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]
