ABSTRACT
The present study aimed to investigate the expression of OIP5‐AS1 in alcohol use disorder and to explore its potential clinical relevance. A total of 78 patients with alcohol use disorder and 36 healthy controls were enrolled in this study. Clinical data were collected for all participants. The expression levels of OIP5‐AS1 were quantified using quantitative real‐time polymerase chain reaction (RT‐PCR). Receiver operating characteristic (ROC) analysis was subsequently performed to evaluate the diagnostic value of OIP5‐AS1. StarBase‐based bioinformatics analysis suggested that OIP5‐AS1 may function within a miRNA‐mediated regulatory network influencing SERPINA3 expression. OIP5‐AS1 expression levels, as determined by RT‐PCR, were markedly higher in patients with alcohol use disorder than in healthy controls (p < 0.001). Patients were stratified into high‐ and low‐expression groups based on the median OIP5‐AS1 level. Comparative analyses of baseline characteristics and clinical parameters showed that body mass index (BMI) was significantly lower in the high‐expression group, and this inverse association between OIP5‐AS1 expression and BMI remained statistically significant in subsequent logistic regression analyses. ROC analysis demonstrated that OIP5‐AS1 had strong diagnostic performance, yielding an area under the curve of 0.9091 (p < 0.0001), with a sensitivity of 100% and a specificity of 75% at the defined cutoff. In conclusion, OIP5‐AS1 expression was significantly increased in patients with alcohol use disorder and was inversely associated with BMI. In addition, OIP5‐AS1 demonstrated good diagnostic performance in distinguishing patients with alcohol use disorder from healthy controls. These findings suggest that OIP5‐AS1 may have potential clinical relevance in alcohol use disorder and merit further investigation.
Keywords: alcohol use disorder, long non‐coding RNA, OIP5‐AS1
Plasma OIP5‐AS1 is significantly elevated in alcohol use disorder. Higher OIP5‐AS1 expression is associated with lower body mass index and shows good diagnostic performance for distinguishing alcohol use disorder from healthy controls (AUC = 0.9091, sensitivity = 100%, specificity = 75%). These findings support OIP5‐AS1 as a candidate biomarker for alcohol use disorder.
1. Introduction
Alcohol use disorder (AUD) is a chronic and relapsing condition characterised by compulsive alcohol consumption and impaired control over alcohol use [1]. AUD poses a significant global health burden and is influenced by both genetic and environmental factors. Twin and adoption studies have consistently estimated the heritability of AUD to be approximately 50%, highlighting a substantial genetic contribution to disease susceptibility [2]. In addition to inherited genetic variation, growing evidence suggests that epigenetic mechanisms play a critical role in mediating the effects of environmental exposures on AUD‐related gene expression. Alcohol‐induced epigenetic alterations, including DNA methylation, histone acetylation and non‐coding RNA regulation, have been implicated in molecular and neurobiological changes associated with AUD and may represent potential biomarkers or therapeutic targets [3].
Long non‐coding RNAs (lncRNAs) are a class of RNA transcripts longer than 200 nucleotides that lack protein‐coding potential [4]. LncRNAs regulate gene expression at transcriptional, post‐transcriptional and epigenetic levels through interactions with DNA, transcription factors, RNAs and chromatin‐associated complexes [5]. Notably, lncRNAs exhibit higher tissue specificity than protein‐coding genes and are particularly enriched in the brain and central nervous system (CNS), suggesting important roles in neural development and function [6]. As key components of gene regulatory networks, lncRNAs have increasingly been implicated in the initiation and progression of AUD [7]. In studies of substance use disorders, lncRNAs—alongside microRNAs—have emerged as major non‐coding RNA species of interest, with accumulating evidence supporting their mechanistic involvement and therapeutic potential in addictive behaviours, including alcohol dependence [8]. Increasing evidence indicates that lncRNAs can function as competing endogenous RNAs (ceRNAs) by acting as molecular sponges for microRNAs, thereby attenuating microRNA‐mediated repression of target mRNA translation [9]. Through this mechanism, lncRNAs participate in complex post‐transcriptional regulatory networks that are sensitive to environmental stimuli. In the context of alcohol exposure, alterations in microRNA and lncRNA expression have been reported to occur via inflammatory signalling pathways such as IL‐6/STAT3, linking non‐coding RNA regulation to neuroinflammatory processes relevant to AUD pathophysiology [10]. OIP5‐AS1 (also known as Oip5os1, 1700020I14Rik or Cyrano) is a pleiotropic lncRNA with broad biological functions across both physiological and pathological contexts, including neurological disorders, cancer and inflammatory diseases. Given its regulatory potential and involvement in inflammation‐ and CNS‐related processes, OIP5‐AS1 may hold translational relevance as a diagnostic, prognostic or therapeutic target in AUD [11].
