https://orcid.org/0000-0002-1095-5455
Figures
Abstract
Background
Ischemic stroke poses a notable global public health challenge, with the Kingdom of Saudi Arabia (KSA) being no exception. This multifaceted condition is affected by a combination of factors, including hypertension, diabetes, and genetic influences. The purpose of the present study was to examine the linkage of long noncoding RNAs (such as ANRIL), plasminogen activator inhibitor-1 (PAI-1), and hepatocyte nuclear factor 1 alpha (HNF1α) gene variations with stroke. Leveraging a substantial cohort comprising both stroke patients and healthy individuals from KSA, our research revealed numerous uncommon genetic variations linked to an increased predisposition to stroke. This insight enhances our comprehension of stroke’s genetic underpinnings and can be invaluable in formulating preventive measures, not only in KSA but also on a global scale.
Methods
In this study we included 100 stroke patients and 100–120 healthy controls from Saudi population. We utilized the amplification refractory mutation system-PCR to genotype the chromosome 9p21 locus, the long noncoding RNA-ANRIL (lncRNA-ANRIL), Hepatocyte Nuclear Factor 1 alpha (HNF1α-A > C [p.I27L]) gene, and the plasminogen activator inhibitor-1 (PAI-1) gene to investigate the association of these gene variations with a stroke. Additionally, WES was performed for 10 stroke patients using the Illumina NovaSeq 6000 platform.
Results
Our investigation revealed significant associations between stroke patients and healthy controls concerning polymorphic variants of lncRNA-ANRIL (A > C), ANRIL (G > A), HNF1α-A > C, and PAI-1-4G > 5G genes (p < 0.05). Notably, the ANRIL rs1333048-GA genotype exhibited a strong connection with stroke susceptibility in both codominant (OR 2.29, RR 1.54, P < 0.007) and dominant (OR 1.83, RR 1.35, P < 0.034) models, while an overdominant inheritance model demonstrated a protective correlation (OR 0.42, RR 0.64, p < 0.004). Similarly, the ANRIL rs10757278-GG genotype was significantly associated with stroke susceptibility in the codominant (OR 2.80, RR 1.66, P < 0.004) and recessive (OR 3.14, RR 1.62, P < 0.0003) models, with the G allele also displaying a significant association. The HNF1α-TT genotype strongly correlated with stroke risk in the codominant (OR 18.36, RR 9.96, P < 0.048) and recessive (OR 22.14, RR 10.18, P < 0.034) models, with the HNF1α-T allele following a similar trend. The PAI-1-4G-5G genotype was also linked to stroke predisposition (OR 2.09, RR 1.34, P < 0.034) and an increased risk in the dominant model (OR 2.27, RR 1.43, P < 0.006). Furthermore, our study identified several novel and common gene variants in stroke patients through WES, including COL4A2, PSEN2, NOTCH3, and RNF2.
Conclusion
Our findings underscore the significant role of genetic determinants in chromosome 9p21, the lncRNA-ANRIL, HNF1α-A > C (p.I27L), and PAI-1-4G > 5G genes in elevating the risk of stroke. Additionally, we report low, novel, and intermediate-genetic-risk variants in COL4A2, PSEN2, NOTCH3, and RNF2 through WES, emphasizing the need for further investigation in larger cohort studies.
Citation: Hamadi A, Mir R, Al-Amer OM, Alasseiri M, Dobie G, Elfaki I, et al. (2025) Exploring the genetic frontier: Unraveling ANRIL, PAI-1, and HNF1α in stroke progression. PLoS One 20(12): e0332252. https://doi.org/10.1371/journal.pone.0332252
Editor: Jianhong Zhou, PLOS: Public Library of Science, UNITED STATES OF AMERICA
Received: December 28, 2024; Accepted: October 30, 2025; Published: December 11, 2025
Copyright: © 2025 Hamadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: This study was supported by funding from Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia. Grant number 0020-S-1442-S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Stroke is a significant global cerebrovascular disorder, claiming around 6 million lives annually or more than 10% of all global fatalities and imposing a substantial economic burden on healthcare systems [1]. In 2019, there were a total of 7.63 million cases of strokes, with ischemic strokes (ISs) comprising 62.4% of them [2]. The World Health Organization (WHO) defines ISs as episodes of neurological dysfunction caused by localized cerebral, spinal, or retinal infarction. Unfortunately, survivors of IS frequently experience significant disability [3]. The incident stroke mean age was estimated as 74.3 years (SD: 13.6), with men showed incident stroke at a younger age in comparison to women (71.4 vs. 76.9 years) [4]. The Kingdom of Saudi Arabia (KSA) is witnessing a rise in stroke-related mortality rates, underscoring the urgent need for comprehensive prevention strategies [5]. In addition to well-known risk factors like metabolic syndrome, characterized by abdominal obesity, hypertension, type 2 diabetes, smoking, physical inactivity, psychosocial stress, depression, hypertension, and cardiac diseases [6], genetic predisposition plays a significant role in stroke causes [7–9]. In KSA and Arab countries, there is a higer the rate of consanguineous marriage in comparison to the Western European and Asian countries [10]. This higer the rate of consanguineous marriage is associated with increasing risk of genetic disorders, including congenital heart diseases, stroke, diabetes mellitus, kidney diseases, and rare blood diseases [10,11]. Recent studies have revealed the substantial impact of genetics on IS, especially in early-onset cases. Various stroke classification systems have emerged, with polygenic disorders accounting for about 38% of all IS cases. These genetic insights offer promising avenues for personalized stroke prevention and innovative treatments, emphasizing the importance of continued research and clinical efforts in stroke management.
Non-protein coding areas make up at least 98% of the human genome. Eighty percent of the 98% areas are transcribed to RNAs. These areas’ transcripts have long been thought of as transcriptional “noise” [12]. Lately, the manifestation, operation, and processes of These non-coding RNAs have garnered a lot of interest. Non-coding RNAs are separated into two categories based on length: large (>200 nt) RNAs (ribosomal RNAs and lncRNAs) and tiny (<200 nt) RNAs (microRNAs and transfer RNAs) [13]. The fact that ncRNAs are expressed differently in different tissues and at different stages of development shows that they play a big role in both healthy and sick processes. Several findings point to ncRNAs as a possible cause of stroke [14].
