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Abstract

Background:

Luteolin, a common dietary flavonoid found in plants, has been shown to have anti-cancer properties. However, its exact mechanisms of action in non-small cell lung cancer (NSCLC) are still not fully understood, particularly its role in regulating broader genomic networks and specific gene targets. In this study, we aimed to elucidate the role of microRNAs (miRNAs) in NSCLC treated with luteolin, using A549 cells as a model system.

Materials and Methods:

miRNA profiling was conducted on luteolin-treated A549 cells using Exiqon microarrays, with validation of selected miRNAs by qRT-PCR. Bioinformatic analysis identified the regulatory roles of miRNAs in biological processes and pathways following luteolin treatment. Computational algorithms were employed to identify potential target genes. A549 cells were transfected with miR-106a-5p mimic and inhibitor or their corresponding controls. The expression levels of 2 genes, twist basic helix-loop-helix transcription factor 1 (TWIST1) and matrix metallopeptidase 2 (MMP2), and cell migration were assessed.

Results:

miRNA profiling identified 341 miRNAs, with 18 exhibiting significantly altered expression (P < 0.05). Subsequent qRT-PCR analysis confirmed altered expression of 6 selected miRNAs. KEGG and GO analyses revealed significant alterations in pathways and biological processes crucial for tumor biology. TWIST1 and MMP2, which both contain conserved miR-106a-5p binding sites, exhibited an inverse correlation with the expression levels of miR-106a-5p. Dual-luciferase reporter assays confirmed TWIST1 and MMP2 as direct targets of miR-106a-5p. Luteolin treatment led to a reduction in A549 cell migration, and this reduction was further amplified by the overexpression of miR-106a-5p.

Conclusion:

Luteolin inhibits A549 cell migration by modulating the miRNA landscape, shedding light on its mechanisms and laying the foundation for miRNA-based therapeutic approaches for NSCLC.

Keywords

luteolin microRNA signature Lung cancer MMP2 TWIST1 miR-106a-5p

Introduction

Lung cancer is the most common form of cancer worldwide and the leading cause of cancer-related mortality.¹ Approximately 80–85% of lung cancers are non-small cell lung cancers (NSCLC).² The number of treatment options has grown considerably in the past few years, with molecularly targeted therapy being one of the most promising options for breakthroughs in recent years. These include inhibitors of the epidermal growth factor receptor (EGFR), PI3K/AKT/mTOR inhibitors, and c-Ros oncogene 1 (ROS1), which have been used in clinical practice.³ Several molecular targets affecting NSCLC are emerging, including mesenchymal-epithelial transition factors (MET), Kirsten rat sarcoma viral oncogenes (KRAS), human epidermal growth factor receptor 2 (HER2), and immune checkpoint inhibitors, leading to the development of new treatments.⁴ To provide tailored therapeutic approaches to patients, it is necessary to develop new knowledge of novel oncogenic drivers^5,6 and their targeted agents.

Recent advances in high-throughput Next-Generation Sequencing (NGS) technologies have led to the discovery of non-coding RNAs (ncRNAs), unveiling novel therapeutic biomarkers in cancer research, particularly in the context of NSCLC. These ncRNAs encompass microRNAs (miRNAs),^7,8 long non-coding RNAs (lncRNAs),^9,10 and circular RNAs (circRNAs).¹¹ Notably, while the biological functions of the majority of circRNAs remain unclear, the circRNA–miRNA–mRNA regulatory networks, comprising circRNAs and their downstream miRNAs and target mRNAs, may play a crucial role in cancer initiation and progression.¹² Among ncRNAs, miRNAs stand out as key regulators, orchestrating post-transcriptional regulation by interacting with the complementary 3′-untranslated regions of target mRNAs,¹³ which results in the inhibition or degradation of mRNAs.¹⁴ Many aspects of tumor pathogenesis have been influenced by miRNAs,¹⁵ and aberrant miRNA signatures have been observed in various cancers,¹⁶ including ovarian, gastric, pancreatic, esophageal, prostate, breast, colorectal, lung,¹⁷ and liver¹⁸ cancers. It is well established that miRNAs play an important role in targeting important cancer cell–regulatory molecules as well as the complex signalling interactions between cancer cells and their microenvironment.¹⁹ The dysregulation of miRNAs has been associated with NSCLC proliferation, invasion, and metastasis.^17,20 Targeting miRNA alterations may lead to the development of miRNA therapeutics for the treatment of NSCLC.

