This article is a correction to:
Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning
Fuze Liu
Yue Huang
Fuhui Liu2
Hai Wang- 1Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- 2School of Clinical Medical, Weifang Medical University, Weifang, China
By Liu F, Huang Y, Liu F and Wang H (2023). Front. Immunol. 14:1126647. doi: 10.3389/fimmu.2023.1126647
In the published article, the wrong dataset was used for the final validation of diagnostic efficacy. The correct dataset should be the atherosclerosis dataset GSE57691. This led to several errors in the article.
There was an error in Figure 7 as published. The wrong dataset GSE73754 has been corrected to GSE57691 in the legend and the ninth ROC curve has been corrected in the Figure. The corrected Figure 7 and its caption appears below.
Figure 7
Figure 7 Construction of the nomogram and the diagnosis value assessment. (A) The ROC curve of each candidate gene (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1), nomogram, and the validation in GSE55235 and GSE57691. (B) Nomogram for diagnosis RA with AS.
There was an error in Supplementary Table 9, Columns S and T. The information from the wrong data was mistakenly indicated in the supplementary material. The supplementary material has been corrected.
A correction has been made to Abstract, Methods section, Paragraph 8. This sentence previously stated:
“We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE73754”.
The corrected sentence appears below:
“We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE57691”.
A correction has been made to 2 Materials and methods, 2.1 Data collection and data procesing section, Paragraph 3 and 7. This sentence previously stated:
“We retrieved four gene expression datasets from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), namely, GSE55457, GSE55235, GSE100927, and GSE73754 (13). The GSE55457 dataset included 11 control samples and 12 RA samples, while GSE55235 included 10 control samples and 10 RA samples. The GSE100927 dataset contained 35 control samples and 69 AS samples, and GSE73754 contained 20 control samples and 52 AS samples”.
The corrected sentence appears below:
“We retrieved four gene expression datasets from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), namely, GSE55457, GSE55235, GSE100927, and GSE57691 (13). The GSE55457 dataset included 11 control samples and 12 RA samples, while GSE55235 included 10 control samples and 10 RA samples. The GSE100927 dataset contained 35 control samples and 69 AS samples, and GSE57691 contained 10 control samples and 9 AS samples”.
A correction has been made to 3 Results, 3.6 Diagnosis value evaluation, Paragraph 10. This sentence previously stated:
“We validated the model with GSE55235 and GSE73754, as shown in Figure 7A”.
The corrected sentence appears below:
“We validated the model with GSE55235 and GSE57691, as shown in Figure 7A”.
The authors apologize for this error 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: rheumatoid arthritis, atherosclerosis, immune infiltration, diagnosis, machine learning
Citation: Liu F, Huang Y, Liu F and Wang H (2024) Corrigendum: Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning. Front. Immunol. 15:1400160. doi: 10.3389/fimmu.2024.1400160
Received: 13 March 2024; Accepted: 02 April 2024;
Published: 16 April 2024.
Edited and Reviewed by:
Mehdi Pirooznia, Johnson & Johnson, United StatesCopyright © 2024 Liu, Huang, Liu and Wang. 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: Hai Wang, d2FuZ2hhaTkwN0Bob3RtYWlsLmNvbQ==