Gene expression data related to systemic lupus erythematosus (SLE) were retrieved from the Gene Expression Omnibus (GEO) database (GSE17755, GSE50772, GSE61635). Jvenn (http://jvenn.toulouse.inra.fr/) was used for intersection analysis of these three SLE-related datasets. The screening criteria for differentially expressed genes (DEGs) were set as |log2FC| > 1 and P< 0.05. DEGs obtained from each dataset were intersected to identify robust SLE-associated genes. Subsequently, the identified DEGs were cross-referenced with immune-related core genes retrieved from the GeneCards database using the keyword “Immunity” (relevance score > 8), in order to further prioritize candidate genes with immunological significance for downstream validation.

A cohort of 360 SLE patients treated at our hospital between March 2023 and January 2025 was included in the study. The group consisted of 312 women and 48 men, with a mean age of 38.23 ± 5.60 years. The inclusion criteria were as follows: (1) meeting the 2019 classification criteria for SLE established jointly by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR) (15); (2) age >18 years; and (3) complete clinical data. The exclusion criteria were as follows: (1) coexisting other types of autoimmune diseases (e.g., rheumatoid arthritis); (2) coexisting infectious diseases; and (3) malignancies or pregnancy.

A control group was established consisting of 360 healthy individuals undergoing routine physical examinations during the same period. This group included 298 females and 62 males, with an average age of 38.42 ± 5.59 years. The healthy participants met none of the diagnostic criteria for SLE, had no history of major diseases, and had no personal or family history of autoimmune diseases.

The severity of disease was assessed using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) (16). Patients with SLE were classified into the active group (SLEDAI ≥ 5) and the inactive group (SLEDAI< 5).

Fasting venous blood samples (5 mL) were collected from each subject and centrifuged at 3000 revolutions per minute (r/min) for 10 minutes using a centrifuge with a rotor diameter of 1000 mm. Following centrifugation, 150 μL of serum was separated and used for immunological analyses. Serum levels of immunoglobulin A (IgA), immunoglobulin M (IgM), immunoglobulin G (IgG), complement components C3 and C4, and C-reactive protein (CRP) were quantified using immunoturbidimetric assays. Peripheral blood cell parameters, including white blood cell (WBC) count, hemoglobin (Hb), and platelet (PLT) count, were analyzed using a Sysmex fully automated modular hematology and body fluid analyzer. Erythrocyte sedimentation rate (ESR) was measured by infrared photo-optical colorimetry. ANA were detected by indirect immunofluorescence assay (IIFA), and anti-double-stranded DNA (anti-dsDNA) antibodies were determined using a line immunoassay (LIA). The 24-hour urinary protein excretion was assessed using a TBA-FX8 fully automated biochemical analyzer.

Blood samples from all participants were collected in EDTA-containing tubes for anticoagulation. Peripheral blood mononuclear cells (PBMCs) were separated through Ficoll-Paque density gradient centrifugation (GE Healthcare, Chicago, USA). Total RNA was isolated from PBMCs with TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. The RNA concentration and purity were determined using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The RNA was then reverse-transcribed into cDNA using a reverse transcription kit (Thermo Fisher Scientific). RT-qPCR was performed on a QuantStudio M7 Flex RT-qPCR system (Thermo Fisher Scientific) using the SYBR Green PCR Master Mix kit (Applied Biosystems, Foster City, CA, USA). The primer sequences for the RT-qPCR reactions were as follows: CLEC4E forward primer: 5′-CCTGTTTCATCACCAGATGTGT-3′, CLEC4E reverse primer: 5′-AACGCCCAGGAAATGGTGT-3′; GAPDH forward primer: 5′-TGCAACCGGGAAGGAAATGA-3′, GAPDH reverse primer: 5′-TTCCCGTTCTCAGCCTTGAC-3′. The PCR amplification was carried out with an initial denaturation at 95°C for 5 minutes, followed by 40 cycles consisting of denaturation at 95°C for 15 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 30 seconds. The relative expression levels of CLEC4E were calculated using the 2 ^{− ΔΔ Ct} method and normalized to GAPDH as an internal control.

ROC curve analysis was used to evaluate the diagnostic performance of CLEC4E expression levels in distinguishing between SLE patients and healthy controls, as well as between active and inactive SLE patients.

SLE patients were categorized into low and high CLEC4E expression groups based on the average expression value. Prognosis was assessed over a 6-month follow-up period post-discharge. Evaluations included updated SLEDAI scores, which were interpreted as follows: Favorable prognosis: SLEDAI score ≤ 4. Unfavorable prognosis: Disease relapse or death. Disease relapse was defined as an increase in the SLEDAI score of more than 3 points compared to the score at the end of treatment. Disease relapse was verified by clinical physicians. Death cases only included those directly related to SLE, such as those caused by disease complications or organ failure. The relationship between CLEC4E expression levels and SLE patient prognosis was assessed using survival analysis with Kaplan-Meier curve analysis.

Statistical analyses were conducted using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 8 (GraphPad Software, San Diego, CA, USA), and R software version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria) was used for interaction effect analysis. For continuous variables with normal distribution, differences between groups were compared using the independent-samples t-test. For continuous variables not following a normal distribution, the Mann-Whitney U test (rank-sum test) was used. Differences in categorical variables were analyzed using the chi-square test. Prognostic rates were analyzed using Kaplan-Meier survival curves. A value of P< 0.05 was considered statistically significant.