Noncoding RNAs (ncRNAs), including microRNA (miRNA), long noncoding RNA (lncRNA), circular RNA (circRNA), and PIWI-interacting RNA (piRNA), play crucial roles in stroke [15]. Recently, elevated expression of antisense non-coding RNA in the INK4 locus (ANRIL), with alleles on chromosome 9p21 was found to be associated with cardiovascular diseases [16], a major risk factor for stroke [17]. More presicely, ANRIL SNP rs1333048, was shown to substantially impact the risk of cardiovascular disease [18]. While its heterozygous carriers exhibited an intermediate risk level, homozygous carriers had a heightened risk of developing coronary atherosclerosis [18]. The genetic rs10757278 polymorphism on human chromosome 9p21 has been suggested to influence lipid metabolic syndrome [8], particularly affecting cholesterol and low-density lipoprotein (LDL) levels, thus impacting the risk of cardiovascular disease [19], and an increased risk of stroke [20,21]. Hu X. et al. have highlighted a robust association between the rs10757278 variant and environmental factors, including smoking, which further modulate the risk of stroke [20,21]. Recent findings indicate a higher prevalence of the rs10757278 variant among individuals of European and Asian descent exhibit an increased risk of cardiovascular disease [22], suggesting it a genetic indicator [23]. Similar to ANRIL, rs1169288 SNP of Hepatocyte Nuclear Factor 1 alpha (HNF1α) has been observed in young diabetic patients [15], and association with insulin resistance [24]. While the role of HNF1α has been observed in hypertension, dyslipidemia, and genetic variations [25], it remains unclear whether HNF1α rs1169288 SNP is involved in metabolic syndrome, cardiovascular disease [26], and stroke [26].
Mutations mostly 4G/5G guanine nucleotides insertion/deletion polymorphism at position 675 of the Plasminogen activator inhibitor-1 (PAI-1) promoter [27,28], leads in its increased levels and result in decreased plasma fibrinolytic activity, a phenomenon observed in metabolic syndrome [29], cardiovascular disease and myocardial ischemia [30,31]. In addition, the thrombotic phenotype in individuals with prothrombotic conditions is correlated with the PAI-1 4G/5G gene polymorphism, which may impact the prognosis of patients with cardiovascular disease [32]. Notably, El-Tarras et al. identified a higher prevalence and allele frequency of this polymorphism in high-altitude cardiovascular patients compared to those residing at sea level, suggesting a relationship between hypoxia, the polymorphism, and the geographic altitude of patients in Saudi Arabia [33]. Though PAI-1 genotype has been shown to influences the likelihood of future stroke [34], role of 4G/4G genotype in the risk of stroke is still debatable [35]. Here in, we investigated the complex interplay between genetic factors, lncRNAs, and various genetic polymorphisms that underscores the intricate web of influences in predisposing indidulas to stroke in the KSA.
Methodology
Study population
Following the ethical approval granted by the University of Tabuk’s Ethics Committee (UT-111-19-2020; date of approval 15/05/2020) following the principles of the Helsinki Declaration, written informed consent was obtained from all participants before sample collection. This genetically homogeneous study exclusively included Saudi Arabs and excluded non-Saudi Arabs, non-Arabs, and recently naturalized Arabs. The study comprised 220 participants, 105 individuals diagnosed with stroke, and 115 healthy controls. Samples were collected from various sources, including King Khaled Hospital, King Salman Military Hospital-Tabuk, and during routine medical checkups from 01/09/2020 till 31/03/2021.
Diagnostic markers for stroke
Physical examinations and analysis of brain scan images. Common stroke mimics include seizures, somatoform disorders (conversion), migraine headaches, and hypoglycemia. Physical exams include blood pressure readings and cardiac auscultation. Neurological examinations assess the impact of a potential stroke on the nervous system. Diagnostic tests may include hematologic testing, magnetic resonance imaging (MRI), carotid ultrasound, computed tomography (CT) scans, cerebral angiograms, and echocardiograms. Data were collected from tests conducted in the hospital and from a standardized questionnaire that included demographic information such as systemic hypertension (blood pressure readings above 140/90 mmHg or past antihypertensive medication usage, age and gender. Samples were collected from various sources, including King Khaled Hospital, King Salman Military Hospital-Tabuk, and during routine medical checkups during the period during the period from January 2021 to july 2023.
Specimen and data collection from the stroke patients
Blood samples from stroke patients, approximately 3 mL in volume, were collected using EDTA or Lavender top tubes. Healthy control samples were obtained during routine blood draws to minimize additional phlebotomy. All blood samples were promptly stored at temperatures ranging from −20 °C to −30°C. Data collection involved the use of a standardized questionnaire that encompassed demographic details such as age and gender, as well as cerebrovascular risk factors, which included systemic hypertension (defined as having two outpatient blood pressure measurements above 140/90 mmHg or prior use of antihypertensive medication), dyslipidemia (defined as total serum cholesterol > 200 mg/dl, LDL > 100 mg/dl, HDL < 50 mg/dl, triglycerides > 150 mg/dl, or medication use, e.g., statins or lipid-lowering agents), diabetes mellitus (as per previous diagnosis or current medication use), any history of atrial fibrillation from prior monitoring, and the current smoking status.
Genomic DNA extraction and genotyping
Genomic DNA was extracted from patient and healthy group samples using the Qiagen DNeasy Blood Kit (Hilden, Germany) following the manufacturer’s instructions. Subsequently, the DNA samples were dissolved in nuclease-free water and stored at 4°C. The quality and integrity of the extracted DNA were assessed using the NanoDrop™ spectrophotometer (Thermo Scientific, Waltham, Massachusetts, USA). The genotypes of ANRIL- rs1333048 A > C, ANRIL- rs10757278 G > A, HNF1α- rs1169288 A > C (I27L), and PAI-14G/5G gene variations were determined using ARMS PCR (Amplification Refractory Mutation System PCR) and Allele-specific PCR methods [36]. Specific primers for the genotyping of these variations were designed using the Primer3 software, and details of these primers can be found in Table 1.
