MicroRNA‑885‑5p regulates cell cycle progression in liver cancer cells

Introduction

Liver cancer is a significant global health challenge, with hepatocellular carcinoma (HCC) being the most common primary liver malignancy, accounting for >75% of cases (1). Liver cancer develops in the setting of chronic liver diseases, which promote the accumulation of DNA mutations and epigenetic changes along with the establishment of a tumor microenvironment (2). Patients in the early stages of liver cancer can be treated by surgical resection, radiofrequency ablation and liver transplantation, whereas those with more advanced disease, particularly HCC, receive systemic therapies that employ sorafenib or lenvatinib as first-line treatment and regorafenib, cabozantinib or ramucirumab as second-line treatment (3,4). Recently, the advent of immunotherapy has demonstrated promising results for liver cancer treatment (5). Despite these successes, current therapies are only effective or suitable for a subset of patients with liver cancer (6). Thus, advancements in the understanding of the molecular pathogenesis underlying liver cancer could provide new insights for more treatment options to combat this malignant tumor.

In the human genome, only 2% of all genes encode proteins, while the majority are transcribed into non-coding RNAs (7). MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression post-transcriptionally by interacting with the 3′ untranslated region (3′ UTR) of target messenger RNAs (mRNAs) to induce mRNA degradation and/or translational repression (8). In recent years, numerous studies have observed dysregulation of miRNAs during the initiation and progression of various liver diseases, including liver cancer (9-11). Modulation of miRNAs has been shown to be a viable approach to treating numerous types of human cancer and other diseases, and several miRNA-based therapeutics have advanced into clinical trials (12,13). For example, the miR-122 inhibitor miravirsen has been evaluated in clinical trials for hepatitis C virus (14), whereas the miR-34a mimic MRX34 has been tested in a clinical trial for various solid tumors, including liver and lung cancer (15). Despite these advancements, no miRNA therapeutics have yet been approved for cancer treatment. Nonetheless, a growing body of preclinical research has demonstrated the immense potential of miRNA-based therapies in various cancer types, including liver cancer (16,17). Thus, given the rapid progress of RNA therapeutic development (18,19), the present study aimed to elucidate the role of miRNAs in liver cancer biology to develop a novel miRNA-based therapy for liver cancer treatment.

The present study aimed to identify potential tumor-suppressing genes from an analysis of miRNA expression profiles of normal and HCC tissue pairs from public datasets, and thus aimed to elucidate the molecular function of miR-885-5p in liver cancer cells by the transcriptomic approach and explore its therapeutic potential for liver cancer therapy.

Materials and methods

Establishment of cell lines and miR-885-5p overexpression

Liver cancer cell lines HepG2 (cat. no. HB-8065) and SNU-449 (cat. no. CRL-2234), and the non-tumorigenic human hepatocyte cell line THLE-2 (cat. no. CRL-2706) were obtained from the American Type Culture Collection. The JHH-4 cell line (cat. no. JCRB0435) was sourced from the Japanese Collection of Research Bioresources Cell Bank. The human 293FT cell line (cat. no. R70007) was obtained from Thermo Fisher Scientific, Inc. Cell lines were authenticated by short tandem repeat analysis (Macrogen, Inc.). Cells were cultured in complete growth media containing 10% fetal bovine serum (cat. no. A5256701; Gibco; Thermo Fisher Scientific, Inc.) following the manufacturers' protocols and maintained at 37°C in a 5% CO2 incubator.

To generate liver cancer cells stably expressing miR-885-5p, a second lentiviral vector system was utilized comprising the packaging plasmids pMD2.G (plasmid #12259; Addgene, Inc.) and psPAX2 (plasmid #12260; Addgene, Inc.) in 293FT cells (cat. no. R70007; Invitrogen; Thermo Fisher Scientific, Inc.). The pre-miRNA sequence of miR-885-5p was amplified from human genomic DNA and cloned into an EcoRI/BamHI-cut pLVX-EF1α-IRES-Puro vector (cat. no. 631988; Takara Bio USA, Inc.). For each 6-well plate, 3.08 μg pLVX-EF1α-pre-miR -885-5p-IRES-Puro, 3.08 μg psPAX2 and 2 μg pMD2.G were co-transfected into 293FT cells with a calcium phosphate transfection kit (cat. no. CAPHOS; Sigma-Aldrich; Merck KGaA) for 8 h at 37°C according to the established protocol (20). Lentiviral particles were collected at 48 h post-transfection and were concentrated before being used to transduce target cells at a multiplicity of infection of 3 using a spinoculation method (800 × g, 40 min, 37°C) (21). Following transduction, cells exhibiting stable integration were selected using puromycin (cat. no. P8833; Sigma-Aldrich; Merck KGaA) at 1-2 μg/ml until all non-transduced control cells were eliminated (typically 7-10 days). Subsequently, the cells were maintained in 0.5-1 μg/ml puromycin for further experimentation. Successful overexpression of miR-885-5p was confirmed via reverse-transcription quantitative PCR (RT-qPCR). Primer sequences used for cloning and RT-qPCR analysis are detailed in Table SI.

