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

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

Yafang Zhong  1 , Wei Zhang  1   2   3 , Dongzhou Liu  4 , Zhipeng Zeng  1 , Shengyou Liao  1 , Wanxia Cai  1 , Jiayi Liu  1 , Lian Li  1 , Xiaoping Hong  4 , Donge Tang  1 , Yong Dai  1
Affiliations

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

Yafang Zhong et al. Front Immunol. .

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.

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Conflict of interest statement

Author WZ was employed by company Fapon Biotech Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Workflow of the study.
Figure 2
Figure 2
Principal Component Analysis and DEGs Screening between The SS versus Controls. (A) Principal component analysis (PCA) plot shows the distribution of each corrected sample. (B) Volcano map of DEGs between the whole blood of SS and control samples based on the discovery data set. P <0.0001 and Fold Change >1.5 considered significant. Green and red represented down-regulation and up-regulation, respectively (SS/Control). (C) The biological processes, (D) cellular components, and (E) molecular functions, (F) KEGG enrichment analysis of the DEGs. (G, H) The GSEA analysis of the SS and control samples.
Figure 3
Figure 3
Identification and Validation of Diagnostic Biomarkers for SS. (A) The LASSO logistic regression, (B) SVM-RFE algorithm, and (C) RF algorithm of SS biomarker screening. (D) Venn diagram of the diagnostic biomarkers extracted by the three algorithms. (E–O) The expression of diagnostic biomarkers based on the validation data set. “Con” represented the control samples, and “SS” represented the SS patients.
Figure 4
Figure 4
The Diagnostic Capability of Potential Biomarkers for SS Based on The Discovery Data Set. (A–J) The ROC curve of BATF2, HES4, IFI27, IFITM3, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2 based on the discovery data set. (K) The ROC curve showing the AUC value of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF based on the discovery data set.
Figure 5
Figure 5
The Diagnostic Capability of Potential Biomarkers for SS Based on The Verification Data Set. (A–J) The ROC curve of BATF2, HES4, IFI27, IFITM3, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2 based on the verification data set. (K) The ROC curve showing the AUC value of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF based on the verification data set.
Figure 6
Figure 6
RT-qPCR Validation of The Potential Diagnostic Markers using Our Own Chinese Cohort. (A–H) The relative mRNA expression levels of HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2 in the PBMC of SS patients and healthy people. (I) The ROC curve showing the AUC value of HES4, IFI27, LY6E, OTOF, TTC21A, and XAF1 based on the Chinese cohort.
Figure 7
Figure 7
The Ratio Changes of Immune Cells in SS Patients, and Their Correlation with The Expression of Diagnostic Markers. (A) The fraction of 22 types of immune cells of SS patients (red) and healthy people (blue). (B) The heat map displaying the 22 different types of immune cells’ proportional association in SS patients. (C–I) The correlation between the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2 with the levels of immune cells in SS. The size of the dots represented the correlation strength. The p value was represented by the color of the dots.
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