Targeting tumor microenvironment in ovarian cancer: Premise and promise

Yuting Jiang1, Chengdi Wang2, Shengtao Zhou1

  • 1Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, PR China.

Insights

Ovarian cancer is a major global health concern, often relapsing due to drug resistance. This review explores the tumor microenvironment

Area of Science:

  • Gynecologic Oncology
  • Cancer Biology
  • Tumor Microenvironment Research

Background:

  • Ovarian cancer is the leading cause of gynecologic cancer mortality worldwide.
  • Most patients experience relapse due to drug resistance, limiting treatment efficacy.
  • Tumor microenvironment (TME) targeted therapies show promise in other solid cancers.

Purpose of the Study:

  • To review recent mechanistic insights into the TME's role in ovarian cancer.
  • To explore TME's influence on ovarian cancer development, progression, and metastasis.
  • To identify novel therapeutic strategies and biomarkers targeting the TME in ovarian cancer.

Main Methods:

  • Comprehensive literature review of recent studies on ovarian cancer and TME.
  • Analysis of TME components' impact on cancer hallmarks.
  • Exploration of TME-driven mechanisms in ovarian cancer progression and metastasis.

Main Results:

  • The TME significantly modulates ovarian cancer hallmarks, influencing disease progression and metastasis.
  • Mechanistic insights reveal TME's critical role in therapeutic resistance.
  • Potential predictive and prognostic biomarkers associated with TME components were identified.

Conclusions:

  • Targeting the tumor microenvironment offers a promising avenue for novel ovarian cancer therapeutics.
  • Understanding TME dynamics is crucial for overcoming drug resistance and improving patient outcomes.
  • Further research into TME biomarkers can enhance prognosis prediction and treatment selection.

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