Li Xi-Xi, Shi Pei, Wu Fei-Fei, Li Dai
Department of Endocrinology, Heping Hospital Affiliated to Changzhi Medical College, Shanxi, China.
Discov Oncol. 2025 Aug 25;16(1):1608. doi: 10.1007/s12672-025-03483-2.
With the sharp increase in the incidence of papillary thyroid carcinoma (PTC), the disease-specific survival rate has not improved significantly. Cholesterol metabolism plays a crucial role in tumor proliferation, regulation of tumor immune escape, and tumor drug resistance. However, there are few studies on the role of cholesterol metabolism in the occurrence and development of thyroid cancer (THCA). This study aimed to investigate the predictive value of cholesterol metabolism-related genes (CMRGs) in THCA and the relationship between immune invasion and drug sensitivity.
Cholesterol metabolism-related genes we identified from the molecular signatures database, and univariate Cox regression and least absolute shrinkage and selection operator(LASSO) were used to construct a predictive model of cholesterol metabolism-related genes based on the TCGA-THCA dataset. The TCGA dataset was randomly divided into a training group and a validation group to verify the model's predictive value and independent prognostic effect. We then constructed a nomogram and performed enrichment analysis, immune cell infiltration, and drug sensitivity analysis. Finally, TCGA-THCA and GSE33630 datasets were used to detect the expression of signature genes, which was further verified by the HPA database.
Six CMRGs (FADS1, NPC2, HSD17B7, ACSL4, APOE, HMGCS2) we identified by univariate Cox and LASSO regression to construct a prognostic model for 155 genes related to cholesterol metabolism. Their prognostic value was confirmed in the validation set, and a highly accurate nomogram was constructed combined with clinical features. We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced. Moreover, through drug sensitivity analysis, we obtained sensitive drugs for different risk groups. The GSE33630 dataset verified the expression of six CMRGs, and the HPA database verified the protein expression of the NPC2 gene.
Cholesterol metabolism-related features are a promising biomarker for predicting THCA prognosis and can potentially guide personalized immunization and targeted therapy.
随着甲状腺乳头状癌（PTC）发病率的急剧上升，其疾病特异性生存率并未显著提高。胆固醇代谢在肿瘤增殖、肿瘤免疫逃逸调节和肿瘤耐药性中起着关键作用。然而，关于胆固醇代谢在甲状腺癌（THCA）发生发展中的作用的研究较少。本研究旨在探讨胆固醇代谢相关基因（CMRGs）在THCA中的预测价值以及免疫浸润与药物敏感性之间的关系。
从分子特征数据库中鉴定出胆固醇代谢相关基因，基于TCGA-THCA数据集，采用单变量Cox回归和最小绝对收缩和选择算子（LASSO）构建胆固醇代谢相关基因的预测模型。将TCGA数据集随机分为训练组和验证组，以验证模型的预测价值和独立预后效应。然后构建列线图并进行富集分析、免疫细胞浸润分析和药物敏感性分析。最后，利用TCGA-THCA和GSE33630数据集检测特征基因的表达，并通过HPA数据库进一步验证。
通过单变量Cox和LASSO回归鉴定出6个CMRGs（FADS1、NPC2、HSD17B7、ACSL4、APOE、HMGCS2），构建了一个包含155个胆固醇代谢相关基因的预后模型。其预后价值在验证集中得到证实，并结合临床特征构建了高度准确的列线图。我们发现高危患者的死亡率增加了11.41倍，高危组免疫细胞浸润显著减少。此外，通过药物敏感性分析，我们获得了不同风险组的敏感药物。GSE33630数据集验证了6个CMRGs的表达，HPA数据库验证了NPC2基因的蛋白表达。
胆固醇代谢相关特征是预测THCA预后的有前景的生物标志物，可能潜在地指导个性化免疫治疗和靶向治疗。
