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Original research
Novel scoring system incorporating lipoproteins to predict outcomes of epithelial ovarian cancer patients
  1. Song Tang1,
  2. Fang Zheng2,
  3. Kelie Chen2,
  4. Yizhen Niu1,
  5. Zhiqin Fu2,
  6. Yihua Wu1,3,
  7. Dajing Xia1,3 and
  8. Weiguo Lu1,4
    1. 1Department of Gynecologic Oncology, Women's Hospital School of Medicine Zhejiang University, Hangzhou, China
    2. 2Department of Gynecology and Obstetrics, First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
    3. 3Department of Toxicology of School of Public Health, Zhejiang University, Hangzhou, China
    4. 4Zhejiang Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, Zhejiang University, Hangzhou, China
    1. Correspondence to Dr Weiguo Lu, Department of Gynecologic Oncology, Women's Hospital School of Medicine Zhejiang University, Hangzhou, China; lbwg{at}zju.edu.cn; Dr Dajing Xia; dxia{at}zju.edu.cn; Dr Yihua Wu, School of Medicine C233, Zijingang Campus of Zhejiang University, Hangzhou, China; georgewu{at}zju.edu.cn

    Abstract

    Objective Epithelial ovarian cancer is the most lethal gynecological malignancy worldwide. While common prognostic factors are identified, the impact of serum lipoproteins remains controversial. This retrospective cohort study aims to investigate the association between specific lipoprotein levels and prognosis.

    Methods Clinical data of 420 participants with epithelial ovarian cancer registered at Women’s Hospital, School of Medicine, Zhejiang University, between January 2014 and April 2021 were included. Cox regression analyses and Kaplan–Meier methods were used to assess prognosis, estimated by hazard ratio (HR) with 95% confidence interval (CI). A novel prognostic model incorporating lipoproteins was developed for evaluating the prognosis. Meta-analysis was applied to assess the impact of low density lipoprotein cholesterol (LDL-C) on prognosis.

    Results Among 420 patients, those in advanced stages exhibited higher low density lipoprotein cholesterol (LDL-C) (p=0.008) and lower high density lipoprotein cholesterol (HDL-C) levels (p<0.001), with no significant differences in total cholesterol or triglyceride levels. Elevated LDL-C level was significantly associated with worse overall survival (HR 1.72; 95% CI 1.15 to 2.58; p=0.010) and progression free survival (HR 1.94; 95% CI 1.46 to 2.58; p<0.001), whereas higher HDL-C level was linked to better overall survival (HR 0.56; 95% CI 0.37 to 0.85; p=0.004) and progression free survival (HR 0.61; 95% CI 0.46 to 0.81; p<0.001). A novel prognostic model, low density lipoprotein cholesterol-high density lipoprotein cholesterol-fibrinogen-lactate dehydrogenase-prealbumin-Fe-stage (LH-FLPFS), was established to enhance prognostic predictive efficacy. The meta-analysis further suggested that higher LDL-C level was associated with worse overall survival (HR 1.82; 95% CI 1.39 to 2.38; p<0.001).

    Conclusions In this study, preoperative LDL-C and HDL-C levels emerged as potential prognostic factors for ovarian cancer. Establishment of a novel prognostic model, LH-FLPFS, holds promise for significantly improving prognostic predictive efficacy.

    • Ovarian Cancer

    Data availability statement

    Data are available upon reasonable request. Data for this retrospective cohort and prognostic model, including clinical data of patients, protocol for the construction of this prognostic model, and other relevant data, are available after publication and may be published as supplementary material or accessed through the corresponding author.

    https://doi.org/10.1136/ijgc-2024-005768

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      Data availability statement

      Data are available upon reasonable request. Data for this retrospective cohort and prognostic model, including clinical data of patients, protocol for the construction of this prognostic model, and other relevant data, are available after publication and may be published as supplementary material or accessed through the corresponding author.

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      Footnotes

      • ST, FZ and KC are joint first authors.

      • Contributors WL: conceptualization, methodology, project administration, resources, supervision, and writing–review and editing. ST: conceptualization, formal analysis, investigation, software, visualization, and writing–original draft. FZ: data curation, investigation, validation, and writing–original draft. KC: formal analysis, funding acquisition, software, and writing–original draft. YN: data curation. ZF: methodology. YW: conceptualization, methodology, funding acquisition, supervision, and writing–review and editing. DX: conceptualization, methodology, funding acquisition, supervision, and writing– review and editing. WL is the guarantor. WL certifies that all listed authors meet the author eligibility criteria and that no other authors who meet the criteria are omitted.

      • Funding This work was supported by the National Natural Science Foundation of China (grant No 32370584), National Natural Science Foundation of China (grant No 82303377), and Zhejiang Provincial Natural Science Foundation of China (grant No LY24H260002).

      • Competing interests None declared.

      • Provenance and peer review Not commissioned; externally peer reviewed.

      • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.