Assessment of cell proliferation and clonogenic survival

Cell proliferation was measured using a standard MTT assay (cat. no. M6494; Invitrogen; Thermo Fisher Scientific, Inc.). Cells were incubated with 0.5 μg/ml MTT for 1 h. The formazan crystals were dissolved in dimethyl sulfoxide (DMSO), and the absorbance was read at a wavelength of 570 nm (BioTek Synergy HTX; Agilent Technologies, Inc.). Colony formation was assessed by plating 500-2,000 cells/well of a 6-well plate and culturing for 2-3 weeks. Colonies were fixed in 100% methanol for 30 min at room temperature, stained with phosphate-buffered saline containing 0.1% crystal violet (cat. no. B21932.14; Thermo Fisher Scientific, Inc.) at room temperature for 30 min and counted for colonies comprising ≥50 cells.

mRNA and miRNA expression analysis by RT-qPCR

mRNA and miRNA fractions were both isolated from liver cancer cells using the GeneAll Hybrid-R miRNA kit (cat. no. HB3520; GeneAll Biotechnology, Co., Ltd.). Complementary DNA was synthesized from the mRNA fraction using iScript Reverse Transcription Supermix (cat. no. 1708840; Bio-Rad Laboratories, Inc.) for mRNA analysis according to the manufacturer's instructions. For miRNA analysis, complementary DNA was synthesized from the miRNA fraction using RevertAid First Strand cDNA Synthesis kit (cat. no. K1621; Thermo Fisher Scientific, Inc.) according to the previous study (22). qPCR was performed on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific, Inc.) with CAPITAL qPCR Green Mix HRox (cat. no. BR0501901; Biotechrabbit GmbH) under the following conditions: 95°C for 10 min, followed by 35 cycles at 95°C for 15 sec and 60°C for 20 sec. Relative gene expression was calculated using the 2−ΔΔCq method (23). RPL19 served as the internal reference gene for mRNA analysis and U6 served as the internal reference gene for miRNA analysis. The sequences of the primers used are listed in Table SI.

Identification of differentially expressed miRNAs from public datasets

To identify key miRNAs in HCC, the Database of Differentially Expressed MiRNAs in Human Cancers (https://www.biosino.org/dbDEMC/index) was utilized (24). The top three datasets derived from HCC tissue samples with the highest number of differentially expressed miRNAs [absolute fold change (|FC|)>1.5 and adjusted P-value <0.05] were selected. Applying this criterion, datasets EXP00409, EXP00410 and EXP00576 were chosen for further analysis. Datasets EXP00409 (comparing miRNA expression between cancerous and normal liver tissues) and EXP00410 (comparing miRNA expression between stage 1 and stage 3 HCC tumors) were obtained from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma Collection project (https://portal.gdc.cancer.gov/projects/TCGA-LIHC). Dataset EXP00576, retrieved from the GSE147889 repository, also compared miRNA expression between normal and cancerous tissues from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147889). For reanalysis, miRNA expression profiles from EXP00576 were processed using the GEO2R web tool (version 3.26.8; https://www.ncbi.nlm.nih.gov/geo/geo2r/). Expression data from the TCGA Liver Hepatocellular Carcinoma Collection datasets were retrieved using the TCGAbiolinks R package (R Core Team; Posit Software, PBC). All expression values were transformed using log2(expression + 1).

Transcriptomic profiling and functional enrichment analysis

RNA-sequencing (RNA-seq) analysis was performed by Beijing Biomarker Technologies Co., Ltd. RNA quality and integrity were assessed using the Agilent Bioanalyzer 2100 system (Agilent Technologies, Inc.) to ensure suitability for sequencing. Libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina® (cat. no. E7770; New England Biolabs, Inc.). The final library concentration was examined using Qubit 2.0 (Thermo Fisher Scientific, Inc.), diluted to a loading concentration of 2 nM, and sequenced in paired-end 150 bp mode on the Illumina NovaSeq 6000 platform (Illumina, Inc.).

Raw sequencing reads were filtered for quality and adapter contamination. The clean reads were then mapped to the reference genome using HISAT2 (version 2.2.1; https://daehwankimlab.github.io/hisat2/). Gene expression levels were quantified using StringTie (version 2.2.1; https://ccb.jhu.edu/software/stringtie/index.shtml). Differentially expressed genes (DEGs) between control and miR-885-5p-overexpressing JHH-4 cells were identified using DESeq (version 1.30.1; http://www.bioconductor.org/packages/release/bioc/html/DESeq.html) with a threshold of |FC|>1.5 and P<0.05.

Functional enrichment analysis of DEGs was performed using g:Profiler web tool (version e109_eg56_p17_63f6c3d4; https://biit.cs.ut.ee/gprofiler_archive3/e109_eg56_p17/gost) to identify enriched Gene Ontology (GO) terms (https://geneontology.org), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (https://www.genome.jp/kegg/pathway.html), Reactome pathways (https://reactome.org), and WikiPathways (https://www.wikipathways.org/organisms/human.html). Statistical significance for GO and KEGG pathway enrichment was determined using a false discovery rate (FDR) <0.05 and P<0.05. Gene Set Enrichment Analysis (GSEA) was performed to identify significantly enriched biological pathways associated with DEGs. The analysis was conducted for KEGG and Reactome pathways using the clusterProfiler and ReactomePA R packages (Posit Software, PBC). Differential expression analysis results, obtained from the RNA-seq data, were used to calculate a ranking metric for each gene, computed as Ranking Metric=sign(logFC)*-log10 (P-value). The most significant negatively enriched pathways in KEGG and Reactome were then visualized using GSEA enrichment plots.

