Skip to main content

CORRECTION article

Front. Endocrinol., 09 May 2024
Sec. Cancer Endocrinology
Volume 15 - 2024 | https://doi.org/10.3389/fendo.2024.1421538

Corrigendum: Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

This article is a correction to:

  1. Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

    1. Read original article
Hao Chi&#x;Hao Chi1†Gaoge Peng&#x;Gaoge Peng1†Jinyan Yang&#x;Jinyan Yang2†Jinhao ZhangJinhao Zhang2Guobin SongGuobin Song2Xixi XieXixi Xie2Dorothee Franziska StrohmerDorothee Franziska Strohmer3Guichuan LaiGuichuan Lai4Songyun ZhaoSongyun Zhao5Rui WangRui Wang1Fang Yang*Fang Yang6*Gang Tian*Gang Tian7*
  • 1Clinical Medical College, Southwest Medical University, Luzhou, China
  • 2School of Stomatology, Southwest Medical University, Luzhou, China
  • 3Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
  • 4Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
  • 5Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
  • 6Department of Ophthalmology, Charité – Universitätsmedizin Berlin, Campus Virchow-Klinikum, Berlin, Germany
  • 7Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China

A Corrigendum on:
Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

By Chi H, Peng G, Yang J, Zhang J, Song G, Xie X, Strohmer DF, Lai G, Zhao S, Wang R, Yang F and Tian G (2022). Front. Endocrinol. 13:1056310. doi: 10.3389/fendo.2022.1056310

In the published article, there was an error. There were missing values for the optimal penalty parameter.

A correction has been made to the section Method, subsection Model construction and validation, This sentence previously stated:

“By performing univariate Cox regression analysis, we identified 27 genes associated with survival, followed by Least absolute shrinkage and selection operator (LASSO) regression analysis using ‘glmnet’ in R and using tenfold cross-validation to determine the penalty regularization parameter λ.”

The corrected sentence appears below.

“By performing univariate Cox regression analysis, we identified 27 genes associated with survival, followed by Least absolute shrinkage and selection operator (LASSO) regression analysis using ‘glmnet’ in R, with tenfold cross-validation to determine the optimal penalty parameter lambda.min=9.”

There was another error. The source of the SVM-RFE algorithm was omitted.

A correction has been made to to the section Method, subsection Model construction and validation. This sentence previously stated:

“Using the SVM-RFE algorithm, we obtained 13 valuable variables.”

The corrected sentence appears below.

“We used the SVM-RFE algorithm from the ‘e1071’ R package, with ten-fold cross-validation to obtain 13 valuable variables.”

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: sphingolipid metabolism, UVM, tumor microenvironment, immunotherapy, predictive signature

Citation: Chi H, Peng G, Yang J, Zhang J, Song G, Xie X, Strohmer DF, Lai G, Zhao S, Wang R, Yang F and Tian G (2024) Corrigendum: Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma. Front. Endocrinol. 15:1421538. doi: 10.3389/fendo.2024.1421538

Received: 22 April 2024; Accepted: 30 April 2024;
Published: 09 May 2024.

Edited and Reviewed by:

Antonino Belfiore, University of Catania, Italy

Copyright © 2024 Chi, Peng, Yang, Zhang, Song, Xie, Strohmer, Lai, Zhao, Wang, Yang and Tian. 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: Hao Chi, Chihao7511@163.com

†These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.