MicroRNA-874 targets phosphomevalonate kinase and inhibits cancer cell growth via the mevalonate pathway

Aersilan, Alimasi; Hashimoto, Naoko; Yamagata, Kazuyuki; Yokoyama, Masataka; Nakayama, Akitoshi; Shi, Xiaoyan; Nagano, Hidekazu; Sakuma, Ikki; Nohata, Nijiro; Kinoshita, Takashi; Seki, Naohiko; Rahmutulla, Bahityar; Kaneda, Atsushi; Zhahara, Siti Nurul; Gong, Yingbo; Nishimura, Motoi; Kawauchi, Shoichiro; Kawakami, Eiryo; Tanaka, Tomoaki

doi:10.1038/s41598-022-23205-w

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Published: 02 November 2022

MicroRNA-874 targets phosphomevalonate kinase and inhibits cancer cell growth via the mevalonate pathway

Alimasi Aersilan¹^na1,
Naoko Hashimoto¹^na1,
Kazuyuki Yamagata¹,
Masataka Yokoyama¹,
Akitoshi Nakayama¹,
Xiaoyan Shi¹,
Hidekazu Nagano¹,
Ikki Sakuma¹,
Nijiro Nohata²^nAff8,
Takashi Kinoshita²,
Naohiko Seki²,
Bahityar Rahmutulla³,
Atsushi Kaneda³,
Siti Nurul Zhahara¹,
Yingbo Gong¹,
Motoi Nishimura⁴,
Shoichiro Kawauchi⁵,
Eiryo Kawakami^5,6 &
…
Tomoaki Tanaka^1,7

Scientific Reports volume 12, Article number: 18443 (2022) Cite this article

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Abstract

The microRNA (miR) miR-874, a potential tumour suppressor, causes cell death via target gene suppression in various cancer types. Mevalonate pathway inhibition also causes cell death in breast cancer. However, the relationship between the mevalonate pathway and miR-874-induced apoptosis or its association with the tumour suppressor p53 has not been elucidated. We identified phosphomevalonate kinase (PMVK), a key mevalonate pathway enzyme, and sterol regulatory element-binding factor 2 (SREBF2), the master cholesterol biosynthesis regulator, as direct miR‑874 targets. Next-generation sequencing analysis revealed a significant miR-874-mediated downregulation of PMVK and SREBF2 gene expression and p53 pathway enrichment. Luciferase reporter assays showed that miR-874 directly regulated PMVK and SREBF2. miR-874-induced apoptosis was p53 dependent, and single-cell RNA sequencing analysis demonstrated that miR-874 transfection resulted in apoptosis and p53 pathway activation. Downregulation of PMVK expression also caused cell cycle arrest and p53 pathway activation, which was rescued by geranylgeranyl pyrophosphate (GGPP) supplementation. Analysis of The Cancer Genome Atlas (TCGA) database indicated a negative correlation between miR-874 and PMVK expression and between miR-874 and SREBF2 expression. These findings suggest that miR-874 suppresses the mevalonate pathway by targeting SREBF2 and PMVK, resulting in GGPP depletion, which activates the p53 pathway and promotes cycle arrest or apoptosis.

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Introduction

Breast cancer is among the most common cancers worldwide. Surgical procedures and recent advances in endocrine therapy, chemotherapy, radiotherapy, and targeted molecular therapies have improved breast cancer treatment efficacy. However, breast cancer remains a leading cause of cancer-related deaths among women globally¹, and multidisciplinary approaches demonstrate limited efficacy in some patients with metastatic breast cancer, requiring the development of novel therapeutic approaches.

Cancer cells undergo metabolic reprogramming, and recent studies have reported the upregulation of the mevalonate pathway in cancer cells^2,3. Statins reduce blood cholesterol levels by inhibiting the rate-limiting enzyme of the mevalonate pathway, hydroxymethylglutaryl-CoA reductase (HMGCR), and statin use in patients with various cancer types has been associated with reduced mortality⁴. Statins suppress cancer cell growth^5,6 and induce apoptosis in various tumour cells, including breast cancer cells^7,8, suggesting the involvement of the mevalonate pathway in breast cancer development. Furthermore, gain-of-function mutants of p53 upregulate the mevalonate pathway, whereas wild-type p53 represses mevalonate pathway genes^2,9.

MicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs 19–25 nucleotides in length that regulate gene expression by binding to mRNAs and promoting mRNA degradation or translation inhibition¹⁰. miRNAs regulate several biological functions, including the cell cycle, cell differentiation, and apoptosis¹⁰, and miRNAs that regulate the mevalonate pathway have been identified. A recent study reported that miR-21 targets HMGCR to regulate triglyceride and cholesterol metabolism in hepatocytes¹¹. In addition, miR-125a directly targets HMGCR to suppress vascular smooth muscle cell proliferation¹².

miR-874 was identified as the most significantly downregulated miRNA in maxillary sinus squamous cell carcinoma tissues compared with corresponding normal tissues and was found to suppress cancer cell proliferation and invasion¹³. miR-874 was reported to inhibit proliferation by targeting cyclin-dependent kinase 9 (CDK9) in breast cancer¹⁴. In addition, several lines of evidence indicate that miR-874 functions as a tumour suppressor in various cancer types, including breast cancer, by targeting multiple genes, including protein phosphatase 1 catalytic subunit alpha (PPP1CA), histone deacetylase 1 (HDAC1), E2F transcription factor 3 (E2F3), signal transducer-activator of transcription 3 (STAT3), matrix metalloproteinase-2 (MMP-2), urokinase-type plasminogen activator (uPA), aquaporin-3 (AQP3), and ETS proto-oncogene 1 (ETS1)^{13,15,16,17,18,19,20}, many of which positively regulate cell proliferation. It was reported that miR-874 expression was downregulated in breast cancer tissues compared with normal tissue adjacent to tumour tissue²¹, indicating that miR-874 is significantly downregulated in breast cancer. The authors also reported that the miR-874 expression level was correlated with the status of the oestrogen receptor, TNM stage and lymph node metastasis. Moreover, patients with high miR-874 expression exhibited a better prognosis. Regarding the expression of miR-874 compared to normal mammary epithelial cell lines such as MCF10A, Kilinc S et al. compared the expression of miRNAs in MCF10A cells with stable expression of oncogenes with empty vectors. They performed qPCR analysis using a panel of 384 miRNA primers, and among them, the expression level of miR-874 was decreased by oncogene expression, such as AURKB and HRAS, compared to the MCF10A control²². These findings indicate that miR-874 is downregulated in breast cancer and that its expression level has prognostic significance. However, the role of miR-874 in breast cancer, especially in cancer cell metabolism, has not been fully elucidated. miR-874 also markedly induces cell death, but its association with the p53 pathway has not been reported.