Assessment of cell proliferation via bromodeoxyuridine incorporation

Cell proliferation was assessed by measuring bromodeoxyuridine (BrdU) incorporation. Cells were incubated with 10 μM BrdU (cat. no. ab142567; Abcam) for 30-60 min at 37°C, fixed with 4% paraformaldehyde for 15 min at room temperature and permeabilized with 2N hydrochloric acid for 30 min at room temperature. BrdU incorporation was detected by immunofluorescence using an anti-BrdU antibody (1:200; cat. no. sc-32323; Santa Cruz Biotechnology, Inc.) overnight at 4°C, followed by Alexa Fluor 488-conjugated secondary antibody (1:500; cat. no. A-21202; Invitrogen) for 1 h at room temperature and DAPI staining (1 μg/ml; cat. no. D9542; Sigma-Aldrich; Merck KGaA) for 10 min at room temperature. Images were captured using an EVOS M7000 cell imaging system (Invitrogen; Thermo Fisher Scientific, Inc.) and BrdU-positive cells were quantified from 10 random fields/group with >2,000 nuclei counted, using ImageJ software (version 1.54g; National Institutes of Health).

Cell cycle phase distribution by flow cytometry

To determine the cell cycle distribution, liver cancer cells were harvested and fixed in ice-cold 70% ethanol overnight. After washing with 1% fetal bovine serum in phosphate-buffered saline, cells were stained by incubation in phosphate-buffered saline containing 50 μg/ml propidium iodide (PI; cat. no. P1304MP; Thermo Fisher Scientific, Inc.) and 100 μg/ml RNase A (cat. no. EN0531; Thermo Fisher Scientific, Inc.) for 30 min at 37°C in the dark. DNA content was then analyzed using a MACSQuant X Flow Cytometer (Miltenyi Biotec, Inc.) by detecting PI fluorescence in the PE channel. Debris was excluded using FSC-A/SSC-A gating, and doublets were removed by pulse-processing (FSC-H vs. FSC-A). The proportion of cells in each phase (G1, S and G2/M) was quantified using the FlowJo software (version 10.10; BD Biosciences).

Assessment of apoptosis via phosphatidylserine translocation

Apoptosis was assessed using the Apoptosis/Necrosis Assay Kit (blue, green, red; cat. no. ab176749; Abcam). Liver cancer cells were washed twice with Assay Buffer and resuspended in Assay Buffer containing 1X Apopxin Green Indicator at room temperature. Cells were incubated at room temperature for 30 min while protected from light. After incubation, cells were washed twice with Assay Buffer and replaced with fresh Assay Buffer. Apoptotic cells were visualized using the fluorescein isothiocyanate channel (Ex/Em=490/525 nm) on an EVOS M7000 cell imaging system (Invitrogen; Thermo Fisher Scientific, Inc.). Images were captured using an EVOS M7000 cell imaging system (Invitrogen; Thermo Fisher Scientific, Inc.), and quantification of Apopxin Green-positive cells was performed from 10 random fields/group with >2,000 nuclei counted, using the ImageJ software (version 1.54g; National Institutes of Health).

Validation of miR-885-5p target genes via dual luciferase reporter assay

To identify potential miRNA-mRNA interactions, TargetScan web tool (version 8.0; https://www.targetscan.org/vert_80/) was used to predict miRNA targets from a pool of downregulated DEGs involved in the G1-to-S transition, and binding sites were further predicted using RNAhybrid web tool (version 2.1.1; https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/). Based on these analyses four genes (CDK6, ORC1, E2F1 and E2F2) were selected for experimental validation using a dual luciferase reporter assay. The 3′ UTRs of these genes containing either wild-type (WT) or mutated (MT) binding sites were cloned into the pmirGLO vector (cat. no. E133A; Promega Corporation). The pmirGLO vector with no insert was used as the negative control. The pre-miRNA sequence of miR-885-5p was cloned into the pSilencer 3.0 H1 vector (cat. no. AM7210; Thermo Fisher Scientific, Inc.). Human 293FT cells (cat. no. R70007; Invitrogen; Thermo Fisher Scientific, Inc.) were co-transfected with these constructs by a standard calcium phosphate transfection (20). Luciferase activity was measured using the Dual-Luciferase Reporter Assay System (cat. no. E1910; Promega Corporation) 48 h post-transfection using a BioTek Synergy HTX Multi-Mode Microplate Reader (BioTek; Agilent Technologies, Inc.). Firefly luciferase activity was normalized to Renilla luciferase activity for data analysis. Primer sequences used for cloning are detailed in Table SI.

