MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance

Weiguo Lu; Hui Huang; Zhanjie Xu; Shumin Xu; Kewei Zhao; Mingfeng Xiao

doi:10.1371/journal.pone.0311419

. 2024 Oct 16;19(10):e0311419. doi: 10.1371/journal.pone.0311419

MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance

Weiguo Lu ¹, Hui Huang ^2,³, Zhanjie Xu ⁴, Shumin Xu ^2,³, Kewei Zhao ^1,^*, Mingfeng Xiao ^2,^3,^*

Editor: Hiromu Suzuki⁵

PMCID: PMC11482689 PMID: 39413115

Abstract

Background

The imbalance between T helper 17 (Th17) and T regulatory (Treg) cells plays a key role in the progression of multiple myeloma (MM).

Methods

The gene expression profiles of MM were acquired and examined from the Gene Expression Omnibus (GEO) database (GSE72213). Our research involved experimental investigations conducted using the MOPC-MM mouse model. Dysregulation of Treg and Th17 cells was evaluated through flow cytometry, while the levels of inflammatory factors were measured using the enzyme-linked immunosorbent assay. Cell proliferation was gauged using the Cell Counting Kit-8 assay, and cell apoptosis was quantified via flow cytometry. Cell metastasis capabilities were determined by conducting transwell assays. To confirm the relationship between miR-27a and PI3K, a dual-luciferase reporter assay was employed. Finally, proteins associated with the PI3K/AKT/mTOR signaling pathway were assessed using western blotting.

Results

MiR-27a exhibited reduced expression levels in MM. Moreover, it exerted control over the equilibrium of Th17 and Treg cells while reducing the expression of inflammatory mediators such as TGF-β1 and IL-10 in an in vivo setting. Elevated miR-27a levels led to the inhibition of cell viability, colony formation capacity, migratory and invasive traits in an in vitro context. The PI3K/AKT/mTOR signaling pathway was identified as a direct target of miR-27a and could reverse the effects induced by miR-27a in MM cells. Notably, PI3K was directly targeted by miR-27a.

Conclusions

Our study revealed that miR-27a inhibited MM evolution by regulating the Th17/Treg balance. Inhibition of the PI3K/AKT/mTOR signaling pathway by miR-27a may play a potential mechanistic role.

Introduction

Multiple myeloma (MM) ranks among the prevalent hematological malignancies and is often associated with elevated immunoglobulin levels. The cytokines generated by plasma cells contribute significantly to localized damage and create a microenvironment conducive to the proliferation of malignant cells. Nevertheless, the underlying pathogenesis of MM remains unclear. The dysregulation of T lymphocyte subsets and the intricate cytokine network likely assume crucial roles in the development of MM [1, 2].

MicroRNAs (miRNAs) are a class of small non-coding RNAs, each consisting of 22 nucleotides in length. Their primary role involves the regulation of gene expression at the post-transcriptional level. Recent studies have highlighted the capacity of aberrantly expressed miRNAs to modulate processes such as cell growth, apoptosis, and tumorigenesis [3]. MiR-27a, located on chromosome 19 (19p13.1), is aberrantly expressed in various malignancies, including renal carcinoma, oral squamous cell carcinoma, and pancreatic cancer, among others [4]. MiR-27a is involved in the regulation of specific signaling pathways in multiple cancers, such as the AKT signaling pathway, Wnt/β-catenin signaling pathway, Ras/MEK/ERK signaling pathway, and TGF-β signaling pathway [5]. MiR-27a plays a diverse role in human cancer by impacting tumorigenesis, proliferation, apoptosis, invasion, migration, and angiogenesis through direct binding to the 3’ untranslated region (3’UTR) of target messenger RNA (mRNA) [6]. In the context of multiple myeloma (MM), miR-27a has been reported to exert its biological influence by targeting Sprouty homolog 2 (SPRY2) [7].

Previous research has established that miR-27a regulates apoptosis in nucleus pulposus cells by targeting PI3K [8]. Another study has identified the pivotal role of PI3K/Akt signaling in balancing Th1/Th2 responses and has highlighted the critical nature of PI3K/Akt signaling in optimal Treg responses in pulmonary sarcoidosis [9]. Depending on the cytokines they produce and their functions, CD4+ T cells can be categorized into four subsets: T helper 1 (Th1), Th2, Th17, and CD4+ CD25+ T regulatory (Treg) cells [10]. Th1 lymphocytes are responsible for the secretion of interferon-gamma (IFN-γ), thus facilitating cell-mediated immune responses [11]. Conversely, Th2 cells are responsible for the production of IL-4, which suppresses the Th1 cell-mediated response. Th17 cells contribute to inflammation in the pathogenesis of various diseases by producing cytokines such as IL-17A, IL-6, and TNF-α [12, 13]. Treg cells inhibit effector T cell proliferation through secreting TGF-β and IL-10 to have immunomodulatory effects [14]. The imbalance between Th17 and Treg cells plays a key role in inflammatory and autoimmune diseases [15]. Recently, it was reported that Th17 cells may contribute to the development of MM and complications [16].

It’s important to emphasize that miR-27a targets the 3’-UTR of the PI3K gene, thereby confirming a direct interaction between PI3K mRNA and miR-27a. Functional assessments have verified that miR-27a diminishes IL-1β-induced apoptosis and autophagy in MM. Conversely, inhibiting miR-27a leads to increased PI3K expression and activation of its downstream components, Akt and mTOR. These findings suggest the potential utility of miR-27a in the diagnosis and treatment of MM. To investigate whether the miR-27a PI3K/AKT/mTOR/Th17/Treg axis contributes significantly to MM development, we conducted both in vivo and in vitro experiments to assess the proportion of Th subsets, serum levels of relevant cytokines, and the expression of specific transcription factors.

Methods

GEO database analysis

The GEO database (http://www.ncbi.nlm.nih.gov/geo) serves as a publicly available repository housing a multitude of datasets acquired through high-throughput sequencing and microarray analyses. These datasets originate from research institutions globally [17]. To pinpoint pertinent gene expression datasets, the keyword "MM" was employed during our search. We adhered to specific inclusion criteria, prioritizing expression profiles generated on the same sequencing platform with the most extensive sample size. Furthermore, we exclusively focused on datasets utilizing human test specimens. Finally, the microarray datasets [GSE72213 [18]] were downloaded from it (Affymetrix GPL570 platform, Affymetrix Human Genome U133 Plus 2.0 Array). The dataset includes 19 patients with three MM and three paired normal tissues. GEO2R (www.ncbi.nlm.nih.gov/geo/ge2r) is an online analysis tool developed using two R packages (GEOquery and Limma) [19]. We employed the GEOquery package to access the data and the Limma package for differential expression analysis. To identify the differentially expressed genes (DEGs) between the control and diseased groups, we used GEO2R to compare gene expression profiles. Any probe sets lacking corresponding gene symbols or genes represented by more than one probe set were excluded or averaged, respectively. The DEGs were limited to genes with adjusted P-value of <0.05 and |logFC (fold change) | values of ≥1.