Gel electrophoresis
The ANRIL- rs1333048 A > C genotyping revealed a 382 bp control band produced by amplifying primers FO and RO to assess DNA quality and quantity. A 185 bp band (A allele) resulted from the amplification of primers FI and RO, while a 253 bp band (C allele) was produced by the amplification of primers FO and RI (Table 1). For ANRIL- rs10757278G>A, a 443 bp control band was generated by amplifying primers FO and RO for DNA quality and quantity assessment. The A allele resulted in a 263 bp band using primers FI and RO, and the G allele produced a 234 bp band. Regarding the Hepatocyte nuclear factor-1 alpha (HNF1Α) rs1169288 A > C (I27L), a 387 bp control band was obtained through the amplification of primers FO and RO for DNA quality and quantity assessment. The C allele produced a 176 bp band using primers FI and RO, while the A allele generated a 257 bp band. For the PAI-1 gene (675, 4G/5G) (rs17998894 4G/5G), the −675 4G/5G polymorphism was detected using ARMS-PCR. A 256 bp control band was produced by amplifying primers FO and RO for DNA quality and quantity assessment. A 138 bp band was generated when the forward 4G allele-specific primer or 5G allele-specific primer combined with the reverse of a common downstream primer.
Whole exome sequencing
Library preparation was carried out for WES as per the Twist 2.0 Exome kit’s instruction manual, and sequencing was performed following the Illumina NovaSeq 6000 platform’s user guide. Quality control of sequencing reads was performed using FastQC v0.11.9, and raw reads were filtered to remove low-quality bases and sequencing adapters using TrimGalore v0.6.6. After mapping the high-quality reads to the hg38 human reference genome, variation calling (SNVs, tiny InDels) was conducted using the GATK (v4.2.4.1) best practice pipeline and the haplotype caller. Various databases and technologies were employed to annotate the identified variants, with gene-related variations described using the RefSeq database. OMIM and ClinVar databases were used to assess the disease associations of the variations. To filter out common variations and polymorphisms, population frequency data from 1000 Genomes, ExAC, GnomAD exome, GnomAD genome, and ESP were utilized. The effects of coding non-synonymous SNVs on protein structure and function were estimated using PolyPhen-2 and SIFT scores. Additionally, in-silico variant effect predictions were performed using multiple prediction algorithms. Subsequently, all variants were classified as pathogenic, potentially pathogenic, or variants of unknown significance based on ACMG standards [37].
Results
Demographic features of stroke patients
A comparative analysis was conducted between the healthy control group (mean age = 35) and the stroke patients (mean age = 58), focusing on various biochemical parameters. The results revealed statistically significant differences in most of the analyzed biochemical markers in stroke patients. Table 2 illustrates substantial variations between the two groups’ serum lipid profile and blood glucose status. Stroke patients exhibited elevated blood sugar levels (fasting), glycated hemoglobin, cholesterol, LDL, VLDL, and triglycerides compared to the healthy controls. However, no significant differences were observed in platelet count and liver enzyme levels (ALT/AST). Additionally, the study explored the significance of gene variations among different allelic forms and statistically correlated them with the gender and age of stroke patients.
Distribution of genotypes and alleles of ANRIL, HNF1α and PAI-1 genes in stroke patients
Our study uncovered significant disparities in the distribution of genotypes for several genetic variants in stroke patients compared to healthy controls. A significant difference was noted in the case of ANRIL-rs1333048 A > C genotypes (p ≤ 0.015). Among stroke patients, the genotypes included CC (40%), CA (50%), and AA (10%), while healthy controls exhibited CC (55%), CA (30%), and AA (15%) genotypes (Table 3). Furthermore, the A allele was more prevalent among stroke patients (0.35) than in healthy controls (0.30). Likewise, a significant difference was observed for lncRNA-ANRIL rs10757278 G > A genotypes (p < 0.0049). In stroke patients, the genotypes were distributed as AA (24%), GA (35%), and GG (41%), whereas in healthy controls, the distribution was AA (34.26%), GA (45%), and TT (20.83%) (Table 3). Additionally, stroke patients had a higher prevalence of the G allele compared to healthy controls, with respective frequencies of 0.58 and 0.43. Regarding HNF1α (rs1169288) G > T (I27L) genotypes, stroke patients exhibited GG (37%), GT (55%), and TT (8%), while healthy controls had GG (16.66%), GA (66.66%), and TT (0%) genotypes (p < 0.004). Stroke patients also showed a slightly higher frequency of the T allele than healthy controls, with frequencies of 0.35 and 0.33, respectively. In the case of PAI-1 (rs1799889) 4G > 5G promoter genotypes, the distribution significantly differed between stroke patients and healthy controls (p < 0.014). Among stroke patients, genotypes included 5G (24%), 5G/4G (45%), and 4G (31%), while healthy controls had 5G (41.81%), 4G/5G (39%), and 4G (19%) genotypes (Table 3). Additionally, it was observed that stroke patients had a higher frequency of the 4G allele compared to healthy controls, with respective frequencies of 0.53 and 0.38 (Table 3).
Association between ANRIL-A > C, ANRIL-G > A, HNF1α G > T and PAI-1 4G > 5G genotypes with clinical charateristcs of stroke patients
We examined the correlation between ANRIL-A > C, ANRIL-G > A, HNF1Α G > T, and PAI-1 4G > 5G genotypes and laboratory parameters such as blood sugar, cholesterol, and LDL among stroke patients. Notably, stroke patients with the CA genotype of ANRIL-A > C, AA genotype of ANRIL-G > A, GT alleles of HNF1α, and 4G genotype of PAI-1 exhibited significantly higher levels of cholesterol and blood sugar when compared to individuals with other genotypes. Additionally, higher levels of LDL were observed in stroke patients carrying the GA and AA genotypes of ANRIL-G > A in comparison to those with the GG genotype, as illustrated in S1 Fig.
