Introduction

Methamphetamine use disorder has emerged as a pressing public health concern worldwide1,2. Long-term abuse of methamphetamine can precipitate psychotic behaviors, encompassing hallucinations, paranoia, violent tendencies, emotional disturbances, and suicidal ideation. Cessation of use often leads to depression, fatigue, intense craving for the drug, and heightened aggression. Research has also revealed that methamphetamine exerts profound effects on the brain, gut, and immune system3, potentially causing a spectrum of complications, including HIV, pulmonary hypertension, periodontal disease, and intestinal inflammation4,5,6. Reports indicate that methamphetamine stimulates synaptic plasticity within reward-related brain regions, such as the midbrain, nucleus accumbens, and prefrontal cortex7. Both acute and chronic administration of methamphetamine can damage monoamine nerve endings, trigger neuronal apoptosis, and elevate levels of astrocytes and microglia8. The drug stimulates neuronal vesicles to release monoamine neurotransmitters into the synaptic cleft, while simultaneously blocking their reuptake, thereby elevating the concentration of dopamine, norepinephrine, and serotonin in the synaptic cleft. This process significantly impacts neuronal plasticity9, ultimately leading to substantial alterations in physiological and biochemical processes throughout the body. However, the underlying mechanisms of methamphetamine use disorder remain incompletely understood, and there is a dearth of objective diagnostic indicators and effective therapeutic interventions. This situation poses significant challenges for national drug rehabilitation efforts.

At the epigenetic level, DNA methylation, histone modification, and non-coding RNA have been implicated in the aberrant expression of genes related to drug addiction10,11,12,13. MicroRNAs (miRNAs), which are non-coding RNAs approximately 22 nucleotides in length, play a crucial role in this process. The biogenesis of miRNAs begins with the synthesis of relatively long primary transcripts, known as pri-miRNAs. These transcripts are then processed by the nuclear microprocessor complex, consisting of Drosha and DGCR8 proteins, into medium-length hairpin precursor miRNAs (pre-miRNAs), which are subsequently exported to the cytoplasm. In the cytoplasm, the Dicer enzyme cleaves these precursors to generate mature miRNAs. These mature miRNAs are then incorporated into cytoplasmic Argonaute (AGO) proteins, forming programmable ribonucleoprotein complexes. These miRNA-programmed complexes, with their complementary information, are essential for recognizing intracellular RNA target sequences. The AGO protein safeguards RNA from degradation, prioritizes one strand of miRNA over the other, spatially organizes the miRNA to bind complementary sequences, and facilitates the search and recognition of intracellular RNA targets. When there is complete complementarity between the short RNAs, AGO2 and to a lesser extent AGO3 can promote the cleavage of target RNA14. MiRNAs mediate post-transcriptional gene silencing by targeting the 3′ untranslated region (3’UTR) of messenger RNA (mRNA). Currently, miR-181a, miR-29a, and miR-143 have been identified as key regulators in the process of methamphetamine use disorder15,16,17. In recent years, circulating miRNAs have emerged as promising biomarkers for reflecting disease status, owing to their excellent stability, strong diagnostic specificity, ease of detection, and dynamic changes during the course of the disease18. However, the alterations in miRNA expression and the underlying mechanisms in the plasma of individuals with methamphetamine use disorder remain to be elucidated.

Thus, this study utilized miRNA sequencing technology to identify miRNAs with heightened plasma expression levels in methamphetamine use disorder, followed by tissue-specific qRT-PCR validation; The potential downstream target molecules of the differentially expressed miRNAs were predicted using bioinformatics and subsequently validated using Western blot experiments and luciferase reporter assays. Furthermore, the study delved into the discriminative power of plasma miRNAs and their target molecules in methamphetamine use disorder, as well as the correlation between their expression levels and the mental state of those afflicted. Primarily, this research investigates the expression levels and diagnostic potential of miRNAs and their target molecules in the plasma of individuals with methamphetamine use disorder, offering novel insights and targets for the detection and treatment of this disease.

