The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells

The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells | 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 The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells Dario Ruiz-Ciancio, Javier Cotignola, Rocío González-Conejero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3072469/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Despite advancements in B-cell acute lymphoblastic leukemia (B-ALL) therapy, a significant number of patients still experience treatment resistance, leading to relapse and poor prognosis. Recent studies have revealed the importance of non-genetic mechanisms in mediating resistance to cancer therapies. MicroRNAs (miRNAs) have emerged among non-genetic mechanisms as crucial regulators of tumor development, progression, and resistance to anticancer therapies. In particular, miR-34a has been implicated in cell invasion, migration, apoptosis, and abnormal response to chemotherapy in various tissues. However, the role of miR-34a-5p in B-ALL cells remains unexplored. Our results discovered that miR-34a-5p was downregulated in B-ALL cells, while its target SIRT1 was upregulated. Although the restoration of miR-34a-5p levels did not affect SIRT1 levels in B-ALL cells, restoring miR-34a-5p sensitized the cells to doxorubicin treatment. Additionally, to explain these results, we performed an extensive bioinformatic analysis in human B-ALL samples downloaded from online repositories to study miR-34a-5p as a potential biomarker for predicting response to B-ALL treatment. Notably, miR-34a-5p was found to be downregulated in B-ALL cells from relapsed patients. We also identified four genes targeted by miR-34a-5p in these patient cells, which had not been previously associated with B-ALL. Finally, miR-34a-5p, PRR11, and SURF4 were identified as independent predictive markers for increased risk of death in B-ALL patients. Overall, these findings shed light on the significance of miR-34a-5p in B-ALL cells, and suggest that the combination of miR-34a-5p, PRR11, and SURF4 hold promise as potential markers for estimating the survival outcomes of B-ALL patients. miRNA miR-34a-5p SURF4 PRR11 B-cell acute lymphoblastic leukemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cancer is a leading cause of death among children worldwide, with acute lymphoblastic leukemia (ALL) being the most common cancer in pediatric oncology [ 1 , 2 ]. ALL is characterized by the clonal proliferation of early B- and T- lymphocyte precursors, predominantly of the B-type (B-ALL) [ 3 ]. Although many favorable advances have been made in B-ALL therapy, a significant number of patients still develop treatment resistance, resulting in relapse and unfavorable prognosis [ 4 ]. Recent evidence suggest that treatment resistance can no longer be reduced to a simple genetic cause, in fact non-genetic mechanisms undeniably contribute to resistance against targeted therapies and conventional chemotherapies [ 5 , 6 ]. Therapeutic pressure exerted on tumor cells can induce alterations in their transcriptional state, providing a selective advantage for escaping treatment [ 7 ]. Previous studies have emphasized the role of epigenetics as a non-genetic cause of this advantage, enabling cells to evade the therapeutic pressure and to proliferate after clinical remission [ 8 ]. These epigenetic resistance mechanisms pose a new challenge to the field and demand innovative strategies for monitoring and counteracting the adaptive processes underlying both endogenous and acquired drug resistance. Among various epigenetic mechanisms, researchers have extensively investigated the role of microRNAs (miRNAs) as prognostic markers of treatment resistance in cancer [ 9 , 10 ]. miRNAs are small endogenous non-coding RNAs, typically 18–22 nucleotides in length, that act as post-transcriptional modulators of gene expression [ 11 ]. They negatively regulate gene expression by suppressing mRNA translation, cleavage, and decay [ 12 ]. Since each miRNA can regulate hundreds of target genes, alterations in miRNA levels have significant implications not only cancer initiation and progression but also for the anticancer therapy resistance acquisition of tumoral cells [ 13 ]. Consequently, researchers have sought to identify several miRNAs as useful biomarkers of drug resistance, thereby developing novel strategies for cancer prevention and therapy. Although numerous miRNAs have demonstrated the potential to sensitize cancer cells to therapy, only a few have become an object of interest in leukemia [ 14 ]. For instance, studies have suggested that miR-21 and miR-222 levels can predict resistance to fludarabine in chronic lymphocytic leukemia (CLL) [ 15 ], while miR-29a, miR-181a, and miR-221 levels may serve as predictors of fludarabine resistance in the same cancer type [ 16 ]. Furthermore, miR34a has emerged as an important biomarker of therapy resistance in CLL [ 17 ] and T-cell ALL(T-ALL) [ 18 ], but its role in B-ALL remains to be determined. In particular, miR-34a has been found to be directly involved in cell invasion, migration, and apoptosis in different tissues [ 19 – 21 ]. These studies stated that downregulation of miR-34a leads to an increase on cell proliferation, as well as an abnormal response to chemotherapy. In addition, a number of authors have considered the effects of miR-34a in SIRT1 protein levels, becoming one of the most characterized miR-34a target [ 22 – 24 ]. SIRT1 is an oncogene known to be is upregulated in several human tumor types, such us breast cancer and CLL [ 25 , 26 ]. In fact, a considerable amount of literature has investigated how SIRT1 levels affects the cell survival ability of T-lymphocytes [ 27 ]. However, the effect of SIRT1 on B-ALL cell proliferation remains to be identified. Therefore, in this work, we combined in vitro and in silico experiments to evaluate the expression levels of miR-34a-5p and SIRT1 in B-ALL cells. We performed an extensive bioinformatic analysis in human B-ALL samples downloaded from online repositories to identify miR-34a-5p, PRR11 and SURF4 as potential biomarkers for predicting response to B-ALL treatment, and thus, as potential prognosis factors of B-ALL patient survival. Materials and Methods Cell culture : JURKAT (ATCC, TIB-152), TOM-1 (DSMZ, ACC 578) and 697 (DSMZ, ACC 42) cell lines were kindly provided by Dr. Felipe Prosper from the University of Navarra (Pamplona, Spain). JURKAT, TOM-1 and 697 cells were maintained in RPMI-160 medium (GIBCO, 11875119) supplemented with 10% fetal bovine serum (FBS) (Atlanta Biologicals, S11550). Cell lines were incubated at 37°C under 5% CO 2 with medium changes every two to three days until confluent. Cell lines were screened for mycoplasma contamination and used within eight passages. Isolation of CD19 + B-lymphocytes from blood : 12 mL of blood were obtained from eight (8) healthy donors without any exclusion criteria. Blood was diluted 1:2 with sterile PBS and layered over with 15 mL of Ficoll-Paque PLUS (Fisher Scientific, 11778538). Leukocytes were collected according to Ficoll-Paque manufacturer instructions. Two leukocyte pools of 4 individuals each were created. To isolate CD19 + B-lymphocytes, previously collected leukocytes were resuspended in 10 mL of sterile autoMACS® Running Buffer (Miltenyi Biotec, Cat. No. 130-091-221). After centrifugation at 700 g for 10 min, the pellet was resuspended in 80 µL of sterile autoMACS® Running Buffer. 20 µL of Dynabeads™ CD19 magnetic microspheres (Miltenyi Biotec) were added to the resuspended pellet, and the solution was incubated at 4°C for 10–15 min. Subsequently, CD19 + B-lymphocytes were recovered using LS columns (Miltenyi Biotec) mounted on the autoMACS™ Pro separator (Miltenyi Biotec), according to manufacturer’s instructions. After consecutive washings with sterile PBS, the recovered CD19 + B-lymphocytes were resuspended in RPMI at 1x10 6 cells/mL. RNA isolation and real-time RT-PCR : Total RNA was isolated from CD19 + B-lymphocytes and B-ALL cell lines (697 and TOM-1) using both TRI Reagent® BD (500 µL/sample) (Sigma Aldrich, T3809) and TRI Reagent® BD-compatible columns according to the manufacturer's instructions (Direct-zol RNA MiniPrep Kit, Zymo Research, R2053). RNA quantification (ng/µl) and quality (A260/A280 and A260/A230 ratios) were measured in NanoDrop™ ND-2000 (ThermoFisher Scientific). Samples were stored at -80°C until use. mRNA cDNA was synthesized from total RNA (400ng) using random hexamer technique (Thermo Fisher Scientific, N8080127) and SuperScript® IV Reverse Transcriptase (SSIV) enzyme (ThermoFisher Scientific, 18090010), according to the manufacturer's protocol in a two-step reaction. Quantitative real-time PCR was performed for SIRT1 mRNA using SYBR™ Green (Thermo Fisher Scientific, Cat. No. 4472908) and both specific 5’ (5’-CAAGGCCACGGATAGGTCC-3’) and 3’ primers (5’- ATTGTTCGAGGATCTGTGCCA-3’). miRNA cDNA was synthesized from total RNA (400ng) using miScriptII RT kit (QIAGEN, 218160) following manufacturer’s instructions. Expression levels of miR-34a-5p were determined by quantitative real-time PCR (qPCR) using the miScript SYBR Green PCR Kit (QIAGEN, 218075), a universal 3’ primer (5’-GAATCGAGCACCAGTTACG-3’) and a specific 5' primers (5’-TGGCAGTGTCTTAGCTGGTTGT-3’). All samples were amplified in the QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). The 2 −(∆∆Ct) method was followed to calculate the relative abundance of miRNA and mRNA compared with endogenous control expression, u6 for miRNA and GAPDH for mRNA [ 28 ]. Total protein extraction and western blotting : Protein extraction was performed from CD19 + B-lymphocytes and B-ALL cell lines (697 and TOM-1) using RIPA lysis buffer (100µl/sample) (ThermoFisher Scientific, 89900). Protein quantification was carried out similarly as described before [ 29 ]. Total protein (30 µg) was loaded in 8% polyacrylamide gels and separated by SDS-PAGE in reducing conditions. Gels were transferred onto PVDF membranes (Amersham Hybond P 0.45, Merk, GE10600023) and blocked 1 hour in 1% v/v PBS-Tween20 and 5% w/v BSA. Antibodies against human SIRT1 (1:1000, Abcam, ab7343) and human GAPDH (1:5000, Abcam, ab128915) were diluted in 1% v/v PBS-Tween20 and 5% w/v BSA, and they were incubated overnight at 4°C. SIRT1 and GAPDH were immunodetected with the appropriate peroxidase-conjugated secondary antibodies (anti-mouse NA931V; anti-rabbit NA9340V; 1:5000; GE Healthcare). ECL™ Prime Detection Kit (GE Healthcare, 12316992) and ImageQuant LAS 4000 Imager (ExonBiotec, GE Healthcare) were used for Western blotting detection. Densitometric analyses were performed with ImageJ software [ 30 ], and sample values were normalized with endogenous GAPDH levels. B-ALL cell transfections : 697 (B-ALL) and TOM-1 (B-ALL) cells were seeded at 1x10 6 cells/mL per well in 6-well culture plates, and they were transfected with 100 nmol/L human miR-34a-5p mimic (ThermoFisher Scientific, 4464067), or miRNA scrambled control (SCR) (ThermoFisher Scientific, 