LncRNAs IFNG-AS1 and TH2LCRR as potential biomarkers of Th1/Th2 imbalance in diabetic nephropathy: From bioinformatics to experimental validation

Abstract

Background

The inflammatory response is a pivotal mechanism underlying the progression of type 2 diabetes mellitus (T2DM) into diabetic nephropathy (DN). Upstream lncRNAs regulate inflammatory molecules, and their dysregulation can disrupt immune homeostasis. Th1/Th2 imbalance is one of the most significant causes of DN progression. This research aims to uncover novel biomarkers and elucidate the underlying molecular mechanisms involved in DN.

Methods

The GSE135390 dataset was analyzed to identify differentially expressed genes (DEGs) associated with Th1 cell differentiation. Based on literature review and NcPath databases, IFNG-AS1(Th1) and TH2LCRR (Th2) were selected as the top lncRNAs. To validate our bioinformatics findings, real-time PCR was conducted on 90 participants categorized into four groups: 30 with T2DM, 30 with DN (15 with microalbuminuria and 15 with ESRD), and 30 healthy controls.

Results

The analysis of real-time PCR results revealed a notable upregulation in IFNG-AS1 expression in ESRD patients compared to individuals with T2DM and healthy controls. Moreover, a significant increase in IFNG-AS1 expression was observed in patients with microalbuminuria relative to healthy subjects. Conversely, TH2LCRR expression was notably reduced in patients with ESRD, microalbuminuria, and T2DM compared to healthy individuals. Expression of IFNG-AS1 and TH2LCRR showed strong correlation with biochemical markers, including HbA1c, ESR, BUN, GFR, and albumin.

Conclusion

This study demonstrates the potential role of IFNG-AS1 and TH2LCRR as key regulators in the immunopathogenesis of DN. Their dysregulated expression may contribute to Th1/Th2 imbalance, providing a deeper understanding of immune-mediated mechanisms involved in DN progression.

Graphical abstract

Highlights

• •Dysregulated immune responses are central to the pathogenesis of DN.
• •The lncRNAs IFNG-AS1 and TH2LCRR modulate Th1/Th2 cytokine balance in DN.
• •IFNG-AS1 upregulation promotes Th1-driven inflammation and renal injury.
• •Reduced TH2LCRR expression impairs Th2 cytokine-mediated anti-inflammatory responses.
• •IFNG-AS1 and TH2LCRR may serve as novel biomarkers for early DN detection.

1. Introduction

Diabetes mellitus, impacting approximately 537 million adults worldwide, is a chronic metabolic disorder hallmarked by insulin resistance, hyperglycemia, and impaired glucose tolerance [1]. Nearly 90 % of diabetes cases are classified as type 2 diabetes mellitus (T2DM), resulting from a multifactorial interaction of genetic predispositions, lifestyle factors, and environmental influences [2]. Uncontrolled T2DM can progress to severe conditions such as diabetic nephropathy (DN), a primary cause of end-stage renal disease (ESRD). Hallmark indicators of DN include deteriorating renal function, reduced glomerular filtration rate (GFR), and persistent proteinuria [3]. Histopathological changes include thickened basement membranes, glomerular mesangial proliferation, glomerular hypertrophy, and tubulointerstitial fibrosis. An estimated 40 % of individuals with T2DM develop DN, underscoring its impact as a major public health issue [4,5].

