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Journal of Ovarian Research logoLink to Journal of Ovarian Research
. 2026 Oct 2;19:303. doi: 10.1186/s13048-026-02267-8

Correction: Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17

Xueyan Geng 1, Maopeng Yin 1, Hongxi Zhao 1, Zeyu Zhang 1, Shichao Liu 1, Yingjie Liu 1, Shoucai Zhang 1, Yongyuan Liang 1, Li Song 3,✉, Guixi Zheng 1,2,✉
PMCID: PMC13633633  PMID: 42827261

Correction: J Ovarian Res 18, 237 (2025)

https://doi.org/10.1186/s13048-025-01809-w

Following publication of the original article [1], In the Methods section of the abstract, the first line currently reads “°C,” which appears to be a typographical error. It should actually be “OC”. The updated statement is given below and the changes have been highlighted in bold typeface.

Incorrect:

We acquired RNA sequencing data of 426 tissues (352 °C tumor and 74 adjacent non-tumor) from the Gene Expression Omnibus (GEO) repository. Following rigorous batch effect correction and normalization procedures, DEGs were screened between tumor and non-tumor specimens.

Correct:

We acquired RNA sequencing data of 426 tissues (352 OC tumor and 74 adjacent non-tumor) from the Gene Expression Omnibus (GEO) repository. Following rigorous batch effect correction and normalization procedures, DEGs were screened between tumor and non-tumor specimens.

The original article [1] has been corrected.

Contributor Information

Li Song, Email: sl0930china@sdu.edu.cn.

Guixi Zheng, Email: zhengg@sdu.edu.cn.

Reference

  • 1.Geng X, Yin M, Zhao H, et al. Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17. J Ovarian Res. 2025;18:237. 10.1186/s13048-025-01809-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

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