In our previous study, differentially expressed genes associated with AUD were screened using the Gene Expression Omnibus database, leading to the identification of SERPINA3 as a significantly upregulated gene [12]. Subsequent bioinformatics analyses suggested that OIP5‐AS1 (Figure 1) may function as a ceRNA by competitively binding to miR‐137‐3p, thereby attenuating the regulatory effect of miR‐137‐3p on SERPINA3. This interaction may result in increased SERPINA3 expression and promote neuroinflammatory responses. However, the expression profile and potential role of OIP5‐AS1 in AUD remain unclear. Therefore, the present study aimed to investigate the expression and functional relevance of OIP5‐AS1 in AUD.
FIGURE 1.
miRNA–lncRNA interactions were predicted using the StarBase database (http://starbase.sysu.edu.cn/), which ultimately identified OIP5‐AS1 as an upstream long non‐coding RNA.
2. Methods
2.1. Clinical Samples
Seventy‐eight patients with AUD and 36 age‐ and sex‐matched healthy controls were recruited from the Third People's Hospital of Huai'an City. AUD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. All participants were aged 18–65 years and underwent semi‐structured clinical interviews conducted by trained psychiatrists. Patients with other substance use disorders (except nicotine dependence), major psychiatric or neurological disorders or severe medical conditions were excluded. All patients were hospitalised for detoxification and had abstained from alcohol for at least 24 h prior to blood sampling. Healthy controls had no history of psychiatric or substance use disorders. Written informed consent was obtained from all participants, and the study was approved by the Ethics Committee of the Third People's Hospital of Huai'an City.
2.2. Sample Collection and Processing
Blood samples were collected from fasting participants between 7:00 and 9:00 a.m. Plasma was separated by centrifugation at 3000 × g for 15 min at room temperature. All plasma samples were processed within 30 min of blood collection and stored immediately at −80°C. Each sample underwent only a single freeze–thaw cycle prior to RNA extraction.
2.3. Quantitative Real‐Time Polymerase Chain Reaction
Total RNA was isolated from plasma samples of patients with AUD and healthy controls using an RNA extraction kit (Tiangen Biotech, China). RNA was reverse‐transcribed into complementary DNA according to the manufacturer's instructions using a reverse transcription kit (SEVEN, China). Quantitative polymerase chain reaction (PCR) amplification was performed using a standard SYBR Green PCR kit (SEVEN, China). The U6 gene was used as an internal reference. The primer sequences used in this study were as follows: OIP5‐AS1, 5′‐AGGCAGCTTGCTTCTTAATCT‐3′ (forward) and 5′‐ATTCTCAGCCCATGCTGCTT‐3′ (reverse) and U6, 5′‐GCTTCGGCAGCACATATACTAAAAT‐3′ (forward) and 5′‐CGCTTCACGAATTTGCGTGTCAT‐3′ (reverse).
