Loss of miR-16-1-3p Activity Predicts Chemoresistance and Progression in Osteosarcoma: An Integrative Clinicopathologic Study

Loss of miR-16-1-3p Activity Predicts Chemoresistance and Progression in Osteosarcoma: An Integrative Clinicopathologic Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Loss of miR-16-1-3p Activity Predicts Chemoresistance and Progression in Osteosarcoma: An Integrative Clinicopathologic Study Wenyu Xue, Yuzhe Wang, Polina Pugacheva, Anna Smirnova, Roman Chuprov-Netochin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7665473/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Chemotherapy resistance contributes significantly to mortality in osteosarcoma (OS) patients. The role of miR-16, miR-16-1-3p, and miR-16-2-3p in modulating chemotherapy response and disease progression in OS remains unclear. Methods RNA-seq and clinical data from 82 OS patients in the TARGET-OS database were analyzed. Patients were classified by progression status (progressive disease [PD] vs. progression-free survivors [PFS]) and chemotherapy response (resistant vs. responsive). Target genes of the three miRNAs were predicted using TargetScan 7.2 and analyzed via Gene Set Enrichment Analysis (GSEA). Kaplan-Meier analysis was used to evaluate the prognostic value of cumulative Z-scores of enriched target gene sets. We used lentivirus to overexpress miRNAs in an osteosarcoma cell line to assess their effect on cisplatin sensitivity. Sensors of miR-16-1/2-3p activity were constructed. miR-16-1-3p-mediated biological effects were tested in vitro and in vivo. Results Overexpression of miR-16, miR-16-1-3p, and miR-16-2-3p sensitized OS cells to cisplatin. Predicted target genes of all three miRNAs were significantly enriched among genes upregulated in chemotherapy-resistant OS samples, suggesting reduced miRNA activity. Target genes of miR-16-1-3p were further enriched in PD cohort. High cumulative Z-scores of target gene sets correlated with poor survival. MiR-Sensor assays confirmed that miR-16-1-3p suppresses protein expression via target mRNA sequence recognition. Functional assays demonstrated significant tumor-suppressive effects of miR-16-1-3p in vitro and in vivo. Conclusion Reduced activities of miR-16, miR-16-1-3p, and miR-16-2-3p are linked to chemoresistance in OS, and only miR-16-1-3p activity loss correlates with disease progression. MiR-16-1-3p may serve as a biomarker for chemoresistance and prognosis in OS. osteosarcoma chemotherapy resistance miR-16 miR-16-1-3p miR-16-2-3p miRNA activity sensors Chick Embryo Chorioallantoic Membrane (CAM) model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 10 Highlights Low activity of miR-16-5p, miR-16-1-3p, and miR-16-2-3p, displayed by increased activity of their target genes, may cause chemoresistance in osteosarcoma. miR-16-1-3p, a low-abundance passenger strand, has important regulatory effects and is linked to disease progression and poor prognosis. Target gene expression patterns may serve as potential biomarkers for predicting chemoresistance and clinical outcome. 1. Introduction Osteosarcoma (OS) is the most common malignant bone and joint tumor, primarily affecting children and adolescents with the highest incidence in the second decade of life[ 1 , 2 ]. In the 1970s, the combination of primary tumor resection and adjuvant chemotherapy greatly improved OS survival rates from under 20% to 70%[ 2 , 3 ]. Currently, the standard treatment strategy combines neoadjuvant (pre-surgical) chemotherapy, surgical resection, and adjuvant chemotherapy. The most effective and widely used combination of chemotherapeutic agents for treating osteosarcoma includes cisplatin, doxorubicin, methotrexate, and ifosfamide [ 4 , 5 ]. It's important to highlight that there is currently no substantial evidence indicating that adding neoadjuvant chemotherapy to the combination of surgery and adjuvant chemotherapy enhances the outcomes of OS therapy[ 6 ]. Following the significant advancements in OS treatment brought about by chemotherapy 40 to 50 years ago, the mortality rate for OS has now stagnated, showing no signs of further improvement[ 7 ]. Additionally, patients with OS diagnosed with metastases face a significantly low 5-year survival rate, averaging around 30% or even lower [ 8 – 10 ]. Crucially, patients who are poor responders to neoadjuvant chemotherapy, defined as having less than 90% necrosis in the resected tumor, face significantly lower 10-year survival rates compared to those who respond well, exhibiting more than 90% necrosis. Specifically, the survival rates are starkly contrasting at 47.2% for poor responders compared to an impressive 73.4% for good responders [ 11 ]. These findings highlight the critical need to deepen our understanding of the pathogenesis of osteosarcoma, particularly the mechanisms behind its chemoresistance. This knowledge is essential for making significant advancements in osteosarcoma therapy. MicroRNAs (miRNAs) are a group of small, non-coding RNAs ranging from 16 to 27 nucleotides in length, with an average size of 20–22 nucleotides[ 12 ]. These molecules play a crucial role in regulating gene expression after transcription. The canonical miRNA biogenesis begins in the cell nucleus, where miRNA genes are transcribed in lengthy primary miRNAs (pri-miRNAs) by DNA-dependent RNA polymerase II or, less frequently, by DNA-dependent RNA polymerase III. Pri-miRNAs undergo processing by the microprocessor complex, consisting of Drosha and DGCR8, to form hairpin-like miRNA precursors (pre-miRNAs). These precursors are generally transported from the nucleus to the cytoplasm by exportin 5. In the cytoplasm, pre-miRNAs are cleaved by Dicer into short RNA duplexes (21–22 nucleotide pairs) with 2-nucleotide 3’-overhangs at each end. One of the strands (arms) of each RNA duplex is bound by an Argonaute protein and included in the RNA-induced silencing complex (RISC) while the other strand (arm) degrades. The RISC-bound miRNA typically identifies target mRNA sites in the 3’-untranslated regions (3’-UTRs) by pairing with a complementary seed region of the miRNA, which spans 2 to 8 nucleotides. The identification of specific signals typically results in translation repression and subsequent degradation of the target mRNA[ 13 – 15 ]. In most cases, one strand is significantly more loaded onto the RISC and is far more prevalent in the cell compared to the less abundant minor strand. In this case, the abundant strand is named “guide” or “lead” miRNA while the minor strand is designated “passenger” or “miR*”. This nomenclature is sometimes ambiguous since both strands may be expressed at similar levels and the strands’ ratio may strongly vary depending on cell type and disease. Therefore, another nomenclature, which classifies miRNAs according to their position in 5’- or 3’-arms of pre-miRNA, has become widely accepted. 5’-arm miRNAs are designated “5p” while 3’-arm miRNAs are designated “3p”. Noteworthily, the leading miRNA strand can be designated as "5p" or "3p," depending on the specific miRNA in question[ 15 ]. Given that miRNAs traditionally operate by regulating target gene expression at the mRNA translation and stability levels, identifying relevant miRNA target genes has emerged as a crucial focus in miRNA research. Experimental high-throughput methods for identifying miRNA target genes are intricate, demanding significant labor and resources. Furthermore, these methods often fail to yield a thorough list of relevant target genes and do not eliminate the risk of false positives[ 16 – 18 ]. The challenges posed by high-throughput experimental methods make bioinformatics tools like TargetScan, miRDB, and miRanda particularly appealing. These tools leverage the complementarity of miRNA seed sequences and evolutionary conservation to effectively predict miRNA-mRNA interactions[ 19 – 21 ]. These bioinformatics tools are essential for identifying target genes of disease-relevant miRNAs using RNA-Seq data from patient samples, especially when high-throughput experimental methods are impractical or unfeasible. Implication of miRNAs in various aspects of oncogenesis has been well established[ 22 , 23 ]. Furthermore, a growing body of evidence indicates that various miRNAs play a significant role in the metastasis of OS and in the development of chemoresistance[ 24 , 25 ]. MiR-16, which is one of the most studied tumor suppressive miRNAs[ 26 ], has been also reported to have tumor suppressive, antimetastatic, and chemotherapy-sensitizing properties in OS [ 27 – 30 ]. This microRNA, classified as miR-16-5p, is encoded by two genetic loci: MiR-16-1 at chromosome 13 and MiR-16-2 at chromosome 3. Both loci encode the same lead strand miR-16 but different passenger strands: miR-16-1-3p (miR-16-1*) and miR-16-2-3p (miR-16-2*). Of note, miR-16-1-3p and miR-16-2-3p are active in osteosarcoma and can match or exceed the activities of the lead strand miR-16 [ 27 ]. Our recent research revealed that the miR-16-1-3p and miR-16-2-3p, exhibit a strong ability to inhibit tumorigenic and metastatic behaviors in lung cancer cells. These miRNAs reduce aggressive traits and increase sensitivity to cisplatin in both radiation-sensitive and resistant A549 non-small cell lung cancer (NSCLC) cells[ 31 ]. This dual function shows their potential in treating lung cancer and enhancing the effectiveness of standard chemotherapy. The findings emphasize the importance of understanding miRNA roles in cancer biology as we look toward more effective treatment strategies. This motivated us to thoroughly investigate the expression of target genes associated with all three strands of miR-16-1/miR-16-2—namely, miR-16, miR-16-1-3p, and miR-16-2-3p—in OS samples that contain RNA-Seq data and clinical information from the TARGET-OS database [ 32 ]. Our study offers crucial insights in association between activities of the studied miRNAs and OS chemoresistance and progression. We also present the OS-relevant target genes of miR-16, miR-16-1-3p, and miR-16-2-3p, exploring the prognostic significance of their expression levels in OS. 2. Materials and Methods 2.1 Acquisition and pre-processing of data The RNA-seq data were obtained from the TARGET-OS database ( https://ocg.cancer.gov/ ), derived from biopsy specimens of primary tumor lesions collected from 88 osteosarcoma patients. Of the 88 patients, therapeutic agent data were available for 17, who were subsequently assigned to the chemotherapy group. To ensure robust and clinically relevant data analysis, three samples lacking corresponding clinical follow-up information were excluded at the initial stage of pre-processing. To assess the consistency of the dataset and identify potential outliers, Principal Component Analysis (PCA) was conducted using the prcomp package [ 33 ]. Outlier points were identified as points significantly distant from the main sample population in the PCA plot (Supplementary Figure S1 ), potentially reflecting technical artifacts or biological heterogeneity unrelated to the study objectives. After pre-processing of patient data, 82 patients were classified into a progressive disease patients (PD pts) group (n = 37) and Progression free survivor (PFS) group (n = 45) based on the clinical follow-up. Additionally, 15 chemotherapy-treated patients were divided into a chemotherapy-resistant group (n = 5) and a chemotherapy-responsive group (n = 10) according to survival outcomes. The RNA-seq libraries were prepared and sequenced using the Illumina HiSeq sequencing platform, ensuring high coverage and accuracy in gene expression profiling. To enable reliable cross-sample comparisons, the raw expression data were normalized using the Transcripts Per Million (TPM) method, accounting for both gene length and sequencing depth. Subsequently, the normalized values were log 2 transformed (log 2 (TPM + 1)) to enhance data distribution and facilitate robust downstream analyses. 2.2 Screening of miRNA target genes TargetScan 7.2 online database ( https://www.targetscan.org/vert_72/ ) was utilized to predict miRNA target genes. Genes with TargetScan context + + scores <-0.2 indicating strong miRNA-gene binding affinity were considered reliable target genes of miRNAs[ 34 , 35 ]. 2.3 Gene Set Enrichment Analysis (GSEA) Differential expression analysis (DEA) was performed to compare gene expression profiles between (1) PD vs PFS patients groups and (2) chemotherapy-resistant vs. chemotherapy-responsive patients groups. The analysis was conducted using the limma package[ 36 ] in R to generate ranked gene lists based on log fold-change (log 2 FC) values for downstream enrichment analyses. GSEA[ 37 ] was performed with the fgsea package [ 38 ] in R to assess the enrichment of predefined gene sets comprising predicted miRNA target genes, within the ranked gene lists. Statistical significance was assessed using 1,000 permutations, with p-adj 1 considered significant. 2.4 GO and KEGG Enrichment Analysis The LogFC value corresponding to the maximum Enrichment Score (ES), which marks the point of highest enrichment for the target genes within the ranked list, was selected as the threshold. Genes with LogFC values exceeding this threshold were considered to significantly contribute to the enrichment of the target gene set, highlighting their potential roles in key biological processes or signaling pathways. GO (Gene Ontology) [ 39 ] and KEGG (Kyoto Encyclopedia of Genes and Genomes) [ 40 ] enrichment analyses were performed using the R package clusterProfiler [ 41 ], with adjusted p-value < 0.05 defined as the cutoff for statistical significance. 2.5 Gene Set Scoring Calculation The expression levels of genes within each target gene set were standardized using Z-score normalization. This normalization was performed for each gene across all patients, ensuring comparability by centering the expression levels around a mean of zero with a standard deviation of one. Subsequently, for each patient, the Z-scores of all genes within a given target gene set were summed to compute a cumulative score, reflecting the combined contribution of the target genes for that patient [ 42 ]. The calculations were implemented in Python using the z-score function from the scipy.stats module [ 43 ]. 2.6 Principal Component Analysis (PCA) Principal component analysis (PCA) was conducted using the PCA module from scikit-learn in Python to reduce the dimensionality of the gene set score data[ 44 , 45 ].The analysis focused on the first two principal components, which were used to generate scatterplots for visualizing patient distributions. Data preprocessing, including expression value standardization and group assignment, was performed with pandas and numpy libraries. Intergroup differences were quantified using PERMANOVA (Permutational Multivariate Analysis of Variance). All visualizations were created with matplotlib and seaborn[ 46 ]. 2.7 Kaplan-Meier Survival Analysis Survival analysis was conducted to evaluate the prognostic relevance of cumulative gene set scores. Patients were categorized into high- and low-score groups based on the median cumulative score for each gene set. Kaplan-Meier (KM) survival curves were used to compare overall survival between the two groups, with statistical significance assessed using log-rank tests. The analysis was performed in R, utilizing the survival package for model fitting and the survminer package for visualization [ 47 ]. Survival time was recorded in days, measured from diagnosis to death or the last follow-up. 2.8 Plasmids A lentiviral vector PLKO.3G was a gift from Christophe Benoist and Diane Mathis (Addgene plasmid # 14748; http://n2t.net/addgene:14748 ; RRID: Addgene_14748). Lentivirus packaging plasmids pLP1, pVSVG, and pLP2 were from Invitrogen (Thermo Fisher Scientific, USA). A mammalian expression vector pKatushka2S-C (cat.# FP761) encoding far-red fluorescent protein Katushka2S was obtained from Evrogen JSC(Russia). The vector effectively facilitates the formation of fusions at the C-terminus of Katushka2S, enabling the expression of Katushka2S fusions, alongside the standalone expression of Katushka2S in mammalian cells. Lentiviral constructs with a reporter EGFP gene for overexpression of miR-16, miR-16-1-3p, miR-16-2-3p, and Scr miR respectively were created in the following way. Pairs of oligonucleotides miR-16-F and miR-16-R, miR-16-1*-F and miR-16-1*-R, miR-16-2*-F and miR-16-2*-R, and shScrambled-F and shScrambled-R were annealed yielding duplexes miR-16, miR-16-1-3p, miR-16-2-3p, and Scr miR, correspondingly (Supplementary Table 1). These duplexes were then cloned in Acc36I and EcoRI restriction sites of PLKO.3G creating lentiviral constructs PLKO.3G-miR-16, PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR, respectively. Accuracy of the generated lentiviral constructs was verified by PCR with primers PLKO-Dir and PLKO-Rev and Sanger sequencing with primers PLKO.1 5’ and PLKO-Rev. The Scr miR sequence was earlier published elsewhere[ 48 ]. To measure miRNA activity directly, Katushka2S genes have been engineered to include miRNA target sequences in their 3′ UTR. Using this approach, the reporter lentivirus constructs PLKO-dsKatushka2S-miR-16-1-3p-Sensor and PLKO-dsKatushka2S-miR-16-2-3p-Sensor have been successfully created. These constructs carry the reporter genes dsKatushka2S-miR-16-1-3p-Sensor and dsKatushka 2S-miR-16-2-3p-Sensor (the respective structures of the miR-16-1-3p/miR-16-2-3p Sensors are presented on Fig. 8 A). Both genes produce a destabilized variant of the Katushka2S reporter protein (dsKatushka2S), characterized by its rapid turnover rates. This is achieved by incorporating a protein degradation sequence from mouse ornithine decarboxylase, specifically amino acids 422–461, which had previously been successfully used to destabilize another reporter fluorescent protein, EGFP[ 49 ]. This version also contains six repeats in the 3' untranslated region that match either hsa-miR-16-1* or hsa-miR-16-2*. In these constructs microRNAs bind to the mRNAs of reporter genes in a silencing complex, causing the mRNAs to degrade. An increase in these microRNAs lowers mRNA levels of reporter genes, resulting in a weaker fluorescence signal from dsKatushka2S protein. This approach enables high-resolution imaging of miRNA activity at the single-cell level. Primers and oligonucleotides used in this study are shown in Supplementary Table 1. 2.9 Cell Culture, Lentivirus Production and Cell Transduction. Human OS cell line U2OS (Russian Cell Culture Collection of Vertebrates, Institute of Cytology, RAS, Russia) and HEK293T cell line (ATCC, USA) were cultured in DMEM complemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM Gln, penicillin (100 I.U./ml), and streptomycin (100 µg/ml). Lentiviruses were produced by co-transfection of each lentiviral plasmid (PLKO.3G-miR-16, PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR, 3.55 µg) with packaging plasmids pLP1 (2.31 µg), pLP2 (0.89 µg), and pVSVG (1.25 µg) using the Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific, USA) in HEK293T cells grown in 100 mm tissue culture plate at 80%-90% confluence. The cell culture medium with lentiviruses was collected in 72 hours after transfections and filtrated through a 0.45 µm PES filter to remove the HEK293T cells. To generate stable miR-overexpressing cell lines, human osteosarcoma U2OS cells were transduced with lentiviruses in a 6 well plates at 20%-30% of confluence by adding 2 ml of medium containing lentiviruses. Subsequently, EGFP-positive U2OS cells were sorted by the BIO-RAD S3e cell sorter (BIO-RAD, USA). The sorted EGFP-positive U2OS cells were utilized in the cisplatin sensitivity, colony formation, wound healing, transwell migration and in ovo tests. 