Tumor models

Experimental research was conducted using a mouse model of marrow-homing multiple myeloma (MOPC)-MM [20]. A total of 40 female 4-week-old BALB/c mice (Changzhou Cavens Experimental Animal Limited Company, Changzhou, China) were randomly assigned to either the MM model group or the normal group. The mice were anesthetized with 1.5% isoflurane, and efforts to alleviate suffering included monitoring the animals for signs of distress and providing appropriate analgesia when necessary. In the MM group, MM was induced by intravenously inoculating 2×10⁵ MOPC cells (MOPC-315.Luc-GFP.BMP3 cells, tested negative for mycoplasma contamination) into the tail veins of the mice. The normal group consisted of healthy BALB/c mice. We compared the weight changes of the mice in different groups over a 2-week period. All animal care and experimental procedures were conducted in compliance with the guidelines established by the Institutional Animal Care and Use Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (GZTCMF1-20230501). The First Affiliated Hospital of Guangzhou University of Chinese Medicine granted ethical approval to conduct this study (Ethical Application Ref: K-2022-033).

Quantitative real‑time PCR

The mRNA expression of miRNA-27a was determined through real-time polymerase chain reaction (PCR). Total RNA was extracted using Trizol Reagent (Servicebio, China) according to the manufacturer’s guidelines. The concentration and purity of RNA were assessed using an ultra-micro spectrophotometer (NanoDrop2000, Thermo, USA). Subsequently, cDNA was synthesized from the mRNA using the RevertAid First Strand cDNA Synthesis Kit (Thermo, USA). For cDNA synthesis, 1 μg of RNA was used with Evo M-MLV RT Premix for qPCR (Accurate Biotechnology Co., Ltd., Hunan, China). The primers sequences are shown in Table 1. A real-time PCR System (ABI, USA) was used to conduct qualitative real-time PCR, employing the FastStart Universal SYBR Green Master (Rox, Servicebio, China). The relative gene expression levels were calculated using the threshold cycle (CT), according to the 2−^ΔΔCT method. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was set as reference.

Table 1. Primers for qRT-PCR analysis.

Genes		Sequences (5’→3’)	Length
MiR-27a	Forward	`GGCTAAGTTCCGCGTCG`	53bp
MiR-27a	Reverse	`CAGTGCGTGTCGTGGAGT`	53bp
GAPDH	Forward	`CACCCGCGAGTACAACCT`	138bp
GAPDH	Reverse	`CCCATACCCACCATCACACC`	138bp

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Flow cytometry analysis and inflammation assessment

Flow cytometry was employed to investigate the frequencies of Th17 and Treg cells. The mice in the various groups were euthanized by cervical dislocation after retroorbital blood collection, and their bodies were immersed in 75% ethanol for 5 minutes. Spleen tissues were collected and placed in sterile PBS. The tissue was gently disrupted by pressing with the base of a sterile syringe, and the cell suspension was filtered through a 200-mesh cell screen. Red blood cells were lysed using a lysis buffer. Following cell counting, 1.0 × 10⁶ cells were collected for flow antibody staining. The cell surface markers APC-CD25, PE-CD4, and BV421-FOXP3 (Biolegend, USA) were employed for the specific labeling of Treg cells, while FITC-CD3, PE-CD4, and BV510-IL-17A (Biolegend, USA) were used to identify Th17 cells. Subsequently, the stained cells were analyzed via flow cytometry. Furthermore, the inflammatory factors IL-2, IL-10, and TGF-β were quantified using enzyme-linked immunosorbent assay (ELISA) (Cusabio, China).

Cell culture and transfection

Human RPMI-8226 (CRM-CCL-155) and NCI-H929 (CRL-9068) cells were procured from the American Type Culture Collection (ATCC; Manassas, VA, USA) and maintained in Roswell Park Memorial Institute 1640 Medium (RPMI1640; Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS, Gibco), 100 U/mL penicillin, and 100 mg/mL streptomycin in a 5% CO₂ humidified incubator at 37°C. MiR-27a mimic (miR-27a) and its corresponding negative control (NC), miR-27a inhibitor (anti-miR-27a), and its negative control (anti-NC) were obtained from GenePharma Co. Ltd. (Shanghai, China). The PI3K activator 740 Y-P and its inhibitor, 3-Methyladenine, were provided by MedChemExpress (Shanghai, China). The above-mentioned oligonucleotides (40 mM) or plasmids (2 μg) were transfected into RPMI-8226 and NCI-H929 cells using Lipofectamine 3000 (Life Technologies Corporation, Carlsbad, CA, USA). After 48 hours, the cells were harvested for subsequent assays.

Cell viability and apoptosis assay

The Cell Counting Kit-8 (CCK-8, Beyotime) reagent was employed to assess the viability of MM RPMI-8226 and NCI-H929 cells according to the manufacturer’s protocols. Cells were cultured in 0.5% serum for 12 h prior to the experiments. Cells were seeded in 96-well plates at a density of 5×10³ cells per well and then transfected for 24, 48, and 72 hours. After the addition of 10 μL of CCK-8 reagent, the cells were further incubated for 1 hour at 37°C. The absorbance at 450 nm of each well was measured using a Microplate Reader (Bio-Rad, Hercules, CA, USA). To determine the apoptotic rate of RPMI-8226 and NCI-H929 cells, we employed the Annexin V-fluorescein isothiocyanate (FITC) Apoptosis Detection Kit (Beyotime). A total of 2×10⁵ cells were seeded in a 6-well plate and incubated for 24 hours. Subsequently, 10 μL of Annexin V-FITC and propidium iodide (PI) solution were added. After a 15-minute incubation, apoptotic cells were analyzed using a flow cytometer (Beckman Coulter, Fullerton, CA, USA), and the apoptotic rate was determined by assessing the proportion of cells that were Annexin V+/PI-.