Determination of protein expression by western blot analysis

Protein extracts from liver cancer cells were prepared using radioimmunoprecipitation assay lysis buffer supplemented with a protease inhibitor cocktail (cat. no. 04693132001; Roche Diagnostics). Protein concentrations were then determined using a Bradford protein assay (cat. no. 5000001; Bio-Rad Laboratories, Inc.). Equal amounts of protein (40 μg) were denatured and separated by 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis. Proteins were then transferred to a nitrocellulose membrane using a semidry method (Bio-Rad Laboratories). Following transfer, membranes were blocked for 30 min in a blocking buffer (cat. no. BM01-500; Visual Protein; Energenesis Biomedical, Co., Ltd.). Membranes were subsequently incubated overnight at 4°C with the following primary antibodies: CDK6 (1:1,000; cat. no. sc-7961; Santa Cruz Biotechnology); Origin Recognition Complex Subunit 1 (ORC1; 1:1,000; cat. no. A14756; ABclonal Biotech Co., Ltd.) and β-actin (1:2,000; cat. no. sc-47778; Santa Cruz Biotechnology). Membranes were then incubated with HRP-conjugated goat anti-rabbit or anti-mouse IgG secondary antibody (1:5,000; cat. no. 7074 and 7076, respectively; Cell Signaling Technology) for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence substrate (cat. no. RPN3004; Cytiva) and imaged using a ChemiDoc Touch Imaging System (Bio-Rad Laboratories, Inc.). Densitometric quantification of protein band intensity was performed using the ImageJ software (version 1.54g; National Institutes of Health) and levels were normalized against those of β-actin as a loading control.

Determination of CDK4/6 inhibitor half-maximal inhibitory concentration values

Abemaciclib (cat. no. HY-16297A), palbociclib (cat. no. HY-50767) and ribociclib (cat. no. HY-15777B) were purchased from MedChemExpress and dissolved in DMSO to prepare stock solutions at a concentration of 20 mM. Serial dilutions were performed to achieve final drug concentrations ranging from 0 to 50 μM. The final concentration of DMSO in all treatment conditions did not exceed 0.5% (v/v), and an equivalent volume of DMSO was added to control cells to ensure consistency across experimental conditions. Cells were seeded at a density of 1×104 cells/well, and drug treatments were carried out for 72 h at 37°C in a 5% CO2 incubator. The half-maximal inhibitory concentration (IC50) of each drug was determined in both control and miR-885-5p-overexpressing cells using the MTT assay as previously mentioned. Dose-response curves were generated and IC50 values were calculated using non-linear regression analysis using the GraphPad Prism software (version 9; Dotmatics).

Statistical analysis

Data are presented as mean ± standard deviation. Statistical significance was determined using GraphPad Prism (version 10; Dotmatics). Unpaired, two-tailed Student's t-tests were used for comparisons between two groups. One-way analysis of variance was used for comparisons among ≥3 groups. Spearman correlation analysis was used to study the relationship between miR-885-5p and cell cycle genes in HCC tissues. Non-linear regression analysis was employed to analyze the effects of CDK4/6 inhibitors on liver cancer cells. P<0.05 was considered to indicate a statistically significant difference. All experiments were performed independently at least twice, with each condition tested in triplicate.

Results

miRNA-885-5p shows tumor-suppressing effects in liver cancer cells

To identify miRNAs with potential roles in liver cancer pathogenesis, miRNA expression profiles were reanalyzed from normal and HCC tissue pairs (dataset IDs. EXP00409 and EXP00576) and low-grade vs. high-grade HCC tissues (dataset ID. EXP00410) using the Database of Differentially Expressed MiRNAs in Human Cancers (24). miRNAs that were significantly downregulated in HCC or high-grade HCC were prioritized, as it was hypothesized that these miRNAs may potentially function as tumor suppressors. The analysis demonstrated five commonly downregulated miRNAs across all three datasets: miR-99a-5p, miR-122-3p, miR-122-5p, miR-139-5p, and miR-885-5p (Fig. 1A and Table SII). While the tumor-suppressive roles of several of these miRNAs are well-documented (25-29), presently, the specific function of miR-885-5p in HCC is the least known. Therefore, investigation was conducted to uncover novel mechanistic insights into its tumor-suppressive role in HCC. Supporting the present hypothesis, miR-885-5p was consistently downregulated in HCC tissues compared with normal tissues across both the TCGA (dataset EXP00409, P=0.005) and GEO (dataset EXP00576, P<0.001) datasets (Fig. 1B). Furthermore, it showed significant downregulation in high-grade tumors (dataset EXP00410, P=0.001). Furthermore, 5-year Kaplan-Meier survival analysis was performed on TCGA-LIHC cohort. Patients were stratified into high- and low-expression groups for miR-885-5p; the high-expression group was defined as patients with expression at or above the 80th percentile, while the low-expression group consisted of those with expression below this threshold. This analysis demonstrated that high miR-885-5p expression was associated with significantly prolonged overall survival in patients with HCC (P=0.0204; hazard ratio, 0.61; 95% confidence interval, 0.40-0.93; Fig. 1C).