2.10 Cisplatin Sensitivity Test The cisplatin sensitivity of miRNA-overexpressing U2OS cells was determined using the sulforhodamine B (SRB) assay[ 50 ]. Briefly, cells were seeded in 96-well plates at a density of 10,000 cells per well and treated with serial dilutions of cisplatin (0.1–80 µM) for 48 hours. After treatment, cells were fixed with 10% trichloroacetic acid (TCA) at 4°C for 1 hour. Fixed cells were washed with distilled water and stained with 0.4% SRB for 30 minutes at room temperature. Excess dye was removed by washing with 1% acetic acid, and the bound dye was dissolved in 10 mM Tris base (pH 10.5). Absorbance was measured at 540 nm using a microplate reader. The IC50 values were calculated applying the GraphPad Prism 8 software by fitting a non-linear regression curve to the dose-response data. 2.11 Colony formation assay 200 miR-overexpressing U2OS cells were seeded into each 10 cm culture dish and cultured in complete medium at 37°C with 5% CO₂ for 10 days.Then,cells were fixed with pre-chilled methanol for 15 minutes and stained with Giemsa solution for 30 minutes. Plates were rinsed with PBS, air-dried, and colonies were imaged and counted manually. 2.12 Wound healing assay MiR-overexpressing U2OS cells were seeded into 6-well plates and grown to approximately 90% confluence. A scratch was introduced through the cell monolayer using a sterile pipette tip. Detached cells were gently removed by washing with PBS, and the medium was replaced with serum-free medium. Images of the wound area were captured at 0 and 24 hours. The wound closure area was quantified using ImageJ software to evaluate cell collective migration capacity. 2.13 Transwell migration assay Serum-starved miR-overexpressing U2OS cells were seeded at a density of 8 × 10⁴ cells per well into the upper chambers of Transwell inserts (8 µm pore size, 24-well format, Corning) containing serum-free medium. The lower chambers were filled with complete medium supplemented with 10% fetal bovine serum as a chemoattractant. After incubation at 37°C in a 5% CO₂ atmosphere for 24 hours, cells remaining on the upper surface of the membrane were gently removed using a cotton swabs. Migrated cells on the lower surface were fixed with pre-chilled methanol for 15 minutes and stained with 0.1% crystal violet for 30 minutes. Following PBS washing, three random non-overlaping fields per well were imaged under an inverted microscope using 10x magnification, and the amount of migrated cells were counted. 2.14 miRNA Activity Assay To validate of miRNA biogenesis and activation within individual living cells, HEK293T and U2OS cells were seeded into 6-well plates and transfected at 60%–70% confluency using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were co-transfected with a lentivirus constructs expressing miR-16-1-3p along with EGFP (PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR), and the Katushka2S reporter plasmid containing the miR-sensor sequence in its 3′UTR (dsKatushka 2S-miR-16-1-3p-Sensor or dsKatushka2S-miR-16-2-3p-Sensor). Forty-eight hours post-transfection, cells were harvested, washed twice with PBS, and subjected to flow cytometry. EGFP fluorescence served as a marker for successful transfection.The Katushka2S fluorescence intensity was assessed specifically in the EGFP-positive population, reflecting the functional activity of miR-16-1/2-3p. Data analysis was performed using FlowJo sorftware. The activity of miRNA within individual living cells was also confirmed by miRNA mimics. HEK293 cells were co-transfected with synthetic mimics of miR-16-1-3p, miR-16-2-3p, or scramble sequences together with the corresponding miRNA sensor (dsKatushka 2S-miR-16-1-3p-Sensor or dsKatushka2S-miR-16-2-3p-Sensor) and the EGFP-expressing PLKO.3G plasmid to identify mimic-transfected cells. At 24 hours after transfection, cells were harvested and analyzed by flow cytometry to measure Katushka2S fluorescence in EGFP-positive cell populations as described above. 2.15 Chick Embryo Chorioallantoic Membrane (CAM) in vivo assay Fertilized specific pathogen-free eggs were obtained from a certified local hatchery (Trade house Ptichnoe, Ltd., https://ptichnoe-td.ru ). Eggs were wiped with sterile water and paper towels, then incubated horizontally at 37°C and 70% relative humidity with automatic rotation, designated as embryonic day 0 (EMD 0). On EMD 3, the air sac and major blood vessels were identified using an egg candler, and approximately 3 mL of albumen was withdrawn to lower the CAM. A circular area of about 2 cm in diameter was marked at the drop site, and the eggshell was opened using a mini rotary saw. The window was sealed with 3M semipermeable film, and eggs were returned to the incubator with the rotation disabled. On EMD 8, tumor cells were implanted onto the CAM to evaluate the tumorigenic effect of miR-16-1-3p. A polytetrafluoroethylene O-ring (PTFE O-ring, inner diameter 6 mm, outer diameter 9 mm) was placed on a vascular-rich region, and 25 µL of cell suspension containing 3 × 10⁶ cells was seeded within the ring. The suspension was prepared by adding cells to the mixture of serum-free medium and Matrigel (Corning, 356234) at a 1:1 (v/v) to enhance tumor formation. The experimental and the control groups consisted of stable U2OS cells sorted out after transduction cells with lentivirus construct PLKO.3G-miR-16-1* (hsa-miR-16-1-3p), and PLKO.3G-Scr miR (SCR miR), respectively (see section 2.9 ). After implantation, the window was resealed and incubation continued. On EMD 16, CAM tissues were harvested, and tumor images were acquired using a Leica M60 stereomicroscope. After excision, tumors were weighed on an electronic analytical balance to determine the tumor mass. Tumor volume was calculated by measuring the maximal diameter (L) and perpendicular height (H) with a vernier caliper and applying the ellipsoid formula: Volume (mm³) = (3/4) × π × (L/2)² × H. According to international legislations, chicken embryos are not classified as live animals before day 17 of incubation[ 51 , 52 ]; therefore, all experiments completed by EMD 16 were exempt from IACUC approval. 2.16 Statistical Analysis Statistical comparisons were performed using Student’s t-test and one-way ANOVA in GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA). Data are presented as mean ± SEM from three independent experiments. A p-value < 0.05 was considered statistically significant. 3. Results Our previous findings[ 31 ] motivated us to explore the potential links between the endogenous activities of the miRNAs we studied in osteosarcoma (OS) and both the chemoresistance and progression of this disease. Notably, OS progression has a significant correlation with chemoresistance[ 11 ]. To evaluate these connections, we employed cutting-edge bioinformatic methods. We extracted the necessary RNA-Seq and clinical data from the TARGET-OS database ( https://ocg.cancer.gov/ ). Using TargetScan software[ 20 ], we compiled a comprehensive list of predicted target genes for miR-16, miR-16-1-3p, and miR-16-2-3p. 3.1 Enrichment of miRNA Target Genes GSEA [ 36 ] was conducted to evaluate the enrichment of target genes of miRNA in osteosarcoma progression and chemoresistance groups. The ranked gene list was constructed based on log 2 FC values, representing the differences in gene expression between specific groups. All genes were used as the background set, while high-confidence target genes predicted by TargetScan with TargetScan score < -0.2 were defined as the predefined gene sets. The GSEA results are visualized as enrichment plots (Fig. 1 ). On these plots, the rank of x-axis represents the position of each gene in the ranked list, arranged from the most upregulated genes (left) to the most downregulated genes (right) based on log 2 FC values. Each black vertical line on the x-axis indicates the position of a target gene in the ranked list. The density of these black lines reflects the clustering of target genes: a higher density in a specific region suggests that the target genes are predominantly enriched in that region. The enrichment score (ES) of y-axis represents the cumulative running sum of the weighted enrichment score as the ranked gene list is traversed. The enrichment score increases when a target gene is encountered and decreases for non-target genes. The peak of the curve indicates the point where the target gene set is most significantly enriched in the ranked list. The normalized enrichment score (NES) adjusts the ES for gene set size and variability across permutations, enabling cross-comparison between gene sets. An NES > 1 indicates that the target gene set is significantly enriched in the top-ranked genes (e.g., upregulated in the progression or chemoresistance group), while an NES < -1 suggests enrichment in the bottom-ranked genes (e.g., downregulated in the progression or chemoresistance group). Statistical significance is determined by the permutation-based p-adj. The results indicate significant enrichment for miR-16-1-3p in both OS progression (NES: 1.344, p-adj: 0.0033) and resistance (NES: 1.273, p-adj: 0.003), underscoring its dual role in tumor progression and drug resistance. miR-16-2-3p exhibits a strong association with OS resistance (NES: 1.263, p-adj: 0.005) but limited relevance in progression (NES: -0.185, p-adj: 0.1575). Conversely, miR-16 displays modest but consistent regulatory roles, with weaker enrichment in progression (NES: 1.05, p-adj: 0.2889) and resistance (NES: 1.188, p-adj: 0.001). These findings emphasize the differential impact of miR-16 family members on OS biology, suggesting their potential as therapeutic targets for addressing tumor progression and chemoresistance. The results demonstrate significant enrichment of miR-16-1-3p target genes among high-rank genes in OS progression, indicating that these target genes are enriched among genes with upregulated expression in the OS progressive group as compared to the OS indolent group. The target gene sets of miR-16-2-3p and miR-16 did not exhibit statistically significant enrichment in any gene group related to OS progression. Similarly, miR-16-1-3p target genes are significantly enriched among high-rank genes in the OS chemoresistance group suggesting their enrichment among genes with upregulated expression in the OS chemotherapy-resistant group as compared to the OS chemotherapy-responsive group. The same is observed for miR-16 and miR-16-2-3p in the case of OS chemoresistance. Target genes of miR-16 and miR-16-2-3p are enriched among genes with upregulated expression in the OS chemotherapy-resistant group as compared to the OS chemotherapy-responsive group. These results collectively suggest that target genes of miR-16, miR-16-1-3p, and miR-16-2-3p are associated with osteosarcoma chemoresistance while miR-16-1-3p target genes are associated with osteosarcoma progression, supporting their role in these processes. 3.2 GO and KEGG Enrichment Analysis We conducted GSEA to investigate how miR-16-5p, miR-16-1-3p, and miR-16-2-3p might regulate gene expression in chemotherapy-resistant and progressive osteosarcoma patients. Subsequently, we selected the log2 fold change (log2FC) corresponding to the peak of the enrichment score (ES)—the point representing the highest degree of enrichment for the target genes in the ranked list—as the threshold (Fig. 2 ). All target genes with log2FC values exceeding this threshold were considered critical regulatory candidates that may contribute to chemoresistance or disease progression in osteosarcoma (see Supplementary Table 2). Based on this selection, we further conducted GO and KEGG pathway enrichment analyses to explore the biological functions and pathways in which these candidate genes might be involved (Figs. 3 and 4 ). Within the biological process (BP) category(Fig. 3 ), the target genes were markedly enriched in “proteasome-mediated ubiquitin-dependent protein degradation”, highlighting the role of the ubiquitin-proteasome system in removing misfolded proteins and degrading critical cell cycle regulators and apoptosis-related proteins, thereby enabling tumor cells to evade elimination and develop chemoresistance[ 53 , 54 ]. Moreover, the genes were also highly enriched in “positive regulation of cell cycle,” indicating their potential to drive rapid proliferation, which allows tumor cells to bypass cell cycle arrest induced by chemotherapy drugs[ 55 ]. Additionally, the enrichment in Rab protein signaling suggests a role in regulating intracellular vesicle transport and cell motility. Studies have shown that Rab GTPase 3C (RAB3C) contributes to the adaptive and invasive behaviors of cancer cells, and its overexpression is a major factor in CRC chemoresistance[ 56 ]. MiR-16 target genes were also prominently enriched in “response to copper ion”. Copper was reported to enhance chemoresistance by modulating mitochondrial metabolism, lipid biosynthesis, and DNA damage repair pathways, underscoring its critical role in tumor resistance mechanisms[ 57 ]. Within the cellular component (CC) category(Fig. 3 ), target genes were significantly enriched in the “histone methyltransferase complex”. Histone methyltransferases (HMTs) are known to alter chromatin structure and gene expression patterns through epigenetic regulation, either activating resistance-related genes or silencing tumor suppressor genes[ 58 ]. The enrichment in the “ubiquitin ligase complex” probably reflects its role in specifically tagging substrate proteins with ubiquitin to trigger degradation, a process closely linked to tumor resistance[ 59 ]. Additionally, the “nuclear envelope” was another enriched term. Curiously, studies showing that micronucleation, nuclear envelope rupture, and repair during chemotherapy affect cellular division stability and drug sensitivity, representing a key factor in cancer cell resistance [ 60 ]. Furthermore, enrichment in the “PcG protein complex” reveals its involvement in maintaining gene silencing through H3K27 methylation mediated by PRC2 and H2AK119 ubiquitination mediated by PRC1, processes that contribute to cancer progression and therapeutic resistance. In the molecular function (MF) category(Fig. 3 ), the target genes were notably enriched in “SMAD binding,” a core component of the TGF-β signaling pathway, which induces EMT-related transcription factors, reducing tumor cell sensitivity to chemotherapy drugs[ 61 ]. The KEGG analysis further revealed critical pathways associated with chemoresistance (Fig. 4 ). Rap1 signaling was enriched and may enhance tumor cell survival by influencing EMT, angiogenesis, and intercellular adhesion[ 62 , 63 ]. The mTOR pathway was identified as a key regulator of chemoresistance, modulating the PI3K/AKT axis, anti-apoptotic factors, metabolic reprogramming to support tumor cell survival and drug resistance [ 64 ]. Similarly, the MAPK pathway is a complex interconnected signaling cascade, that enhances survival and drug efflux by inducing Mdr-1 and anti-apoptotic protein Bcl-2 expression through downstream transcription factors [ 65 ]. Besides, the HIF-1 pathway facilitates resistance by promoting angiogenesis and metabolic adaptation, further sustaining tumor survival during chemotherapy [ 66 ]. Together, these results from the GO and KEGG analysis emphasize the critical role of miR-16 target genes in mediating tumor chemoresistance mechanisms. In contrast, the target genes of miR-16-1-3p and miR-16-2-3p did not show significant GO and KEGG enrichment results, which may be related to the more dispersed biological functions of their target genes or the more complex signaling pathways involved. 3.3 Kaplan-Meier Analysis of OS Groups with Differential Expression of miRNA Target Genes We conducted a systematic analysis of miRNA target gene expression to explore their regulatory roles in osteosarcoma progression and chemoresistance. First, we selected differentially upregulated target genes (log 2 FC > 0, p < 0.05) (Supplementary Table 3) and normalized their expression values using Z-scores to eliminate intergroup biases. We performed principal component analysis (PCA) on the expression of miRNA target genes to investigate their roles in osteosarcoma progression and chemoresistance. PCA reduces the dimensionality of high-dimensional gene expression data, projecting it onto a few principal components to visually display the distribution patterns of different clinical groups. To further quantify intergroup differences, we applied PERMANOVA (Permutational Multivariate Analysis of Variance). A higher pseudo-F value indicates that the differences between groups are larger relative to the variability within groups. Additionally, p-values are derived from a distribution of randomized permutations and indicate the probability of observing similar or greater differences under random conditions. Statistically significant p-values < 0.05 and higher pseudo-F values together suggest significant differences between groups (Fig. 6 ). Our results showed that PCA for miR-16-1-3p target genes revealed statistically significant differences between the PD pts and PFS group (PERMANOVA: p = 0.001, pseudo-F = 17.67), indicating a clear divergence in overall gene expression patterns between these groups (Fig. 5 A). Similarly, in the chemo-resistant and chemo-sensitive groups, PCA for miR-16-1-3p, miR-16-2-3p, and miR-16 target genes also demonstrated significant differences, with PERMANOVA results as follows: miR-16-1-3p (p = 0.002, pseudo-F = 19.32), miR-16-2-3p (p = 0.002, pseudo-F = 18.45), and miR-16 (p = 0.002, pseudo-F = 24.32) (Fig. 5 B,C,D). The PCA results demonstrated that the overall expression profiles of miRNA target genes effectively distinguish between distinct phenotypic groups, indicating strong discriminatory and predictive capabilities. Subsequently, we calculated the cumulative gene set scores for each sample by summing the Z-scores of the selected genes (log 2 FC > 0, p < 0.05), thereby quantifying the overall expression levels of the target gene set in each sample. The Mann-Whitney U test was then employed to evaluate the differences in cumulative scores between distinct phenotypic groups. The results revealed that the cumulative scores of miR-16-1-3p target genes were significantly higher in the PD pts group compared to the PFS group (Fig. 5 A). Similarly, the cumulative scores of miR-16-1-3p, miR-16-2-3p, and miR-16 target genes were significantly higher in the chemo-resistant group compared to the chemo-sensitive group (Fig. 5 B,C,D). These findings demonstrate that the cumulative scores effectively distinguish between different phenotypic groups and exhibit robust predictive capabilities. While PCA primarily captures the distribution patterns of gene set expression across samples, cumulative scoring focuses on constructing a quantitative metric directly applicable to clinical research, providing a clear and actionable variable for analyzing the relationship between target gene set expression and clinical outcomes. 