTGS2, SERPINE1, IDO1, TGFB1, CXCR2 and MMP9, was identified from the consensus genes, with areas under the receiver operating characteristic (ROC) curve (AUCs) values of 0.84 and 0.793 for the training and test cohorts, respectively. In the herb-compound-target network, twenty-four compounds were shown to interact with the 11 prognostic genes, which were significantly enriched in the IL-17 signaling, arachidonic acid metabolism and metabolic pathways. Single-cell analysis of the prognostic genes confirmed that their abnormal expression was associated with the TME of CRC.This study organically integrated network pharmacology and bioinformatics analyses to identify prognostic genes in CRC IIP patients from the targets of YYFZBJS. Although this data mining work was limited to the study of mechanisms related to prognosis based on the immune microenvironment, the methodology provides new perspectives in the search for novel therapeutic targets of traditional Chinese medicines (TCMs) and accurate diagnostic indicators of cancers targeted by TCMs.Copyright © 2023 Elsevier Ltd. All rights reserved.