In the present study, we investigated the molecular mechanism of miR-874-mediated tumour suppression in breast cancer, particularly in the context of cancer metabolism and the p53 pathway. Next-generation sequencing (NGS) analysis revealed that miR-874 transfection resulted in the downregulation of sterol regulatory element-binding factor 2 (SREBF2), a transcription factor and the master cholesterol biosynthesis regulator²³, and phosphomevalonate kinase (PMVK), a mevalonate pathway enzyme, both of which were identified as direct miR-874 targets. miR-874 transfection induced p53-dependent apoptosis in MCF-7 cells. PMVK suppression caused cell cycle arrest and p53 pathway activation, which was rescued by geranylgeranyl pyrophosphate (GGPP) supplementation, a mevalonate pathway metabolite. Our findings reveal the involvement of the mevalonate pathway in the mechanisms underlying the tumour-suppressive function of miR-874 and suggest that miR-874 could be a potent therapeutic target in breast cancer.

Results

Identification of the miR-874-modulated molecular pathway and putative target genes in breast cancer cells

Several lines of evidence indicate that c-Myc and p53 are essential for the induction of apoptosis or cell cycle arrest^24,25. To evaluate the tumour-suppressive effects of miR-874 in breast cancer cells, we employed RNA-seq to compare WT MCF-7 cells transfected with miR-874 or control miRNA (Fig. 1a). Principal component analysis (PCA) showed the effects of miR-874 transfection on the gene expression profile represented by the PC1 and PC2 axes (Supplementary Fig. S1a). Differentially expressed genes (DEGs) between WT MCF-7 cells transfected with control miRNA and miR-874 were identified (Supplementary Fig. S1b). To characterize the genes whose expression was altered by miR-874, we focused on upregulated or downregulated genes/pathways in miR-874-transfected cells relative to control miRNA-transfected cells (Fig. 1b). As expected, the DNA damage response, apoptosis, and p53 transcriptional gene network were highly upregulated. Furthermore, using TRRUST analysis to search for upstream factors, p53 was the most enriched transcription factor, and Myc was also highly enriched (Fig. 1c). Therefore, we re-analysed RNA-seq for miR-874 effects on p53 KO and p53/c-Myc DKO cells compared to WT cells. The PCA is shown in Supplementary Fig. S1a. DEGs between p53 KO and p53/c-Myc DKO MCF-7 cells transfected with control miRNA and miR-874 were also identified (Supplementary Fig. S1b). Therefore, we first focused on the genes that were upregulated by miR-874 transfection specifically for WT p53. The genotoxic pathway and p53 transcriptional gene network were the most upregulated WT-specific pathways (Fig. 1d). Next, analysis of the DEGs common to the three cell lines revealed an enrichment of the cell cycle rather than apoptosis (Supplementary Fig. S1e). In contrast, the apoptotic pathway was not upregulated in either p53 KO or p53/c-Myc DKO MCF-7 cells (Supplementary Fig. S1e). Since miRNAs exert their function by repressing target mRNAs, to examine the pathways repressed by miR-874, we turned to the downregulated genes in WT MCF-7 cells to explore the point of action of miR-874. As a result, cholesterol biosynthesis and the mevalonate pathway were found to be highly enriched (Fig. 1d). Expression level analysis of fragments per kilobase of exon per million mapped sequence reads (FPKM) from RNA-seq data confirmed that 5 of 7 genes (HMGCS, HMGCR, PMVK, MVD, and FDPS) in the mevalonate pathway and SREBF1/2, master regulators of lipid metabolism, were significantly suppressed by miR-874, including an 85% reduction in PMVK expression (Fig. 1e, Table 1). The effect of miR-874 on the mevalonate pathway was also examined in p53 KO and p53/c-Myc DKO cells. Similar to WT MCF-7 cells, miR-874-transfected p53 KO and p53/c-Myc DKO cells exhibited significantly decreased expression of mevalonate pathway genes (Table 1), according to FPKM RNA-seq data analysis, including more than 85% reduction in PMVK expression, although the mevalonate pathway was not identified as a top enriched pathway in pathway enrichment analysis (Supplementary Fig. S1f). Furthermore, heatmap analysis confirmed that PMVK, along with PPP1CA, one of the previously reported targets of miR-874, was among the 15 most significantly downregulated genes in cells transfected with miR-874 compared with control miRNA, not only in WT but also in p53 KO and p53/c-Myc DKO MCF-7 cells (Fig. 1f).

Table 1 Mevalonate pathway and SREBF expression determined by RNA-seq (FPKM).

