The correlation of long non‐coding RNA intersectin 1‐2 with disease risk, disease severity, inflammation, and prognosis of acute ischemic stroke

Abstract Background This study aimed to evaluate the predictive value of long non‐coding RNA intersectin 1‐2 (lnc‐ITSN1‐2) for acute ischemic stroke (AIS) risk, and investigate its correlation with disease severity, inflammation, and recurrence‐free survival (RFS) in AIS patients. Methods Three hundred and twenty AIS patients were recruited, and plasma samples were collected within 24 hours after admission. lnc‐ITSN1‐2 expression form plasma was detected by reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR). The National Institute of Health Stroke Scale (NIHSS) score was assessed, and RFS was calculated. Meanwhile, 320 controls were enrolled and plasma samples were collected on the enrollment, and lnc‐ITSN1‐2 expression was detected by RT‐qPCR. Results lnc‐ITSN1‐2 expression was increased in AIS patients compared to controls (P < .001), and receiver operating characteristic curve revealed its predictive value for AIS risk (area under the curve: 0.804, 95% confidence interval, 0.763‐0.845). In AIS patients, lnc‐ITSN1‐2 expression was positively correlated with NIHSS score (r = 0.464, P < .001). For inflammation, lnc‐ITSN1‐2 expression was positively correlated with CRP (r = 0.398, P < .001), TNF‐α (r = 0.502, P < .001), IL‐1β (r = 0.313, P < .001), IL‐6 (r = 0.207, P < .001), IL‐8 (r = 0.400, P < .001), IL‐17 (r = 0.272, P < .001), and IL‐22 (r = 0.222, P < .001). In terms of predicted target microRNAs, lnc‐ITSN1‐2 expression was negatively correlated with microRNA (miR)‐107 (r = −0.467, P < .001), miR‐125a (r = −0.494, P < .001), and miR‐146a (r = −0.126, P = .025). For prognosis, high lnc‐ITSN1‐2 expression was correlated with worse RFS in AIS patients. Conclusion lnc‐ITSN1‐2 exerts a good predictive value for AIS risk; meanwhile, its increased expression is correlated with enhanced disease severity, elevated inflammation, and worse RFS in AIS patients.


| INTRODUC TI ON
Stroke, ranking as the second cause of worldwide mortality, influences over 17 million people and causes more than $300 billion in economic losses annually, which is divided into ischemic stroke (counting on over 80% of stroke incidences) and hemorrhagic stroke. [1][2][3] Acute ischemic stroke (AIS), one of the common types of ischemic stroke, is caused by a deficiency of blood and oxygen supply to the brain tissue, subsequently leading to irreversible damage to the brain, and finally disability or even premature death within hours. 4,5 In such pathological processes of AIS, inflammation plays an important role by increasing neurocyte death and subsequently exacerbates the severity of AIS. 6 Although current treatments against AIS (including intra-arterial therapy and intravenous thrombolysis) have greatly progressed, there is still a part of patients who are unable to receive recommended therapy partly due to the narrow therapeutic window, causing over 3 million cases of mortality in 2017. [7][8][9] Thus, it is necessary to search for new predictive biomarkers for early prevention and monitoring disease progression to improve prognosis in AIS patients.
Long non-coding RNAs (lncRNAs), defined as non-protein-coding RNAs with lengths exceeding 200 nucleotides, display various biological functions including chromatin modification, transcriptional regulation, post-transcriptional regulation. 10 Long noncoding RNA intersectin 1-2 (lnc-ITSN1-2) is a lncRNA located on chromatin 21 with a length of 451 bp and with NONCODE gene ID NONHSAG032726.2. 11 The function of lnc-ITSN1-2 is reported by only a few studies, which reveal that it acts as a potential biomarker in inflammation-related diseases (such as rheumatoid arthritis (RA), sepsis, and coronary artery disease (CAD)). [11][12][13] Considering the abovementioned data and the implication of inflammation in AIS, we hypothesized that lnc-ITSN1-2 can also promote the pathological progression in AIS patients, while relevant research on the role of lnc-ITSN1-2 in AIS has not been studied before. 14 Thus, we performed this study to explore the predictive value of lnc-ITSN1-2 for AIS risk and investigate its correlation with disease severity, inflammation, and recurrence-free survival (RFS) in AIS patients.