3.4 Kaplan-Meier Survival Analysis In the survival analysis, we stratified patients into high-score and low-score groups based on the median cumulative Z-scores of each gene set, aiming to evaluate the prognostic significance of miRNA target gene expression. For progression-related prognostic analysis, we utilized data from 85 patients. In chemotherapy resistance-related survival analysis, data from 15 chemotherapy-treated patients were used. Additionally, PCA was employed to validate the separation between high-score and low-score groups in terms of overall gene set expression patterns, revealing distinct clustering of high-score and low-score groups on the PCA plot, which indicated that the cumulative score serves as a reliable metric for differentiating patient gene expression profiles (Fig. 6 A). Survival analysis results demonstrated that patients with high cumulative scores for miR-16-1-3p progression-related target genes had significantly worse progression-free survival and overall survival compared to those in the low-score group (Fig. 6 B). This finding suggests that the cumulative expression of miR-16-1-3p targets reflects both early disease progression and long-term prognosis in osteosarcoma patients. Notably, patients with high scores for miR-16-1-3p, miR-16-2-3p, and miR-16 target genes associated with drug resistance exhibited significantly lower overall survival than those with low scores, highlighting a strong link between elevated expression of these genes and chemotherapy failure. Due to the limited number of chemotherapy-treated cases with complete follow-up data, Cox proportional hazards regression was not applied to the chemotherapy resistance–related curves. However, the consistency of the results across all three miRNA target gene sets supports their prognostic relevance (Fig. 6 B). Notably, the high- and low-score groupings of patients were identical across all three miRNA target gene sets (Fig. 6 A). This observation suggests that, while the target genes of these miRNAs may differ functionally, they consistently capture gene expression patterns characteristic of chemotherapy-resistant patients. 3.5 Overexpressing miR-16-1-3p and miR-16-2-3p increases the chemosensitivity of the OS cell line. To assess how miR-16-1-3p, miR-16-2-3p, and miR-16 affect cisplatin chemosensitivity in vitro, U2OS cells were transduced with PLKO.3G-based lentiviral vectors encoding the indicated miRNAs together with GFP gene as a fluorescent reporter. FACS was used to isolate the brightest reporter-positive cells, thereby enriching for high miRNA expressers. Cell viability was evaluated following a 48-hour incubation with different concentrations of cisplatin, leading to the creation of dose-response curves. Our findings revealed that the overexpression of miR-16-1-3p, miR-16-2-3p, and miR-16 markedly increased sensitivity to cisplatin when compared to both the PLKO-3G and parental control groups (Fig. 7 ). Cells that overexpressed these miRNAs demonstrated a significant decrease in IC50 values, indicating enhanced chemosensitivity. These findings underscore the pivotal roles of miR-16-1-3p, miR-16-2-3p, and miR-16 overexpression in increasing osteosarcoma cell sensitivity to cisplatin, highlighting their potential as powerful therapeutic agents to enhance the effectiveness of osteosarcoma chemotherapy. Next, we examined whether synthetic miR-16-1-3p, and miR-16-2-3p mimics could augment cisplatin sensitivity in OS cells. All three synthetic miRNAs sensitized U2OS cells to cisplatin similarly (Supplementary Figure S2 ). Thus, use of these synthetic miRNAs may also increase osteosarcoma cell sensitivity to cisplatin, potentially improving outcomes in standard chemotherapy. To validate the target-binding activity of miR-16-1-3p and miR-16-2-3p, we constructed a sensor plasmid dsKatushka 2S-miR-16-1/2-3p-Sensor in which six tandem-repeated sequences fully complementary to the seed region of miR-16-1/2-3p—specifically nucleotides 2 to 8—were inserted into the 3′ untranslated region (3′UTR) of the red fluorescent reporter gene dsKatushka2S (see Fig. 8 A). Binding of miR-16-1/2-3p to these sequences leads to translational repression or mRNA degradation, resulting in decreased red fluorescence intensity of dsKatushka2S fluorescent protein. miR-16-1/2-3p and EGFP were co-expressed from the same plasmid, with EGFP serving as a marker for successful transfection (see Materials and Methods). Red fluorescence intensity was measured specifically within the GFP-positive cell population to assess miRNA-mediated repression. In HEK293T (Fig. 8 B) and U2OS (Fig. 8 D) cells, miR-16-1-3p overexpression markedly suppress mean fluorescent intensity (MFI) of dsKatushka signal compared to the scramble-miR control. The GFP-negative population exhibited a background MFI, indicating that the observed reduction was specific. After overexpressing miR-16-2-3p, HEK293T (Fig. 8 C) and U2OS (Fig. 8 E) cells also exhibited lower red fluorescence intensity of the dsKatushka signal compared to the scramble-miR control. This confirms the target-binding and suppressive activity of miR-16-2-3p in a cancer-related context. 3.6 Impact of miR-16-1-3p Overexpression on Osteosarcoma Cell Behavior In Vitro and In Vivo Our bioinformatics analysis revealed that low levels of miR-16-1-3p are significantly associated with osteosarcoma progression and poor patient outcomes. We examined the therapeutic effects of increasing miR-16-1-3p expression in OS cells based on these compelling findings. We increased miR-16-1-3p levels in OS cells to study their effect on tumor growth and patient outcomes in advanced cancer. First, we wanted to assess the ability of OS cells to repopulate. The clonogenic anchorage-dependent growth assay effectively assesses how well individual cells can grow and form colonies. A higher plating efficiency is a clear indicator of enhanced survival and tumorigenicity in vivo[ 67 ]. The colony formation assay clearly demonstrated that the overexpression of miR-16-1-3p led to a substantial decrease in the number of U2OS cell colonies compared to the scrambled miR control (Fig. 9 A). OS-related deaths primarily occur due to metastasis, a process involving the migration and invasion of cancer cells. In most solid tumors, metastasis occurs via 2D collective cell migration (CCM), where groups of connected cells move together[ 68 ]. The "scratch" test, or wound healing assay, is a common lab method used to visualize and measure 2D cell motility under serum-depleted (1% FBS) conditions. This assay enables real-time observation of cell movement in a monolayer for 24 hours after a wound is made. The wound healing assay on U2OS cells that overexpressed hsa-miR-16-1-3p showed a noticeable delay in scratch closure within 24 hours compared to and. The 2D collective migration of these cells was half that of the non-transfected and miR-scramble-overexpressing control cells (Fig. 9 B). Metastatic cells have special traits that allow them to navigate through confined spaces between other cells and the extracellular matrix[ 69 ]. We used the Boyden Chamber assay to assess a fraction of cancer cells able to migrate in a constrained microenvironment. The cells, sized 12 to 20 µm, moved through an 8 µm pore membrane due to the serum concentration difference between the upper and lower chambers. This Transwell 3D migration assay clearly demonstrated that cells overexpressing miR-16-1-3p exhibited significantly reduced confined migration through the membrane pores compared to both the non-transfected and miR-scramble-overexpressing control cells (Fig. 9 C). To evaluate how overexpressing miR-16-1-3p affects human OS cells' tumor formation in vivo, we utilized the well-known in vivo CAM model[ 70 ]. U2OS cells with high levels of miR-16-1-3p formed tumors of much lower weights and volumes than those from cells overexpressing control miR sequences (Fig. 10 ). These results suggest that miR-16-1-3p possesses strong tumor suppressive activities in vivo. 4. Discussion In murine models, the deletion of the miR-15a/miR-16-1 and/or miR-15b/miR-16-2 loci has been shown to trigger the development of multiple forms of leukemia and lymphoma; however, it is particularly noteworthy that these deletions do not lead to the onset of osteosarcoma[ 71 – 73 ]. These findings suggest that the mature strands of miR-16-1 and miR-16-2 play a significant role in osteosarcoma primarily during the later stages of disease progression, rather than at the onset of tumor initiation. Our bioinformatics analysis indicates that lower levels of miR-16-1-3p activity correlate with more progressive osteosarcoma cases. Furthermore, a reduction in the activity of miR-16-5p, miR-16-1-3p, and miR-16-2-3p is linked to chemotherapy-resistant osteosarcoma. Our functional studies revealed that the overexpression of these miRNAs greatly enhanced the sensitivity of human osteosarcoma cell lines to cisplatin treatment (Fig. 7 ). These findings collectively underscore the essential regulatory roles of these miRNAs during the later stages of osteosarcoma progression. Remarkably, the passenger strand, often dismissed as a mere non-functional byproduct, revealed extraordinary regulatory potential in this study. Although its expression levels were significantly lower than those of the dominant strand miR-16-5p, the passenger strands miR-16-1-3p and miR-16-2-3p displayed strong functional effects that contributed to the progression of osteosarcoma and enhanced chemoresistance. Recent studies have shown that many passenger strands are not merely degraded remnants but instead play an active role in intricate regulatory networks within specific cellular contexts[ 74 – 79 ]. Collectively, these findings challenge the traditional belief that the regulatory effects of microRNA are solely determined by their expression levels. Recent studies indicate that the expression levels of microRNA may not be strong indicators of their regulatory functions[ 80 ]. Furthermore, in certain instances, the recruitment of miRNA to the RNA-induced silencing complex (RISC) and consequently its activity show no correlation with its expression levels [ 81 ]. MicroRNA activity is shaped by a dynamic and complex regulatory environment. Key factors influencing the efficiency of microRNA-target binding include the assembly and subcellular localization of RISC complexes. Additionally, post-translational modifications of the Ago2 protein, particularly phosphorylation, play a significant role in determining RISC stability and functional effectiveness[ 14 , 82 ]. Moreover, RNA-binding proteins (RBPs) are crucial in regulating microRNA activity. For example, the RBP Dnd1 interacts with specific mRNAs, effectively preventing certain microRNAs from carrying out their regulatory functions and modifying their activity [ 80 ]. Moreover, the prevalence of target genes significantly impacts microRNA activity. An elevated expression of these target genes can competitively sequester microRNAs, thus reducing their regulatory influence on other targets through a phenomenon known as the sponging effect. This adds another layer of complexity to the already intricate microRNA regulatory network [ 83 ]. Highly expressed circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) compete for binding to microRNAs, thereby diminishing the microRNAs' accessibility to their target genes. Together, these non-coding RNAs are referred to as competing endogenous RNAs (ceRNAs) and serve as natural miRNA sponges. For instance, the interaction of miR-7 with the circular RNA circ7as effectively separates its functional activity from its expression level. This highlights the profound influence that RNA sponge mechanisms have on microRNA-mediated regulation[ 84 , 85 ]. Significantly, ceRNAs have also been identified for miR-16, including lncRNA TTN-AS1[ 86 ] and circRNA Circ-CUX1 [ 87 ]. These discoveries underscore the complex interactions that govern microRNA activity, offering profound insights into the intricate dynamics of microRNA-mediated regulation in osteosarcoma. Here, we harnessed RNA sequencing and clinical data from the TARGET-OS database, focusing on osteosarcoma patients. By integrating clinical gene expression profiles with advanced computational target prediction tools, we conducted a thorough investigation into the regulatory capabilities of miRNA activity in osteosarcoma. Measuring the biological activity of miRNA directly presents noteworthy technical challenges. As a result, researchers increasingly favor indirect assessments through the analysis of target mRNA expression patterns, a method that has gained broad acceptance in the scientific community[ 80 , 81 , 88 – 91 ]. This approach is grounded in the classical miRNA-mediated regulatory mechanism, where miRNAs attach to target mRNAs to inhibit translation or promote degradation. Consequently, an increase in the expression of miRNA target genes usually signifies diminished miRNA activity, while a decrease in target gene expression indicates enhanced miRNA activity. Among the various prediction tools available, TargetScan stands out as a leading platform for high-throughput identification of miRNA targets. It utilizes a combination of factors, including complementarity to miRNA seed sequences, the free energy of miRNA binding, and the conservation of miRNA binding sites, to accurately predict potential binding locations. While the accuracy of these predictions is affected by various factors such as the translational state of the mRNA and the structural context of the binding site, TargetScan provides an effective and trustworthy approach for pinpointing potential targets, even without experimental validation[ 20 ]. This methodology addresses the gap between miRNA expression levels and their functional impacts, offering a comprehensive framework for revealing the biological and pathological roles of miRNAs. Analyzing miRNA expression levels provides useful insights, but these levels don't always reflect miRNA activity in biological processes[ 92 ]. MiRNA activity cannot be determined solely by expression data, as other regulatory factors and environmental influences can affect their functions[ 93 ]. To gain a comprehensive understanding of miRNA roles, a more integrative approach that considers both expression and activity is essential. This complexity underscores the necessity of additional experimental validation to truly decipher the roles of miRNAs in various cellular contexts. For the first time, we proved the target-binding activity of miR-16-1-3p and miR-16-2-3p using a new sensor plasmid called dsKatushka2S-miR-16-1/2-3p-Sensor (Fig. 6 ). This plasmid contains six tandem-repeated sequences complementary to the seed region of miR-16-1/2-3p (nucleotides 2 to 8) inserted into the 3′ UTR of the red fluorescent reporter gene Katushka2S. However, Katushka2S has a slow turnover rate due to its high stability. Hence, the Katushka2S-assisted monitoring of the rapid dynamic expression of miRNA was not convenient. We created a destabilized version of the Katushka2S fluorescent protein (dsKatushka2S) that includes a PEST sequence, allowing for quick imaging of miRNA activity. The feasibility of such an approach was reported using GFP as a fluorophore recently[ 93 ]. Our sensor data bring certainty to cisplatin chemosensitization of miR-16-1/2-3p overexpression in U2OS (Fig. 7 and Supplementary Fig. S2 ) was likely due to their functional effects in cells. Next, we investigated the anti-tumor effects of enhancing miR-16-1-3p expression in osteosarcoma cells to confirm its link to disease progression and poor outcomes. Our findings indicate that overexpressing miR-16-1-3p significantly lowers the self-renewal and long-term growth of osteosarcoma (OS) cells (Fig. 9 A), and restricts their migration (Fig. 9 B) and invasion (Fig. 9 C), traits associated with metastasis. Moreover, we demonstrate using the CAM model that overexpressing miR-16-1-3p effectively suppresses tumor formation in human OS cells in vivo(Fig. 10 ). Consistent with this mechanism, our experimental data demonstrated that miR-16-1-3p can recognize its target sequence and suppress protein translation, while exerting tumor-suppressive effects in osteosarcoma cells, thereby confirming its intrinsic regulatory potential as a functional miRNA. Unlike traditional single-gene studies, our research employs a comprehensive analysis approach that concentrates on complete sets of target genes. The multi-gene composite expression scoring method has been confirmed as a powerful tool for predicting clinical outcomes and therapeutic responses in cancer patients [ 94 , 95 ]. Our research indicates that the expression profiles of target genes for miR-16-5p, miR-16-2-3p, and miR-16-1-3p are closely linked to chemotherapy responsiveness and post-treatment outcomes in osteosarcoma patients. To rigorously assess the robustness of our approach, we randomly selected additional miRNAs and performed GSEA on their corresponding target gene sets. Our findings revealed that not all miRNA target gene sets exhibited significant enrichment. This underscores the precision of the enrichment analysis, revealing that significant results are not merely coincidental but instead demonstrate the functional importance of particular miRNAs within distinct biological contexts. For example, the target gene sets of miR-140-5p and miR-144-3p exhibited notable enrichment in the progressive phenotype group, aligning with earlier research that has identified their involvement in the progression of osteosarcoma[ 96 , 97 ]. These findings are consistent with the current literature, reinforcing the reliability and biological significance of our approach. Our findings indicate that analyzing the expression patterns of miRNA target genes can serve as a powerful predictive tool for determining chemotherapy sensitivity in osteosarcoma. Notably, the gene expression profile targeted by miR-16-1-3p not only serves as a predictor of chemotherapy response but also reveals a significant correlation with the overall progression of the disease, as evidenced by the current disease progression data (Supplementary Table 2). This observation highlights the significant regulatory role of miR-16-1-3p in driving chemoresistance and progression in osteosarcoma. MiR-16-1/2-3p has a role in suppressing tumors in various malignancies[ 98 ]. MiRNAs are an integral part of the p53 network[ 99 , 100 ]. It was reported that miR-16-1 activates p53 and inhibits anti-apoptotic factors like FAP-1, Bcl2, and IGF, leading to cell death[ 101 ]. However, the mechanism of p53 activation was unclear. The presesnt study confirms that miR-16-1-3p can target MDM2 and supports recent findings about circNUDT21, a circular RNA from the NUDT21 gene. CircNUDT21 is an oncogenic circRNA that significantly contributes to bladder cancer progression through the sponging miR-16-1-3p that modulates MDM2/p53 pathway[ 