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Abstract

Objective

Carotid artery stenosis (CAS) is a significant contributor to cerebral ischemic events (CIEs). This study investigated the expression pattern and clinical significance of lncRNA NEXN-AS1 in CAS and CIEs.

Methods

132 patients with CAS and 98 controls were enrolled. RT-qPCR was employed to quantify serum levels of NEXN-AS1 and miR-92a-1-5p. The diagnostic utility of NEXN-AS1 for CAS was assessed using ROC curves. Logistic regression pinpointed potential risk factors for severe CAS. Patients were followed for 2 years, and Kaplan-Meier and Cox methods evaluated the prognostic role of NEXN-AS1 and risk factors for CIEs in CAS cases. RIP and DLR assays were conducted to confirm the association between NEXN-AS1 and miR-92a-1-5p.

Results

Serum NEXN-AS1 was less expressed in CAS patients than in controls, which could effectively distinguish between the two groups with high sensitivity and specificity. CAS patients with severe stenosis had lower serum NEXN-AS1 levels than those with moderate stenosis. Patients with low NEXN-AS1 expression were more prone to developing CIEs compared to those with high expression (log-rank P = .0051). Cox regression analysis identified NEXN-AS1 as an independent risk factor for the development of CIEs. Molecularly, the target of NEXN-AS1 is miR-92a-1-5p.

Conclusion

Patients with low NEXN-AS1 expression could serve as diagnostic indicators for CAS and may predict the occurrence of CIEs. This study may offer new insights into the management of CAS and CIEs.

Keywords

LncRNA NEXN-AS1 carotid artery stenosis cerebral ischemic event diagnostic biomarker

Introduction

Carotid artery stenosis (CAS) results from atherosclerosis, characterized by plaque accumulation on the artery wall that obstructs blood flow. CAS can decrease cerebral blood flow or cause plaque detachment at the stenotic site, leading to intracranial embolism and cerebral ischemic events (CIEs). CAS is reported to account for 20%–30% of CIEs and is linked to over 60% of cerebral infarctions, potentially causing severe disability or death.¹ However, CAS onset is insidious, with patients remaining asymptomatic until a transient ischemic attack or stroke, thus missing the optimal treatment window.² Effective CAS diagnosis and progression inhibition may reduce CIEs. Currently, digital subtraction angiography is the gold standard for CAS diagnosis, but it is invasive. Non-invasive modalities like MRI and CTA are widely used for screening, yet they are not ideal for universal applications. Therefore, identifying effective noninvasive molecular markers for CAS diagnosis and CIE prediction may offer new insights into CAS pathogenesis and aid in CIE prediction, prevention, and treatment.

Emerging evidence shows that the abundance and dysfunction of long non-coding RNA (lncRNAs) > 200 nt are linked to various diseases and hold potential as biomarkers.^3,4 For instance, LINC01088 effectively distinguished CAS patients from controls and played a role in the migration and apoptosis of human aortic endothelial cells.³ NORAD was upregulated in CAS patients and correlated with cerebrovascular events.⁴ NEXN antisense RNA 1 (NEXN-AS1, Gene ID: HSALNG0004596), also known as FLJ90637 and Clorf118, is a novel lncRNA located on human chromosome 1p31.1 with three exons. Hu et al (2019) found that NEXN-AS1 is poorly expressed in atherosclerosis plaques and contributes to their progression by regulating endothelial cell adhesion molecules and inflammatory factors.⁵ Furthermore, increased NEXN-AS1 levels slow atherosclerotic progression by preserving endothelial homeostasis and enhancing plaque stability.⁶ Atorvastatin upregulates NEXN-AS1 expression in a time- and dose-dependent manner, potentially counteracting atherosclerosis development.⁷ What's more, Zheng et al identified differentially expressed lncRNAs in ischemic stroke, noting a significant decrease in NEXN-AS1.⁸ However, the expression pattern and clinical mechanisms of NEXN-AS1 in CAS remain unclear.

Based on prior studies, we hypothesized that NEXN-AS1's aberrant expression may contribute to CAS progression and hold clinical significance. To investigate this, we enrolled CAS patients and conducted a follow-up analysis to examine NEXN-AS1's expression pattern and its predictive significance for CIEs, addressing the knowledge gap in CAS research.