Materials and methods

Study object

From March to June 2024, 112 individuals with methamphetamine use disorder (case group) were recruited from 14 community drug rehabilitation centers in Shenyang, Liaoning Province. Simultaneously, 112 healthy individuals (control group) were selected from those undergoing physical examinations at Shenyang Fourth People’s Hospital, ensuring age and gender matching with the case group. All participants in the case group underwent urine toxicology screening and self-reporting to confirm methamphetamine use at the outset. A diagnosis of methamphetamine use disorder was established by professional practicing psychiatrists based on DSM-5 structured clinical interviews. To be included in the study, all participants had to meet the following criteria: (1) a history of methamphetamine use for at least 12 months prior to the intervention; (2) an age range of 18 to 60 years; (3) no substance abuse other than smoking and drinking; and (4) no use of antidepressants or other psychotropic drugs during the study period. Exclusion criteria included: (1) a lifetime history of using other illegal substances, such as heroin, ketamine, marijuana, etc. (alcohol and tobacco excluded); (2) a previous or current diagnosis of severe physical or organic brain diseases; (3) a past or current mental disorder unrelated to substance use or addiction, such as schizophrenia, depression, bipolar disorder, etc.; and (4) an inability to comprehend the interview and questionnaire content, thus preventing accurate responses.

This study has received approval from the ethics committee and adheres to the principles outlined in the Helsinki Declaration and medical ethics guidelines. All participating research subjects were voluntarily enrolled after thoroughly comprehending the research objectives and content, and each signed an informed consent form.

Investigation methods

The survey was administered through a comprehensive questionnaire, that encompassed general information about drug users, as well as assessments using the visual analog scale (VAS), the self-rating anxiety scale (SAS), the self-rating depression scale (SDS), and the Pittsburgh sleep quality index (PSQI). The general information section collected details such as name, age, gender, ethnicity, marital status, current health status, and drug use history.

Experimental methods

Serum collection

Collected 2 ml of venous blood from the subjects using EDTA anticoagulant tubes. Allowed the tubes to stand at room temperature for 30 min, then centrifuged them at a low temperature for 10 min at 3000 rpm. Carefully collected the upper layer of yellow, clear serum using a quantitative pipette, and transferred it into 1 ml centrifuge tubes that were free of nucleases.

Preliminary screening of differential microRNA chips

The detection and analysis of miRNA chips were conducted by Shanghai Biochip Co., Ltd. Five samples from individuals with methamphetamine use disorders and five normal control samples were selected from the research cohort and transported on dry ice to Shanghai Biochip Co., Ltd. for miRNA chip analysis. The miRNA chip encompassed 2578 human mature microRNAs, with the microRNA data derived from the Sanger miRbase V20.0 database. Each chip featured eight loading sites. A total of 100 ng RNA was labeled with Cy3. The chip was scanned using the XDR Scan (PMT 100, PMT5) to capture the signals. The labeling and hybridization procedures were performed in accordance with the Agilent micro RNA chip system manual. The chip image data was converted into intensity values using Feature Extraction Software Rev. 9.5.3 (Agilent Technologies, Santa Clara, CA). After eliminating background noise, the signal intensity values were directly imported into GeneSpring GX 10 (Agilent Technologies, Santa Clara, CA) software for comprehensive analysis.

Extraction of total RNA from serum

Added 900 µL of TRIzol to 1300 µL of plasma sample LS reagent lysate. Mixed thoroughly and incubated at room temperature for 5 min; Added 5 µL 200 µM cel-miRNA-39-3p standard to each sample, mixed well, and incubated again at room temperature for 5 min. Then, added 240 µL of chloroform to each sample, mixed until a milky white solution formed, and incubated at room temperature for 3 min. Centrifuge the samples at 12,000 g for 15 min at 4 ℃. After centrifugation, the sample was separated into three layers: a colored lower organic phase, a white DNA middle layer, and an upper aqueous phase containing RNA. Carefully transferred the upper aqueous phase to a new EP tube; Added 600 µL of isopropanol, mixed by inverting the tube, and incubated overnight at 4 ℃. Centrifuge the samples at 12,000 g for 10 min at 4 ℃. A feather-like RNA precipitated at the bottom of the tube and discarded the supernatant. Added 1000 µL of 75% ethanol to each sample, mixed by inverting the tube, centrifuged at 7500 rpm for 5 min at 4 ℃, and discarded the supernatant. Air-dry the RNA precipitation. Added 20 µL of DEPC water (ribonuclease-free water) to each sample, pipetted to mix well and dissolved the RNA. Finally, used a spectrophotometer to measure the purity and concentration of RNA. A valid OD value ranged between 1.8 and 2.1.