Transwell assay

The invasion and migration capacities of RPMI-8226 and NCI-H929 cells were assessed using a Transwell assay with Transwell chambers (Corning Inc., Corning, NY, USA). To evaluate invasion ability, 4×10⁴ cells in serum-free medium were placed into the upper chambers coated with Matrigel (Corning Inc.). For migration ability assessment, 1×10⁴ cells in serum-free medium were seeded into the upper chambers. The lower chambers were filled with medium containing 10% FBS. After 24 hours of incubation, the migrated or invasive cells were stained with 0.1% crystal violet and counted under a light microscope (at 200× magnification).

Dual‑luciferase reporter assay

Wild-type luciferase reporter vectors (PI3K-wt) and mutant-type ones (PI3K-mut) were established by inserting corresponding sequences into luciferase reporter vector pGL3 (Promega, Madison, WI, USA). The constructed vectors and miR-27a or NC were cotransfected into RPMI-8226 and NCI-H929 cells, and after 2 h, luciferase activity was evaluated using the Dual-Lucy Assay Kit (Solarbio).

Western blot

First, RPMI-8226 and NCI-H929 cells were subjected to protein isolation using a protein extraction kit (Solarbio, Beijing, China). After quantification with a bicinchoninic acid protein assay kit (Beyotime), 30 μg protein samples were loaded on a 12% sodium dodecyl sulfate–polyacrylamide gel and then transferred onto polyvinylidene difluoride membranes (Pall Corporation, East Hills, NY, USA). The membranes were blocked with skim milk, followed by incubation with specific primary antibodies: PI3K antibody (1:2500, ab227204, Abcam), p-PI3K antibody (1:2500, ab182651, Abcam), mTOR antibody (1:1000, ab2732, Abcam), p-mTOR antibody (1:2000, ab109268, Abcam), Akt antibody (1:2000, ab8805, Abcam), p-Akt antibody (1:500, ab38449, Abcam), and β-actin (1:2000, ab8227, Abcam). Subsequently, the membranes were incubated with the secondary antibodies conjugated with Amersham ECL peroxidase: goat anti-rabbit IgG (1:5,000) or goat anti-mouse IgG (1:5,000) at room temperature for an additional 60 minutes. The immunoreactivity was measured using a Super Signal West Femto Maximum Sensitivity Substrate Kit (Thermo) on a C-DiGit Blot Scanner.

Statistical analysis

For data analysis and figure generation, IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, N.Y., USA) and GraphPad Prism, version 8.0.1 (GraphPad Software, San Diego, CA, USA) were employed, respectively. All samples were measured in triplicate and the average values were calculated. The data are presented as the means ± standard deviation (SD). The normality and homogeneity of the results were assessed using the Shapiro-Wilk test. The experimental data were analyzed using one-way analysis of variance (ANOVA) and two-way ANOVA, followed by Tukey’s multiple comparisons test. Statistical significance was determined at p < 0.05.

Results

Identification of DEGs

After normalizing the microarray data, differentially expressed genes (DEGs) were identified from the GSE72213 dataset. Through an intersection analysis, it was observed that the expression of miR-27a was significantly higher in normal samples compared to that in MM tissues. Furthermore, when compared to healthy controls, the expression of PI3K, AKT, and mTOR was significantly elevated in MM samples (Fig 1A and 1B). Consequently, miR-27a was selected for further validation and in-depth investigation.

Fig 1 — (A) Different expression levels of a set of DEGs in GSE72213. Green, low expression levels; red, high expression levels. (B) Histograms displaying the different expressions of miR-27a, PI3K, AKT, and mTOR, as detected by the microarray analysis of MM samples relative to healthy controls. *P < 0.05, **P < 0.01, ***P < 0.001, ***P < 0.0001.

Verification of the relationship between miR-27a and Th17/Treg balance in vivo

As depicted in Fig 2A, MOPC-MM mice exhibited a significant decrease in weight gain within a 2-week period compared to that of the control group (Control 2 Weeks: 18.38±1.29 g vs. MOPC-MM 2 Weeks: 17.09±1.14 g, p <0.001). The levels of miR-27a were assessed by qRT-PCR. The specific miR-27a expression levels were notably higher in the spleens of the control group compared to those in the MOPC-MM group (Fig 2B).

Fig 2C and 2D illustrate an analysis of the populations of CD25+ T cells expressing Foxp3, showing that the number of Treg cells in the spleens of the MOPC-MM group increased by 3.27-fold compared to that of the control group (Control: 1.78±0.22% vs. MOPC-MM: 5.83±0.12%, p = 0.004). Conversely, the number of Th17 cells in the spleens of the MOPC-MM group decreased by 5.21-fold compared to that of the control group (Control: 5.68±0.49% vs. MOPC-MM: 1.09±0.17%, p = 0.02). Results of ELISA demonstrated a marked increase in IL-2 levels in the spleens of the control group compared to those in the MOPC-MM group. However, the levels of IL-10 and TGF-β were reduced in the control group relative to those in the MOPC-MM group (Fig 2E). These findings suggested that the MOPC-MM model experienced a severe inflammatory state owing to an imbalance in the Th17/Treg ratio.

Low expression of miR-27a promoted proliferation and inhibited apoptosis as well as invasion of MM cells

To elucidate the impact of miR-27a on the cellular behavior of MM in vitro, we transfected RPMI-8226 and NCI-H929 cells with the miR-27a mimic and miR-27a inhibitor, while cells transfected with miR-27a-NC served as controls. The CCK-8 assay revealed an inhibitory effect of miR-27a on the viability of RPMI-8226 and NCI-H929 cells (Fig 3A). Our data demonstrate that introducing the miR-27a mimic for 48h and 72h inhibited the proliferation of both RPMI-8226 and NCI-H929 cells in a dose-dependent manner (P <0.05). Flow cytometry analysis indicated that the overexpression of miR-27a promoted cell apoptosis and led to cell cycle arrest in the G1 phase when compared to the control and anti-miR-27a groups (Fig 3B). Furthermore, the introduction of miR-27a mimic reduced the number of migrated and invaded RPMI-8226 and NCI-H929 cells (Fig 3C). In summary, the introduction of miR-27a suppressed proliferation and metastasis but enhanced apoptosis in MM RPMI-8226 and NCI-H929 cells.

Fig 3 — (A) Cell viability of two transfected cell lines was analyzed using the CCK-8 assay. (B) The apoptosis rate and cell cycle distribution were tested using the flow cytometry assay. (C) The migration and invasion abilities of transfected cells were examined via the transwell assay. *P < 0.05, **P < 0.01, ***P < 0.001.