Materials and Methods

Patients and Sample Collection

This study enrolled 138 asymptomatic CAS patients admitted to Xingtai People's Hospital from March 2020 to April 2022. The inclusion criteria were: 1) aged over 18 years; b) ipsilateral internal carotid artery stenosis ≥50% on carotid Doppler ultrasound, with no prior history of ischemic stroke, transient ischemic attack, or focal neurological deficits.^9,10 Furthermore, the following subjects were excluded: a) those with a history of carotid artery revascularization; b) individuals with severe cardiac, cerebral, hepatic, or renal insufficiency; c) patients with a stroke diagnosis within the last 2 months; d) those with traumatic brain injury; e) patients with vascular malformations; f) individuals with severe psychiatric disorders; and g) pregnant or lactating women. At the same time, 89 age- and sex-matched controls with asymptomatic CAS and <20% carotid stenosis on Doppler ultrasound, without acute/chronic inflammatory diseases, autoimmune disorders, or cancer, were recruited via the health screening center. Furthermore, according to the North American Asymptomatic Carotid Endarterectomy Trial (NASCET),¹¹ moderate carotid stenosis is defined as 50%-69% stenosis, while severe carotid stenosis is ≥70%. The study protocol received approval from the Xingtai People's Hospital Ethics Committee, adhered to the Declaration of Helsinki, and obtained informed consent from participants before sampling.

Blood Sampling and Baseline Clinical Data Collection

Baseline clinical data (age, gender, BMI, Medical history: dyslipidemia, hypertension, diabetes, drinking, and smoking) were collected. Subsequently, 5 mL venous blood was collected, clotted at room temperature for 20 min, centrifuged at 1359 × g for 10 min, and serum stored at −80°C for analysis. Routine blood biochemical parameters, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), Total cholesterol (TC), and Triglyceride (TG), were measured using a fully automated hematology analyzer (XN-10) and biochemical analyzer.

Follow-up Protocol and Event Outcomes

Patients underwent a 2-year follow-up protocol encompassing outpatient, inpatient, and telephone follow-ups, with the primary endpoint defined as the occurrence of cerebral ischemic events (CIEs). Documented CIEs included transient ischemic attack (TIA), stroke, and mortality. TIA was characterized as a transient neurological deficit resulting from focal ischemia of the cerebral, spinal, or retinal circulation, without evidence of permanent cerebral infarction or tissue injury, and with complete resolution of symptoms within 24 h. Mortality was defined as death attributed to CAS. During follow-up, 6 patients were uncontactable and lost to follow-up, with a loss rate of approximately 4.4%. They were excluded from the study. Finally, 132 patients were included in the research.

Reverse transcription and quantitative PCR (RT-qPCR)

Total RNA from serum was extracted using the RNAprep purification kit, and the concentration and purity of RNA were monitored under the NanoDrop 2000. 500 ng RNA was reverse-transcribed to cDNA using M-MLV Reverse Transcriptase and the PrimeScript RT reagent kit. Subsequently, primers, cDNA, SYBR Green PCR kit, and ddH₂O were added to the ABI 7500 Fast real-time PCR system in a 20 μl mixture for reverse transcription. For miRNA analysis, reverse transcription was conducted using the Mir-X miRNA First-Strand Synthesis kit, followed by RT-qPCR with TB Green Premix Ex Taq kit. GAPDH and U6 served as the internal reference (endogenous control) for normalizing NEXN-AS1 and miRNA expression, and the levels were determined via the 2^−ΔΔCt method.

Bioinformatics Analysis

The lncRNASNP2 bioinformatics database predicts target miRNAs of NEXN-AS1, while miRDB, miRDB, Targetscan, miRWalk, mirDIP, and microT_Interactions databases predict target mRNA of miR-91a-1-5p. The LncLocator database predicts the subcellular localization of NEXN-AS1. GO encompassing biological processes (BP), molecular function (MF), cellular components (CC), and KEGG pathway enrichment analysis were conducted using SRplot.

Cell Culture

Human aortic endothelial cells (HAECs) were obtained from BeNa Culture Collection, and cultured in DMEM supplemented with 10% FBS at 5% CO2 and 37°C. The study was initiated once the cells reached the logarithmic growth phase.

Subcellular Localization Analyses

Total nuclear and cytoplasmic extracts were prepared using the SurePreTM Nuclear/cytoplasmic RNA purification Kit following the manufacturer's guidelines. RNA extraction and RT-qPCR were conducted as previously outlined, utilizing GAPDH as the cytoplasmic internal control and U6 as the nuclear internal control.

Dual Luciferase Reporter (DLR) and RNA-Binding Protein Immunoprecipitation (RIP) Assay

The NEXN-AS1 sequence harboring the miR-92a-1-5p binding site was subcloned into a luciferase reporter vector to generate the NEXN-AS1 wild-type luciferase reporter plasmid (NEXN-AS-WT). The sequence with the mutated binding site was similarly inserted into the vector to produce the NEXN-AS1 mutant luciferase reporter plasmid (NEXN-AS1-MT). HACE cells were seeded into 24-well plates. Following overnight incubation, NEXN-AS1-WT or NEXN-AS1-MT was co-transfected into the cells with either the miR-92a-1-5p inhibitor or miR NC using transfection reagents Lipofectamine 3000. Luciferase activity was measured 48 h later. A Magna RIP kit was employed for the RIP assay. Magnetic beads, coated with 10 μg of antibodies against Rabbit IgG and Ago2, were used for RNA precipitation followed by RT-qPCR.

