Xie Pu, Yin Qinglei, Wang Shu, Song Dalong
Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Biomedicines. 2024 Sep 10;12(9):2066. doi: 10.3390/biomedicines12092066.
Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan-Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy.
甲状腺癌（THCA）是全球最常见的癌症之一。整合先进的基因组和蛋白质组分析以构建基于蛋白质的预后模型，有望识别有效的生物标志物并探索新的治疗途径。在本研究中，使用了来自癌症蛋白质组图谱（TCPA）的蛋白质组数据和来自癌症基因组图谱（TCGA）的临床数据。通过Kaplan-Meier、Cox回归和LASSO惩罚Cox分析，我们开发了一个包含13种蛋白质（S100A4、PAI1、IGFBP2、RICTOR、B7-H3、胶原蛋白VI、PAR、SNAIL、FAK、连接蛋白-43、Rheb、EVI1和P90RSK_pT359S363）的预后风险模型。基于风险曲线、生存分析、受试者工作特征曲线和独立预后分析，该蛋白质预后模型被验证为THCA患者生存时间的独立预测指标。此外，我们还探讨了与这些特征相关的免疫细胞浸润和肿瘤突变负荷（TMB）。值得注意的是，我们的研究证明了一种预测THCA患者治疗反应的新方法，包括接受化疗和靶向治疗的患者。