3. Statistics
For continuous variables with homogeneity of variance, independent‐samples t tests were used to compare differences in laboratory indicators between groups, whereas categorical variables were compared using χ 2 tests. Logistic regression analysis was further performed to identify variables independently associated with OIP5‐AS1 levels. In the adjusted logistic regression model, body mass index (BMI) was entered as the primary independent variable, with age, sex, marital status and occupation included as covariates to control for potential confounding effects. In addition, a BMI × sex interaction term was tested in a separate model to explore whether sex moderated the association between BMI and OIP5‐AS1 expression.
The diagnostic value of OIP5‐AS1 for AUD was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. The optimal cutoff value was determined using the Youden index. Each plasma sample was analysed in triplicate, and the mean value was used for statistical analysis to ensure high reproducibility and assay reliability. All statistical tests were two‐sided, and a p value < 0.05 was considered statistically significant.
4. Results
4.1. Experimental Validation of OIP5‐AS1
Bioinformatics prediction based on the StarBase database identified OIP5‐AS1 as a candidate upstream lncRNA within a miRNA‐mediated regulatory network, which is hypothesised to influence SERPINA3 expression (Figure 1). Quantitative real‐time PCR (RT‐qPCR) analysis demonstrated that OIP5‐AS1 expression levels were significantly higher in patients with AUD than in healthy controls (p < 0.001) (Figure 2). The general demographic and clinical characteristics of patients with AUD and healthy controls are summarised in Table 1. Based on the median OIP5‐AS1 expression level, patients were stratified into a high‐expression group (≥ 1.2755, n = 39) and a low‐expression group (< 1.2755, n = 39). Comparisons of clinical characteristics between the two groups are presented in Table 2. The results showed that BMI was significantly lower in the high OIP5‐AS1 expression group, indicating an inverse association between OIP5‐AS1 expression and BMI (p = 0.005). Further binary logistic regression analysis confirmed that the difference in BMI between the two groups remained statistically significant (p = 0.009) (Table 3). To further examine whether sex moderated the association between BMI and OIP5‐AS1 expression, an additional regression model including BMI, sex and their interaction term (BMI × sex) was fitted. The interaction term was not statistically significant (β = −0.001, p = 0.513), and the model explained a limited proportion of variance (adjusted R 2 = 0.056), indicating no evidence of a sex‐specific moderation effect in the present sample.
FIGURE 2.
Comparison of plasma OIP5‐AS1 levels between patients with alcohol use disorder and the healthy control group.
TABLE 1.
Comparison of general characteristics between the alcohol use disorder (AUD) group and the healthy control group.
| Alcohol use disorder (n = 78) | Health control (n = 36) | p | |
|---|---|---|---|
| Demographics | |||
| Age, years | 49 ± 10 | 46 ± 12 | 0.144 |
| Gender | 0.890 | ||
| Male, n (%) | 72 (92.3) | 34 (94.4) | |
| Female, n (%) | 6 (7.7) | 2 (5.6) | |
| BMI, kg/m2 | 21.2 ± 4.2 | 22.4 ± 2.8 | 0.127 |
TABLE 2.
Comparison of demographic and clinical characteristics between the high and low OIP5‐AS1 expression groups.