qRT-PCR reaction

Performed reverse transcription and real-time fluorescence quantitative PCR on the RNA extracted from the aforementioned plasma. The reverse transcription reaction involved incubating at 16 ℃ for 30 min, followed by 42 ℃ for 30 min, and finally 85 ℃ for 10 min. Upon completion, promptly removed the cDNA product, rapidly cooled it on ice, and conducted all subsequent steps on ice. Diluted the cDNA product 3–4 fold, mixed thoroughly, and then extracted 2 µL (for a 20 µL reaction system) as the template for quantitative PCR. Stored the remaining product at −20 ℃. The PCR reaction was carried out on an ABI7500 real-time quantitative PCR instrument, following a program of initial denaturation at 95 ℃ for 3 min, followed by 40 cycles of 95℃ for 12 s and 62℃ for 1 min, and a final step at 95 ℃ for 15 s. After the reaction, verified the amplification and melting curves of Real-Time PCR, and established standard curves for PCR quantification. The data obtained from three independent experiments were analyzed using the method of formula 2−△△Ct19, where △△Ct was calculated as follows: (average Ct of the target gene in the test sample−average Ct of the housekeeping gene in the test sample)−(average Ct of the target gene in the control sample - average Ct of the housekeeping gene in the control sample).

Western blot analysis

The exosomes were lysed with RIPA lysate, and the protein concentration was determined by BCA assay. Subsequently, the proteins were separated by gel electrophoresis. Then, the separated proteins were transferred onto an NC membrane, blocked with milk, and incubated overnight at 4 ℃ with mouse anti-human CD63 antibody (Abcam, 1:500), added mouse anti-human CD63 antibody (Abcam, 1:500), rabbit anti-human TSG101 antibody (Abcam, 1:1000), and rabbit anti-human CD9 antibody (Abcam, 1:2000), respectively. After washing with TBST, the membrane was incubated with HRP-labeled goat anti-rabbit or anti-mouse IgG (Abcam, 1:5000) at room temperature for 1 h. Finally, the membrane was washed again with TBST, and imaging and photography were performed.

Cell culture and transfection

293T cells were cultivated in DMEM medium supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin (100U/mL penicillin, 100 µg/mL streptomycin). Upon reaching exponential growth, the cells were seeded onto a 24-well plate (500 µL/well) and incubated at 37 ℃, in a 5% CO2 incubator. Then, transient transfection of the cells was performed according to the manufacturer’s instructions for the X-tremeGENE HP transfection reagent. The wild-type BDNF 3′UTR and GSK3B 3’UTR luciferase reporter genes, or their mutant counterparts, were co-transfected into 293T cells along with miR-9-3p mimetics, let-7-3p mimetics, miR-9-3p inhibitors, miR-9-3p inhibitors, pre-miR-9-3p plasmids, pre-let-7-3p plasmids, or their corresponding negative controls.

Validation of luciferase reporter assays

To assess targeted luciferase activity, luciferase reporter assays were conducted. Following 48 h of transfection, 300 µL of lysis buffer was added to each sample and incubated at 4 ℃ for 20 min to ensure complete lysis. After thorough mixing, 40 µL of the cell lysis buffer was transferred to a Lockwell Maxsorp detection plate. Subsequently, 20 µL of firefly luciferase detection reagent was added, mixed well, and the fluorescence value of firefly luciferase was immediately measured using an enzyme-linked immunosorbent assay (ELISA) reader. Next, 20 µL Stop&Glo® Reagent was added, and the fluorescence value of sea kidney luciferase was detected using the ELISA reader. This process was repeated three times to ensure accuracy.