Figure 1 miR-885-5p shows tumor-suppressing effects in liver cancer cells. (A) Reanalysis of microRNA profiling from three datasets curated by the Database of Differentially Expressed MiRNAs in Human Cancers to identify downregulated miRNAs in HCC tissues. (B) Expression of miR-885-5p in normal liver tissues, low-grade and high-grade HCC tissues from Database of Differentially Expressed MiRNAs in Human Cancers datasets. Statistical significance was determined using the Mann-Whitney test for clinical data. (C) Kaplan-Meier survival analysis of patients with HCC with low and high expression of miR-885-5p from TCGA database. (D) RT-qPCR analysis of miR-885-5p in normal hepatocyte cell line (THLE-2) and liver cancer cell lines (HepG2, JHH-4 and SNU-449). Statistical significance was determined using the one-way ANOVA with Tukey's multiple comparison test. (E) RT-qPCR analysis of miR-885-5p in liver cancer cell lines with 885-OE. (F) MTT-based proliferation assays in control and 885-OE liver cancer cells. (G) Colony formation assay of control and 885-OE liver cancer cells with representative images and quantification of colony numbers/well. Data are shown as the mean ± standard deviation from n ≥3 replicates from at least two independent experiments. Statistical significance was determined using Student's t-test for in vitro assays. *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001. miR, microRNA; HCC, hepatocellular carcinoma; 885-OE, miR-885-5p overexpressed; TCGA, The Cancer Genome Atlas; LIHC, Liver Hepatocellular Carcinoma Collection; RT-qPCR, reverse transcription-quantitative PCR.

To investigate the functional roles of miR-885-5p in HCC, the pre-miRNA sequence of miR-885-5p was cloned into a lentiviral vector and transduced into three liver cancer cell lines: HepG2, JHH-4 and SNU-449. These cell lines exhibited reduced expression levels of endogenous miR-885-5p compared with that of a normal hepatocyte cell line, THLE-2 (P<0.001; Fig. 1D). Cells transduced with a lentivirus containing a scrambled miRNA sequence served as controls. Following puromycin selection, successful miR-885-5p overexpression (885-OE) was confirmed by RT-qPCR. Significant miR-885-5p induction was achieved in each cell line compared with its respective control (HepG2, P<0.001; JHH-4, P=0.001; SNU-449, P=0.002; Fig. 1E). While the absolute levels of overexpression varied among cell lines, consistent and robust relative induction was observed. MTT assays demonstrated that 885-OE cells significantly impaired cell proliferation in all three liver cancer cell lines compared with that of control cells (HepG2, P=0.01; JHH-4, P=0.002; SNU-449, P<0.001; Fig. 1F). Consistent with this defect, 885-OE cells also demonstrated reduced colony formation ability compared with that of control cells (HepG2, P=0.013; JHH-4, P=0.001; SNU-449, P=0.045; Fig. 1G).

Overexpression of miR-885-5p downregulates multiple genes involved in the G1-to-S transition

To elucidate the molecular mechanisms underlying the tumor-suppressive function of miR-885-5p, RNA-seq on control and 885-OE JHH-4 cells (GEO ID. GSE290378) was performed. Transcriptome analysis demonstrated 1,190 upregulated and 990 downregulated DEGs in 885-OE cells compared with controls (|FC|>1.5 and P<0.05; Fig. 2A; Tables SIII and SIV). Consistent with the typical inhibitory effect of miRNAs on mRNA targets, further analysis on downregulated DEGs was conducted. KEGG analysis indicated that miR-885-5p overexpression significantly affected the cell cycle, DNA replication and homologous recombination (P<0.001; Fig. 2B). GO enrichment analysis demonstrated the G1/S transition as a key affected process (P<0.001; Fig. 2B). Reactome and WikiPathways analyses further supported this finding, demonstrating the potential role of miR-885-5p in the G1/S checkpoint (P<0.001; Fig. 2B). GSEA corroborated these findings, demonstrating a significant association between miR-885-5p overexpression and downregulation of key G1/S transition genes, including CDK6, E2F Transcription Factor (E2F1), E2F2 and ORC1 (GSEA analysis, q<0.001; Fig. 2C and D). To validate these findings, RT-qPCR analysis was performed on a representative list of top downregulated genes involved in the G1/S transition, including CDK6, CCNA2, E2F1, E2F2, PPP2CA and ORC1 in two independent liver cancer cell lines, which confirmed their significant downregulation in 885-OE cells (Fig. 2E).

Figure 2 RNA-sequencing analysis of JHH-4 cells with miR-885-5p overexpression. (A) The volcano plot represents log2 fold change and statistical significance (-log10 P-value) for gene expression of control and JHH-4 cell lines with overexpression of miR-885-5p. DEGs were identified based on an absolute fold change >1.5 and P-value <0.05. Significantly upregulated and downregulated DEGs are shown as red and blue dots, respectively. (B) Overall functional enrichment analysis of downregulated DEGs using Kyoto Encyclopedia of Genes and Genomes, Reactome, Gene Ontology, and WikiPathways with an adjusted P<0.05. (C) Gene Set Enrichment Analysis identified a downregulation of DEGs involved in the cell cycle and G1/S transition. (D) The heatmap visualized gene expression patterns of cell cycle-related genes where red and green represent upregulated and downregulated levels, respectively. (E) RT-qPCR validation of cell cycle-related genes identified in RNA-seq analysis from three replicates. RT-qPCR experiments were normalized to the control group and analyzed using Student's t-test. Data are presented as mean ± standard deviation, with significance levels. miR, microRNA; NES, Normalized enrichment score; Differentially expressed genes, DEGs; 885-OE, miR-885-5p overexpression; RT-qPCR, reverse transcription-quantitative PCR.