Despite considerable research efforts, the full etiological basis of DN remains unresolved. Still, systemic inflammatory responses and immune dysregulation are strongly implicated in its progression. Coordination between the innate and adaptive immune systems is central to regulating the inflammatory processes in DN [6,7]. As pivotal regulators of adaptive immunity and inflammation, T helper 1 (Th1) and Th2 cells play a significant role in the development and progression of DN [8]. Th1 cells primarily release interferon-gamma (IFN-γ), a key pro-inflammatory cytokine that drives cellular immune responses and promotes inflammation [9]. In contrast, Th2-mediated immunity involves B cell proliferation, immunoglobulin production, and the secretion of cytokines with anti-inflammatory properties such interleukin-4 (IL-4), IL-5, and IL-13 [10]. In diabetic patients, there is a Th1/Th2 imbalance characterized by increased Th1 cell counts and activity. This pro-inflammatory shift contributes to insulin resistance, β-cell dysfunction, and the progression of diabetes-related complications [11,12]. The balance between Th1/Th2 cells is regulated by complex molecular mechanisms involving cytokine signaling, transcription factors, and epigenetic modulators [13]. An altered Th1/Th2 ratio, marked by excessive Th1 activity and suppressed Th2 response, is associated with renal injury, fibrosis, and proteinuria in DN [14,15]. However, upstream regulatory elements affecting this Th1/Th2 shift remain insufficiently explored.

Among regulatory elements, long non-coding RNAs (lncRNAs) serve as critical regulators of T cell differentiation and inflammatory signaling at both the transcriptional and post-transcriptional levels. LncRNAs are a heterogeneous group of RNA molecules that exceed 200 nucleotides in length, involved in diverse biological processes, including immune regulation, fibrosis, and cell survival [[16], [17], [18]]. In DN, lncRNAs modulate mesangial cell proliferation, fibrosis, and inflammation under high-glucose conditions [19]. Nevertheless, little is known about how specific lncRNAs contribute to the Th1/Th2 imbalance in DN.

The study aimed to evaluate the expression and potential clinical roles of IFNG-AS1 and TH2LCRR, two lncRNAs linked to Th1 and Th2 immune responses in DN. We hypothesize that dysregulation of these lncRNAs contributes to Th1/Th2 immune imbalance by modulating cytokine expression and promoting pro-inflammatory pathways through mechanisms such as chromatin remodeling, recruitment of transcription factors, and regulation of mRNA stability [20,21]. Using bioinformatics analysis and real-time PCR validation in clinical samples, we investigated the dysregulation of these lncRNAs with Th1/Th2 imbalance and renal dysfunction in diabetic patients. Our findings highlight immune-related lncRNAs as biomarkers and emphasize the need for further therapeutic research in DN.

2. Materials and methods

2.1. Identifying genes associated with Th1 cell line differentiation

Dataset GSE135390, containing RNA-seq expression data, was sourced from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Transcriptional profiling of peripheral blood CD4⁺ T helper cell subsets in this dataset was conducted using the Illumina GPL15456 platform [22]. The dataset contains 33 Peripheral blood mononuclear cell (PBMC) samples from healthy individuals, categorized into three groups: naïve T cells (n = 3), Th cells (n = 15), and T regulatory (Treg) cells (n = 15), with sample IDs ranging from GSM4007509 to GSM4007541. To identify differentially expressed genes (DEGs) related to Th1 cell differentiation, we compared gene expression between Th1 and naïve T cell groups using the DESeq2 package in R [23].

P-values were adjusted for multiple comparisons using the Benjamini–Hochberg False Discovery Rate (FDR) method. Genes with an adj. p-value (q-value) less than 0.01 and log2 fold change (|log2 FC|) greater than 2 were considered significantly differentially expressed.

2.2. Investigation of Th1/Th2 cytokine-targeted ncRNAs using the NcPath database

The NcPath database (http://ncpath.pianlab.cn/) is a bioinformatics tool used for analyzing non-coding RNA (ncRNA) pathways [24]. In this study, NcPath was utilized to investigate lncRNAs associated with the Th1/Th2 cell pathway. This tool aids in identifying differentially expressed lncRNAs and their target genes and predicts the potential functions of these lncRNAs through their co-expression with protein-coding genes. The interaction network of key ncRNAs that influence Th1/Th2 cytokine function was identified using NcPath.

