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. 2025 Dec 31;21:6. doi: 10.1186/s13008-025-00175-9

MiR-124-3p inhibits stomach adenocarcinoma progression by targeting AHR to induce autophagy

Qian Wan 1,#, Siwei Wang 1,2,#, Wei Dong 1,#, Xinyi Liu 1,2,#, Xiangyun Li 3, Chuan Liu 3, Shanling Xu 1,✉
PMCID: PMC12866595  PMID: 41476313

Abstract

Background

MicroRNA-124-3p (miR-124-3p) has been widely reported as an important tumor-suppressive regulator in multiple malignancies. Nevertheless, its precise biological function in stomach adenocarcinoma (STAD) remains insufficiently clarified.

Methods

We applied large-scale bioinformatics interrogation of The Cancer Genome Atlas (TCGA) STAD cohort, combined with in vitro cellular assays and in vivo xenograft experiments, to explore both the biological significance and molecular mechanisms of miR-124-3p in STAD progression.

Results

MiR-124-3p expression was significantly downregulated in STAD tissues and correlated with advanced pathological stage, poor prognosis, and reduced survival outcomes. Functional investigations confirmed that miR-124-3p directly interacts with the 3′-UTR of the aryl hydrocarbon receptor (AHR) mRNA, suppressing its expression and inducing autophagy. This regulation led to impaired proliferation, migration, and invasiveness of STAD cells. Restoration of AHR expression reversed these tumor-suppressive effects. Moreover, in vivo delivery of miR-124-3p inhibited tumor growth and mitigated cancer-induced cachexia in nude mice.

Conclusion

These findings establish miR-124-3p as a key suppressor of STAD progression via AHR-mediated autophagy, underscoring its promise as both a diagnostic biomarker and a therapeutic candidate.

Graphical abstract

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Supplementary Information

The online version contains supplementary material available at 10.1186/s13008-025-00175-9.

Keywords: MiR-124-3p, Stomach adenocarcinoma, Aryl hydrocarbon receptor, Biomarker, Autophagy

Introduction

Stomach adenocarcinoma (STAD) represents one of the most prevalent malignancies worldwide, characterized by a multifactorial etiology and typically detected at advanced stages owing to the lack of specific early clinical manifestations [1, 2]. The World Health Organization reports that STAD is a major contributor to global cancer mortality, with an especially high incidence in East Asian regions [3–5]. The geographic variation in STAD incidence and mortality highlights the significant influence of environmental, dietary, and genetic factors on its development [1, 6]. Despite recent progress in therapeutic approaches, including surgical resection, radiotherapy, chemotherapy, and targeted interventions, the five-year overall survival (OS) of patients with STAD remains poor [2, 7, 8]. This is mainly due to the low early diagnostic rate and ineffective early biomarkers. Therefore, identifying and validating novel biomarkers is essential for improving STAD’s early diagnosis and treatment outcomes.

MicroRNA-124-3p (miR-124-3p) has emerged as a pivotal post-transcriptional regulator implicated in multiple cellular processes, such as differentiation, proliferation, and programmed cell death [9–12]. It is frequently downregulated in neurological disorders and various cancers, and its function as a tumor suppressor has been increasingly recognized [13–15]. For instance, decreased expression of miR-124-3p is closely linked to malignant progression in liver, breast, and colorectal cancers [16, 17]. Conversely, the aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor, orchestrates a wide spectrum of intracellular responses and contributes to essential biological processes, including detoxification, cell proliferation, and differentiation [18–20]. AHR exhibits abnormal activity in various cancers, and its dysregulation is associated with tumor initiation and progression. In STAD, increased AHR expression correlates with disease progression, suggesting its potential as a therapeutic target [21, 22].

In the study of STAD, miR-124-3p and the AHR have attracted considerable attention as independent molecular factors. Accumulating evidence supports the tumor-suppressive function of miR-124-3p across diverse malignancies, with reduced expression strongly linked to unfavorable prognosis in STAD [23, 24]. On the other hand, AHR, a ligand-activated transcription factor, has been increasingly recognized as a pivotal contributor to STAD initiation and progression [21, 25]. Research has demonstrated that AHR is highly expressed in STAD tissues and is involved in regulating multiple oncogenic biological processes, such as promoting cell proliferation and migration, inhibiting apoptosis, and modulating the tumor immune microenvironment [21, 22, 26]. Although no direct evidence has yet confirmed the collaborative involvement of miR-124-3p and AHR in STAD, bioinformatics tools such as TargetScan and miRDB predicted that AHR may be a potential target of miR-124-3p, suggesting a possible molecular link between them. This hypothesis provides a new direction for exploring the role of miRNA regulatory networks in STAD.

This study aimed to elucidate the role of miR-124-3p in STAD by examining its expression, biological functions, and regulatory relationship with the AHR. We hypothesized that miR-124-3p may inhibit STAD cell invasion, migration, and proliferation by downregulating AHR expression and modulating the AHR-mediated autophagy pathway. Confirmation of this mechanism would advance understanding of microRNA-mediated regulation in STAD and highlight AHR signaling as a potential therapeutic target. In addition, the altered expression of miR-124-3p in clinical samples suggests its utility as a biomarker for diagnosis and prognosis. Therefore, through a combination of bioinformatic predictions and in vitro and in vivo experimental validation, this study aimed to clarify the regulatory mechanism of miR-124-3p targeting AHR in STAD and to provide theoretical support for future molecular targeted therapies.

Results

AHR expression was elevated in STAD patients

Analysis of transcriptomic profiles revealed that AHR mRNA levels were significantly elevated in multiple cancer types compared with normal tissues. As shown in Fig. 1, AHR mRNA was upregulated in lymphoma, esophageal cancer, glioblastoma, renal cancer, pancreatic cancer, STAD, and thymoma. We further analyzed the HPA database (Fig. 2A and E) and found that IHC expression of AHR was observed in 354 tumor tissue samples from 32 patients. The results showed strong staining in 1 case, moderate staining in 28 cases, weak staining in 2 cases, and no expression in 1 case. Protein-level expression of AHR across pan-cancer types (Fig. 2F) indicated high AHR protein expression in STAD, ovarian cancer, and melanoma tissues.

Fig. 1.

Fig. 1

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Expression levels of AHR mRNA across various cancer types. A A Schematic representation of AHR mRNA expression in human organs was generated using the GEPIA database. Expression levels are presented as Log(TPM + 1); red indicates tumor samples, and green indicates normal tissue samples. B Scatter plot of AHR mRNA expression in the GEPIA database. The x-axis represents tissue types; the y-axis shows transcripts per million. Colored dots represent the comparison between cancer and normal tissues across various cancer types

Fig. 2.

Fig. 2

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Representative expression levels of AHR in STAD and normal tissues. A Normal gastric tissue; B STAD tissue with undetectable AHR expression; C STAD tissue with low AHR expression; D STAD tissue with moderate AHR expression; E STAD tissue with high AHR expression. Data sourced from the Protein Atlas database (scale bar = 100 μm); F Proportion of AHR expression levels across pan-cancer samples

Correlation between miR-124-3p and clinical features of STAD, and its prognostic potential

Analysis using the LinkedOmics database identified significantly dysregulated miRNAs targeting AHR, which were classified into upregulated and downregulated groups across 415 STAD patients (Fig. 3A-B; Table 1). Among them, miR-124-3p emerged as a key regulator of AHR and was notably elevated in gastric adenocarcinoma tissues, showing a positive correlation with AHR expression (Pearson’s correlation = 0.4267, p = 4.83 × 10− 3) (Fig. 3C-D). Although miR-124-3p levels were generally low across samples, its overall expression in tumors was significantly higher than in normal tissues, and single outliers did not drive the difference. However, this correlation was not validated in vitro or in vivo, where an opposite trend was observed.