Full size table

These results suggest that the tumour-suppressive function of miR-874 is associated with apoptosis via the p53 and c-Myc signalling pathways. Moreover, our analysis of genes downregulated by miR-874 transfection indicates that miR-874 represses genes involved in multiple steps of the mevalonate pathway, most notably PMVK, in a p53-independent manner. These results suggest that PMVK may be a miR-874 target.

miR-874 directly targets both PMVK and SREBF2, inhibiting their expression

The RNA-seq results showed that miR-874 acts on multiple components of the mevalonate pathway, including PMVK, which is necessary for the maintenance of cell cycle progression²⁶ (Fig. 1e, Table 1). However, even if PMVK is a direct target, it alone does not explain the repression of genes in multiple steps of the mevalonate pathway. In our RNA-seq analysis, we found that the expression of SREBF2 was also downregulated by miR-874 transfection. To validate the RNA-seq results, we assessed whether miR-874 affects endogenous PMVK and SREBF2 expression in breast cancer cell lines. MCF-7 is an oestrogen receptor (ER)-positive and progesterone receptor (PgR)-positive breast cancer cell line that expresses WT p53²⁷, whereas MDA-MB-231 is a triple-negative breast cancer cell line that expresses mutant p53 (R280K), which promotes tumour progression²⁸. miR-874 overexpression markedly suppressed PMVK and SREBF2 mRNA expression in both MCF-7 and MDA-MB-231 cells (Fig. 2a,b), and HMGCR was also downregulated in both MCF-7 and MDA-MB-231 cells (Supplementary Fig. S2), despite a lack of predicted miR-874 target sites in the 3′-UTR of HMGCR according to the TargetScan database. The protein expression levels of PMVK and SREBP2 also decreased following transfection with miR-874 compared with control miRNA in both cell lines (Fig. 2c,d). To explore the mechanism of PMVK and SREBF2 downregulation by miR-874, we employed the TargetScan database to predict miR-874 target genes. Putative miR-874 target sites were located in positions 216–222 of the PMVK 3′-UTR and in positions 222–228 of the SREBF2 3′-UTR (Fig. 2e). The genes that were downregulated also contained predicted seed sites for miR-874 and are shown in Supplementary Table S1. STarMir predicted that the seed sequence for PMVK and miR-874 binding had a minimum free energy of − 23.2 kcal/mol, suggesting that miR-874 might form a stable structure with PMVK and SREBF2, respectively (Fig. 2f,h). To investigate whether miR-874 targets PMVK and SREBF2 directly, a reporter assay was performed by constructing psiCHECK2 vectors containing an intact PMVK 3′-UTR or mutant PMVK 3’-UTR lacking the miR-874 binding site. Similarly, WT or miR-874 binding site-deleted SREBF2 3′-UTR fragments were cloned into the psiCHECK2 vector downstream of the Renilla luciferase reporter gene. p53/c-Myc DKO MCF-7 cells were cotransfected with miR-874 and the reporter vectors. Compared with the control miRNA, miR-874 reduced the luciferase activities of the PMVK 3′-UTR and SREBF2 3′-UTR WT reporters, but it had no effect on the constructs lacking the binding site (Fig. 2g,i). Deletion of positions 216–222 of the 3′-UTR of PMVK increased the luminescence (Fig. 2g, right). These results suggest that PMVK and SREBF2 are direct targets of miR-874.

miR-874 activates p53 and induces apoptotic cell death and tumour cell growth inhibition

To assess the tumour-suppressive functions of miR-874 in breast cancer cells, MCF-7 cells were transfected with control miRNA or miR-874 mimic. The RNA-seq results showed the upregulation of p53 and c-Myc pathway components following miR-874 mimic transfection; therefore, we investigated the effects of miR-874 on p53 and c-Myc expression. First, we examined changes in c-Myc and p53 protein expression over time after miR-874 transfection, which revealed that c-Myc expression peaked 24 h after miR-874 transfection before slowly decreasing. p53 expression increased gradually, peaking 36 h after transfection (Fig. 3a, Supplementary Fig. S3). These results suggest that miR-874 activates c-Myc and p53 sequentially, resulting in apoptosis. To investigate the time course of miR-874-induced apoptosis, we performed an annexin V apoptosis assay. Consistent with the p53 activation results, apoptosis was initiated 36 h after miR-874 transfection and increased at 48 h (Fig. 3b). To investigate the effects of miR-874 on cell cycle regulation over time, we performed an EdU assay in miR-874-transfected MCF-7 cells. A decrease in the proportion of S phase cells and the arrest of cell cycles in the G1 phase were observed as early as 12 h after transfection (Fig. 3c). These data corroborate the findings that miR-874 activates the c-Myc and p53 signalling pathways. The importance of p53 and c-Myc status on the effects of miR-874 transfection in WT, p53 KO, and p53/c-Myc DKO MCF-7 cells was examined. Both p53 and p53/c-Myc KO cell generation were confirmed to be successful. p53 upregulation was observed in WT MCF-7 cells transfected with miR-874 but not in p53 KO or p53/c-Myc DKO MCF-7 cells transfected with miR-874 (Fig. 3d). Consistently, although miR-874 overexpression caused a significant increase in the fraction of annexin V-positive p53 WT MCF-7 cells, the apoptosis rate after miR-874 transfection increased in p53 KO cells but was lower than that in p53 WT cells. Furthermore, the transfection of miR-874 did not increase the fraction of apoptotic cells relative to control miR transfection in p53/c-Myc DKO cells (Fig. 3e,f). Correspondingly, the RNA-seq results revealed increased expression of BAX and PUMA in WT MCF-7 cells but not in p53 KO or p53/c-Myc DKO MCF-7 cells (Fig. 3g). These findings suggest that apoptosis induction by miR-874 is dependent on p53 and c-Myc. We also examined the effects of miR-874 on apoptosis and cell cycle regulation in MDA-MB-231 cells containing the p53 R280K missense mutation, which promotes tumour progression and metastasis. Annexin V assays showed no induction of apoptotic cell death in miR-874-transfected MDA-MB-231 cells (Supplementary Fig. S4a,b). In contrast, cell growth was suppressed in miR-874-transfected MDA-MB-231 cells compared with control miRNA-transfected cells (Supplementary Fig. S4c). An EdU assay was performed to analyse the cell cycle by flow cytometry, showing that compared with control miRNA-transfected cells, miR-874-transfected cells displayed increased G1 arrest and decreased progression into the S phase at 48 h (Supplementary Fig. S4d,e). These data indicate that miR-874 induces p53-dependent apoptosis and cell cycle arrest in a partially p53-independent manner.

