DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution

Abstract

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and prognosis. The occurrence of ovarian cancer is influenced by multiple complex mechanisms including genomics, transcriptomics, and proteomics. Integrating multiple types of omics data aids in predicting the survival rate of ovarian cancer patients. However, existing methods only fuse multi-omics data at the feature level, neglecting the shared and complementary neighborhood information among samples of multi-omics data, and failing to consider the potential interactions between different omics data at the molecular level. In this paper, we propose a prognostic model for ovarian cancer prediction named Dual Fusion Channels and Stacked Graph Convolutional Neural Network (DFASGCNS). The DFASGCNS utilizes dual fusion channels to learn feature representations of different omics data and the associations between samples. Stacked graph convolutional network is used to comprehensively learn the deep and intricate correlation networks present in multi-omics data, enhancing the model's ability to represent multi-omics data. An attention mechanism is introduced to allocate different weights to important features of different omics data, optimizing the feature representation of multi-omics data. Experimental results demonstrate that compared to existing methods, the DFASGCNS model exhibits significant advantages in ovarian cancer prognosis prediction and survival analysis. Kaplan-Meier curve analysis results indicate significant differences in the survival subgroups predicted by the DFASGCNS model, contributing to a deeper understanding of the pathogenesis of ovarian cancer and providing more reliable auxiliary diagnostic information for the prognosis assessment of ovarian cancer patients.

Fig 1. The architecture of DFASGCNS.

Fig 2. The results of ovarian cancer prognosis prediction with different values of k.

Fig 3. Prognostic prediction of OV by different number of hidden units in attention mechanism.

Fig 4. The results of DFASGCNS compared to other existing methods on the GEO datasets.

(a) GSE26712 dataset. (b) GSE32062 dataset. (c) GSE17260 dataset. (d) GSE140082 dataset.

Fig 5. The Kaplan-Meier survival curves of ovarian cancer patients generated by different methods.

Fig 6. Pathway enrichment analysis of identified genes.

The x-axis represents the -log10 p-value for each term, and the y-axis represents the KEGG pathway terms. (a) GO pathway enrichment analysis. (b) KEGG pathway enrichment analysis.