| High OIP5‐AS1 level (n = 39) | Low OIP5‐AS1 level (n = 39) | p | |
|---|---|---|---|
| Demographics | |||
| Age, years | 46 ± 10 | 47 ± 11 | 0.148 |
| Gender | 0.089 | ||
| Male, n (%) | 34 (87.2) | 38 (97.4) | |
| Female, n (%) | 5 (12.8) | 1 (2.6) | |
| Education (years), n (%) | 0.917 | ||
| ≤ 6 | 26 (68.4) | 25 (67.6) | |
| 6–12 | 7 (18.4) | 6 (16.2) | |
| ≥ 12 | 5 (13.2) | 6 (16.2) | |
| Marital status, n (%) | 0.399 | ||
| Unmarried | 3 (7.7) | 3 (7.7) | |
| Married | 30 (76.9) | 26 (66.7) | |
| Divorced | 5 (12.8) | 10 (25.6) | |
| Widowed, n (%) | 1 (2.6) | 0 (0.0) | |
| Occupation, n (%) | 0.319 | ||
| Unemployed | 33 (84.6) | 30 (76.9) | |
| Physical labour | 4 (10.3) | 3 (7.7) | |
| Mental labour | 2 (5.1) | 6 (15.4) | |
| BMI, kg/m2 | 19.7 ± 3.00 | 22.5 ± 4.77 | 0.005* |
| Drinking duration (years) | 21 ± 10 | 23 ± 10 | 0.404 |
| Heart rate, bpm | 97 ± 14 | 95 ± 16 | 0.667 |
| Systolic blood pressure, mmHg | 138 ± 20 | 136 ± 18 | 0.709 |
| Diastolic blood pressure, mmHg | 90 + 14 | 87 ± 11 | 0.249 |
| Smoking duration (years) | 9 ± 13 | 14 ± 15 | 0.175 |
| Laboratory tests | |||
| RBC | 3.97 ± 0.629 | 4.17 ± 0.730 | 0.220 |
| Haemoglobin, g/L | 131.08 ± 18.541 | 135.13 ± 19.802 | 0.354 |
| MCV | 99.03 ± 7.135 | 98.31 ± 5.642 | 0.619 |
| WBC count, ×109/L | 6.81 ± 2.151 | 6.59 ± 2.013 | 0.646 |
| Neutrophils, ×109/L | 4.36 ± 2.019 | 4.01 ± 1.862 | 0.429 |
| Lymphocytes, ×109/L | 1.74 ± 0.672 | 1.88 ± 0.728 | 0.410 |
| Platelets, ×109/L | 205.44 ± 83.332 | 225.67 ± 103.018 | 0.343 |
| AST, U/L | 58.9 ± 60.024 | 65.79 ± 73.942 | 0.655 |
| ALT, U/L | 49.55 ± 61.194 | 46.37 ± 47.503 | 0.799 |
| GGT, U/L | 213.39 ± 349.104 | 221.78 ± 391.575 | 0.921 |
| TBIL, μmol/L | 20.89 ± 19.100 | 20.38 ± 14.301 | 0.895 |
| DBIL, μmol/L | 7.99 ± 12.506 | 7.37 ± 6.766 | 0.786 |
| IBIL, μmol/L | 12.9 ± 8.416 | 13.01 ± 8.600 | 0.954 |
|
Serum creatinine, μmol/L |
60.53 ± 16.984 | 57.37 ± 16.257 | 0.415 |
| UA, μmol/L | 352.04 ± 126.890 | 332.21 ± 145.301 | 0.513 |
| BUN, mmol/L | 4.09 ± 1.467 | 4.26 ± 1.775 | 0.645 |
| CK, U/L | 726.32 ± 2400.116 | 678.22 ± 2379.598 | 0.935 |
| CK‐MB, U/L | 22.17 ± 35.202 | 20.90 ± 32.798 | 0.879 |
Note: Continuous variables were compared using independent‐samples t test or Mann–Whitney U test, as appropriate, and categorical variables were compared using χ 2 tests.
Statistically significant difference.
TABLE 3.
Binary logistic regression models for OIP5‐AS1.
| B | SE | Sig. | Exp(B) | 95% CI | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| BMI | 0.203 | 0.078 | 0.009 | 1.225 | 1.051 | 1.429 |
| Constant | −4.201 | 1.641 | 001 | 0.015 | ||
Abbreviations: CI, confidence interval; SE, standard error.
4.2. Diagnostic Value of OIP5‐AS1
ROC analysis was used to test the diagnostic value of OIP5‐AS1, wherein it revealed that OIP5‐AS1 had an AUC of 0.9091 (p < 0.0001), sensitivity of 100% and specificity of 75% (Figure 3).
FIGURE 3.
Receiver operating characteristic (ROC) curve based on OIP5‐AS1 levels in patients with alcohol use disorder. The area under the curve (AUC) for OIP5‐AS1 was 0.9091 (p < 0.0001), with a sensitivity of 100% and a specificity of 75% at an optimal cutoff value of 0.75, as determined by the Youden index.