Considering its pro-apoptotic and anti-migration properties, luteolin (3′, 4′, 5,7-tetrahydroxyflavone), a common dietary component, may offer a novel anti-cancer treatment option.²¹ However, identifying predictive factors for response to luteolin in NSCLC continues to be challenging, and a more detailed understanding of molecular biomarkers may result in effective treatment options. Therefore, through miRNA profiling, in silico computational analysis, and in vitro validation, our study aimed to explore the comprehensive action of luteolin on miRNA regulatory networks critical for tumorigenesis and metastasis, while also aiming to identify specific biomarkers to unravel novel mechanisms of luteolin action. Specifically, we demonstrated that luteolin inhibits epithelial-mesenchymal transition (EMT) phenotype in lung cancer by decreasing Twist1 and MMP2 expressions via miR-106a-5p. This suggests that treatment with luteolin in combination with a miR-106a-5p activator may hold promise in reducing the risk of metastasis in NSCLC patients.

Materials and Methods

Reagents

The luteolin (greater than 99% purity) was obtained from Sigma (St. Louis, MO), and dissolved in dimethyl sulfoxide (DMSO). RPMI 1640 medium and penicillin/streptomycin were purchased from Gibco/Life Technologies (Grand Island, NY). Gene-specific qPCR primers and miRNAs were obtained from Integrated DNA Technologies Inc. (Coralville, IA, USA) and Sigma (St. Louis, MO, USA), respectively. TWIST1, MMP2, and GAPDH antibodies were obtained from Cell Signaling Technology (Beverly, MA).

miRNA Microarray and Array Data Analysis

Microarray expression profiling was performed using the 7th generation of miRCURY LNATM microRNA Array (v.18.0) from Exiqon (Vedbaek, Denmark), which comprises 1918 well-characterized human microRNAs among 3100 capture probes covering human, mouse, and rat miRNAs. Total RNA including miRNAs was isolated using TRIzol (Invitrogen), and further purified using the miRNeasy mini kit (QIAGEN) as per the manufacturer’s instructions. RNA quantity was assessed by NanoDrop Spectrophotometer (ND-1000, Nanodrop Technologies, Wilmington, DE, USA), and RNA integrity was determined by gel electrophoresis, respectively. The purified miRNAs were labeled with the miRCURY™ Hy3™/Hy5™ Power labeling kit (Exiqon, Vedbaek, Denmark) and hybridized on the miRCURYTM LNA Array (v.18.0) (Exiqon) for 16 to 20 hours in a 12-Bay Hybridization Systems (Hybridization System- Nimblegen Systems, Inc., Madison, WI, USA) at 56°C. After hybridization, the processed slides were scanned with Axon GenePix 4000B microarray scanner (Axon Instruments, Foster City, CA) at a scan resolution of 10 µm. GenePix Pro 6.0 (Axon) was used for grid alignment and data extraction. Normalization was performed using the Median Normalization method, and the replicated miRNAs’ normalization factor was calculated by averaging their intensities across all samples with intensities ≥30. Differentially expressed miRNAs were identified through Fold Change filtering by volcano plot analysis by applying a cutoff fold change of 2.0 and a statistical cutoff of P < .05. Afterward, for the multiple corrections, the false discovery rate (FDR) was used based on the Benjamini-Hochberg procedure, and adjusted, P < .05 was considered statistically significant. Hierarchical clustering plots were generated with MEV software (v4.6, TIGR).

Bioinformatic Analysis and Target Gene Identification

To identify global molecular networks and canonical pathways associated with differentially expressed miRNAs, the DIANA pathway enrichment analysis was used (http://diana.imis.athena-innovation.gr/DianaTools/index.php).^22,23 The miRNA target genes are compared with all KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways (https://www.kegg.jp)²⁴ to determine the degree of enrichment in each set. In addition, Targetscan (http://www.targetscan.org/),²⁵ miRTarBase 9.0 (http://miRTarBase.cuhk.edu.cn/),²⁶ and microT-CDS (http://www.microrna.gr/webServer)²⁷ were used to retrieve predicted or experimentally validated miRNA targets. In addition, we used the Gene Ontology (GO) project, a controlled vocabulary for describing gene and gene product attributes (http://www.geneontology.org),²⁸ which involves the following 3 categories: biological process (BP), molecular function (MF) and cell composition (CC). Using Fisher’s exact test with Bonferroni correction, the differentially expressed gene list was compared to the GO annotation list to determine whether they were more similar than expected. Statistical significance for enrichment was represented by p values, where we considered pathways to be significantly enriched between classes when P < .05.

