Open Access

REVIEW

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

GUANTING PANG^1,4,#, YAOHAN LI^2,4,#, QIWEN SHI³, JINGKUI TIAN⁴, HANMEI LOU^4,5,*, YUE FENG^4,5,*

1 College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, 310014, China
2 College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China
3 Collaborative Innovation Center for Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, China
4 Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
5 Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China

* Corresponding Authors: HANMEI LOU. Email: ; YUE FENG. Email:
# Contributed equally as first authors

Oncology Research 2025, 33(4), 821-836. https://doi.org/10.32604/or.2024.053772

Received 10 May 2024; Accepted 01 August 2024; Issue published 19 March 2025

Abstract

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

---

Keywords

---