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Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer

  • Gynecologic Oncology
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

Abstract

Objective

Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC.

Methods and results

The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan–Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.

Conclusion

A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

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Availability of data and materials

We obtained the data sets from TCPA (https://www.tcpaportal.org/) and TCGA (http://cancergenome.nih.gov/).

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Acknowledgements

We acknowledge the contributions of the TCPA Database and the TCGA Database gratefully.

Funding

This work was supported by the Shandong Province Nature Science Foundation (No. ZR2016HM08).

Author information

Author notes

  1. Xiaoyu Ji and Guangdi Chu have contribute equally to this research.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Affiliated Hospital of Qingdao University, No. 1677 Wutaishan Road, Qingdao, 266000, China

    Xiaoyu Ji, Yulong Chen, Jinwen Jiao, Teng Lv & Qin Yao

  2. Department of Urology, Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China

    Guangdi Chu

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  1. Xiaoyu Ji
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Contributions

XJ: Conceptualization, Writing-original draft, Writing-review & editing. GC: Writing-original draft, Methodology, Software. YC: Visualization, Investigation and Writing-review & editing. JJ: Software, Validation. TL: Methodology, Visualization. QY: Writing-Review & Editing, Project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qin Yao.

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Ji, X., Chu, G., Chen, Y. et al. Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer. Arch Gynecol Obstet 307, 903–917 (2023). https://doi.org/10.1007/s00404-022-06642-w

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