Quantitative RT-PCR Analysis

RNA was extracted from the cells using TRIzol (Invitrogen) following the manufacturer’s protocol. The total RNA was reverse transcribed to cDNA using the Revert Aid First Strand cDNA Synthesis Kit (Thermo scientific, USA). SYBR Green quantitative PCR master mix from Qiagen was used for quantitative RT-PCR analysis on the Applied Biosystems 7500 real-time RT-PCR system from Life Technologies (Grand Island, NY, USA). The expression of microRNAs was measured by utilizing TaqMan microRNA assay kit from Applied Bio-systems^TM (life Technologies, Carlsbad, California, USA). As internal normalization controls for mRNA and miRNA, GAPDH and U6 were used. The relative quantitation of miRNA expression was calculated using the 2^−∆∆Ct method, with the ∆∆Ct value determined as the difference between the Ct value of the treatment group and the control group. A list of primers used in this study can be found in Supplemental Material Tables S1 and S2.

Cell Culture and Transfections

Human lung cancer A549 cells were purchased from ATCC (American Type Culture Collection; Rockville, MD). A549 cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) (Sijiqin Biotechnology Co. Ltd, Hangzhou, PRC) at 37°C with 5% CO₂. The cells were seeded in 6-well plates and were transfected the following day using Lipofectamine 2000 (Thermo Fisher Scientific, Inc.) following the manufacturer’s instructions. The oligonucleotide miR-106a-5p mimics (mimic-miR-106a-5p), mimic negative control (mimic-NC), miR-106a-5p inhibitor (anti-miR-106a-5p), and the inhibitor negative control (anti-NC) were purchased from GenePharma (Shanghai, China). Each well was treated with equal concentrations (100 pmol) of mimic-NC, mimic-miR-106a-5p, anti-miR-106a-5p, or anti-NC. The sequences were as follows: miR-106a-5p mimic, 5′-AAAAGUGCUUACAGUGCAGGUAG-3′; mimic-NC, 5′-UUCUCCGAACGUGUCACGUTT-3′; miR-106a-5p inhibitor, 5′-CUACCAGCACUGUACAUUAGUACAA-3′; inhibitor-NC, 5′-CAGUUAGUUAGUACAA-3′. Cells were harvested 24 hours after transfection.

Dual-Luciferase Reporter Assay

The miR-106a-5p binding sites in wild-type (WT) and mutant (MT) TWIST1 and MMP2 were analyzed utilizing the Dual-Luciferase Reporter assay system (Promega, Madison, WI, USA). Briefly, the wild-type and mutant 3'UTRs of TWIST1 and MMP2 were cloned into pMIR-REPORT vector containing firefly luciferase. Both plasmids were co-transfected into A549 cells treated with either miR-106a-5p mimic-NC or mimic-miR-106a-5p. The activities of firefly or Renilla fluorescence were measured 48 hours after transfection according to manufacturer’s protocols (Promega). The firefly luciferase activities were normalized to the Renilla luciferase activities.

Western Blot Analysis

A549 cells following different treatments were collected and lysed in ice-cold lysis buffer [25 mM Tris (pH 7.4), 150 mM NaCl, 5 mM EDTA, 1 mM phenylmethylsulfonyl fluoride, 1 mM Na₃VO₄, and 1 mM NaF, and protease inhibitor (Roche, Indianapolis, IN)]. The lysate was further centrifuged at 14,000g for 15 minutes at 4°C, and the resulting supernatant protein extract was stored at −80°C. The protein concentration was determined by using a DC protein assay kit (Bio-Rad Laboratories, Hercules, CA). Total protein (50 µg) was separated on a 10% SDS-PAGE gel and were then transferred to a PVDF membrane. The membranes were blotted with primary antibodies at 4°C overnight and then with secondary antibody for 2 hours. The signal was detected using the Amersham ECL system (Amersham-Pharmacia Biotech, Arlington Heights, IL), and the relative expression of TWIST1, MMP2 were quantified by densitometry using Quantity One from BioRad (Hercules, CA). Each experiment was repeated at least 3 times.