102 ]. Lysyl oxidase (LOX), one of the specific targets of miR-16-1-3p suggested here, has recently been shown to facilitate RANKL-induced osteoclast differentiation and significantly enhance IL-6 production by tumor cells in vitro. Moreover, it plays a crucial role in promoting metastatic osteolytic lesions in breast[ 103 ] and other cancers[ 104 – 107 ]. Abnormal expression and activity of this protein observed in several other cancers gives the rationale for its pharmacological targeting[ 108 , 109 ]. LOX and lysyl oxidase-like 2 (LOXL2), the two most studied members of the lysyl oxidase (LOX(L)) family, regulate the expression of E-Cadherin (CDH1) and vimentin, promoting epithelial-to-mesenchymal transition (EMT) and aiding the metastatic behavior of cancer cells[ 110 ]. LOX and LOXL2 were involved in neovascualrization[ 111 ], and have a critical intracellular role in transcriptional regulation targeting histones, placing this family of proteins within the epigenetic field[ 112 ]. High levels of ATP-binding cassette (ABC) transporter 13 (ABCA13), another highly likely specific target of miR-16-1-3p identified here, in primary tumors were statistically and independently associated with adverse outcomes contributing to drug resistance in ovarian carcinoma, breast cancer, gastric adenocarcinoma and glioblastoma[ 113 – 120 ]. The ABCA13 gene maps to chromosome 7p12.3, a region that contains a locus involved in T-cell tumor invasion and metastasis (INM7), and therefore is a positional candidate for this pathology[ 121 ]. Current evidence indicates that reducing ABCA13 levels makes cancer cells more sensitive to standard chemotherapy drugs like Temsirolimus and Docetaxel, leading to a lower IC50. This indicates that ABCA13 may contribute significantly to the drug resistance observed in stem-like renal cell carcinoma[ 122 ]. On the other hand, three (VEGFA, APLN and FGF2) suggested miR-16-5p-specific targets found here, were also strongly associated with vascularization, which is pivotal for tumor progression. Notably, four mRNAs (for MDM2, PDK1, HIF1A and CDK1 genes) out of eight suggested miR-16-1/2-3p targets found here are related to proliferation/cell cycle regulation[ 102 , 123 – 125 ]. Behind, two were implicated in amino acid transport (SLC38A1)[ 126 ], and chemotaxis (CCL20)[ 127 ].. Collectively, our findings emphasize the significant impact that the expression profiles of target genes regulated by miR-16, as well as miR-16-1-3p and miR-16-2-3p, have on clinical outcomes for patients, illustrating their prognostic potential specifically for OS. The data suggest a robust correlation between these miRNAs and various clinical outcome measures, indicating their possible role as biomarkers. This reinforces the notion that strategically modulating the activity of these critical miRNAs might serve as a promising therapeutic avenue for treating OS. Overall, these insights point towards a greater understanding of the molecular mechanisms behind OS and highlight potential strategies for improving patient management and treatment options. Conclusions This research shows that lower activity of miR-16, particularly miR-16-1-3p, is linked to chemotherapy resistance and osteosarcoma progression. Our research on the TARGET-OS cohort indicates that high expression of miRNA target genes correlates with worse survival rates. Reactivating miR-16-1-3p boosts the effectiveness of cisplatin on osteosarcoma cells and slows tumor growth in in vitro and in vivo studies. Our findings emphasize the overlooked regulatory role of passenger strands like miR-16-1-3p, showing they serve as functional miRNAs instead of just byproducts. These findings offer new insights into how osteosarcoma develops and resists treatment and indicate that miR-16-1-3p could be a valuable biomarker and treatment target. Further validation in larger clinical cohorts and preclinical models will be essential to translate these findings into clinical applications. Abbreviations OS osteosarcoma PD progressive disease PFS progression-free survivors GSEA Gene Set Enrichment Analysis CAM Chick Embryo Chorioallantoic Membrane model MiRNAs MicroRNAs pri-miRNAs primary miRNAs pre-miRNAs miRNA precursors RISC RNA-induced silencing complex 3’-UTRs 3’-untranslated regions miR-16-1/2* miR-16-1/2-3p NSCLC non-small cell lung cancer GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes PCA Principal component analysis PERMANOVA Permutational Multivariate Analysis of Variance KM Kaplan-Meier FBS fetal bovine serum SRB sulforhodamine B IACUC Institutional Animal Care and Use Committee RBPs RNA-binding proteins circRNAs circular RNAs lncRNAs long noncoding RNAs ceRNAs competing endogenous RNAs ES enrichment score NES normalized enrichment score BP biological process CC cellular component MFI mean fluorescent intensity CCM collective cell migration HMTs Histone methyltransferases MF molecular function Declarations Supplementary Information The online version contains supplementary material available at “ ”. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. Author details 1 Institute of Future Biophysics, 141701 Dolgoprudny, Russia 2 Phystech school of biological and medical physics, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia 3 Institute of Cell Biophysics of Russian Academy of Sciences, 142290 Pushchino, Russia Funding This work was supported by the Russian Science Foundation (project No. 23-14-00220). Authors' contributions Wenyu Xue : Formal analysis, Writing- Original draft preparation, Visualization, Investigation. Yuzhe Wang : Writing- Original draft preparation, Investigation, In ovo data analysis. Polina Pugacheva : Visualization, Reviewing and Editing. Anna V. Smirnova: Visualization, Investigation. Roman Chuprov-Netochin: FACS data analysis and visualization, Reviewing and Editing. Margarita Pustovalova : Resources, Reviewing and Editing. Denis V Kuzmin : Funding Acquisition, Reviewing and Editing, Supervision. Sergey Leonov : Conceptualization, Methodology, Resources, Writing- Reviewing and Editing, Supervision. Acknowledgements We express our gratitude for the invaluable editing suggestions provided by Dr. Vadim Maximov. Additionally, we would like to thank the Russian Scientific Fund for its generous financial support for project No. 23-14-00220. References Longhi A, Errani C, De Paolis M, Mercuri M, Bacci G. Primary bone osteosarcoma in the pediatric age: State of the art. Cancer Treatment Reviews 2006, 32:423-436.https://doi.org/10.1016/j.ctrv.2006.05.005 Ottaviani G, Jaffe N. The Epidemiology of Osteosarcoma. In; 2009: 3-1310.1007/978-1-4419-0284-9_1 Isakoff MS, Bielack SS, Meltzer P, Gorlick R. Osteosarcoma: Current Treatment and a Collaborative Pathway to Success. 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Journal of Huazhong University of Science and Technology [Medical Sciences] 2017, 37:30-36.https://doi.org/10.1007/s11596-017-1690-3 Hippe A, Braun SA, Oláh P, Gerber PA, Schorr A, Seeliger S, Holtz S, Jannasch K, Pivarcsi A, Buhren B. EGFR/Ras-induced CCL20 production modulates the tumour microenvironment. British journal of cancer 2020, 123:942-954.https://doi.org/10.1038/s41416-020-0943-2 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:47:20","extension":"html","order_by":57,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":326965,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/713896ecdfdedfcb6464006b.html"},{"id":95818918,"identity":"39b52457-8a84-46c4-a38c-ba97f0f93ea4","added_by":"auto","created_at":"2025-11-13 10:35:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1331994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment of iR-16-1-3p, miR-16-2-3p, and miR-16 target genes in osteosarcoma progression.\u003c/strong\u003e (A) and resistance (B) groups. This figure illustrates the enrichment analysis of miR-16-1-3p, miR-16-2-3p, and miR-16 target genes in OS progression (A) and chemoresistance (B) using Gene Set Enrichment Analysis (GSEA). The enrichment score (green curve) reflects the cumulative distribution of miRNA target genes along the ranked gene list. Black vertical bars represent the positions of the target genes across the ranking. The red dashed line denotes the statistical significance threshold, with p-values and normalized enrichment scores (NES) displayed for each analysis.\u003c/p\u003e\n\u003cp\u003eThe results indicate significant enrichment for miR-16-1-3p in both OS progression (NES: 1.344, p-adj: 0.0033) and resistance (NES: 1.273, p-adj: 0.003), underscoring its dual role in tumor progression and drug resistance. miR-16-2-3p exhibits a strong association with OS resistance (NES: 1.263, p-adj: 0.005) but limited relevance in progression (NES: -0.185, p-adj: 0.1575). Conversely, miR-16 displays modest but consistent regulatory roles, with weaker enrichment in progression (NES: 1.05, p-adj: 0.2889) and resistance (NES: 1.188, p-adj: 0.001). These findings emphasize the differential impact of miR-16 family members on OS biology, suggesting their potential as therapeutic targets for addressing tumor progression and chemoresistance.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/a4dac8322356b16ccf8564ab.jpg"},{"id":95806203,"identity":"d9b5918f-669f-4e3b-9112-eb4f5a8a20e5","added_by":"auto","created_at":"2025-11-13 08:47:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":739361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of target gene sets for miR-16-1-3p, miR-16 and miR-16-2-3p based on the peak enrichment score (ES) in GSEA analysis. \u003c/strong\u003eThe peak ES point in the green enrichment curve was used to determine the corresponding logFC, which served as a threshold for selecting genes with higher log2FC values for subsequent GO and KEGG analysis. For miR-16-1-3p in osteosarcoma progression, the peak ES occurs at rank 6352 with a log2FC of 0.136, while in chemotherapy resistance, the peak ES is at rank 11276 with a log2FC of 0.192. For miR-16-2-3p in chemotherapy resistance, the peak ES is at rank 9648 with a log2FC of 0.369.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/9d46e6193cefc41dbcb0633b.jpg"},{"id":95818683,"identity":"5e740e84-a6a0-4bc9-b035-90ff098706d9","added_by":"auto","created_at":"2025-11-13 10:29:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":735968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO enrichment analysis of miR-16 target genes in the OS chemoresistant group. \u003c/strong\u003eBar lengths indicate the number of enriched genes, and color intensity reflects significance according to the adjusted p-value (p.adiust).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/6f079ee3575fe5a80b70f6cf.jpg"},{"id":95805981,"identity":"00384b33-f71f-4182-b3ce-aaa5dd507d23","added_by":"auto","created_at":"2025-11-13 08:47:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":499124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway enrichment analysis of miR-16 target genes in the OS chemoresistant group. \u003c/strong\u003eBar lengths represent the number of enriched genes, and color intensity reflects significance according to the adjusted p-value (p.adiust).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/b11d8ff729f06a4c8a7d40bb.jpg"},{"id":95806207,"identity":"0a1cf253-86c5-4582-8aca-5e343b6fd4a8","added_by":"auto","created_at":"2025-11-13 08:47:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1403751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA and gene set score analysis demonstrate the role of miR-16-1-3p, miR-16-2-3p, and miR-16 in OS progression and chemotherapy resistance. \u003c/strong\u003ePanels\u003cstrong\u003e (A)\u003c/strong\u003e and \u003cstrong\u003e(C)\u003c/strong\u003e represent PCA of cumulative expression Z-scores of miR-16-1-3p target genes, which are associated with OS progression and chemotherapy resistance respectively, showing distinct group separations in PCA with PERMANOVA test. The gene set cumulative scores for progression and chemo-resistance are significantly higher than those for the indolent and chemo-responsive groups, respectively. Panels\u003cstrong\u003e (B)\u003c/strong\u003e and \u003cstrong\u003e(D)\u003c/strong\u003e display PCA of cumulative expression Z-scores of miR-16 and miR-16-2-3p target genes, which are associated with OS chemotherapy resistance, where PCA with PERMANOVA test highlights clear group segregation, and gene set cumulative scores are significantly elevated in the chemotherapy resistant group. Statistical significance is indicated as ***p \u0026lt; 0.001 or ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/5fdcb359c5d00822037b8b20.jpg"},{"id":95806260,"identity":"394e452e-d5f5-4f75-8c4b-f245ffe06554","added_by":"auto","created_at":"2025-11-13 08:47:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1478242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA and Kaplan-Meier Survival Analysis of miR-16-1-3p, miR-16-2-3p, and miR-16 target gene groups\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) PCA for miR-16-1-3p, miR-16-2-3p, and miR-16 targets associated with OS progression and chemotherapy resistance. Patients with high and low expression group scores are clearly separated. \u003cstrong\u003e(B)\u003c/strong\u003eKaplan–Meier survival curves for miR-16-1-3p, miR-16-2-3p, and miR-16 in patients in the high- and low-scoring groups based on the cumulative expression scores of target genes associated with OS progression and chemotherapy resistance. Significant differences in overall survival and progression-free survival were observed.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/b9effed45dcd372c7b40105b.jpg"},{"id":95806418,"identity":"5b82f88e-d770-4d77-b889-c01c3fd95e0c","added_by":"auto","created_at":"2025-11-13 08:47:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":485746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response curves for cisplatin treatment in microRNA overexpressing U2OS cell lines and control cell lines\u003c/strong\u003e. Cell survival (%) was assessed following cisplatin treatment at various concentrations (log [M]) using standard Sulphorodamnin B (SRB) assay. IC50 values (M) are displayed in the table below the graph, showing the differential sensitivity of each group. U2OS-miR-16 (red), U2OS-hsa-miR-16-1-3p (orange), and U2OS-hsa-miR-16-2-3p (black) have significantly decreased IC50 in comparison with control PLKO.3G-Scr miR (blue) and parental (green) U2OS cells, indicating enhanced sensitivity to cisplatin.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/18aed7f348bf8429fb328c8a.jpg"},{"id":95806134,"identity":"e7ca43b3-0408-41d3-9871-13be98d29afa","added_by":"auto","created_at":"2025-11-13 08:47:17","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":443768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of miR-16-1/2-3p activity using Katushka2S sensor plasmid in HEK293T and U2OS cells. A\u003c/strong\u003e. Structure of miR-16-1/2-3p sensors. Either dsKatushka2S-miR-16-1-3p Sensor (\u003cstrong\u003eB, D\u003c/strong\u003e) or dsKatushka2S-miR-16-2-3p Sensor (\u003cstrong\u003eC,E\u003c/strong\u003e) plasmid was co-transfected with PLKO.3G-hsa-miR-16-1-3p, PLKO.3G-hsa-miR-16-2-3p, or PLKO.3G-miR-scramble expression plasmids into either HEK293T (\u003cstrong\u003eB, C\u003c/strong\u003e) or U2OS (\u003cstrong\u003eD, E\u003c/strong\u003e) cells Forty-eight hours after transfection, cells were harvested for flow cytometry analysis. EGFP fluorescence was used to identify miRNA-expressing cell populations, and Katushka2S fluorescence was used to evaluate miRNA-mediated downregulation of the sensor. Yellow curves: cells transfected only with the PLKO.3G-hsa-miR-16-1/2-3p and respective dsKatushka2S-miR-16-1/2-3p Sensor plasmid; Red curves: cells co-transfected with PLKO.3G-miR-scramble and the sensor plasmid; Blue curves: non-transfected cells were included as a negative control for setting fluorescence channel parameters and background compensation; X-axis represents Katushka2S fluorescence intensity (YL2-A); Y-axis indicates the percentage of cells (normalized to mode). Changes in Katushka2S expression are assessed by the mean fluorescence intensity, reflecting the degree of translational repression mediated by miR-16-1/2-3p.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/6cbcf179220eb050ad54c7dd.jpg"},{"id":95805976,"identity":"2941088e-fff5-453b-b7c2-21b9e9797e61","added_by":"auto","created_at":"2025-11-13 08:47:11","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1726646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of miR-16-1-3p overexpression on tumor growth in the CAM model. \u003c/strong\u003eOn EMD8, 3 × 10^6 U2OS cells stably overexpressing either miR-16-1-3p (hsa-miR-16-2-3p) or a scrambled control (SCR miR) were suspended in serum-free medium : Matrigel (1:1, v/v) mixture solution and implanted onto the CAM within O-rings. On EMD16, tumor nodules were excised and photographed (left). Tumor burden (right) was quantified as volume (mm³) and wet weight (mg). Bars show mean ± s.e.m.; two-sided Student’s t-test. * P \u0026lt; 0.05; ** P \u0026lt; 0.01;\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/6a81ae42c168f7d3b0678a24.jpg"},{"id":99318013,"identity":"9c08642d-9e9a-4871-808f-dcd73b8bd356","added_by":"auto","created_at":"2025-12-31 16:31:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10518673,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/6d4a5a41-6b80-49f1-a6f2-e83aa3399d43.pdf"},{"id":95806347,"identity":"7acdcf7e-6161-48c4-a6e3-c05628f051cb","added_by":"auto","created_at":"2025-11-13 08:47:24","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":158429,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/76e8d3be7f494c8a1ded923c.xlsx"},{"id":95806391,"identity":"06845497-a441-40cd-a30e-5dae003fa127","added_by":"auto","created_at":"2025-11-13 08:47:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":103424,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/3998a3558fc2270a9ef35231.xlsx"},{"id":95806225,"identity":"53c9c26f-b9d0-4d45-b0ad-c301450e83ca","added_by":"auto","created_at":"2025-11-13 08:47:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":236705,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7665473/v1/455801cd315b4afe9e53053c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Loss of miR-16-1-3p Activity Predicts Chemoresistance and Progression in Osteosarcoma: An Integrative Clinicopathologic Study","fulltext":[{"header":"Highlights","content":"\u003cp\u003eLow activity of miR-16-5p, miR-16-1-3p, and miR-16-2-3p, displayed by increased activity of their target genes, may cause chemoresistance in osteosarcoma.\u003c/p\u003e\u003cp\u003emiR-16-1-3p, a low-abundance passenger strand, has important regulatory effects and is linked to disease progression and poor prognosis.\u003c/p\u003e\u003cp\u003eTarget gene expression patterns may serve as potential biomarkers for predicting chemoresistance and clinical outcome.