Statistical Analysis

For data analysis and visualization, SPSS and GraphPad Prism 9.0 were utilized. Independent t-test compared the measurements between two groups, while ANOVA with Tukey's post hoc test was applied for multiple group comparisons. Count data were analyzed using the chi-square test. Binary logistic regressed risk factors for stenosis degree. The ROC curve evaluated the diagnostic significance of carotid stenosis. Kaplan-Meier (K-M) curves and Cox regression identified prognostic indicators for CIEs. A two-tailed probability (P) value of <.05 was considered statistically significant.

Results

Serum NEXN-AS1 Expression is Notably Suppressed in CAS Patients

132 patients with CAS and 98 controls were enrolled. Table 1 displays the clinical baseline characteristics of the two subject groups. CAS patients had higher TG and LDL-C levels (P < .05), but otherwise, the two groups were similar in demographics (age, gender, BMI, medical histories) and biochemical markers (TC, HDL-C, FBG, and HbA1c, P > .05), indicating baseline comparability. Additionally, CAS patients exhibited notably lower serum NEXN-AS1 levels than the controls (P < .001, Figure 1A).

Figure 1.

Serum NEXN-AS1 Expression and its Diagnostic Utility in CAS Patients. A. Serum NEXN-AS1 Levels in Participants. B. ROC Curve Analysis to Determine the Diagnostic Ability of serum NEXN-AS1 in Distinguishing CAS Patients from Controls. **** P < .0001.

Table 1.

Demographic and Clinical Baseline Characteristics of the Subjects.

Indicators	Control(n = 98)	CAS(n = 132)	P value
Demographics
Age (years)	63.16 ± 6.69	64.05 ± 7.32	.349
Male (n, %)	47 (47.96)	73 (55.30)	.689
BMI (kg/m²)	21.76 ± 3.41	22.02 ± 2.64	.519
Medical history
Smoking (n, %)	41 (41.84)	51 (38.64)	.684
Drinking (n, %)	44 (44.90)	61 (46.21)	.893
Diabetes (n, %)	45 (45.92)	73 (55.30)	.183
Dyslipidemia (n, %)	47 (47.96)	78 (59.09)	.109
Hypertension (n, %)	44 (44.90)	67 (50.76)	.424
Medication
Stains (n, %)	31 (31.63)	61 (46.21)	.178
ACEIs (n, %)	21 (21.43)	52 (39.39)
Antidiabetic drugs (n, %)	28 (28.57)	36 (27.27)
Laboratory indicators
TC (mmol/L)	4.99 ± 0.78	5.19 ± 1.03	.115
TG (mmol/L)	1.33 ± 0.37	1.41 ± 0.20	.032
HDL-C (mmol/L)	1.22 ± 0.19	1.19 ± 0.21	.217
LDL-C (mmol/L)	2.55 ± 0.34	2.64 ± 0.31	.041
FBG (mmol/L)	6.52 ± 1.55	6.76 ± 1.58	.255
HbA1c (%)	6.36 ± 1.10	6.51 ± 1.17	.303
Degree of carotid artery stenosis (%)	-	73.84 ± 12.09	-

Abbreviations: BMI, body mass index; ACEIs, Angiotensin-converting enzyme inhibitors; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FBG, fasting blood glucose; HbA1c, Glycosylated Hemoglobin; CAS, carotid artery stenosis.

LncRNA NEXN-AS1 Holds Promise as a Novel Biomarker for Identifying CAS Patients

To elucidate the role of NEXN-AS1 in CAS, its association with patients’clinical characteristics was examined. The patients were stratified into high- and low-NEXN-AS1 expression groups based on the mean serum NEXN-AS1 level. As revealed in Table 2, lower NEXN-AS1 levels were associated with dyslipidemia (P = .034), elevated TG (P = .036), increased LDL-C (P = .023), and a higher degree of carotid stenosis (P < .001) in CAS patients, suggesting a potential role in disease progression. Notably, the ROC curve revealed that serum NEXN-AS1, with a sensitivity of 94.70% and a specificity of 74.49%, could effectively distinguish CAS patients from controls, indicating its diagnostic potential (AUC = 0.884, Figure 1B).

Table 2.

Relationship Between lncRNA NEXN-AS1 Expression Levels and Clinical Baseline Characteristics.