Cell Migration Assay

Transwell assay was performed by a modified Boyden chamber assay. Cells transfected with mimic-miR-106a-5p, anti-miR-106a-5p, or their corresponding negative controls were suspended in serum-free cold RPMI 1640, and then added into the upper chamber at a density of 4 × 10⁴ cells/well. The lower chambers were filled with 10% FBS and luteolin. After 24 hours of incubation, the non-migrated cells on the top of the transwell were removed using a cotton swab, and the migrated cells were fixed in 4% paraformaldehyde and stained with 0.5% crystal violet. The migrated cells were photographed and quantified using light microscopy at a magnification of × 100 by counting the stained cells via at least 5 randomly selected fields. The mean number of migrating cells was expressed as a percentage relative to the control.

Statistics

All data were processed using SPSS version 20 (SPSS Inc., Chicago, IL, USA) and expressed as mean ± SD. Statistical comparisons between 2 groups were conducted using either a Student’s t-test or Mann-Whitney U-test, whereas comparisons among multiple groups were performed using one-way ANOVA followed by Dunnett’s post hoc test. A P-value of less than .5 was considered statistically significant.

Results

Luteolin-Induced Modulation of microRNA Profiles

The Exiqon miRCURY^TM LNA miRNA array was used to analyze RNA samples from luteolin treated or untreated A549 cells. We observed 341 miRNAs out of 3100 miRNAs that had a difference more than two-fold between luteolin-treated and control cells, with 200 upregulated and 141 downregulated as shown in the volcano plot (Figure 1A). In addition, we identified 18 miRNAs exhibiting significantly different expression levels with a false discovery rate (FDR) P < .05, which were highlighted in the volcano plot (Figure 1 and Table 1). Among these miRNAs, 12 miRNAs were found to be increased by luteolin, and 6 miRNAs showed a decrease. Further validation of the array data was performed using real-time qPCR, which validated 6 miRNA genes, miR-17-5p, miR-27a-3p, miR-20a-5p, miR-106b-5p, miR-29a-5p, and miR-374b-3p. In line with the microarray experiments, the qRT-PCR analysis indicated that miR-17-5p, miR-27a-3p, miR-20a-5p and miR-106b-5p were highly expressed, with expression levels greater than 2 times those of the controls (Figure 2). Contrarily, miR-29a-5p and miR-374b-3p were expressed at a significantly lower level in the treated group than in the control group (Figure 2). Collectively, the results of qRT-PCR validated those obtained by microarrays, suggesting that miRNAs exhibiting distinct expression patterns in lung cancer cells after treatment with luteolin.

Figure. 1.

The miRNA expression profile in A549 cells subjected to luteolin treatment compared to untreated cells. (A) Volcano plot representing the differentially expressed miRNAs—log₁₀P values (Y-axis) were plotted against log₂FC values (X-axis). Red dots represented P < .05, while the grey dots represented P ≥ .05. (B) Hierarchical clustering analysis (i.e., Heatmap) of the top 18 differentially expressed miRNAs between cells treated with luteolin (25 μM) and control cells. The intensity of expression is indicated by the color scale, with red indicating increased expression and green representing decreased expression.

Table 1.

Differentially Expressed miRNAs After Luteolin Treatment by miRNA Array.

miRNA	Regulation (luteolin vs. control)	Fold change	P-value
hsa-miR-17-5p	↑	8.98	1.34 × 10⁻⁴
hsa-miR-27a-3p	↑	12.68	2.46 × 10⁻⁴
hsa-miR-27b-3p	↑	5.46	1.03 × 10⁻⁵
hsa-miR-23b-3p	↑	6.28	7.57 × 10⁻⁴
hsa-miR-107	↑	8.59	3.95 × 10⁻⁴
hsa-miR-24-3p	↑	5.14	4.01 × 10⁻³
hsa-miR-20a-5p	↑	4.46	2.23 × 10⁻⁵
hsa-miR-20b-5p	↑	6.25	7.35 × 10⁻⁴
hsa-miR-106a-5p	↑	6.56	1.21 × 10⁻⁶
hsa-miR-106b-5p	↑	8.28	5.94 × 10⁻⁵
hsa-miR-183-3p	↑	3.77	9.61 × 10⁻⁴
hsa-let-7g-5p	↑	14.48	4.33 × 10⁻³
hsa-miR-181a-2-3p	↓	4.71	2.82 × 10⁻³
hsa-miR-29a-5p	↓	13.07	9.12 × 10⁻⁴
hsa-miR-24-1-5p	↓	3.78	6.23 × 10⁻³
hsa-miR-205-3p	↓	10.10	1.08 × 10⁻⁴
hsa-miR-374b-3p	↓	4.19	2.13 × 10⁻³
hsa-miR-32-3p	↓	5.39	5.83 × 10⁻⁴

Note. Expression analysis of miRNA using Exiqon miRCURY LNA^TM miRNA microarray in luteolin-treated A549 cells compared with the control. Relative miRNA levels are presented as fold change increase (↑) or decrease (↓) compared with the control.