2.3. Human subjects

A case-control association study was conducted with 90 adult participants, including 15 individuals with microalbuminuria, 15 with ESRD, 30 T2DM patients, and 30 healthy controls. Approval for this research was obtained from the Ethics Committee at Hormozgan University of Medical Sciences (IR.HUMS.REC.1403.119). After obtaining informed consent, samples were collected from individuals admitted to Shahid Mohammadi Hospital, located in Bandar Abbas, Iran. The study followed specific inclusion and exclusion criteria, as presented in Table 1. The inclusion criteria were specific to the study population, while the exclusion criteria were designed to minimize potential confounding factors and ensure the homogeneity of the sample. By following these criteria, the study aimed to provide accurate and meaningful results, contributing to the understanding of DN and its associated factors.

Table 1.

Inclusion and exclusion criteria.

Inclusion criteria for the study
Age	Between 35 and 70 years
Diagnosis	T2DM and DN patients with a 2-year history of insulin use, confirmed by a specialist doctor
Insulin medication	T2DM patients treated with injectable insulin medication
FBS	Levels of ≥126 mg/dL for diabetic patients
Microalbuminuri	Urinary albumin levels between 30 mg/d and 300 mg/d on two or more separate occasions
ESRD	Diagnosed based on clinical criteria, including irreversible loss of kidney function and the need for renal replacement therapy
Exclusion criteria for the study
Diabetes-related complications	Diabetic neuropathy, diabetic retinopathy, etc.
Physiological special condition	Pregnant women
Other types or stages of diabetes	Type 1 diabetes
	Prediabetes
Immunological special condition	Inflammatory and autoimmune diseases
Other diseases	Cancer
	Individuals using psychoactive drugs

T2DM; type 2 diabetes mellitus, DN; diabetic nephropathy, ESRD; end-stage renal disease, FBS; fasting blood sugar.

2.4. PBMCs isolation

Peripheral blood samples were diluted at a 1:1 ratio with phosphate-buffered saline (PBS) (Merck, Germany). PBMCs were then separated through Ficoll density gradient centrifugation (mBio, Germany). To remove any remaining contaminants, the separated cells were centrifuged following two washes with PBS. After carefully discarding the supernatant, purified PBMCs were obtained.

2.5. RNA extraction and cDNA synthesis

The Trizol method (Yekta Tajhiz, Iran) was utilized to extract total RNA from PBMCs. The integrity of the extracted RNA was assessed by running the samples on an agarose gel (Merck, Germany). High-quality RNA was indicated by well-defined bands without smearing. RNA quantification was performed using a Nanodrop spectrophotometer (Thermo Scientific, USA), which estimates RNA concentration based on absorbance at 260 nm. Subsequently, the extracted RNA was converted into complementary DNA (cDNA) suitable for amplification using the Sinaclon cDNA synthesis kit.

2.6. Primer design and real-time PCR

Primers for lncRNAs in Th1/Th2 cells, along with the internal control gene (GAPDH), were designed using Primer3 and Gene Runner software (Table 2). Real-time PCR was conducted on the Corbett RG 3000 device (Qia Gene, Australia) with SYBR Green Master mix (Ampliqon, Denmark). During real-time PCR, SYBR Green binds specifically to double-stranded DNA, enabling the sensitive detection of amplified fragments through fluorescence. The 2-ΔΔCt method was applied to measure the expression of target lncRNAs, using GAPDH as the internal control.

Table 2.

List of primer sequences used for amplification of target genes.

Gene names	Forward primer	Reverse primer
IFNG-AS1	TATACCTTCAGAGAAATGCCAGC	CTTTTCAAACTCCTCGTTGTGC
TH2LCRR	GTCTCCCTTCAGCATCACCAC	CCAGTAAAGCCTCTTCCTCATCC
GAPDH	GGTGTGAACCATGAGAAGTAT	AGTCCTTCCACGATACCAA

2.7. Statistical analysis

GraphPad Prism (version 9.0; GraphPad Software, La Jolla, CA) and SPSS (version 26.0, Chicago, IL, USA) were used for the statistical analyses. Assessment of data normality was conducted using the Kolmogorov–Smirnov and Shapiro–Wilk tests. The chi-square test was used for analyzing nominal data. One-way ANOVA was conducted for data with normal distribution, and the Kruskal–Wallis test was utilized for data that did not follow a normal distribution. Statistical significance was defined as p < 0.05. Additionally, the relationship between the two scale variables was determined using the Spearman correlation. Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the biomarker potential of each DN-associated gene, considering both the area under the curve (AUC) and the 95 % confidence interval (CI). The Youden Index (J = sensitivity + specificity – 1) was utilized in ROC curve analysis to define the optimal cut-off, reflecting the best compromise between sensitivity and specificity.