MiR-27a alleviated the severity of inflammatory state of MM cells

The concentrations of serum cytokines, including IL-2, IL-10, and TGF-β, were assessed using ELISA in various groups of RPMI-8226 and NCI-H929 cells. As depicted in Fig 4, the levels of IL-2 were notably elevated in the miR-27a mimic group, while the levels of IL-10 and TGF-β were lower than those in the anti-miR-27a group. Additionally, the levels of IL-10 were significantly elevated in the anti-miR-27a group.

Fig 4 — (A) The protein levels of IL-2, IL-10 and TGF-β in the RPMI-8226 cells of each group. (B) The protein levels of IL-2, IL-10, and TGF-β in the NCI-H929 cells of each group. All values are expressed as the means ± S.D. *P < 0.05, **P < 0.01, ***P < 0.001.

MiR-27a directly targeted PI3K/AKT/mTOR signaling

Subsequently, our aim was to elucidate the impact of miR-27a on MM development through the PI3K/AKT/mTOR signaling pathway. The results from the dual-luciferase reporter assay indicated that the miR-27a mimic (with control miRNA transfection as a reference) significantly suppressed the expression of the reporter gene containing the 3’UTR of PI3K, while it had no effect on the expression of the reporter gene containing the mutated variant of the 3’UTR of PI3K (Fig 5A). This observation underscores the direct binding and inhibitory effect of miR-27a on PI3K expression. Subsequent western blot analysis revealed a reduction in the levels of p-PI3K, p-AKT, and p-mTOR in RPMI-8226 cells transfected with the MiR-27a mimic (Fig 5B).

Fig 5 — (A) The luciferase activity in the PI3K-wt group and PI3K-mut group of RPMI-8226 cells was determined via the dual-luciferase reporter assay. (B) The expression of PI3K, p-PI3K, p-AKT, and p-mTOR proteins in RPMI-8226 cells was measured via western blot assay. *P < 0.05, **P < 0.01, ***P < 0.001.

Upregulation of miR-27a counteracted the effects of PI3K/AKT/mTOR signaling on the cellular behaviors of MM cells

Next, we conducted a rescue experiment to investigate how miR-27a modulates the cellular behaviors of RPMI-8226 and NCI-H929 cells. The enhancing effects of PI3K/AKT/mTOR signaling on cell viability and colony formation ability in RPMI-8226 cells and NCI-H929 were attenuated by the presence of the miR-27a mimic. Furthermore, the introduction of miR-27a reinforced apoptosis and induced cell cycle arrest in the G1 phase, as revealed by flow cytometry (Fig 6A). Additionally, the inclusion of miR-27a inhibited the metastatic potential of both two MM cell lines induced by PI3K/AKT/mTOR signaling (as illustrated in Fig 6B). As anticipated, the miR-27a mimic-mediated decrease in p-PI3K, p-AKT, and p-mTOR levels in RPMI-8226 and NCI-H929 cells was reversed upon PI3K activation (Fig 6C). In summary, the overexpression of miR-27a inhibited the progression of MM by suppressing the expression of PI3K/AKT/mTOR signaling.

Fig 6 — (A) The apoptosis rate and cell cycle distribution were tested via the flow cytometry assay. (B) The migration and invasion abilities of transfected cells were examined using the transwell assay. (C) The expression of PI3K, p-PI3K, p-AKT, and p-mROT proteins in RPMI-8226 and NCI-H929 cells was measured via western blot assay. *P < 0.05, **P < 0.01, ***P < 0.001.

Discussion

It is widely accepted that MM and signaling pathways are implicated in the occurrence and development of MM, including miRNAs [21]. In the present study, the inhibitory role of miR-27a was established in MM both in vitro and in vivo. Furthermore, we provided the initial validation of the regulatory impact of the MiR-27a/PI3K/AKT/mTOR axis on the modulation of Th17/Treg balance in MM.

miRNAs are small non-coding RNAs that have been highly conserved throughout evolution and can modulate gene expression at the translational level [22]. They have been implicated in various cellular processes and exert downstream regulatory effects on various genes. Additionally, they exhibit stability in biological fluids, such as urine, blood, and saliva, facilitating sample collection and advancing miRNA research across multiple domains [23]. In tumor tissues, certain miRNAs demonstrate reduced expression levels, acting as suppressors of tumorigenesis, whereas others exhibit increased expression, promoting tumor advancement [24]. Consequently, miRNAs can serve as potential diagnostic markers and prognostic indicators for tumor detection and evaluation.

Previous research has indicated that miR-27a can enhance autophagy and apoptosis in IL-1β-treated articular chondrocytes in osteoarthritis [25]. Additionally, miR-27a has been shown to increase cisplatin sensitivity in hepatocellular carcinoma cells by inhibiting the PI3K/Akt pathway [26]. These findings underscore the dual functionality of miR-27a in human malignancies, potentially influenced by variations in the tumor microenvironment.

In this basic bioinformatics study, we observed that miR-27a is down-regulated in MM tissue, serving as a potential indicator of favorable prognosis for MM patients. Our in vitro and in vivo assays consistently demonstrated that miR-27a functions as a tumor suppressor gene in MM, inhibiting cell viability, inducing apoptosis, and affecting the cell cycle. However, it remained unclear whether miR-27a played a role in regulating the balance between Th17 and Treg cells. Significantly, for the first time, we report that the overexpression of miR-27a markedly reduces the proportion of Treg cells while enhancing Th17 cells in vivo. This finding suggests that miR-27a plays a crucial role in regulating the balance between Th17 and Treg cells. Additionally, we have preliminary evidence indicating that miR-27a achieves this by targeting the PI3K/AKT/mTOR signaling pathway. Our data underscore the potential of miR-27a as a novel molecular target for MM chemotherapy.

In our in vitro assays, we demonstrated that the overexpression of miR-27a inhibits MM cell viability, promotes apoptosis, and induces cell cycle arrest in the G0/G1 phase. This highlights the pivotal role of miR-27a in MM tumorigenesis. However, it’s worth noting that the functions of miR-27a can vary in different types of cancer. For instance, in nasopharyngeal carcinoma, Li et al. found that miR-27a promotes cell proliferation, enhances migration, and facilitates invasion by targeting the Mapk10 protein [27]. Additionally, Mertens-Talcott et al. observed an oncogenic role of miR-27a in breast cancer, where it increased the percentage of breast cancer cells in the G2-M phase by targeting the Myt-1 gene [28]. These examples illustrate that miR-27a can play distinct roles in different cancer types.