Figure 2.

qRT-PCR validation of 6 selected miRNAs. The expression levels of (A) miR-17-5p, (B) miR-27a-3p, (C) miR-20a-5p, (D) miR-106b-5p, (E) miR-29a-5p, and (F) miR-374b-3p were assessed following exposure to luteolin (25 μM) for 24 hours. Data are expressed as the means ± SD (n = 3).

The Regulatory Roles of miRNAs in Biological Processes and Controlled Pathways in Response to Luteolin

Then, we analyzed miRNA regulatory roles in luteolin-treated lung cancer cells and identified miRNAs significantly controlling selected pathways or belonging to specific Gene Ontology (GO) categories based on miRNA target prediction algorithms. Our analysis of the impact of the 12 miRNAs strikingly upregulated following luteolin treatment as indicated in Table 1 on biological processes and KEGG pathways was conducted using DIANA miRPath v3.0, an online computational tool based on a highly accurate target prediction algorithm (http://diana.imis.athena-innovation.gr/DianaTools/index.php).^22,23

The KEGG pathway enrichment analysis revealed significant overrepresentation of pathways potentially associated with cancer (FDR P < .05). These enriched pathways include cancer-related pathways (non-small cell lung cancer, small cell lung cancer, glioma, colorectal cancer); signalling pathways related to cancer progression and metastasis (cell cycle, apoptosis, p53 signaling pathway, TGF-β signaling pathway, ErbB signaling pathway, MAPK signaling pathway, Wnt signaling pathway, FoxO signaling pathway, PI3K-Akt signaling pathway); adhesion- and mobility-related pathways (ECM-receptor interactions, adherens junction, focal adhesion); metabolic reprogramming pathways (central carbon metabolism in cancer, proteoglycans in cancer, fatty acid metabolism, N-Glycan biosynthesis) (Figure 3A). These pathways have been demonstrated to play a critical role in driving tumor biology, including tumor initiation, progression, and metastasis.^29
-33 The up-regulation of miRNAs in cancers that regulate the expression of these genes may lead to abnormally deactivated pathways, resulting in decreased proliferation and migration abilities after luteolin treatment.

Figure 3.

KEGG and GO enrichment analyses of significant miRNA targets. (A) KEGG pathway enrichment analysis revealed overrepresented pathways associated with cancer with FDR P value <.05. The bubble plot shows enrichment scores [−log10 (P value), x-axis] of the significantly enriched pathways (y-axis). (B) Top 10 ranked GO terms of 3 categories (BP: biology process; CC: cell component; MF: molecular function) based on P values.

Furthermore, the GO functional analysis identified 266 biological processes (BPs), 20 cell composition (CC), and 33 molecular functions (MFs) associated with up-regulated miRNAs by luteolin. The top ten enrichment values for each category were listed (Figure 3B). The top-ranked miRNA targets were linked to BPs such as regulation of transcription from RNA polymerase II promoter, cellular lipid metabolic process, apoptotic signaling pathway, and nuclear-transcribed mRNA catabolic process. In terms of CC, the top targets were enriched in focal adhesion, vacuole, cytoplasmic stress granule, cell projection, and endosome. Regarding molecular functions, the enriched targets were involved in kinase activity, transcription coactivator activity, transcription corepressor activity, ubiquitin protein ligase binding, and ligase activity.

The Putative Functional Implications of Luteolin-Responsive miRNAs

To further assess the combined miRNA actions in modulating the luteolin-induced pro-apoptotic and anti-migration effects in lung cancer, a variety of computational algorithms were applied to identify potential targets that could be affected by miRNAs upregulated by luteolin (including TargetScan, microT-CDS, and mirTarbase).^34
-36 Interestingly, a list of essential genes that are known to mediate cancer cell growth (cell cycle control, cell proliferation, cell differentiation), migration and EMT, angiogenesis, inflammatory and immune responses, and apoptosis were predicted as potential target genes for miRNAs induced by luteolin (Table 2), suggesting that these genes may play a fundamental role in controlling the growth and migration of A549 cells during luteolin treatment. As an example, vascular endothelial growth factor A (VEGFA), which plays a prominent role in tumor angiogenesis, growth and metastasis,³⁷ was identified as a target gene for 5 miRNAs; miR-106a-5p, miR-17-5p, miR-20a-5p, miR-20b-5p, and miR-106b-5p. Furthermore, matrix metallopeptidase 2 (MMP2), which stimulates migration,³⁸ was also identified as a common target gene for 3 miRNAs, miR-17-5p, miR-106a-5p, and miR-106b-5p, suggesting it may contribute to altered migration processes in A549 cells under luteolin. Interestingly, twist basic helix-loop-helix transcription factor 1 (TWIST1), which regulates MMP2 transcription and tumor invasion,³⁹ was also predicted to be a target gene for miR-106a-5p, the same miRNA targeting MMP2. As it has previously demonstrated luteolin has anti-migratory effects,²¹ we have paid particular attention to these genes.