Fig. 3.

Fig. 3

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Correlation between AHR mRNA and related miRNAs. A Heatmap of upregulated miRNAs associated with AHR alterations in the TCGA-STAD dataset based on the LinkedOmics database. B Heatmap of downregulated miRNAs associated with AHR alterations. C Pearson correlation analysis between AHR and associated miRNAs. D Pearson correlation analysis between miR-124-3p and AHR. Expression values are presented as log2(pseudo-count + raw counts). Statistical significance was assessed using the non-parametric Mann-Whitney U test

Table 1.

GO annotation results

ONTOLOGY ID Description GeneRatio BgRatio p value p.adjust q value
BP GO:2,000,134 negative regulation of G1/S transition of mitotic cell cycle 4/12 125/18,670 9.09e-07 4.67e-04 2.18e-04
BP GO:1,902,807 negative regulation of cell cycle G1/S phase transition 4/12 131/18,670 1.10e-06 4.67e-04 2.18e-04
BP GO:0048146 positive regulation of fibroblast proliferation 3/12 51/18,670 4.15e-06 7.28e-04 3.41e-04
BP GO:2,000,045 regulation of G1/S transition of mitotic cell cycle 4/12 184/18,670 4.25e-06 7.28e-04 3.41e-04
BP GO:1,901,990 regulation of mitotic cell cycle phase transition 5/12 444/18,670 5.13e-06 7.28e-04 3.41e-04
CC GO:0000307 cyclin-dependent protein kinase holoenzyme complex 3/12 42/19,717 1.95e-06 8.58e-05 5.54e-05
CC GO:1,902,554 serine/threonine protein kinase complex 3/12 88/19,717 1.84e-05 4.04e-04 2.61e-04
CC GO:1,902,911 protein kinase complex 3/12 109/19,717 3.49e-05 5.11e-04 3.30e-04
CC GO:0005667 transcription factor complex 4/12 365/19,717 5.08e-05 5.59e-04 3.61e-04
CC GO:0061695 transferase complex, transferring phosphorus-containing groups 3/12 259/19,717 4.52e-04 0.004 0.003
MF GO:0030332 cyclin binding 3/12 30/17,697 9.57e-07 7.08e-05 3.63e-05
MF GO:0016538 cyclin-dependent protein serine/threonine kinase regulator activity 3/12 49/17,697 4.31e-06 1.60e-04 8.17e-05
MF GO:0004861 cyclin-dependent protein serine/threonine kinase inhibitor activity 2/12 12/17,697 2.77e-05 6.84e-04 3.50e-04
MF GO:0004693 cyclin-dependent protein serine/threonine kinase activity 2/12 29/17,697 1.69e-04 0.002 0.001
MF GO:0097472 cyclin-dependent protein kinase activity 2/12 30/17,697 1.81e-04 0.002 0.001
KEGG hsa04934 Cushing syndrome 12/12 155/8076 1.63e-21 1.66e-19 9.25e-20
KEGG hsa04927 Cortisol synthesis and secretion 5/12 65/8076 2.19e-08 1.12e-06 6.23e-07
KEGG hsa05225 Hepatocellular carcinoma 6/12 168/8076 6.18e-08 2.10e-06 1.17e-06
KEGG hsa05224 Breast cancer 5/12 147/8076 1.33e-06 3.40e-05 1.90e-05
KEGG hsa05218 Melanoma 4/12 72/8076 2.72e-06 3.45e-05 1.92e-05
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Comparative analysis of TCGA and GTEx datasets revealed significant differences in AHR mRNA and miR-124-3p abundance between gastric cancer (GC), adjacent noncancerous tissues, and healthy controls (Fig. 4A-C). Notably, miR-124-3p expression was markedly increased in STAD compared with GTEx normal tissues (Fig. 4C). Violin plots further showed a positive association between AHR mRNA levels and advanced clinical stage in STAD (Fig. 4D).

Fig. 4.

Fig. 4

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Expression, prognostic value, and diagnostic performance of miR-124-3p and AHR. A Comparison of AHR mRNA expression between tumor tissues and adjacent normal tissues in the TCGA-STAD dataset; B Comparison of AHR mRNA expression between tumor tissues (TCGA) and normal tissues (GTEx); C Expression levels of miR-124-3p in normal versus gastric cancer tissues; D Violin plot from the GEPIA database showing the association between AHR mRNA expression and clinical stage; E Kaplan-Meier (K-M) survival analysis evaluating the hazard ratio of AHR; F K-M survival analysis evaluating the hazard ratio of hsa-miR-124-3p; G ROC curve assessing the predictive value of AHR; H ROC curve assessing the predictive value of hsa-miR-124-3p; I Association between AHR expression levels and pathological stage in the GSE15459 dataset (n = 200). Normal samples were obtained from the GTEx project (stomach, n = 174) and adjacent normal tissues from the TCGA-STAD cohort (n = 36), processed and merged using the Toil pipeline. Tumor samples were gastric adenocarcinoma tissues from TCGA-STAD (n = 414). Statistical significance was determined using multiple t-tests with Holm-Sidak correction. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001

Survival analysis demonstrated that elevated AHR expression was associated with worse OS (HR = 1.46, p = 6.2 × 10− 5) (Fig. 4E), whereas upregulation of miR-124-3p was not a significant predictor of prognosis (HR = 0.75, p = 0.061) (Fig. 4F). Receiver operating characteristic (ROC) analysis further assessed the diagnostic value of AHR and miR-124-3p (Fig. 4G-H). In the TCGA STAD dataset, AHR mRNA achieved an AUC of 0.956, indicating high accuracy in distinguishing tumors from normal tissues, while miR-124-3p yielded an AUC of 0.667, reflecting limited diagnostic utility. Similar findings were obtained in the validation cohort GSE_15459, where AHR expression correlated with pathological stage (Fig. 4I). In two independent validation cohorts (GSE_15459 and GSE_30070), AHR mRNA consistently outperformed miR-124-3p in predictive capacity, with AUCs of 0.626 and 0.603, respectively.

Additional validation supported these findings. Kaplan-Meier analysis indicated that high AHR expression was linked to reduced survival probability (p = 0.002) (Fig. S1A). In the GSE_15459 (Fig. S1B) and GSE_30070 (Fig. S1C) validation cohorts, ROC analyses confirmed the inferior predictive value of miR-124-3p (AUC = 0.603) compared with AHR (AUC = 0.626).

In STAD tumors, miR-124-3p expression was also downregulated in association with clinical features: sex (female vs. normal, log2(RPM + 1) = 4.571, p.adj < 0.001; male vs. normal, log2(RPM + 1) = 4.480, p.adj < 0.001) (Fig. S2A), Helicobacter pylori infection (infected vs. uninfected, log₂(RPM + 1) = 5.266, p.adj < 0.001) (Fig. S2B), and pathological stage (stage I-IV vs. normal) (Fig. S2C). Moreover, miR-124-3p was associated with OS, progression-free interval (PFI), and disease-specific survival (DSS) (Fig. S2D-F). Expression differences were also evident across TNM stages, with significant statistical variation (Fig. S2G-I).

miR-124-3p specifically bound to AHR mRNA

Candidate miRNAs targeting AHR were screened using TargetScan 7.1 and miRDB and filtered based on context percentile scores (Table 1). A Venn diagram illustrated the overlap of the miR-124-3p family between the two databases (Fig. 5A and B). The interaction between miR-124-3p and AHR mRNA was experimentally validated using a dual-luciferase reporter assay. In AHR-WT MKN-45 cells, overexpression of a miR-124-3p mimic led to a significant reduction in luciferase activity, whereas no effect was observed in AHR-MUT cells (Fig. 5C-D), consistent with the correlation observed in the LinkedOmics analysis (Fig. 3D). Figure S3A-E showed the transfection efficiency of the PC luciferase plasmid and the empty vector, further confirming the reliability of the transfection.