Allelic genotypes of ANRIL, HNF1α and PAI-1 genes predict the risk of stroke
Our findings demonstrated a significant association between the lncRNA ANRIL rs1333048-GA genotype and susceptibility to stroke in the codominant model, with an odds ratio (OR) of 2.29 (95% CI = 1.2465 to 4.213), a relative risk (RR) of 1.54 (95% CI = 1.1090 to 2.149), and a p-value of less than 0.007 (Table 4). In the dominant inheritance model, a strong association was observed between the ANRIL-CC genotype and the combined (CA + AA) genotypes with susceptibility to stroke, yielding an OR of 1.83 (95% CI = 1.0456 to 3.214), RR of 1.35 (95% CI = 0.9811 to 1.579), and a p-value less than 0.034. Conversely, in the dominant inheritance model, a protective correlation was found between the ANRIL-CA genotype and the combined CC + AA genotypes with stroke susceptibility, resulting in an OR of 0.42 (95% CI = 0.239–0.765), RR of 0.64, and a p-value less than 0.0042 (Table 4). In allelic comparison, the ANRIL-A allele was not associated with susceptibility to stroke, as indicated by an OR of 1.25 (95% CI = 0.8260 to 1.911), RR of 1.12, and a p-value of 0.0001.
Our findings revealed a robust association between the ANRIL rs10757278 GG genotype and susceptibility to stroke in the codominant model, with an OR of 2.80 (95% CI = 1.3803 to 5.686), a RR of 1.66 (95% CI = 1.1611 to 2.388), and a p-value less than 0.004 (Table 5). In the recessive inheritance model, our study reported a significant association between the ANRIL genotypes (AA + AG) and susceptibility to stroke, with an OR of 3.14 (95% CI = 1.694 to 5.833), RR of 1.62 (95% CI = 1.1672 to 2.272), and a p-value less than 0.0003. In allelic comparison, the ANRIL-G allele exhibited a significant association with stroke susceptibility, as evidenced by an OR of 1.84 (95% CI = 1.2608 to 2.695), RR of 1.31 (1.108 to 1.570), and a p-value less than 0.0016. Our findings unveiled a significant association between the HNF1α-TT genotype and susceptibility to stroke in the codominant model, with an OR of 18.36 (95% CI = 1.0239 to 329.235), a RR of 9.96 (95% CI = 0.6267 to 139.376), and a p-value less than 0.048 (Table 5).
In the recessive inheritance model, a strong association was observed between the HNF1α (GG + GT) genotypes and the TT genotype with susceptibility to stroke, yielding an OR of 22.14 (95% CI = 1.261 to 388.66), RR of 10.18 (95% CI = 0.686 to 150.96), and a p-value less than 0.034 (Table 6). In allelic comparison, the HNF1α-T allele was notably associated with susceptibility to stroke, as indicated by an OR of 1.55 (95% CI = 1.0661 to 2.254), RR of 1.24, and a p-value of 0.021 (Table 6).
PAI-1 rs1799889 4G > 5G or PAI-1 4G > 5G promoter rs1799889 4G > 5G genotypes predicts the risk of stroke
A multivariate analysis using logistic regression, including OR or RR with 95% CI, was performed for each group to assess the relationship between PAI-1 rs1799889 4G > 5G genotypes and stroke risk (Table 7). In the codominant model, the PAI-1 -4G/5G genotype was associated with increased stroke risk, with an OR of 2.09 (95% CI = 1.0505 to 3.829), RR of 1.34 (1.0240 to 1.766), and a p-value of less than 0.034. A significant association was also found in the codominant model between the PAI-1-5G and PAI-1-4G genotypes, indicating increased stroke risk, with an OR of 2.00 (95% CI = 1.0505 to 3.829), RR of 1.62 (1.1228 to 2.358), and a p-value of less than 0.034. In the dominant inheritance model, a strong association was noted between the PAI-1-5G and PAI-1-(4G5G+4G) genotypes, reflecting enhanced stroke risk, with an OR of 2.27 (95% CI = 1.2552 to 4.127), RR of 1.43 (1.1224 to 1.841), and a p-value of less than 0.006. Similarly, a significant association was observed in the dominant inheritance model between the PAI-1-(5G + 4G/5G) and PAI-1-4G genotypes, indicating an increased stroke risk, with an OR of 1.90 (95% CI = 1.0071 to 3.600), RR of 1.39 (0.9754 to 1.994), and a p-value of less than 0.047. Furthermore, the 4G allele was protective against stroke risk, with an OR of 0.54 (95% CI = 0.3711 to 0.807), RR of 0.74 (0.6172 to 0.905), and a p-value of less than 0.0023.
Association between ANRIL-A > C, ANRIL-G > A, HNF1α G > T and PAI-1 4G > 5G genotypes with clinical charateristcs of stroke patients
We examined the correlation between ANRIL-A > C, ANRIL-G > A, HNF1Α G > T, and PAI-1 4G > 5G genotypes and laboratory parameters such as blood sugar, cholesterol, and LDL among stroke patients. Notably, stroke patients with the CA genotype of ANRIL-A > C, AA genotype of ANRIL-G > A, GT alleles of HNF1α, and 4G genotype of PAI-1 exhibited significantly higher levels of cholesterol and blood sugar when compared to individuals with other genotypes. Additionally, higher levels of LDL were observed in stroke patients carrying the GA and AA genotypes of ANRIL-G > A in comparison to those with the GG genotype, as illustrated in Fig 1.
(A) Comparing the p value of Blood sugar, Cholesterol and LDL with ANRIL rs1333048A>C, (B) Comparing the p value of Blood sugar, Cholesterol and LDL with LNCRNA(ANRIL) rs10757278 G > A. (C) Comparing the p value of Blood sugar, Cholesterol and LDL with Hepatocyte nuclear factor-1 alpha HNF1Α (rs1169288) G > T. (C) Comparing the p value of Blood sugar, Cholesterol and LDL with Plasminogen activator inhibitor-1 (PAI-1) 4G > 5G promoter rs1799889 4G > 5G genotypes.