Parameters	Cases No.(n = 132)	NEXN-AS1 Expression		P value
Parameters	Cases No.(n = 132)	High (n = 63)	Low (n = 69)	P value
Age (years)				.862
< 64.05	67	31	36
≥ 64.05	65	32	33
Sex				.220
Female	59	32	31
Male	73	31	42
BMI (kg/m²)				.296
< 22.02	66	35	31
≥ 22.02	66	28	38
Smoking				.139
No	81	42	39
Yes	51	21	30
Drinking				.080
No	70	38	31
Yes	62	24	38
Diabetes				.382
No	59	31	28
Yes	73	32	41
Dyslipidemia				.034
No	54	32	22
Yes	78	31	47
Hypertension				.116
No	65	36	29
Yes	67	27	40
TC (mmol/L)				.057
< 5.19	70	39	31
≥ 5.19	62	24	38
TG (mmol/L)				.036
< 1.41	60	35	25
≥ 1.41	72	28	44
HDL-C (mmol/L)				.382
＞ 1.19	59	31	28
≤ 1.19	73	32	41
LDL-C (mmol/L)				.023
< 2.64	63	37	26
≥ 2.64	69	26	43
FBG (mmol/L)				.301
< 6.76	69	36	33
≥ 6.76	63	27	36
HbA1c (%)				.601
< 6.51	63	32	31
≥ 6.51	69	31	38
Degree of carotid artery stenosis				.000
50%–69%	57	39	18
70%–99%	75	24	51

The Degree of Carotid Artery Stenosis was Greater with the Reduction of NEXN-AS1

Pearson's correlation coefficient analysis revealed a significant negative correlation between patients’ serum NEXN-AS1 levels and the degree of carotid artery stenosis (r = −0.699, P < .001, Figure 2A). Patients were categorized into moderate and severe stenosis subgroups, with those exhibiting severe stenosis showing reduced serum NEXN-AS1 levels (P < .001, Figure 2B). Furthermore, univariate logistic regression analysis indicated that dyslipidemia, hypertension, TG, HDL-C, LDL-C, and NEXN-AS1 levels were correlated with the severity of carotid artery stenosis (Table 3). When incorporating variables with P < .1 into multivariate logistic regression, Figure 2C depicted that both dyslipidemia (OR: 2.706, 95%CI: 1.158-6.321, P = .021) and NEXN-AS1 (OR: 0.155, 95%CI: 0.063-0.381, P < .000) independently influenced severe carotid stenosis.

Figure 2.

Serum NEXN-AS1 levels influence carotid artery stenosis severity. A. Association between serum NEXN-AS1 levels and carotid arthrosis severity. B. Serum NEXN-AS1 Levels in Patients with Moderate and Severe CAS. C. Forest Plot of Multivariate Logistic Regression for Risk Factors of Carotid Artery Stenosis Severity. **** P < .0001.

Table 3.

Relationship Between a Degree of Carotid Artery Stenosis and Clinical Indexes.

Indicators	OR	95% CI	P value
Age	1.438	0.720–2.871	.303
Male	1.554	0.775–3.113	.214
BMI	1.064	0.534–2.118	.861
Smoking	1.377	0.676–2.803	.378
Drinking	1.474	0.733–2.964	.277
Diabetes	1.554	0.775–3.113	.214
Dyslipidemia	2.071	1.021–4.202	.044
Hypertension	1.848	0.920–3.710	.084
Stains	1.723	0.856–3.470	.127
TC	1.689	0.841–3.392	.140
TG	2.149	1.065–4.335	.033
HDL-C	2.001	0.993–4.032	.052
LDL-C	2.524	1.244–5.119	.010
FBG	1.046	0.525–2.084	.989
HbA1c	1.490	0.744–2.982	.260
LncRNA NEXN-AS1	0.217	0.104–0.455	.000

Prognostic Significance of LncRNA NEXN-AS1 in Predicting CIEs in Patients with CAS

Over the 2-year follow-up, 32 CAS patients experienced CIEs, yielding an incidence rate of 24.24%. As depicted in Figure 3A, CAS patients who suffered CIEs exhibited diminished levels of NEXN-AS1 (P < .05). Furthermore, univariate Cox regression analysis revealed that NEXN-AS1, along with the degree of carotid stenosis, LDL-C, TG, Dyslipidemia, and Hypertension, influenced the occurrence of CIEs in patients with CAS (Table 4). Subsequent multivariate Cox regression analysis was conducted on variables exhibiting significance (P < .1) in the multivariate Cox regression assessment. As presented in Figure 3B, both NEXN-AS1 and the degree of carotid stenosis were independent risk factors for CIE onset (P < .05). Additionally, Kaplan-Meier curves indicated that CAS patients exhibiting low NEXN-AS1 expression had a higher incidence of CIEs compared to those with high expression (log-rank P = .005, Figure 3C).

Figure 3.

Prognostic Significance of LncRNA NEXN-AS1 in predicting CIEs in patients with CAS. A. LncRNA NEXN-AS1 mRNA levels in CAS patients with CIEs and non-CIEs. B. Forest plot depicting results of multivariable Cox analysis for independent risk factors of CIEs onset. C. Employ Kaplan-Meier curves to evaluate the prognostic impact of CIEs in CAS patients. *P < .05.

Table 4.

Univariate COX Regression Analysis of Risk Factors Influencing the Development of CIEs in CAS Patients.