As we have learned, the imbalance between Th17 and Treg cells is a key factor in many diseases [29]. Although there is conflicting evidence regarding the role of Th17 cells in cancer, excessive inflammation caused by Th17 cells or an overactive immunosuppression by Treg cells may contribute to carcinogenesis [30, 31]. The effect of Th17 cells on cancer development depends on the tumor’s phenotype, while Treg cells are typically linked to tumor progression and reduced survival rates in cancer patients [32]. In tumors, the expression of GARP has been shown to enhance active TGF-β, promoting Treg induction within the cancer microenvironment and thus inhibiting immune responses [33]. The presence of Treg cells in the tumor microenvironment is associated with advanced malignancy stages, invasive growth, and poorer prognoses [32]. In our study, we observed a significantly higher percentage of Treg cells and a lower percentage of Th17 cells in MM samples compared to healthy controls. Following the assessment of relevant inflammatory factors, we discovered that the increase in Treg cells was associated with higher levels of IL-10 and TGF-β, and it was also closely correlated with miR-27a expression. In addition, the inflammatory effects of IL-2, IL-10, and TGF-β have significant implications in MM. IL-2, known for its role in T-cell proliferation and activation, appears to be suppressed in MM, contributing to impaired immune responses. Conversely, IL-10, with its anti-inflammatory properties, may create a microenvironment conducive to MM progression by dampening immune surveillance and promoting tumor cell survival [34]. Additionally, TGF-β, a multifunctional cytokine, plays a dual role in MM pathogenesis. Although it exerts tumor-suppressive effects by inhibiting cell proliferation and inducing apoptosis, its immunosuppressive actions can foster MM growth and evasion of immune surveillance [35]. Thus, the interplay between these inflammatory mediators underscores their intricate involvement in MM pathophysiology and suggests potential therapeutic targets.

Based on these prior studies, PI3K and its downstream components, namely Akt and mTOR, have been associated with multiple biological processes [36, 37]. In particular, phosphorylated Akt influences various genes, especially pro-apoptotic factors such as Bcl-xL, Bcl2, Bax, VEGF, and MMPs [38–40]. In our study, we observed that miR-27a impacts the development of MM through the PI3K/AKT/mTOR signaling pathway. Notably, the reduction in p-PI3K, p-AKT, and p-mTOR levels induced by miR-27a mimic was reversed upon PI3K activation. Although our research briefly addressed the effect of tumor microenvironment variations on miR-27a ’s dual functionality, a more comprehensive examination of potential limitations, particularly the specific conditions dictating miR-27a ’s diverse roles, is warranted. Investigating these aspects would offer a more nuanced interpretation of our findings and shed light on the context-dependent nature of miR-27a ’s functions. Despite these limitations, our findings suggest that miR-27a likely plays a significant role in suppressing MM progression by inhibiting the expression of the PI3K/AKT/mTOR signaling pathway.

Conclusions

To conclude, miR-27a functions as a tumor suppressor gene in MM. Significantly, we verified the inhibition of MM growth and metastasis by miR-27a through regulation of the Th17/Treg balance. Additionally, we acknowledge the potential mechanistic role of the PI3K/AKT/mTOR signaling pathway in this process. Our findings emphasize the potential of miR-27a as a molecular target in MM chemotherapy strategies.

Supporting information

S1 File. This file contains the original PCR data used in the study, including the amplification curves, threshold cycles (Ct values), and gel electrophoresis images for the target genes.

(ZIP)

pone.0311419.s001.zip^{(970.9KB, zip)}

S2 File. This file includes the original Western blot data, featuring raw images of the blots for the target proteins, along with corresponding molecular weight markers.

Detailed information on the antibody used, sample preparation, and exposure times is also provided.

(ZIP)

pone.0311419.s002.zip^{(3.6MB, zip)}

S3 File. This file contains the original flow cytometry images, including dot plots and histograms for the analyzed samples.

(ZIP)

pone.0311419.s003.zip^{(3.2MB, zip)}

S4 File. This file includes the original images from the transwell assays, showcasing the migratory and invasive capacities of the cell populations under various experimental conditions.

(ZIP)

pone.0311419.s004.zip^{(77.8MB, zip)}

Acknowledgments

We would like to thank Editage for providing language editing for this manuscript.

Data Availability

The original contributions presented in the study are included in the Supplementary Material section of this manuscript. We have uploaded our original research data to the Harvard Dataverse. The data can be accessed at the following link: https://doi.org/10.7910/DVN/BZATPL.

Funding Statement

Administration of Traditional Chinese Medicine of Guangdong Province, China, Fund No.20241093; 2.Foundation of Guangdong Province, China, Fund No.C2023074 The recipients of the funding were responsible for the conception and design of this study.

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PLoS One. doi: 10.1371/journal.pone.0311419.r001

Decision Letter 0

Hiromu Suzuki

16 Nov 2023

PONE-D-23-34574MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance via PI3K/AKT/mTOR signalingPLOS ONE

Dear Dr. Xiao,

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This manuscript was carefully reviewed by 2 experts. Although the results presented in this study are of potential importance and interest, the reviewers raised a number of issues which need to be addressed before acceptance. Please respond to each of the reviewer comments.

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Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Title: MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating

Th17/Treg balance via PI3K/AKT/mTOR signaling

This study investigates the role of MiR-27a in regulating the balance between T helper 17 (Th17) and T regulatory (Treg) cells in multiple myeloma (MM). The research reveals that MiR-27a has reduced expression in MM and suggests an influence on the equilibrium of Th17 and Treg cells, reducing the expression of inflammatory mediators. Elevated MiR-27a levels inhibit cell viability, migration, and invasiveness, induce apoptosis, and arrest cell cycle progression in MM cells by targeting the PI3K/AKT/mTOR signaling pathway. This suggests that MiR-27a may play a role in inhibiting MM progression by modulating this pathway.

Comments:

1. Text on line 121 has a different font color.

2. Was DNA purity tested by running a gel too?

3. Where were primers purchased from?

4. Were cells serum-starved prior to viability and apoptosis assays?

5. GEO database analysis lacks details of how analysis was conducted, there is not sufficient detail for the study to be replicated.