Whole exome sequencing in stroke patients
The most prevalent COL4A2 gene variants, identified through WES in all our stroke patients, include COL4A2 rs4238272 (c.297G > A), COL4A2 rs439831 (c.3804T > A), COL4A2 rs409858 (c.3807T > C), and COL4A2 rs438758 (c.4083T > C) (Table 8). In the case of PSEN2 gene variants, the most common ones detected in all our stroke patients through WES are PSEN2 rs11405 (c.69T > C), PSEN2 rs6759 (c.129C > T), and PSEN2 rs1046240 (c.129C > T) (Table 8). For RNF213 gene variants, the predominant ones identified in all our stroke patients through whole exome sequencing are RNF213 rs8082521 (c.3544C > A), RNF213 rs8074015 (c.4139A > G), and RNF213 rs4890009 (c.4797G > A). Likewise, the most common NOTCH3 gene variants, as revealed by whole exome sequencing in all our stroke patients, include NOTCH3 rs15174241 (c.4563A > G), NOTCH3 rs15181626 (c.2742A > G), and NOTCH3 rs15192033 (c.606A > G) (Table 8).
Discussion
Stroke ranks as the second-leading global cause of death. Disturbingly, there has been a notable surge in new stroke cases (70%), existing stroke cases (85%), and related fatalities (43%) over the past decade [38]. Numerous risk factors, such as high blood pressure, diabetes, smoking, inactivity, and age and gender, significantly increase the likelihood of stroke [6]. Gender plays a significant role in stroke risk, as IS is more commonly observed in younger men and tends to become more prevalent in older women [39]. In contrast to earlier reports, which indicated that the age associated with IS risk was approximately 69.2 years in 2005, recent evidence suggests a shifting trend toward younger age groups [40]. Consistent with this evolving trend, our study presents data indicating a mean age of 58 years among stroke patients.
Polymorphic variations in genes have been the subject of extensive global studies, contributing valuable insights into various health conditions, including stroke. These variations playas crucial roles in the susceptibility and outcomes of stroke patients. Clinically, this is a significant endeavor since the identification of genetic markers that can predict the risk of stroke and coronary disease holds the potential to target individuals at risk for intensive risk modification or drug therapy. However, despite the wealth of research globally, there has been a notable dearth of such studies in KSA. Remarkably, our study addresses this gap as the first to investigate the genotype distribution in relation to gender and age among stroke patients in KSA. Our research focuses on four key genes (HNF1α, PAI1, lnc-RNA rs1333048 A > C, and ANRIL rs10757278 G > A), shedding light on polymorphic variations within this specific population. This groundbreaking work not only adds to the global understanding of stroke but also addresses the unique challenges and genetic factors within the context of Saudi Arabia, where stroke is a critical health concern.
Comprehensive evaluations are currently in progress to assess the therapeutic potential of lncRNAs in a wide range of medical conditions, including cardiovascular disorders [41]. In this connection, specific variations in the ANRIL gene within the INK4 locus have been proposed as potential contributors to stroke onset [21]. Our data analysis revealed higher cholesterol levels in patients with the lncRNA rs1333048 polymorphism. However, we observed a significant difference between the CA and AA alleles, leading us to conclude there’s no direct correlation with stroke risk. Previous research, like Na Bai’s work, emphasized a strong link between ANRIL and IS risk in Asian populations [42]. Future investigations could use Mendelian randomization analysis to clarify the roles of SNPs in ANRIL associated with IS [42]. Regarding the ANRIL SNP rs10757278, our findings showed a slight increase in cholesterol levels among patients with this polymorphism. Notably, both SNPs, rs1333048 and rs10757278, were connected to higher blood sugar and LDL levels in these patients, indicating a robust link with stroke and highlighting their potential as cardiovascular disease risk factors [17]. Xuemei Han and colleagues identified rs10757278 as a risk locus for IS, despite its protective effects in certain contexts, emphasizing the growing significance of genetic biomarker research in understanding disease susceptibility [43].
Genetic variations within the HNF-1α gene, which exert pleiotropic effects by influencing multiple causative pathways, offer compelling prospects for connections with intricate, multifactorial vascular conditions, such as stroke [44]. Zhou, YJ et al showed that HNF1α SNPs are associated with elevated blood ApoA1 levels [45]. These findings suggest that polymorphic variations in the HNF1α locus may indeed serve as risk factors for both coronary artery disease (CAD) and IS [45]. These findings are supported by our study, which shows that polymorphic variations in the HNF1α locus may possibly raise the risk of IS. Our results also show a strong association between the HNF1α SNP, rs1169288 Ile27Leu, and elevated levels of cholesterol and LDL. It is important to note that the connection between this SNP, serum lipid traits, and its impact on stroke risk had not been established previously [26]. Our findings also reveal increased blood sugar levels in patients with the same SNP. In summary, our findings align with existing studies, collectively suggesting the potential involvement of HNF-1α SNPs in elevating the risk of stroke. This research contributes to the expanding body of evidence underscoring the significance of HNF-1α SNPs in susceptibility to stroke.
Within the domain of stroke risk factors, some researchers have emphasized the protective role attributed to the 4G/4G genotype of the PAI-1 gene [34]. In a meta-analysis conducted by Mohammad Ali Jafari and colleagues, they convincingly demonstrated that the PAI-1 rs1799889 polymorphism significantly heightens the risk of IS, especially among Asian populations [46]. In contrast, our own data revealed that individuals carrying the PAI-1 4G/5G polymorphism displayed elevated levels of blood sugar, cholesterol, and LDL, implying a potentially significant role in stroke risk. Furthermore, a study conducted by Liu, et al, delved into the functions of PAI-1 gene polymorphisms within the context of atherosclerotic diseases. Their findings suggested that specific polymorphisms, including rs1799889, could serve as potential genetic biomarkers for atherosclerotic conditions [26]. These contrasting findings underscore the intricate and multifaceted genetic contributions to stroke risk, underscoring the ongoing need for extensive research in this field.