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eOsteosarcoma (OS) is the most common malignant bone and joint tumor, primarily affecting children and adolescents with the highest incidence in the second decade of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the 1970s, the combination of primary tumor resection and adjuvant chemotherapy greatly improved OS survival rates from under 20% to 70%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, the standard treatment strategy combines neoadjuvant (pre-surgical) chemotherapy, surgical resection, and adjuvant chemotherapy. The most effective and widely used combination of chemotherapeutic agents for treating osteosarcoma includes cisplatin, doxorubicin, methotrexate, and ifosfamide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It's important to highlight that there is currently no substantial evidence indicating that adding neoadjuvant chemotherapy to the combination of surgery and adjuvant chemotherapy enhances the outcomes of OS therapy[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Following the significant advancements in OS treatment brought about by chemotherapy 40 to 50 years ago, the mortality rate for OS has now stagnated, showing no signs of further improvement[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, patients with OS diagnosed with metastases face a significantly low 5-year survival rate, averaging around 30% or even lower [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Crucially, patients who are poor responders to neoadjuvant chemotherapy, defined as having less than 90% necrosis in the resected tumor, face significantly lower 10-year survival rates compared to those who respond well, exhibiting more than 90% necrosis. Specifically, the survival rates are starkly contrasting at 47.2% for poor responders compared to an impressive 73.4% for good responders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings highlight the critical need to deepen our understanding of the pathogenesis of osteosarcoma, particularly the mechanisms behind its chemoresistance. This knowledge is essential for making significant advancements in osteosarcoma therapy.\u003c/p\u003e\u003cp\u003eMicroRNAs (miRNAs) are a group of small, non-coding RNAs ranging from 16 to 27 nucleotides in length, with an average size of 20\u0026ndash;22 nucleotides[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These molecules play a crucial role in regulating gene expression after transcription. The canonical miRNA biogenesis begins in the cell nucleus, where miRNA genes are transcribed in lengthy primary miRNAs (pri-miRNAs) by DNA-dependent RNA polymerase II or, less frequently, by DNA-dependent RNA polymerase III. Pri-miRNAs undergo processing by the microprocessor complex, consisting of Drosha and DGCR8, to form hairpin-like miRNA precursors (pre-miRNAs). These precursors are generally transported from the nucleus to the cytoplasm by exportin 5. In the cytoplasm, pre-miRNAs are cleaved by Dicer into short RNA duplexes (21\u0026ndash;22 nucleotide pairs) with 2-nucleotide 3\u0026rsquo;-overhangs at each end. One of the strands (arms) of each RNA duplex is bound by an Argonaute protein and included in the RNA-induced silencing complex (RISC) while the other strand (arm) degrades. The RISC-bound miRNA typically identifies target mRNA sites in the 3\u0026rsquo;-untranslated regions (3\u0026rsquo;-UTRs) by pairing with a complementary seed region of the miRNA, which spans 2 to 8 nucleotides. The identification of specific signals typically results in translation repression and subsequent degradation of the target mRNA[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In most cases, one strand is significantly more loaded onto the RISC and is far more prevalent in the cell compared to the less abundant minor strand. In this case, the abundant strand is named \u0026ldquo;guide\u0026rdquo; or \u0026ldquo;lead\u0026rdquo; miRNA while the minor strand is designated \u0026ldquo;passenger\u0026rdquo; or \u0026ldquo;miR*\u0026rdquo;. This nomenclature is sometimes ambiguous since both strands may be expressed at similar levels and the strands\u0026rsquo; ratio may strongly vary depending on cell type and disease. Therefore, another nomenclature, which classifies miRNAs according to their position in 5\u0026rsquo;- or 3\u0026rsquo;-arms of pre-miRNA, has become widely accepted. 5\u0026rsquo;-arm miRNAs are designated \u0026ldquo;5p\u0026rdquo; while 3\u0026rsquo;-arm miRNAs are designated \u0026ldquo;3p\u0026rdquo;. Noteworthily, the leading miRNA strand can be designated as \"5p\" or \"3p,\" depending on the specific miRNA in question[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven that miRNAs traditionally operate by regulating target gene expression at the mRNA translation and stability levels, identifying relevant miRNA target genes has emerged as a crucial focus in miRNA research. Experimental high-throughput methods for identifying miRNA target genes are intricate, demanding significant labor and resources. Furthermore, these methods often fail to yield a thorough list of relevant target genes and do not eliminate the risk of false positives[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The challenges posed by high-throughput experimental methods make bioinformatics tools like TargetScan, miRDB, and miRanda particularly appealing. These tools leverage the complementarity of miRNA seed sequences and evolutionary conservation to effectively predict miRNA-mRNA interactions[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These bioinformatics tools are essential for identifying target genes of disease-relevant miRNAs using RNA-Seq data from patient samples, especially when high-throughput experimental methods are impractical or unfeasible.\u003c/p\u003e\u003cp\u003eImplication of miRNAs in various aspects of oncogenesis has been well established[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, a growing body of evidence indicates that various miRNAs play a significant role in the metastasis of OS and in the development of chemoresistance[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. MiR-16, which is one of the most studied tumor suppressive miRNAs[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], has been also reported to have tumor suppressive, antimetastatic, and chemotherapy-sensitizing properties in OS [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This microRNA, classified as miR-16-5p, is encoded by two genetic loci: \u003cem\u003eMiR-16-1\u003c/em\u003e at chromosome 13 and \u003cem\u003eMiR-16-2\u003c/em\u003e at chromosome 3. Both loci encode the same lead strand miR-16 but different passenger strands: miR-16-1-3p (miR-16-1*) and miR-16-2-3p (miR-16-2*). Of note, miR-16-1-3p and miR-16-2-3p are active in osteosarcoma and can match or exceed the activities of the lead strand miR-16 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our recent research revealed that the miR-16-1-3p and miR-16-2-3p, exhibit a strong ability to inhibit tumorigenic and metastatic behaviors in lung cancer cells. These miRNAs reduce aggressive traits and increase sensitivity to cisplatin in both radiation-sensitive and resistant A549 non-small cell lung cancer (NSCLC) cells[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This dual function shows their potential in treating lung cancer and enhancing the effectiveness of standard chemotherapy. The findings emphasize the importance of understanding miRNA roles in cancer biology as we look toward more effective treatment strategies. This motivated us to thoroughly investigate the expression of target genes associated with all three strands of miR-16-1/miR-16-2\u0026mdash;namely, miR-16, miR-16-1-3p, and miR-16-2-3p\u0026mdash;in OS samples that contain RNA-Seq data and clinical information from the TARGET-OS database [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our study offers crucial insights in association between activities of the studied miRNAs and OS chemoresistance and progression. We also present the OS-relevant target genes of miR-16, miR-16-1-3p, and miR-16-2-3p, exploring the prognostic significance of their expression levels in OS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Acquisition and pre-processing of data\u003c/h2\u003e\u003cp\u003eThe RNA-seq data were obtained from the TARGET-OS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ocg.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://ocg.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), derived from biopsy specimens of primary tumor lesions collected from 88 osteosarcoma patients. Of the 88 patients, therapeutic agent data were available for 17, who were subsequently assigned to the chemotherapy group. To ensure robust and clinically relevant data analysis, three samples lacking corresponding clinical follow-up information were excluded at the initial stage of pre-processing. To assess the consistency of the dataset and identify potential outliers, Principal Component Analysis (PCA) was conducted using the prcomp package [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Outlier points were identified as points significantly distant from the main sample population in the PCA plot (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), potentially reflecting technical artifacts or biological heterogeneity unrelated to the study objectives.\u003c/p\u003e\u003cp\u003eAfter pre-processing of patient data, 82 patients were classified into a progressive disease patients (PD pts) group (n\u0026thinsp;=\u0026thinsp;37) and Progression free survivor (PFS) group (n\u0026thinsp;=\u0026thinsp;45) based on the clinical follow-up. Additionally, 15 chemotherapy-treated patients were divided into a chemotherapy-resistant group (n\u0026thinsp;=\u0026thinsp;5) and a chemotherapy-responsive group (n\u0026thinsp;=\u0026thinsp;10) according to survival outcomes.\u003c/p\u003e\u003cp\u003eThe RNA-seq libraries were prepared and sequenced using the Illumina HiSeq sequencing platform, ensuring high coverage and accuracy in gene expression profiling. To enable reliable cross-sample comparisons, the raw expression data were normalized using the Transcripts Per Million (TPM) method, accounting for both gene length and sequencing depth. Subsequently, the normalized values were log\u003csub\u003e2\u003c/sub\u003e transformed (log\u003csub\u003e2\u003c/sub\u003e(TPM\u0026thinsp;+\u0026thinsp;1)) to enhance data distribution and facilitate robust downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Screening of miRNA target genes\u003c/h2\u003e\u003cp\u003eTargetScan 7.2 online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_72/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_72/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to predict miRNA target genes. Genes with TargetScan context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;scores \u0026lt;-0.2 indicating strong miRNA-gene binding affinity were considered reliable target genes of miRNAs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\u003cp\u003eDifferential expression analysis (DEA) was performed to compare gene expression profiles between (1) PD vs PFS patients groups and (2) chemotherapy-resistant vs. chemotherapy-responsive patients groups. The analysis was conducted using the limma package[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] in R to generate ranked gene lists based on log fold-change (log\u003csub\u003e2\u003c/sub\u003eFC) values for downstream enrichment analyses. GSEA[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] was performed with the fgsea package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in R to assess the enrichment of predefined gene sets comprising predicted miRNA target genes, within the ranked gene lists. Statistical significance was assessed using 1,000 permutations, with p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 andཛྷNormalized Enrichment Score (NES)ཛྷ\u0026gt;1 considered significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 GO and KEGG Enrichment Analysis\u003c/h2\u003e\u003cp\u003eThe LogFC value corresponding to the maximum Enrichment Score (ES), which marks the point of highest enrichment for the target genes within the ranked list, was selected as the threshold. Genes with LogFC values exceeding this threshold were considered to significantly contribute to the enrichment of the target gene set, highlighting their potential roles in key biological processes or signaling pathways. GO (Gene Ontology) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and KEGG (Kyoto Encyclopedia of Genes and Genomes) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] enrichment analyses were performed using the R package clusterProfiler [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], with adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defined as the cutoff for statistical significance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene Set Scoring Calculation\u003c/h2\u003e\u003cp\u003eThe expression levels of genes within each target gene set were standardized using Z-score normalization. This normalization was performed for each gene across all patients, ensuring comparability by centering the expression levels around a mean of zero with a standard deviation of one. Subsequently, for each patient, the Z-scores of all genes within a given target gene set were summed to compute a cumulative score, reflecting the combined contribution of the target genes for that patient [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The calculations were implemented in Python using the z-score function from the scipy.stats module [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Principal Component Analysis (PCA)\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) was conducted using the PCA module from scikit-learn in Python to reduce the dimensionality of the gene set score data[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].The analysis focused on the first two principal components, which were used to generate scatterplots for visualizing patient distributions. Data preprocessing, including expression value standardization and group assignment, was performed with pandas and numpy libraries. Intergroup differences were quantified using PERMANOVA (Permutational Multivariate Analysis of Variance). All visualizations were created with matplotlib and seaborn[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Kaplan-Meier Survival Analysis\u003c/h2\u003e\u003cp\u003eSurvival analysis was conducted to evaluate the prognostic relevance of cumulative gene set scores. Patients were categorized into high- and low-score groups based on the median cumulative score for each gene set. Kaplan-Meier (KM) survival curves were used to compare overall survival between the two groups, with statistical significance assessed using log-rank tests. The analysis was performed in R, utilizing the survival package for model fitting and the survminer package for visualization [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Survival time was recorded in days, measured from diagnosis to death or the last follow-up.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Plasmids\u003c/h2\u003e\u003cp\u003eA lentiviral vector PLKO.3G was a gift from Christophe Benoist and Diane Mathis (Addgene plasmid # 14748; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:14748\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:14748\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; RRID: Addgene_14748). Lentivirus packaging plasmids pLP1, pVSVG, and pLP2 were from Invitrogen (Thermo Fisher Scientific, USA). A mammalian expression vector pKatushka2S-C (cat.# FP761) encoding far-red fluorescent protein Katushka2S was obtained from Evrogen JSC(Russia). The vector effectively facilitates the formation of fusions at the C-terminus of Katushka2S, enabling the expression of Katushka2S fusions, alongside the standalone expression of Katushka2S in mammalian cells.\u003c/p\u003e\u003cp\u003eLentiviral constructs with a reporter EGFP gene for overexpression of miR-16, miR-16-1-3p, miR-16-2-3p, and Scr miR respectively were created in the following way. Pairs of oligonucleotides miR-16-F and miR-16-R, miR-16-1*-F and miR-16-1*-R, miR-16-2*-F and miR-16-2*-R, and shScrambled-F and shScrambled-R were annealed yielding duplexes miR-16, miR-16-1-3p, miR-16-2-3p, and Scr miR, correspondingly (Supplementary Table\u0026nbsp;1). These duplexes were then cloned in \u003cem\u003eAcc36I\u003c/em\u003e and \u003cem\u003eEcoRI\u003c/em\u003e restriction sites of PLKO.3G creating lentiviral constructs PLKO.3G-miR-16, PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR, respectively. Accuracy of the generated lentiviral constructs was verified by PCR with primers PLKO-Dir and PLKO-Rev and Sanger sequencing with primers PLKO.1 5\u0026rsquo; and PLKO-Rev. The Scr miR sequence was earlier published elsewhere[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo measure miRNA activity directly, Katushka2S genes have been engineered to include miRNA target sequences in their 3\u0026prime; UTR. Using this approach, the reporter lentivirus constructs PLKO-dsKatushka2S-miR-16-1-3p-Sensor and PLKO-dsKatushka2S-miR-16-2-3p-Sensor have been successfully created. These constructs carry the reporter genes dsKatushka2S-miR-16-1-3p-Sensor and dsKatushka 2S-miR-16-2-3p-Sensor (the respective structures of the miR-16-1-3p/miR-16-2-3p Sensors are presented on Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Both genes produce a destabilized variant of the Katushka2S reporter protein (dsKatushka2S), characterized by its rapid turnover rates. This is achieved by incorporating a protein degradation sequence from mouse ornithine decarboxylase, specifically amino acids 422\u0026ndash;461, which had previously been successfully used to destabilize another reporter fluorescent protein, EGFP[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This version also contains six repeats in the 3' untranslated region that match either hsa-miR-16-1* or hsa-miR-16-2*. In these constructs microRNAs bind to the mRNAs of reporter genes in a silencing complex, causing the mRNAs to degrade. An increase in these microRNAs lowers mRNA levels of reporter genes, resulting in a weaker fluorescence signal from dsKatushka2S protein. This approach enables high-resolution imaging of miRNA activity at the single-cell level. Primers and oligonucleotides used in this study are shown in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Cell Culture, Lentivirus Production and Cell Transduction.\u003c/h2\u003e\u003cp\u003eHuman OS cell line U2OS (Russian Cell Culture Collection of Vertebrates, Institute of Cytology, RAS, Russia) and HEK293T cell line (ATCC, USA) were cultured in DMEM complemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM Gln, penicillin (100 I.U./ml), and streptomycin (100 \u0026micro;g/ml).\u003c/p\u003e\u003cp\u003eLentiviruses were produced by co-transfection of each lentiviral plasmid (PLKO.3G-miR-16, PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR, 3.55 \u0026micro;g) with packaging plasmids pLP1 (2.31 \u0026micro;g), pLP2 (0.89 \u0026micro;g), and pVSVG (1.25 \u0026micro;g) using the Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific, USA) in HEK293T cells grown in 100 mm tissue culture plate at 80%-90% confluence. The cell culture medium with lentiviruses was collected in 72 hours after transfections and filtrated through a 0.45 \u0026micro;m PES filter to remove the HEK293T cells.