Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells

Yafang Zhong; Wei Zhang; Dongzhou Liu; Zhipeng Zeng; Shengyou Liao; Wanxia Cai; Jiayi Liu; Lian Li; Xiaoping Hong; Donge Tang; Yong Dai

doi:10.3389/fimmu.2023.1023248

Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells

Front Immunol. 2023 Jun 13:14:1023248. doi: 10.3389/fimmu.2023.1023248. eCollection 2023.

Authors

Yafang Zhong¹, Wei Zhang^{1

2

3}, Dongzhou Liu⁴, Zhipeng Zeng¹, Shengyou Liao¹, Wanxia Cai¹, Jiayi Liu¹, Lian Li¹, Xiaoping Hong⁴, Donge Tang¹, Yong Dai¹

Affiliations

¹ Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China.
² South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China.
³ Innovative Markers Department, Fapon Biotech Inc., Dongguan, China.
⁴ Department of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.

Abstract

Background: Sjögren's syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis.

Methods: We downloaded three datasets of SS patients' and healthy pepole's whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers' diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied.

Results: We obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2.

Conclusion: In this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients.

Keywords: CIBERSORT; Sjogren’s Syndrome; immune cell disturbance; machine learning; potential biomarker.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Area Under Curve
Biomarkers
Computers
Humans
Sjogren's Syndrome* / diagnosis
Sjogren's Syndrome* / genetics

Substances

Biomarkers

Grants and funding

This study was supported by grants from the Key Research and Development Program of Guangdong Province (No. 2019B020229001), the science and technology plan of Shenzhen (No. JCYJ20200109144218597 and No: JCYJ20210324113013035), Shenzhen Key Medical Discipline Construction Fund (No. SZXK011), and the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515111071).