| Patients
Between January 2013 and June 2016, 320 first-episode AIS patients were consecutively enrolled in our hospital. The inclusion criteria were as follows: (a) newly diagnosed as AIS according to the criteria of World Health Organization (WHO), 15  and (f) pregnant or lactating woman. In addition, 320 non-AIS subjects who were complicated with stroke risk factors were recruited as controls. The screening criteria of controls included (a) complicated with at least three of following risk factors: hypertension, diabetes mellitus, heart disease, transient ischemic attack, obesity, hyperlipidemia, smoking, alcoholism, infections, platelet hyperaggregability, elevated blood lipid levels, and so on 15 ; (b) no history of stroke, hematological malignancies, or solid tumors; (c) no severe infections, and inflammatory or autoimmune diseases; (d) age ≥18 years; and (e) not pregnant or lactating woman. This study was approved by the ethics committee of our hospital. All participants or their guardians provided written informed consents before enrollment.

| Data collection
For all the participants, the clinical characteristics (age, gender, body mass index [BMI], current smoke, hypertension, hyperlipidemia, hyperuricemia, diabetes mellitus, and chronic kidney disease [CKD]) were recorded after the written informed consents were provided. Besides, C-reactive protein (CRP) level was collected and the National Institute of Health Stroke Scale (NIHSS) score was assessed in the AIS patients.
The NIHSS included 11 items (total score ranges from 0 to 42), and the higher score was corresponding to increased severity of stroke. 16

| Sample collection
Within 24 hours after admission, peripheral blood samples were collected from AIS patients, which were subsequently centrifuged at 1000 g for 20 minutes under 4°C. The plasma was separated and stored at −80°C for further detection. In addition, peripheral blood samples were also collected from controls on the enrollment, and the plasma was isolated using the same method described above.

| lnc-ITSN1-2 and microRNA (miRNA) relative expression detection
The relative expression of lnc-ITSN1-2 in plasma of AIS patients and controls, and the relative expressions of microRNA (miR)-107, miR-125a, and miR-146a in plasma of AIS patients were detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). GAPDH was set as the internal reference for lnc-ITSN1-2, and U6 was set as the internal reference for miRNAs. RNA was

| Follow-up
After enrollment, all AIS patients received routine treatments based on their clinical status. Regular follow-up was conducted for the AIS patients until 36 months or stroke recurrence or death, and the median follow-up duration was 36 months (range 0.0-36.0 months).
During follow-up, stroke recurrence or death was recorded, and RFS was calculated from the date of admission to the date of stroke recurrence or death. Besides, 38 (11.9%) AIS patients lost follow-up, and in the final analysis, they were censored on the date of stroke recurrence or last visit.

| Statistical analysis
Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR), while categorical variables were expressed as count (percentage). Comparison between two groups was determined by Student's t test, the chi-square test, or the Wilcoxon rank-sum test. Correlation between continuous variables was analyzed by Spearman's rank correlation test. Receiver operating characteristic (ROC) curve and the area under the curve (AUC) with 95% confidence interval (CI) were used to assess the ability of lnc-ITSN1-2 in discriminating AIS patients and controls.
RFS was displayed by the Kaplan-Meier curve, and the difference in RFS between two groups was determined by log-rank test. SPSS 24.0 statistical software (IBM) was used for statistical analysis, and GraphPad Prism 7.00 software (GraphPad Software) was used for figures plotting. P value <.05 was considered significant.

| Baseline characteristics
For demographic characteristics, the mean age in AIS patients and controls was 62.6 ± 10.8 years and 61.6 ± 9.1 years, respectively.  Table 1.

| The expression of lnc-ITSN1-2 and its predictive value for AIS risk
The median value of lnc-ITSN1-2 expression was 2.421 (1.361-4.274) in AIS patients and 1.098 (0.587-1.798) in the controls, and its expression was increased in AIS patients compared with the controls (P < .001; Figure 1A). In addition, the ROC curve revealed that lnc-ITSN1-2 presented with a good predictive value for increased AIS risk (AUC: 0.804, 95% CI: 0.763-0.845; Figure 1B).

| Correlation of lnc-ITSN1-2 expression with NIHSS score
In order to evaluate the potential of lnc-ITSN1-2 as a biomarker for monitoring disease severity in AIS patients, NIHSS score was assessed and the correlation between lnc-ITSN1-2 expression and NIHSS score was performed, which displayed that lnc-ITSN1-2 expression was positively associated with NIHSS score in AIS patients (r = 0.464, P < .001; Figure 2).

