Chen Yongzhi, Cui Rongjie, Xiong Dun, Zhao Yuan, Pang Jianyu, Gul Samina, Qi Qi, Tang Yuheng, Zhou Xuhong, Tang Wenru
Medicine School, Kunming University of Science and Technology, Kunming, PR China.
Department of Thyroid and Breast Surgery, Pu'er People's Hospital, Puer, Yunnan, PR China.
Heliyon. 2024 May 4;10(9):e30746. doi: 10.1016/j.heliyon.2024.e30746. eCollection 2024 May 15.
As the second most common gynecological cancer, cervical cancer (CC) seriously threatens women's health. The poor prognosis of CC is closely related to the post-infection microenvironment (PIM). This study investigated how lipid metabolism-related genes (LMRGs) affect CC PIM and their role in diagnosing CC.
We analyzed lipid metabolism scores in the CC single-cell landscape by AUCell. The differentiation trajectory of epithelial cells to cancer cells was revealed using LMRGs and Monocle2. Consensus clustering was used to identify novel subgroups using the LMRGs. Multiple immune assessment methods were used to evaluate the immune landscape of the subgroups. Prognostic genes were determined by the LASSO and multivariate Cox regression analysis. Finally, we perform molecular docking of prognostic genes to explore potential therapeutic agents.
We revealed the differentiation trajectory of epithelial cells to cancer cells in CC by LMRGs. The higher LMRGs expression cluster had higher survival rates and immune infiltration expression. Functional enrichment showed that two clusters were mainly involved in immune response regulation. A novel LMR signature (LMR.sig) was constructed to predict clinical outcomes in CC. The expression of prognostic genes was correlated with the PIM immune landscape. Small molecular compounds with the best binding effect to prognostic genes were obtained by molecular docking, which may be used as new targeted therapeutic drugs.
We found that the subtype with better prognosis could regulate the expression of some critical genes through more frequent lipid metabolic reprogramming, thus affecting the maturation and migration of dendritic cells (DCs) and the expression of M1 macrophages, reshaping the immunosuppressive environment of PIM in CC patients. LMRGs are closely related to the PIM immune landscape and can accurately predict tumor prognosis. These results further our understanding of the underlying mechanisms of LMRGs in CC.
宫颈癌（CC）作为第二常见的妇科癌症，严重威胁着女性健康。CC的不良预后与感染后微环境（PIM）密切相关。本研究调查了脂质代谢相关基因（LMRGs）如何影响CC的PIM及其在CC诊断中的作用。
我们通过AUCell分析了CC单细胞图谱中的脂质代谢评分。使用LMRGs和Monocle2揭示上皮细胞向癌细胞的分化轨迹。使用共识聚类法基于LMRGs识别新的亚组。采用多种免疫评估方法评估亚组的免疫格局。通过LASSO和多变量Cox回归分析确定预后基因。最后，我们对预后基因进行分子对接以探索潜在的治疗药物。
我们通过LMRGs揭示了CC中上皮细胞向癌细胞的分化轨迹。LMRGs表达较高的簇具有更高的生存率和免疫浸润表达。功能富集表明，两个簇主要参与免疫反应调节。构建了一种新的LMR特征（LMR.sig）来预测CC的临床结局。预后基因的表达与PIM免疫格局相关。通过分子对接获得了与预后基因结合效果最佳的小分子化合物，其可能用作新的靶向治疗药物。
我们发现预后较好的亚型可通过更频繁的脂质代谢重编程来调节一些关键基因的表达，从而影响树突状细胞（DCs）的成熟和迁移以及M1巨噬细胞的表达，重塑CC患者PIM的免疫抑制环境。LMRGs与PIM免疫格局密切相关，能够准确预测肿瘤预后。这些结果进一步加深了我们对LMRGs在CC中潜在机制的理解。