scRNA-seq analysis reveals that miR-874 upregulates the p53 signalling pathway and downregulates the mevalonate pathway

miR-874 affects not only the mevalonate pathway but also cell cycle arrest and p53-mediated apoptosis. To extensively characterize the miR-874-mediated cell cycle mechanism and its association with PMVK downregulation, we performed scRNA-seq analysis of MCF-7 cells transfected with control miRNA or miR-874 for 36 h or control siRNA or siPMVK for 48 h. Cell types were classified according to DEGs and known marker expression. The cells were divided into 13 clusters (Fig. 4a, Supplementary Fig. S5a). The relative cell proportions of Clusters 5, 7, 9, and 10 increased, whereas those of Clusters 0, 1, 2, 3, 4, 6, and 8 decreased in cells transfected with miR-874 compared with control miRNA (Fig. 4a,b). In contrast, the proportions of Clusters 2, 5, 9 and 11 increased, whereas those of Clusters 0, 1, 3, 4, 6, 7, 8, 10, and 12 decreased in cells transfected with siPMVK compared with control siRNA (Fig. 4a,c). Gene Ontology (GO) terms associated with the cell cycle were enriched in approximately half of the 13 clusters (Supplementary Fig. S6). The increased proportions of Clusters 5 and 9 reflected common characteristics between cells transfected with miR-874 and siPMVK. Cluster 5 contained genes involved in the cell cycle, and Cluster 9 contained genes related to the cellular response and p53 pathway (Supplementary Fig. S6). We examined cell cycle variations and visualized the cell cycle phases (Fig. 4d,e, Supplementary Fig. S5c). The proportions of S phase cells decreased among cells transfected with siPMVKs compared with control siRNA, whereas the proportions of G1 phase cells increased (Fig. 4d,e). No visible changes were observed among the cell cycle phases in cells transfected with miR-874 compared with control miRNA, which was in contrast with the cell cycle analysis by flow cytometry (Figs. 3c, 4e). This difference may be due to the harvesting of cells just prior to cell death and the exclusion of dead cells from flow cytometry analysis. When we characterized clusters according to cell cycle phases, Clusters 0, 3 and 6 primarily consisted of S phase cells, whereas Clusters 2 and 9 were primarily G1 phase cells (Fig. 4f). These results are consistent with the finding that miR-874 transfection induces S phase blockade and indicate that PMVK knockdown is associated with G1 arrest.

We investigated alterations in gene expression following miR-874 or siPMVK transfection, especially p53 downstream genes and mevalonate pathway genes. First, we compared gene expression profiles among clusters, which showed variations in the enrichment of p53 target genes and mevalonate pathway genes (Supplementary Fig. S5d). To determine common components between bulk RNA-seq and scRNA-seq, we compared the DEGs, revealing 349 common DEGs between the two analyses (Fig. 4g), which were associated with biological process GO terms related to the cell cycle or cell death pathways (Fig. 4h). Furthermore, p53 and c-Myc were identified as upstream regulators based on transcription factor prediction analysis (Fig. 4i). Similar to the bulk RNA-seq results (Fig. 1), several p53 target genes were upregulated, and mevalonate pathway genes were downregulated in miR-874-transfected cells (Fig. 4j,k).

GO enrichment analysis and transcription factor enrichment analysis were applied to DEGs identified by scRNA-seq between cells transfected with control siRNA and siPMVK. In addition to the p53 pathway, GO enrichment analysis indicated enrichment of the cell cycle and apoptosis pathways in cells transfected with siPMVK compared with control siRNA (Supplementary Fig. S5e). Similar to the results for miR-874, p53 and c-Myc were identified as regulatory transcription factors for the identified DEGs (Fig. 4l). For data validation, the expression levels of PMVK, SREBF2, and PPP1CA, a previously reported miR-874 target, were determined. PMVK expression was effectively suppressed by miR-874 or siPMVK transfection (Fig. 4m, Supplementary Fig. S5b). SREBF2 and PPP1CA expression was also downregulated by miR-874 transfection (Fig. 4j,n, Supplementary Fig. S5b).

To examine common gene expression patterns between cells transfected with miR-874 and siPMVK, we compared DEGs from the miR-874 vs. control miRNA comparison with those identified in the siPMVK vs. control siRNA comparison (Supplementary Fig. S5f). Among the 89 identified common DEGs, GO enrichment analysis revealed the enrichment of cell cycle-associated pathways and p53 signalling pathways, including several p53 target genes upregulated by PMVK repression (Fig. 4k, Supplementary Fig. S5g). Consistent with our findings in Figs. 1, 2 and 3, miR-874 expression altered the cell cycle and activated the p53 pathway. The gene expression profile for miR-874 expression was similar to that for PMVK knockdown, suggesting that miR-874 activates the p53 pathway at least partially through the suppression of the mevalonate pathway.

Supplementation with isoprenoids partially rescues miR-874-induced apoptosis

We investigated whether supplementation with mevalonate pathway metabolites could block miR-874-induced apoptosis. Cell proliferation was measured using Alamar Blue. Among the metabolites, only GGPP, which prenylates small GTPases and tethers them to plasma membranes, was able to restore cell growth following miR-874 transfection (Fig. 5a). Unlike statins, which are inhibitors of HMGCR, miR-874 acts on multiple genes in the cholesterol synthesis pathway, and treatment with mevalonic acid did not restore cell growth. To evaluate the effects of miR-874 in MCF-7 cells, we investigated whether miR-874-induced apoptosis could be prevented through GGPP supplementation. Similar to previous results, miR-874 significantly induced apoptosis in p53 WT MCF-7 cells, and GGPP treatment partially decreased the percentages of visibly detached cells and apoptotic cells, accompanied by an increase in the proportion of live cells (Fig. 5b,c). The p53 protein expression level, which increased in response to miR-874 expression, decreased following GGPP supplementation (Fig. 5d). The expression levels of PUMA and BAX, which are downstream of p53 and increased in response to miR-874 expression, decreased following GGPP supplementation (Fig. 5e). In contrast, the protein expression level of c-Myc increased in response to miR-874 and was further increased by GGPP supplementation (Fig. 5d). These results indicate that the mevalonate pathway influences the apoptotic function of miR-874.