6. Just to clarify, only 3 normal and 3 MM datasets were selected? This is what is represented in the clusterogram.

7. Figure 2B, perhaps include MiR-27a in the y axis label.

8. Figure 3C legend does not fully describe what is seen. This is also not well described in text.

9. I am not fully convinced by this statement: initial validation of the regulatory impact of the MiR-27a/PI3K/AKT/mTOR axis on the modulation of Th17/Treg balance in MM. The study does show the dual functionality of MiR-27A but does not clearly validate the regulatory impact of the MiR-27a/PI3K/AKT/mTOR axis on the modulation of Th17/Treg balance in MM. Although this study showed that miR-27a impacts the development of MM through the PI3K/AKT/mTOR signaling pathway, this cannot infer its modulation of the Th17/Treg balance. This is a major concern as it is the basis of the study and a conclusion that is drawn. The study does hold significant merit in its findings regarding the PI3K/AKT/mTOR pathway and MiR-27a.

Reviewer #2: The manuscript "MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance via PI3K/AKT/mTOR signaling" provides a good insight into miRNA based regulation of multiple myeloma. However, addressing the following comments can improve the manuscript.

1. In the abstract the conclusion is presented very vaguely, please improve the sentence structure to make it more scientific and clear to understand.

2. Introduction is satisfactory, the last lines on page 4 (84to 87) needs clarity of the aim of studies.

3. Methods are presented well, however certain details are missing. Please explain what is meant by control in case of cells are they untransfected cells or mock transfected? The information about the designing of miR-27a mimic and anti-miR-27a is missing. How the mutant of PI3K was constructed? Experiments replication number is not mentioned. The ethical permissions for animal studies should also be mentioned in methods section.

4. In the Results the statistical analysis in all the figures is not clear, the authors should double check all the analysis as at some points e.g in fig 2 it says **** but in the figure caption it is not mentioned. Also in the mRNA analysis the values on y-axis and stats doesnot coordinate. Please double check.

Figure 3c shows unequal contrast of the images

Fig 4 is not visible in the document

The authors may show some other in-vitro metastasis assays such as ECM degradation to support their hypothesis with appropriate controls

5. The discussion section needs improvement in corelating the results to the conclusion made. The authors have provided enough references from the other studies but failed to corelate their own results with the conclusions made. Please write few sentences in this regard.

6. Overall the sentence structure could be improved throughout the document.

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Oct 16;19(10):e0311419. doi: 10.1371/journal.pone.0311419.r002

Author response to Decision Letter 0

22 Jul 2024

Responses to Reviewer #1

1.The first three sentences need references.

"Their primary role involves the regulation of gene expression at the translation level...": Is this accurate? In my understanding, microRNAs act at the post-transcriptional level and not at the translational level itself. Please provide references.

After carefully considering your inquiry and conducting further research, we have verified that microRNAs indeed primarily function at the post-transcriptional level rather than directly affecting translation. We apologize for any confusion caused by the previous statement in our manuscript.

In light of this clarification, we have revised the relevant section of the manuscript to accurately reflect the role of microRNAs in gene expression regulation at the post-transcriptional level. Additionally, we have incorporated appropriate references to support this updated interpretation.

"MiR-27a, located on chromosome 19 (19p13.1), exhibits expression in various malignancies, including renal carcinoma, oral squamous cell carcinoma, and pancreatic cancer, among others": Should it be "Aberrant expression"?

Upon further investigation, we have confirmed that the appropriate term should indeed be "aberrant expression" rather than simply "expression." We apologize for any confusion caused by the oversight in our manuscript. Accordingly, we have revised the relevant section of the manuscript to accurately reflect the aberrant expression of MiR-27a in various malignancies, including renal carcinoma, oral squamous cell carcinoma, and pancreatic cancer, among others. This correction ensures the clarity and accuracy of our findings.

2.The sentences "Th1 lymphocytes are responsible for the secretion of interferon-gamma (IFN-γ), thus facilitating cell-mediated immune responses" and "Conversely, Th2 cells are responsible for the production of IL-4, which suppresses the Th1 cell-mediated response" need references.

The last paragraph requires references.

Reference 15, in the sentence "Recently, it was reported that Th17 cells may contribute to the development of MM and complications," is quite relevant to the present study, but it may not be widely known to the community, so a brief explanation of the findings is essential.

We have carefully addressed your concerns regarding the need for references in specific sentences and the requirement for a brief explanation of the findings related to Reference 15. We have now included references to support the statements regarding Th1 and Th2 cells, as well as the information in the last paragraph of the manuscript. Additionally, in response to your suggestion for providing a brief explanation of the findings related to Reference 15, we have included the following statement: "Multiple studies have demonstrated that an imbalance between Th17 and Treg cells, often characterized by an elevated Th17/Treg ratio, contributes to the development and progression of various inflammatory and autoimmune conditions. In conditions such as rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, and psoriasis, there is evidence of an increased Th17 response coupled with impaired Treg cell function or decreased Treg cell numbers." We trust that these revisions adequately address your concerns and improve the clarity and accuracy of our manuscript. Please feel free to contact us if you require any further information or clarification. Thank you for your continued support and valuable feedback.

3.I did not understand what the authors meant by "19 PAIRED patients."

For Q-PCR, were quality controls performed? Such as RNA quality, melt curve analysis, etc.

Thank you for your feedback on our manuscript. We apologize for the confusion regarding the statement "19 PAIRED patients." We have revised this to accurately reflect the cohort as "19 patients with 3 MM and 3 paired normal tissues." Regarding the qPCR analysis, we confirm that we performed comprehensive quality controls, including RNA quality assessment, melt curve analysis, and other necessary checks. All raw data pertaining to the qPCR experiments have been provided as supplementary material upon submission to the editor.

4.The section “Identification of DEGs” is lacking. It is not clear how the authors normalized the data and how the differential expression analysis was conducted. Was the data normalization for small RNAs and coding genes done in the same way?Additionally, the authors mention the Venn Diagram but do not show it, and to be honest, I did not understand the utility of this analysis. Does the Heatmap presented include all DEGs or only some of them? If it includes only some, what were the criteria for selection? If it includes all, how do the authors explain the small number of DEGs in a cancer vs. normal comparison? Why are there only 3 MM samples when there are 19 in the methods? In my opinion, a volcano plot for the differential expression analysis would be more appropriate in this case.

We have addressed the concerns raised in your review in our revised submission. We have provided a comprehensive explanation of our data normalization and differential expression analysis methods in the Methods section. Regarding the Venn Diagram, we have removed any mention of it from the manuscript to avoid confusion. We opted for a heatmap visualization over a volcano plot as we believe it provides a clearer representation of the specific genes that are upregulated or downregulated. Additionally, we apologize for the misunderstanding regarding the number of samples in our dataset. While there are indeed 19 patients included, only 3 pairs of tumor and normal tissue samples were available for analysis.