Recent technological advancements, notably the availability of WES, have opened up new opportunities for comprehensive investigations of multiple genes [47]. This approach allows for the development of gene panels that assist in interpreting exome sequencing results related to monogenic stroke. These gene panels can be invaluable in evaluating the pathogenicity of novel variants within these genes, as exemplified by the study conducted by Hartl et al, [48]. Their study highlighted the pathogenic nature of the NOTCH3 c.1672C > T (p.Arg558Cys) genotype, associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [48]. Interestingly, the same individual was found to carry a previously unreported COL4A1 c.161C > T (p.Pro54Leu) variant, which appeared to segregate with the disease within their family and was also considered pathogenic. Xiaoling Yuan et al, [49] reported on the association of NOTCH3 381C > T and 1735T > C gene variants with stroke, suggesting their role as risk factors in stroke development [49]. However, the NOTCH3 605C > T polymorphism did not exhibit the same association. In concordance with these studies, we identified three of the most common gene variants through WES in the NOTCH3 gene: NOTCH3 rs15174241 (c.4563A > G), rs15181626 (c.2742A > G), and rs15192033 (c.606A > G) (Table 8). Similarly, mutations in presenilin 1 (PSEN1) on chromosome 14, presenilin 2 (PSEN2) on chromosome 1, and Amyloid precursor protein (APP) on chromosome 21 genes have been linked to most early-onset, autosomal dominant Alzheimer’s disease [50]. Identification of pathogenic mutations in the PSEN2 gene in a Korean patient with early-onset Alzheimer’s disease emphasizes the significance of PSEN2 mutations [51]. In concordinance with these earier studies, we discovered three prevalent PSEN2 gene variants, PSEN2 rs11405 (c.69T > C), rs6759 (c.129C > T), and rs1046240 (c.129C > T) in our stroke patient cohort (Table 8). As per previous strudies highlighting the pathogenicity of the COL4A2 p.Glu1123Gly variant [52], our research also identified four of the most prevalent COL4A2 gene variants in our stroke patients: COL4A2 rs4238272 (c.297G > A), rs439831 (c.3804T > A), rs409858 (c.3807T > C), and rs438758 (c.4083T > C) (Table 8).
Teppei Kamimura and colleagues’ study shed light on the importance of the RNF213 p.R4810K variant in early-onset IS, particularly in the context of anterior circulation stenosis in Japan [53]. This variant was more prevalent in women than men and was associated with vasculogenesis rather than atherogenesis. Patients with this variant were predisposed to small intracranial arteries, potentially leading to hemodynamic compromise in the presence of intracranial atherosclerosis [54]. Similrly, we have identified several of the most common RNF213 gene variants through WES in our stroke patients: RNF213 rs8082521 (c.3544C > A), rs8074015 (c.4139A > G), and rs4890009 (c.4797G > A) (Table 8). In conclusion, the application of whole-exome sequencing has significantly expanded our capacity to explore and understand the genetic underpinnings of stroke. These advances have revealed potential pathogenic variants in genes associated with stroke and other neurological conditions. Such insights contribute to our growing understanding of the genetic factors involved in stroke susceptibility and pave the way for targeted approaches to diagnosis and treatment.
Conclusion
In conclusion, our study provides persuasive evidence that particular genetic factors found within the lncRNA on the ANRIL locus, including the HNF1A-A > C (p.I27L) variant and the PAI-14G > 5G gene, are associated with a higher risk of stroke. This study has uncovered low, novel, and intermediate-genetic-risk variants within COL4A2, PSEN2, NOTCH3, and RNF213 through WES. To deepen our comprehension and validate these findings, further investigations in larger cohort studies are essential. Additionally, conducting functional analyses to assess the causal effects of these genetic variants on specific stroke subtypes is of paramount importance. Our study lays the groundwork for ongoing exploration of the genetic factors contributing to stroke susceptibility and its various subtypes. These findings require further verification in large scale case-control and protein functional studies before being used for genetic testing for prevention and treatment of stroke. Our study lays the groundwork for ongoing exploration of the genetic factors contributing to stroke susceptibility and its various subtypes.
Limitations of this study include a relatively small sample size in one population, a cross-sectional design, and the imbalanced age distribution between cases and controls. Further protein functional studies and large-scale longitudinal case-control studies considering environmental factors (e.g., diet and physical activity) in different populations are required to validate these findings before these SNVs considered for genetic testing.
Supporting information
S1 Fig. Association between ANRIL-A > C (A), ANRIL-G > A (B), HNF1α G > T (C) and PAI-1 4G > 5G (D) genotypes with clinical charateristcs of stroke patients.
https://doi.org/10.1371/journal.pone.0332252.s001
(DOCX)
Acknowledgments
The authors express their gratitude to all stroke patients who participated in this study.
References
- 1. Li R, Zhang X, Yin W, Wang Y, Liu Y. Common genetic variants on chromosome 9p21 confers risk of ischemic stroke: a large-scale genetic association study. Cell Mol Biol (Noisy-le-grand). 2021;67(2):132–7. pmid:34817327
- 2. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021. pmid:33309175
- 3. Zhang SR, Phan TG, Sobey CG. Targeting the immune system for ischemic stroke. Trends Pharmacol Sci. 2021;42(2):96–105. pmid:33341247
- 4. Almqvist T, Berglund A, Sjöstrand C, Eriksson E, Mazya MV. Prehospital triage accuracy in patients with stroke symptoms assessed within 6 to 24 hours or with an unknown time of onset. Stroke. 2021;52(4):1441–5. pmid:33641383
- 5. Alqahtani BA, Alenazi AM, Hoover JC, Alshehri MM, Alghamdi MS, Osailan AM, et al. Incidence of stroke among Saudi population: a systematic review and meta-analysis. Neurol Sci. 2020;41(11):3099–104. pmid:32564272
- 6. Gladstone DJ, Lindsay MP, Douketis J, Smith EE, Dowlatshahi D, Wein T, et al. Canadian stroke best practice recommendations: secondary prevention of stroke update 2020. Can J Neurol Sci. 2022;49(3):315–37. pmid:34140063
- 7. Liu M, Wu B, Wang W-Z, Lee L-M, Zhang S-H, Kong L-Z. Stroke in China: epidemiology, prevention, and management strategies. Lancet Neurol. 2007;6(5):456–64. pmid:17434100
- 8. Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng Y-C, et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11(11):951–62. pmid:23041239
- 9. Bayoglu B, Cakmak HA, Yuksel H, Can G, Karadag B, Ulutin T, et al. Chromosome 9p21 rs10757278 polymorphism is associated with the risk of metabolic syndrome. Mol Cell Biochem. 2013;379(1–2):77–85. pmid:23535969
- 10. Khayat AM, Alshareef BG, Alharbi SF, AlZahrani MM, Alshangity BA, Tashkandi NF. Consanguineous marriage and its association with genetic disorders in Saudi Arabia: a review. Cureus. 2024;16(2):e53888. pmid:38465157
- 11. Dahbi N, El khair A, Cheffi K, Habibeddine L, Talbi J, Hilali A, et al. Consanguinity, complex diseases and congenital disabilities in the Souss population (Southern Morocco): a cross-sectional survey. Egypt J Med Hum Genet. 2024;25(1).