Indicators	HR	95% CI	P value
Age	1.552	0.766–3.144	.223
Male	1.602	0.758–3.385	.217
BMI	1.658	0.817–3.362	.161
Smoking	1.378	0.650–2.918	.403
Drinking	1.091	0.545–2.183	.806
Diabetes	1.449	0.723–2.902	.296
Dyslipidemia	2.125	1.059–4.266	.034
Hypertension	2.301	1.104–4.793	.026
Stains	1.255	0.624–2.527	.524
TC	1.873	0.886–3.958	.100
TG	2.417	1.142–5.116	.021
HDL-C	1.649	0.822–3.308	.210
LDL-C	2.224	1.085–4.558	.029
FBG	1.752	0.855–3.589	0.125
HbA1c	1.166	0.579–2.349	.667
Degree of carotid artery stenosis	2.184	1.021–4.672	.044
LncRNA NEXN-AS1	0.327	0.141–0.756	.009

miR-92a-1-5p is a Direct Target miRNA of NEXN-AS1

Cytoplasmic lncRNAs serve as miRNA sponges. To elucidate the molecular mechanism of NEXN-AS1 in CAS, its subcellular localization was assessed. Figure 4A illustrates that the LncLocator database predicts NEXN-AS1 to be primarily cytoplasmic. Consistently, in HACE cells, NEXN-AS1 was also predominantly found in the cytoplasm (Figure 4B). Additionally, the lncRNASNP2 database predicted a binding site for miR-92a-1-5p on NEXN-AS1 (Figure 4C). DLR assays demonstrated that the miR-92a-1-5p inhibitor typically suppressed the luciferase activity of NEXN-AS1-WT (P < .001, Figure 4D), whereas it had no impact on NEXN-AS1-MT luciferase activity (P > .05). Furthermore, both miR-92a-1-5p and NEXN-AS1 exhibited significant enrichment on the Ago2 antibody compared to IgG (P < .001, Figure 4E). Serum miR-92a-1-5p levels were markedly elevated in CAS patients (P < .001, Figure 4F) and demonstrated a significant negative correlation with serum NEXN-AS1 levels in these patients (r = −0.621, Figure 4G).

Figure 4.

miR-92a-1-5p is a Target of NEXN-AS1. Prediction and Analysis of NEXN-AS1 Subcellular Location in the LncLocator Database (A) and HAEC Cells (B). C. The Predicted Binding Site of NEXN-AS1 and miR-92a-1-5p. DLR (D) and RIP (E) Assays Were Utilized to Assess the Binding Between miR-92a-1-5p and NEXN-AS1. Analysis of serum miR-92a-1-5p Expression in CAS (F) and its Correction with serum NEXN-AS1 (G). Five Bioinformatics Databases Predicted Downstream Targets of miR-92a-1-5p (H), and GO (I) and KEGG (J) Analyses Were Performed to Explore Their Potential Mechanisms. **** P < .0001.

Preliminary Exploration of the NEXN-AS1/miR-92a-1-5p in ACS Pathogenesis

Further analysis of the downstream targets of miR-92a-1-5p identified 57 overlapping targets across five databases (Figure 4H). GO analysis indicated that these genes were predominantly enriched in biological processes like “regulation of vasculogenesis” and cellular components such as “Lamellipodium” and molecular functions such as “peptide binding” (Figure 4I). Additionally, KEGG pathway enrichment analysis demonstrated that these overlapping targets were principally enriched in the AMPK signaling pathway, glucagon signaling pathway, and longevity regulation pathway (Figure 4J)

Discussion

Cerebral ischemia refers to brain tissue injury resulting from inadequate blood perfusion to the brain, typically triggered by carotid or cerebral artery stenosis, occlusion, or thrombosis. When blood fails to reach the brain efficiently, brain cells may suffer damage or die from oxygen and nutrient deprivation. While carotid artery stenosis often presents asymptomatically in its early stages, acute manifestation can account for 30% of cerebral ischemic incidents.¹² Therefore, an accurate initial diagnosis and prompt evaluation of the stenosis severity are of paramount importance in patients with CAS or cerebral ischemia. In the present research, we conducted a preliminary investigation into NEXN-AS1 levels in CAS patients and evaluated their significance in clinical diagnosis, prognosis, and disease assessment. Notably, we confirmed for the first time that NEXN-AS1 is generally downregulated in the serum of CAS patients. Furthermore, NEXN-AS1 exhibited a distinctly inverse correlation with the severity of CAS, a known risk factor for stenosis progression.