Table 2.

Predicted Genes Targeted by Upregulated Luteolin-Responsive miRNAs.

Number of miRNAs	Putative target genes	Function
8 (hsa-miR-17-5p, hsa-miR-27a-3p, hsa-miR-24-3p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-106b-5p, hsa-miR-106a-5p, hsa-miR-107)	CCND1, CDK1, CDK4, CDK6, CDK8	Cell cycle control
7 (hsa-miR-17-5p, hsa-miR-107, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-106a-5p, hsa-miR-106b-5p, hsa-let-7g-5p)	HIF1A, E2F1, STAT3, E2F5, SMAD2, SMAD4, SMAD7	Transcription factor
5 (hsa-miR-106a-5p, hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-106b-5p)	VEGFA	Angiogenesis
5 (hsa-miR-106a-5p, hsa-miR-23b-3p, hsa-let-7g-5p, hsa-miR-17-5p, hsa-let-7g-5p)	IL1A, IL6R, IL8, IL13	Inflammatory and immune responses
4 (hsa-miR-27a-3p, hsa-miR-23b-3p, hsa-miR-24-3p, hsa-let-7g-5p)	MET, MYC	Proto-oncogenic
3 (hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-106b-5p)	MMP2	Migration
3 (hsa-miR-106a-5p, hsa-miR-23b-3p, hsa-let-7g-5p)	TWIST1, ZEB1, FN1	EMT
2 (hsa-miR-23b-3p, hsa-miR-20a-5p)	BCL2, MCL1	Anti-apoptotic
2 (hsa-miR-24-3p, hsa-miR-20b-5p)	TGFB1, MAPK9	Cell proliferation, cell differentiation and apoptosis
1 (hsa-miR-27a-3p)	KRAS, EGFR	Oncogenic

Inhibition of Cell Migration by Luteolin Was Associated With the Negative Regulation of TWIST1 and MMP2 by miR-106a-5p

TWIST1 and MMP2 were selected among the predicted luteolin-targeted genes, since both of them enhance epithelial-mesenchymal transition (EMT) and cancer cell migration and invasion, thus promoting metastasis.⁴⁰ The expression levels of TWIST1 and MMP2 were reduced by luteolin in a dose-dependent manner, suggesting that luteolin negatively regulates these genes (Figure 4A). In particular, there are conserved miR-106a-5p binding sites in the 3'UTRs of TWIST1 (position 183-189) and MMP2 (position 515-521) respectively (Figure 4B), suggesting that miRNA-106a-5p may influence extracellular matrix remodeling by targeting these 2 mRNAs. A549 cells were then transfected with miR-106a-5p mimic, anti-miR-106a-5p (inhibitor), or their corresponding negative controls (NC) and tested for migration and expression levels of TWIST1and MMP2. Both quantitative PCR and western blotting analyses demonstrated that miR-106a-5p negatively regulated TWIST1 and MMP2 expression at both transcriptional (Figure 4C) and post-transcriptional levels (Figure 4D). Following luteolin treatment, miR-106a-5p mimic inhibited TWIST1 and MMP2 expression, whereas miR-106a-5p inhibitor increased TWIST1 and MMP2 levels. Moreover, dual-luciferase reporter assays were performed to confirm the binding of miR-106a-5p to TWIST1 and MMP2. According to the results (Figure 4E), miR-106a-5p mimic significantly reduced luciferase activity of wide-type (WT) TWIST1 and MMP2, but had a limited effect on mutant (MT) TWIST1 and MMP2. This suggest that miR-106a-5p directly targets TWIST1 and MMP2. Further, the findings from the transwell experiments demonstrated a notable decrease in migratory capabilities of A549 cells after they were treated with luteolin (Figure 4F). Notably, this reduction in migratory potential was further augmented with the additional overexpression of miR-106a-5p. Together, these findings suggest that luteolin inhibits A549 cell migration by suppressing the expression of TWIST1 and MMP2 via miR-106a-5p, thereby suppressing cancer metastasis.