Fig. 5.

Fig. 5

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Correlation analysis between miR-124-3p and AHR mRNA and validation by dual-luciferase assay. A, B Predicted AHR binding sites and potential binding sequences from TargetScan and miRDB online tools, with a Venn diagram showing the correlation of the miR-124-3p family with AHR across the two databases; C predicted target binding sequence between miR-124-3p and AHR mRNA; D dual-luciferase reporter assay validating the direct targeting of AHR mRNA by miR-124-3p. Statistical significance was determined using a two-tailed t-test. ****p < 0.0001 (three independent biological replicates)

miR-124-3p inhibited STAD cell proliferation and migration by suppressing AHR expression in vitro

To examine the functional consequences of miR-124-3p upregulation in GC cells, we assessed AHR expression, autophagy-related markers, and cellular motility. Immunoblotting revealed that transfection with a miR-124-3p mimic markedly reduced AHR and p62 expression, while increasing the LC3II/I ratio and Beclin-1 levels (Fig. 6A-B, Supplementary Materials A-B). These effects were opposite to those induced by chloroquine (CQ), which suppressed Beclin-1 expression and decreased the LC3II/I ratio, and CQ treatment reversed the miR-124-3p-mediated downregulation of p62 and AHR. Conversely, the miR-124-3p inhibitor elicited opposite effects. Quantitative analyses of Western blot results are shown in Fig. S4A-H. Wound-healing and Transwell assays further demonstrated that the miR-124-3p mimic markedly suppressed STAD cell migration and invasion (Fig. 6C-D), effects that were partially restored by CQ treatment. In contrast, the miR-124-3p inhibitor enhanced migration and invasion, with quantitative results shown in Fig. S4I-L.

Fig. 6.

Fig. 6

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Regulation of AHR and autophagy-related molecules by miR-124-3p in STAD cells in vitro. A Expression levels of AHR and autophagy-related biomarkers (p62, Beclin-1, and LC3) under different treatment conditions (NC-mimic, miR-124-3p mimic, miR-124-3p mimic + CQ, and miR-124-3p inhibitor) in MKN-45 cells; B Expression of AHR and autophagy-related biomarkers in AGS cells; C Wound-healing assays of MKN-45 and AGS cells (scale bar = 200 μm) under different treatments, including NC-mimic, miR-124-3p mimic, miR-124-3p mimic + CQ, and miR-124-3p inhibitor; D Transwell invasion assays of MKN-45 and AGS cells (scale bar = 100 μm)

To clarify the role of autophagy in the miR-124-3p/AHR axis, STAD cell lines with Beclin-1 knockdown were established, and knockdown efficiency was confirmed at both mRNA and protein levels (Fig. 7A-D). Wound-healing and Transwell assays revealed that Beclin-1 knockdown significantly reversed the inhibitory effects of miR-124-3p overexpression on cell migration and invasion (Fig. 7E-J), indicating that Beclin-1-mediated autophagy plays a critical role in this regulatory axis.

Fig. 7.

Fig. 7

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Knockdown of Beclin-1 reverses miR-124-3p-mediated effects on gastric cancer cell behavior. Negative control siRNA (NC) and BECN1 siRNA (siBECN1) were transfected into MKN-45 and AGS cells, and knockdown efficiency of BECN1 mRNA and protein levels was measured at 48 h in (A, C) MKN-45 cells and (B, D) AGS cells. E Wound-healing assays of MKN-45 and AGS cells (scale bar = 200 μm) under different treatments, including NC-mimic, miR-124-3p mimic, miR-124-3p mimic + siNC, and miR-124-3p mimic + siBeclin-1. F Transwell invasion assays of MKN-45 and AGS cells (scale bar = 100 μm). G, I Wound-healing rate and number of invasive cells in MKN-45 cells, and (H, J) corresponding results in AGS cells. Statistical significance was determined using multiple t-tests with Holm-Sidak correction. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Further rescue experiments demonstrated that transfection with miR-124-3p reduced wound closure, invasion, and migration of MKN-45 cells (Figs. 8A-C, S5A-C), while also suppressing cell proliferation and viability (Figs. 8D-E, S5D). Overexpression of AHR restored these cellular functions that had been impaired by miR-124-3p. In AGS cells, Western blot analysis similarly confirmed that AHR expression was regulated by miR-124-3p (Fig. S5E, Supplementary Materials C).

Fig. 8.

Fig. 8

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AHR overexpression reverses miR-124-3p-mediated changes in STAD cell behavior in vitro. A Wound-healing assay (scale bar = 200 μm) and Transwell invasion assay (scale bar = 50 μm) in MKN-45 cells; B–D quantitative analysis of migrated cell numbers and wound-healing rates in MKN-45 cells, analyzed using GraphPad Prism 8.0; E CCK-8 proliferation assay in MKN-45 cells. Corresponding experiments in AGS cells are shown in Fig. S4. Statistical significance was determined using multiple t-tests with Holm-Sidak correction. *p < 0.05, **p < 0.01 (three independent biological replicates)

Collectively, the data demonstrate that miR-124-3p inhibits GC cell growth, migration, and invasion through AHR downregulation and autophagy activation, while AHR overexpression can partially reverse these inhibitory effects.

MiR-124-3p inhibited STAD growth in vivo and regulated AHR/β-catenin-related autophagy in vitro

Increasing evidence indicates that AHR regulates β-catenin and plays a critical role in autophagy induction, thereby influencing tumor cell apoptosis [27, 28]. In vivo, transfection with miR-124-3p agomir significantly reduced tumor weight in nude mice bearing subcutaneous STAD xenografts (Fig. 9A-B), whereas no difference was detected between the NC-agomir group and the negative control. Moreover, miR-124-3p agomir treatment markedly decreased the number of nuclear AHR-positive cells in xenograft tumors (Fig. 9C-D). In vitro, miR-124-3p mimic transfection significantly reduced MKN-45 cell viability (Fig. 9E).

Fig. 9.

Fig. 9

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miR-124-3p regulates tumor growth through the AHR/β-catenin axis In vivo and in vitro. A Subcutaneous xenograft tumor model in nude mice; B tumor weight at week 4; C immunohistochemical staining of AHR in tumor tissues (scale bar = 20 μm); D quantification of the average optical density of AHR protein expression; E CCK-8 proliferation assay in MKN-45 cells. Statistical significance was determined using multiple t-tests with Holm-Sidak correction. *p < 0.05 (three independent biological replicates)

To further investigate the role of the AHR/β-catenin pathway in STAD, GO annotation was performed for miR-124-3p-targeted proteins and AHR-related biological processes (Table 2), and results were visualized using Metascape (Fig. 10A). AHR was found to be enriched in tumor-associated pathways, including response to wounding and positive regulation of cell migration. STRING analysis further revealed the PPI network of AHR downstream regulators, highlighting a potential interaction with β-catenin (Fig. 10B).

Table 2.