\u003c/p\u003e\u003cp\u003eTo generate stable miR-overexpressing cell lines, human osteosarcoma U2OS cells were transduced with lentiviruses in a 6 well plates at 20%-30% of confluence by adding 2 ml of medium containing lentiviruses. Subsequently, EGFP-positive U2OS cells were sorted by the BIO-RAD S3e cell sorter (BIO-RAD, USA). The sorted EGFP-positive U2OS cells were utilized in the cisplatin sensitivity, colony formation, wound healing, transwell migration and in ovo tests.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Cisplatin Sensitivity Test\u003c/h2\u003e\u003cp\u003eThe cisplatin sensitivity of miRNA-overexpressing U2OS cells was determined using the sulforhodamine B (SRB) assay[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Briefly, cells were seeded in 96-well plates at a density of 10,000 cells per well and treated with serial dilutions of cisplatin (0.1\u0026ndash;80 \u0026micro;M) for 48 hours. After treatment, cells were fixed with 10% trichloroacetic acid (TCA) at 4\u0026deg;C for 1 hour. Fixed cells were washed with distilled water and stained with 0.4% SRB for 30 minutes at room temperature. Excess dye was removed by washing with 1% acetic acid, and the bound dye was dissolved in 10 mM Tris base (pH 10.5). Absorbance was measured at 540 nm using a microplate reader. The IC50 values were calculated applying the GraphPad Prism 8 software by fitting a non-linear regression curve to the dose-response data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Colony formation assay\u003c/h2\u003e\u003cp\u003e200 miR-overexpressing U2OS cells were seeded into each 10 cm culture dish and cultured in complete medium at 37\u0026deg;C with 5% CO₂ for 10 days.Then,cells were fixed with pre-chilled methanol for 15 minutes and stained with Giemsa solution for 30 minutes. Plates were rinsed with PBS, air-dried, and colonies were imaged and counted manually.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Wound healing assay\u003c/h2\u003e\u003cp\u003eMiR-overexpressing U2OS cells were seeded into 6-well plates and grown to approximately 90% confluence. A scratch was introduced through the cell monolayer using a sterile pipette tip. Detached cells were gently removed by washing with PBS, and the medium was replaced with serum-free medium. Images of the wound area were captured at 0 and 24 hours. The wound closure area was quantified using ImageJ software to evaluate cell collective migration capacity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Transwell migration assay\u003c/h2\u003e\u003cp\u003eSerum-starved miR-overexpressing U2OS cells were seeded at a density of 8 \u0026times; 10⁴ cells per well into the upper chambers of Transwell inserts (8 \u0026micro;m pore size, 24-well format, Corning) containing serum-free medium. The lower chambers were filled with complete medium supplemented with 10% fetal bovine serum as a chemoattractant. After incubation at 37\u0026deg;C in a 5% CO₂ atmosphere for 24 hours, cells remaining on the upper surface of the membrane were gently removed using a cotton swabs. Migrated cells on the lower surface were fixed with pre-chilled methanol for 15 minutes and stained with 0.1% crystal violet for 30 minutes. Following PBS washing, three random non-overlaping fields per well were imaged under an inverted microscope using 10x magnification, and the amount of migrated cells were counted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 miRNA Activity Assay\u003c/h2\u003e\u003cp\u003eTo validate of miRNA biogenesis and activation within individual living cells, HEK293T and U2OS cells were seeded into 6-well plates and transfected at 60%\u0026ndash;70% confluency using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer\u0026rsquo;s instructions. Cells were co-transfected with a lentivirus constructs expressing miR-16-1-3p along with EGFP (PLKO.3G-miR-16-1*, PLKO.3G-miR-16-2*, and PLKO.3G-Scr miR), and the Katushka2S reporter plasmid containing the miR-sensor sequence in its 3\u0026prime;UTR (dsKatushka 2S-miR-16-1-3p-Sensor or dsKatushka2S-miR-16-2-3p-Sensor). Forty-eight hours post-transfection, cells were harvested, washed twice with PBS, and subjected to flow cytometry. EGFP fluorescence served as a marker for successful transfection.The Katushka2S fluorescence intensity was assessed specifically in the EGFP-positive population, reflecting the functional activity of miR-16-1/2-3p. Data analysis was performed using FlowJo sorftware.\u003c/p\u003e\u003cp\u003eThe activity of miRNA within individual living cells was also confirmed by miRNA mimics. HEK293 cells were co-transfected with synthetic mimics of miR-16-1-3p, miR-16-2-3p, or scramble sequences together with the corresponding miRNA sensor (dsKatushka 2S-miR-16-1-3p-Sensor or dsKatushka2S-miR-16-2-3p-Sensor) and the EGFP-expressing PLKO.3G plasmid to identify mimic-transfected cells. At 24 hours after transfection, cells were harvested and analyzed by flow cytometry to measure Katushka2S fluorescence in EGFP-positive cell populations as described above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Chick Embryo Chorioallantoic Membrane (CAM) in vivo assay\u003c/h2\u003e\u003cp\u003eFertilized specific pathogen-free eggs were obtained from a certified local hatchery (Trade house Ptichnoe, Ltd., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ptichnoe-td.ru\u003c/span\u003e\u003cspan address=\"https://ptichnoe-td.ru\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Eggs were wiped with sterile water and paper towels, then incubated horizontally at 37\u0026deg;C and 70% relative humidity with automatic rotation, designated as embryonic day 0 (EMD 0). On EMD 3, the air sac and major blood vessels were identified using an egg candler, and approximately 3 mL of albumen was withdrawn to lower the CAM. A circular area of about 2 cm in diameter was marked at the drop site, and the eggshell was opened using a mini rotary saw. The window was sealed with 3M semipermeable film, and eggs were returned to the incubator with the rotation disabled.\u003c/p\u003e\u003cp\u003eOn EMD 8, tumor cells were implanted onto the CAM to evaluate the tumorigenic effect of miR-16-1-3p. A polytetrafluoroethylene O-ring (PTFE O-ring, inner diameter 6 mm, outer diameter 9 mm) was placed on a vascular-rich region, and 25 \u0026micro;L of cell suspension containing 3 \u0026times; 10⁶ cells was seeded within the ring. The suspension was prepared by adding cells to the mixture of serum-free medium and Matrigel (Corning, 356234) at a 1:1 (v/v) to enhance tumor formation. The experimental and the control groups consisted of stable U2OS cells sorted out after transduction cells with lentivirus construct PLKO.3G-miR-16-1* (hsa-miR-16-1-3p), and PLKO.3G-Scr miR (SCR miR), respectively (see section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.9\u003c/span\u003e). After implantation, the window was resealed and incubation continued.\u003c/p\u003e\u003cp\u003eOn EMD 16, CAM tissues were harvested, and tumor images were acquired using a Leica M60 stereomicroscope. After excision, tumors were weighed on an electronic analytical balance to determine the tumor mass. Tumor volume was calculated by measuring the maximal diameter (L) and perpendicular height (H) with a vernier caliper and applying the ellipsoid formula: Volume (mm\u0026sup3;) = (3/4) \u0026times; π \u0026times; (L/2)\u0026sup2; \u0026times; H.\u003c/p\u003e\u003cp\u003eAccording to international legislations, chicken embryos are not classified as live animals before day 17 of incubation[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]; therefore, all experiments completed by EMD 16 were exempt from IACUC approval.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical comparisons were performed using Student\u0026rsquo;s t-test and one-way ANOVA in GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM from three independent experiments. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOur previous findings[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] motivated us to explore the potential links between the endogenous activities of the miRNAs we studied in osteosarcoma (OS) and both the chemoresistance and progression of this disease. Notably, OS progression has a significant correlation with chemoresistance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To evaluate these connections, we employed cutting-edge bioinformatic methods. We extracted the necessary RNA-Seq and clinical data from the TARGET-OS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ocg.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://ocg.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Using TargetScan software[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we compiled a comprehensive list of predicted target genes for miR-16, miR-16-1-3p, and miR-16-2-3p.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Enrichment of miRNA Target Genes\u003c/h2\u003e\u003cp\u003eGSEA [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was conducted to evaluate the enrichment of target genes of miRNA in osteosarcoma progression and chemoresistance groups. The ranked gene list was constructed based on log\u003csub\u003e2\u003c/sub\u003eFC values, representing the differences in gene expression between specific groups. All genes were used as the background set, while high-confidence target genes predicted by TargetScan with TargetScan score \u0026lt; -0.2 were defined as the predefined gene sets. The GSEA results are visualized as enrichment plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On these plots, the rank of x-axis represents the position of each gene in the ranked list, arranged from the most upregulated genes (left) to the most downregulated genes (right) based on log\u003csub\u003e2\u003c/sub\u003eFC values. Each black vertical line on the x-axis indicates the position of a target gene in the ranked list. The density of these black lines reflects the clustering of target genes: a higher density in a specific region suggests that the target genes are predominantly enriched in that region. The enrichment score (ES) of y-axis represents the cumulative running sum of the weighted enrichment score as the ranked gene list is traversed. The enrichment score increases when a target gene is encountered and decreases for non-target genes. The peak of the curve indicates the point where the target gene set is most significantly enriched in the ranked list. The normalized enrichment score (NES) adjusts the ES for gene set size and variability across permutations, enabling cross-comparison between gene sets. An NES\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates that the target gene set is significantly enriched in the top-ranked genes (e.g., upregulated in the progression or chemoresistance group), while an NES \u0026lt; -1 suggests enrichment in the bottom-ranked genes (e.g., downregulated in the progression or chemoresistance group). Statistical significance is determined by the permutation-based p-adj.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results indicate significant enrichment for miR-16-1-3p in both OS progression (NES: 1.344, p-adj: 0.0033) and resistance (NES: 1.273, p-adj: 0.003), underscoring its dual role in tumor progression and drug resistance. miR-16-2-3p exhibits a strong association with OS resistance (NES: 1.263, p-adj: 0.005) but limited relevance in progression (NES: -0.185, p-adj: 0.1575). Conversely, miR-16 displays modest but consistent regulatory roles, with weaker enrichment in progression (NES: 1.05, p-adj: 0.2889) and resistance (NES: 1.188, p-adj: 0.001). These findings emphasize the differential impact of miR-16 family members on OS biology, suggesting their potential as therapeutic targets for addressing tumor progression and chemoresistance.\u003c/p\u003e\u003cp\u003eThe results demonstrate significant enrichment of miR-16-1-3p target genes among high-rank genes in OS progression, indicating that these target genes are enriched among genes with upregulated expression in the OS progressive group as compared to the OS indolent group. The target gene sets of miR-16-2-3p and miR-16 did not exhibit statistically significant enrichment in any gene group related to OS progression. Similarly, miR-16-1-3p target genes are significantly enriched among high-rank genes in the OS chemoresistance group suggesting their enrichment among genes with upregulated expression in the OS chemotherapy-resistant group as compared to the OS chemotherapy-responsive group. The same is observed for miR-16 and miR-16-2-3p in the case of OS chemoresistance. Target genes of miR-16 and miR-16-2-3p are enriched among genes with upregulated expression in the OS chemotherapy-resistant group as compared to the OS chemotherapy-responsive group. These results collectively suggest that target genes of miR-16, miR-16-1-3p, and miR-16-2-3p are associated with osteosarcoma chemoresistance while miR-16-1-3p target genes are associated with osteosarcoma progression, supporting their role in these processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.2 GO and KEGG Enrichment Analysis\u003c/h2\u003e\u003cp\u003eWe conducted GSEA to investigate how miR-16-5p, miR-16-1-3p, and miR-16-2-3p might regulate gene expression in chemotherapy-resistant and progressive osteosarcoma patients. Subsequently, we selected the log2 fold change (log2FC) corresponding to the peak of the enrichment score (ES)\u0026mdash;the point representing the highest degree of enrichment for the target genes in the ranked list\u0026mdash;as the threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All target genes with log2FC values exceeding this threshold were considered critical regulatory candidates that may contribute to chemoresistance or disease progression in osteosarcoma (see Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on this selection, we further conducted GO and KEGG pathway enrichment analyses to explore the biological functions and pathways in which these candidate genes might be involved (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin the biological process (BP) category(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the target genes were markedly enriched in \u0026ldquo;proteasome-mediated ubiquitin-dependent protein degradation\u0026rdquo;, highlighting the role of the ubiquitin-proteasome system in removing misfolded proteins and degrading critical cell cycle regulators and apoptosis-related proteins, thereby enabling tumor cells to evade elimination and develop chemoresistance[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Moreover, the genes were also highly enriched in \u0026ldquo;positive regulation of cell cycle,\u0026rdquo; indicating their potential to drive rapid proliferation, which allows tumor cells to bypass cell cycle arrest induced by chemotherapy drugs[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Additionally, the enrichment in Rab protein signaling suggests a role in regulating intracellular vesicle transport and cell motility. Studies have shown that Rab GTPase 3C (RAB3C) contributes to the adaptive and invasive behaviors of cancer cells, and its overexpression is a major factor in CRC chemoresistance[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. MiR-16 target genes were also prominently enriched in \u0026ldquo;response to copper ion\u0026rdquo;. Copper was reported to enhance chemoresistance by modulating mitochondrial metabolism, lipid biosynthesis, and DNA damage repair pathways, underscoring its critical role in tumor resistance mechanisms[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Within the cellular component (CC) category(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), target genes were significantly enriched in the \u0026ldquo;histone methyltransferase complex\u0026rdquo;. Histone methyltransferases (HMTs) are known to alter chromatin structure and gene expression patterns through epigenetic regulation, either activating resistance-related genes or silencing tumor suppressor genes[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The enrichment in the \u0026ldquo;ubiquitin ligase complex\u0026rdquo; probably reflects its role in specifically tagging substrate proteins with ubiquitin to trigger degradation, a process closely linked to tumor resistance[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additionally, the \u0026ldquo;nuclear envelope\u0026rdquo; was another enriched term. Curiously, studies showing that micronucleation, nuclear envelope rupture, and repair during chemotherapy affect cellular division stability and drug sensitivity, representing a key factor in cancer cell resistance [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Furthermore, enrichment in the \u0026ldquo;PcG protein complex\u0026rdquo; reveals its involvement in maintaining gene silencing through H3K27 methylation mediated by PRC2 and H2AK119 ubiquitination mediated by PRC1, processes that contribute to cancer progression and therapeutic resistance. In the molecular function (MF) category(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the target genes were notably enriched in \u0026ldquo;SMAD binding,\u0026rdquo; a core component of the TGF-β signaling pathway, which induces EMT-related transcription factors, reducing tumor cell sensitivity to chemotherapy drugs[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe KEGG analysis further revealed critical pathways associated with chemoresistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Rap1 signaling was enriched and may enhance tumor cell survival by influencing EMT, angiogenesis, and intercellular adhesion[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The mTOR pathway was identified as a key regulator of chemoresistance, modulating the PI3K/AKT axis, anti-apoptotic factors, metabolic reprogramming to support tumor cell survival and drug resistance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Similarly, the MAPK pathway is a complex interconnected signaling cascade, that enhances survival and drug efflux by inducing Mdr-1 and anti-apoptotic protein Bcl-2 expression through downstream transcription factors [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Besides, the HIF-1 pathway facilitates resistance by promoting angiogenesis and metabolic adaptation, further sustaining tumor survival during chemotherapy [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTogether, these results from the GO and KEGG analysis emphasize the critical role of miR-16 target genes in mediating tumor chemoresistance mechanisms. In contrast, the target genes of miR-16-1-3p and miR-16-2-3p did not show significant GO and KEGG enrichment results, which may be related to the more dispersed biological functions of their target genes or the more complex signaling pathways involved.