4464058). Transfection reactions were performed using siPORT™ NeoFX™ (ThermoFisher Scientific, AM4510) as described before [ 31 ]. After 48 h, cells were collected for subsequent RNA and protein analyses. Cell proliferation assay : 697 (B-ALL) and TOM-1 (B-ALL) cells were seeded at 1x10 5 cells/mL in 96-well plates. Cells were incubated for 24h and 48h in OPTIM-MEM medium. B-ALL cells were transfected with 100 nmol/L human miR-34a-5p mimic or 100 nmol/L SCR, with or without the addition of 0.1 µM doxorubicin (DOX) (DOXPLAX®, Laboratorio LKM S.A., Argentina). Cell viability was assessed using the XTT Cell Proliferation II kit (ATCC, 30-1011K), according to the manufacturer's protocol. Cell viability was measured by colorimetry using a semiautomatic plate reader (BioTek® Synergy HT). Transcriptome datasets selection of B-ALL patients : Gene Expression Omnibus (GEO) repository was used to identify potentially relevant studies with transcriptomic data from B-ALL patients [ 32 ]. In this repository, the following keywords and expressions were used: For miRNA search: [(Acute Lymphoblastic Leukemia) OR (ALL)] AND [(miRNA) OR (microRNA)] AND [(transcriptomics) OR (miRNA-seq) OR (microarray)]. For mRNA search: [(Acute Lymphoblastic Leukemia) OR (ALL)] AND [(ARNm) OR (messenger RNA)] AND [(transcriptomics) OR (RNA-seq) OR (microarray)]. From all potentially relevant datasets, the eligibility conditions included: (i) publicly available transcriptomic data (gene expression microarray, RNA-seq); (ii) detailed information on the sample and the protocol; (iii) patients with B-ALL among those studied. We selected the following datasets that complied with these criteria: GSE109868 [ 33 ]: The study included three plasma samples from healthy children, and nine paired samples of pediatric ALL individuals, which accounts for three newly diagnosed patient samples (D), three complete remission patient samples (CR), and three relapse patient samples (RE). miRNA expression profiles were detected by Taqman miRNA Array. GSE45839 [ 34 ]: From the forty-eight bone marrow samples collected, thirty-six represented B-ALL samples with different chromosomal alterations. All samples were obtained at diagnosis. miRNA expression profiles were detected by miRXplore_v4.0 TM Microarray. Associated clinical data included age, cell of origin (T or B), white blood cell count, response to prednisone, and relapse status. GSE28460 [ 35 ]: A total of ninety-eight paired samples were collected from pediatric B-ALL patients at two different time points: diagnosis (n = 49) and relapse (n = 49). Gene expression profiles were obtained by Affymetrix Human Genome U133 Plus 2.0 Array. GSE18497 [ 36 ]: A total of eighty-two paired samples were collected from pediatric B-ALL patients at two different time points: diagnosis (n = 41) and relapse (n = 41). Gene expression profiles were obtained by Affymetrix Human Genome U133 Plus 2.0 Array. Additionally, cBioPortal from the Cancer Genome Atlas (TCGA) repository was used to download miRNA and mRNA transcriptomic data from B-ALL patients [ 37 , 38 ]. In this repository, the "Pediatric Acute Lymphoid Leukemia - Phase II (TARGET, 2018)" database was selected, which contains miRNA (miRNA-seq) and mRNA (mRNA-seq) raw data from a total of 181 B-ALL patients. Clinical data associated included gender, age, BCR-ABL status, cell of tumor origin, site of relapse, days to last follow-up, and 5-year survival. miRNA bioinformatic analysis : For the GSE109868 and GSE45839 datasets, we employed the GEO2R online tool from the GEO repository to identify differentially expressed miRNA [ 32 ]. Using the GSE109868 dataset, we compared "Healthy children vs. B-ALL newly diagnosed patients”; and “B-ALL newly diagnosed patients vs. patients in complete remission, vs. patients in relapse". In the GSE45839 dataset, we utilized “relapse status” as the comparison condition. The miR34a-5p values obtained were downloaded in “.csv” format for statistical analysis. To study differentially expressed miRNA from TCGA data, rows with zero counts (no expression) were removed, and the differential expression analysis was conducted using the R package DEseq2 (version 1.36.0) [ 39 ]. miRNAs were considered differentially expressed if the Log2 Fold Change (Log2 FC) was greater than 1, and the adjusted p-value (Bonferroni correction) was below 0.05. The results were downloaded in ".csv" format for statistical analysis. mRNA bioinformatic analysis : To identify differential mRNA expression levels in the GSE28460 and GSE18497 datasets, the GEO2R online tool from the GEO repository was used [ 32 ]. The comparison condition was based on the disease relapse status. Furthermore, the differential expression analysis of mRNA data from TCGA was conducted using the R package DEseq2 (version 1.36.0) [ 39 ]. In this case, the comparison condition was based on the 5-year survival status, categorizing individuals as "alive" or "deceased". In all genes expression analysis, genes with both a Log2 FC value greater than 0 and p-values less than 0.05 were filtered, and the list of filtered genes with associated expression values was downloaded in “.csv” format for further analysis. Filtered out of potential direct targets of miR-34a-5p : A total of 899 potential target genes of miR34a-5p were downloaded from the miRDB database of microRNA-mRNA interactions [ 40 ]. The "Bioinformatics & Evolutionary Genomics" web server tool (https:/ /bioinformatics.psb.ugent.be/webtools/Venn/) was employed to compare these mRNAs with the filtered gene list obtained from the GSE28460, GSE18497, and TCGA dataset analysis. The results of common mRNAs were illustrated as a Venn diagram suing the R package VennDiagram (version 1.7.3.) [ 41 ]. Risk scoring system analysis : A previously described risk scoring model [ 42 ] was utilized to perform a multivariable Cox regression analysis based on the gene expression levels obtained from the TCGA, GSE28460 and GSE18497 dataset analysis. B-ALL patients were categorized in different scoring groups according to the level of risk (high/low) using the Cutoff Finder software [ 43 ]. Kaplan-Meier (KM) survival analysis were employed to calculate the overall survival (OS) between the dichotomized (high-risk/low-risk) patient groups [ 42 ]. Statistical analyses : Mean and standard error of the mean (SEM) were calculated using either GraphPad Prism 9 or R programming language. Non-parametric statistical calculations were used for transcriptomic data analysis, and the analysis of variance (ANOVA) was employed for multiple comparison analysis. Spearman's rank coefficient of correlation was utilized to calculate the association between continuous variables. The impact of miRNA and mRNA expression in the overall survival (OS) was evaluated using the Kaplan-Meier (KM) method [ 42 ] and the Log-rank test. We used the R package survminer [ 44 ] to plot the OS. Statistical significance was set at a p-value less than 0.05. Results To determine the expression levels of miR-34a-5p, two pools of CD19 + B-lymphocytes were created from four healthy individuals each (Figure S1 ) . We initially assessed miR-34a-5p levels by RT-qPCR. Compared with CD19 + B-lymphocytes, the level of miR-34a-5p was significantly downregulated in in the B-ALL cell lines TOM-1 and 697 (p = 0.0014, and p = < 0.0001; respectively) ( Fig. 1 A ) . Then, we interrogated Jiang et al. dataset (GSE109868) [ 33 ]. miR-34a-5p was also shown to be significantly downregulated in the newly diagnosed pediatric B-ALL patients (B-ALL Diagnosis) compared to healthy children used as control (Healthy control) (p = 0.0032) ( Fig. 1 B ) . These findings suggest a decreased expression level of miR-34a-5p in B-ALL cells. Since several studies have demonstrated the role of miR-34a-5p in cell survival and drug sensitivity [ 19 , 45 , 46 ], we next hypothesized that expression levels of miR-34a-5p might be altered in patients with different treatment responses. Thus, we analyzed Jiang et al. dataset (GSE109868) [ 33 ], Avigad et al. dataset (GSE45839) [ 34 ], and B-ALL patients from TCGA to evaluate miR-34a-5p expression under different conditions. In the Jiang et al. dataset, the expression level of miR-34a-5p showed a tendency to increase in patients who achieved complete remission (CR) compared to newly diagnosed patients (ND) ( Fig. 2 A ) . However, the level of miR-34a-5p decreased in relapsed patients (RE), when compared to the other two conditions (ND and CR) ( Fig. 2 A ). Similarly, miR-34a-5p expression significantly dropped in B-ALL relapsed patients from the Avigad et al. dataset (p = 0.041) ( Fig. 2 B ) . Furthermore, analyzing the TCGA data, we observed a lower expression of miR-34a-5p in deceased patients (p = < 0.0001) ( Fig. 2 C ) . In summary, the levels of miR-34a-5p tended to decrease in patients who experience relapse or death. These results were also associated with the overall survival (OS) of B-ALL patients from TCGA. As shown in Fig. 2 D, a higher expression of miR-34a-5p was significantly correlated with better OS [Hazard Ratio (HR) = 0.44, Cox p = 0.00043] ( Fig. 2 D ) . Assuming that patients who responded more favorably to treatment achieved complete remission or were still alive, these results suggest that miR-34a-5p may play a role in sensitizing tumor cells to chemotherapeutic drugs. Next, to evaluate the involvement of miR-34a-5p in the treatment response of B-ALL, 100 nM of miR-34a-5p mimic or miRNA scramble (SCR) were transfected into B-ALL cells in order to increase the levels of ectopic miR-34a-5p. As shown in Fig. 3 A, the expression level of miR-34a-5p was significantly increased in B-ALL cells transfected with miR-34a-5p compared to the cells transfected with SCR ( Fig. 3 A ) . Thereafter, TOM-1 and 697 cells were transfected either with a miR-34a-5p mimic or with a SCR, and treated in two groups: the first group was transfected only with miRNA (miR-34a-5p or SCR), while the second group was treated with the combination of miRNA and 0.1 µM of doxorubicin (DOX) ( Fig. 3 B ) . The results of the XTT assay revealed a slight reduction in cell viability mediated by miR-34a-5p in 697 cell line, while the cell viability effect was more pronounced in TOM-1 cells. In the SCR-only group, we also observed that DOX reduced cell viability in B-ALL cells, as expected. Interestingly, the result of the miR-34a-5p/DOX combination therapy showed a significant reduction in cell survival rate compared to both control groups (SCR with and without DOX) ( Fig. 3 B ) . This finding supports the notion that miR-34a-5p levels and B-ALL-drug sensitivity may be related. We hypothesized the effect mediated by miR-34a-5p on B-ALL drug sensitivity may involve SIRT1, as it is the most characterized miR-34a-5p target [ 23 – 25 ]. SIRT1 is known to participate in tumor development and lymphocytes proliferation [ 26 , 47 ]. Therefore, the mRNA and protein levels of SIRT1 were analyzed in B-ALL cells, with CD19 + B-lymphocytes used as the control. Contrary to expectations, SIRT1 mRNA levels