Knockdown and overexpression experiments

Mimics and inhibitors, including miRNA mimics and inhibitors, miR-9-3p mimics, let-7b-3p mimics, miR-9-3p inhibitors, and let-7b-3p inhibitors (chemically modified with 2′-O-Me), as well as control oligonucleotides, were obtained from GenePharma. These mimics and inhibitors (miR-9-3p mimic, let-7-3p mimic, miR-9-3p inhibitor, and let-7-3p inhibitor) were transfected into 293 cells. After 48 h of culture, total RNA, microRNA, and total protein were extracted. The changes in mRNA and protein expression levels of BDNF and GSK3B were then detected by qRT-PCR and Western blot analyses, respectively.

Statistical analysis

The data for each group were expressed as mean ± standard deviation (mean ± S.D). Statistical analysis was performed using SPSS 22.0 software. For data conforming to a normal distribution, the commonly employed statistical method was the independent sample t-test. Conversely, non-parametric testing was utilized when the data do not follow a normal distribution. One-way ANOVA was appropriate for experiments involving three or more groups, with the Levene test used to assess the homogeneity of variance. The repeated measures statistical analysis method was applied when multiple observations of a specific indicator were made at different time points for the same research subject. P < 0.05 denoted statistical significance. All experimental data graphs were generated using GraphPad 8.2 software.

Results

Study subject characteristics

The study includes 112 individuals with methamphetamine use disorder and 112 normal controls. Table 1 presents the basic characteristics of the study participants, summarizing their age, gender, marital status, education level, frequency of drug use, and mental state specific to those with methamphetamine use disorder. The mean age of the methamphetamine use disorder group is 35.37 ± 7.21 years old. This group exhibits a high frequency of drug use, with 42% reporting daily methamphetamine use. Additionally, during the withdrawal period, individuals with methamphetamine use disorder display a significant psychological craving for drugs, with a craving level VAS score of 4. Psychiatric symptom assessment results revealed that those with methamphetamine use disorder have a mean SDS score of 52.89 ± 10.67 7, indicating significant depression during the withdrawal period (P < 0.01), compared to the normal control group, which has an SDS score of 40.36 ± 9.58.SDS = 40.36 ± 9.58).

Table 1 Human subject characteristics between the case and control.
Full size table

Differential microRNA screening

Based on the miRNA expression levels in each sample, we conducted a differential expression analysis between the case group and the control group. MicroRNAs that were differentially expressed, meeting the criteria of |log2 fold change (FC)|>1 and p < 0.05, were selected. Figure 1 illustrates that a total of 59 miRNAs exhibited significantly different expression levels at p < 0.05. Specifically, in comparison to the control group, 27 miRNAs were notably upregulated, while 22 were downregulated in the methamphetamine use disorder group. For validation experiments, we selected miR-9-3p and let-7b-3p, which had higher fold change values.

Fig. 1
figure 1

Cluster heatmap of differentially expressed miRNAs in plasma.

Full size image

Changes in miRNA expression in the plasma of methamphetamine use disorder

In this study, the samples were split into a training set (25%) and a testing set (75%). The training set comprised 28 individuals with methamphetamine use disorder and 28 normal controls, while the testing set included 84 individuals with methamphetamine use disorder and 84 normal controls. The results of the RT-qPCR analysis are presented in Table 2. Within the training set, the expression levels of miR-9-3p and let-7b-3p in plasma samples from individuals with methamphetamine use disorder were significantly elevated compared to those from normal individuals (p < 0.05). Furthermore, rank sum tests were conducted on the expression levels of the aforementioned five miRNAs. As shown in Fig. 2, the expression levels of miR-9-3p and let-7b-3p in the plasma of individuals with methamphetamine use disorder were notably higher than those in normal controls (P < 0.01). Specifically, the median expression level was 1 in the normal control group, whereas it was 2.277 and 2.288 in the methamphetamine use disorder group for miR-9-3p and let-7b-3p, respectively.

