Division of Anatomical Science, Department of Functional Morphology, Nihon University School of Medicine, 30-1 Ohyaguchi-Kami-Cho, Itabashi-Ku, Tokyo, 173-8610, Japan.
Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinano-Machi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Sci Rep. 2024 Sep 6;14(1):20833. doi: 10.1038/s41598-024-71606-w.
Despite widespread cervical cancer (CC) screening programs, low participation has led to high morbidity and mortality rates, especially in developing countries. Because early-stage CC often has no symptoms, a non-invasive and convenient diagnostic method is needed to improve disease detection. In this study, we developed a new approach for differentiating both CC and cervical intraepithelial neoplasia (CIN)2/3, a precancerous lesion, from healthy individuals by exploring CC fatty acid metabolic reprogramming. Analysis of public datasets suggested that various fatty acid metabolizing enzymes were expressed at higher levels in CC tissues than in normal tissues. Correspondingly, 11 free fatty acids (FFAs) showed significantly different serum levels in CC patient samples compared with healthy donor samples. Nine of these 11 FFAs also displayed significant alterations in CIN2/3 patients. We then generated diagnostic models using combinations of these FFAs, with the optimal model including stearic and dihomo-γ-linolenic acids. Receiver operating characteristic curve analyses suggested that this diagnostic model could detect CC and CIN2/3 more accurately than using serum squamous cell carcinoma antigen level. In addition, the diagnostic model using FFAs was able to detect patients regardless of clinical stage or histological type. Overall, the serum FFA diagnostic model developed in this study could be a powerful new tool for the non-invasive early detection of CC and CIN2/3.
尽管广泛开展了宫颈癌（CC）筛查计划，但由于参与率低，导致发病率和死亡率居高不下，尤其是在发展中国家。由于早期 CC 通常没有症状，因此需要一种非侵入性和方便的诊断方法来提高疾病检测率。在这项研究中，我们通过探索 CC 脂肪酸代谢重编程，开发了一种新方法来区分 CC 和宫颈上皮内瘤变（CIN）2/3，这是一种癌前病变，与健康个体区分开来。对公共数据集的分析表明，各种脂肪酸代谢酶在 CC 组织中的表达水平高于正常组织。相应地，11 种游离脂肪酸（FFAs）在 CC 患者样本中的血清水平与健康供体样本相比存在显著差异。这 11 种 FFAs 中有 9 种在 CIN2/3 患者中也显示出明显的改变。然后，我们使用这些 FFAs 的组合生成诊断模型，最优模型包括硬脂酸和二同型-γ-亚麻酸。受试者工作特征曲线分析表明，与使用血清鳞状细胞癌抗原水平相比，该诊断模型可以更准确地检测 CC 和 CIN2/3。此外，使用 FFAs 的诊断模型能够检测到无论临床分期或组织学类型的患者。总体而言，本研究中开发的血清 FFA 诊断模型可能是一种用于非侵入性早期检测 CC 和 CIN2/3 的强大新工具。