Emerging evidence shows that lncRNAs not only exert an essential function as therapeutic targets in the biological process of CAS but also hold growing promise as clinical biomarkers. For instance, low LINC01088 expression serves as a potential diagnostic marker for CAS progression.³ Elevated THRIL levels are identified as an independent risk factor for poor prognosis in CAS.¹³ Furthermore, NOARD stands as an independent risk factor for cerebrovascular events in patients with CAS, demonstrating clinical utility in predicting such events.⁴ Elevated expression of lncRNA PCA3 markedly suppresses cell proliferation and invasion, thereby impeding the prognosis of CAS.¹⁴ NEXN-AS1, a newly identified lncRNA, has been sparsely studied. Nonetheless, Zhang et al detected differentially expressed lncRNAs in ischemic stroke patients and observed a notable decrease in NEXN-AS1 levels.⁸ Atherosclerosis serves as the pathological foundation for CAS development and underlies approximately 90% of CAS cases.¹⁵ Currently, three studies have confirmed the association between NEXN-AS1 and atherosclerosis. Specifically, Hu et al (2019) demonstrated that NEXN-AS1 levels are markedly decreased in atherosclerotic plaques and contribute to plaque progression by modulating endothelial cell adhesion molecules and inflammatory factor expression.⁵ Atorvastatin upregulates NEXN-AS1 expression in a time- and dose-dependent fashion, thereby actively countering atherosclerosis development.⁷ Increased NEXN-AS1 levels impede atherosclerosis prognosis through the maintenance of endothelial homeostasis and enhancement of plaque stability.⁶ In the present study, we have, for the first time shown a marked reduction in NEXN-AS1 expression in the serum of patients with CAS.

In studies exploring risk factors and potential biomarkers of CAS, substantial evidence indicates that elevated TC, TG, and LDL-C are native contributors to CAS development, potentially resulting in arterial wall thickening.¹⁶ In alignment with prior research, our findings also revealed notable elevations in these parameters among CAS patients. Notably, this study uniquely identifies a significant correlation between reduced NEXN-AS1 expression and dyslipidemia, as well as elevated TG and LDL-C levels in CAS patients. Furthermore, our study is the first to demonstrate a significant inverse correlation between NEXN-AS1 levels and the severity of carotid stenosis, identifying it as a risk factor for severe stenosis. This implies that ongoing monitoring of NEXN-AS1 levels could offer valuable insights into the progression of a patient's condition. Given that lncRNAs continue to attract widespread attention as biomarkers.^17–19 We evaluated the diagnostic significance of NEXN-AS1 in CAS patients. Our results indicated that serum NEXN-AS1 levels effectively distinguished CAS patients from controls. This finding implies that serum NEXN-AS1 detection could be beneficial in the initial clinical assessment of CAS, particularly in early stages where stenosis may not yet exhibit obvious symptoms or be accurately assessed by imaging. Thus, NEXN-AS1 may offer supplementary diagnostic information. We also made the novel discovery that patients exhibiting low NEXN-AS1 levels are at an elevated risk of developing CIEs, a finding that may offer additional diagnostic utility in guiding subsequent clinical management of these patients.

Prior research has demonstrated that cytoplasmic lncRNAs can function as miRNA molecular sponges, thereby suppressing miRNA expression. Our current study revealed that NEXN-AS1 predominantly resides in the cytoplasm, implying its potential to serve as a miRNA molecular sponge. As a group of short non-coding RNAs, miRNAs have been implicated in crucial roles in CAS. Chen et al (2020) showed that miR-92a-1-5p levels were markedly increased in asymptomatic CAS patients and correlated with cerebrovascular events.²⁰ In 2021, Gerrit et al conducted a comparative analysis to identify differentially expressed miRNAs between symptomatic and asymptomatic CAS patients. Several miRNAs, including miR-92a, miR-126, and miR-155, showed differential expression. Notably, miR-92a levels were consistently higher in symptomatic CAS patients compared to asymptomatic ones, and they exhibited the most pronounced difference among the differentially expressed miRNAs.²¹ Furthermore, miR-92a has been identified as an endogenous suppressor of angiogenesis in vascular endothelial cells, with its downregulation linked to reduced levels of the pro-angiogenic factor CD93.²² In line with Chen et al's observations,²⁰ we observed a notable decrease in serum miR-92a-1-5p levels in CAS patients. Importantly, our study is the first to demonstrate that NEXN-AS1 targets miR-92a-5p and negatively modulates its expression in CAS. What's more, CAS is a prevalent vascular disorder primarily characterized by reduced cerebral blood flow secondary to obstruction within the carotid arteries. The resultant local ischemic-hypoxic milieu triggers compensatory angiogenic mechanisms aimed at restoring adequate perfusion. In addition, prior research has highlighted angiogenesis-related factors like vascular endothelial growth factor as biomarkers for CAS progression and adverse prognosis.¹⁵ Intraplaque angiogenesis serves as a crucial predictor for carotid stenosis, plaque hemorrhage, and rupture.²³ We anticipated that NEXN-AS1/miR-92a-1-5p may participate in CAS progression through modulation of angiogenesis.