All primer sequences for qPCR

Gene Forward Primer(5’-3’) Reverse Primer(5’-3’)
AHR ACATCACCTACGCCAGTCG CGCTTGGAAGGATTTGACTTGA
CTNNB1 AGCTTCCAGACACGCTATCAT CGGTACAACGAGCTGTTTCTAC
Wnt4 CTCCACACTCGACTCCTTGC CCGAAGAGATGGCGTACACG
GSK-3β GGCAGCATGAAAGTTAGCAGA GGCGACCAGTTCTCCTGAATC
c-myc TCCCTCCACTCGGAAGGAC CTGGTGCATTTTCGGTTGTTG
cyclin D1 GCTGCGAAGTGGAAACCATC CCTCCTTCTGCACACATTTGAA
GAPDH GGAGCGAGATCCCTCCAAAAT GGCTGTTGTCATACTTCTCATGG
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Fig. 10.

Fig. 10

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Biological effects of AHR expression and qPCR results of related molecules in MKN-45 cells. A GO pathway annotations from the Metascape database; B PPI network constructed using the STRING database; C–H RT-PCR quantification of related genes analyzed using GraphPad Prism8.0. Statistical significance was determined using multiple t-tests with Holm-Sidak correction. *p < 0.05 (three independent biological replicates)

Functional validation showed that treatment of MKN-45 and AGS cells with the AHR inhibitor CH-223,191 (0.03 µM), agonist ITE (3 nM), and miR-124-3p mimic significantly downregulated AHR mRNA and protein expression (Figs. 10C, S6A, S7F), suppressed CTNNB1 mRNA and β-catenin protein levels (Figs. 10D, S6B, S7G), and upregulated GSK-3β mRNA (Fig. 10H). By contrast, no significant changes were detected in the mRNA levels of c-Myc, Cyclin D1, or Wnt4 (Fig. 10E-G). Notably, these in vitro findings were opposite to the overall positive correlation between miR-124-3p and AHR expression observed in the LinkedOmics database (Fig. 3D), suggesting that database-derived associations may be influenced by cohort composition or normalization methods and require functional validation.

Further analysis demonstrated that AHR inhibition increased the LC3II/I ratio and Beclin-1 expression, whereas miR-124-3p mimic transfection decreased p-p65 protein levels (Figs. 11A-B, S6, Supplementary Materials D-E). Collectively, these results indicate that miR-124-3p suppresses STAD cell proliferation and migration by downregulating AHR and β-catenin, thereby activating autophagy.

Fig. 11.

Fig. 11

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miR-124-3p regulates tumor growth via the AHR/β-catenin axis In vivo and in vitro. A Western blot was used to detect the expression of molecules related to the AHR/β-catenin signaling pathway; B Expression levels of the corresponding proteins in the AGS cell line. All protein expression levels were quantitatively analyzed using GraphPad Prism 8.0, and the statistical results are shown in Fig. S6. Statistical significance was determined using multiple t-tests with Holm-Sidak correction. ns, not significant; *p < 0.05, **p < 0.01 (three independent biological replicates)

Discussion

miR-124-3p is a microRNA with significant research value in multiple cancers, and its expression pattern and biological functions have been gradually revealed in recent years [29]. In the current study, GC tissues exhibited pronounced downregulation of miR-124-3p, and reduced expression was significantly correlated with unfavorable outcomes, including OS, DSS, and PFI. Previous studies in hepatocellular carcinoma, prostate cancer, and bladder cancer have confirmed the tumor-suppressive role of miR-124-3p through regulation of the cell cycle, apoptosis induction, and metastasis inhibition [15, 30]. Consistent with these findings, the data suggest that miR-124-3p also functions as a tumor suppressor in GC. Furthermore, its reduced expression showed a negative association with TNM stage and histological grade, suggesting involvement in key pathological events from the early phases of tumor development. ROC curve analysis suggested its diagnostic potential, with moderate sensitivity and specificity. Although the AUC value did not reach the ideal level for clinical application, its prognostic relevance indicates that miR-124-3p remains a promising biomarker worthy of further investigation.

AHR is a well-characterized ligand-activated transcription factor that was initially recognized for its role in xenobiotic detoxification [31–33]. With advancing research, the role of AHR in immune regulation, inflammation, and tumorigenesis has become increasingly apparent. In breast cancer, glioblastoma, and hepatocellular carcinoma, AHR has been shown to promote malignant transformation by regulating the cell cycle and apoptosis [34, 35]. In this study, we observed significantly elevated AHR expression in GC tissues, which correlated with poor prognosis, consistent with some previous reports. However, other studies have suggested that AHR may exert anti-tumor effects in certain cancers [36], highlighting the tissue-specific nature of its functions. Importantly, we experimentally confirmed that AHR acts as an oncogenic factor in GC and that it can be directly downregulated by miR-124-3p, thereby supporting its potential as a therapeutic target.

At the mechanistic level, our findings demonstrated that miR-124-3p downregulates AHR, thereby activating autophagy and suppressing GC cell proliferation, migration, and invasion. Functional validation using the autophagy inhibitor CQ further established the central role of autophagy in this pathway [37–40]. This not only corroborates previous findings but also identifies a novel miR-124-3p/AHR/autophagy regulatory axis that may provide new opportunities for autophagy-targeted therapies. Interestingly, bioinformatics analyses suggested a positive correlation between miR-124-3p and AHR, whereas in vitro and in vivo experiments revealed a negative regulatory relationship. This discrepancy may be attributed to sample heterogeneity, differences in sequencing platforms and data normalization methods [41–43], as well as the multi-target and multi-pathway characteristics of miRNAs [44, 45]. These results highlight the importance of integrating bioinformatic predictions with experimental validation.

Beyond AHR-mediated regulation of β-catenin, β-catenin itself has also been shown to positively regulate AHR transcription and activity. For instance, in colorectal cancer cells, β-catenin enhances AHR-dependent gene transcription by inducing AHR expression [46], while elevated AHR expression was observed in CTNNB1-overexpressing liver tumor models [46–48]. Reviews further suggest extensive crosstalk between Wnt/β-catenin and AHR signaling, potentially forming a bidirectional feedback loop [49]. Together with our findings—that miR-124-3p downregulates the AHR/β-catenin axis and activates autophagy to suppress GC progression—these results raise the possibility that β-catenin may also regulate AHR expression in GC cells, a hypothesis warranting further exploration.

The role of AHR in GC may extend beyond the autophagy pathway, as indicated by the present findings. Previous studies have shown that AHR activation in breast cancer enhances resistance to chemotherapy and UV-induced apoptosis [50]. At the immune level, AHR upregulates PD-L1 and IDO expression, suppressing T-cell function and promoting immune evasion [51]. Furthermore, AHR influences the Th17/Treg balance and Tr1 differentiation, thereby shaping the tumor immune microenvironment [52, 53]. AHR has also been implicated in regulating the cell cycle, migration, genomic stability, and stem cell fate [54]. These findings indicate that AHR may promote GC progression through multiple pathways. Functional experiments showed that miR-124-3p mimics reduced AHR protein abundance, enhanced autophagy, and suppressed the invasive phenotype of GC cells in vitro. In vivo, miR-124-3p overexpression in xenograft models resulted in reduced tumor weight and mitigated body weight loss, indicating an anti-tumor effect. Importantly, AHR overexpression partially reversed these changes, confirming the central role of the miR-124-3p/AHR axis. Collectively, these results establish AHR as a multifaceted oncogenic driver in GC and highlight miR-124-3p as a tumor suppressor that counteracts its activity, providing convergent evidence from both computational prediction and experimental validation.