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Kaplan-Meier Analysis of OS Groups with Differential Expression of miRNA Target Genes\u003c/h2\u003e\u003cp\u003eWe conducted a systematic analysis of miRNA target gene expression to explore their regulatory roles in osteosarcoma progression and chemoresistance. First, we selected differentially upregulated target genes (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;3) and normalized their expression values using Z-scores to eliminate intergroup biases. We performed principal component analysis (PCA) on the expression of miRNA target genes to investigate their roles in osteosarcoma progression and chemoresistance. PCA reduces the dimensionality of high-dimensional gene expression data, projecting it onto a few principal components to visually display the distribution patterns of different clinical groups. To further quantify intergroup differences, we applied PERMANOVA (Permutational Multivariate Analysis of Variance). A higher pseudo-F value indicates that the differences between groups are larger relative to the variability within groups. Additionally, p-values are derived from a distribution of randomized permutations and indicate the probability of observing similar or greater differences under random conditions. Statistically significant p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and higher pseudo-F values together suggest significant differences between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Our results showed that PCA for miR-16-1-3p target genes revealed statistically significant differences between the PD pts and PFS group (PERMANOVA: p\u0026thinsp;=\u0026thinsp;0.001, pseudo-F\u0026thinsp;=\u0026thinsp;17.67), indicating a clear divergence in overall gene expression patterns between these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Similarly, in the chemo-resistant and chemo-sensitive groups, PCA for miR-16-1-3p, miR-16-2-3p, and miR-16 target genes also demonstrated significant differences, with PERMANOVA results as follows: miR-16-1-3p (p\u0026thinsp;=\u0026thinsp;0.002, pseudo-F\u0026thinsp;=\u0026thinsp;19.32), miR-16-2-3p (p\u0026thinsp;=\u0026thinsp;0.002, pseudo-F\u0026thinsp;=\u0026thinsp;18.45), and miR-16 (p\u0026thinsp;=\u0026thinsp;0.002, pseudo-F\u0026thinsp;=\u0026thinsp;24.32) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB,C,D). The PCA results demonstrated that the overall expression profiles of miRNA target genes effectively distinguish between distinct phenotypic groups, indicating strong discriminatory and predictive capabilities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we calculated the cumulative gene set scores for each sample by summing the Z-scores of the selected genes (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), thereby quantifying the overall expression levels of the target gene set in each sample. The Mann-Whitney U test was then employed to evaluate the differences in cumulative scores between distinct phenotypic groups. The results revealed that the cumulative scores of miR-16-1-3p target genes were significantly higher in the PD pts group compared to the PFS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Similarly, the cumulative scores of miR-16-1-3p, miR-16-2-3p, and miR-16 target genes were significantly higher in the chemo-resistant group compared to the chemo-sensitive group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB,C,D). These findings demonstrate that the cumulative scores effectively distinguish between different phenotypic groups and exhibit robust predictive capabilities. While PCA primarily captures the distribution patterns of gene set expression across samples, cumulative scoring focuses on constructing a quantitative metric directly applicable to clinical research, providing a clear and actionable variable for analyzing the relationship between target gene set expression and clinical outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Kaplan-Meier Survival Analysis\u003c/h2\u003e\u003cp\u003eIn the survival analysis, we stratified patients into high-score and low-score groups based on the median cumulative Z-scores of each gene set, aiming to evaluate the prognostic significance of miRNA target gene expression. For progression-related prognostic analysis, we utilized data from 85 patients. In chemotherapy resistance-related survival analysis, data from 15 chemotherapy-treated patients were used. Additionally, PCA was employed to validate the separation between high-score and low-score groups in terms of overall gene set expression patterns, revealing distinct clustering of high-score and low-score groups on the PCA plot, which indicated that the cumulative score serves as a reliable metric for differentiating patient gene expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Survival analysis results demonstrated that patients with high cumulative scores for miR-16-1-3p progression-related target genes had significantly worse progression-free survival and overall survival compared to those in the low-score group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This finding suggests that the cumulative expression of miR-16-1-3p targets reflects both early disease progression and long-term prognosis in osteosarcoma patients. Notably, patients with high scores for miR-16-1-3p, miR-16-2-3p, and miR-16 target genes associated with drug resistance exhibited significantly lower overall survival than those with low scores, highlighting a strong link between elevated expression of these genes and chemotherapy failure. Due to the limited number of chemotherapy-treated cases with complete follow-up data, Cox proportional hazards regression was not applied to the chemotherapy resistance\u0026ndash;related curves. However, the consistency of the results across all three miRNA target gene sets supports their prognostic relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, the high- and low-score groupings of patients were identical across all three miRNA target gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This observation suggests that, while the target genes of these miRNAs may differ functionally, they consistently capture gene expression patterns characteristic of chemotherapy-resistant patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Overexpressing miR-16-1-3p and miR-16-2-3p increases the chemosensitivity of the OS cell line.\u003c/h2\u003e\u003cp\u003eTo assess how miR-16-1-3p, miR-16-2-3p, and miR-16 affect cisplatin chemosensitivity in vitro, U2OS cells were transduced with PLKO.3G-based lentiviral vectors encoding the indicated miRNAs together with GFP gene as a fluorescent reporter. FACS was used to isolate the brightest reporter-positive cells, thereby enriching for high miRNA expressers. Cell viability was evaluated following a 48-hour incubation with different concentrations of cisplatin, leading to the creation of dose-response curves. Our findings revealed that the overexpression of miR-16-1-3p, miR-16-2-3p, and miR-16 markedly increased sensitivity to cisplatin when compared to both the PLKO-3G and parental control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Cells that overexpressed these miRNAs demonstrated a significant decrease in IC50 values, indicating enhanced chemosensitivity. These findings underscore the pivotal roles of miR-16-1-3p, miR-16-2-3p, and miR-16 overexpression in increasing osteosarcoma cell sensitivity to cisplatin, highlighting their potential as powerful therapeutic agents to enhance the effectiveness of osteosarcoma chemotherapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we examined whether synthetic miR-16-1-3p, and miR-16-2-3p mimics could augment cisplatin sensitivity in OS cells. All three synthetic miRNAs sensitized U2OS cells to cisplatin similarly (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Thus, use of these synthetic miRNAs may also increase osteosarcoma cell sensitivity to cisplatin, potentially improving outcomes in standard chemotherapy.\u003c/p\u003e\u003cp\u003eTo validate the target-binding activity of miR-16-1-3p and miR-16-2-3p, we constructed a sensor plasmid dsKatushka 2S-miR-16-1/2-3p-Sensor in which six tandem-repeated sequences fully complementary to the seed region of miR-16-1/2-3p\u0026mdash;specifically nucleotides 2 to 8\u0026mdash;were inserted into the 3\u0026prime; untranslated region (3\u0026prime;UTR) of the red fluorescent reporter gene dsKatushka2S (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Binding of miR-16-1/2-3p to these sequences leads to translational repression or mRNA degradation, resulting in decreased red fluorescence intensity of dsKatushka2S fluorescent protein. miR-16-1/2-3p and EGFP were co-expressed from the same plasmid, with EGFP serving as a marker for successful transfection (see Materials and Methods). Red fluorescence intensity was measured specifically within the GFP-positive cell population to assess miRNA-mediated repression. In HEK293T (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) and U2OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD) cells, miR-16-1-3p overexpression markedly suppress mean fluorescent intensity (MFI) of dsKatushka signal compared to the scramble-miR control. The GFP-negative population exhibited a background MFI, indicating that the observed reduction was specific. After overexpressing miR-16-2-3p, HEK293T (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC) and U2OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) cells also exhibited lower red fluorescence intensity of the dsKatushka signal compared to the scramble-miR control. This confirms the target-binding and suppressive activity of miR-16-2-3p in a cancer-related context.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Impact of miR-16-1-3p Overexpression on Osteosarcoma Cell Behavior In Vitro and In Vivo\u003c/h2\u003e\u003cp\u003eOur bioinformatics analysis revealed that low levels of miR-16-1-3p are significantly associated with osteosarcoma progression and poor patient outcomes. We examined the therapeutic effects of increasing miR-16-1-3p expression in OS cells based on these compelling findings. We increased miR-16-1-3p levels in OS cells to study their effect on tumor growth and patient outcomes in advanced cancer.\u003c/p\u003e\u003cp\u003eFirst, we wanted to assess the ability of OS cells to repopulate. The clonogenic anchorage-dependent growth assay effectively assesses how well individual cells can grow and form colonies. A higher plating efficiency is a clear indicator of enhanced survival and tumorigenicity in vivo[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The colony formation assay clearly demonstrated that the overexpression of miR-16-1-3p led to a substantial decrease in the number of U2OS cell colonies compared to the scrambled miR control (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eOS-related deaths primarily occur due to metastasis, a process involving the migration and invasion of cancer cells. In most solid tumors, metastasis occurs via 2D collective cell migration (CCM), where groups of connected cells move together[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The \"scratch\" test, or wound healing assay, is a common lab method used to visualize and measure 2D cell motility under serum-depleted (1% FBS) conditions. This assay enables real-time observation of cell movement in a monolayer for 24 hours after a wound is made. The wound healing assay on U2OS cells that overexpressed hsa-miR-16-1-3p showed a noticeable delay in scratch closure within 24 hours compared to and. The 2D collective migration of these cells was half that of the non-transfected and miR-scramble-overexpressing control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eMetastatic cells have special traits that allow them to navigate through confined spaces between other cells and the extracellular matrix[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. We used the Boyden Chamber assay to assess a fraction of cancer cells able to migrate in a constrained microenvironment. The cells, sized 12 to 20 \u0026micro;m, moved through an 8 \u0026micro;m pore membrane due to the serum concentration difference between the upper and lower chambers. This Transwell 3D migration assay clearly demonstrated that cells overexpressing miR-16-1-3p exhibited significantly reduced confined migration through the membrane pores compared to both the non-transfected and miR-scramble-overexpressing control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate how overexpressing miR-16-1-3p affects human OS cells' tumor formation in vivo, we utilized the well-known in vivo CAM model[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. U2OS cells with high levels of miR-16-1-3p formed tumors of much lower weights and volumes than those from cells overexpressing control miR sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These results suggest that miR-16-1-3p possesses strong tumor suppressive activities in vivo.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn murine models, the deletion of the miR-15a/miR-16-1 and/or miR-15b/miR-16-2 loci has been shown to trigger the development of multiple forms of leukemia and lymphoma; however, it is particularly noteworthy that these deletions do not lead to the onset of osteosarcoma[\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. These findings suggest that the mature strands of miR-16-1 and miR-16-2 play a significant role in osteosarcoma primarily during the later stages of disease progression, rather than at the onset of tumor initiation.\u003c/p\u003e\u003cp\u003eOur bioinformatics analysis indicates that lower levels of miR-16-1-3p activity correlate with more progressive osteosarcoma cases. Furthermore, a reduction in the activity of miR-16-5p, miR-16-1-3p, and miR-16-2-3p is linked to chemotherapy-resistant osteosarcoma. Our functional studies revealed that the overexpression of these miRNAs greatly enhanced the sensitivity of human osteosarcoma cell lines to cisplatin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings collectively underscore the essential regulatory roles of these miRNAs during the later stages of osteosarcoma progression.\u003c/p\u003e\u003cp\u003eRemarkably, the passenger strand, often dismissed as a mere non-functional byproduct, revealed extraordinary regulatory potential in this study. Although its expression levels were significantly lower than those of the dominant strand miR-16-5p, the passenger strands miR-16-1-3p and miR-16-2-3p displayed strong functional effects that contributed to the progression of osteosarcoma and enhanced chemoresistance. Recent studies have shown that many passenger strands are not merely degraded remnants but instead play an active role in intricate regulatory networks within specific cellular contexts[\u003cspan additionalcitationids=\"CR75 CR76 CR77 CR78\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Collectively, these findings challenge the traditional belief that the regulatory effects of microRNA are solely determined by their expression levels.\u003c/p\u003e\u003cp\u003eRecent studies indicate that the expression levels of microRNA may not be strong indicators of their regulatory functions[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Furthermore, in certain instances, the recruitment of miRNA to the RNA-induced silencing complex (RISC) and consequently its activity show no correlation with its expression levels [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. MicroRNA activity is shaped by a dynamic and complex regulatory environment. Key factors influencing the efficiency of microRNA-target binding include the assembly and subcellular localization of RISC complexes. Additionally, post-translational modifications of the Ago2 protein, particularly phosphorylation, play a significant role in determining RISC stability and functional effectiveness[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Moreover, RNA-binding proteins (RBPs) are crucial in regulating microRNA activity. For example, the RBP Dnd1 interacts with specific mRNAs, effectively preventing certain microRNAs from carrying out their regulatory functions and modifying their activity [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Moreover, the prevalence of target genes significantly impacts microRNA activity. An elevated expression of these target genes can competitively sequester microRNAs, thus reducing their regulatory influence on other targets through a phenomenon known as the sponging effect. This adds another layer of complexity to the already intricate microRNA regulatory network [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Highly expressed circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) compete for binding to microRNAs, thereby diminishing the microRNAs' accessibility to their target genes. Together, these non-coding RNAs are referred to as competing endogenous RNAs (ceRNAs) and serve as natural miRNA sponges. For instance, the interaction of miR-7 with the circular RNA circ7as effectively separates its functional activity from its expression level. This highlights the profound influence that RNA sponge mechanisms have on microRNA-mediated regulation[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Significantly, ceRNAs have also been identified for miR-16, including lncRNA TTN-AS1[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] and circRNA Circ-CUX1 [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. These discoveries underscore the complex interactions that govern microRNA activity, offering profound insights into the intricate dynamics of microRNA-mediated regulation in osteosarcoma.\u003c/p\u003e\u003cp\u003eHere, we harnessed RNA sequencing and clinical data from the TARGET-OS database, focusing on osteosarcoma patients. By integrating clinical gene expression profiles with advanced computational target prediction tools, we conducted a thorough investigation into the regulatory capabilities of miRNA activity in osteosarcoma. Measuring the biological activity of miRNA directly presents noteworthy technical challenges. As a result, researchers increasingly favor indirect assessments through the analysis of target mRNA expression patterns, a method that has gained broad acceptance in the scientific community[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan additionalcitationids=\"CR89 CR90\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. This approach is grounded in the classical miRNA-mediated regulatory mechanism, where miRNAs attach to target mRNAs to inhibit translation or promote degradation. Consequently, an increase in the expression of miRNA target genes usually signifies diminished miRNA activity, while a decrease in target gene expression indicates enhanced miRNA activity. Among the various prediction tools available, TargetScan stands out as a leading platform for high-throughput identification of miRNA targets. It utilizes a combination of factors, including complementarity to miRNA seed sequences, the free energy of miRNA binding, and the conservation of miRNA binding sites, to accurately predict potential binding locations. While the accuracy of these predictions is affected by various factors such as the translational state of the mRNA and the structural context of the binding site, TargetScan provides an effective and trustworthy approach for pinpointing potential targets, even without experimental validation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This methodology addresses the gap between miRNA expression levels and their functional impacts, offering a comprehensive framework for revealing the biological and pathological roles of miRNAs.