In this investigation, we noted that although the patient group exhibited comorbidities including diabetes, dyslipidaemia, and hypertension, there was no statistically significant difference in NEXN-AS1 levels between the patient and control groups. This finding implies that patients with these specific comorbidities are comparable in terms of NEXN-AS1 expression, and that diabetes, dyslipidaemia, and hypertension, in isolation, do not substantially alter NEXN-AS1 levels. However, it is crucial to recognize that CAS patients frequently present with multiple comorbidities, such as peripheral arterial disease (PAD), coronary artery disease (CAD), or diabetic angiopathy. These comorbidities may modulate NEXN-AS1 expression or metabolism through intricate pathophysiological pathways, potentially confounding our results. A key limitation of this study is the challenge of fully eliminating the impact of comorbidities. Strict exclusion of patients with comorbid conditions would inevitably reduce the sample size, potentially weakening the statistical power and compromising the clinical representativeness of our results. Given the high prevalence of comorbidities in patients with CAS, complete exclusion of such patients is impractical in a real-world research context. Consequently, despite our efforts to control for comorbidities, we cannot definitively exclude the possibility that other comorbidities may have influenced NEXN-AS1 levels to some extent. Despite this, our study provides initial insights into NEXN-AS1's role in CAS, even with comorbidities. It helps identify CAS patients and has some predictive value for CIEs. To better assess NEXN-AS1's clinical value, future studies will use a more rigorous design to minimize comorbidity interference and improve risk and prognosis assessment in CAS patients.

Taken together, NEXN-AS1 expression is reduced in CAS patients, associated with dyslipidemia, elevated LDL-C and TG levels, and the severity of carotid stenosis. It may emerge as a potential diagnostic biomarker for CAS that may predict the risk of CIEs, and potentially influence CAS progression by targeting miR-92a-1-5p.

Footnotes

ORCID iD

Zhenzeng Fan

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

References

Dong

. Long non-coding RNA cyclin-dependent kinase inhibitor 2B antisense ribonucleic acid 1 is associated with in-stent restenosis and promotes human carotid artery smooth muscle cell proliferation and migration by sponging miR-143-3p. Exp Ther Med. 2021;21(3):2334. 2021/02/20. DOI: https://doi.org/10.3892/etm.2021.9665

Hersant

Ramondou

Douillet

, et al. Comparison between conventional duplex ultrasonography and the dual-gate Doppler mode for hemodynamic measurements of the carotid arteries. Ultrasonography. 2022;41(2):373-381. 2022/01/03. DOI: https://doi.org/10.14366/usg.21175

Tang

Sun

Zhang

, et al. LINC01088 Targets miR-195-5p to promote proliferation and migration and reduce apoptosis in the inhibition of carotid artery stenosis. Clin Appl Thromb Hemost. 2025;31:10760296251319281. 2025/03/25. DOI: https://doi.org/10.1177/10760296251319281

Fan

Wang

, et al. Prospective study of lncRNA NORAD for predicting cerebrovascular events in asymptomatic patients with carotid artery stenosis. Clin Appl Thromb Hemost. 2025;31:10760296241299792. 2025/01/07. DOI: https://doi.org/10.1177/10760296241299792

Guo

, et al. Long noncoding RNA NEXN-AS1 mitigates atherosclerosis by regulating the actin-binding protein NEXN. J Clin Invest. 2019;129(3):1115-1128. 2018/12/28. DOI: https://doi.org/10.1172/JCI98230

Chen

Lin

Chen

, et al. Anti-atherosclerotic effects of LncRNA NEXN-AS1 by regulation of canonical inflammasome pathway of pyroptosis via NEXN. Folia Histochem Cytobiol. 2024;62(4):191-202. 2025/01/13. DOI: https://doi.org/10.5603/fhc.102206

Chen

, et al. Atorvastatin inhibits pyroptosis through the lncRNA NEXN-AS1/NEXN pathway in human vascular endothelial cells. Atherosclerosis. 2020;293:26-34. 2019/12/13. DOI: https://doi.org/10.1016/j.atherosclerosis.2019.11.033

Zheng

Sun

, et al. Identification of potential hub-lncRNAs in ischemic stroke based on subpathway-LNCE method. J Cell Biochem. 2019;120(8):12832-12842. 2019/03/19. DOI: https://doi.org/10.1002/jcb.28554

Palamuthusingam

Quigley

Golledge

. Implications of the finding of no significant carotid stenosis based on data from a regional Australian vascular unit. Ann Vasc Surg. 2011;25(8):1050-1056. 2011/08/13. DOI: https://doi.org/10.1016/j.avsg.2011.05.015

10.

Carreira

Duarte-Gamas

Rocha-Neves

, et al. Management of the carotid artery stenosis in asymptomatic patients. Rev Port Cir Cardiotorac Vasc. 2020;27(3):159-166. 2020/10/18.

11.

Liang

Yan

Sun

, et al. Cardiovascular risk factor profiles for peripheral artery disease and carotid atherosclerosis among Chinese older people: A population-based study. PLoS One. 2014;9(1):e85927. 2014/01/28. DOI: https://doi.org/10.1371/journal.pone.0085927

12.