This study combined bioinformatic prediction and experimental validation, representing a methodological innovation. Using the TCGA-STAD dataset for initial screening, and integrating TargetScan, miRDB, Metascape, and STRING, we systematically constructed a predicted regulatory network of miR-124-3p and AHR, suggesting involvement in multiple downstream pathways. Unlike previous studies that relied on single databases or small-sample experiments, we adopted a comprehensive approach integrating bioinformatics with in vitro and in vivo experiments, thus achieving a closed-loop research path from large-scale prediction to experimental confirmation.

In terms of experimental design, we went beyond traditional cell-based miRNA studies by incorporating both in vitro and in vivo models to strengthen the reliability and translational value of our findings. In vitro, miR-124-3p mimics significantly suppressed GC cell migration and invasion while activating autophagy via AHR downregulation. In vivo, miR-124-3p overexpression reduced tumor weight and attenuated body weight loss. Importantly, AHR overexpression partially reversed these effects, confirming the central role of the miR-124-3p/AHR axis in GC biology. This integrative strategy, which combines bioinformatics prediction, experimental validation, and in vitro/in vivo studies, enhanced the robustness of our conclusions and provides a valuable model for future biomarker research.

miR-124-3p demonstrated strong translational potential in this study. Its expression correlated significantly with pathological stage, TNM stage, and overall survival, underscoring its value as a prognostic biomarker. Functional experiments confirmed that miR-124-3p suppresses GC progression by downregulating AHR and activating autophagy, thereby supporting its dual role as a diagnostic/prognostic marker and a therapeutic target. Building on these findings, future therapeutic strategies may focus on the miR-124-3p/AHR axis, including the development of miR-124-3p mimics or AHR inhibitors, to improve clinical outcomes. Importantly, our results suggest that miR-124-3p may function as a multifunctional biomarker integrating diagnosis, treatment, and disease monitoring. Its expression not only correlates with clinicopathological features and OS but also links to autophagy-mediated metabolic reprogramming, revealing a mechanism by which miR-124-3p overcomes apoptotic resistance via AHR targeting. Future studies may investigate its application in personalized therapy and develop multimodal detection systems, including: (i) early diagnostic models incorporating radiomics, (ii) intraoperative localization of micrometastases using nanoprobe-based technologies, and (iii) dynamic monitoring of serum miR-124-3p levels to guide AHR-targeted maintenance therapy. These strategies highlight new opportunities for precision medicine in GC.

Despite the novel findings, our study has limitations. In the validation cohort, ROC analysis yielded an AUC of 0.603, indicating only moderate predictive accuracy when used as a single biomarker. Additionally, the study was mainly based on TCGA data and a limited number of clinical samples, lacking validation in large, multi-center cohorts, which may affect generalizability. The in vivo experiments included only three mice per group, reducing statistical power. Furthermore, while our work focused on the miR-124-3p/AHR/autophagy axis, GC progression involves complex molecular networks, and miR-124-3p may regulate other signaling pathways yet to be explored. Finally, the clinical application of miRNAs still faces challenges, including stability, delivery efficiency, and potential off-target effects, which require further investigation.

Future research directions may be explored from multiple perspectives to further investigate the biological functions and clinical applications of miR-124-3p in STAD. First, the clinical sample size should be expanded, and diverse population-based clinical datasets should be incorporated to validate the stability and reliability of miR-124-3p as a biomarker and explore its potential synergistic effects with other established STAD biomarkers. Second, further mechanistic studies are required to identify the upstream regulatory factors of miR-124-3p and to elucidate its roles in additional signaling pathways, thereby advancing a more comprehensive understanding of its molecular network in STAD. In addition, developing optimized miRNA delivery systems, such as lipid nanoparticles, viral vectors, or exosome-mediated delivery, may enhance the in vivo stability and targeting efficiency of miR-124-3p, laying the groundwork for its clinical application. Simultaneously, combining CRISPR-Cas9 technology, small-molecule inhibitors, or AHR antagonists may help further refine miR-124-3p-mediated AHR-targeted therapeutic strategies, offering new possibilities for precision treatment of STAD. Ultimately, in-depth evaluation through animal models and clinical trials will be essential to assess the safety and efficacy of miR-124-3p-based therapies, accelerating their clinical translation in the context of STAD precision medicine.

Conclusion

This study demonstrated that miR-124-3p exerted a significant tumor-suppressive effect in the progression of STAD by negatively regulating AHR-mediated autophagy. Both bioinformatics analysis and experimental validation revealed that miR-124-3p serves as a prognostic predictor in STAD, suppressing the proliferation, migration, and invasion of STAD cells while promoting tumor cell death through the activation of autophagy. Furthermore, AHR overexpression significantly reversed the tumor-suppressive effects mediated by miR-124-3p, suggesting that the miR-124-3p/AHR axis provides a novel therapeutic target distinct from traditional apoptosis-related pathways. In vivo xenograft experiments in nude mice further confirmed that miR-124-3p overexpression suppressed tumor growth and reduced tumor burden. Collectively, these findings establish miR-124-3p as a prognostic biomarker and functional tumor suppressor, highlighting its central role in the molecular pathogenesis of STAD.

Materials and methods

Public database analysis

Pan-cancer expression and prognostic relevance of the AHR were profiled using Gene Expression Profiling Interactive Analysis (GEPIA) and the Gene Expression Omnibus (GEO); RNA and protein levels in stomach/gastric adenocarcinoma were retrieved from the Human Protein Atlas (HPA). Differential expression in stomach adenocarcinoma (STAD) used an integrated The Cancer Genome Atlas-STAD (TCGA-STAD) and Genotype-Tissue Expression (GTEx) cohort: the Normal group combined GTEx stomach tissues (n = 174) and TCGA-STAD adjacent normals (n = 36) processed with the Toil pipeline, and the Tumor group comprised TCGA-STAD primary tumors (n = 414). All statistical comparisons contrasted Tumor vs. Normal; visualizations were generated in R (version 3.6.3) with ggplot2 (version 3.3.3). Candidate AHR-targeting microRNAs (miRNAs) were predicted with TargetScan (version 7.1) and miRDB, followed by correlation analysis, candidate prioritization, and pathway enrichment in LinkedOmics. Clinical and prognostic features of miR-124-3p in STAD and controls were derived from TCGA and GTEx. Survival associations for AHR mRNA and miR-124-3p were evaluated using Kaplan-Meier Plotter. Functional annotation of the 20 most altered AHR-associated genes was obtained via Metascape, and protein-protein interaction (PPI) networks for annotated pathways were reconstructed and visualized using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins)(Fig. S7).

RNA mimic and plasmid transfection

MKN-45 and AGS cells were seeded in 6-well plates at a density of 1 × 106 cells per well. When cultures reached 70%–80% confluence, cells were transfected with the indicated RNA mimics. The sequence of the miR-124-3p mimic was: 5’-UAAGGCACGCGUGGAAUGCCAA-3’ (sense), 3’-AUUCCGUGCGCCACUUACGGUU-5’ (antisense); the sequence of the negative control (NC mimic) was: 5’-UUUGUACUACACAAAAGUACUG-3’ (sense), 3’-AAACAUGAUGUGUUUCAUGAC-5’ (antisense). The miR-124-3p inhibitor and the corresponding luciferase plasmids (including positive control (PC), empty vector, and AHR expression plasmid) were co-transfected into STAD cells. After transfection, cells were incubated in a complete medium for 48 h and used for subsequent experimental analysis.

SiRNA transfection

MKN-45 and AGS cells were plated in 6-well dishes at 1 × 106 cells per well. Upon reaching 70%–80% confluence, cells were transfected with the indicated siRNAs [55]. The sequences of BECN1 siRNAs were 5′-UUCAGACCCAUCUUAUUGGTT-3′ and 5′-UUGUUGGACGUCUUAGACCTT-3′. The sequence of the negative control siRNA was 5′-AUCUUAGGCAGAUCGUCGCdTdT-3′. Following transfection, cells were maintained in growth medium for 48 h before subsequent analyses.