Overexpression of miR-885-5p promotes G1 arrest in the cell cycle

The G1/S transition, a critical checkpoint for DNA replication, is frequently dysregulated in cancer, leading to uncontrolled proliferation (30,31). To investigate if miR-885-5p regulates this transition, BrdU incorporation assays were performed. This method identifies cells in S phase and assesses G1/S progression, where a decrease in BrdU incorporation indicates impaired transition from G1 (gap phase 1) into S phase (DNA synthesis) (32). A significant decrease in BrdU incorporation in 885-OE cells was demonstrated, indicating a reduced proportion of cells in S phase (HepG2, P=0.004; JHH-4, P<0.001; SNU-449, P=0.019; Fig. 3A). Cell cycle analysis by flow cytometry was subsequently performed to precisely quantify the proportion of cells in each cell cycle phase (G1, S and G2/M). This analysis confirmed G1 arrest in 885-OE cells, demonstrating an increased proportion of cells in the G1 phase (HepG2, P=0.002; JHH-4, P=0.007; SNU-449, P<0.001; Fig. 3B). These findings were consistent across three liver cancer cell lines and aligned with the reduced proliferation and colony formation observed in aforementioned MTT and colony formation assays. Furthermore, the G1 accumulation in 885-OE cells was consistent with the enrichment of pathways governing G1/S transition identified in the present RNA-seq analysis. To further assess the impact of miR-885-5p on cell fate, its effect on apoptosis was investigated. Apoptosis, which is characterized by the translocation of phosphatidylserine to the outer plasma membrane leaflet, was measured using Apopxin Green Indicator staining. Overexpression of miR-885-5p significantly increased the proportion of apoptotic cells (HepG2, P<0.001; JHH-4, P<0.001; SNU-449, P=0.003; Fig. S1).

Figure 3 Overexpression of miR-885-5p promotes G1 arrest in the cell cycle. (A) BrdU incorporation assay in control and liver cancer cell lines with overexpression of miR-885-5p with representative images and quantification of BrdU-positive cells from 10 randomly selected fields. (B) Cell cycle analysis of control and liver cancer cell lines with overexpression of miR-885-5p (n=3). All experiments were performed in three independent liver cancer lines. Fluorescent images were captured under identical exposure settings for comparison. Data are presented as mean ± standard deviation and analyzed by Student's t-test for statistical significance. *P<0.05, **P<0.01 and ***P<0.001. Scale bar, 100 μm. miR, microRNA; BrdU, Bromodeoxyuridine; DAPI, 4′,6-diamidino-2-phenylindole; 885-OE, miR-885-5p overexpression.

miR-885-5p directly targeted CDK6 and ORC1

To investigate whether miR-885-5p directly modulates genes involved in the G1/S transition, potential miR-885-5p targets within the 3′ UTRs of key genes identified from the G1/S transition term highlighted by the present RNA-seq analysis were analyzed. The TargetScan tool was used to predict four genes as potential direct targets. CDK6, ORC1, E2F1 and E2F2 were prioritized for further validation and determined their lowest predicted minimum free energy of binding sites using the RNAhybrid web tool (Fig. 4A). WT and MT 3′ UTR constructs were generated. To confirm the specificity of miR-885-5p binding, the 5′ seed regions within the predicted binding sites of the MT constructs were engineered to abolish interaction. Cotransfection of miR-885-5p expression plasmids with the WT constructs significantly decreased luciferase activity for the firefly luciferase reporter fused with the 3′ UTRs of CDK6 (P<0.001 vs. no-insert control) and ORC1 (P<0.001 vs. no-insert control) but not those of E2F1 and E2F2 (Fig. 4B). Mutations within the miR-885-5p binding sites in the MT constructs significantly abolished this repressive effect compared with their respective WT constructs (CDK6, P<0.001 vs. WT; ORC1, P<0.001 vs. WT), thereby demonstrating the specificity of the interaction (Fig. 4B). Clinical data further supported the regulation of miR-885-5p as evidenced by a stronger negative correlation between miR-885-5p and CDK6/ORC1 (r=−0.448 and −0.221) compared with E2F1/E2F2 (r=-0.095 and −0.173; Fig. 4C). To validate the physiological relevance of this direct targeting, the endogenous protein expression levels of CDK6 and ORC1 upon miR-885-5p overexpression were assessed. Western blot analysis confirmed a significant downregulation of both CDK6 and ORC1 protein levels in 885-OE cells compared with control cells (P<0.001; Fig. S2).