Eilenberg

Stojkovic

Piechota-Polanczyk

, et al. Neutrophil gelatinase associated lipocalin (NGAL) is elevated in type 2 diabetics with carotid artery stenosis and reduced under metformin treatment. Cardiovasc Diabetol. 2017;16(1):98. 2017/08/10. DOI: https://doi.org/10.1186/s12933-017-0579-6

13.

Wang

Guo

. LncRNA THRIL functions as a marker for carotid artery stenosis and affects the biological function of human aortic endothelial cell. J Inflamm Res. 2023;16:2437-2446. 2023/06/14. DOI: https://doi.org/10.2147/JIR.S409679

14.

Liu

, et al. Upregulated lncRNA NORAD can diagnose acute cerebral ischemic stroke patients and predict poor prognosis. Folia Neuropathol. 2023;61:105-110. 2023/04/28. DOI: https://doi.org/10.5114/fn.2022.121478

15.

Wang

Xie

, et al. Investigating the pharmacological mechanisms of clopidogrel for carotid stenosis treatment based on network pharmacology and molecular docking techniques. Clin Exp Med. 2025;25(1):84. 2025/03/16. DOI: https://doi.org/10.1007/s10238-025-01602-5

16.

Yan

, et al. The predictive value of nontraditional lipid parameters for intracranial and extracranial atherosclerotic stenosis: A hospital-based observational study in China. Lipids Health Dis. 2023;22(1):16. 2023/01/29. DOI: https://doi.org/10.1186/s12944-022-01761-4

17.

Tao

. Diagnostic and prognostic significance of lncRNA SOX2-OT in patients with carotid atherosclerosis. BMC Cardiovasc Disord. 2022;22(1):211. 2022/05/11. DOI: https://doi.org/10.1186/s12872-022-02634-5

18.

Wang

Zhao

, et al. Long non-coding RNA RMST serves as a diagnostic biomarker in patients with carotid artery stenosis and predicts the occurrence of cerebral ischemic event: A retrospective study. Vascular. 2023;31(5):908-913. 2022/05/10. DOI: https://doi.org/10.1177/17085381221100095

19.

Song

Zhu

Cai

, et al. LncRNA MBNL1-AS1 functions as an alternative atherosclerosis biomarker in elderly hypertensive patients and regulates vascular smooth muscle cell function. Minerva Cardiol Angiol. 2025;73(3):258-266. https://doi.org/10.23736/S2724-5683.24.06619-5

20.

Chen

Gao

Sheng

, et al. Diagnostic value of miR-92a in asymptomatic carotid artery stenosis patients and its ability to predict cerebrovascular events. Diagn Pathol. 2020;15(1):74. 2020/06/12. DOI: https://doi.org/10.1186/s13000-020-00987-z

21.

Grosse

Derda

Stauss

, et al. Circulating microRNAs in symptomatic and asymptomatic carotid stenosis. Front Neurol. 2021;12:755827. 2021/12/14. DOI: https://doi.org/10.3389/fneur.2021.755827

22.

Piani

Tossetta

Cara-Fuentes

, et al. Diagnostic and prognostic role of CD93 in cardiovascular disease: A systematic review. Biomolecules. 2023;13(6):910. 2023/06/28. DOI: https://doi.org/10.3390/biom13060910

23.

Sun

, et al. The relationship between intracarotid plaque neovascularization and Lp (a) and Lp-PLA2 in elderly patients with carotid plaque stenosis. Dis Markers. 2022;2022:6154675. 2022/05/03. DOI: https://doi.org/10.1155/2022/6154675

LncRNA NEXN-AS1 Serves as a Diagnostic Biomarker for Carotid Artery Stenosis,Forecasting Cerebral Ischemic Events

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Materials and Methods

Patients and Sample Collection

Blood Sampling and Baseline Clinical Data Collection

Follow-up Protocol and Event Outcomes

Reverse transcription and quantitative PCR (RT-qPCR)

Bioinformatics Analysis

Cell Culture

Subcellular Localization Analyses

Dual Luciferase Reporter (DLR) and RNA-Binding Protein Immunoprecipitation (RIP) Assay

Statistical Analysis

Results

Serum NEXN-AS1 Expression is Notably Suppressed in CAS Patients

LncRNA NEXN-AS1 Holds Promise as a Novel Biomarker for Identifying CAS Patients

The Degree of Carotid Artery Stenosis was Greater with the Reduction of NEXN-AS1

Prognostic Significance of LncRNA NEXN-AS1 in Predicting CIEs in Patients with CAS

miR-92a-1-5p is a Direct Target miRNA of NEXN-AS1

Preliminary Exploration of the NEXN-AS1/miR-92a-1-5p in ACS Pathogenesis

Discussion

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

References