Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses

Sep 21, 2026

Genome-scale CRISPR screening and multi-cohort transcriptomic analyses are used to clarify how cancer stemness shapes responses to immune checkpoint inhibitors in ovarian cancer, where reliable biomarkers remain limited. A cancer stemness index (CSI) is built from public ovarian and pan-cancer datasets and validated in single-cell and PD1/PD-L1 cohorts. The CSI shows strong prognostic and immunotherapy predictive performance with mean AUC values above 0.8, while experiments confirm SNRPE roles in proliferation, migration, and tumor microenvironment remodeling that supports immune evasion.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background

Methods

Results

Conclusions

Introduction

TYPE Original Research PUBLISHED 11 December 2024 DOI 10.3389/fimmu.2024.1486652 OPEN ACCESS EDITED BY Wenyi Jin, City University of Hong Kong, China REVIEWED BY Ming Yi, Zhejiang University, China Zhiling Yang, Clinical Medical College and Afﬁliated Hospital of Chengdu University, China Guang Lei, Central South University, China *CORRESPONDENCE Xiuwen Wang [xiuwenwang12@sdu.edu.cn](xiuwenwang12@sdu.edu.cn)[ ](xiuwenwang12@sdu.edu.cn)Fangling Ning [ningfangling@126.com](ningfangling@126.com)†These authors have contributed equally to this work RECEIVED 26 August 2024 ACCEPTED 25 November 2024 PUBLISHED 11 December 2024 CITATION Liu Z, Han L, Ji X, Wang X, Jian J, Zhai Y, Xu Y, Wang F, Wang X and Ning F (2024) Multiomics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses. Front. Immunol. 15:1486652 . doi: 10.3389/fimmu.2024.1486652 COPYRIGHT © 2024 Liu, Han, Ji, Wang, Jian, Zhai, Xu, Wang, Wang and Ning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses Zhibing Liu 1,2†, Lei Han 3†, Xiaoyu Ji 4†, Xiaole Wang 1, Jinbo Jian 1, Yujie Zhai 1, Yingjiang Xu 5, Feng Wang 1, Xiuwen Wang 2* and Fangling Ning 1* 1 Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China, 2 Department of Oncology, Qilu Hospital of Shandong University, Jinan, Shandong, China, 3 Department of Reproductive Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China, 4 Department of Oncology, Huashan Hospital Fudan University, Shanghai, China, 5 Department of Interventional Vascular Surgery, Binzhou Medical University Hospital, Binzhou, Shandong, China Background: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to ﬁnd reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended. Methods: Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV) . Thirteen publicly accessible OV transcriptomic datasets, seven pan-cancer ICI transcriptomic cohorts, and one single-cell RNA dataset from melanoma patients treated with PD-1 were utilized to scale a novel cancer stemness index (CSI) . An OV single-cell RNA dataset was amassed and scrutinized to uncover the role of Small Nuclear Ribon uc leoprotein Polypeptide E (SNRPE) in the tumor microenvironment (TME) . Vitro experiments were performed to validate the function of SNRPE in promoting proliferation and migration of ovarian cancer. Results: Through the analysis of extensive datasets on ovarian cancer, a speciﬁc gene set that impacts the stemness characteristics of tumors has been identiﬁed and we unveiled a negative correlation between cancer stemness, and beneﬁts of ICI treatment in single cell ICI cohorts. This identiﬁed gene set underpinned the development of the CSI, a groundbreaking tool leveraging advanced machine learning to predict prognosis and immunotherapy responses in ovarian cancer patients. The accuracy of the CSI was further conﬁrmed by applying PD1/PD-L1 ICI transcriptomic cohorts, with a mean AUC exceeding 0.8 for predicting tumor progression and immun

FAQs

Why is predicting immunotherapy response in ovarian cancer difficult?

Cancer heterogeneity and the complex tumor immune microenvironment make response prediction challenging. The contribution of cancer stemness to resistance to immune checkpoint inhibitors is not fully understood.

How is the cancer stemness index (CSI) constructed and validated?

CSI is developed using genome-scale CRISPR screening of Achilles RNA-seq data and transcriptomic datasets, including ovarian and pan-cancer ICI cohorts, plus single-cell RNA evidence. Validation uses PD1/PD-L1 transcriptomic cohorts and single-cell ICI settings.

What role does SNRPE play according to the study results?

SNRPE amplification is linked to remodeling the tumor microenvironment and enabling malignant cells to evade immune recognition. Functional in vitro experiments support SNRPE involvement in ovarian cancer proliferation and migration.

Sep 21, 2026

Uploaded by Genevieve