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作者

Guanting Pang, Yaohan Li, Qiwen Shi, Jingkui Tian, Hanmei Lou, Yue Feng

作者单位

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

刊名

Oncology research

年份

2025

卷号

Vol.33 No.4

页码

821-836

ISSN

1555-3906

关键词

Cervical cancer (CC) Omics Precision medicine Tumor immune microenvironment (TIME).

摘要

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer . 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 . 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 an...更多

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer . 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 . 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.收起

文献类型

期刊