Downregulation of PMVK inhibits MCF-7 breast cancer cell proliferation

PMVK is a cytoplasmic enzyme that catalyses the conversion of mevalonate 5-phosphate into mevalonate 5-diphosphate in the mevalonate pathway²⁹. To investigate the functional role of PMVK, we assessed the effects of silencing PMVK on apoptosis and the cell cycle in MCF-7 cells. The expression levels of PMVK mRNA and protein decreased following PMVK knockdown in MCF-7 cells (Fig. 6a,b). p21/CDKN1A, a cell cycle regulator downstream of p53, exhibited increased expression following PMVK knockdown. BAX and PUMA, apoptotic genes downstream of p53, were also affected by PMVK silencing (Fig. 6a,b). PMVK knockdown decreased the proliferation rate of p53 WT MCF-7 cells (Fig. 6c). Consistently, although PMVK knockdown did not increase the proportion of apoptotic MCF-7 cells after 48 h (data not shown), PMVK silencing induced apoptosis after 64 h (Fig. 6d,e). To test whether cell cycle arrest and the suppression of cell proliferation could be rescued by supplementation with mevalonate pathway metabolites, we treated PMVK knockdown cells with GGPP, which partially restored S phase progression (Fig. 6f,g). The GGPP treatment of PMVK knockdown MCF-7 cells also partially decreased p21/CDKN1A, BAX, and NOXA expression (Fig. 6a). These results suggest that miR-874 suppresses the proliferation of breast cancer cells, at least in part, by inhibiting the mevalonate pathway via the downregulation of PMVK.

High PMVK expression appears to be a risk factor in breast cancer patients

We confirmed the relationship between miR-874 and PMVK expression and between miR-874 and SREBF2 expression in clinical specimens by analysing the TCGA breast cancer dataset. Both PMVK and SREBF2 expression were elevated in tumour tissues (n = 1093) compared with adjacent normal tissues (n = 104), whereas miR-874 expression was decreased in tumour tissues compared with corresponding normal tissues (Fig. 7a). Furthermore, PMVK expression was negatively correlated with miR-874 expression (Fig. 7b), whereas the expression of SREBF2 and other mevalonate pathway genes was not correlated with miR-874 expression (Supplementary Fig. S7). We performed an integrated meta-analysis for PMVK using public datasets representing approximately 3000 patients with breast cancer (Fig. 7c). High PMVK expression tended to be associated with a poor prognosis (HR = 1.13) for relapse-free survival (not significant; Fig. 7c). Kaplan‒Meier survival analysis revealed that the overall survival of patients was worse in the high PMVK expression group than in the low PMVK expression group (p = 0.0059; Fig. 7d upper panel). The results of the analysis using protein expression showed a similar trend (p = 0.011, Fig. 7d lower panel), suggesting that PMVK expression might serve as a prognostic marker. On the other hand, for SREBF2 mRNA, the overall survival analysis showed the opposite trend., i.e., the overall survival of patients was worse in the low SREBF2 mRNA expression group than in the high expression group (p = 5.4e−07; Supplementary Fig. S7b). A schematic model summarizing the study results is shown in Fig. 7e.

Discussion

The mevalonate pathway is responsible for de novo cholesterol synthesis, in addition to the synthesis of many important nonsterol isoprenoid derivatives³⁰. Elevated or dysregulated mevalonate pathway activity has been identified in various tumours, including breast cancer, and a number of studies have suggested that malignant cells are more dependent on the continuous availability of mevalonate pathway metabolites than their nonmalignant counterparts^31,32,33. In this study, we focused on the functional role of miR-874 in breast cancer and its association with the mevalonate pathway. miR-874 induces apoptosis in MCF-7 cells in a p53-dependent manner, affecting the mevalonate pathway through the direct targeting of PMVK and SREBF2. RNA-seq analysis of miR-874-transfected MCF-7 cells identified the enrichment of p53 signaling pathways and the downregulation of several genes associated with the mevalonate pathway and SREBF2. scRNA-seq analysis revealed the effect of miR-874 on the cell cycle. Some of the miR-874 target genes previously reported are known to be involved in p53 activation. Regarding CDK9 function, suppression of CDK9 increases p53 stability. Inhibition of CDK9 not only reduces the MDM2-mediated ubiquitination and degradation of p53 but also increases p53 stability by suppressing the deacetylase activity of SIRT1³⁴. E2F3 is a key repressor of the p19(Arf)-p53 pathway³⁵. Regarding Stat3, Stat3 inhibits p53 gene transcription by binding to the p53 promoter³⁶. Thus, miR-874 may activate the p53 pathway via multiple target genes. Analysis of a microRNA expression panel of 51 breast cancer cell lines showed no significant difference in miR-874 expression among the cell lines³⁷, suggesting that the expression level of endogenous miR-874 in breast cancer cell lines does not differ between p53 WT and mutant. In p53 WT MCF-7 cells, miR-874 overexpression resulted in cell death and cell cycle arrest. On the other hand, p53 mutant MDA-MB-231 did not induce cell death, cell cycle arrest or cell proliferation inhibition. miR-874 overexpression suppressed cell proliferation in another p53-mutant triple-negative breast cancer cell line, MDA-MB-468³⁸. The possibility that ER/PR + status and c-Myc have some influence could not be ruled out. Indeed, we tried to establish c-Myc single KO MCF-7-cell lines using CRISPR/Cas9. However, c-Myc KO cells were very slow to proliferate, and we could not maintain them. Apoptosis induction by miR-874 overexpression completely abolished p53/c-Myc DKO (Fig. 3f).