3. Results

3.1. Identifying lncRNAs involved in Th1 cell differentiation

To investigate the genes involved in Th1 cell differentiation, a comprehensive analysis of the GSE135390 dataset was conducted using the DESeq2 package. Applying the statistical thresholds of |log2 FC| > 2 and an adj. p-value < 0.01 identified 416 DEGs in Th1 cells. Then, a volcano plot was used to visualize Th1 gene expression data distribution (Fig. 1). To find lncRNAs involved in Th1 cell differentiation, we extracted a complete list of human lncRNAs from the NON-CODE database, and through comparison with DEGs of Th1 cells, we reached 16 important lncRNAs (Fig. 2).

Fig. 1.

Volcano plot of DEGs in the Th1 cell line from the GSE135390 dataset. Red dots represent DEGs with p-value <0.01 and |log₂FC| > 1. Gray dots indicate genes with no statistically significant change. Green dots indicate genes with |log₂FC| > 1 but non-significant p-value (p > 0.01). Blue dots represent genes with statistically significant p-value (p < 0.01) but with |log₂FC| ≤ 1.

Fig. 2.

Venn diagram of DEGs in GSE135390 and lncRNA of database NON-CODE: The overlap indicates that 16 lncRNAs are differentially expressed.

3.2. Investigating the regulatory network of lncRNAs on IFN-γ (Th1) and IL-4 (Th2) cytokines using the NcPath database

The NcPath database was used to investigate the effect of the ncRNA regulatory network on the main Th1 and Th2 cytokines. Fig. 3 illustrate that IL-4, the main Th2 cytokine interacts with 5 miRNAs and 3 lncRNAs. After reviewing the literature, TH2LCRR was identified as a novel lncRNA that may contribute to the inflammatory responses involved in kidney damage. Additionally, NcPath data revealed that IFN‐γ, one of the key cytokines of Th1 cells, interacts with 10 miRNAs and 11 lncRNAs, including IFNG-AS1, SNHG14, and MIRLET7A1HG. These findings also highlighted the regulatory function of IFNG-AS1 in Th1 cells.

Fig. 3.

Analysis of the regulatory ncRNA network for IL-4 and IFN-γ cytokines using the NcPath database.

3.3. Demographic characteristics of patients and the correlation of lncRNAs with laboratory parameters

Patient characteristics, including demographic information (age, gender, family history) and laboratory parameters (FBS, HbA1C, ESR, BUN, GFR), are summarized in Table 3. Findings indicate a significant correlation between laboratory markers and diabetes progression, highlighting the importance of ESR, BUN, and GFR in distinguishing various stages of diabetes and kidney impairment.

Table 3.

An overview of the participants' demographic characteristics.

