Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications

Abstract

Background: Gynecological malignancies, particularly ovarian cancer, pose a formidable challenge to women's wellbeing, as evidenced by the global incidence and mortality rates, emphasizing the pressing need for advanced diagnostic and treatment modalities. The heterogeneity of ovarian cancer poses challenges for traditional therapeutic approaches, necessitating the exploration of novel, precision medicine techniques.

Methods: This study leveraged multi-dataset analysis to construct and validate an Artificial Intelligence-Derived Prognostic Index (AIDPI) for ovarian cancer. Transcriptome data from the TCGA, ICGC, and GEO databases were utilized, encompassing bulk and single-cell RNA sequencing. The AIDPI model was developed and refined using univariate Cox regression analysis and an ensemble of machine learning algorithms. Functional analysis, immunoprofiling, and the role of the MFAP4 gene were investigated to elucidate the biological mechanisms underlying the model.

Results: The AIDPI model demonstrated superior accuracy in predicting ovarian cancer prognosis compared to existing models. It correlated with clinical treatment outcomes, including chemotherapy responsiveness, and was integrated into a nomogram for improved prognostic stratification. Functional analysis revealed the influence of AIDPI genes on tumor immune infiltration and cell cycle regulation. Single-cell analysis exposed cell type-specific expression patterns, and the MFAP4 gene was identified as a potential therapeutic target due to its association with patient prognosis and modulation of cellular behavior. In clinical samples of ovarian cancer patients, MFAP4 is highly expressed in metastatic lesions and is associated with poor prognosis. In vitro and in vivo experiments, knockdown of MFAP4 reduces the metastasis of ovarian cancer cells.

Conclusion: The AIDPI model offers a highly accurate tool for ovarian cancer prognosis and treatment decision-making, underscored by the integration of multi-omics data and artificial intelligence. The model's performance and biological insights provide a foundation for advancing precision medicine in ovarian cancer. MFAP4's functionality and the influence of DNA methylation present opportunities for prospective research endeavors and potential therapeutic interventions.

Keywords: AIDPI; Immune microenvironment; MFAP4; Ovarian Cancer; Precision medicine; Prognostic model.

Fig. 1

Construction and Comparison of the AIDPI Model.

Fig. 2

Cox Analysis and Nomogram Construction of AIDPI in Relation to Clinical Treatment.

Fig. 3

Functional Analysis of AIDPI Model Genes.

Fig. 4

Single-Cell Analysis Results of Ovarian Cancer.

Fig. 5

Research Results of the MFAP4 Gene.

Fig. 6

Comprehensive Analysis of MFAP4 Expression and Functionality in Ovarian Cancer.

Fig. 7

Validation of the effects of MFAP4 in vivo experiments. A. Fluorescence intensity analysis of abdominal metastasis model constructed after knocking down MFAP4 in ES2. B-C. Statistical chart of fluorescence intensity tumor formation quantity. The data are presented as the means ± SEM; two-tailed t-test, ***p < 0.001; n = 5. D.Visual display of mouse abdominal anatomy, with tumor location marked with white arrows.