Figure 4.

The role of miR-106a-5p and TWIST1/MMP2 in cell migration during luteolin treatment. (A) Western blot analysis of the expressions of TWIST1 and MMP2. (B) Schematic representation of miR-106a-5p binding sites within the TWIST1 3’UTR regions (position 183-189) and MMP2 3’UTR regions (position 515-521). The sequence alignments between miR-106a-5p and its target genes are shown in “bounds.” A549 cells were transfected with the miR-106a mimic, inhibitor (anti-miR-106a), and relative controls, and the protein and mRNA expression of MMP 2 and Twist1 levels were measured by RT-PCR (C) and Western blotting (D), respectively. (E) Luciferase reporter assay for determining miR-106a’s binding to TWIST1 and MMP2. WT: wild type; MT: mutant. (F) Transwell assay based on crystal violet staining was used to determine the migration ability of A549 cells.

Discussion

Regulatory roles of miRNAs in cancers have been established in recent years and have been demonstrated to be clinically relevant.⁴¹ Despite the rapid increase in the number of cancer-related miRNAs, little is known about their precise cellular functions. Luteolin demonstrates promising anti-cancer effects in NSCLC, including inhibition of cell proliferation, invasion, migration, and induction of apoptosis.^21,42 However, previous studies have predominantly focused on specific molecular targets or pathways such as Caspase-3 and -9,²¹ MMP-2 and -9,⁴³ MAPK signaling,¹⁶ PI3K/Akt signaling,⁴³ FAK-Src signaling,⁴⁴ and AIM2 inflammasome.⁴⁵ Yet, a comprehensive understanding of luteolin’s mechanisms of action remains unclear, particularly in elucidating its involvement in broader genomic regulatory networks and interactions within the NSCLC tumor microenvironment. This gap in knowledge highlights the need for further investigation into the multifaceted roles of luteolin in NSCLC. In this study, we performed miRNA expression profiling in an NSCLC cell line to unravel the pivotal regulatory role of miRNAs during the treatment with luteolin. The results of our study demonstrate that luteolin exerts its effects through a novel mechanism, by modulating a complex miRNA landscape critical to tumorigenesis and metastasis, which would lead to a deeper understanding of luteolin’s therapeutic potential and facilitate the development of more effective treatment strategies for NSCLC patients. In addition, our study demonstrates that miR-106a-5p contributes to the anti-cancer action of luteolin by negatively modulating TWIST1 and MMPs, thereby inhibiting lung cancer cell migration.

There has been considerable interest in miRNA as an early screening tool for lung cancer.⁴¹ The present study identified a number of miRNAs that were differentially expressed in response to luteolin treatment, specifically those that functioned as tumor suppressors or oncogenes in NSCLC. For instance, the let-7 family, the first known human miRNA, is located in regions that are commonly deleted in lung cancer, including let-7g at 3p21.1-21.2, let-7a-2 at 11q23-q24, and let-7c at 21q11.1.⁴⁶ let-7g functions as tumor suppressor as it could effectively induce cell cycle arrest and slow the proliferation of lung cancer cells by repressing KRAS.⁴⁷ Interestingly, miR-183-3p, another miRNA elevated by luteolin, has been found to inhibit invasion and metastasis in lung cancers by targeting Foxf2.⁴⁸ In our study, luteolin may enlarge the tumor suppressant function of these tumor suppressing miRNAs by upregulating their levels of expression. It was surprising to find that luteolin treatment had increased miR-17, one of the most commonly amplified miRNAs and oncogenic miRNAs in NSCLC.¹⁷ The mechanism by which luteolin exerts its anti-cancer effect may not be formulated, such as by reducing oncogenic miRNAs or by increasing tumor-suppressor miRNAs, but rather by a balanced modulation that skews miRNA signatures in a path toward tumor suppression.