Figure 4 miR-885-5p directly regulates CDK6 and ORC1. (A) Hybridization patterns predicted by the RNAhybrid tool for binding sites of miR-885-5p with WT and MT 3′ UTRs of CDK6, ORC1, E2F1 and E2F2. (B) Dual luciferase reporter assay for miR-885-5p and the 3′ UTR of genes involved in the G1/S transition. Relative luciferase activity was normalized to the no-insert group and analyzed using one-way ANOVA with Tukey's multiple comparison test (n=3). Data are presented as mean ± standard deviation, with significance levels indicated as ***P<0.001 and ****P<0.0001. (C) Spearman correlation (r) assessing the relationship between miR-885-5p expression (x-axis) and genes involved in the G1/S transition (y-axis) in hepatocellular carcinoma tissue samples from The Cancer Genome Atlas database. miR, microRNA; UTR, untranslated region; WT, wild-type; MT, mutated.

miR-885-5p sensitizes liver cancer cells to CDK4/6 inhibitors

Given that miR-885-5p downregulated CDK6 expression, its potential to sensitize liver cancer cells to CDK4/6 inhibitors was investigated. Drug sensitivity was quantified by IC50, where lower IC50 values indicated increased drug sensitivity, and higher IC50 values indicated decreased sensitivity or resistance. Overexpression of miR-885-5p significantly increased the sensitivity of liver cancer cells to palbociclib, ribociclib and abemaciclib as demonstrated by significantly reduced IC50 values in the liver cancer cell lines examined (Fig. 5A-C). In SNU-449 cells, the IC50 for palbociclib decreased from 7.49±0.15 μM in control cells to 1.50±0.17 μM in 885-OE cells (P<0.001). In JHH-4 cells, the IC50 for palbociclib decreased from 8.25±1.12 μM in control cells to 3.35±0.41 μM in 885-OE cells (P=0.028). Similar significant reductions in IC50 values were observed for ribociclib (SNU-449, P=0.034; JHH-4, P= 0.024) and abemaciclib (SNU-449, P= 0.011; JHH-4, P= 0.035).

Figure 5 Overexpression of miR-885-5p sensitized liver cancer cells to CDK4/6 inhibitors. JHH-4 and SNU-449 liver cancer cells were treated with (A) abemaciclib, (B) palbociclib and (C) ribociclib with final drug concentrations ranging from 0 to 50 μM. The half-maximal inhibitory concentration of each drug was determined in both control and liver cancer cell lines with overexpression of miR-885-5p using the MTT assay. Dose-response curves were generated, and the half-maximal inhibitory concentration values were calculated using non-linear regression analysis. Each data point is presented as mean ± standard deviation from at least eight technical replicates from three independent experiments. miR, microRNA; 885-OE, miR-885-5p overexpression.

Discussion

The present study demonstrated that miR-885-5p acts as a tumor suppressor in liver cancer by modulating the G1/S transition of the cell cycle. Significant downregulation of miR-885-5p in HCC tissues and liver cancer cells was demonstrated, while miR-885-5p overexpression inhibited cell proliferation, induced G1 arrest and increased sensitivity to CDK4/6 inhibitors. Mechanistically, miR-885-5p directly repressed the expression of CDK6 and ORC1, two key proteins that promote S phase entry. Based on these findings, the present study proposed a model wherein miR-885-5p maintains controlled cell cycle progression in normal hepatocytes, but its suppression in liver cancer cells allows for the overexpression of cell cycle-related genes, thereby driving uncontrolled proliferation (Fig. 6). Collectively, the present data suggested that miR-885-5p may be a potential therapeutic target in liver cancer.

Figure 6 Proposed mechanistic model of the tumor-suppressive role of miR-885-5p in liver cancer. In normal cells (left panel), miR-885-5p is expressed at normal levels, leading to the translational repression of CDK6 and ORC1 mRNA. This results in controlled levels of cell cycle-related genes and normal cell proliferation. In liver cancer cells (right panel), miR-885-5p expression is downregulated, leading to the loss of translational repression of CDK6 and ORC1 mRNAs. Consequently, there is an increase in the levels of cell cycle-related genes, resulting in increased cell proliferation. Red and green lines represent CDK6 and ORC1 mRNAs, respectively. Blue lines represent miR-885-5p. The 'X' in the liver cancer cell panel represents the loss of translational repression. miR, microRNA; mRNA, messenger RNAs.

Previous studies have shown miR-885-5p as a serum biomarker for several types of cancer, including HCC (33-36). For example, Wang (34) demonstrated that low serum miR-885-5p was an independent prognostic factor for poor survival in patients with HCC, suggesting its utility as a prognostic biomarker. Similarly, other studies have evaluated its diagnostic value in other types of cancer, such as osteosarcoma, cervical cancer and clear cell renal carcinoma (33,34,36). While these studies primarily evaluated its diagnostic and prognostic value, often relying on bioinformatics to predict its targets, the present study is novel in that it investigated the functional role of miR-885-5p at a global transcriptomic level by performing RNA-seq, and analyzing the phenotypic and genotypic changes following its overexpression. This approach provided direct, data-driven insights into its novel role as a tumor suppressor. Notably, miR-885-5p has been reported to promote growth and metastasis in colorectal cancer (37) and gastric cancer (38), whereas it serves a tumor suppressor role in osteosarcoma (39) and cholangiocarcinoma (40). However, the present study demonstrated that miR-885-5p acted as a tumor suppressor in liver cancer. The contrasting roles of miR-885-5p may be attributed to tissue-specific differences in its expression and function. miRNAs can regulate different sets of target genes depending on the cellular context, leading to diverse effects on cell behavior and tumorigenesis (41). Furthermore, cancer is a heterogeneous disease, and even within the same cancer type, there can be significant variations in genetic and epigenetic profiles, tumor stage and microenvironment (42). These variations can influence the expression and function of miR-885-5p, leading to different roles in different subtypes or stages of cancer.

