- 1Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- 2Liaoning Provincial Key Laboratory of Urological Digital Precision Diagnosis and Treatment, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- 3Department of Urology, Liaoning Engineering Research Center of Integrated Precision Diagnosis and Treatment Technology for Urological Cancer, Dalian, Liaoning, China
- 4Dalian Key Laboratory of Prostate Cancer Research, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- 5Department of Colorectal Surgery, Dalian Municipal Central Hospital, Dalian, Liaoning, China
- 6First Clinical College, Dalian Medical University, Dalian, Liaoning, China
- 7Department of Anatomy, College of Basic Medicine, Dalian Medical University, Dalian, Liaoning, China
- 8College of Humanities and Social Sciences, Dalian Medical University, Dalian, Liaoning, China
- 9Second Clinical College, Dalian Medical University, Dalian, Liaoning, China
- 10Department of Pathology, Dalian Friendship Hospital, Dalian, China
By Wang Y, Yu A, Gao Z, Yuan X, Du X, Shi P, Guan H, Wen S, Wang H, Wang L, Fan B and Liu Z (2025) Front. Endocrinol. 16:1568665. doi: 10.3389/fendo.2025.1568665
There was a mistake in Figure 4A as published. The image in Figure 4A was inadvertently duplicated in Figure 7A due to a technical oversight during the figure assembly process. The corrected Figure 4A appears below.
Figure 4. Prognostic modeling and survival analysis in transition zone prostate cancer. (A) Nomogram integrating clinicopathological variables for predicting 3-, 4-, and 5-year overall survival probabilities. (B) Kaplan-Meier curves stratified by pathological T stage. (C) Survival differentiation across Gleason grade groups. (D) Comparative survival analysis by TET2 mutation status.
The original version of this article has been updated.
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Keywords: transition zone, prostate cancer, whole-exome sequencing, driver genes, medication prediction, TET2 mutation, machine learning models
Citation: Wang Y, Yu A, Gao Z, Yuan X, Du X, Shi P, Guan H, Wen S, Wang H, Wang L, Fan B and Liu Z (2025) Correction: TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning models. Front. Endocrinol. 16:1738698. doi: 10.3389/fendo.2025.1738698
Received: 03 November 2025; Accepted: 28 November 2025;
Published: 10 December 2025.
Edited and reviewed by:
Jorge Adrián Ramírez De Arellano Sánchez, University of Guadalajara, MexicoCopyright © 2025 Wang, Yu, Gao, Yuan, Du, Shi, Guan, Wen, Wang, Wang, Fan 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) and the copyright owner(s) 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: Liang Wang, d2FuZ2xkb2N0QHNpbmEuY29t; Bo Fan, ZmFuYm9AZG11LmVkdS5jbg==; Zhiyu Liu, bHp5ZG9jdEAxNjMuY29t
†These authors have contributed equally to this work
‡ORCID: Bo Fan, orcid.org/0000-0001-6598-3237
Yutong Wang1,2,3,4,5†