\u003c/p\u003e\u003cp\u003eAnalyzing miRNA expression levels provides useful insights, but these levels don't always reflect miRNA activity in biological processes[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. MiRNA activity cannot be determined solely by expression data, as other regulatory factors and environmental influences can affect their functions[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. To gain a comprehensive understanding of miRNA roles, a more integrative approach that considers both expression and activity is essential. This complexity underscores the necessity of additional experimental validation to truly decipher the roles of miRNAs in various cellular contexts.\u003c/p\u003e\u003cp\u003eFor the first time, we proved the target-binding activity of miR-16-1-3p and miR-16-2-3p using a new sensor plasmid called dsKatushka2S-miR-16-1/2-3p-Sensor (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This plasmid contains six tandem-repeated sequences complementary to the seed region of miR-16-1/2-3p (nucleotides 2 to 8) inserted into the 3\u0026prime; UTR of the red fluorescent reporter gene Katushka2S. However, Katushka2S has a slow turnover rate due to its high stability. Hence, the Katushka2S-assisted monitoring of the rapid dynamic expression of miRNA was not convenient. We created a destabilized version of the Katushka2S fluorescent protein (dsKatushka2S) that includes a PEST sequence, allowing for quick imaging of miRNA activity. The feasibility of such an approach was reported using GFP as a fluorophore recently[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Our sensor data bring certainty to cisplatin chemosensitization of miR-16-1/2-3p overexpression in U2OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) was likely due to their functional effects in cells. Next, we investigated the anti-tumor effects of enhancing miR-16-1-3p expression in osteosarcoma cells to confirm its link to disease progression and poor outcomes. Our findings indicate that overexpressing miR-16-1-3p significantly lowers the self-renewal and long-term growth of osteosarcoma (OS) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), and restricts their migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), traits associated with metastasis. Moreover, we demonstrate using the CAM model that overexpressing miR-16-1-3p effectively suppresses tumor formation in human OS cells in vivo(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Consistent with this mechanism, our experimental data demonstrated that miR-16-1-3p can recognize its target sequence and suppress protein translation, while exerting tumor-suppressive effects in osteosarcoma cells, thereby confirming its intrinsic regulatory potential as a functional miRNA.\u003c/p\u003e\u003cp\u003eUnlike traditional single-gene studies, our research employs a comprehensive analysis approach that concentrates on complete sets of target genes. The multi-gene composite expression scoring method has been confirmed as a powerful tool for predicting clinical outcomes and therapeutic responses in cancer patients [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Our research indicates that the expression profiles of target genes for miR-16-5p, miR-16-2-3p, and miR-16-1-3p are closely linked to chemotherapy responsiveness and post-treatment outcomes in osteosarcoma patients. To rigorously assess the robustness of our approach, we randomly selected additional miRNAs and performed GSEA on their corresponding target gene sets. Our findings revealed that not all miRNA target gene sets exhibited significant enrichment. This underscores the precision of the enrichment analysis, revealing that significant results are not merely coincidental but instead demonstrate the functional importance of particular miRNAs within distinct biological contexts. For example, the target gene sets of miR-140-5p and miR-144-3p exhibited notable enrichment in the progressive phenotype group, aligning with earlier research that has identified their involvement in the progression of osteosarcoma[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. These findings are consistent with the current literature, reinforcing the reliability and biological significance of our approach. Our findings indicate that analyzing the expression patterns of miRNA target genes can serve as a powerful predictive tool for determining chemotherapy sensitivity in osteosarcoma. Notably, the gene expression profile targeted by miR-16-1-3p not only serves as a predictor of chemotherapy response but also reveals a significant correlation with the overall progression of the disease, as evidenced by the current disease progression data (Supplementary Table\u0026nbsp;2). This observation highlights the significant regulatory role of miR-16-1-3p in driving chemoresistance and progression in osteosarcoma. MiR-16-1/2-3p has a role in suppressing tumors in various malignancies[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. MiRNAs are an integral part of the p53 network[\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. It was reported that miR-16-1 activates p53 and inhibits anti-apoptotic factors like FAP-1, Bcl2, and IGF, leading to cell death[\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. However, the mechanism of p53 activation was unclear. The presesnt study confirms that miR-16-1-3p can target MDM2 and supports recent findings about circNUDT21, a circular RNA from the NUDT21 gene. CircNUDT21 is an oncogenic circRNA that significantly contributes to bladder cancer progression through the sponging miR-16-1-3p that modulates MDM2/p53 pathway[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLysyl oxidase (LOX), one of the specific targets of miR-16-1-3p suggested here, has recently been shown to facilitate RANKL-induced osteoclast differentiation and significantly enhance IL-6 production by tumor cells in vitro. Moreover, it plays a crucial role in promoting metastatic osteolytic lesions in breast[\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e] and other cancers[\u003cspan additionalcitationids=\"CR105 CR106\" citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Abnormal expression and activity of this protein observed in several other cancers gives the rationale for its pharmacological targeting[\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. LOX and lysyl oxidase-like 2 (LOXL2), the two most studied members of the lysyl oxidase (LOX(L)) family, regulate the expression of E-Cadherin (CDH1) and vimentin, promoting epithelial-to-mesenchymal transition (EMT) and aiding the metastatic behavior of cancer cells[\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. LOX and LOXL2 were involved in neovascualrization[\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], and have a critical intracellular role in transcriptional regulation targeting histones, placing this family of proteins within the epigenetic field[\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHigh levels of ATP-binding cassette (ABC) transporter 13 (ABCA13), another highly likely specific target of miR-16-1-3p identified here, in primary tumors were statistically and independently associated with adverse outcomes contributing to drug resistance in ovarian carcinoma, breast cancer, gastric adenocarcinoma and glioblastoma[\u003cspan additionalcitationids=\"CR114 CR115 CR116 CR117 CR118 CR119\" citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. The ABCA13 gene maps to chromosome 7p12.3, a region that contains a locus involved in T-cell tumor invasion and metastasis (INM7), and therefore is a positional candidate for this pathology[\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Current evidence indicates that reducing ABCA13 levels makes cancer cells more sensitive to standard chemotherapy drugs like Temsirolimus and Docetaxel, leading to a lower IC50. This indicates that ABCA13 may contribute significantly to the drug resistance observed in stem-like renal cell carcinoma[\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. On the other hand, three (VEGFA, APLN and FGF2) suggested miR-16-5p-specific targets found here, were also strongly associated with vascularization, which is pivotal for tumor progression. Notably, four mRNAs (for MDM2, PDK1, HIF1A and CDK1 genes) out of eight suggested miR-16-1/2-3p targets found here are related to proliferation/cell cycle regulation[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan additionalcitationids=\"CR124\" citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. Behind, two were implicated in amino acid transport (SLC38A1)[\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e], and chemotaxis (CCL20)[\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e].. Collectively, our findings emphasize the significant impact that the expression profiles of target genes regulated by miR-16, as well as miR-16-1-3p and miR-16-2-3p, have on clinical outcomes for patients, illustrating their prognostic potential specifically for OS. The data suggest a robust correlation between these miRNAs and various clinical outcome measures, indicating their possible role as biomarkers. This reinforces the notion that strategically modulating the activity of these critical miRNAs might serve as a promising therapeutic avenue for treating OS. Overall, these insights point towards a greater understanding of the molecular mechanisms behind OS and highlight potential strategies for improving patient management and treatment options.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research shows that lower activity of miR-16, particularly miR-16-1-3p, is linked to chemotherapy resistance and osteosarcoma progression. Our research on the TARGET-OS cohort indicates that high expression of miRNA target genes correlates with worse survival rates. Reactivating miR-16-1-3p boosts the effectiveness of cisplatin on osteosarcoma cells and slows tumor growth in in vitro and in vivo studies. Our findings emphasize the overlooked regulatory role of passenger strands like miR-16-1-3p, showing they serve as functional miRNAs instead of just byproducts. These findings offer new insights into how osteosarcoma develops and resists treatment and indicate that miR-16-1-3p could be a valuable biomarker and treatment target. Further validation in larger clinical cohorts and preclinical models will be essential to translate these findings into clinical applications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; osteosarcoma\u003c/p\u003e\n\u003cp\u003ePD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; progressive disease\u003c/p\u003e\n\u003cp\u003ePFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;progression-free survivors\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eCAM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Chick Embryo Chorioallantoic Membrane model\u003c/p\u003e\n\u003cp\u003eMiRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; MicroRNAs\u003c/p\u003e\n\u003cp\u003epri-miRNAs \u0026nbsp; \u0026nbsp;primary miRNAs\u003c/p\u003e\n\u003cp\u003epre-miRNAs \u0026nbsp; \u0026nbsp;miRNA precursors\u003c/p\u003e\n\u003cp\u003eRISC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RNA-induced silencing complex\u003c/p\u003e\n\u003cp\u003e3\u0026rsquo;-UTRs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u0026rsquo;-untranslated regions\u003c/p\u003e\n\u003cp\u003emiR-16-1/2* \u0026nbsp; \u0026nbsp;miR-16-1/2-3p\u003c/p\u003e\n\u003cp\u003eNSCLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Principal component analysis\u003c/p\u003e\n\u003cp\u003ePERMANOVA \u0026nbsp;Permutational Multivariate Analysis of Variance\u003c/p\u003e\n\u003cp\u003eKM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kaplan-Meier\u003c/p\u003e\n\u003cp\u003eFBS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; fetal bovine serum\u003c/p\u003e\n\u003cp\u003eSRB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; sulforhodamine B\u003c/p\u003e\n\u003cp\u003eIACUC \u0026nbsp; \u0026nbsp; \u0026nbsp; Institutional Animal Care and Use Committee\u003c/p\u003e\n\u003cp\u003eRBPs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RNA-binding proteins\u003c/p\u003e\n\u003cp\u003ecircRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; circular RNAs\u003c/p\u003e\n\u003cp\u003elncRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; long noncoding RNAs\u003c/p\u003e\n\u003cp\u003eceRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;competing endogenous RNAs\u003c/p\u003e\n\u003cp\u003eES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; enrichment score\u003c/p\u003e\n\u003cp\u003eNES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; normalized enrichment score\u003c/p\u003e\n\u003cp\u003eBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; biological process\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;cellular component\u003c/p\u003e\n\u003cp\u003eMFI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; mean fluorescent intensity\u003c/p\u003e\n\u003cp\u003eCCM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; collective cell migration\u003c/p\u003e\n\u003cp\u003eHMTs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Histone methyltransferases\u003c/p\u003e\n\u003cp\u003eMF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;molecular function\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at \u0026ldquo; \u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003eInstitute of Future Biophysics, 141701 Dolgoprudny, Russia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003ePhystech school of biological and medical physics, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003eInstitute of Cell Biophysics of Russian Academy of Sciences, 142290 Pushchino, Russia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Russian Science Foundation (project No. 23-14-00220).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenyu Xue\u003c/strong\u003e: Formal analysis, Writing- Original draft preparation, Visualization, Investigation. \u003cstrong\u003eYuzhe Wang\u003c/strong\u003e: Writing- Original draft preparation, Investigation, In ovo data analysis. \u003cstrong\u003ePolina Pugacheva\u003c/strong\u003e: Visualization, Reviewing and Editing. \u003cstrong\u003eAnna V. Smirnova:\u003c/strong\u003e Visualization, Investigation. \u003cstrong\u003eRoman Chuprov-Netochin:\u0026nbsp;\u003c/strong\u003eFACS data analysis and visualization, Reviewing and Editing.\u003cstrong\u003e\u0026nbsp;Margarita Pustovalova\u003c/strong\u003e: Resources, Reviewing and Editing. \u003cstrong\u003eDenis V Kuzmin\u003c/strong\u003e: Funding Acquisition, Reviewing and Editing, Supervision. \u003cstrong\u003eSergey Leonov\u003c/strong\u003e: Conceptualization, Methodology, Resources, Writing- Reviewing and Editing, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude for the invaluable editing suggestions provided by Dr. Vadim Maximov. Additionally, we would like to thank the Russian Scientific Fund for its generous financial support for project No. 23-14-00220.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLonghi A, Errani C, De Paolis M, Mercuri M, Bacci G. Primary bone osteosarcoma in the pediatric age: State of the art. \u003cem\u003eCancer Treatment Reviews \u003c/em\u003e2006, 32:423-436.https://doi.org/10.1016/j.ctrv.2006.05.005\u003c/li\u003e\n\u003cli\u003eOttaviani G, Jaffe N. The Epidemiology of Osteosarcoma. In; 2009: 3-1310.1007/978-1-4419-0284-9_1\u003c/li\u003e\n\u003cli\u003eIsakoff MS, Bielack SS, Meltzer P, Gorlick R. Osteosarcoma: Current Treatment and a Collaborative Pathway to Success. \u003cem\u003eJournal of Clinical Oncology \u003c/em\u003e2015, 33:3029-3035.https://doi.org/10.1200/JCO.2014.59.4895\u003c/li\u003e\n\u003cli\u003eZhang B, Zhang Y, Li R, Li J, Lu X, Zhang Y. 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EGFR/Ras-induced CCL20 production modulates the tumour microenvironment. \u003cem\u003eBritish journal of cancer \u003c/em\u003e2020, 123:942-954.https://doi.org/10.1038/s41416-020-0943-2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"osteosarcoma, chemotherapy resistance, miR-16, miR-16-1-3p, miR-16-2-3p, miRNA activity sensors, Chick Embryo Chorioallantoic Membrane (CAM) model","lastPublishedDoi":"10.21203/rs.3.rs-7665473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7665473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eChemotherapy resistance contributes significantly to mortality in osteosarcoma (OS) patients. The role of miR-16, miR-16-1-3p, and miR-16-2-3p in modulating chemotherapy response and disease progression in OS remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eRNA-seq and clinical data from 82 OS patients in the TARGET-OS database were analyzed. Patients were classified by progression status (progressive disease [PD] vs. progression-free survivors [PFS]) and chemotherapy response (resistant vs. responsive). Target genes of the three miRNAs were predicted using TargetScan 7.2 and analyzed via Gene Set Enrichment Analysis (GSEA). Kaplan-Meier analysis was used to evaluate the prognostic value of cumulative Z-scores of enriched target gene sets. We used lentivirus to overexpress miRNAs in an osteosarcoma cell line to assess their effect on cisplatin sensitivity. Sensors of miR-16-1/2-3p activity were constructed. miR-16-1-3p-mediated biological effects were tested in vitro and in vivo.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOverexpression of miR-16, miR-16-1-3p, and miR-16-2-3p sensitized OS cells to cisplatin. Predicted target genes of all three miRNAs were significantly enriched among genes upregulated in chemotherapy-resistant OS samples, suggesting reduced miRNA activity. Target genes of miR-16-1-3p were further enriched in PD cohort. High cumulative Z-scores of target gene sets correlated with poor survival. MiR-Sensor assays confirmed that miR-16-1-3p suppresses protein expression via target mRNA sequence recognition. Functional assays demonstrated significant tumor-suppressive effects of miR-16-1-3p in vitro and in vivo.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eReduced activities of miR-16, miR-16-1-3p, and miR-16-2-3p are linked to chemoresistance in OS, and only miR-16-1-3p activity loss correlates with disease progression. MiR-16-1-3p may serve as a biomarker for chemoresistance and prognosis in OS.\u003c/p\u003e","manuscriptTitle":"Loss of miR-16-1-3p Activity Predicts Chemoresistance and Progression in Osteosarcoma: An Integrative Clinicopathologic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:50:57","doi":"10.21203/rs.3.rs-7665473/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c833229f-cc92-43a9-b772-7f335d90a5d8","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T08:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 07:50:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7665473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7665473","identity":"rs-7665473","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}