Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study

Area of Science:

• Oncology
• Cell Death Research
• Bioinformatics

Background:

• Ovarian cancer (OC) is a deadly gynecological cancer with unexplored prognostic factors.
• PANoptosis, a complex cell death pathway, has not been studied in relation to OC outcomes.

Purpose of the Study:

• To investigate the prognostic role of PANoptosis in ovarian cancer.
• To develop a deep learning-based prognostic model for OC using pathomics features.
• To identify key genes and cellular mechanisms involved in OC prognosis.

Main Methods:

• Analysis of TCGA, GTEx, and GEO datasets (GSE184880) for ovarian cancer data.
• Feature extraction from spatial and pathological images using CellProfiler and ResNet-50.
• Development of a deep learning PANoptosis-related pathomics prognostic model (PANPM).

Main Results:

• Higher PANoptosis levels were associated with a better prognosis in ovarian cancer patients.
• The developed PANPM demonstrated excellent performance in predicting OC prognosis.
• STAT4 was identified as a hub gene potentially acting as a protective factor.

Conclusions:

• PANoptosis is a significant prognostic indicator for ovarian cancer.
• The PANPM offers a promising tool for clinical application in OC diagnosis and treatment.
• STAT4+ T cells may inhibit OC by activating epithelial cell PANoptosis, suggesting novel therapeutic avenues.