5. Discussion
AUD is a multifactorial condition shaped by the interplay between genetic susceptibility and environmentally driven molecular adaptations. Growing evidence highlights the pivotal role of epigenetic regulation—particularly non‐coding RNAs—in the pathophysiology of AUD [13]. In the present study, we examined the expression pattern and potential clinical relevance of the lncRNA OIP5‐AS1 in patients with AUD. Using RT‐qPCR analysis, we demonstrated that OIP5‐AS1 expression was significantly elevated in individuals with AUD compared with healthy controls, providing direct clinical evidence of its dysregulation in AUD and extending previous bioinformatics‐based predictions. Given that lncRNAs are highly enriched in the CNS and exhibit pronounced tissue and cell‐type specificity [14], altered OIP5‐AS1 expression may reflect disease‐associated molecular changes linked to chronic alcohol exposure. Notably, OIP5‐AS1—also known as Cyrano according to NCBI Gene and Ensembl annotations—is an evolutionarily conserved lncRNA with high expression in the CNS [15], further supporting its potential relevance to neurobiological processes underlying AUD.
In neuroscience research, lncRNAs have been shown to play critical roles in neuronal differentiation, maintenance of synaptic function and regulation of neural circuit homeostasis, and their dysregulated expression has been consistently observed across a broad spectrum of neurological diseases and psychiatric disorder models [16]. These findings underscore the importance of lncRNAs in preserving molecular homeostasis within the nervous system. In this context, OIP5‐AS1, as a lncRNA with well‐defined expression characteristics and regulatory potential in neural tissues, may participate in adaptive cellular responses to both intrinsic and extrinsic environmental challenges. Importantly, chronic alcohol exposure is known to induce widespread and long‐lasting transcriptomic and epigenetic remodelling [17], with pronounced effects on neural plasticity [18], stress‐response systems [19] and metabolism‐related pathways [20, 21]. Against this background, the observed upregulation of OIP5‐AS1 is more likely to reflect a disease‐associated reorganisation of molecular networks under sustained alcohol insult. Accordingly, alterations in OIP5‐AS1 expression not only carry potential pathological significance but also provide a biologically plausible rationale for considering OIP5‐AS1 as a candidate molecular biomarker and/or regulatory node in the pathophysiology of AUD.
Emerging evidence indicates that lncRNAs play critical roles in the regulation of systemic metabolism and energy homeostasis through both transcriptional and post‐transcriptional mechanisms [22, 23, 24]. In line with this notion, OIP5‐AS1 has been implicated in multiple biological processes closely linked to metabolic regulation, including mitochondrial function, cellular stress responses and inflammatory signalling pathways [25, 26]. Notably, these processes are profoundly disrupted in the context of AUD, as chronic ethanol exposure is known to induce substantial metabolic derangements, such as altered lipid utilisation [27], insulin resistance [20] and progressive weight loss [28].
In the present study, we observed a significant inverse association between OIP5‐AS1 expression and BMI, with lower BMI values detected in patients exhibiting higher OIP5‐AS1 levels. Importantly, this relationship remained statistically significant after adjustment for potential confounders in logistic regression analyses. Although the precise molecular mechanisms underlying this relationship remain to be elucidated, these findings raise the possibility that OIP5‐AS1 dysregulation may be linked to metabolic or nutritional alterations that are commonly observed in individuals with AUD.
Previous studies have demonstrated that chronic alcohol consumption disrupts metabolic homeostasis and inflammatory signalling—two tightly interconnected processes in which non‐coding RNAs are increasingly recognised as key regulatory elements [29]. Clinically, AUD is frequently accompanied by nutritional insufficiency, altered appetite regulation, impaired nutrient absorption and systemic catabolic states, all of which may contribute to reduced BMI.