- 12. Walter NG. Are non-protein coding RNAs junk or treasure?: An attempt to explain and reconcile opposing viewpoints of whether the human genome is mostly transcribed into non-functional or functional RNAs. Bioessays. 2024;46(4):e2300201. pmid:38351661
- 13. Ma L, Bajic VB, Zhang Z. On the classification of long non-coding RNAs. RNA Biol. 2013;10(6):925–33. pmid:23696037
- 14. López-Jiménez E, Andrés-León E. The implications of ncRNAs in the development of human diseases. Noncoding RNA. 2021;7(1):17. pmid:33668203
- 15. Bao M-H, Szeto V, Yang BB, Zhu S-Z, Sun H-S, Feng Z-P. Long non-coding RNAs in ischemic stroke. Cell Death Dis. 2018;9(3):281. pmid:29449542
- 16. Holdt LM, Beutner F, Scholz M, Gielen S, Gäbel G, Bergert H, et al. ANRIL expression is associated with atherosclerosis risk at chromosome 9p21. Arterioscler Thromb Vasc Biol. 2010;30(3):620–7. pmid:20056914
- 17. Razeghian-Jahromi I, Karimi Akhormeh A, Zibaeenezhad MJ. The role of ANRIL in atherosclerosis. Dis Markers. 2022;2022:8859677. pmid:35186169
- 18. Xu B, Xu Z, Chen Y, Lu N, Shu Z, Tan X. Genetic and epigenetic associations of ANRIL with coronary artery disease and risk factors. BMC Med Genomics. 2021;14(1):240. pmid:34615528
- 19. Razavi AC, Jain V, Grandhi GR, Patel P, Karagiannis A, Patel N, et al. Does elevated high-density lipoprotein cholesterol protect against cardiovascular disease? J Clin Endocrinol Metab. 2024;109(2):321–32. pmid:37437107
- 20. Hu X, Wang D, Cui C, Wu Q. Association of single-nucleotide polymorphisms of rs2383206, rs2383207, and rs10757278 with stroke risk in the Chinese population: a meta-analysis. Front Genet. 2022;13:905619. pmid:35860475
- 21. Biros E, Cooper M, Palmer LJ, Walker PJ, Norman PE, Golledge J. Association of an allele on chromosome 9 and abdominal aortic aneurysm. Atherosclerosis. 2010;212(2):539–42. pmid:20605023
- 22. Bogari N, Dannoun A, Athar M, Elkhateeb O, Porqueddu M, Allam R, et al. Genetic association of rs10757278 on chromosome 9p21 and coronary artery disease in a Saudi population. Int J Gen Med. 2021;14:1699–707. pmid:33981157
- 23. Lemmens R, Abboud S, Robberecht W, Vanhees L, Pandolfo M, Thijs V, et al. Variant on 9p21 strongly associates with coronary heart disease, but lacks association with common stroke. Eur J Hum Genet. 2009;17(10):1287–93. pmid:19319159
- 24. Yang C, Ji L, Han X. Low C-reactive protein alleles in hepatocyte nuclear factor 1A are associated with an increased risk of cardiovascular disease. J Clin Endocrinol Metab. 2025;110(2):592–600. pmid:39210612
- 25. Wung S-F, Hickey KT, Taylor JY, Gallek MJ. Cardiovascular genomics. J Nurs Scholarsh. 2013;45(1):60–8. pmid:23368089
- 26. Zhou Y-J, Yin R-X, Hong S-C, Yang Q, Cao X-L, Chen W-X. Association of the HNF1A polymorphisms and serum lipid traits, the risk of coronary artery disease and ischemic stroke. J Gene Med. 2017;19(1–2):e2941. pmid:28035729
- 27. Morrow GB, Whyte CS, Mutch NJ. A serpin with a finger in many PAIs: PAI-1’s central function in thromboinflammation and cardiovascular disease. Front Cardiovasc Med. 2021;8:653655. pmid:33937363
- 28. Choi GH, Cho SH, An HJ, Park HS, Lee JY, Ko EJ, et al. Association between PAI-1 polymorphisms and ischemic stroke in a South Korean case-control cohort. Int J Mol Sci. 2023;24(9):8041. pmid:37175749
- 29. Zhang X, Gao B, Xu B. Association between plasminogen activator inhibitor-1 (PAI-1) 4G/5G polymorphism and risk of Alzheimer’s disease, metabolic syndrome, and female infertility: a meta-analysis. Medicine (Baltimore). 2020;99(50):e23660. pmid:33327353
- 30. de Paula Sabino A, Ribeiro DD, Domingueti CP, Dos Santos MS, Gadelha T, Dusse LMS, et al. Plasminogen activator inhibitor-1 4G/5G promoter polymorphism and PAI-1 plasma levels in young patients with ischemic stroke. Mol Biol Rep. 2011;38(8):5355–60. pmid:21373825
- 31. van Goor M-L, Gómez García E, Leebeek F, Brouwers G-J, Koudstaal P, Dippel D. The plasminogen activator inhibitor (PAI-1) 4G/5G promoter polymorphism and PAI-1 levels in ischemic stroke. A case-control study. Thromb Haemost. 2005;93(1):92–6. pmid:15630497
- 32. Kumar S, Verma AK, Sagar V, Ranjan R, Sharma R, Tomar P, et al. Genotype variations and association between PAI-1 promoter region (4G/5G and -844G/A) and susceptibility to acute myocardial infarction and chronic stable angina. Cardiol Res Pract. 2021;2021:5551031. pmid:34258054
- 33.