Similar to miR-874 overexpression, PMVK knockdown induced apoptotic cell death accompanied by p53 pathway activation, which could be partially restored by GGPP supplementation in MCF-7 cells. Although it is not known how PMVK knockdown causes apoptosis, PMVK has been associated with the development of porokeratosis, a group of keratinization disorders characterized by circular or annular skin lesions with a distinct hyperkeratotic rim termed the cornoid lamella³⁹. In addition to PMVK, MVD, mevalonate kinase (MVK), FDPS, and SLC17A9 are known as causative genes for porokeratosis^40,41,42. A combination of cholesterol and statins that replace deficient end products, preventing the accumulation of potentially toxic metabolites, efficiently treats porokeratosis⁴³. Premature apoptosis and incomplete keratinocyte differentiation were observed in the lesion tissues of PMVK-deficient patients²⁹, and several studies have reported that p53 expression increases in the cornoid lamella of these lesions^44,45, consistent with our results. Future studies may explore the mechanisms of apoptosis caused by inhibition of the mevalonate pathway. PMVK knockdown suppresses the downstream production of isoprenoids, such as farnesyl pyrophosphate (FPP) and GGPP, which are necessary for the synthesis of important biomolecules, such as cholesterol, dolichol and ubiquinone. FPP and GGPP isoprenylate small GTPases, such as Ras and Rho, which are involved in tumorigenesis, tethering these GTPases to the cell membrane for signal transduction⁴⁶. GGPP and FPP inhibition induces GTPase dissociation from the cell membrane, suppressing Ras- and Rho-mediated signalling and inducing cancer cell death. Our results also showed that c-Myc expression increased following GGPP supplementation. Agabiti et al. reported that geranylgeranyl diphosphate synthase, which is an enzyme in the isoprenoid biosynthesis pathway that produces a pathway that produces GGPP, reduces the expression of Notch1 and affects the expression of its downstream target, c-Myc⁴⁷.

The expression of PMVK in breast cancer tissue as reported in the TCGA database was higher than that in normal tissue adjacent to tumour tissue. Consistent with this finding, survival analysis in breast cancer showed that high PMVK expression was associated with poor prognosis. These results suggest that the suppression of breast cancer by the miR-874/PMVK axis may be relevant in many types of breast cancers, not just certain breast cancer cell lines. Although few previous reports have examined the association between PMVK and cancer, PMVK is reported to be differentially related to the multidrug response of ER-positive and ER-negative cells, with expression positively correlated with the drug response in ER-positive cells and negatively correlated with the drug response in ER-negative cells in breast cancer⁴⁸, suggesting that PMVK might serve as a prognostic marker for breast cancer. In contrast, high PMVK expression is reported as a potential biomarker for favourable progression-free survival in ovarian cancer⁴⁹. p53 status might also be associated with prognosis, and further clarification regarding which factors are involved in mediating antitumour effects remains necessary. PMVK knockdown induced apoptosis and cell cycle arrest, but the proportion of apoptotic cells was lower than that following miR-874 transfection. A single miRNA can regulate dozens or even hundreds of target mRNAs^50,51, and the downregulation of one target may have a minimal effect on apoptosis; however, our results indicate that PMVK downregulation has p53-dependent tumour-suppressive activity in breast cancer cells.

Unlike in the case of PMVK, SREBF2 mRNA and miR-874 expression did not show a correlation. Higher expression of SREBF2 mRNA was associated with a better prognosis. When intracellular cholesterol levels are low, cleaving enzymes in the Golgi apparatus cut out the N-terminal region of SREBPs, and active SREBPs are transported to the nucleus, where they act as transcription factors to positively regulate the expression of genes related to cholesterol metabolism in the nucleus⁵². Therefore, SREBF mRNA levels do not necessarily reflect the protein expression or expression of genes related to cholesterol metabolism.

Several reports indicate that miR-874 functions as a tumour suppressor and is downregulated in several cancers. The DNA methylation of the miR-874 promoter region is upregulated in breast cancer tissues compared with normal tissues. Demethylation treatment with 5-Aza-2′-deoxycytidine (5-Aza-CdR) upregulated miR-874 expression in breast cancer cell lines, suggesting that DNA methylation could be a mechanism of downregulation of miR-874 expression²¹. There is also emerging evidence that long noncoding RNAs such as lncRNA H19, lncRNA NEAT1, lncRNA miR210HG and lncRNA DCST1-AS1 act as sponges for miR-874, suppressing miR-874 expression and contributing to cancer progression^53,54,55,56. Further studies are needed to identify long noncoding RNAs that suppress the miR-874/PMVK axis. Our study revealed that miR-874 activates the p53 signalling pathway by suppressing the mevalonate pathway via the depletion of GGPP, suggesting the therapeutic potential of miR-874 for targeting the mevalonate pathway, especially in p53 WT breast cancers.

Materials and methods

Cell lines and cell culture

Two breast cancer cell lines, MCF-7 and MDA-MB-231, were obtained from the American Type Tissue Culture Collection (ATCC, Manassas, VA, USA). MCF-7 cells were cultured in Dulbecco’s modified Eagle medium (DMEM; Sigma-Aldrich, St Louis, MO, USA) supplemented with 10% (v/v) foetal bovine serum (FBS) and penicillin and streptomycin (Sigma-Aldrich). MDA-MB-231 cells were maintained in L-15 media (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% FBS and penicillin and streptomycin. p53 knockout (KO) and p53/c-Myc double-knockout (DKO) MCF-7 cells were generated by CRISPR‒Cas9 genome editing, as previously described⁵⁷.

miRNA transfection and small interfering RNA treatment

The following RNA species were used in this study: a pre-miRNA™ miRNA precursor (hsa-miR-874; premiR ID: PM12355) that mimics endogenous precursor miRNAs, two negative control (cont) miRNAs (P/N: AM17110 and AM17111; Thermo Fisher Scientific), small interfering RNAs (siRNAs; PMVK siRNA_1 and PMVK siRNA_2; Japan Bio Services, Saitama, Japan, and Thermo Fisher Scientific, respectively), and a negative control siRNA (Thermo Fisher Scientific) (Supplementary Table S2). RNA was reverse transfected into MCF-7 cells or MDA-MB-231 cells using RNAiMAX (Thermo Fisher Scientific) following the manufacturer’s protocol. Cells were harvested at the indicated time points after transfection and subjected to RNA sequencing (RNA-seq), apoptosis analysis, quantitative reverse-transcription polymerase chain reaction (qRT-PCR), dual-luciferase reporter assay, and western blot analysis.