Table 2 Expression levels of plasma mir-9-3p and let-7b-3p in the training and testing sets.
Full size table
Fig. 2
figure 2

Relative expression levels of plasma miRNAs between the case group and the control group.

Full size image

Prediction of miRNA targets

To identify the mRNA targets corresponding to the upregulated miRNAs in methamphetamine use disorder, we conducted a joint analysis utilizing the Targeted Scan and miRDB databases, screening a total of 2514 relevant mRNAs. These mRNAs were then subjected to KEGG20,21,22 enrichment and GO (encompassing Molecular Function, Cellular Component, and Biological Process) analysis using Metascope software. The top 20 pathways and functions were carefully selected and are presented in Fig. 3A, B. Notably, 43 mRNAs were enriched in the PI3K-Akt signaling pathway, and in GO functional analysis, numerous mRNAs were enriched in neural system development and function. Figure 3C highlights a subset of mRNAs that are enriched in pathways and functions closely associated with the nervous system and immune system. Among these, genes such as GSK3B, BNDF, SMAD4, CTNNB1, TIAM1, PTCH1 were predicted by multiple pathways or functions. To further visualize these relationships, we employed Cytoscope to map the interaction network of mRNAs and miRNAs enriched in these relevant pathways and functions. Ultimately, we screened and identified two key mRNA types, BDNF and GSK3B, which were specifically associated with miR-9-3p and let-7b-3p.

Fig. 3
figure 3

Identification of gene targets of miRNA in plasma of methamphetamine use disorder. (A) Functional association of over-mutated miRNA genes with KEGG pathway. (B) Up-regulation miRNA GO analysis (BP, CC, MF) result. (C) MiRNA pathways and functional networks predicted by Cytoscape.

Full size image

Targeted validation results of mir-9-3p and let-7-3p with BDNF and GSK3B

The results of the luciferase reporter assays are presented in Fig. 4. When comparing the wild-type plasmid (BDNF wt-3′ UTR) + miR-NC group, we observed a significant reduction in the relative expression levels of luciferase in both the wild-type plasmid (BDNF wt-3′ UTR) + miR-9-3p mimics group and the (BDNF wt-3′ UTR) + let-7-3p mimics group. This difference was statistically significant (P < 0.05). Conversely, there was no statistically significant difference (P ≥ 0.05) in luciferase expression between the mutant plasmid (BDNF mt-3′ UTR) + miR-NC group and the mutant plasmid groups treated with miR-9-3p mimics or let-7-3p mimics. Similarly, when comparing the wild-type plasmid (GSK3B-wt-3′ UTR) + miR-NC group, we found a significant decrease in luciferase expression in both the wild-type plasmid (GSK3B-wt-3′ UTR) + miR-9-3p mimics group and the (GSK3B-wt-3′ UTR) + let-7-3p mimics group, with a statistically significant difference (P < 0.05). Again, no statistically significant difference (P ≥ 0.05) in luciferase expression was observed between the mutant plasmid (GSK3B-mt-3′ UTR) + miR-NC group and the mutant plasmid groups treated with miR-9-3p mimics or let-7-3p mimics.

Fig. 4
figure 4

Targeted validation results of miR-9-3p and let-7-3p with BDNF and GSK3B.

Full size image

MiR-9-3p and let-7-3p regulate the expression of BDNF and GSK3B

The mRNA and protein expression levels of BDNF and GSK3B were assessed using qRT-PCR and Western blot analyses, respectively. The results revealed that, compared to the control group, transfection with the miR-9-3p mimic led to a significant overexpression of miR-9-3p, while transfection with the let-7-3p mimic resulted in a marked overexpression of let-7-3p. Both treatments significantly inhibited the protein expression of BDNF and GSK3B in cells (P < 0.05). Conversely, transfection with the miR-9-3p inhibitor suppressed the expression of endogenous miR-9-3p, and the let-7-3p inhibitor reduced the levels of endogenous let-7-3p, leading to a notable increase in the expression of BDNF and GSK3B (P < 0.05) (Fig. 5).