It should also be acknowledged that BMI represents a relatively coarse surrogate for nutritional and metabolic status. Consequently, future studies incorporating more granular phenotypic measures—such as body composition analysis, dietary intake assessment, liver function indices, inflammatory biomarkers and metabolic hormones—will be necessary to determine whether OIP5‐AS1 is more closely associated with malnutrition, hepatic dysfunction, systemic inflammation or other metabolic domains relevant to AUD. Such integrative approaches may help clarify whether OIP5‐AS1 functions as an active regulator within alcohol‐related metabolic pathology or reflects a downstream molecular signature of systemic metabolic stress.
In addition, ROC curve analysis demonstrated that OIP5‐AS1 exhibited good diagnostic performance in distinguishing patients with AUD from healthy controls, with a high AUC, sensitivity and specificity. These results indicate that OIP5‐AS1 may have potential value as a biomarker for AUD. Importantly, this diagnostic performance was achieved using peripheral plasma samples, suggesting that OIP5‐AS1 could serve as a minimally invasive indicator of disease status.
Previous studies have suggested that OIP5‐AS1 may function as a ceRNA involved in inflammatory and neurobiological pathways. In our earlier bioinformatics analyses, OIP5‐AS1 was predicted to interact with miR‐137‐3p and modulate the expression of SERPINA3, a gene implicated in inflammatory responses. Although the present study did not directly investigate this regulatory axis, the observed upregulation of OIP5‐AS1 in AUD patients is consistent with a potential role in AUD‐related neuroinflammatory processes and warrants further mechanistic investigation.
Several limitations of this study should be acknowledged. First, the sample size was relatively modest and all participants were recruited from a single centre, which may limit the generalizability of the findings. Second, although a BMI × sex interaction was tested, no significant sex‐moderating effect was observed; sex‐stratified analyses were not performed due to the small number of female participants, and future studies with larger, sex‐balanced samples are warranted. Third, the cross‐sectional design and the lack of systematically collected AUD severity measures constrained causal inference and precluded more refined analyses of the relationship between OIP5‐AS1 expression and clinical features of AUD. Finally, functional validation experiments were not performed, which limits mechanistic interpretation of the observed associations. Accordingly, future studies may incorporate standardised assessments of AUD severity and functional approaches, such as OIP5‐AS1 knockdown or overexpression in relevant cellular models, together with integrative analyses of lncRNA–miRNA–mRNA networks and inflammation‐ or metabolism‐related pathways to further clarify downstream targets and the biological relevance of OIP5‐AS1 in AUD.
In summary, the present findings indicate that OIP5‐AS1 is significantly elevated in AUD, is negatively associated with BMI and exhibits promising diagnostic performance. Collectively, these results support OIP5‐AS1 as a candidate biomarker for AUD and highlight a potentially meaningful link between AUD‐related molecular changes and metabolic status. Further validation and mechanistic studies are warranted to define its clinical utility and biological relevance.
6. Conclusion
In conclusion, this study demonstrates that OIP5‐AS1 expression is significantly increased in patients with AUD and is inversely associated with BMI. Moreover, OIP5‐AS1 shows good diagnostic performance in distinguishing patients with AUD from healthy controls. These findings suggest that OIP5‐AS1 may have potential clinical relevance in AUD and provide a basis for further investigation into its biological function and potential utility as a biomarker.
Author Contributions
Meng ya Zhu and Jian Wang collected the data and conducted the experiments. Jia Jia Song and Yue Wang participated in the statistical analysis and reference collection. Bo Zhang drafted the manuscript and performed revisions.
Funding
The authors have nothing to report.
Ethics Statement
All participants provided written informed consent. The study was authorised and approved by the Third People's Hospital Ethics Committee of Huai'an and carried out in compliance with the Declaration of Helsinki (IRB Number: 2021‐07).
