目的 筛选泛素结合酶E2S（UBE2S）互作蛋白并构建肝细胞癌（HCC）基于UBE2S互作蛋白的预后模型（UIPM），分析UIPM评估HCC患者预后的价值。 方法 采用免疫共沉淀（Co-IP）技术筛选与Flag-UBE2S结合的蛋白复合体，经十二烷基硫酸钠-聚丙烯酰胺凝胶电泳（SDS-PAGE）和Western blotting法验证后，采用液相色谱-质谱联用仪（LC-MS）分析鉴定UBE2S互作蛋白，并对互作蛋白进行基因本体论（GO）功能和京都基因与基因组百科全书（KEGG）信号通路富集分析。采用R软件survival包筛选癌症基因组图谱（TGGA）中HCC预后相关蛋白与UBE2S互作蛋白取交集，通过LASSO回归分析从交集蛋白中获取关键蛋白构建UIPM，并建立预后模型风险评分公式，按照风险评分的中位值将TGGA中HCC患者分为高风险组和低风险组，通过受试者工作特征曲线（ROC）评估UIPM的预测准确性，并采用国际癌症基因组联盟（ICGC）数据库对UIPM预测准确性进行再次验证。采用单因素和多因素Cox回归分析评估UIPM风险评分是否为HCC的预后独立危险因素，并进一步构建列线图模型。 结果 Co-IP联合LC-MS分析得到97个UBE2S互作蛋白。GO功能和KEGG信号通路富集分析，互作蛋白主要与半胱氨酸型内肽酶活性、氧化应激和细胞死亡有密切关联。TCGA筛选出5 163个HCC预后相关蛋白，与UBE2S互作蛋白取交集，获得40个预后相关互作蛋白，LASSO回归分析得到7个关键蛋白，包括UBE2S、热休克蛋白家族A成员8（HSPA8）、异质性胞核核糖核蛋白H1 （HNRNPH1）、含TCP1伴侣蛋白亚基3（CCT3）、真核翻译起始因子2亚基1（EIF2S1）、活化蛋白C激酶1受体（RACK1）和肌动蛋白相关蛋白2/3复合体亚基4（ARPC4），并构建了UIPM，高和低风险组HCC患者生存率存比较差异有统计学意义（P<0.05）。ROC曲线，UIPM预测HCC患者1、2和3年UIPM风险评分的ROC曲线下面积（AUC）值均大于0.7，表明预测模型准确度较高。ICGC数据库数据也证实UIPM预测准确度较高。单因素和多因素Cox回归分析，UIPM风险评分是HCC患者的独立预后危险因素（P<0.05）。列线图预测HCC患者生存率与实际生存率之间有较好的一致性。 结论 97个互作蛋白与UBE2S相互作用，可能通过氧化应激和铁死亡相关通路的失调促进HCC发生发展。UIPM风险评分是HCC预后的独立危险因素，可以用于预测HCC患者预后。而UBE2S、HSPA8、HNRNPH1、CCT3、EIF2S1、RACK1和ARPC4有望成为HCC新的生物标志物和治疗靶点。

Objective To screen the interacting protein of ubiquitin-conjugating enzyme E2S （UBE2S） and construct the hepatocellular carcinoma （HCC） based on UBE2S interacting protein prognosis model （UIPM），and to discuss the value of UIPM in assessing the prognosis of the HCC patients. Methods Co-immunoprecipitation （Co-IP） was used to screen the protein complexes binding to Flag-UBE2S. After validation by sodium dodecyl sulphate-polyacrylamide gel electrophoresis （SDS-PAGE） and Western blotting methods；liquid chromatography-mass spectrometer （LC-MS） was used to identify the UBE2S interacting proteins； Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway enrichment analysis were conducted on these proteins； the prognosis-related proteins from The Cancer Genome Atlas （TCGA） were cross-referenced with UBE2S interacting proteins by survival package of R software；the key proteins were extracted through LASSO regression analysis to build the UIPM； the prognostic model risk scoring formula was established. The HCC patients in TCGA were divided into high risk group and low risk group based on median value of the risk scores. The predictive accuracy of UIPM was evaluated by receiver operating characteristic curve （ROC）， and the predictive accuracy was further validated by International Cancer Genome Consortium （ICGC） Database； univariate regression analysis and multivariate Cox regression analysis were used to detect whether the UIPM risk score was an independent prognostic factor for HCC. Furthermore， the nomogram model was built. Results A total of 97 UBE2S interacting proteins were identified through Co-IP combined with LC-MS analysis.The GO functional enrichment analysis and KEGG signaling pathway enrichment analysis results showed that the interacting proteins were closely associated with cysteine-type endopeptidase activity， oxidative stress， and cell death. The TCGA revealed 5 163 HCC prognosis-related proteins； after intersecting with UBE2S interacting proteins， 40 prognosis-related interacting proteins were found. Seven key proteins were determined through LASSO regression analysis， including UBE2S， heat shock protein family A member 8 （HSPA8）， heterogeneous nuclear ribonucleoprotein H1 （HNRNPH1），chaperonin containing TCP1 subunit 3 （CCT3），eukaryotic translation initiation factor 2 subunit 1 （EIF2S1）， receptor for activated C kinase 1 （RACK1）， and actin related protein 2/3 complex subunit 4 （ARPC4）， and the UIPM was constructed. There was significant difference in survival rate of the patients between high risk group and low risk group （P<0.05）. The ROC curve analysis results showed the area under ROC curve（AUC） values of UIPM for predicting 1-year， 2-year， and 3-year survival risk scores of the HCC patients were all greater than 0.7， indicating the model had high predictive accuracy. This was also confirmed by ICGC Database data. The univariate and multivariate Cox regression analysis results showed that the UIPM risk score was an independent prognostic risk factor for the HCC patients （P<0.05）. The nomogram results showed good consistency between predicted survival rate and actual survival rate of the patient. Conclusion A total of 97 interacting proteins that interact with UBE2S may promote the occurence and devolopment of HCC through oxidative stress and dysregulation of ferroptosis pathways. The UIPM risk score is an independent risk factor for the prognosis of HCC and can be used to predict the outcomes of the patients. UBE2S， HSPA8， HNRNPH1， CCT3， EIF2S1， RACK1， and ARPC4 could be regarded as the new biomarkers and therapeutic targets for HCC.