Fig. 5
figure 5

MiR-9-3p and let-7-3p regulate the expression of BDNF and GSK3B.

Full size image

ROC curve analysis

To assess the discriminatory power of significantly elevated plasma miRNAs and their downstream target proteins between normal individuals and those with methamphetamine use disorder, we conducted ROC curve analysis on the selected miRNAs (miR-9-3p and Let-7b-3p), as well as BDNF and GSK3B. The area under the curve (AUC) was calculated for each. As illustrated in Fig. 6, the AUC values were as follows: miR-9-3p, AUC = 0.782 (95% CI 0.716–0.848); Let-7b-3p, AUC = 0.720 (95% CI 0.650–0.789); BDNF, AUC = 0.884 (95% CI 0.837–0.933); and GSK3B, AUC = 0.685 (95% CI, 0.614–0.757). Notably, BDNF and miR-9-3p demonstrated excellent discriminatory efficacy, with optimal cutoff points yielding sensitivity and specificity of (0.968, 0.719) and (0.722, 0.729), respectively.

Furthermore, logistic regression was employed for joint prediction, and the combination of hsa-miR-9-3p and BDNF yielded the highest discriminative ability, with an AUC of 0.901 (95% CI 0.836–0.945) and optimal cutoff points of (0.805, 0.861). Additionally, the combination of BDNF, miR-9-3p, and the visual analog scale (VAS) resulted in an AUC of 0.918 (95% CI 0.850–0.936; P < 0.001), with a sensitivity of 81.9% and specificity of 89.4% for distinguishing between normal individuals and those with methamphetamine use disorder. Similarly, the combination of BDNF, miR-9-3p, and the Verbal Descriptor Scale (VDS) produced an AUC of 0.932 (95% CI 0.862–0.954; P < 0.001), with a sensitivity of 83.7% and specificity of 92.6% for differentiating methamphetamine use disorder from healthy subjects.

Fig. 6
figure 6

Receiver-operator characteristic (ROC) plot.

Full size image

Spearman correlation analysis

The statistical analysis of the mental state of individuals with methamphetamine use disorder is presented in Fig. 7; Table 3. During the withdrawal period, a significant general depression was observed (SDS = 52.89 ± 10.67, P < 0.01). However, the anxiety level was not significantly elevated, with the SAS score showing no statistical significance (P = 0.162). The craving level for drugs, measured on a VAS ranging from 0 to 10, reached a maximum of 10, with a median score of 4. Notably, there was no significant correlation found between the expression levels of target miRNAs and target proteins and the psychiatric symptoms of methamphetamine use disorder.

Fig. 7
figure 7

Mental status of methamphetamine use disorder.

Full size image
Table 3 Mir-9-3p and let-7b-3p expression in subjects with different clinicopathological features.
Full size table

Cox proportional hazards model analysis

The Cox proportional hazards model was employed to investigate the factors influencing psychological symptoms (assessed by VAS and SAS) in methamphetamine use disorder. Both univariate and multivariate Cox regression analyses revealed that daily drug use dosages and poor social support status were independent risk factors for the development of psychological symptoms in this disorder (as shown in Table 4).

Table 4 Cox regression analysis of psychological symptoms affecting methamphetamine use disorder.
Full size table

Discussion

Methamphetamine, a highly addictive central nervous system stimulant with a high relapse rate, poses significant physical and mental health risks, and excessive use can be fatal. Its increasing availability and harmful effects are intertwined with the ongoing opioid overdose crisis, further complicating the resurgence of the methamphetamine threat23. As gene sequencing technology continues to advance, numerous studies have revealed alterations in miRNA expression levels in specific brain regions during the period of drug dependence. For instance, Rikki discovered changes in the expression of miR-101b, miR-137, miR-212, and miR-132 in the nucleus accumbens core, nucleus accumbens shell, and dorsomedial striatum of a rat cocaine self-administration model23. Similarly, Wang found that miR-181-5p was upregulated in HT-22 cells treated with morphine and could negatively regulate MAPK1 expression by specifically binding to it; moreover, knockdown of MAPK1 reversed the effect of miR-181-5p on morphine-induced phenotypic changes in these cells24. Additionally, evidence suggests that the miR-31-3p/RhoA pathway in the dorsal hippocampus regulates methamphetamine-induced conditioned place preference in mice, with RhoA protein levels significantly reduced and miR-31-3p identified as a direct target of RhoA25. Despite extensive research on miRNAs in methamphetamine use disorder, effective methods for prognostic treatment and relapse prevention remain elusive. Therefore, a thorough investigation into the mechanisms underlying methamphetamine addiction, identification of potential biomarkers, and discovery of effective therapeutic targets are crucial for mitigating and preventing methamphetamine abuse.