Dual-Luciferase reporter assay

The wild-type (AHR-WT) 3′-UTR fragment of AHR containing the predicted miR-124-3p binding site was cloned into the pmirGLO vector, and a mutant construct (AHR-MUT) with disrupted binding sequences was generated accordingly. MKN-45 cells were co-transfected with AHR-WT, AHR-MUT, or empty pmirGLO vectors together with miR-124-3p mimics, inhibitors, or negative controls. After 24 h, luciferase activity was determined using the Dual-Luciferase Reporter Assay System (Promega) and quantified with a GloMax fluorescence reader.

Wound healing assay

MKN-45 and AGS cells were seeded in 6-well plates and transfected with the indicated mimics, inhibitors, or plasmids. At 70–80% confluence, a linear scratch was introduced using a 200 µL pipette tip. After washing with PBS to remove debris, cells were cultured in serum-free medium for 24 h. Wound closure was photographed at 0 and 24 h and quantified to assess migratory capacity.

Transwell assay

Matrigel was diluted 1:8 in RPMI-1640 medium and coated onto Transwell chamber membranes, followed by incubation at 37 °C for 6 h to allow gel polymerization. Transfected MKN-45 and AGS cells (1 × 106) were seeded into the upper chamber in serum-free medium, while 500 µL of medium containing 20% fetal bovine serum was added to the lower chamber as a chemoattractant. After 48 h of incubation at 37 °C in 5% CO₂, migrated and invaded cells on the lower surface were fixed with 4% paraformaldehyde (PFA), stained with crystal violet, and counted in five randomly selected high-power fields per well.

RNA extraction and quantitative PCR (qPCR)

Total RNA from MKN-45 cells was extracted using TRIzol reagent, and reverse transcription was performed using the PrimeScript RT reagent kit. qPCR was conducted in triplicate using the Applied Biosystems 7900 real-time PCR system, with a maximum of 40 amplification cycles. CT values from different samples were analyzed using the ΔΔCt method, and relative expression levels were calculated utilizing the 2−ΔΔCt method. Untreated normal cells were used as the control group, and mRNA levels were normalized to relative values. The average of all non-missing values was calculated to compensate for any missing data. Primer sequences for the specific genes are listed in Table 3.

Table 3.

Screening of correlated MiRNAs targeting AHR based on site type and contextual scores

miRNA Site type Context + + score Context + + score percentile Conserved branch length PCT
hsa-miR-124-3p.1 8mer -0.37 98 6.131 0.97
hsa-miR-124-3p.2 7mer-m8 -0.22 80 6.131 0.86
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Western blot

Cells were lysed using RIPA buffer supplemented with PMSF, NaF, Na3VO4, and protease inhibitors. After lysis, the protein was separated on a 10% Tris-glycine gel and transferred to a PVDF membrane at 200 mA for 2 h. After transfer, membranes were blocked in 5% non-fat milk at ambient temperature for 1 h and incubated overnight at 4 °C with specific primary antibodies (AHR, β-Catenin, p62, Beclin-1, LC3, p-p65, and α-tubulin; all purchased from Cell Signaling Technology) at a 1:2000 dilution. After three washes in TBST, membranes were incubated with HRP-conjugated secondary antibodies (1:5000, Cell Signaling Technology) for 1 h at ambient temperature, followed by additional washes. Signals were developed using ECL reagents (Thermo Fisher Scientific) and visualized with the ChemiDoc™ XRS + imaging system (Bio-Rad).

Animal experiments

Male BALB/c nude mice (4–6 weeks old, n = 3 per group) were randomly assigned to the NC, NC-agomir, and miR-124-3p agomir groups. All mice were purchased from Vital River Laboratories (V-TEX, Cat. No.: VTX-703). Each group received a 200 nmol dose of the respective agomir via tail vein injection once a week for three injections. After the first injection, mice were subcutaneously inoculated with 200 µL of SGC-7901 cell suspension. Following tumor development, the mice were maintained for 4 weeks, during which body weight was measured weekly. After 4 weeks, all mice were euthanized, and tumor specimens were collected for analysis.

All procedures complied with the Regulations on the Administration of Laboratory Animals and institutional ethical standards (Approval No. 20240309E). Experiments were conducted under the 3Rs principle (Replacement, Reduction, Refinement) to minimize animal suffering.

Immunohistochemistry (IHC)

IHC data for normal and tumor tissues were obtained from the Human Protein Atlas (HPA) database, with all annotations and validations performed by domain experts.

For xenograft tumor samples, tissues were fixed in 4% PFA, paraffin-embedded, and sectioned. After deparaffinization, antigen retrieval was performed in 10 mM citrate buffer. Sections were incubated with anti-AHR antibody (1:800; Cell Signaling Technology), followed by biotinylated anti-rabbit IgG secondary antibody (1:200; Cell Signaling Technology). Staining intensity was quantified as average optical density (AOD).

Statistical analysis

All analyses were conducted using GraphPad Prism (v8.0). Data are presented as mean ± standard error of the mean (SEM) from three independent replicates (n = 3). Comparisons between two groups were performed with a two-tailed unpaired Student’s t-test, while one-way ANOVA with Holm–Sidak post hoc correction was applied for multiple groups. For experiments with two independent variables, two-way ANOVA followed by Holm–Sidak correction was used. When multiple pairwise comparisons were required, unpaired t-tests with Holm–Sidak correction were performed. Data normality was assessed with the Shapiro–Wilk test; if normality was not met, non-parametric tests were used (Mann–Whitney U test for two groups, Kruskal–Wallis test with Dunn’s post hoc for multiple groups). Statistical significance was defined as *p < 0.05 (significant), **p < 0.01, ***p < 0.001, ****p < 0.0001.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (311.4KB, jpg)

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Supplementary Material 2 (569.8KB, jpg)

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Supplementary Material 8 (8.9MB, jpg)

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Acknowledgements

None.

Abbreviations

AHR

Aryl Hydrocarbon Receptor

AOD

Average Optical Density

AUC

Area Under the Curve

CI

Confidence Interval

CQ

Chloroquine

DSS

Disease-Specific Survival

GEPIA

Gene Expression Profiling Interactive Analysis

GEO

Gene Expression Omnibus

GO

Gene Ontology

H. pylori

Helicobacter pylori

HPA

Human Protein Atlas

HR

Hazard Ratio

IHC

Immunohistochemistry

K-M

Kaplan-Meier

OS

Overall Survival

PC

Positive Control

PFI

Progression-Free Interval

PPI

Protein-Protein Interaction

qPCR

Quantitative PCR

ROC

Receiver Operating Characteristic

STAD

Stomach Adenocarcinoma

TCGA

The Cancer Genome Atlas

TNM

Tumor-Node-Metastasis

Author contributions

QW, SW, WD, and XL performed bioinformatics analyses, conducted in vitro and in vivo experiments, and drafted the manuscript. XL and CL provided technical support and contributed to data analysis. SX conceived and supervised the study, interpreted the data, and revised the manuscript critically. All authors read and approved the final version of the manuscript.

Funding

This work was supported by a grant from the Sichuan Province Key Clinical Specialty Construction Project Sichuan Medical Research Project (No. S22037).

Data availability

All data generated or analyzed during this study are included in this article and/or its supplementary material files. Further inquiries can be directed to the corresponding author on a reasonable request.

