Corrigendum to “Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response” [iScience 29 (2026) 116229]

Main text

DOI of original article: https://doi.org/10.1016/j.isci.2026.116229.

In the original published version of this article, [the affiliations for the first author (Yueqiong Lao) and co-first author (Jie Yang) were incorrectly cross-listed; an affiliation of the corresponding author and several co-authors to the Guangzhou Institute of Cardiovascular Disease was inappropriately included; and the postal code for the Second Affiliated Hospital of Guangzhou Medical University was erroneous.]

[Yueqiong Lao^1,2,3,8, Jie Yang^1,2,4,8, Minli Hu^3,5, Jing Li^3,5, Weikang Xu^3,5, Jiahui Xu^3,5, Huan Wang^3,5, Hechenhao Jiang^3,5, Zhihao Pei^3,5, Xinyi Qiu⁵, Kunyuan Wang^3,5, Xuan Li^6,7,∗, Hui Yang^3,5,9,∗∗

¹Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China

²Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China

³Guangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China

⁴Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China

⁵Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China

⁶Guangzhou Key Laboratory for Research and Development of Nano-Biomedical Technology for Diagnosis and Therapy&Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumour Microenvironment, Department of Oncology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China

⁷Department of Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China

∗Corresponding author lixuan881206@126.com

∗∗Corresponding author yanghui@gzhmu.edu.cn

⁸These authors contributed equally

⁹Lead contact]

[The authors have corrected the affiliation numbers for Yueqiong Lao and Jie Yang to accurately reflect their respective institutional affiliations. The authors also removed the inappropriate Guangzhou Institute of Cardiovascular Disease affiliation from all authors and added an acknowledgment to thank that institute for providing laboratory resources. Additionally, the postal code for the Second Affiliated Hospital of Guangzhou Medical University has been revised from 510245 to 510260 in all relevant addresses]. The correct version of Title page and Acknowledgments sections can be found below.

[Title page

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response

Yueqiong Lao^1,6, Jie Yang^2,3,6, Minli Hu¹, Jing Li¹, Weikang Xu¹, Jiahui Xu¹, Huan Wang¹, Hechenhao Jiang¹, Zhihao Pei¹, Xinyi Qiu¹, Kunyuan Wang¹, Xuan Li^4,5,∗, Hui Yang^1,7,∗

¹Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.

²Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.

³Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.

⁴Guangzhou Key Laboratory for Research and Development of Nano-Biomedical Technology for Diagnosis and Therapy&Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumour Microenvironment, Department of Oncology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.

⁵Department of Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.

⁶These authors contributed equally.

⁷Lead contact

∗Correspondence: lixuan881206@126.com (X.L.), yanghui@gzhmu.edu.cn (H.Y.)

Acknowledgments

This study was supported by the General Project of Guangdong Natural Science Foundation (no. 2021A1515010795), the National Natural Science Foundation of China (nos. 82102813, 82100610, and 82403294), the Guangzhou Science and Technology Plan Project (nos. 202201020157, 2023A03J0416, and 2024A03J1003), the National Foundation of Postdoctoral Program of China (no. GZC20233187), and the Guangdong Province ordinary universities characteristic innovation project (no. 2024KTSCX125). We also thank the Guangzhou Institute of Cardiovascular Disease of the Second Affiliated Hospital of Guangzhou Medical University for providing laboratory space and experimental equipment.].

The authors/publisher apologize for the errors.

Declaration of interests

The authors declare no competing interests.