Basic Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Oct 24, 2023; 14(10): 357-372
Published online Oct 24, 2023. doi: 10.5306/wjco.v14.i10.357
Hub genes and their key effects on prognosis of Burkitt lymphoma
Yan-Feng Xu, Guan-Yun Wang, Ming-Yu Zhang, Ji-Gang Yang
Yan-Feng Xu, Guan-Yun Wang, Ming-Yu Zhang, Ji-Gang Yang, Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
Author contributions: Xu YF and Yang JG designed the research; Xu YF, Wang GY and Zhang MY performed the research; Xu YF, Wang GY contributed new reagents/analytic tools; Xu YF analyzed the data; Xu YF, Zhang MY, Wang GY and Yang JG wrote the paper.
Supported by National Natural Science Foundation of China (General Program), No. 82272034.
Institutional review board statement: Institutional review board statement is not applied to our manuscript.
Institutional animal care and use committee statement: Institutional animal care and use committee statement is not applied to our manuscript.
Conflict-of-interest statement: All authors declare that they have no conflicts of interest and have never published the manuscript.
Data sharing statement: The datasets analyzed (GSE4475 and GSE69051) during this study are publicly available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), the original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author (yangjigang@ccmu.edu.cn).
ARRIVE guidelines statement: The authors have read the ARRIVE Guidelines, and the manuscript was prepared and revised according to the ARRIVE Guidelines.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ji-Gang Yang, MD, PhD, Chief Doctor, Professor, Researcher, Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing 100050, China. yangjigang@ccmu.edu.cn
Received: July 19, 2023
Peer-review started: July 19, 2023
First decision: August 24, 2023
Revised: September 6, 2023
Accepted: September 18, 2023
Article in press: September 18, 2023
Published online: October 24, 2023
Processing time: 96 Days and 20.1 Hours
Abstract
BACKGROUND

Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.

AIM

To identify hub genes and perform gene ontology (GO) and survival analysis in BL.

METHODS

Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed.

RESULTS

In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (SRC, TLR4, CD40, STAT3, SELL, CXCL10, IL2RA, IL10RA, CCR7 and FCGR2B) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (CXCL10, P = 0.029 and IL2RA, P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.

CONCLUSION

From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.

Keywords: Burkitt lymphoma; Weighted gene co-expression network analysis; Microarray data; Functional enrichment analysis; Prognosis; Therapeutic target

Core Tip: This study represents the pioneering investigation of gene expression in Burkitt lymphoma (BL) using weighted gene co-expression network analysis, coupled with functional enrichment analysis. In this study, we have successfully identified and validated 10 hub genes. Survival analysis has demonstrated that the overexpression of CXCL10 and IL2RA in BL may serve as robust prognostic indicators. Furthermore, an integrated mRNA signature and age nomogram potentially provide valuable prognostic insights for patients with BLs.