Declarations

Ethics approval and consent to participate

All animal experiments were approved by the Animal Ethics Committee of Wuhan University (No. 20240309E).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Qian Wan, Siwei Wang, Wei Dong and Xinyi Liu are regarded as co-first authors.

References

  • 1.Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48. 10.1016/s0140-6736(20)31288-5. [DOI] [PubMed] [Google Scholar]
  • 2.Guan WL, He Y, Xu RH. Gastric cancer treatment: recent progress and future perspectives. J Hematol Oncol. 2023;16(1). 10.1186/s13045-023-01451-3. [DOI] [PMC free article] [PubMed]
  • 3.López MJ, Carbajal J, Alfaro AL, et al. Characteristics of gastric cancer around the world. Crit Rev Oncol/Hematol. 2023;181:103841. 10.1016/j.critrevonc.2022.103841. [DOI] [PubMed] [Google Scholar]
  • 4.Xia JY, Aadam AA. Advances in screening and detection of gastric cancer. J Surg Oncol. 2022;125(7):1104–9. 10.1002/jso.26844. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Yang WJ, Zhao HP, Yu Y, et al. Updates on global epidemiology, risk and prognostic factors of gastric cancer. World J Gastroenterol. 2023;29(16):2452–68. 10.3748/wjg.v29.i16.2452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Machlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric cancer: Epidemiology, risk Factors, Classification, genomic characteristics and treatment strategies. IJMS. 2020;21(11):4012. 10.3390/ijms21114012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Song Z, Wu Y, Yang J, Yang D, Fang X. Progress in the treatment of advanced gastric cancer. Tumour Biol. 2017;39(7):101042831771462. 10.1177/1010428317714626. [DOI] [PubMed] [Google Scholar]
  • 8.Ling Q, Huang ST, Yu TH, et al. Optimal timing of surgery for gastric cancer after neoadjuvant chemotherapy: a systematic review and meta-analysis. World J Surg Onc. 2023;21(1). 10.1186/s12957-023-03251-y. [DOI] [PMC free article] [PubMed]
  • 9.Bo W, Wang X, Zhang M, Zhang Z. ZNF655 mediated by LINC01210/miR-124‐3p axis promotes the progression of gastric cancer. Kaohsiung J Med Scie. 2022;39(3):200–8. 10.1002/kjm2.12634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lu Y, Zhang T, Shan S, et al. MiR-124 regulates transforming growth factor-β1 induced differentiation of lung resident mesenchymal stem cells to myofibroblast by repressing Wnt/β-catenin signaling. Dev Biol. 2019;449(2):115–21. 10.1016/j.ydbio.2019.02.010. [DOI] [PubMed] [Google Scholar]
  • 11.Roshani Asl E, Rasmi Y, Baradaran B. MicroRNA-124‐3p suppresses PD‐L1 expression and inhibits tumorigenesis of colorectal cancer cells via modulating STAT3 signaling. J Cell Physiol. 2021;236(10):7071–87. 10.1002/jcp.30378. [DOI] [PubMed] [Google Scholar]
  • 12.Reyes-Long S, Cortes-Altamirano JL, Clavijio-Cornejo D, et al. Nociceptive related MicroRNAs and their role in rheumatoid arthritis. Mol Biol Rep. 2020;47(9):7265–72. 10.1007/s11033-020-05700-3. [DOI] [PubMed] [Google Scholar]
  • 13.Deng D, Luo K, Liu H, et al. p62 acts as an oncogene and is targeted by miR-124-3p in glioma. Cancer Cell Int. 2019;19(1). 10.1186/s12935-019-1004-x. [DOI] [PMC free article] [PubMed]
  • 14.Wang Y, Hong Y, Mao S, et al. Downregulation of miR-124-3p suppresses the development of the deep retinal blood vessels by enhancing the Stat1/Ripk1 pathway in mouse retinal microglia. Exp Eye Res. 2023;233:109551. 10.1016/j.exer.2023.109551. [DOI] [PubMed] [Google Scholar]
  • 15.Song Bfeng, Xu Lzhe, Jiang K, Cheng F. MiR-124-3p inhibits tumor progression in prostate cancer by targeting EZH2. Funct Integr Genomics. 2023;23(2). 10.1007/s10142-023-00991-8. [DOI] [PMC free article] [PubMed]
  • 16.Xiang H, Luo M, Hou P, et al. miR-124-3p combined with miR-506-3p delay hepatic carcinogenesis via modulating Sirtuin 1. Biomarkers. 2021;26(3):196–206. 10.1080/1354750x.2020.1854856. [DOI] [PubMed] [Google Scholar]
  • 17.Bartoszewicz Z, Kościelak J, Pacuszka T. Structure of a new disialoganglioside GD1c from spontaneous murine thymoma. Carbohydr Res. 1986;151:77–88. 10.1016/s0008-6215(00)90331-8. [DOI] [PubMed] [Google Scholar]
  • 18.Rothhammer V, Quintana FJ. The Aryl hydrocarbon receptor: an environmental sensor integrating immune responses in health and disease. Nat Rev Immunol. 2019;19(3):184–97. 10.1038/s41577-019-0125-8. [DOI] [PubMed] [Google Scholar]
  • 19.Arenas-Huertero F, Zaragoza-Ojeda M, Sánchez-Alarcón J, et al. Involvement of Ahr pathway in toxicity of aflatoxins and other Mycotoxins. Front Microbiol. 2019;10. 10.3389/fmicb.2019.02347. [DOI] [PMC free article] [PubMed]
  • 20.Minacori M, Fiorini S, Perugini M, et al. AhR and STAT3: A dangerous duo in chemical carcinogenesis. IJMS. 2025;26(6):2744. 10.3390/ijms26062744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhang X, Zhao Y, Chen X. Collagen extracellular matrix promotes gastric cancer immune evasion by activating IL4I1-AHR signaling. Translational Oncol. 2024;49:102113. 10.1016/j.tranon.2024.102113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Xie J, Pang Y, Wu X. Taxifolin suppresses the malignant progression of gastric cancer by regulating the AhR/CYP1A1 signaling pathway. Int J Mol Med. 2021;48(5). 10.3892/ijmm.2021.5030. [DOI] [PMC free article] [PubMed]
  • 23.Li T, Deng L, He X, et al. MST4 Predicts Poor Prognosis And Promotes Metastasis By Facilitating Epithelial–Mesenchymal Transition In Gastric Cancer CMAR. 2019;Volume 11:9353–9369. doi:10.2147/cmar.s219689. [DOI] [PMC free article] [PubMed]
  • 24.LI H, XIE S, LIU M, et al. The clinical significance of downregulation of mir-124-3p, mir-146a-5p, mir-155-5p and mir-335-5p in gastric cancer tumorigenesis. Int J Oncol. 2014;45(1):197–208. 10.3892/ijo.2014.2415. [DOI] [PubMed] [Google Scholar]
  • 25.Jiang Y, Xiao H, Sun L, Zhang Y, Liu S, Luo B. LMP2A suppresses the role of AHR pathway through ERK signal pathway in EBV-associated gastric cancer. Virus Res. 2021;297:198399. 10.1016/j.virusres.2021.198399. [DOI] [PubMed] [Google Scholar]
  • 26.Wang Y, Liu Y, Tang T, et al. The antitumour activity of 2-(4‐amino‐3‐methylphenyl)‐5‐fluorobenzothiazole in human gastric cancer models is mediated by AhR signalling. J Cell Mol Medi. 2019;24(2):1750–9. 10.1111/jcmm.14869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chen CT, Wu PH, Hu CC, et al. Aberrant upregulation of indoleamine 2,3-Dioxygenase 1 promotes proliferation and metastasis of hepatocellular carcinoma cells via coordinated activation of AhR and β-Catenin signaling. IJMS. 2021;22(21):11661. 10.3390/ijms222111661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Kim HR, Kang SY, Kim HO, Park CW, Chung BY. Role of Aryl hydrocarbon receptor activation and autophagy in Psoriasis-Related inflammation. IJMS. 2020;21(6):2195. 10.3390/ijms21062195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Chen G, Wang Y, Zhang L, Yang K, Wang X, Chen X. Research progress on miR-124-3p in the field of kidney disease. BMC Nephrol. 2024;25(1). 10.1186/s12882-024-03688-7. [DOI] [PMC free article] [PubMed]