In this study, we observed increased expression levels of miR-9-3p and let-7b-3p in individuals with methamphetamine use disorder. Let-7, the first human miRNA discovered, and its family members are involved in various biological processes in both humans and animals. As a member of this family, let-7b-3p has been frequently implicated in neurological disorders. James reported differential expression of specific miRNAs, including let-7b-3p and let-hsa-7c-3p, in the hippocampus and cortex of patients with surgically resected medial temporal lobe epilepsy, suggesting their potential role as key regulatory factors in this condition26. Yota found that the ratio of circulating hsa-miR-4455 to hsa-let-7b-3p provided a diagnostic index with 97% accuracy for distinguishing patients with mild cognitive impairment from healthy subjects27. Furthermore, other studies have indicated that miR-let-7e and miR-let-7d may play a significant role in the pathology of methamphetamine use disorder and could potentially serve as peripheral biomarkers for this condition, pending further confirmation28. In our study, the expression level of let-7b-3p was notably elevated in the methamphetamine group.

MiR-9 plays a crucial role in the onset and progression of various neurological diseases. Meng discovered that the combination of miR-331-3p and miR-9-5p, along with autophagic activity and amyloid plaques, can distinguish between different stages of Alzheimer’s disease, enabling more precise and timely diagnosis of early lesions. It has also been suggested that inhibiting miR-331-3p and miR-9-5p while enhancing autophagy could offer a novel therapeutic approach for Alzheimer’s disease patients29. Zhen revealed that miR-9-5p directly targets Parkinson’s disease by regulating the β-catenin signaling pathway of SCRIB, thereby inhibiting dopaminergic neuron apoptosis and improving motor function in Parkinson’s disease mice30. In the context of drug dependence, Judit found that miR-9, miR-153, and miR-124 were downregulated in dopamine cell models following acute cocaine exposure, potentially contributing to cocaine dependence31. In our study, we observed a significant increase in the expression level of miR-9-3p in the plasma of patients with methamphetamine use disorder, demonstrating its strong discriminatory power. Furthermore, ROC analysis revealed an AUC of 0.782 for miR-9-3p, highlighting its considerable diagnostic value for methamphetamine use disorder. Additionally, the AUC for let-7b-3p was 0.720.

