Zhou Yanjun, Zhu Jiahao, Gu Mengxuan, Gu Ke
Department of Radiotherapy and Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214000, China.
Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150000, China.
J Oncol. 2023 Mar 8;2023:6851036. doi: 10.1155/2023/6851036. eCollection 2023.
Increasing evidence suggests that diverse activation patterns of metabolic signalling pathways may lead to molecular diversity of cervical cancer (CC). But rare research focuses on the alternation of fatty acid metabolism (FAM) in CC. Therefore, we constructed and compared models based on the expression of FAM-related genes from the Cancer Genome Atlas by different machine learning algorithms. The most reliable model was built with 14 significant genes by LASSO-Cox regression, and the CC cohort was divided into low-/high-risk groups by the median of risk score. Then, a feasible nomogram was established and validated by -index, calibration curve, net benefit, and decision curve analysis. Furthermore, the hub genes among differential expression genes were identified and the post-transcriptional and translational regulation networks were characterized. Moreover, the somatic mutation and copy number variation landscapes were depicted. Importantly, the specific mutation drivers and signatures of the FAM phenotypes were excavated. As a result, the high-risk samples were featured by activated de novo fatty acid synthesis, epithelial to mesenchymal transition, angiogenesis, and chronic inflammation response, which might be caused by mutations of oncogenic driver genes in RTK/RAS, PI3K, and NOTCH signalling pathways. Besides the hyperactivity of cytidine deaminase and deficiency of mismatch repair, the mutations of POLE might be partially responsible for the mutations in the high-risk group. Next, the antigenome including the neoantigen and cancer germline antigens was estimated. The decreasing expression of a series of cancer germline antigens was identified to be related to reduction of CD8 T cell infiltration in the high-risk group. Then, the comprehensive evaluation of connotations between the tumour microenvironment and FAM phenotypes demonstrated that the increasing risk score was related to the suppressive immune microenvironment. Finally, the prediction of therapy targets revealed that the patients with high risk might be sensitive to the RAF inhibitor AZ628. Our findings provide a novel insight for personalized treatment in CC.
越来越多的证据表明，代谢信号通路的不同激活模式可能导致宫颈癌（CC）的分子多样性。但很少有研究关注CC中脂肪酸代谢（FAM）的变化。因此，我们通过不同的机器学习算法，基于癌症基因组图谱中FAM相关基因的表达构建并比较了模型。通过LASSO-Cox回归，利用14个显著基因构建了最可靠的模型，并根据风险评分的中位数将CC队列分为低/高风险组。然后，通过C指数、校准曲线、净效益和决策曲线分析建立并验证了一个可行的列线图。此外，还鉴定了差异表达基因中的枢纽基因，并对转录后和翻译调控网络进行了表征。此外，还描绘了体细胞突变和拷贝数变异图谱。重要的是，挖掘了FAM表型的特定突变驱动因素和特征。结果显示，高风险样本的特征是从头脂肪酸合成激活、上皮-间质转化、血管生成和慢性炎症反应，这可能是由RTK/RAS、PI3K和NOTCH信号通路中致癌驱动基因的突变引起的。除了胞苷脱氨酶的过度活跃和错配修复的缺陷外，POLE的突变可能部分导致了高风险组中的突变。接下来，估计了包括新抗原和癌症种系抗原在内的抗原组。一系列癌症种系抗原的表达降低被确定与高风险组中CD8 T细胞浸润的减少有关。然后，对肿瘤微环境和FAM表型之间内涵的综合评估表明，风险评分的增加与免疫抑制微环境有关。最后，治疗靶点预测显示，高风险患者可能对RAF抑制剂AZ628敏感。我们的研究结果为CC的个性化治疗提供了新的见解。