In the context of these varied findings, it is important to detail the reports on the functional role of miR-885-5p in liver cancer, which have also yielded diverse and sometimes contrasting findings (43-45). For example, Zhang et al (43) reported that miR-885-5p acts as a tumor suppressor by inhibiting HCC metastasis via the Wnt/β-catenin signaling pathway. Similarly, Xu et al (45) demonstrated a tumor-suppressive role, showing that miR-885-5p negatively regulates the Warburg effect by silencing hexokinase 2. Conversely, Zou et al (44) described an oncogenic role for miR-885-5p, suggesting it promotes liver tumorigenesis by promoting glycolysis. The findings of the present study, which suggested a tumor-suppressive role for miR-885-5p, contribute to the growing body of evidence supporting its anticancer activity in liver cancer. However, the current study is distinct in its methodology and scope. The present study employed an unbiased approach to investigate the function of miR-885-5p in liver cancer. By utilizing transcriptomic analysis (RNA-seq), the gene expression changes associated with miR-885-5p overexpression were examined, which led to identification of the novel role of miR-885-5p in regulating the G1/S transition of the cell cycle, a critical checkpoint often dysregulated in cancer. By contrast, previous studies primarily focused on specific pathways or phenotypes, such as cancer metabolism, potentially introducing bias in their investigation of the function of miR-885-5p (43-45). The present transcriptome-wide analysis provided a broader perspective, demonstrating the impact of miR-885-5p on a fundamental process driving cell proliferation by promoting the G1/S transition in cell cycle. This novel finding contributed to the current understanding of the tumor-suppressive role of miR-885-5p in liver cancer and may have implications for therapeutic strategies targeting the G1/S transition Furthermore, regarding the cell lines used in the present study, while JHH-4 and SNU-449 cells have been characterized as HCC models, reports suggest the origin of HepG2 could be either from HCC or hepatoblastoma (46,47).

The G1/S transition stage represents a promising target for therapeutic intervention. CDK4/6 kinases serve a pivotal role in this transition, leading to the development of CDK4/6 inhibitors as anticancer agents. Although clinically approved primarily for hormone receptor-positive, HER2-negative advanced or metastatic breast cancer, these inhibitors, including palbociclib, ribociclib and abemaciclib, are also being actively studied in preclinical models for a variety of other cancer types, including non-small cell lung cancer, ovarian cancer and liver cancer (48-50). However, resistance mechanisms often emerge, highlighting the need for combination therapies to enhance their effectiveness (51). Previous studies have shown that decreasing CDK6 expression can enhance sensitivity to CDK4/6 inhibitors (52-54). The present findings demonstrated that miR-885-5p effectively downregulated CDK6, suggesting its potential to augment the antitumor activity of CDK4/6 inhibitors. The combination of miR-885-5p and CDK4/6 inhibitors represents a novel therapeutic strategy, as the synergistic potential of miR-885-5p and CDK4/6 inhibitors has not been previously explored. Notably, successful combinatorial therapies based on miRNAs and conventional drugs have been reported in preclinical models of various types of cancer, supporting the feasibility of this approach (55). For instance, in breast cancer, miR-20a-5p increases the cytotoxicity of vinorelbine, doxorubicin and paclitaxel (56); in gastric cancer, miR-129-5p augments cisplatin-induced apoptosis (57); and in liver cancer, miR-27-3p enhances the sensitivity of multidrug-resistant cells to 5-fluorouracil (58). Therefore, further investigations are warranted to explore the therapeutic potential of miR-885-5p and CDK4/6 inhibitor combinations in liver cancer.

In conclusion, the present study provided evidence for the tumor-suppressive role of miR-885-5p in liver cancer. By regulating the G1/S transition and sensitizing liver cancer cells to CDK4/6 inhibitors, miR-885-5p represents a promising therapeutic target for liver cancer treatment. Future research should focus on validating these findings in vivo and exploring the clinical potential of miR-885-5p-based therapies.

Supplementary Data

Availability of data and materials

The data generated in the present study may be requested from the corresponding author. The RNA-seq data sets generated in the present study may be found in the National Center for Biotechnology Information Gene Expression Omnibus repository under the accession number GSE290378 or at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290378.

Authors' contributions

CA and PT conceived and designed the study. AN and CA performed and analyzed the experiments. AN provided support for bioinformatic analysis. CA and PT wrote the manuscript. PT supervised the project. CA and AN confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Use of artificial intelligence tools

During the preparation of this work, AI tools (https://gemini.google.com/) were used to improve the readability and language of the manuscript, and subsequently, the authors revised and edited the content produced by the AI tools as necessary, taking full responsibility for the ultimate content of the present manuscript.

Acknowledgements

Not applicable.

Funding

The present work was funded by the National Research Council of Thailand (NRCT; grant no. N42A670089), the Ratchadaphiseksomphot Fund, Graduate Affairs, Faculty of Medicine, Chulalongkorn University (grant no. GA66/077), the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B, grant no. B36G660010) and the Center of Excellence in Hepatitis and Liver Cancer, Faculty of Medicine, Chulalongkorn University. Further support was provided by the Second Century Fund, Chulalongkorn University and NRCT (grant no. N41A670267) for the doctoral fellowship.

References