An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma

Tianxiang Geng; Mengxue Zheng; Yongfeng Wang; Janne Elin Reseland; Athina Samara

doi:10.3389/fmolb.2023.1200354

An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma

Front Mol Biosci. 2023 Jun 14:10:1200354. doi: 10.3389/fmolb.2023.1200354. eCollection 2023.

Authors

Tianxiang Geng¹, Mengxue Zheng², Yongfeng Wang³, Janne Elin Reseland¹, Athina Samara¹

Affiliations

¹ Department of Biomaterials, FUTURE, Center for Functional Tissue Reconstruction, Faculty of Dentistry, University of Oslo, Oslo, Norway.
² Laboratory of Reproductive Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
³ Department of Obstetrics and Gynecology, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Abstract

Background: Ovarian Serous Adenocarcinoma is a malignant tumor originating from epithelial cells and one of the most common causes of death from gynecological cancers. The objective of this study was to develop a prediction model based on extracellular matrix proteins, using artificial intelligence techniques. The model aimed to aid healthcare professionals to predict the overall survival of patients with ovarian cancer (OC) and determine the efficacy of immunotherapy. Methods: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection was used as the study dataset, whereas the TCGA-Pancancer dataset was used for validation. The prognostic importance of 1068 known extracellular matrix proteins for OC were determined by the Random Forest algorithm and the Lasso algorithm establishing the ECM risk score. Based on the gene expression data, the differences in mRNA abundance, tumour mutation burden (TMB) and tumour microenvironment (TME) between the high- and low-risk groups were assessed. Results: Combining multiple artificial intelligence algorithms we were able to identify 15 key extracellular matrix genes, namely, AMBN, CXCL11, PI3, CSPG5, TGFBI, TLL1, HMCN2, ESM1, IL12A, MMP17, CLEC5A, FREM2, ANGPTL4, PRSS1, FGF23, and confirm the validity of this ECM risk score for overall survival prediction. Several other parameters were identified as independent prognostic factors for OC by multivariate COX analysis. The analysis showed that thyroglobulin (TG) targeted immunotherapy was more effective in the high ECM risk score group, while the low ECM risk score group was more sensitive to the RYR2 gene-related immunotherapy. Additionally, the patients with low ECM risk scores had higher immune checkpoint gene expression and immunophenoscore levels and responded better to immunotherapy. Conclusion: The ECM risk score is an accurate tool to assess the patient's sensitivity to immunotherapy and forecast OC prognosis.

Keywords: artificial intelligence; extracellular matrix; immunity therapy; ovarian serous adenocarcinoma; prognosis.

Grants and funding

This work was supported by the Research Council of Norway (287953).