Furthermore, throughout our analysis of KEGG pathway enrichment, we have discovered that the set of twelve elevated miRNAs is associated with a variety of cancer-related pathways that are critical for tumorigenesis and metastasis. The most significant pathway TGF-β has been well studied for its ability to inhibit cell proliferation in early stages of tumorigenesis while encouraging epithelial-mesenchymal transition and invasion in advanced cancer. A growing body of evidence suggests that TGF- signaling may be an important contributor to cancer therapy resistance.³⁰ The ErbB receptor family, also known as the EGF receptor family or type I receptor family, is overexpressed or mutated in many cancers, especially in breast cancer, ovarian cancer, and non-small cell lung cancer, resulting in poor prognosis, drug resistance, cancer metastasis, and lower survival rate.³² In addition, the MAPK cascade plays a critical role in the survival, dissemination, and resistance to drug therapy of human cancer cells.³¹ The aberrant Wnt signaling pathway also promotes cancer stem cell renewal, proliferation, and differentiation, thus having a crucial role in tumorigenesis and response to therapy.³³ Additionally, FOXO are considered tumour suppressors due to their ability to regulate genes necessary for cell proliferation, cell death, senescence, angiogenesis, cell migration, and metastasis.²⁹ These findings of this study will presumably provide information regarding the mechanisms by which luteolin exerts its anticancer properties.

The miR-106a-5p is a newly discovered tumor-associated miRNA, and to date, it has not been conclusively established whether it functions as tumor suppressor or tumor promoter. Several studies have shown that miR-106a-5p suppresses cell proliferation, migration, and apoptosis in renal cell carcinomas⁴⁹ and astrocytomas.⁵⁰ In contrast, miR-106a-5p has been reported to promote cancer in gastric cancer,⁵¹ hepatocellular carcinoma,⁵² and lung adenocarcinoma.⁵³ Moreover, miR-106a-5p levels were found to be decreased in NSCLC when compared with controls.⁵⁴ There is a possibility that miR-106a-5p plays different roles in cancer growth and migration depending on its target genes.

In the present study, luteolin significantly elevated miR-106a-5p expression levels in A549 cells; in addition, miR-106a-5p overexpression decreased the migration of A549 cells after luteolin treatment. Further investigation revealed that miR-106a-5p inhibits both transcriptional and post-transcriptional activation of TWIST1 and MMP2. There is evidence that Twist1 plays a major role in the regulation of EMT and, therefore, in promoting carcinoma metastasis.⁵⁵ It is interesting to note that Twist 1, a basic helix-loop-helix transcription factor of class II, could directly activate MMP2 expression through binding to its promoter.⁵⁶ As a result of these findings, it is suggested that miR-106a-5p exerts its tumor suppressive effects on NSCLC by inhibiting the expression of Twist1/MMP2.

Conclusion

Our study identified a complex miRNA landscape in NSCLC lung cancer cells treated with luteolin, suggesting luteolin plays a role in modulating tumor progression through a novel mechanism. Furthermore, our data demonstrate that luteolin inhibits EMT by reducing TWIST1 and MMP2 mediated migration via a potent tumor suppressor miR-106a-5p. While this study acknowledges the limitation of relying solely on computational analysis and validation through an in vitro model, its strength lies in elucidating a novel mechanism of luteolin action through its modulation of the miRNA-target mRNA network crucial in cancer biology. The findings from our study serve as a stepping stone for understanding luteolin’s therapeutic potential and the exploration of miRNA-based anti-cancer strategies for NSCLC. Moving forward, establishing a preclinical testing platform with an effective engineered miRNA delivery system will be essential to explore the intricate interplay between therapeutic miRNAs and the NSCLC tumor microenvironment, thus amplifying the translational significance of our findings.

Supplemental Material

sj-docx-1-ict-10.1177_15347354241247223 – Supplemental material for Luteolin Inhibits Lung Cancer Cell Migration by Negatively Regulating TWIST1 and MMP2 Through Upregulation of miR-106a-5p

Supplemental material, sj-docx-1-ict-10.1177_15347354241247223 for Luteolin Inhibits Lung Cancer Cell Migration by Negatively Regulating TWIST1 and MMP2 Through Upregulation of miR-106a-5p by Qiang Wang, Mengyuan Chen and Xiaofang Tang in Integrative Cancer Therapies

Footnotes

Acknowledgements

We thank Shanghai NewCore Biotechnology Co., Ltd. (, last accessed on 10 July 2023) for providing data visualization support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Basic Public Welfare Research Project of Zhejiang Province, China (Grant No. LGF22H190004), the Medical, Health Science and Technology Planning Project of Zhejiang Province, China (Grant No.2021KY840, 2022KY927), and the Chinese Medicine Scientific Research Foundation of Zhejiang Province, China (Grant No. 2021ZB137).

ORCID iD

Xiaofang Tang

Supplemental Material

Supplemental material for this article is available online.

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