were significantly decreased in tumor cells relative to control B-lymphocytes (Figure S2A) , even though B-ALL cells showed higher expression levels of the SIRT1 protein (Figure S2B) . To investigate the potential effect of the miR-34a-5p/ SIRT1 axis in B-ALL cells, mRNA and protein levels of SIRT1 were examined in transfected B-ALL cells. SIRT1 mRNA levels did not changed in both 697 and TOM-1 transfected cells (Figure S2C) . Similarly, SIRT1 protein levels showed a tendency to decrease after transfection with miR-34a-5p mimic, although this decrease was not significant (Figure S2D) . To confirm these results, a linear regression analysis was performed between miR-34a-5p and SIRT1 mRNA levels in B-ALL patients obtained from the TCGA Pediatric ALL TARGET II Project (n = 181). The analyzed data verified that there is no relationship between miR-34a-5p and SIRT1 (Spearman's correlation, r = 0.04, p = 0.26) (Figure S2E) . Previously, miR-34a-5p was found to be downregulated in B-ALL relapsed patients ( Figs. 1 and 2 ) , and its levels were associated with cell viability in combination with DOX ( Fig. 3 B ) ; however, the effect of miR-34a-5p does not appear to be mediated through the SIRT1 pathway (Fig. S2) . Therefore, to explore other potential targets of miR-34a-5p associated with treatment response, we analyzed gene expression data obtained from the TCGA Pediatric ALL TARGET II Project (n = 181), as well as the datasets GSE28460 [ 35 ] (n = 98) and GSE18497 [ 36 ] (n = 82). Our hypothesis was that the miR-34a-5p targets would be significantly upregulated in non-responsive patients. Initially, we filtered genes that showed significantly increased expression (Log2 FC > 0 and p < 0.05) between treatment response groups (“complete remission vs. relapse”; or “dead vs. alive”) across all three datasets. Next, we downloaded a list of miR-34a-5p target genes (n = 899) from the miRDB online database ( https://mirdb.org/ ). We then identified the genes common to both the filtered gene list and the miR-34a-5p target gene list, resulting in a combined list of significantly upregulated and miR-34a-5p-target genes for each dataset, illustrated a in a Venn Diagram ( Fig. 4 A ) . We found five genes that were consistently present in all three datasets: PRR11, SEC61A1, STRAP, SURF4, and PKP4 ( Table 1 ) . Importantly, to the best of our knowledge, none of these genes have been previously described in B-ALL. Table 1 List of target genes of miR-34a-5p selected in our work as potential biomarkers in B-ALL. Gene symbol Full gene name Function B-ALL PRR11 Proline rich 11. Plays a critical role in cell cycle progression [ 48 ]. Highly-expressed PRR11 is a predictor of poor prognosis in 10 cancer types [ 49 ]. Not described SEC61A1 Sec61 subunit alpha isoform 1. The protein encoded by this gene belongs to the SECY/SEC61- alpha family. It appears to play a crucial role in the insertion of secretory and membrane polypeptides into the endoplasmic reticulum [ 50 ]. Not described STRAP Serine/Threonine Kinase Receptor Associated Protein. STRAP participates in the assembly of small nuclear ribonucleoproteins and thereby plays an important role in the splicing of cellular pre-mRNAs [ 51 ]. STRAP participates in cell proliferation and cell death in response to various stresses by regulating both TGF-β and p53 signaling [ 52 ]. Not described SURF4 Surfeit locus protein 4 SURF4 is involved in trafficking soluble proteins from the endoplasmic reticulum to the golgi bodies [ 53 ]. Not described PKP4 Plakophilin 4 (also known as p0071) Member of the armadillo-like proteins, PKP4 is a component of the adhesion plaques and is thought to be involved in regulating junctional plaque organization and cadherin function [ 54 ]. Not described To validate our findings, we examined the expression levels of the five selected genes in B-ALL patients from the TCGA Pediatric ALL TARGET II Project. The results confirmed a significant increase in gene expression for PRR11 (p = 0.0005), SEC61A1 (p = 0.0098), STRAP (p < 0.0001), and SURF4 (p < 0.0001) when comparing the levels at the time of diagnosis between patients who later died and those who remained alive until the last follow-up ( Fig. 5 B ). Linear regression analysis further demonstrated a significant inverse relationship between the expression levels of these four genes and miR-34a-5p ( Figure S3) . However, we did not observe a significant difference in the expression of PKP4 ( Fig. 5 B ) . and there was no association with miR-34a-5p expression (Figure S3) . Further, we investigated the potential of PRR11, SEC61A1, STRAP and SURF4 as survival biomarkers. Analyses of overall survival (OS) revealed a significant association between high expression levels of these genes and poorer prognosis in B-ALL patients (Figure S4) . Considering the univariate results of OS, we evaluated the potential of miR-34a-5p and the selected genes (PRR11, SEC61A1, STRAP, and SURF4) as independent risk predictors in B-ALL through a multivariate Cox proportional hazards analysis. Our findings demonstrated that PRR11 (p < 0.0001), SURF4 (p = 0.0194), and miR-34a-5p (p = 0.0117) could be considered independent biological risk factors for predicting OS in B-ALL patients. Notably, the inclusion of miR-34a-5p, PRR11, and SURF4 significantly improved the predictive model (p < 0.0001) ( Fig. 5 A ) . The results indicated that patients classified as "risk group 4", characterized by both low miR-34a-5p expression and high PRR11/SURF4 expression, had the worst OS compared to patients in other groups. Interestingly, the "risk group 1", characterized by both high miR-34a-5p expression and low PRR11/SURF4 expression, exhibited the best OS among all B-ALL patients. Next, we conducted separate analyses for: miR-34a-5p and PRR11 ( Fig. 5 B ) , miR-34a-5p and SURF4 ( Fig. 5 C ) , and PRR11 and SURF4 ( Fig. 5 D ) . Interestingly, the presence of miR-34a-5p improves the OS prediction model in B-ALL patients: miR-34a-5p/PRR11 (HR = 3.58, p = 9.99x10 − 06 ) ( Fig. 5 B ) , miR-34a-5p/SURF4 (HR = 4.21, p = 5.09x10 − 07 ) ( Fig. 5 C ) , and PRR11/SURF4 (HR = 3.02, p = 1.08x10 − 06 ) ( Fig. 5 D ) . These results underscore the importance of evaluating different genes such as miR-34a-5p, PRR11, and SURF4 as combined biological factors of clinical significance in B-ALL. Discussion Several reports have demonstrated the downregulation of miR-34a-5p in various types of cancer, indicating its role as a tumor suppressor gene [ 55 ]. For example, Tarasov et al. observed low expression of miR-34a in breast and lung cancer, as well as osteosarcoma cells [ 45 ]. Similarly, Corney et al. revealed low expression of miR-34a in ovarian cancer [ 46 ]. Nakagama's and Stallings's labs also reported low expression of miR-34a in colorectal [ 47 ] and neuroblastoma [ 56 ], respectively. Consistent with the literature, our results pioneered in providing evidence of lower expression of miR-34a-5p in B-ALL cell lines and newly diagnosed B-ALL patients compared to normal B cells. In addition, an important finding wof this study was the downregulation of miR-34a-5p in relapsed patients compared to those who achieve complete remission. Furthermore, decreased expression of this miRNA was observed in deceased patients compared to alive patients, suggesting a potential role of miR-34a-5p levels in B-ALL treatment response. Interestingly, high-expression of miR-34a-5p favorably altered the overall survival of B-ALL patients. These results are in agreement with those obtained by Vinalli et al, who exposed that upregulating miR-34a sensitizes tumor cells to treatment, supporting the idea of using miR-34a-5p to monitor treatment resistance during chemotherapy [ 57 ]. Another elegant study revealed that transfection of miR-34a into skin cancer squamous cells not only inhibits their growth and proliferation but also enhances sensitivity to DOX in these tumoral cells [ 58 ]. In a similar study, Najjary et al. demonstrated that the combination of miR-34a and DOX effectively induces apoptosis in T-ALL cells [ 18 ]. Moreover, upregulation of miR-34a increased DOX sensitivity in breast cancer cells [ 59 ], while miR-34a-transfected lung cancer cells showed heightened sensitivity to cisplatin [ 60 ]. These findings further support the hypothesis of a potential association between miR-34a-5p and DOX, although the specific link remains unclear for B-ALL cells. To investigate this, we transfected B-ALL cells with miR-34a-5p mimic, and our results demonstrated a significant reduction in cell survival rate with the miR-34a-5p/DOX combination therapy compared to the control group. Previous studies have noted the importance of SIRT1 as one of the main target genes of miR-34a, along with SIRT1 participation in tumor development and cell proliferation of B-lymphocytes [ 25 , 61 ]. The miR-34a/SIRT1 axis has been extensively described in various cancer types including liver [ 62 ], esophageal [ 63 ], prostate [ 24 ], colorectal [ 64 ], and breast cancer [ 65 ]. In our study, we contributed to the understanding of the axis miR-34a-5p/SIRT1 in B-ALL. As expected, SIRT1 protein levels were significantly increased in B-ALL cells compared to control B-lymphocytes. Surprisingly, SIRT1 mRNA levels were significantly decreased in these tumoral cells related to the control. To validate our findings, we analyzed miR-34a-5p-transfected B-ALL cells. Although miR-34a-5p expression was increased in the transfected cells, there was no change in SIRT1 mRNA levels, while SIRT1 protein levels showed only a slight decrease in the transfected cells. Also, we did not find a significant correlation between miR-34a-5p and SIRT1 levels in B-ALL patients samples. Taken together, these results suggest that SIRT1 appears to be unaffected by miR-34a-5p in B-ALL cells, highlighting the need for a better understanding of the biological role of miR-34a-5p in this disease. It is well established that a specific miRNA can target several genes. Therefore, to identify potential miR-34a-5p targets, we carried out a screening method based on gene expression. Briefly, we filtered out miR-34a-5p target genes that were significantly upregulated in B-ALL relapsed or diseased patients using transcriptomic data obtained from the GEO and TCGA databases. As a result, we identified a total of five genes, of which four showed a significant correlation with miR-34a-5p. Interestingly, to the best of our knowledge, PRR11, SEC61A1, STRAP, and SURF4 have not been previously described in B-ALL. Furthermore, we demonstrated that the levels of these four genes significantly modified B-ALL patient’s overall survival. More important, the multivariate analysis revealed that only PRR11, SURF4, and miR-34a-5p significantly altered the B-ALL patient’s overall survival. In fact, the best overall survival prediction model in B-ALL patients was the model that consider the three genes together (miR-34a-5, PRR11 and SURF4), confirming the importance of considering miR-34a-5p as an independent prognostic marker of relapse. However, the generalizability of these results is subject to certain limitations. For instance, while these studies focused on initial in-vitro proof-of-concept, further in vivo studies that analyze the expression of miR-34a-5p, PRR11 and SURF4 in a larger number of B-ALL patients and evaluate the gene-mediated response to chemotherapy are warranted. In summary, the findings reported here shed new light on the potential importance of miR-34a-5p along with PRR11 and SURF4, as new functionally relevant biomarkers in B-ALL. Declarations Author Contributions DRC, RGC, CM and JC designed experiments. DRC conducted experiments. DRC, RGC and CM analyzed the data and conducted statistical analysis. RGC and CM contributed reagents and materials. DRC wrote the manuscript. All authors reviewed and accepted the manuscript. Funding RGC and CM were supported by grants from INSTITUTO DE SALUD CARLOS III (PI20/00136). DRC was also supported by CONICET PhD fellowship (2018-2023) and SANTANDER IBEROAMERICA - UCCUYO scholarship 2019. 