D'Andréa Grégoire, Jing Lun, Peyrottes Isabelle, Guigonis Jean-Marie, Graslin Fanny, Lindenthal Sabine, Sanglier Julie, Gimenez Isabel, Haudebourg Juliette, Vandersteen Clair, Bozec Alexandre, Guevara Nicolas, Pourcher Thierry
Otorhinolaryngology and Head and Neck Surgery Department, Institut Universitaire de la Face et du Cou, GCS Nice University Hospital-Antoine Lacassagne Center, Côte d'Azur University, 31 Avenue de Valombrose, 06103 Nice, France.
Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), UMR E4320 TIRO-MATOs, Direction de la Recherche Fondamentale (DRF), Institut des Sciences du Vivant Fréderic Joliot, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Faculté de Médecine, Côte d'Azur University, 28 Avenue de Valombrose, CEDEX 2, 06107 Nice, France.
Metabolites. 2023 Jun 23;13(7):782. doi: 10.3390/metabo13070782.
Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.
尽管术前细针穿刺抽吸细胞学检查（FNAC）样本的细胞学分析是评估甲状腺结节（TN）恶性程度的金标准，但仍有20%-30%的病例结果为不确定病变（ITN）。由于这些病变中有三分之二在诊断性手术后会显示为良性，因此需要开发改进的术前诊断方法。在这项初步研究中，我们评估良性和恶性结节的液基（CytoRich）FNAC样本的代谢组学谱是否能够实现TN的分子诊断。我们在一项单中心回顾性研究中对CytoRich FNAC进行了非靶向代谢组学分析。该队列由细胞学良性TN、组织学良性或细胞学ITN的乳头状甲状腺癌（PTC）以及组织学确诊为PTC的可疑/恶性TN组成。使用几种监督分类方法评估所识别的代谢组学特征的诊断性能。78名患者纳入本研究。我们识别出7690个峰，其中2697个离子纳入进一步分析。我们选择了由前15种代谢物组成的代谢组学特征。在所有监督分类方法中，监督自动编码器深度神经网络表现最佳，准确率为0.957（0.842-1），曲线下面积（AUC）为0.945（0.833-1），F1分数为0.947（0.842-1）。在此，我们报告了一种有前景的新辅助分子技术，可基于FNAC样本液的代谢组学特征将PTC与良性TN（包括ITN）区分开来。现在需要更大队列的进一步研究来识别更多生物标志物并获得更可靠的特征。