Saeed MSA, Awad NS, El-tarras AE. Prevalence of some genetic polymorphisms among cardiovascular patients residing at high altitude and sea level. 2015.
- 34. Wiklund P-G, Nilsson L, Ardnor SN, Eriksson P, Johansson L, Stegmayr B, et al. Plasminogen activator inhibitor-1 4G/5G polymorphism and risk of stroke: replicated findings in two nested case-control studies based on independent cohorts. Stroke. 2005;36(8):1661–5. pmid:16020771
- 35. Akhter MS, Biswas A, Abdullah SM, Behari M, Saxena R. The role of PAI-1 4G/5G promoter polymorphism and its levels in the development of ischemic stroke in young Indian population. Clin Appl Thromb Hemost. 2017;23(8):1071–6. pmid:28460568
- 36. Hussain M, Khan HN, Abbas S, Ali A, Aslam MN, Awan FR. Tetra-ARMS-PCR assay development for genotyping of AGT rs699 T/C polymorphism, its comparison with PCR-RFLP and application in a case-control association study of cardiovascular disease patients. Nucleos Nucleot Nucl Acids. 2023;42(8):603–18. pmid:36815563
- 37. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. pmid:25741868
- 38. Katan M, Luft A. Global burden of stroke. Semin Neurol. 2018;38(2):208–11.
- 39. Rexrode KM, Madsen TE, Yu AYX, Carcel C, Lichtman JH, Miller EC. The impact of sex and gender on stroke. Circ Res. 2022;130(4):512–28. pmid:35175851
- 40. Boehme AK, Esenwa C, Elkind MSV. Stroke risk factors, genetics, and prevention. Circ Res. 2017;120(3):472–95. pmid:28154098
- 41. Sayad A, Mirzajani S, Gholami L, Razzaghi P, Ghafouri-Fard S, Taheri M. Emerging role of long non-coding RNAs in the pathogenesis of periodontitis. Biomed Pharmacother. 2020;129:110362. pmid:32563981
- 42. Bai N, Liu W, Xiang T, Zhou Q, Pu J, Zhao J, et al. Genetic association of ANRIL with susceptibility to Ischemic stroke: a comprehensive meta-analysis. PLoS One. 2022;17(6):e0263459. pmid:35653368
- 43. Han X, Wang C, Tang D, Shi Y, Gao M. Association of genetic polymorphisms in chromosome 9p21 with risk of ischemic stroke. Cytokine. 2020;127:154921. pmid:31810024
- 44. Reiner AP, Gross MD, Carlson CS, Bielinski SJ, Lange LA, Fornage M, et al. Common coding variants of the HNF1A gene are associated with multiple cardiovascular risk phenotypes in community-based samples of younger and older European-American adults: the Coronary Artery Risk Development in Young Adults Study and The Cardiovascular Health Study. Circ Cardiovasc Genet. 2009;2(3):244–54. pmid:20031592
- 45. Zhou Y-J, Yin R-X, Hong S-C, Yang Q, Cao X-L, Chen W-X. Association of the HNF1A polymorphisms and serum lipid traits, the risk of coronary artery disease and ischemic stroke. J Gene Med. 2017;19(1–2):e2941. pmid:28035729
- 46. Jafari M, Jarahzadeh MH, Dastgheib SA, Seifi-Shalamzari N, Raee-Ezzabadi A, Sadeghizadeh-Yazdi J, et al. Association of PAI-1 rs1799889 polymorphism with susceptibility to ischemic stroke: a huge meta-analysis based on 44 studies. Acta Med (Hradec Kralove). 2020;63(1):31–42. pmid:32422114
- 47. Jiang T, Tan M-S, Tan L, Yu J-T. Application of next-generation sequencing technologies in neurology. Ann Transl Med. 2014;2(12):125. pmid:25568878
- 48. Härtl J, Hartberger J, Wunderlich S, Cordts I, Bafligil C, Sturm M, et al. Exome-based gene panel analysis in a cohort of acute juvenile ischemic stroke patients:relevance of NOTCH3 and GLA variants. J Neurol. 2023;270(3):1501–11. pmid:36411388
- 49. Yuan X, Dong Z. The association between the genetic variants of the NOTCH3 gene and ischemic stroke risk. Med Sci Monit. 2016;22:3910–4. pmid:27770607
- 50. Piaceri I, Nacmias B, Sorbi S. Genetics of familial and sporadic Alzheimer’s disease. Front Biosci (Elite Ed). 2013;5(1):167–77. pmid:23276979
- 51. Kim DH, Yeo SH, Park J-M, Choi JY, Lee T-H, Park SY, et al. Genetic markers for diagnosis and pathogenesis of Alzheimer’s disease. Gene. 2014;545(2):185–93. pmid:24838203
- 52. Jeanne M, Gould DB. Genotype-phenotype correlations in pathology caused by collagen type IV alpha 1 and 2 mutations. Matrix Biol. 2017;57–58:29–44. pmid:27794444
- 53. Kamimura T, Okazaki S, Morimoto T, Kobayashi H, Harada K, Tomita T, et al. Prevalence of RNF213 p.R4810K variant in early-onset stroke with intracranial arterial stenosis. Stroke. 2019;50(6):1561–3. pmid:31060437
- 54. Choi E-H, Lee H, Chung J-W, Seo W-K, Kim G-M, Ki C-S, et al. Ring finger protein 213 variant and plaque characteristics, vascular remodeling, and hemodynamics in patients with intracranial atherosclerotic stroke: a high-resolution magnetic resonance imaging and hemodynamic study. J Am Heart Assoc. 2019;8(20):e011996. pmid:31590595