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Oncotarget 6(12):10432–10444. 10.18632/oncotarget.3394 Supplementary Files SupplementalFileAnnalsofHematologyRuiz.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 08 Jul, 2024 Reviewers agreed at journal 15 Jul, 2023 Reviewers invited by journal 26 Jun, 2023 Editor assigned by journal 20 Jun, 2023 First submitted to journal 16 Jun, 2023 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3072469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":249033475,"identity":"c2162293-2d8d-4b1c-bec9-ad52bdf5a5df","order_by":0,"name":"Dario Ruiz-Ciancio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgYGHgYQYj4A5EnIkKKFLQGkhYc4iyDKeAwQbHzAfEZy84c3Nfdk+Nl7Pr+6UWPBw8B++OgGfFpkbiQ2GM45Vswj2XN2m3XOMaDDeNLSbuDTIiGR2JDMw5bAY3Ajd5txDhtQiwSPGUEth3n+AbXcf/PMOOcfcVoam3nbQLbwMD/ObSNGC8/DZsa5fQlAv6SZMef2SfCwEfQLe/rjD2++Jdjzsx9+/DnnW50ckHEMrxZkwCYBJolVDgLMH0hRPQpGwSgYBSMHAABLy0IQlJ3TOAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5039-9588","institution":"Universidad Catolica de Cuyo Facultad de Ciencias Medicas","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dario","middleName":"","lastName":"Ruiz-Ciancio","suffix":""},{"id":249033476,"identity":"ade9429d-6534-40bc-b1f4-f18d31ace111","order_by":1,"name":"Javier Cotignola","email":"","orcid":"","institution":"Universidad de Buenos Aires Facultad de Ciencias Exactas y Naturales","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Cotignola","suffix":""},{"id":249033477,"identity":"7a1d7b79-6235-4877-b038-f4b6348fd01b","order_by":2,"name":"Rocío González-Conejero","email":"","orcid":"","institution":"IMIB Arrixaca: Instituto Murciano de Investigacion Biosanitaria Virgen de la Arrixaca","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rocío","middleName":"","lastName":"González-Conejero","suffix":""},{"id":249033478,"identity":"8c0f9c6f-2a58-4e16-8d4b-5d41f47479fe","order_by":3,"name":"Constantino Martínez","email":"","orcid":"","institution":"IMIB Arrixaca: Instituto Murciano de Investigacion Biosanitaria Virgen de la Arrixaca","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Constantino","middleName":"","lastName":"Martínez","suffix":""}],"badges":[],"createdAt":"2023-06-16 12:49:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3072469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3072469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46493358,"identity":"e97ba197-067f-4b19-8148-10823b64e475","added_by":"auto","created_at":"2023-11-15 14:55:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiR-34a-5p expression levels in B-ALL cell lines and B-ALL patients.\u003c/strong\u003e (A) The expression levels of miR-34a-5p were measured by RT-qPCR in the B-ALL cell lines 697 and TOM-1, using a pool of CD19+ B-lymphocytes obtained from healthy individuals (n=8) as control. The 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e method was used to calculate the miR-34a-5p expression, normalized to U6 expression level. Data was plotted as the mean ± SEM of the 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e value; n = 3 biological replicates; One-way ANOVA, *p \u0026lt; 0.05; **p \u0026lt; 0.01. (B) The expression level of miR-34a-5p was assessed in newly diagnosed patients (B-ALL Diagnosis) (n = 3) and healthy individuals (Healthy control) (n = 3). Data obtained from the Jiang et al. data set (GSE109868). Data was plotted as the mean ± SEM of the Log2 of 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e value; Unpaired T-test, *p \u0026lt; 0.05; **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/0bf2915dbbb5d0e641316efb.png"},{"id":46493359,"identity":"ca979c53-7fe1-402a-8fea-a8d49e8150d4","added_by":"auto","created_at":"2023-11-15 14:55:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiR-34a-5p expression levels in different datasets.\u003c/strong\u003e(A) The expression level of miR-34a-5p was assessed in newly diagnosed patients (B-ALL Diagnosis) (n = 3), complete remission patients (CR) (n = 3) and relapsed patients (RE) (n = 3). Data obtained from the Jiang et al. data set (GSE109868). Data was plotted as the mean ± SEM of the 2–∆∆Ct value; One-way ANOVA, ns= not significant, *p \u0026lt; 0.05. (B) The expression level of miR-34a-5p was evaluated in complete remission patients (CR) and patients in relapse (RE) from the Avigad et al data set (GSE45839) (n = 36). Each point represents an individual measurement, and lines represents the mean ± SEM; Mann-Whitney test, *p \u0026lt; 0.05 (C) The expression level of miR-34a-5p was studied in dead and alive patients, obtained from the Pediatric ALL Project TARGET II dataset of the Cancer Genome Atlas (TCGA) database. Data was plotted as the mean ± SEM. T-test, ***p \u0026lt; 0.001. (D) Kaplan-Meier curve using data extracted from the TCGA database for overall survival (OS) of B-ALL patients segregated based on the expression of miR-34a-5p, considering patients with low expression (blue line) as the reference group. HR = Hazard ratio. Cox p = p value from the Cox proportional hazards model. Statistical significance was set at Cox p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage22.png","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/a2d15bf95d46f947939a5f47.png"},{"id":46493362,"identity":"a6ab568d-dc85-433d-b4a2-171ffd573722","added_by":"auto","created_at":"2023-11-15 14:55:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA transfection in B-ALL cells and cell viability assay.\u003c/strong\u003e (A) 697 and TOM-1 cells were transfected either with 100nM of miR-34a-5p or a miRNA scramble used as control. Expression of miR-34a-5p was investigated by RT-qPCR. The 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e method was used to calculate the miR-34a-5p expression, normalized to U6 expression level. Data was plotted as the mean ± SEM of the 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e value; n = 3 biological replicates. (B) XTT cell viability assay performed using transfected 697 and TOM-1 cells. B-ALL cells were transfected either with miR-34a-5p or with a miRNA scramble, and they were divided into two groups according to DOX treatment. The effect on cell viability was studied 48hs after treatment. Data are expressed as the mean relative absorbance ± SEM, of three experimental replicates. nM = nanomolar.\u003c/p\u003e","description":"","filename":"floatimage31.png","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/5464370768a1cc50ea6b6042.png"},{"id":46493361,"identity":"e38ebe68-19d9-4c39-ab34-3d07130ef576","added_by":"auto","created_at":"2023-11-15 14:55:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential target genes of miR-34a-5p selected \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (A) We filtered out all genes that were significantly increased (Log2 FC \u0026gt; 0 and p \u0026lt; 0.05) and target of miR-34a-5p, in the TCGA Pediatric ALL TARGET II Project, and in GEO datasets GSE28460 (n = 98)[35] and GSE18497 (n = 82)[36]. The gene list shared common to different datasets was illustrated in a Venn diagram. (B) The expression level of PRR11, SEC61A1, STRAP, SURF4 and PKP4, was studied in dead and alive patients, obtained from the Pediatric ALL Project TARGET II dataset of the Cancer Genome Atlas (TCGA) database (n = 181). Data was plotted as the mean ± SEM. Mann-Whitney test; ns = not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Screenshot20231115at9.53.15AM.png","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/c360e59c82f5e06d554ba380.png"},{"id":46493360,"identity":"2e30ecda-3e08-4dac-bacf-788b82ef0521","added_by":"auto","created_at":"2023-11-15 14:55:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":356767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient stratification according to a risk scoring model based on multivariate regression analysis.\u003c/strong\u003e Kaplan-Meier curve using data extracted from the TCGA database for overall survival (OS) of B-ALL patients segregated based on a risk score generated by a multivariate Cox proportional hazards model. (A) The model was generated with the expression levels of miR-34a-5p, PRR11 and SURF4. Patients were stratified into four risk groups: group “1”- red color (miR-34a-5p high, PRR11/SURF4 low), group “2”- green color (miR-34a-5p/SURF4 high, PRR11 low ; miR-34a-5p/PRR11 high, SURF4 low, or all three genes with low expression), group “3”- light blue color (miR-34a-5p/SURF4 low, PRR11 high; miR-34a-5p/PRR11 low, SURF4 high, or the three genes with high expression), and group “4”- violet color (miR-34a-5p low, PRR11/SURF4 high). Group 4 was considered as the reference group. (B-D) The model was generated with the expression levels of miR-34a-5p and PRR11 (B), miR-34a-5p and SURF4 (C) or PRR11 and SURF4 (D. Patients were stratified into three risk groups: group \"1\"- red color (both genes at normal levels), group \"2\"- green color (one of the genes was altered), group \"3\"- light blue color (both genes altered). Group 1 was considered as the reference group. HR = Hazard ratio. Cox p = p value from the Cox proportional hazards model. Statistical significance was set at Cox p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Screenshot20231115at9.53.26AM.png","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/7d864df7720c5f17de26eff5.png"},{"id":46494095,"identity":"8b0ba49b-45e7-4d2d-9c8d-83f3872c7ae0","added_by":"auto","created_at":"2023-11-15 15:03:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1239104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/2eda8824-cf55-4fdb-8950-02becf68fca1.pdf"},{"id":46493364,"identity":"9a7f3180-e8a0-4e54-b1e7-91af89a9933d","added_by":"auto","created_at":"2023-11-15 14:55:49","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":507530,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFileAnnalsofHematologyRuiz.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3072469/v1/697e342ce50e909136b9943d.pdf"}],"financialInterests":"","formattedTitle":"The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is a leading cause of death among children worldwide, with acute lymphoblastic leukemia (ALL) being the most common cancer in pediatric oncology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ALL is characterized by the clonal proliferation of early B- and T- lymphocyte precursors, predominantly of the B-type (B-ALL) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although many favorable advances have been made in B-ALL therapy, a significant number of patients still develop treatment resistance, resulting in relapse and unfavorable prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent evidence suggest that treatment resistance can no longer be reduced to a simple genetic cause, in fact non-genetic mechanisms undeniably contribute to resistance against targeted therapies and conventional chemotherapies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therapeutic pressure exerted on tumor cells can induce alterations in their transcriptional state, providing a selective advantage for escaping treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have emphasized the role of epigenetics as a non-genetic cause of this advantage, enabling cells to evade the therapeutic pressure and to proliferate after clinical remission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These epigenetic resistance mechanisms pose a new challenge to the field and demand innovative strategies for monitoring and counteracting the adaptive processes underlying both endogenous and acquired drug resistance.\u003c/p\u003e \u003cp\u003eAmong various epigenetic mechanisms, researchers have extensively investigated the role of microRNAs (miRNAs) as prognostic markers of treatment resistance in cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. miRNAs are small endogenous non-coding RNAs, typically 18\u0026ndash;22 nucleotides in length, that act as post-transcriptional modulators of gene expression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They negatively regulate gene expression by suppressing mRNA translation, cleavage, and decay [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since each miRNA can regulate hundreds of target genes, alterations in miRNA levels have significant implications not only cancer initiation and progression but also for the anticancer therapy resistance acquisition of tumoral cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, researchers have sought to identify several miRNAs as useful biomarkers of drug resistance, thereby developing novel strategies for cancer prevention and therapy. Although numerous miRNAs have demonstrated the potential to sensitize cancer cells to therapy, only a few have become an object of interest in leukemia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For instance, studies have suggested that miR-21 and miR-222 levels can predict resistance to fludarabine in chronic lymphocytic leukemia (CLL) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while miR-29a, miR-181a, and miR-221 levels may serve as predictors of fludarabine resistance in the same cancer type [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, miR34a has emerged as an important biomarker of therapy resistance in CLL [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and T-cell ALL(T-ALL) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], but its role in B-ALL remains to be determined.\u003c/p\u003e \u003cp\u003eIn particular, miR-34a has been found to be directly involved in cell invasion, migration, and apoptosis in different tissues [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These studies stated that downregulation of miR-34a leads to an increase on cell proliferation, as well as an abnormal response to chemotherapy. In addition, a number of authors have considered the effects of miR-34a in SIRT1 protein levels, becoming one of the most characterized miR-34a target [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. SIRT1 is an oncogene known to be is upregulated in several human tumor types, such us breast cancer and CLL [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In fact, a considerable amount of literature has investigated how SIRT1 levels affects the cell survival ability of T-lymphocytes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the effect of SIRT1 on B-ALL cell proliferation remains to be identified. Therefore, in this work, we combined \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein silico\u003c/em\u003e experiments to evaluate the expression levels of miR-34a-5p and SIRT1 in B-ALL cells. We performed an extensive bioinformatic analysis in human B-ALL samples downloaded from online repositories to identify miR-34a-5p, PRR11 and SURF4 as potential biomarkers for predicting response to B-ALL treatment, and thus, as potential prognosis factors of B-ALL patient survival.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCell culture\u003c/span\u003e: JURKAT (ATCC, TIB-152), TOM-1 (DSMZ, ACC 578) and 697 (DSMZ, ACC 42) cell lines were kindly provided by Dr. Felipe Prosper from the University of Navarra (Pamplona, Spain). JURKAT, TOM-1 and 697 cells were maintained in RPMI-160 medium (GIBCO, 11875119) supplemented with 10% fetal bovine serum (FBS) (Atlanta Biologicals, S11550). Cell lines were incubated at 37\u0026deg;C under 5% CO\u003csub\u003e2\u003c/sub\u003e with medium changes every two to three days until confluent. Cell lines were screened for mycoplasma contamination and used within eight passages.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIsolation of CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes from blood\u003c/span\u003e: 12 mL of blood were obtained from eight (8) healthy donors without any exclusion criteria. Blood was diluted 1:2 with sterile PBS and layered over with 15 mL of Ficoll-Paque PLUS (Fisher Scientific, 11778538). Leukocytes were collected according to Ficoll-Paque manufacturer instructions. Two leukocyte pools of 4 individuals each were created. To isolate CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes, previously collected leukocytes were resuspended in 10 mL of sterile autoMACS\u0026reg; Running Buffer (Miltenyi Biotec, Cat. No. 130-091-221). After centrifugation at 700 g for 10 min, the pellet was resuspended in 80 \u0026micro;L of sterile autoMACS\u0026reg; Running Buffer. 20 \u0026micro;L of Dynabeads\u0026trade; CD19 magnetic microspheres (Miltenyi Biotec) were added to the resuspended pellet, and the solution was incubated at 4\u0026deg;C for 10\u0026ndash;15 min. Subsequently, CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes were recovered using LS columns (Miltenyi Biotec) mounted on the autoMACS\u0026trade; Pro separator (Miltenyi Biotec), according to manufacturer\u0026rsquo;s instructions. After consecutive washings with sterile PBS, the recovered CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes were resuspended in RPMI at 1x10\u003csup\u003e6\u003c/sup\u003e cells/mL.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRNA isolation and real-time RT-PCR\u003c/span\u003e: Total RNA was isolated from CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes and B-ALL cell lines (697 and TOM-1) using both TRI Reagent\u0026reg; BD (500 \u0026micro;L/sample) (Sigma Aldrich, T3809) and TRI Reagent\u0026reg; BD-compatible columns according to the manufacturer's instructions (Direct-zol RNA MiniPrep Kit, Zymo Research, R2053). RNA quantification (ng/\u0026micro;l) and quality (A260/A280 and A260/A230 ratios) were measured in NanoDrop\u0026trade; ND-2000 (ThermoFisher Scientific). Samples were stored at -80\u0026deg;C until use.\u003c/p\u003e \u003cp\u003emRNA cDNA was synthesized from total RNA (400ng) using random hexamer technique (Thermo Fisher Scientific, N8080127) and SuperScript\u0026reg; IV Reverse Transcriptase (SSIV) enzyme (ThermoFisher Scientific, 18090010), according to the manufacturer's protocol in a two-step reaction. Quantitative real-time PCR was performed for SIRT1 mRNA using SYBR\u0026trade; Green (Thermo Fisher Scientific, Cat. No. 4472908) and both specific 5\u0026rsquo; (5\u0026rsquo;-CAAGGCCACGGATAGGTCC-3\u0026rsquo;) and 3\u0026rsquo; primers (5\u0026rsquo;- ATTGTTCGAGGATCTGTGCCA-3\u0026rsquo;).\u003c/p\u003e \u003cp\u003emiRNA cDNA was synthesized from total RNA (400ng) using miScriptII RT kit (QIAGEN, 218160) following manufacturer\u0026rsquo;s instructions. Expression levels of miR-34a-5p were determined by quantitative real-time PCR (qPCR) using the miScript SYBR Green PCR Kit (QIAGEN, 218075), a universal 3\u0026rsquo; primer (5\u0026rsquo;-GAATCGAGCACCAGTTACG-3\u0026rsquo;) and a specific 5' primers (5\u0026rsquo;-TGGCAGTGTCTTAGCTGGTTGT-3\u0026rsquo;).\u003c/p\u003e \u003cp\u003eAll samples were amplified in the QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). The 2\u003csup\u003e\u0026minus;(∆∆Ct)\u003c/sup\u003e method was followed to calculate the relative abundance of miRNA and mRNA compared with endogenous control expression, u6 for miRNA and GAPDH for mRNA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal protein extraction and western blotting\u003c/span\u003e: Protein extraction was performed from CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes and B-ALL cell lines (697 and TOM-1) using RIPA lysis buffer (100\u0026micro;l/sample) (ThermoFisher Scientific, 89900). Protein quantification was carried out similarly as described before [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Total protein (30 \u0026micro;g) was loaded in 8% polyacrylamide gels and separated by SDS-PAGE in reducing conditions. Gels were transferred onto PVDF membranes (Amersham Hybond P 0.45, Merk, GE10600023) and blocked 1 hour in 1% v/v PBS-Tween20 and 5% w/v BSA. Antibodies against human SIRT1 (1:1000, Abcam, ab7343) and human GAPDH (1:5000, Abcam, ab128915) were diluted in 1% v/v PBS-Tween20 and 5% w/v BSA, and they were incubated overnight at 4\u0026deg;C. SIRT1 and GAPDH were immunodetected with the appropriate peroxidase-conjugated secondary antibodies (anti-mouse NA931V; anti-rabbit NA9340V; 1:5000; GE Healthcare). ECL\u0026trade; Prime Detection Kit (GE Healthcare, 12316992) and ImageQuant LAS 4000 Imager (ExonBiotec, GE Healthcare) were used for Western blotting detection. Densitometric analyses were performed with ImageJ software [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and sample values were normalized with endogenous GAPDH levels.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eB-ALL cell transfections\u003c/span\u003e: 697 (B-ALL) and TOM-1 (B-ALL) cells were seeded at 1x10\u003csup\u003e6\u003c/sup\u003e cells/mL per well in 6-well culture plates, and they were transfected with 100 nmol/L human miR-34a-5p mimic (ThermoFisher Scientific, 4464067), or miRNA scrambled control (SCR) (ThermoFisher Scientific, 4464058). Transfection reactions were performed using siPORT\u0026trade; NeoFX\u0026trade; (ThermoFisher Scientific, AM4510) as described before [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. After 48 h, cells were collected for subsequent RNA and protein analyses.