Bioinformatics predictions have consistently identified the PI3K-Akt signaling pathway as enriched in downstream targets, with BDNF and GSK3B serving as effector proteins for multiple miRNAs on this pathway. Notably, BDNF and GSK3B, shared targets of miR-9-3p and let-7b-3p, may contribute to methamphetamine use disorder, as evidenced by decreased expression levels in plasma Western blot analysis. BDNF, one of the most vital and abundant neurotrophic factors in the nervous system, plays a pivotal role in neuronal development, survival, differentiation, and synaptic plasticity. Shelley emphasized that glial cell-derived GDNF, NGF, and BDNF are essential for the survival, maintenance, and regeneration of specific neuronal populations in the adult brain32. The depletion of these neurotrophic factors is linked to disease pathology and symptoms, making them potential therapeutic targets for neurodegenerative diseases such as Parkinson’s, Alzheimer’s, and Huntington’s. Erik’s research revealed that 282 biomarkers in the cerebrospinal fluid or serum of bipolar disorder patients were associated with 90 different CACNA1C variants, and the CACNA1C risk allele correlated with serum BDNF levels and tissue plasminogen activator, which converts precursor BDNF into its mature form33. In the context of drug dependence, Bianca Cotto found that cocaine self-administration enhances microglia activation, with microglia MeCP2 being a sensitive cocaine target that triggers increased BDNF release and potential synaptic plasticity alterations34. Kerim Abdelkhalek reported that BDNF is implicated in nicotine dependence, with smokers exhibiting significantly elevated plasma BDNF and methylation levels, and a notable decrease during smoking cessation35. Andrew’s study demonstrated that methamphetamine can counteract the effects of voluntary aerobic exercise on BDNF and DRE2 gene expression in the frontal cortex and striatum36. Our study also uncovered that miR-9-3p and let-7b-3p regulate GSK3B function. Prior research has shown that abnormal GSK3B activation can induce tau protein phosphorylation, leading to neuronal damage and cognitive impairment in Alzheimer’s disease37. Additionally, studies have indicated that the GSK3B inhibitor YQ138 can protect neurons from glutamate- and cerebral ischemia-induced damage by activating the Nrf2 signaling pathway38, suggesting its potential relevance in methamphetamine use disorder, which requires further investigation. In this study, we observed that plasma levels of BDNF and GSK3B were lower in individuals with methamphetamine use disorder compared to controls. The differences in BDNF levels were particularly pronounced, likely due to its predominant presence in the nervous system.

All individuals with methamphetamine use disorder exhibit specific clinical manifestations, including psychotic symptoms, depression, anxiety, sleep disorders, and cravings39,40. In this study, the results of the Spearman correlation analysis indicated that there was no significant association between the expression levels of target miRNAs and target proteins and the psychiatric symptoms associated with methamphetamine use disorder.

Limitations of this study. Firstly, our study did not detect any association between serum let-7b-3p, miR-9-3p, and psychiatric symptoms or medication use in methamphetamine use disorder. This may be attributed to the limited sample size in each group. Further multi-center and large-scale exploration and validation analyses are warranted to address this issue. Secondly, while this study explored the diagnostic value of plasma miR-9-3p and let-7b-3p, the results, though promising, are somewhat one-sided. The occurrence and development of dependence are influenced by multiple factors working in concert. Therefore, it is essential to construct a more reliable diagnostic model by incorporating other microRNAs identified during the screening stage. Thirdly, as potential biomarkers for the early screening and diagnosis of methamphetamine use disorder, the sensitivity and specificity of these microRNAs require further scientific validation in large, diverse sample populations. This study focused on preliminary screening biomarkers, and all participants were self-reported patients with methamphetamine use disorder, rather than a representative sample from the general population. This may have led to an overestimation of the diagnostic value. These limitations can be addressed in future research endeavors. Lastly, the combined AUC of hsa-miR-9-3p and BDNF was 0.901, indicating strong diagnostic potential. However, to assess the real-world applicability of these biomarkers, further validation in clinical settings is crucial. This could involve testing the miRNAs in a larger, independent patient cohort and evaluating their performance in a clinical trial environment.

In summary, this study employed high-throughput miRNA sequencing technology to screen and validate the elevated expression levels of miR-9-3p and let-7b-3p in the plasma of individuals with methamphetamine use disorder. Using the ROC curve for identification, we found that the combination of miR-9-3p and BDNF exhibited the highest diagnostic efficacy. Additionally, the target proteins BDNF and GSK3B were found to have decreased plasma levels in those with methamphetamine use disorder. These findings propose a novel approach for identifying methamphetamine use disorder and offer potential targets for its treatment and prevention.

Conclusions

The expression levels of miR-9-3p, let-7b-3p, and their target molecules, BDNF and GSK3B, are altered in the plasma of individuals with methamphetamine use disorder. These alterations and their interactions may play a crucial role in the development of this disorder. Furthermore, the combination of miR-9-3p, let-7b-3p, and BDNF demonstrates high diagnostic accuracy for methamphetamine use disorder, suggesting its potential as a valuable biomarker for diagnosing this condition.