Characteristics	Normal (n = 30)	T2DM (n = 30)	DN (n = 30)	P-value
			Microalbuminuria (n = 15)	ESRD (n = 15)
Gender (Frequency %)					0.849
Male	15 (50 %)	14 (46.7 %)	7 (46.7 %)	9 (60 %)
Female	15 (50 %)	16 (53.3 %)	8 (53.3 %)	6 (40 %)
Familial history (Frequency %)					<0.001
No	21 (70 %)	8 (26.7 %)	3 (20 %)	2 (13.3 %)
Yes	9 (30 %)	22 (73.3 %)	12 (80 %)	13 (86.7 %)
Age (Mean/SD)	60.4 (4.3)	60.3 (4.1)	61.2 (7.6)	65.9 (10.4)	0.026
FBS (Mean/SD)	90.2 (6.8)	182.6 (61.2)	205 (77.1)	115.3 (31.7)	<0.001
HbA1C (Mean/SD)	5.4 (0.3)	8.3 (1.5)	9.4 (1.4)	7.4 (1.1)	<0.001
ESR (Mean/SD)	9.9 (4.2)	17.6 (11.2)	34.1 (12.3)	52.9 (15.8)	<0.001
BUN (Mean/SD)	13.8 (3.9)	23.9 (10.8)	31.9 (10.1)	67.2 (32.1)	<0.001
GFR (Mean/SD)	111.8 (24.6)	79.4 (16.9)	45.4 (14.4)	12.3 (2.6)	<0.001

T2DM; type 2 diabetes mellitus, DN; diabetic nephropathy, ESRD; end-stage renal disease, FBS; fasting blood sugar, HbA1C; hemoglobin A1c, ESR; erythrocyte sedimentation rate, GFR; glomerular filtration rate.

The relationship between laboratory parameters and the expression levels of TH2LCRR and IFNG-AS1 is depicted in Table 4. Significant positive correlations were observed between IFNG-AS1 and HbA1c, ESR, LDL, BUN, creatinine, urea, and albumin levels and a negative correlation with GFR. In contrast, TH2LCRR was positively correlated with Hb, MCV, MCH, GFR, and TSH but negatively correlated with FBS, HbA1c, BS2hpp, ESR, BUN, albumin, and blood pressure.

Table 4.

Correlation coefficients between the expression levels of lncRNAs in Th1/Th2 cells and laboratory factors.

Variable	IFNG-AS1		TH2LCRR
	Correlation	P-value	Correlation	P-value
FBS (mg/dl)	0.065	0.301	−0.589	<0.01
HbA1c (%)	0.222	0.036	−0.620	<0.01
BS2hpp (mg/dl)	0.135	0.138	−0.642	<0.01
RBC( × 106/μl)	−0.052	0.339	0.14	0.136
Hb (g/dl)	−0.157	0.102	0.293	0.01
MCV (fl)	0.117	0.172	0.401	<0.01
MCH (Pg)	−0.06	0.313	0.311	<0.01
WBC ( × 103/μl)	0.098	0.214	−0.018	0.446
Neutrophil (%)	−0.059	0.317	−0.178	0.081
Lymphocyte (%)	0.141	0.128	0.232	0.034
Basophil (%)	−0.107	0.2	−0.05	0.348
Monocyte (%)	−0.148	0.122	0.019	0.441
Eosinophil (%)	−0.014	0.135	0.008	0.475
ESR (mm/hr)	0.414	<0.01	−0.326	<0.01
Triglyceride (mg/dl)	−0.088	0.241	−0.046	0.359
Cholesterol (mg/dl)	0.05	0.343	−0.094	0.231
HDL (mg/dl)	0.126	0.155	−0.112	0.191
LDL (mg/dl)	0.205	0.048	−0.041	0.374
ALT (IU/l)	0.121	0.164	−0.196	0.062
AST (IU/l)	0.013	0.458	−0.103	0.211
BUN (mg/dl)	0.303	<0.01	−0.282	0.013
Creatinine (mg/dl)	0.226	0.033	−0.146	0.127
Urea (mg/dl)	0.303	<0.01	−0.121	0.173
GFR (ml/min/1.73 m2)	−0.247	0.022	0.471	<0.01
Albumin (g/dl)	0.461	<0.01	−0.444	<0.01
TSH (μIU/ml)	0.210	0.048	0.319	<0.01
Vitamin D	−0.196	0.06	0.194	0.063
BMI (Kg/m2)	0.066	0.303	−0.17	0.091
Hypertension (%)	0.092	0.23	−0.300	<0.01

FBS; fasting blood sugar, HbA1c; glycated hemoglobin, BS2hpp; blood sugar 2 h postprandial, RBC; red blood cell, MCV; Mean Corpuscular Volume, MCH; Mean corpuscular hemoglobin, WBC; white blood cells, ESR; erythrocyte sedimentation rate, HDL; high-density lipoprotein, LDL; low-density lipoprotein, ALT; alanine transaminase, AST; aspartate aminotransferase, BUN; blood urea nitrogen, GFR; glomerular filtration rate, TSH; thyroid stimulating hormone, BMI; body mass index.