| Correlation of lnc-ITSN1-2 expression with RFS
According to the median value of lnc-ITSN1-2 expression in AIS patients, the patients were further divided into two groups: the lnc-ITSN1-2 high-expression group and the lnc-ITSN1-2 low-expression group, and the Kaplan-Meier curve was performed to investigate the correlation between lnc-ITSN1-2 expression and RFS in AIS patients, which presented that RFS was poorer in the lnc-ITSN1-2 high-expression group compared with the lnc-ITSN1-2 low-expression group (P = .007; Figure 3).

| D ISCUSS I ON
In the present study, we discovered that (a) lnc-ITSN1-2 was highly and JAK/STAT pathway. [19][20][21][22] For instance, increased lncRNA H19 expression is associated with impaired neurological function and increased TNF-α level in AIS animal models. 23 Antisense non-coding RNA in the cyclin-dependent kinase inhibitor 4 locus (ANRIL), an antisense lncRNA co-clustered with p15/CDKN2B-p16/CDKN2A-p14/ ARF, is overexpressed in cerebral infarction rat models and plays a pro-inflammatory role by activating NF-κB pathway. 24 Likewise, ln-cRNA Gm4419 could activate NF-κB pathway and contributes to cell damage in oxygen-glucose-deprived cerebral microglial cells. 25 Another in vitro and in vivo study discloses that lncRNA SNHG14 elevates the expression of pro-inflammatory factors (such as TNF-α and nitric oxide), thereby aggravating neuron damage by regulating miR-145-5p/PLA2G4A. 26 Therefore, these previous findings suggest that lncRNAs might be regulators in inflammation or biomarkers for disease progression in AIS. Items  CRP  TNF-α  IL-1β  IL-6  IL-8  IL-17  IL- F I G U R E 3 Association of lnc-ITSN1-2 expression with RFS in AIS patients. Kaplan-Meier curve was conducted to display RFS. Comparison of RFS between the lnc-ITSN1-2 high-expression group and the lnc-ITSN1-2 low-expression group was conducted by logrank test. P value <.05 was considered significant. lnc-ITSN1-2, long non-coding RNA intersectin 1-2; RFS, recurrence-free survival Similarly, few studies have been performed to investigate the role of lnc-ITSN1-2 in inflammation-related diseases. Just three previous studies reveal that the enhanced lnc-ITSN1-2 expression is associated with elevated inflammation and disease severity of RA, CAD, and sepsis. [11][12][13] For instance, a previous study reveals that lnc-ITSN1-2 is positively correlated with disease activity score in 28 joints, as well as CRP in RA patients. 11 Another study discloses that lnc-ITSN1-2 is positively associated with acute physiology and chronic health evaluation II score, as well as inflammatory factor ex- the anti-angiogenesis effect of miR-107, miR-125a, and miR-146a to increase the alteration of vascular structure, thereby increasing disease severity in AIS patients. 27,28 In order to investigate the correlation of lnc-ITSN1-2 expression with AIS patients' prognosis, we further recorded stroke recurrence and death with follow-ups of 36 months, and we discovered that to that the AIS patients who died within 24 hours were with worse disease severity, which might cause deviation in this study, also for F I G U R E 4 Association of lnc-ITSN1-2 expression with predicted target miRNAs in AIS patients. A, Correlation of lnc-ITSN1-2 expression with miR-107. B, Correlation of lnc-ITSN1-2 expression with miR-125a. C, Correlation of lnc-ITSN1-2 expression with miR-146a.

TA B L E 3 Correlation of lnc-ITSN1-2 relative expression with inflammatory markers
Correlations between lnc-ITSN1-2 expression and miRNAs expressions were determined by Spearman's rank correlation test. P value <.05 was considered significant. lnc-ITSN1-2, long non-coding RNA intersectin 1-2; miRNAs, microRNAs; miR-107, microRNA-107; miR-125a, microRNA-125a; miR-146a, microRNA-146a these patients who died within 24 hours, there might not be enough time to collect blood samples and clinical data. Hence, based on the above reasons, patients died within 24 hours were excluded in this study, which might be a bias, and therefore, a further study including them is needed. (f) As the expressions of targeted miRNAs in controls were not detected, a further study is needed.
In conclusion, lnc-ITSN1-2 displays a good predictive value for AIS risk, and it is correlated with increased disease severity and inflammation, as well as worse RFS in AIS patients, which provides a potential biotarget for early prevention and monitoring disease progression to further improve prognosis in AIS patients.