自噬是一种分解代谢过程，真核生物的成分受到损害，受影响的或多余的成分会发生自我降解。然而，自噬可以促进癌细胞凋亡或促进细胞生长。本研究旨在探讨自噬相关基因（ARG）在预测Cremastra干预乳腺癌（BC）预后中的意义。利用中药系统药理学数据库和分析平台（TCMSP）获得活性成分和作用靶点。和 SwissTargetPrediction。然后，BC 转录组和临床数据被下载到癌症基因组图谱 (TCGA) 中，而 ARG 则被收集到人类自噬数据库 (HADb) 中。同时利用Perl和R软件进行数据处理和分析。首先，将BC的转录组数据映射到ARG，筛选BC-ARG。其次，将上述基因定位到Cremastra的作用靶点，筛选出Cremastra干预的BC的ARG。此外，还进行了生物学功能的富集分析。对BC的ARG进行单变量Cox回归，初步筛选独立预后基因并构建自噬预后模型。这些基因被定位到与 Cremastra 干预的 BC 相关的 ARG。最后，通过多因素Cox回归对定位的基因进行优化，获得关键的ARG和潜在的化合物。最后，根据中位风险评分将所有病例分为低风险组或高风险组。进行受试者工作特征（ROC）曲线、Kaplan-Meier（K-M）生存、独立预后和临床相关性分析，以进行模型评估和独立预测预后的因素识别。Cremastra干预自噬的总共66个活性成分和38个靶点筛选了 BC，自噬预后模型表现出良好的预测性能。生存曲线表明，与高危患者相比，低危患者的生存率显着提高（P < .01）。此外，高危组的基因表达水平随着患者风险评分的增加而增加。经过单变量回归，筛选出 34 个与 BC 治疗相关的差异表达 ARG。多元回归确定了 4 个关键的 ARG，主要来源于糖苷、木酚素、黄酮类化合物和二苄基化合物。此后对关键基因进行临床病理特征与预后的相关性分析，其中BCL2和TP63表现出独立的预后价值。本研究建立自噬预后模型，通过Cremastra干预BC预测BCL2和TP63。生物信息学，将应用于进一步的工作。

Autophagy is the catabolic process where the components of eukaryotes experience damage, and the affected or superfluous components undergo self-degradation. However autophagy can promote cancer cell apoptosis or facilitate cell growth. This work aimed to investigat the significance of autophagy-related genes (ARGs) in predicting the prognosis of breast cancer (BC) intervened with Cremastra.Active ingredients and action targets were obtained using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction. Then, the BC transcriptome and clinical data were downloaded in The Cancer Genome Atlas (TCGA), whereas ARGs were collected in the Human Autophagy Database (HADb). Meanwhile, Perl and R software were used for data processing and analysis. Firstly, the transcriptome data of BC were mapped to ARGs to screen the BC-ARGs. Secondly, the above genes were mapped to the action targets of Cremastra, ARGs of Cremastra-intervened BC were then screened out. Moreover, an enrichment analysis of biological function was carried out. Univariate Cox regression was carried out on ARGs of BC for preliminarily selecting the independent prognostic genes and constructing the autophagy prognosis model. These genes were mapped to ARGs involved in Cremastra-intervened BC. Finally, those mapped genes were optimized by multi-factor Cox regression, and the key ARGs and potential compounds were obtained. Finally, all cases were classified as low- or high-risk group based on the median risk score. Receiver operating characteristic (ROC) curve, Kaplan-Meier (K-M) survival, independent prognosis and clinical correlation analyses were conducted for model evaluation and identification of factors to independently predict prognosis.Altogether, 66 active components and 38 targets of the Cremastra-intervened autophagy of BC were screened and the autophagy prognosis model demonstrate good predictive performance. As suggested by the survival curve, low-risk patients had a markedly increased survival rate compared with high-risk patients (P < .01). Besides, the gene expression levels of the high-risk group increased with the increases in patients' risk scores. Upon univariate regression, 34 differentially expressed ARGs related to BC treatment were screened. Multivariate regression identified 4 key ARGs, which were mainly derived from glycosides, lignans, flavonoids, and dibenzyl compounds. Thereafter, key genes were subjected to correlation analysis between clinicopathological features and prognosis, among which BCL2 and TP63, showed independent prognostic value.In this study, an autophagy prognosis model was established, and BCL2 and TP63 were predicted for the Cremastra intervention of BC by Bioinformatics, which will be applied to further work.