Chemical compounds and antibodies

Anti-actin antibody (#A2066), mevalonolactone (M4667), mevalonic acid 5-phosphate lithium salt hydrate (79849), mevalonic acid 5-pyrophosphate tetralithium salt (#94259), isopentenyl pyrophosphate triammonium salt (I0503), farnesyl pyrophosphate ammonium salt (F6892) and geranylgeranyl pyrophosphate ammonium salt (G6025) were purchased from Sigma-Aldrich. Anti-p21 (Ab1) antibody was purchased from Cell Signaling Technology (Danvers, MA, USA). Anti-PMVK (SC-390775) antibody was purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-PMVK (15674-1-AP) antibody was purchased from Proteintech (Rosemont, IL, USA). Anti-c-Myc (ab32072), anti-SREBP2 (ab30682), and anti-p53 (DO1) antibodies were purchased from Abcam (Cambridge, UK).

Apoptosis analysis

Harvested cells were stained with the Annexin V-633 Apoptosis Detection Kit (Nacalai Tesque, Kyoto, Japan) according to the manufacturer’s protocol. In brief, 1 × 10⁵ cells were incubated with 5 µl annexin V and 5 µl propidium iodide (PI) at room temperature for 15 min in the dark. The cells were analysed immediately with a flow cytometer [FACSCanto^TMII, (BD Biosciences, Franklin Lakes, NJ, USA)].

RNA-seq and data analysis

Total RNA was extracted from cells with the FastGene RNA Premium kit (Nippon Gene, Tokyo, Japan). RNA concentration and RNA integrity were measured using an Agilent 2100 bioanalyzer. Library construction and sequencing were performed as previously described⁵⁷. RNA-seq libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit (Illumina, San Diego, CA, USA) following the manufacturer’s protocol. Deep sequencing was performed on the Illumina NextSeq 500 platform according to the manufacturer’s protocol.

Sequenced reads from RNA-seq experiments were aligned by using HISAT2, and Cufflinks was used for transcript assembly. Gene expression levels were expressed as fragments per kilobase of exon per million mapped sequence reads (FPKM). Differentially expressed genes (DEGs) were identified using the R/BioConductor software package (version 3.11)⁵⁸. Heatmaps and dendrograms were generated by R software. DEGs related terms of Gene Ontology were analysed in Enrichr⁵⁹, and the WikiPathway 2021 Human results were assessed. TRRUST Transcription Factors 2019 by Enrichr was used to identify the transcription factors that regulate both up- and downregulated genes.

Single-cell RNA-seq library preparation and sequencing

Single-cell suspensions of MCF-7 cells were transfected with control miRNA or miR-874 for 36 h or control siRNA or siPMVK for 48 h. Cells were washed twice with phosphate-buffered saline, trypsinized, and collected in DMEM. scRNA-seq libraries were prepared according to 10x Genomics specifications (Chromium Next GEM single-cell 3’ reagent kits and library construction kit, 10x Genomics, Pleasanton, CA, USA). Briefly, cell suspensions were loaded onto the 10x Genomics Chromium Controller to generate gel beads in emulsions (GEMs). After GEM generation, the samples were incubated at 53 °C for 45 min in a thermal cycler (Thermo Fisher Scientific) to generate polyA cDNA barcoded at the 5′ end by the addition of a template switch oligo linked to a cell barcode and unique molecular identifiers (UMIs). The GEMs were broken, and the single-strand cDNA was cleaned using DynaBeads My One Silane Beads (Thermo Fisher Scientific). cDNA was amplified (98 °C for 3 min; 11 cycles of 98 °C for 15 s and 63 °C for 20 s; 72 °C for 1 min), and cDNA quality was assessed using an Agilent TapeStation. cDNA was enzymatically fragmented, end-repaired, A-tailed, subjected to double-sided size selection with SPRIselect beads (Beckman Coulter, Indianapolis, Indiana, USA), and ligated to adaptors provided in the kit. A unique sample index for each library was introduced through 14 PCR amplification cycles using the indices provided in the kit (98 °C for 45 s; 14 cycles of 98 °C for 20 s, 54 °C for 30 s, and 72 °C for 20 s; 72 °C for 1 min; held at 4 °C). Indexed libraries were subjected to a second round of double-sided size selection, and libraries were quantified and quality assessed with an Agilent TapeStation. Libraries were submitted to GeneWiz (South Plainfield, NJ, USA), clustered using NovaSeq on a paired-end read flow cell, and sequenced on R1 (10x barcode and the UMIs), followed by 8 cycles of the I7 index (sample index) and 89 cycles based on R2 (transcript). 10x Genomics Cell Ranger Single Cell Software was used for sample demultiplexing, alignment with the human reference genome, GRCh38, filtering, UMI counting, single-cell 3′-end gene counting, and quality control using the manufacturer’s parameters⁵⁸.

scRNA-seq data analysis

The R package Seurat (v4.0) was used to cluster cells in a merged matrix. MCF-7 cells with > 10% mitochondrial gene expression were filtered out as low-quality cells. Individual gene counts for each cell were divided by the total gene counts for the cell and multiplied by a scale factor of 10,000; natural-log transformation was applied to all counts. The FindVariableFeatures function was used to select variable genes with default parameters. The ScaleData function was used to scale and centre the counts in the dataset. Principal component analysis was performed on variable genes, and 50 principal components were used for cell clustering (resolution = 5) and visualized as uniform manifold approximation and projection (UMAP) overlays, dot heat maps, and violin plots. A volcano plot was also constructed by R. Cluster markers were found using the FindAllMarkers function, and cell types were manually annotated based on cluster markers. We illustrated the cell cycle phase (G1, S, G2 M) using the CellCycleScoring function based on cell cycle-related genes⁶⁰.

Analysis of The Cancer Genome Atlas data

To investigate the clinical significance of miRNAs and genes in breast cancer, we used the RNA sequence database in TCGA (https://tcga-data.nci.nih.gov/tcga/). The gene expression and clinical data were obtained from the GDC Data Portal (https://portal.gdc.cancer.gov, the provisional data downloaded on 5th July 2020). PMVK and miR-874 expression in an RNA-seq dataset was compared between normal (n = 104) samples and tumour (n = 1093) samples. For correlation analysis, Pearson’s correlation coefficients for miR-874 gene expression versus PMVK, SREBF2, and other mevalonate pathway gene expression were calculated.

