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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Cancer Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1434705
This article is part of the Research Topic Metabolic Abnormalities on the Incidence and Prognosis of Gynecological Malignant Tumors View all 5 articles

Comprehensive identification of a disulfidptosis-associated long non-coding RNA signature to predict the prognosis and treatment options in ovarian cancer

Provisionally accepted
Shouze Liu Shouze Liu 1,2,3*Rulan Jiang Rulan Jiang 4*Xinxin Wang Xinxin Wang 1*Qianqian Zhang Qianqian Zhang 5*Feida Du Feida Du 2*Pengtao Zheng Pengtao Zheng 2*Yanpeng Tian Yanpeng Tian 3*Zhongkang Li Zhongkang Li 2*Ruixia Guo Ruixia Guo 3*Shikai Liu Shikai Liu 1*
  • 1 Cangzhou Central Hospital, Cangzhou, Hebei, China
  • 2 Second Hospital of Hebei Medical University, Shijiazhuang, China
  • 3 First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
  • 4 Third Hospital of Cangzhou, Heibei, China
  • 5 Beijing Tsinghua Changgeng Hospital, Tsinghua University, Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Distinguished from cuproptosis and ferroptosis, disulfidptosis has been described as a newly discovered form of non-programmed cell death tightly associated with glucose metabolism. However, the prognostic profile of disulfidptosis-related lncRNAs (DRLRs) in ovarian cancer (OC) and their biological mechanisms need to be further elucidated.First, we downloaded the profiles of RNA transcriptome, clinical information for OC patients from the TCGA database. Generated from Cox regression analysis, prognostic lncRNAs were utilized to identify the risk signature by least absolute shrinkage and selection operator analysis. Then, we explored the intimate correlations between disulfidptosis and lncRNAs. What's more, we performed a series of systemic analyses to assess the robustness of the model and unravel its relationship with the immune microenvironment comprehensively.We identified two DRLR clusters, in which OC patients with low-risk scores exhibited a favorable prognosis, up-regulated immune cell infiltrations and enhanced sensitivity to immunotherapy. Furthermore, validation of the signature by clinical features and Cox analysis demonstrated remarkable consistency, suggesting the universal applicability of our model. It's worth noting that high-risk patients showed more positive responses to immune checkpoint inhibitors and potential chemotherapeutic drugs. 3 Conclusion Our findings provided valuable insights into DRLRs in OC for the first time, which indicated an excellent clinical value in the selection of management strategies, spreading brilliant horizons into individualized therapy.

    Keywords: disulfidptosis, lncRNA, ovarian cancer, Signature, Immunotherapy

    Received: 18 May 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Liu, Jiang, Wang, Zhang, Du, Zheng, Tian, Li, Guo and Liu. 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) or licensor 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.

    * Correspondence:
    Shouze Liu, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
    Rulan Jiang, Third Hospital of Cangzhou, Heibei, China
    Xinxin Wang, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
    Qianqian Zhang, Beijing Tsinghua Changgeng Hospital, Tsinghua University, Beijing, 102218, Beijing Municipality, China
    Feida Du, Second Hospital of Hebei Medical University, Shijiazhuang, China
    Pengtao Zheng, Second Hospital of Hebei Medical University, Shijiazhuang, China
    Yanpeng Tian, First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
    Zhongkang Li, Second Hospital of Hebei Medical University, Shijiazhuang, China
    Ruixia Guo, First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
    Shikai Liu, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.