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
References
- 1. Koob G. F. and Volkow N. D., “Neurobiology of Addiction: A Neurocircuitry Analysis,” Lancet Psychiatry 3, no. 8 (2016): 760–773, 10.1016/S2215-0366(16)00104-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Verhulst B., Neale M. C., and Kendler K. S., “The Heritability of Alcohol Use Disorders: A Meta‐Analysis of Twin and Adoption Studies,” Psychological Medicine 45, no. 5 (2015): 1061–1072, 10.1017/S0033291714002165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Wu L., Zhang Y., and Ren J., “Epigenetic Modification in Alcohol Use Disorder and Alcoholic Cardiomyopathy: From Pathophysiology to Therapeutic Opportunities,” Metabolism 125 (2021): 154909, 10.1016/j.metabol.2021.154909. [DOI] [PubMed] [Google Scholar]
- 4. Dahariya S., Paddibhatla I., Kumar S., Raghuwanshi S., Pallepati A., and Gutti R. K., “Long Non‐Coding RNA: Classification, Biogenesis and Functions in Blood Cells,” Molecular Immunology 112 (2019): 82–92, 10.1016/j.molimm.2019.04.011. [DOI] [PubMed] [Google Scholar]
- 5. Fernandes J. C. R., Acuña S. M., Aoki J. I., Floeter‐Winter L. M., and Muxel S. M., “Long Non‐Coding RNAs in the Regulation of Gene Expression: Physiology and Disease,” Noncoding RNA 5, no. 1 (2019): 17, 10.3390/ncrna5010017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Derrien T., Johnson R., Bussotti G., et al., “The GENCODE v7 Catalog of Human Long Noncoding RNAs: Analysis of Their Gene Structure, Evolution, and Expression,” Genome Research 22, no. 9 (2012): 1775–1789, 10.1101/gr.132159.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Denham A. N., Drake J., Gavrilov M., Taylor Z. N., Bacanu S. A., and Vladimirov V. I., “Long Non‐Coding RNAs: The New Frontier Into Understanding the Etiology of Alcohol Use Disorder,” Noncoding RNA 8, no. 4 (2022): 59, 10.3390/ncrna8040059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Seyednejad S. A. and Sartor G. C., “Noncoding RNA Therapeutics for Substance Use Disorder,” Advances in Drug and Alcohol Research 2 (2022): 10807, 10.3389/adar.2022.10807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Yu Y., Gao F., He Q., Li G., and Ding G., “lncRNA UCA1 Functions as a ceRNA to Promote Prostate Cancer Progression via Sponging miR143,” Molecular Therapy ‐ Nucleic Acids 19 (2020): 751–758. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Soares do Amaral N., Cruz E Melo N., de Melo Maia B., and Malagoli Rocha R., “Noncoding RNA Profiles in Tobacco‐ and Alcohol‐Associated Diseases,” Genes 8, no. 1 (2016): 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Wooten S. and Smith K. N., “Long Non‐Coding RNA OIP5‐AS1 (Cyrano): A Context‐Specific Regulator of Normal and Disease Processes,” Clinical and Translational Medicine 12, no. 1 (2022): e706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Zhang B., Wang G., C. Huang, B , J. Zhu, N , Xue Y., and Hu J., “Exploration of the Role of Serine Proteinase Inhibitor A3 in Alcohol Dependence Using Gene Expression Omnibus Database,” Frontiers in Psychiatry 12 (2022): 779143, 10.3389/fpsyt.2021.779143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Upreti D., Kumar R. P., Chen J. B. J., et al., “Noncoding RNA and Alcohol Use Disorder: A Scoping Review of Current Research and Knowledge Gaps,” Alcohol Research: Current Reviews 45, no. 1 (2025): 06, 10.35946/arcr.v45.1.06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Policarpo R., Sierksma A., De Strooper B., and d'Ydewalle C., “From Junk to Function: LncRNAs in CNS Health and Disease,” Frontiers in Molecular Neuroscience 14 (2021): 714768, 10.3389/fnmol.2021.714768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kleaveland B., Shi C. Y., Stefano J., and Bartel D. P., “A Network of Noncoding Regulatory RNAs Acts in the Mammalian Brain,” Cell 174, no. 2 (2018): 350–362.e17, 10.1016/j.cell.2018.05.