Meta-analysis

We searched the public database PROGgeneV2, which compiles data associated with cohort studies from public repositories, including GEO, EBI Array Express, and TCGA. Studies through September 2021 with a study duration of > 5 years of relapse-free survival were selected for inclusion in our meta-analysis, resulting in 20 cohort studies entered into the meta-analysis.

Microarray or RNA sequencing data for PMVK in breast cancer are available online at PROGgeneV2 (http://www.progtools.net/gene/). Data were combined by means of random-effects models using the number of cases in each cohort as weights. The meta-analysis was performed using the R package metafor^61,62. For Kaplan–Meier analysis, Kaplan–Meier plotter (https://kmplot.com/analysis) was used^63,64.

Statistical analysis

Quantitative data are presented as the mean ± standard deviation (SD). Comparisons between groups were performed by two-tailed Student’s t test or one-way analysis of variance (ANOVA) unless otherwise specified. The correlation between groups was determined by Pearson’s correlation analysis. JMP Pro 14 (https://www.jmp.com) and GraphPad Prism 9 software (https://www.graphpad.com) were used for statistical analyses. For Kaplan–Meier analysis, overall survival was assessed using the log-rank test for comparisons of the Kaplan–Meier event-free format using Kaplan–Meier plotter (https://kmplot.com/analysis)^63,64. A p value of < 0.05 was considered significant.

Data availability

The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) database, with accession number GSE214509. All data will be made available upon reasonable request. Please contact Tomoaki Tanaka (tomoaki@restaff.chiba-u.jp).

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Acknowledgements

This work was supported by grants from the Japan Society for the Promotion of Science KAKENHI [Grants-in-Aid: for Scientific Research (S) 26221305, JP19H05650, (B) #21H02974, #19H03708, #22300325; (C) #21K07145, #21K08524, #20K08397, #20K07561, #19K07635, 19K08972, #18K07439, #18K08464; Challenging Research (Exploratory) #21K19398; Early-Career Scientists #20K17527]. This work was partly supported by The Uehara Memorial Foundation, Mochida Memorial Foundation for Medical and Pharmaceutical Research, The Naito Foundation, Mitsui Life Social Welfare Foundation, Princes Takamatsu Cancer Research Fund, Takeda Science Foundation, Senshin Medical Research Foundation, Kose Cosmetology Research Foundation, Japan Diabetes Foundation, Yamaguchi Endocrine Research Foundation, The Cell Science Research Foundation, The Ichiro Kanehara Foundation for the Promotion of Medical Sciences and Medical Care, the Yasuda Memorial Medical Foundation, MSD Life Science Foundation, The Hamaguchi Foundation for the Advancement of Biochemistry, The Novartis Foundation (Japan) for Promotion of Science and the Medical Institute of Bioregulation Kyushu University Cooperative Research Project Program. We acknowledge Masanori Fujimoto for his technical assistance.

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Nijiro Nohata
Present address: MSD K.K., Tokyo, Japan
These authors contributed equally: Alimasi Aersilan and Naoko Hashimoto.

Authors and Affiliations

Department of Molecular Diagnosis, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8677, Japan
Alimasi Aersilan, Naoko Hashimoto, Kazuyuki Yamagata, Masataka Yokoyama, Akitoshi Nakayama, Xiaoyan Shi, Hidekazu Nagano, Ikki Sakuma, Siti Nurul Zhahara, Yingbo Gong & Tomoaki Tanaka
Department of Functional Genomics, Graduate School of Medicine, Chiba University, Chiba, Japan
Nijiro Nohata, Takashi Kinoshita & Naohiko Seki
Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
Bahityar Rahmutulla & Atsushi Kaneda
Division of Laboratory Medicine, Clinical Genetics and Proteomics, Chiba University Hospital, Chiba, Japan
Motoi Nishimura
Advanced Data Science Project (ADSP), RIKEN, Kanagawa, Japan
Shoichiro Kawauchi & Eiryo Kawakami
Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
Eiryo Kawakami
Research Institute of Disaster Medicine, Chiba University, Chiba, Japan
Tomoaki Tanaka

Authors

Alimasi Aersilan
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Naoko Hashimoto
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Kazuyuki Yamagata
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Masataka Yokoyama
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Akitoshi Nakayama
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Xiaoyan Shi
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Hidekazu Nagano
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Ikki Sakuma
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Nijiro Nohata
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Takashi Kinoshita
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Naohiko Seki
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Bahityar Rahmutulla
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Atsushi Kaneda
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Siti Nurul Zhahara
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Yingbo Gong
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Motoi Nishimura
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Shoichiro Kawauchi
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Eiryo Kawakami
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Tomoaki Tanaka
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Contributions

N.H. and T.T. designed the experiments. N.H. performed most experiments. X.S. performed the western blotting, qRT-PCR and cell growth analysis experiments. and K.Y., B.R., S.N.Z., and YB.G. performed the RNA-seq-related experiments. K.Y. analysed sequencing experiments and performed bioinformatic analysis and prepared RNA-seq-related figures. A.A. performed the qPCR and cell growth analyses of MDA-MB-231 cells. N.H. and M.Y. performed experiments with flow cytometry. N.N. and T.K. performed the cell viability experiments. S.K. and E.K. performed the meta-analysis. N.S. provided critical advice on the study design. A.K., M.N., H.N., and I.S. interpreted the RNA-seq data and provided advice. N.H. and T.T. wrote the manuscript, with all authors contributing to the writing and providing feedback. T.T. conceived and supervised the project.

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Correspondence to Tomoaki Tanaka.

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Aersilan, A., Hashimoto, N., Yamagata, K. et al. MicroRNA-874 targets phosphomevalonate kinase and inhibits cancer cell growth via the mevalonate pathway. Sci Rep 12, 18443 (2022). https://doi.org/10.1038/s41598-022-23205-w

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Received: 23 May 2022
Accepted: 26 October 2022
Published: 02 November 2022
DOI: https://doi.org/10.1038/s41598-022-23205-w

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