PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer.

Cervical carcinoma (CC) remains a significant global health issue despite advancements in screening and treatment. To improve prognostic accuracy and therapeutic strategies, we developed a multi-machine learning prognostic model based on metabolic-associated genes. This study integrated genomic, transcriptomic, and spatial data from multiple databases to identify key metabolic genes with a causal relationship to CC. We identified 112 key metabolic genes, which were used to construct and validate a prognostic model through various machine learning algorithms. GO and KEGG enrichment analysis revealed the MAPK cascade plays a crucial role in metabolic processes. To pinpoint key metabolic genes, we constructed WGCNA and extracted 337 key genes. Supervised principal component analysis and random survival forests were incorporated into the final model, which showed strong predictive ability in classifying patients. Furthermore, the model demonstrated notable variations in immune cell infiltration among risk categories, which shown regulatory T cells may be involved in immune suppression, and natural killer cells might have a limited effect in tumor clearance. Spatial transcriptomics and single-cell analyses further validated the model, uncovering tumor heterogeneity and distinct intercellular communication patterns associated with different risk levels. The functional experiment results indicated that down expression of PLOD3 could suppress the proliferation of CC cell. In this study, offer a precision medicine methods for predicting patient outcomes as well as fresh insights into the metabolic foundations, which may contribute to the prognosis and immunotherapy of CC. Additionally, we discovered PLOD3 to be a novel oncogene in CC. These findings imply that this model may be applied to assess prognostic risk and identify potential therapeutic targets for CC patients.