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCell proliferation assay\u003c/span\u003e: 697 (B-ALL) and TOM-1 (B-ALL) cells were seeded at 1x10\u003csup\u003e5\u003c/sup\u003e cells/mL in 96-well plates. Cells were incubated for 24h and 48h in OPTIM-MEM medium. B-ALL cells were transfected with 100 nmol/L human miR-34a-5p mimic or 100 nmol/L SCR, with or without the addition of 0.1 \u0026micro;M doxorubicin (DOX) (DOXPLAX\u0026reg;, Laboratorio LKM S.A., Argentina). Cell viability was assessed using the XTT Cell Proliferation II kit (ATCC, 30-1011K), according to the manufacturer's protocol. Cell viability was measured by colorimetry using a semiautomatic plate reader (BioTek\u0026reg; Synergy HT).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTranscriptome datasets selection of B-ALL patients\u003c/span\u003e: Gene Expression Omnibus (GEO) repository was used to identify potentially relevant studies with transcriptomic data from B-ALL patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this repository, the following keywords and expressions were used:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor miRNA search: [(Acute Lymphoblastic Leukemia) OR (ALL)] AND [(miRNA) OR (microRNA)] AND [(transcriptomics) OR (miRNA-seq) OR (microarray)].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor mRNA search: [(Acute Lymphoblastic Leukemia) OR (ALL)] AND [(ARNm) OR (messenger RNA)] AND [(transcriptomics) OR (RNA-seq) OR (microarray)].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFrom all potentially relevant datasets, the eligibility conditions included: (i) publicly available transcriptomic data (gene expression microarray, RNA-seq); (ii) detailed information on the sample and the protocol; (iii) patients with B-ALL among those studied. We selected the following datasets that complied with these criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGSE109868 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: The study included three plasma samples from healthy children, and nine paired samples of pediatric ALL individuals, which accounts for three newly diagnosed patient samples (D), three complete remission patient samples (CR), and three relapse patient samples (RE). miRNA expression profiles were detected by Taqman miRNA Array.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGSE45839 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]: From the forty-eight bone marrow samples collected, thirty-six represented B-ALL samples with different chromosomal alterations. All samples were obtained at diagnosis. miRNA expression profiles were detected by miRXplore_v4.0 TM Microarray. Associated clinical data included age, cell of origin (T or B), white blood cell count, response to prednisone, and relapse status.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGSE28460 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]: A total of ninety-eight paired samples were collected from pediatric B-ALL patients at two different time points: diagnosis (n\u0026thinsp;=\u0026thinsp;49) and relapse (n\u0026thinsp;=\u0026thinsp;49). Gene expression profiles were obtained by Affymetrix Human Genome U133 Plus 2.0 Array.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGSE18497 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]: A total of eighty-two paired samples were collected from pediatric B-ALL patients at two different time points: diagnosis (n\u0026thinsp;=\u0026thinsp;41) and relapse (n\u0026thinsp;=\u0026thinsp;41). Gene expression profiles were obtained by Affymetrix Human Genome U133 Plus 2.0 Array.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAdditionally, cBioPortal from the Cancer Genome Atlas (TCGA) repository was used to download miRNA and mRNA transcriptomic data from B-ALL patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this repository, the \u003cem\u003e\"Pediatric Acute Lymphoid Leukemia - Phase II (TARGET, 2018)\"\u003c/em\u003e database was selected, which contains miRNA (miRNA-seq) and mRNA (mRNA-seq) raw data from a total of 181 B-ALL patients. Clinical data associated included gender, age, BCR-ABL status, cell of tumor origin, site of relapse, days to last follow-up, and 5-year survival.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003emiRNA bioinformatic analysis\u003c/span\u003e: For the GSE109868 and GSE45839 datasets, we employed the GEO2R online tool from the GEO repository to identify differentially expressed miRNA [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Using the GSE109868 dataset, we compared \"Healthy children vs. B-ALL newly diagnosed patients\u0026rdquo;; and \u0026ldquo;B-ALL newly diagnosed patients vs. patients in complete remission, vs. patients in relapse\". In the GSE45839 dataset, we utilized \u0026ldquo;relapse status\u0026rdquo; as the comparison condition. The miR34a-5p values obtained were downloaded in \u0026ldquo;.csv\u0026rdquo; format for statistical analysis.\u003c/p\u003e \u003cp\u003eTo study differentially expressed miRNA from TCGA data, rows with zero counts (no expression) were removed, and the differential expression analysis was conducted using the R package DEseq2 (version 1.36.0) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. miRNAs were considered differentially expressed if the Log2 Fold Change (Log2 FC) was greater than 1, and the adjusted p-value (Bonferroni correction) was below 0.05. The results were downloaded in \".csv\" format for statistical analysis.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003emRNA bioinformatic analysis\u003c/span\u003e: To identify differential mRNA expression levels in the GSE28460 and GSE18497 datasets, the GEO2R online tool from the GEO repository was used [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The comparison condition was based on the disease relapse status. Furthermore, the differential expression analysis of mRNA data from TCGA was conducted using the R package DEseq2 (version 1.36.0) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this case, the comparison condition was based on the 5-year survival status, categorizing individuals as \"alive\" or \"deceased\". In all genes expression analysis, genes with both a Log2 FC value greater than 0 and p-values less than 0.05 were filtered, and the list of filtered genes with associated expression values was downloaded in \u0026ldquo;.csv\u0026rdquo; format for further analysis.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFiltered out of potential direct targets of miR-34a-5p\u003c/span\u003e: A total of 899 potential target genes of miR34a-5p were downloaded from the miRDB database of microRNA-mRNA interactions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The \u003cem\u003e\"Bioinformatics \u0026amp; Evolutionary Genomics\"\u003c/em\u003e web server tool (https:/ /bioinformatics.psb.ugent.be/webtools/Venn/) was employed to compare these mRNAs with the filtered gene list obtained from the GSE28460, GSE18497, and TCGA dataset analysis. The results of common mRNAs were illustrated as a Venn diagram suing the R package VennDiagram (version 1.7.3.) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRisk scoring system analysis\u003c/span\u003e: A previously described risk scoring model [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] was utilized to perform a multivariable Cox regression analysis based on the gene expression levels obtained from the TCGA, GSE28460 and GSE18497 dataset analysis. B-ALL patients were categorized in different scoring groups according to the level of risk (high/low) using the Cutoff Finder software [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Kaplan-Meier (KM) survival analysis were employed to calculate the overall survival (OS) between the dichotomized (high-risk/low-risk) patient groups [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical analyses\u003c/span\u003e: Mean and standard error of the mean (SEM) were calculated using either GraphPad Prism 9 or R programming language. Non-parametric statistical calculations were used for transcriptomic data analysis, and the analysis of variance (ANOVA) was employed for multiple comparison analysis. Spearman's rank coefficient of correlation was utilized to calculate the association between continuous variables. The impact of miRNA and mRNA expression in the overall survival (OS) was evaluated using the Kaplan-Meier (KM) method [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and the Log-rank test. We used the R package survminer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] to plot the OS. Statistical significance was set at a p-value less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo determine the expression levels of miR-34a-5p, two pools of CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes were created from four healthy individuals each \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. We initially assessed miR-34a-5p levels by RT-qPCR. Compared with CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes, the level of miR-34a-5p was significantly downregulated in in the B-ALL cell lines TOM-1 and 697 (p\u0026thinsp;=\u0026thinsp;0.0014, and p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; respectively) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Then, we interrogated Jiang et al. dataset (GSE109868) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. miR-34a-5p was also shown to be significantly downregulated in the newly diagnosed pediatric B-ALL patients (B-ALL Diagnosis) compared to healthy children used as control (Healthy control) (p\u0026thinsp;=\u0026thinsp;0.0032) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These findings suggest a decreased expression level of miR-34a-5p in B-ALL cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince several studies have demonstrated the role of miR-34a-5p in cell survival and drug sensitivity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], we next hypothesized that expression levels of miR-34a-5p might be altered in patients with different treatment responses. Thus, we analyzed Jiang et al. dataset (GSE109868) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Avigad et al. dataset (GSE45839) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and B-ALL patients from TCGA to evaluate miR-34a-5p expression under different conditions. In the Jiang et al. dataset, the expression level of miR-34a-5p showed a tendency to increase in patients who achieved complete remission (CR) compared to newly diagnosed patients (ND) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. However, the level of miR-34a-5p decreased in relapsed patients (RE), when compared to the other two conditions (ND and CR) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e Similarly, miR-34a-5p expression significantly dropped in B-ALL relapsed patients from the Avigad et al. dataset (p\u0026thinsp;=\u0026thinsp;0.041) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Furthermore, analyzing the TCGA data, we observed a lower expression of miR-34a-5p in deceased patients (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In summary, the levels of miR-34a-5p tended to decrease in patients who experience relapse or death. These results were also associated with the overall survival (OS) of B-ALL patients from TCGA. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, a higher expression of miR-34a-5p was significantly correlated with better OS [Hazard Ratio (HR)\u0026thinsp;=\u0026thinsp;0.44, Cox p\u0026thinsp;=\u0026thinsp;0.00043] \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Assuming that patients who responded more favorably to treatment achieved complete remission or were still alive, these results suggest that miR-34a-5p may play a role in sensitizing tumor cells to chemotherapeutic drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, to evaluate the involvement of miR-34a-5p in the treatment response of B-ALL, 100 nM of miR-34a-5p mimic or miRNA scramble (SCR) were transfected into B-ALL cells in order to increase the levels of ectopic miR-34a-5p. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the expression level of miR-34a-5p was significantly increased in B-ALL cells transfected with miR-34a-5p compared to the cells transfected with SCR \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Thereafter, TOM-1 and 697 cells were transfected either with a miR-34a-5p mimic or with a SCR, and treated in two groups: the first group was transfected only with miRNA (miR-34a-5p or SCR), while the second group was treated with the combination of miRNA and 0.1 \u0026micro;M of doxorubicin (DOX) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The results of the XTT assay revealed a slight reduction in cell viability mediated by miR-34a-5p in 697 cell line, while the cell viability effect was more pronounced in TOM-1 cells. In the SCR-only group, we also observed that DOX reduced cell viability in B-ALL cells, as expected. Interestingly, the result of the miR-34a-5p/DOX combination therapy showed a significant reduction in cell survival rate compared to both control groups (SCR with and without DOX) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. This finding supports the notion that miR-34a-5p levels and B-ALL-drug sensitivity may be related.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe hypothesized the effect mediated by miR-34a-5p on B-ALL drug sensitivity may involve SIRT1, as it is the most characterized miR-34a-5p target [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. SIRT1 is known to participate in tumor development and lymphocytes proliferation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, the mRNA and protein levels of SIRT1 were analyzed in B-ALL cells, with CD19\u0026thinsp;+\u0026thinsp;B-lymphocytes used as the control. Contrary to expectations, SIRT1 mRNA levels were significantly decreased in tumor cells relative to control B-lymphocytes \u003cb\u003e(Figure S2A)\u003c/b\u003e, even though B-ALL cells showed higher expression levels of the SIRT1 protein \u003cb\u003e(Figure S2B)\u003c/b\u003e. To investigate the potential effect of the miR-34a-5p/ SIRT1 axis in B-ALL cells, mRNA and protein levels of SIRT1 were examined in transfected B-ALL cells. SIRT1 mRNA levels did not changed in both 697 and TOM-1 transfected cells \u003cb\u003e(Figure S2C)\u003c/b\u003e. Similarly, SIRT1 protein levels showed a tendency to decrease after transfection with miR-34a-5p mimic, although this decrease was not significant \u003cb\u003e(Figure S2D)\u003c/b\u003e. To confirm these results, a linear regression analysis was performed between miR-34a-5p and SIRT1 mRNA levels in B-ALL patients obtained from the TCGA Pediatric ALL TARGET II Project (n\u0026thinsp;=\u0026thinsp;181). The analyzed data verified that there is no relationship between miR-34a-5p and SIRT1 (Spearman's correlation, r\u0026thinsp;=\u0026thinsp;0.04, p\u0026thinsp;=\u0026thinsp;0.26) \u003cb\u003e(Figure S2E)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003ePreviously, miR-34a-5p was found to be downregulated in B-ALL relapsed patients \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, and its levels were associated with cell viability in combination with DOX \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e; however, the effect of miR-34a-5p does not appear to be mediated through the SIRT1 pathway \u003cb\u003e(Fig. S2)\u003c/b\u003e. Therefore, to explore other potential targets of miR-34a-5p associated with treatment response, we analyzed gene expression data obtained from the TCGA Pediatric ALL TARGET II Project (n\u0026thinsp;=\u0026thinsp;181), as well as the datasets GSE28460 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (n\u0026thinsp;=\u0026thinsp;98) and GSE18497 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (n\u0026thinsp;=\u0026thinsp;82). Our hypothesis was that the miR-34a-5p targets would be significantly upregulated in non-responsive patients. Initially, we filtered genes that showed significantly increased expression (Log2 FC\u0026thinsp;\u0026gt;\u0026thinsp;0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between treatment response groups (\u0026ldquo;complete remission vs. relapse\u0026rdquo;; or \u0026ldquo;dead vs. alive\u0026rdquo;) across all three datasets. Next, we downloaded a list of miR-34a-5p target genes (n\u0026thinsp;=\u0026thinsp;899) from the miRDB online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003cspan address=\"https://mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We then identified the genes common to both the filtered gene list and the miR-34a-5p target gene list, resulting in a combined list of significantly upregulated and miR-34a-5p-target genes for each dataset, illustrated a in a Venn Diagram \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. We found five genes that were consistently present in all three datasets: PRR11, SEC61A1, STRAP, SURF4, and PKP4 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Importantly, to the best of our knowledge, none of these genes have been previously described in B-ALL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of target genes of miR-34a-5p selected in our work as potential biomarkers in B-ALL.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull gene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB-ALL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline rich 11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlays a critical role in cell cycle progression [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Highly-expressed PRR11 is a predictor of poor prognosis in 10 cancer types [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot described\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEC61A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSec61 subunit alpha isoform 1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe protein encoded by this gene belongs to the SECY/SEC61- alpha family. It appears to play a crucial role in the insertion of secretory and membrane polypeptides into the endoplasmic reticulum [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot described\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerine/Threonine Kinase Receptor Associated Protein.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTRAP participates in the assembly of small nuclear ribonucleoproteins and thereby plays an important role in the splicing of cellular pre-mRNAs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. STRAP participates in cell proliferation and cell death in response to various stresses by regulating both TGF-β and p53 signaling [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot described\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSURF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurfeit locus protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSURF4 is involved in trafficking soluble proteins from the endoplasmic reticulum to the golgi bodies [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot described\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePKP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlakophilin 4 (also known as p0071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMember of the armadillo-like proteins, PKP4 is a component of the adhesion plaques and is thought to be involved in regulating junctional plaque organization and cadherin function [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot described\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo validate our findings, we examined the expression levels of the five selected genes in B-ALL patients from the TCGA Pediatric ALL TARGET II Project. The results confirmed a significant increase in gene expression for PRR11 (p\u0026thinsp;=\u0026thinsp;0.0005), SEC61A1 (p\u0026thinsp;=\u0026thinsp;0.0098), STRAP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and SURF4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) when comparing the levels at the time of diagnosis between patients who later died and those who remained alive until the last follow-up \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e Linear regression analysis further demonstrated a significant inverse relationship between the expression levels of these four genes and miR-34a-5p (\u003cb\u003eFigure S3)\u003c/b\u003e. However, we did not observe a significant difference in the expression of PKP4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. and there was no association with miR-34a-5p expression \u003cb\u003e(Figure S3)\u003c/b\u003e. Further, we investigated the potential of PRR11, SEC61A1, STRAP and SURF4 as survival biomarkers. Analyses of overall survival (OS) revealed a significant association between high expression levels of these genes and poorer prognosis in B-ALL patients \u003cb\u003e(Figure S4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eConsidering the univariate results of OS, we evaluated the potential of miR-34a-5p and the selected genes (PRR11, SEC61A1, STRAP, and SURF4) as independent risk predictors in B-ALL through a multivariate Cox proportional hazards analysis. Our findings demonstrated that PRR11 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), SURF4 (p\u0026thinsp;=\u0026thinsp;0.0194), and miR-34a-5p (p\u0026thinsp;=\u0026thinsp;0.0117) could be considered independent biological risk factors for predicting OS in B-ALL patients. Notably, the inclusion of miR-34a-5p, PRR11, and SURF4 significantly improved the predictive model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The results indicated that patients classified as \"risk group 4\", characterized by both low miR-34a-5p expression and high PRR11/SURF4 expression, had the worst OS compared to patients in other groups. Interestingly, the \"risk group 1\", characterized by both high miR-34a-5p expression and low PRR11/SURF4 expression, exhibited the best OS among all B-ALL patients. Next, we conducted separate analyses for: miR-34a-5p and PRR11 