3.4. Expression results

The expression of IFNG-AS1 and TH2LCRR, which regulate Th1 and Th2 cells, was assessed by Real-time PCR in PBMCs obtained from individuals with ESRD, T2DM, microalbuminuria, and healthy subjects. Analysis revealed a statistically significant overexpression of IFNG-AS1 in microalbuminuria subjects versus healthy participants (p = 0.0217; log2 FC = 2.78). Additionally, ESRD cases demonstrated higher expression compared to both the healthy group (p = 0.0131; log2 FC = 2.79) and individuals with T2DM (p = 0.0383; log2 FC = 2.46). Furthermore, compared to the healthy control group, TH2LCRR was markedly downregulated in T2DM subjects (p < 0.0001, log₂FC = −5.57), in patients with microalbuminuria (p < 0.0001, log₂FC = −4.98), and in those with ESRD (p < 0.0001, log₂FC = −4.78). It suggests that these two lncRNAs could be key factors in developing DN and disseminating inflammation (Fig. 4).

Fig. 4.

Box plots showing normalized expression levels of IFNG-AS1 and TH2LCRR across four groups: healthy controls (n = 30), T2DM (n = 30), microalbuminuria (n = 15), and ESRD (n = 15). The boxes represent the interquartile range (IQR), with the horizontal line indicating the median. Whiskers represent the minimum and maximum values. Expression data were normalized using the ΔCt method. Statistical comparisons were performed using the ANOVA test followed by Tukey post-hoc test. ∗p < 0.05, ∗∗∗∗p < 0.0001.

3.5. Analysis of ROC curve

ROC curve analysis was employed to evaluate the diagnostic potential of candidate genes as biomarkers for DN. The analysis showed an AUC of 0.78 (p = 0.001) for IFNG-AS1 and an AUC of 0.98 (p < 0.0001) for TH2LCRR. Optimal cut-off values were identified as −12.30 for IFNG-AS1 (sensitivity: 70.83 %, specificity: 72.73 %) and −13.80 for TH2LCRR (sensitivity: 100 %, specificity: 95.00 %) based on the highest Youden Index (Fig. 5).

Fig. 5.

The ROC curve results demonstrate the high diagnostic value of TH2LCRR and IFNG-AS1 for distinguishing DN from healthy individuals.

4. Discussion

DN is a progressive chronic inflammatory disease that can cause ESRD if not properly treated [25]. Th1/Th2 immune cell dysregulation is a key contributor to the initiation of inflammation [26]. LncRNAs play a crucial role in disrupting Th1/Th2 immune balance by epigenetically regulating the expression of lineage-specific cytokine genes [27,28]. Our study focused on the lncRNAs IFNG-AS1 and TH2LCRR to evaluate their potential roles as immunomodulatory factors and non-invasive biomarkers in DN progression.

Recent studies have highlighted the increased Th1/Th2 ratio in DN [14,15]. Th1 and Th2 cells exert opposing effects on insulin resistance and renal injury in diabetes. Th1 cells promote inflammation and impair insulin signaling primarily through the secretion of proinflammatory cytokines such as IFN-γ [12]. In DN, sustained Th1-driven immune activation exacerbates glomerular and tubular injury and accelerates renal fibrosis [8,29]. In contrast, cytokines secreted by Th2 cells, including IL-4, IL-5, and IL-13, demonstrate anti-inflammatory activity that could reduce kidney injury [6,10]. However, as diabetic complications progress, the protective effects mediated by Th2 responses tend to decline. Therefore, an imbalance in Th1/Th2 cytokines is a critical factor in the immunopathogenesis of DN [12].