5.In the case of Figure 1B, it is more appropriate, but I believe the caption would benefit from including the name of the statistical test used (this can be repeated in other figures as well).

Since the authors are evaluating the dynamics of T cells, a deconvolution analysis would be welcome (i.e., evaluating the cell proportions from expression signatures). For example, the authors could analyze different cancer samples and see if differences in microRNA expression alter the proportion of cells of interest.

We will include the name of the statistical test used in the methodology section of the manuscript. Regarding the deconvolution analysis, we appreciate the suggestion. However, in this study, the focus is on experimental validation following the bioinformatics analysis. Therefore, incorporating extensive bioinformatics analyses may overshadow the experimental findings. We will consider integrating such analyses in future research endeavors in line with the reviewer's recommendations.

6.Figure 2B would benefit from a title/axis indicating that they are analyzing microRNA27a.

Figures 2C and D are difficult to interpret for readers who are not familiar with the technique. A bar graph summarizing the results could address this issue.

Thank you for your constructive feedback. We have carefully revised Figure 2B to include a clear title and axis indicating the analysis of microRNA27a. Additionally, to enhance clarity for readers less familiar with the technique, we have incorporated a bar graph summarizing the results in Figures 2C and D. These modifications aim to improve the interpretability of the figures and provide a more accessible presentation of our findings. We appreciate your valuable input and have taken steps to address these concerns.

7.I understood what the authors meant in this sentence: "These findings suggest that the MOPC-MM models experienced a severe inflammatory state due to an imbalance in the Th17/Treg ratio," but some information about the pro- and anti-inflammatory roles of Treg, Th17, IL2, IL10, and TGF-beta would help non-specialist readers.

Thank you for your feedback. We have addressed the pro-and antiinflammatory roles of Treg, Th17, IL2, IL10, and TGF-β in the discussion section of the manuscript. We have provided detailed explanations based on the literature and our research findings to help non-specialist readers understand the implications of these factors in the context of our study.

8.The ELISA assay is not described in detail for reproducibility in the methodology.

Thank you for your feedback. We have revised the methodology section to include additional details regarding the ELISA assay. Specifically, we have clarified that all samples were measured in triplicate, and the average values were calculated for analysis.

9."Low expression of MiR-27a promoted proliferation and metastasis and inhibited apoptosis as well as invasion of MM cells." The title is not accurate, as metastasis was not evaluated, only invasion. Please adjust it to avoid misleading the reader (in the discussion/text, you can mention that metastasis requires invasion).

Thank you for your suggestion. We have revised the title as per your recommendation, and we have also made the necessary adjustments in the text to clarify the distinction between invasion and metastasis.

10.Figure 3A needs a statistical test. Figure 3B faces the same problem as Figures 2C and D.

Thank you for your valuable feedback. We have carefully addressed the concerns regarding Figure 3A and Figures 2C and D. Statistical tests have been incorporated into Figure 3A, and similar adjustments have been made to Figures 3B, 2C, and 2D to ensure the robustness of our results. We appreciate your attention to detail and have taken the necessary steps to enhance the clarity and rigor of our manuscript.

11.There is a typographical error in the caption of Figure 5 (p-mROT instead of p-mTOR).

Thank you for bringing this to our attention. We have corrected the typographical error in the caption of Figure 5, replacing "p-mROT" with the correct term "p-mTOR" as requested.

12.I believe it would be worthwhile for the authors to discuss why, at the protein level, only the phosphorylated version of PI3K was reduced. Again, graphs to summarize the flow cytometry results in Figure 6.

Thank you for your comments. Firstly, we have accurately presented our research findings, which may indicate that PI3K functions through its phosphorylated modification in multiple myeloma. Secondly, based on other literature reports, when studying the role of the PI3K protein and its related pathways in tumors, it is often through modulation of its phosphorylation that its effects are exerted. Regarding Figure 6, similar issues have been addressed in above responses.

13.The section "Upregulation of MiR-27a counteracted the effects of PI3K/AKT/mTOR signaling on the cellular behaviors of MM cells" is somewhat confusing. Please expand, explaining the groups ("control," "Mir27a mimic," "mir27a nc," etc.). I honestly did not understand the groups, making interpretation difficult.

Thank you for your feedback. The group labels are distinguished based on different treatments given to the cells. To address the reviewer's confusion, we have provided more specific descriptions of these group labels in the corresponding sections of the results description.

14.I believe these sections can be reviewed as a whole (see my initial comment). Some information in the discussion can be toned down to avoid overstatement. For example, "In this bioinformatics study," the authors conducted a basic bioinformatics analysis, not a comprehensive study.

Thank you for your suggestion. We have revised the sections accordingly, addressing the points you raised. We have adjusted the language in the discussion to ensure accuracy and avoid overstatement, as recommended.

Responses to Reviewer #2

1: Lack of Human Clinical Data: While the study explores the impact of MiR-27a on Th17/Treg equilibrium in MM, it heavily relies on the MOPC-MM mouse model. The lack of human clinical data or validation in human samples raises questions about the direct relevance and translatability of the findings to human MM patients. It is crucial to establish the clinical significance of the observed effects in the context of human disease.

Thank you for your suggestion.While validation in human samples is indeed important, limitations such as the difficulty of clinical trials and ethical and regulatory constraints have hindered us from conducting clinical validation in this study. However, our research has yielded significant results through animal and cell experiments, demonstrating considerable academic value. We aim to use the findings from this study as a foundation for future research to pursue clinical validation in human subjects.

2: Molecular Pathways Complexity: The study focuses on the PI3K/AKT/mTOR signaling pathway as a target of MiR-27a, but these pathways are highly complex and interconnected. The study lacks a comprehensive exploration of downstream effectors and potential compensatory mechanisms that might be activated in response to the observed changes. A more in-depth analysis of the molecular pathways involved would strengthen the study.

We greatly appreciate the reviewer's perspective. Indeed, the complexity of the pathways warrants a more detailed and thorough experimental design for validation. Our findings serve as preliminary validation of this molecular pathway, as we have emphasized in the discussion section of the manuscript. Moving forward, we intend to conduct more rigorous animal experiments, bioinformatics analyses, and cell experiments to delve deeper into the mechanisms of the pathway and the roles of individual molecules involved, thus strengthening our study.

