Front. Endocrinol. Frontiers in Endocrinology Front. Endocrinol. 1664-2392 Frontiers Media S.A. 10.3389/fendo.2025.1738698 Correction 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 WangYutong 1 2 3 4 5 † Project administration Formal analysis Methodology Data curation Writing – original draft Investigation Conceptualization YuAiling 1 2 3 4 6 † Project administration Writing – review & editing Formal analysis Conceptualization Methodology Data curation Investigation GaoZiping 1 2 3 4 † Project administration Writing – review & editing Data curation Methodology Formal analysis Investigation Conceptualization YuanXiaoying 7 Validation Formal analysis Data curation Writing – review & editing DuXiaochen 8 Formal analysis Data curation Validation Writing – review & editing ShiPeng 9 Validation Writing – review & editing Formal analysis Visualization GuanHaoyun 9 Writing – review & editing Validation Formal analysis Visualization WenShuang 10 Writing – review & editing Supervision Resources WangHonglong 10 Supervision Resources Writing – review & editing WangLiang 1 2 3 4 * Funding acquisition Writing – review & editing Conceptualization Supervision FanBo 1 2 3 4 * ‡ Writing – review & editing Supervision Funding acquisition Conceptualization LiuZhiyu 1 2 3 4 * Conceptualization Writing – review & editing Supervision Funding acquisition Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China Liaoning Provincial Key Laboratory of Urological Digital Precision Diagnosis and Treatment, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China Department of Urology, Liaoning Engineering Research Center of Integrated Precision Diagnosis and Treatment Technology for Urological Cancer, Dalian, Liaoning, China Dalian Key Laboratory of Prostate Cancer Research, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China Department of Colorectal Surgery, Dalian Municipal Central Hospital, Dalian, Liaoning, China First Clinical College, Dalian Medical University, Dalian, Liaoning, China Department of Anatomy, College of Basic Medicine, Dalian Medical University, Dalian, Liaoning, China College of Humanities and Social Sciences, Dalian Medical University, Dalian, Liaoning, China Second Clinical College, Dalian Medical University, Dalian, Liaoning, China Department of Pathology, Dalian Friendship Hospital, Dalian, China Correspondence: Liang Wang, wangldoct@sina.com; Bo Fan, fanbo@dmu.edu.cn; Zhiyu Liu, lzydoct@163.com

These authors have contributed equally to this work

ORCID: Bo Fan, orcid.org/0000-0001-6598-3237

 10 12 2025 2025 16 1738698 03 11 2025 28 11 2025 Copyright © 2025 Wang, Yu, Gao, Yuan, Du, Shi, Guan, Wen, Wang, Wang, Fan and Liu. 2025 Wang, Yu, Gao, Yuan, Du, Shi, Guan, Wen, Wang, Wang, Fan and Liu https://creativecommons.org/licenses/by/4.0/ 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. A Correction on 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 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 transition zone prostate cancer whole-exome sequencing driver genes medication prediction TET2 mutation machine learning models section-at-acceptance Cancer Endocrinology

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.

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.

A composite image with four panels: A: A nomogram assessing survival probability based on Gleason score, pathological stage, TET2 mutation, and perineural invasion, showing total points and survival rates over three to five years. B: Kaplan-Meier survival curve comparing overall survival for pathological T1c versus T2 or more, with T1c having higher survival probability. P-value is less than 0.001. C: Kaplan-Meier survival curve comparing overall survival based on Gleason scores. Lower scores show higher survival probability. P-value is 0.005. D: Kaplan-Meier survival curve comparing TET2 mutant versus non-mutant, with non-mutant showing better survival. P-value is less than 0.001.

The original version of this article has been updated.

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Edited and reviewed by: Jorge Adrián Ramírez De Arellano Sánchez, University of Guadalajara, Mexico