Cervical cancer subtype identification and model building based on lipid metabolism and post-infection microenvironment immune landscape

Yongzhi Chen; Rongjie Cui; Dun Xiong; Yuan Zhao; Jianyu Pang; Samina Gul; Qi Qi; Yuheng Tang; Xuhong Zhou; Wenru Tang

doi:10.1016/j.heliyon.2024.e30746

Cervical cancer subtype identification and model building based on lipid metabolism and post-infection microenvironment immune landscape

Heliyon. 2024 May 4;10(9):e30746. doi: 10.1016/j.heliyon.2024.e30746. eCollection 2024 May 15.

Authors

Yongzhi Chen¹, Rongjie Cui¹, Dun Xiong², Yuan Zhao², Jianyu Pang¹, Samina Gul¹, Qi Qi¹, Yuheng Tang¹, Xuhong Zhou³, Wenru Tang¹

Affiliations

¹ Medicine School, Kunming University of Science and Technology, Kunming, PR China.
² Department of Thyroid and Breast Surgery, Pu'er People's Hospital, Puer, Yunnan, PR China.
³ Office of Science and Technology, Yunnan University of Chinese Medicine, Kunming, PR China.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: LMRGs; Molecular docking; PIM; Targeted therapy; scRNA-seq.