3: Sample Size and Reproducibility: The study's reliance on the Gene Expression Omnibus (GEO) database for gene expression profiles introduces potential limitations regarding sample size and variability. Additionally, the study would benefit from independent validation in a larger cohort or different datasets to enhance the robustness and reproducibility of the results.

We appreciate the reviewer's perspective and acknowledge the validity of their comment. However, it's important to note that our utilization of the Gene Expression Omnibus (GEO) database for bioinformatics analysis serves as just one aspect of our study, providing initial insights rather than comprehensive coverage of our research focus. We believe that all bioinformatics findings require experimental validation to bolster their credibility. Consequently, we have not emphasized bioinformatics analyses extensively nor included additional datasets from other databases in our study.

4: Functional Validation of MiR-27a Targets: While the study identifies the PI3K/AKT/mTOR pathway as a direct target of MiR-27a, a more extensive experimental validation of these interactions would strengthen the conclusions. This could include additional assays, such as knockdown or overexpression of specific pathway components, to confirm the direct regulatory effects of MiR-27a on the PI3K/AKT/mTOR axis.

We appreciate the reviewer's insightful feedback and agree with their suggestion. Indeed, our study includes a rescue experiment in cell assays, demonstrating the significant impact of MiR-27a on the PI3K/AKT/mTOR pathway. However, we acknowledge the limitations in our experimental design, which only provide preliminary findings. To address the reviewer's concerns comprehensively, additional experiments such as genetic knockout animal models would be necessary. These experiments are essential for fully validating our hypothesis. We are committed to addressing these aspects in future research endeavors to enhance the robustness of our conclusions.

5: Inadequate Discussion of Limitations: The discussion section briefly touches upon variations in the tumor microenvironment affecting MiR-27a's dual functionality. However, a more thorough discussion of potential limitations, including the specific conditions under which MiR-27a might exhibit different roles, would provide a more nuanced interpretation of the results.

We appreciate the reviewer's valuable feedback and agree with their suggestion regarding the discussion of limitations. In response to their insightful input, we have revised the discussion section to provide a more comprehensive exploration of potential limitations, particularly focusing on the conditions under which MiR-27a may exhibit varied roles in the tumor microenvironmen

Attachment

Submitted filename: Responses.docx

pone.0311419.s005.docx^{(24.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0311419.r003

Decision Letter 1

Hiromu Suzuki

6 Aug 2024

PONE-D-23-34574R1MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance via PI3K/AKT/mTOR signalingPLOS ONE

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PLoS One. 2024 Oct 16;19(10):e0311419. doi: 10.1371/journal.pone.0311419.r004

Author response to Decision Letter 1

14 Aug 2024

Responses to Reviewer #2

1. There should be consistency in writing miR-27a. At some places it's written with 'M" and at others with "m".

Thank you for your feedback. We have revised the manuscript to ensure consistency in writing miR-27a. All instances of "M" have been changed to "m" except at the beginning of sentences.

2. Please look for sentence structure errors.

Thank you for your suggestion. We have had the manuscript professionally reviewed and edited by native English speaker to address any sentence structure errors.

Attachment

Submitted filename: Responses.docx

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PLoS One. doi: 10.1371/journal.pone.0311419.r005

Decision Letter 2

Hiromu Suzuki

18 Sep 2024

MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance

PONE-D-23-34574R2

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Additional Editor Comments (optional):

The authors addressed the issues raised by the reviewer.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0311419.r006

Acceptance letter

Hiromu Suzuki

7 Oct 2024

PONE-D-23-34574R2

PLOS ONE

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. This file contains the original PCR data used in the study, including the amplification curves, threshold cycles (Ct values), and gel electrophoresis images for the target genes.

(ZIP)

pone.0311419.s001.zip^{(970.9KB, zip)}

S2 File. This file includes the original Western blot data, featuring raw images of the blots for the target proteins, along with corresponding molecular weight markers.

Detailed information on the antibody used, sample preparation, and exposure times is also provided.

(ZIP)

pone.0311419.s002.zip^{(3.6MB, zip)}

S3 File. This file contains the original flow cytometry images, including dot plots and histograms for the analyzed samples.

(ZIP)

pone.0311419.s003.zip^{(3.2MB, zip)}

S4 File. This file includes the original images from the transwell assays, showcasing the migratory and invasive capacities of the cell populations under various experimental conditions.

(ZIP)

pone.0311419.s004.zip^{(77.8MB, zip)}

Attachment

Submitted filename: Responses.docx

pone.0311419.s005.docx^{(24.6KB, docx)}

Attachment

Submitted filename: Responses.docx

pone.0311419.s006.docx^{(17.3KB, docx)}

Data Availability Statement

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PERMALINK

MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance

Weiguo Lu

Hui Huang

Zhanjie Xu

Shumin Xu

Kewei Zhao

Mingfeng Xiao

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

GEO database analysis

Tumor models

Quantitative real‑time PCR

Table 1. Primers for qRT-PCR analysis.

Flow cytometry analysis and inflammation assessment

Cell culture and transfection

Cell viability and apoptosis assay

Transwell assay

Dual‑luciferase reporter assay

Western blot

Statistical analysis

Results

Identification of DEGs

Fig 1. Microarray data.

Verification of the relationship between miR-27a and Th17/Treg balance in vivo

Fig 2. Relationship between MiR-27a and MM in vivo.

Low expression of miR-27a promoted proliferation and inhibited apoptosis as well as invasion of MM cells

Fig 3. Overexpression of miR-27a inhibited proliferation and invasion, but promoted apoptosis of MM cells.

MiR-27a alleviated the severity of inflammatory state of MM cells

Fig 4. MiR-27a alleviated the severity of inflammatory state of MM cells.

MiR-27a directly targeted PI3K/AKT/mTOR signaling

Fig 5. MiR-27a could target PI3K/AKT/mTOR signaling in MM.

Upregulation of miR-27a counteracted the effects of PI3K/AKT/mTOR signaling on the cellular behaviors of MM cells

Fig 6. Upregulation of miR-27a counteracted the effects of PI3K/AKT/mTOR signaling on RPMI-8226 and NCI-H929 cells.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Hiromu Suzuki

Roles

Author response to Decision Letter 0

Decision Letter 1

Hiromu Suzuki

Roles

Author response to Decision Letter 1

Decision Letter 2

Hiromu Suzuki

Roles

Acceptance letter

Hiromu Suzuki

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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