To identify upstream regulators of Th1/Th2 cytokines, we analyzed the GSE135390 dataset and integrated the results with NcPath findings. Based on a comprehensive literature review, IFNG-AS1 and TH2LCRR were identified as key epigenetic regulators of IFNG and IL-4, respectively.

IFNG impairs insulin signaling through the induction of SOCS1 and SOCS3, which promote the degradation of insulin receptor substrates, thereby contributing to insulin resistance and elevated blood glucose levels [30]. Elevated IFNG levels in T2DM patients correlate with increased counts of lymphocytes and inflammation markers [31]. In diabetic mice, IFNG expression in the kidneys is notably higher compared to the control group. This upregulation is related to the recruitment and activation of T cells [32]. Additionally, the increase in insulin resistance in individuals with ESRD is linked to higher levels of IFNG [33]. Genetic polymorphisms influence lncRNA-mediated regulatory networks, leading to altered expression of genes associated with inflammatory signaling pathways [34].

IFNG-AS1 is a Th1-specific lncRNA that enhances IFNG transcription by modulating chromatin structure and interacting with the transcription factor TBX21. Its expression is rapidly upregulated following T cell activation and positively correlates with IFNG levels. Knockdown of IFNG-AS1 significantly reduces IFNG expression, further supporting its role as a transcriptional enhancer [35]. Upregulation of IFNG-AS1 has been reported in several diseases, including brucellosis [36] and multiple sclerosis [37], where it modulates proinflammatory pathways. In inflammatory bowel disease [38] and Hashimoto's thyroiditis [35] IFNG-AS1 upregulates IFNG expression and promotes Th1-mediated inflammation. These findings highlight the potential involvement of IFNG-AS1 in regulating inflammatory responses. However, the epigenetic interactions between IFNG-AS1 and IFNG in DN remain poorly understood.

This study revealed a notable elevation in IFNG-AS1 expression in individuals with microalbuminuria and ESRD. With an AUC of 0.78, IFNG-AS1 appears to be a reliable biomarker for identifying patients with DN. Notably, IFNG-AS1 expression showed strong positive correlations with key indicators of renal injury, including BUN, creatinine, and urea, as well as with inflammatory markers such as ESR. These associations suggest that IFNG-AS1 may function as an upstream enhancer of IFNG transcription and Th1 polarization, contributing to the progression of renal dysfunction and systemic inflammation in DN.

IL-4 is integral to the regulation of insulin secretion and the preservation of pancreatic islet cell viability, playing a key role in maintaining metabolic balance [39]. IL-4 gene expression is lower in T2DM patients with chronic kidney disease (CKD) compared to both T2DM patients without CKD and healthy controls. Moreover, reduced IL-4 expression shows a negative correlation with diabetes severity markers such as HbA1c, blood pressure, and renal dysfunction indicators [40]. LncRNAs contribute to Th2 polarization of CD4⁺ T cells by affecting IL-4 gene expression through the activity of transcription factors GATA3 and STAT6 [41].

TH2LCRR, located on the reverse strand of chromosome 5, is vital for the functioning of the immune system. It enables chromatin remodeling of the IL-4, IL-5, and IL-13 genes, which are crucial for initiating the Th2 response [42]. Under in vitro conditions, the absence of TH2LCRR in mice resulted in a notable decline in Th2 cytokine production, reinforcing its essential role in immune regulation [43]. Interestingly, previous studies have demonstrated disease-specific regulation of TH2LCRR expression, with upregulation observed in conditions such as asthma [44] and neuromyelitis optica [45] and downregulation reported in systemic lupus erythematosus [46] and brucellosis [47].