  • 30.Zhao Q, Jiang F, Zhuang H, Chu Y, Zhang F, Wang C. MicroRNA miR-124-3p suppresses proliferation and epithelial–mesenchymal transition of hepatocellular carcinoma via ARRDC1 (arrestin domain containing 1). Bioengineered. 2022;13(4):8255–65. 10.1080/21655979.2022.2051686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Schiering C, Wincent E, Metidji A, et al. Feedback control of AHR signalling regulates intestinal immunity. Nature. 2017;542(7640):242–5. 10.1038/nature21080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Rakateli L, Huchzermeier R, van der Vorst EPC. AhR, PXR and CAR: from xenobiotic receptors to metabolic sensors. Cells. 2023;12(23):2752. 10.3390/cells12232752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kou Z, Tran F, Colon T, et al. AhR signaling modulates ferroptosis by regulating SLC7A11 expression. Toxicol Appl Pharmcol. 2024;486:116936. 10.1016/j.taap.2024.116936. [DOI] [PubMed] [Google Scholar]
  • 34.Benoit L, Jornod F, Zgheib E, et al. Adverse outcome pathway from activation of the AhR to breast cancer-related death. Environ Int. 2022;165:107323. 10.1016/j.envint.2022.107323. [DOI] [PubMed] [Google Scholar]
  • 35.Gabriely G, Wheeler MA, Takenaka MC, Quintana FJ. Role of AHR and HIF-1α in glioblastoma metabolism. Trends Endocrinol Metabolism. 2017;28(6):428–36. 10.1016/j.tem.2017.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Baker JR, Sakoff JA, McCluskey A. The Aryl hydrocarbon receptor (AhR) as a breast cancer drug target. Med Res Rev. 2019;40(3):972–1001. 10.1002/med.21645. [DOI] [PubMed] [Google Scholar]
  • 37.Debnath J, Gammoh N, Ryan KM. Autophagy and autophagy-related pathways in cancer. Nat Rev Mol Cell Biol. 2023;24(8):560–75. 10.1038/s41580-023-00585-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Onorati AV, Dyczynski M, Ojha R, Amaravadi RK. Targeting autophagy in cancer. Cancer. 2018;124(16):3307–18. 10.1002/cncr.31335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Li X, He S, Ma B. Autophagy and autophagy-related proteins in cancer. Mol Cancer. 2020;19(1). 10.1186/s12943-020-1138-4. [DOI] [PMC free article] [PubMed]
  • 40.Zhang J, Xiang Q, Wu M, et al. Autophagy regulators in cancer. IJMS. 2023;24(13):10944. 10.3390/ijms241310944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Russi S, Calice G, Ruggieri V, et al. Gastric normal adjacent mucosa versus healthy and cancer tissues: distinctive transcriptomic profiles and biological features. Cancers. 2019;11(9):1248. 10.3390/cancers11091248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Mounir M, Lucchetta M, Silva TC, et al. New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx. PLoS Comput Biol. 2019;15(3):e1006701. 10.1371/journal.pcbi.1006701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Wang Q, Armenia J, Zhang C, et al. Unifying cancer and normal RNA sequencing data from different sources. Sci Data. 2018;5(1). 10.1038/sdata.2018.61. [DOI] [PMC free article] [PubMed]
  • 44.Svoronos AA, Engelman DM, Slack FJ. OncomiR or tumor suppressor? The duplicity of MicroRNAs in cancer. Cancer Res. 2016;76(13):3666–70. 10.1158/0008-5472.can-16-0359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Disner GR, Lopes-Ferreira M, Lima C. Where the Aryl hydrocarbon receptor Meets the micrornas: literature review of the last 10 years. Front Mol Biosci. 2021;8. 10.3389/fmolb.2021.725044. [DOI] [PMC free article] [PubMed]
  • 46.Shiizaki K, Kido K, Mizuta Y. Insight into the relationship between aryl-hydrocarbon receptor and β-catenin in human colon cancer cells. PLoS ONE. 2019;14(11):e0224613. 10.1371/journal.pone.0224613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Schneider A, Branam A, Peterson R. Intersection of AHR and Wnt signaling in Development, Health, and disease. IJMS. 2014;15(10):17852–85. 10.3390/ijms151017852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Braeuning A. Interplay of β-Catenin with Xenobiotic-Sensing receptors and its role in glutathione S-Transferase expression. CDM. 2012;13(2):203–14. 10.2174/138920012798918381. [DOI] [PubMed] [Google Scholar]
  • 49.Grishanova AY, Klyushova LS, Perepechaeva ML. AhR and Wnt/β-Catenin signaling pathways and their interplay. CIMB. 2023;45(5):3848–76. 10.3390/cimb45050248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Bekki K, Vogel H, Li W, et al. The Aryl hydrocarbon receptor (AhR) mediates resistance to apoptosis induced in breast cancer cells. Pestic Biochem Physiol. 2015;120:5–13. 10.1016/j.pestbp.2014.12.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Kenison JE, Wang Z, Yang K, Snyder M, Quintana FJ, Sherr DH. The Aryl hydrocarbon receptor suppresses immunity to oral squamous cell carcinoma through immune checkpoint regulation. Proc Natl Acad Sci USA. 2021;118(19). 10.1073/pnas.2012692118. [DOI] [PMC free article] [PubMed]
  • 52.Quintana FJ, Basso AS, Iglesias AH, et al. Control of Treg and TH17 cell differentiation by the Aryl hydrocarbon receptor. Nature. 2008;453(7191):65–71. 10.1038/nature06880. [DOI] [PubMed] [Google Scholar]
  • 53.Mascanfroni ID, Takenaka MC, Yeste A, et al. Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α. Nat Med. 2015;21(6):638–46. 10.1038/nm.3868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Feng S, Cao Z, Wang X. Role of Aryl hydrocarbon receptor in cancer. Biochim Et Biophys Acta (BBA) - Reviews Cancer. 2013;1836(2):197–210. 10.1016/j.bbcan.2013.05.001. [DOI] [PubMed] [Google Scholar]
  • 55.Shao T, Ke H, Liu R, et al. Autophagy regulates differentiation of ovarian granulosa cells through degradation of WT1. Autophagy. 2022;18(8):1864–78. 10.1080/15548627.2021.2005415. [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.

Supplementary Materials

Supplementary Material 1 (311.4KB, jpg)
Supplementary Material 2 (569.8KB, jpg)
Supplementary Material 3 (1.2MB, jpeg)
Supplementary Material 4 (1.4MB, jpg)
Supplementary Material 5 (1.1MB, jpg)
Supplementary Material 6 (1.8MB, jpg)
Supplementary Material 7 (6.8MB, jpg)
Supplementary Material 8 (8.9MB, jpg)

Data Availability Statement

All data generated or analyzed during this study are included in this article and/or its supplementary material files. Further inquiries can be directed to the corresponding author on a reasonable request.

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