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Li L., Zhuang Y., Zhao X., and Li X., “Long Non‐Coding RNA in Neuronal Development and Neurological Disorders,” Frontiers in Genetics 9 (2019): 744, 10.3389/fgene.2018.00744. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Renthal W. and Nestler E. J., “Epigenetic Mechanisms in Drug Addiction,” Trends in Molecular Medicine 14, no. 8 (2008): 341–350, 10.1016/j.molmed.2008.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Clapp P., Bhave S. V., and Hoffman P. L., “How Adaptation of the Brain to Alcohol Leads to Dependence: A Pharmacological Perspective,” Alcohol Research & Health 31, no. 4 (2008): 310–339. [PMC free article] [PubMed] [Google Scholar]
- 19. Becker H. C., “Influence of Stress Associated With Chronic Alcohol Exposure on Drinking,” Neuropharmacology 122 (2017): 115–126, 10.1016/j.neuropharm.2017.04.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. de la Monte S., Derdak Z., and Wands J. R., “Alcohol, Insulin Resistance and the Liver–Brain Axis,” Journal of Gastroenterology and Hepatology 27, no. Suppl 2 (2012): 33–41, 10.1111/j.1440-1746.2011.07023.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Popova D., Sun J., Chow H. M., and Hart R. P., “A Critical Review of Ethanol Effects on Neuronal Firing: A Metabolic Perspective,” Alcoholism, Clinical and Experimental Research 48, no. 3 (2024): 450–458, 10.1111/acer.15266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Sun L. and Lin J. D., “Function and Mechanism of Long Noncoding RNAs in Adipocyte Biology,” Diabetes 68, no. 5 (2019): 887–896, 10.2337/dbi18-0009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Zhang T. N., Wang W., Yang N., Huang X. M., and Liu C. F., “Regulation of Glucose and Lipid Metabolism by Long Non‐Coding RNAs: Facts and Research Progress,” Frontiers in Endocrinology 11 (2020): 457, 10.3389/fendo.2020.00457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Tang S., Zhu W., Zheng F., et al., “The Long Noncoding RNA Blnc1 Protects Against Diet‐Induced Obesity by Promoting Mitochondrial Function in White Fat,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 13 (2020): 1189–1201, 10.2147/DMSO.S248692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Jiang Z., Zhang W., and Zhang J., “LncRNA OIP5‐AS1 Regulates Ferroptosis and Mitochondrial Dysfunction–Mediated Apoptosis in Spinal Cord Injury by Targeting the miR‐128‐3p/Nrf2 Axis,” Heliyon 10, no. 18 (2024): e37704, 10.1016/j.heliyon.2024.e37704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Cai X. J., Huang L. H., Zhu Y. K., and Huang Y. J., “LncRNA OIP5‐AS1 Aggravates House Dust Mite–Induced Inflammatory Responses in Human Bronchial Epithelial Cells via the miR‐143‐3p/HMGB1 Axis,” Molecular Medicine Reports 22, no. 6 (2020): 4509–4518, 10.3892/mmr.2020.11536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Jeon S. and Carr R., “Alcohol Effects on Hepatic Lipid Metabolism,” Journal of Lipid Research 61, no. 4 (2020): 470–479, 10.1194/jlr.R119000547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Rajendram R. and Preedy V. R., “Effect of Alcohol Consumption on the Gut,” Digestive Diseases 23, no. 3–4 (2005): 214–221, 10.1159/000090168. [DOI] [PubMed] [Google Scholar]
- 29. Zhang L., Wang R., Nan Y., and Kong L., “Molecular Regulators of Alcoholic Liver Disease: A Comprehensive Analysis of microRNAs and Long Non‐Coding RNAs,” Frontiers in Medicine 12 (2025): 1482089, 10.3389/fmed.2025.1482089. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.