To date, no study has specifically investigated alterations in TH2LCRR expression as an IL–4–associated lncRNA in patients with DN. Our findings revealed that TH2LCRR expression was significantly reduced in patients with ESRD, microalbuminuria, and T2DM compared to healthy controls. Moreover, elevated levels of FBS, HbA1c, albumin, BUN, and BS2hpp were significantly negatively correlated with TH2LCRR expression, while a positive correlation was observed with GFR. Notably, TH2LCRR demonstrated excellent diagnostic performance for detecting kidney damage in diabetic patients, with an AUC of 0.98. Given the role of TH2LCRR in the epigenetic regulation of Th2 cytokines, its downregulation may impair the Th2 response and result in a Th1/Th2 imbalance favoring a Th1-dominant immune environment. This shift exacerbates chronic inflammation and renal injury in DN.

These findings support the hypothesis that dysregulation of Th1/Th2-related lncRNAs is essential to the immune processes that contribute to DN. Restoring the Th1/Th2 balance through targeted therapeutic approaches could offer a valuable avenue for future investigations. Although the differences observed between the various DN groups were statistically significant, further studies with larger cohorts and investigations into other diabetes-related complications are necessary to confirm these findings and assess their generalizability to broader populations.

5. Conclusion

Our study reveals that IFNG-AS1 and TH2LCRR play critical roles in the Th1/Th2 imbalance during DN. Upregulation of IFNG-AS1 promotes Th1-mediated inflammation and kidney injury, while downregulation of TH2LCRR impairs Th2 responses, contributing to disease progression. Modulating the expression of these lncRNAs may offer a promising approach to restoring immune balance and protecting renal function. However, further investigations, including in vivo studies using animal models, functional assays employing inhibitory RNA techniques, and detailed analyses of epigenetic regulation, are needed to demonstrate their effectiveness as therapies and to shed light on their precise contributions to disease pathophysiology.

CRediT authorship contribution statement

Seyed Amir Hossein Hosseini: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Parisa Ajorlou: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Maryam Salehian: Writing – original draft. Aghdas Dehghani: Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Funding acquisition.

Ethics approval and consent to participate

This research was approved by the Ethics Committee of HUMS and conducted following the Declaration of Helsinki. Written informed consent was secured from all participants prior to their involvement (Ethics Code: IR.HUMS.REC.1403.119).

Consent for publication

Not applicable.

Funding

The Vice-Chancellor for Research at Hormozgan University of Medical Sciences, Bandar Abbas, Iran, provided grant number 4030002 to support this study.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Aghdas Dehghani reports financial support was provided by Hormozgan University of Medical Sciences Endocrinology and Metabolism Research Center. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

List of abbreviations

DN

Diabetic Nephropathy

T2DM

Type 2 Diabetes Mellitus

ESRD

End-Stage Renal Disease

IFN-γ:

Interferon-gamma

IL-4

Interleukin-4

Th1

T helper 1

Treg

T regulatory

LncRNA

Long non-coding RNA

DEGs

Differentially Expressed Genes

GEO

Gene Expression Omnibus

FC

Fold Change

FDR

False Discovery Rate

ncRNA

non-coding RNA

CI

Confidence Interval

CKD

Chronic Kidney Disease

GFR

Glomerular Filtration Rate

FBS

Fasting Blood Sugar

RBC

Red Blood Cell

MCV

Mean Corpuscular Volume

MCH

Mean corpuscular hemoglobin

PBMC

Peripheral Blood Mononuclear Cell

PBS

Phosphate-Buffered Saline

BMI

Body Mass Index

ROC:

Receiver Operating Characteristic

AUC

Area Under Curve

BS2hpp:

Blood Sugar 2 h postprandial

HbA1C

Hemoglobin A1c

BUN

Blood Urea Nitrogen

ESR

Erythrocyte Sedimentation Rate

WBC

White Blood Cell

HDL:

High-Density Lipoprotein

LDL:

Low-Density Lipoprotein

ALT

Alanine Transaminase

AST

Amino Aspartate Transferase

TSH

Thyroid Stimulating Hormone

cDNA

complementary DNA

IQR

Inter Quartile Range

Data availability

Upon reasonable request, the data that support the findings of this research are available from the corresponding author.