Gene expression datasets related to SLE, including GSE17755, GSE50772, and GSE61635, were retrieved from the GEO database. Intersection analysis was conducted with the Jvenn tool, applying a screening criterion of |log2FC| > 1 and P< 0.05. A total of six DEGs were identified: ISG15, HERC5, TNFAIP6, IFIT3, OASL, and CLEC4E. To further prioritize genes with immunological relevance, the identified DEGs were cross-referenced with immune-related genes from the GeneCards database (relevance score > 8). Only ISG15 and CLEC4E met these criteria. Considering that ISG15 has already been extensively investigated in clinical studies of SLE and its role and clinical significance have been systematically elucidated, additional validation would provide limited novelty. In contrast, CLEC4E has been rarely reported in SLE, remains to be clinically validated, and is feasible for detection in peripheral blood, thus offering both innovation and translational potential. On this basis, CLEC4E was selected as the key gene for subsequent clinical validation. See Figure 1 .

A comparison was made between the SLE patient group and the healthy control group (Control) regarding general demographic characteristics and lifestyle habits. No significant differences were observed between the two groups regarding age, gender ratio, BMI, smoking history, alcohol consumption, or education level (P > 0.05), suggesting comparability, as shown in Table 1 .

The expression of the CLEC4E gene was detected in both the SLE patient group and the healthy control group using RT-qPCR. The results indicated that CLEC4E expression was significantly elevated in the SLE group relative to the control group (P< 0.05). See Table 2 and Figure 2 .

The diagnostic value of CLEC4E in SLE patients was assessed using ROC curve analysis. The AUC for CLEC4E was 0.7744, with a sensitivity of 63.06% and a specificity of 86.39%, indicating that CLEC4E has high diagnostic performance for SLE. See Table 3 and Figure 3 .

Based on the SLEDAI scores, SLE patients were divided into active and inactive groups. Clinical parameters were compared between the two groups. No significant differences were found in disease duration, WBC, HB, PLT, IgA, IgG, and IgM between the two groups (P > 0.05). However, the active group exhibited significantly elevated levels of immunological and inflammatory markers, ANA positivity, anti-dsDNA antibody levels, 24-hour urinary protein levels, ESR, and CRP. In contrast, serum levels of C3 and C4 were significantly reduced in this group (P< 0.05), indicating higher disease activity in active SLE patients. See Table 4 .

The correlation between CLEC4E and SLEDAI score, IgA, IgG, and IgM was 0.7200, 0.7549, and 0.8269, respectively (P< 0.0001), and negatively correlated with C3 and C4, with correlations of -0.7195 and -0.7247, respectively (P< 0.0001). In addition, the correlation of CLEC4E with ANA positivity and anti-dsDNA antibody was 0.7571 and 0.6193 (P< 0.0001), respectively ( Table 5 and Figure 4 ).

CLEC4E expression was significantly higher in the active SLE group compared to that in the inactive group (P< 0.05). This finding suggests that CLEC4E may be closely related to the disease activity of SLE. See Table 6 and Figure 5 .

A multivariate logistic regression analysis was conducted to explore potential independent risk factors for disease activity in SLE. The factors with a P-value of<0.05 from the previous differential analysis were subjected to collinearity diagnostics. The results showed that the variance inflation factor (VIF) for all independent variables was less than 10, and the tolerance values for each variable were greater than 0.1, indicating that there was no significant linear correlation between the independent variables. Based on the conventional criteria for VIF and tolerance, no potential risk for multicollinearity was observed, as shown in Table 7 . The regression analysis revealed that C3, positive ANA, anti-dsDNA antibodies, and CLEC4E expression levels were independent risk factors for SLE disease activity (P< 0.05). This suggests the potential prognostic value of CLEC4E in SLE progression. See Table 8 .

To explore the potential interaction between CLEC4E expression and ANA status, an interaction term (ANA × CLEC4E) was added to the aforementioned multivariate model. The analysis showed that the regression coefficient of the interaction term was 0.9043 with a P value of 0.572, indicating that the effect of CLEC4E on disease activity (SLEDAI) did not differ significantly between ANA-positive and ANA-negative patients ( Figure 6 ). Therefore, the interaction term was not included in the main-effect model, and subsequent analyses and conclusions were based on the multivariate model without the interaction term.

The diagnostic value of CLEC4E in different disease activity phases of SLE was evaluated using ROC curve analysis. The results showed that CLEC4E had an AUC of 0.6360, a sensitivity of 44.37%, and a specificity of 79.36%, indicating its potential diagnostic value in assessing SLE activity, particularly in distinguishing patients with high specificity. Although CLEC4E’s standalone diagnostic performance has certain limitations, its diagnostic capacity significantly improves when combined with other markers (such as C3, C4, ANA, and anti-dsDNA antibodies). The combined diagnostic performance showed a remarkable AUC of 0.9407, with a sensitivity of 95.07% and a specificity of 82.11%, further highlighting CLEC4E’s potential in SLE diagnosis, especially when used in conjunction with multiple biomarkers. See Table 6 and Figure 6 . See Table 9 and Figure 7 .

Kaplan-Meier survival analysis was used to assess the relationship between CLEC4E expression levels and prognosis in SLE patients (χ² = 13.46, P = 0.0002). The analysis demonstrated that patients with high CLEC4E expression had significantly poorer outcomes than those with low expression levels (P = 0.0002). These results suggest that high CLEC4E expression may be associated with worse clinical outcomes. See Figure 8 .