Identification of important modules and biomarkers in tuberculosis based on WGCNA

Jing Dong; Ruixue Song; Xuetian Shang; Yingchao Wang; Qiuyue Liu; Zhiguo Zhang; Hongyan Jia; Mailing Huang; Chuanzhi Zhu; Qi Sun; Boping Du; Aiying Xing; Zihui Li; Lanyue Zhang; Liping Pan; Zongde Zhang

doi:10.3389/fmicb.2024.1354190

Identification of important modules and biomarkers in tuberculosis based on WGCNA

Front Microbiol. 2024 Feb 8:15:1354190. doi: 10.3389/fmicb.2024.1354190. eCollection 2024.

Authors

Jing Dong^#^{1

2

3}, Ruixue Song^#^{1

2

3}, Xuetian Shang^{1

2

3}, Yingchao Wang^{1

2

3}, Qiuyue Liu^{3

4}, Zhiguo Zhang⁵, Hongyan Jia^{1

2

3}, Mailing Huang^{3

6}, Chuanzhi Zhu^{1

2

3}, Qi Sun^{1

2

3}, Boping Du^{1

2

3}, Aiying Xing^{1

2

3}, Zihui Li^{1

2

3}, Lanyue Zhang^{1

2

3}, Liping Pan^{1

2

3}, Zongde Zhang^{1

2

3}

Affiliations

¹ Beijing Chest Hospital, Capital Medical University, Beijing, China.
² Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, China.
³ Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
⁴ Department of Intensive Care Unit, Beijing Chest Hospital, Capital Medical University, Beijing, China.
⁵ Changping Tuberculosis Prevent and Control Institute of Beijing, Beijing, China.
⁶ Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing, China.

^# Contributed equally.

Abstract

Background: Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.

Methods: We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.

Results: Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.

Conclusion: This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.

Keywords: WGCNA; diagnosis; lncRNA; mRNA; tuberculosis.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the National Natural Science Foundation (82172279 and 82100011), the Beijing Natural Science Foundation (L234055), Tongzhou Science and Technology Project (KJ2022CX042), and Tongzhou Yunhe Project (YH201807 and YH202001).