NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma

Abstract

To evaluate the prognostic significance and molecular mechanism of NETosis markers in ovarian serous cystadenocarcinoma (OSC), we constructed a machine learning-based pathomic model utilizing hematoxylin and eosin (H&E) slides. We analyzed 333 patients with OSC from The Cancer Genome Atlas for prognostic-related neutrophil extracellular trap formation (NETosis) genes through bioinformatics analysis. Pathomic features were extracted from 54 cases with complete pathological images, genetic matrices, and clinical information. Two pathomic prognostic models were constructed using support vector machine (SVM) and logistic regression (LR) algorithms. Additionally, we established a predictive scoring system that integrated pathomic scores based on the NETcluster subtypes and clinical signature. We identified four NETosis genes significantly correlated with OSC prognosis, which were functionally associated with immune response, somatic mutations, tumor invasion, and metastasis. Five robust pathomic features were selected for overall survival prediction. The LR and SVM pathomic models demonstrated strong predictive performance for the NETcluster subtype classification through five-fold cross-validation. Time-dependent ROC analysis revealed excellent prognostic capability of the LR pathomic model's score for the overall survival (AUC values of 0.658, 0.761, and 0.735 at 36, 48, and 60 months, respectively), further validated by Kaplan-Meier analysis. The expression levels of NETosis genes greatly affected OSC patients' prognoses. The pathomic analysis of H&E slide pathological images provides an effective approach for predicting both NETcluster subtype and overall survival in OSC patients.

Keywords: Clinical prognosis; Machine learning; NETosis; Ovarian serous cystadenocarcinoma; Pathomics.