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, miR-34a-5p and SURF4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, and PRR11 and SURF4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Interestingly, the presence of miR-34a-5p improves the OS prediction model in B-ALL patients: miR-34a-5p/PRR11 (HR\u0026thinsp;=\u0026thinsp;3.58, p\u0026thinsp;=\u0026thinsp;9.99x10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, miR-34a-5p/SURF4 (HR\u0026thinsp;=\u0026thinsp;4.21, p\u0026thinsp;=\u0026thinsp;5.09x10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, and PRR11/SURF4 (HR\u0026thinsp;=\u0026thinsp;3.02, p\u0026thinsp;=\u0026thinsp;1.08x10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. These results underscore the importance of evaluating different genes such as miR-34a-5p, PRR11, and SURF4 as combined biological factors of clinical significance in B-ALL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral reports have demonstrated the downregulation of miR-34a-5p in various types of cancer, indicating its role as a tumor suppressor gene [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For example, Tarasov et al. observed low expression of miR-34a in breast and lung cancer, as well as osteosarcoma cells [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Similarly, Corney et al. revealed low expression of miR-34a in ovarian cancer [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Nakagama's and Stallings's labs also reported low expression of miR-34a in colorectal [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and neuroblastoma [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], respectively. Consistent with the literature, our results pioneered in providing evidence of lower expression of miR-34a-5p in B-ALL cell lines and newly diagnosed B-ALL patients compared to normal B cells.\u003c/p\u003e \u003cp\u003eIn addition, an important finding wof this study was the downregulation of miR-34a-5p in relapsed patients compared to those who achieve complete remission. Furthermore, decreased expression of this miRNA was observed in deceased patients compared to alive patients, suggesting a potential role of miR-34a-5p levels in B-ALL treatment response. Interestingly, high-expression of miR-34a-5p favorably altered the overall survival of B-ALL patients. These results are in agreement with those obtained by Vinalli et al, who exposed that upregulating miR-34a sensitizes tumor cells to treatment, supporting the idea of using miR-34a-5p to monitor treatment resistance during chemotherapy [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Another elegant study revealed that transfection of miR-34a into skin cancer squamous cells not only inhibits their growth and proliferation but also enhances sensitivity to DOX in these tumoral cells [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In a similar study, Najjary et al. demonstrated that the combination of miR-34a and DOX effectively induces apoptosis in T-ALL cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, upregulation of miR-34a increased DOX sensitivity in breast cancer cells [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], while miR-34a-transfected lung cancer cells showed heightened sensitivity to cisplatin [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These findings further support the hypothesis of a potential association between miR-34a-5p and DOX, although the specific link remains unclear for B-ALL cells. To investigate this, we transfected B-ALL cells with miR-34a-5p mimic, and our results demonstrated a significant reduction in cell survival rate with the miR-34a-5p/DOX combination therapy compared to the control group.\u003c/p\u003e \u003cp\u003ePrevious studies have noted the importance of SIRT1 as one of the main target genes of miR-34a, along with SIRT1 participation in tumor development and cell proliferation of B-lymphocytes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The miR-34a/SIRT1 axis has been extensively described in various cancer types including liver [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], esophageal [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], prostate [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], colorectal [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and breast cancer [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In our study, we contributed to the understanding of the axis miR-34a-5p/SIRT1 in B-ALL. As expected, SIRT1 protein levels were significantly increased in B-ALL cells compared to control B-lymphocytes. Surprisingly, SIRT1 mRNA levels were significantly decreased in these tumoral cells related to the control. To validate our findings, we analyzed miR-34a-5p-transfected B-ALL cells. Although miR-34a-5p expression was increased in the transfected cells, there was no change in SIRT1 mRNA levels, while SIRT1 protein levels showed only a slight decrease in the transfected cells. Also, we did not find a significant correlation between miR-34a-5p and SIRT1 levels in B-ALL patients samples. Taken together, these results suggest that SIRT1 appears to be unaffected by miR-34a-5p in B-ALL cells, highlighting the need for a better understanding of the biological role of miR-34a-5p in this disease.\u003c/p\u003e \u003cp\u003eIt is well established that a specific miRNA can target several genes. Therefore, to identify potential miR-34a-5p targets, we carried out a screening method based on gene expression. Briefly, we filtered out miR-34a-5p target genes that were significantly upregulated in B-ALL relapsed or diseased patients using transcriptomic data obtained from the GEO and TCGA databases. As a result, we identified a total of five genes, of which four showed a significant correlation with miR-34a-5p. Interestingly, to the best of our knowledge, PRR11, SEC61A1, STRAP, and SURF4 have not been previously described in B-ALL. Furthermore, we demonstrated that the levels of these four genes significantly modified B-ALL patient\u0026rsquo;s overall survival. More important, the multivariate analysis revealed that only PRR11, SURF4, and miR-34a-5p significantly altered the B-ALL patient\u0026rsquo;s overall survival. In fact, the best overall survival prediction model in B-ALL patients was the model that consider the three genes together (miR-34a-5, PRR11 and SURF4), confirming the importance of considering miR-34a-5p as an independent prognostic marker of relapse. However, the generalizability of these results is subject to certain limitations. For instance, while these studies focused on initial in-vitro proof-of-concept, further in vivo studies that analyze the expression of miR-34a-5p, PRR11 and SURF4 in a larger number of B-ALL patients and evaluate the gene-mediated response to chemotherapy are warranted. In summary, the findings reported here shed new light on the potential importance of miR-34a-5p along with PRR11 and SURF4, as new functionally relevant biomarkers in B-ALL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDRC, RGC, CM and JC designed experiments. DRC conducted experiments. DRC, RGC and CM analyzed the data and conducted statistical analysis. RGC and CM contributed reagents and materials. DRC wrote the manuscript. All authors reviewed and accepted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRGC and CM were supported by grants from INSTITUTO DE SALUD CARLOS III (PI20/00136). DRC was also supported by CONICET PhD fellowship (2018-2023) and SANTANDER IBEROAMERICA - UCCUYO scholarship 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource data are provided with this paper. Further data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflicts of Interest:\u003c/u\u003e\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTerwilliger T, Abdul-Hay M (2017) Acute lymphoblastic leukemia: a comprehensive review and 2017 update. 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Oncotarget 6(12):10432\u0026ndash;10444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.3394\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.3394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"miRNA, miR-34a-5p, SURF4, PRR11, B-cell acute lymphoblastic leukemia","lastPublishedDoi":"10.21203/rs.3.rs-3072469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3072469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite advancements in B-cell acute lymphoblastic leukemia (B-ALL) therapy, a significant number of patients still experience treatment resistance, leading to relapse and poor prognosis. Recent studies have revealed the importance of non-genetic mechanisms in mediating resistance to cancer therapies. MicroRNAs (miRNAs) have emerged among non-genetic mechanisms as crucial regulators of tumor development, progression, and resistance to anticancer therapies. In particular, miR-34a has been implicated in cell invasion, migration, apoptosis, and abnormal response to chemotherapy in various tissues. However, the role of miR-34a-5p in B-ALL cells remains unexplored. Our results discovered that miR-34a-5p was downregulated in B-ALL cells, while its target SIRT1 was upregulated. Although the restoration of miR-34a-5p levels did not affect SIRT1 levels in B-ALL cells, restoring miR-34a-5p sensitized the cells to doxorubicin treatment. Additionally, to explain these results, we performed an extensive bioinformatic analysis in human B-ALL samples downloaded from online repositories to study miR-34a-5p as a potential biomarker for predicting response to B-ALL treatment. Notably, miR-34a-5p was found to be downregulated in B-ALL cells from relapsed patients. We also identified four genes targeted by miR-34a-5p in these patient cells, which had not been previously associated with B-ALL. Finally, miR-34a-5p, PRR11, and SURF4 were identified as independent predictive markers for increased risk of death in B-ALL patients. Overall, these findings shed light on the significance of miR-34a-5p in B-ALL cells, and suggest that the combination of miR-34a-5p, PRR11, and SURF4 hold promise as potential markers for estimating the survival outcomes of B-ALL patients.\u003c/p\u003e","manuscriptTitle":"The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-15 14:55:44","doi":"10.21203/rs.3.rs-3072469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-07-08T12:58:20+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-07-15T18:54:05+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-26T08:03:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-20T08:35:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2023-06-16T08:49:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f0f761ba-35b0-4862-be2f-91340633b498","owner":[],"postedDate":"November 15th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-11-15T14:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-15 14:55:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3072469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3072469","identity":"rs-3072469","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}