Abstract Background Colorectal cancer (CRC) is a common gastrointestinal cancer, and even though oxaliplatin chemotherapy is effective, there is a high likelihood of relapse, indicating the presence of oxaliplatin-resistant CRC. Therefore, it is crucial to comprehend the molecular mechanisms of oxaliplatin resistance and develop effective strategies to counter drug resistance. Numerous studies have demonstrated the close association between microRNAs (miRNAs) and drug resistance in CRC. In this study, we aimed to identify the essential exosomal and cellular miRNA related to oxaliplatin resistance in the CRC cell line HCT-116. Methods The miRNA expression profile of CRC cells with resistance to oxaliplatin was analyzed. The effectiveness of diagnostics and biomarker potency of miRNAs were evaluated by receiver operating characteristic (ROC) analysis. Target miRNAs were identified, and the enrichment analysis was assessed based on Gene Ontology (GO), Reactome, and Human Disease Ontology (DO). In vitro experiments, oxaliplatin-resistant HCT-116 cells (HCT116-OXA) were developed, and the exosomes were isolated and characterized from HCT116-OXA and HCT116 cells. The expression of the selected miRNAs was evaluated in HCT116-OXA cells and their exosomes, and they were compared to HCT-116 cells using quantitative real-time PCR. Results This study revealed that a combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p exhibited the highest area under the curve (AUC), sensitivity, specificity, and superior diagnostic and predictive performance. In vitro experiments, HCT116-OXA cells displayed reduced early and late apoptosis, a bypass of S phase arrest, prolonged doubling time, and higher IC[50] compared to parental cells. The expression of miR-454-3p, miR-130b-3p, miR-7974, miR-3615, and miR-4326 was decreased in HCT116-OXA cells as compared to sensitive cells. However, a significantly higher expression of miR-130b-3p and miR-4326 was observed in the isolated exosomes of HCT116-OXA cells as compared to the sensitive cells. Conclusions The low expression of miR-454-3p, miR-7974, and miR-3615 in CRC cells or high expression of miR-130b-3p and miR-4326 in isolated exosomes could predict the response to oxaliplatin therapy. This indicates the potential of these specific miRNAs to serve as predictive markers for the response to oxaliplatin therapy. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-024-13392-2. Keywords: Colorectal cancer, MicroRNAs, Oxaliplatin, Chemotherapy resistance Background Colorectal cancer (CRC) has been recognized to be one of the most prevalent types of gastrointestinal cancer and ranked the third most prevalent cancer affecting both men and women worldwide [[34]1]. CRC has high mortality and morbidity, and most CRC patients face a low chance of surviving for five years, mostly due to delay in diagnosis, the metastatic potential of CRC, lack of effective treatment strategy, and drug resistance. Surgery, radiotherapy, chemotherapy, and targeted therapy are all treatment strategies used for colorectal cancer [[35]2]. While surgery and chemotherapy have been considered the main treatment options for CRC, most patients, especially metastatic CRC patients, don’t respond satisfactorily to these treatments due to tumor recurrence and development of chemotherapy resistance [[36]2, [37]3]. As a result, it is crucial to study the molecular mechanism related to CRC chemotherapy resistance. There are different types of chemotherapy for CRC, including single-agent therapy that uses 5-fluorouracil (5-FU) and multiple-agent regimens that may include drugs like oxaliplatin, irinotecan, and capecitabine [[38]4]. Despite the effectiveness of these chemotherapy drugs, there is a high chance of relapse, suggesting the presence of CRCs resistant to treatment [[39]5, [40]6]. While 5-FU alone, as the first-line chemotherapy drug for CRC, is moderately effective, it is more effective when combined with other chemotherapeutic agents like oxaliplatin and irinotecan [[41]7]. Oxaliplatin belongs to the platinum family and has been shown to be effective in treating CRC by disrupting DNA replication and transcription through forming intrastrand adducts between guanine and adenine or two adjacent guanine residues. Oxaliplatin also induces various apoptosis signaling pathways [[42]8]. The mechanisms of resistance to oxaliplatin are not well understood; however, it has been suggested that CRCs could acquire resistance to oxaliplatin through suppression of apoptosis [[43]9, [44]10]. Besides gene alterations, increasing evidence has revealed that microRNAs (miRNAs) are closely linked to drug resistance in CRC [[45]11]. MiRNAs are small RNA molecules (19–25 nucleotides) that complement messenger RNAs (mRNAs) and inhibit the expression of related genes and proteins at the translational level. They play a vital role in controlling gene expression in most biological processes, including cell proliferation, differentiation, apoptosis, and various pathological processes, including cancer [[46]12, [47]13]. It has been reported that miRNAs can act as either oncogenes or tumor suppressors and play a key role in tumor initiation, progression, and development of drug resistance in CRC [[48]14, [49]15]. Moreover, it has been proposed that exosomes transfer miRNA from drug-resistant to drug-sensitive cells, thereby enhancing the resistance capacity of sensitive cancer cells [[50]16, [51]17]. Exosomes are a type of small extracellular vehicle (EV) with sizes ranging from 30 to 150 nanometers that various cell types secreted them into the extracellular environment through an endocytic pathway. Exosomes selectively package biomolecular cargo and transport it to cells, influencing cellular communication, gene expression, and recipient cell biology. They transfer drug-resistance-associated materials, such as proteins and nucleic acids (mRNAs, miRNAs, and other noncoding RNAs) to drug-sensitive cells, protecting cancer cells from chemotherapeutic agents by modifying signal transduction, inhibiting apoptosis, and immune escape. The composition of exosomes is highly variable, influenced by cellular origin and physiopathologic condition, indicating that the incorporation of components into exosomes may be a controlled process [[52]18, [53]19]. MiRNA is exported into exosomes via the ESCRT-independent pathway; also, RNA-binding proteins like Ago2 and hnRNP family proteins, post-transcriptional modifications and cellular levels of miRNAs or their target mRNAs play roles in miRNA sorting. Recent research indicates that exosomes have distinct miRNA profiles from their parent cells, and their expression levels changed under different pathophysiological conditions [[54]20]. Exosomal miRNA possesses the potential to serve as a biomarker for cancer diagnosis and responses to chemotherapy.Identification of specific cellular and exosomal miRNAs related to oxaliplatin resistance to be targeted by miRNA-based interventions (e.g., anti-miRs, antagomiRs, locked nucleic acids, small molecules, and miRNA sponges) may be a promising therapeutic strategy to overcome drug resistance in CRC [[55]21]. In the present investigation, we aim to identify the essential exosomal and cellular miRNA related to oxaliplatin resistance in the CRC cell line HCT-116. Methods miRNA selection The dataset with accession number [56]GSE119481 was downloaded from the Gene Expression Omnibus (GEO) database ([57]https://www.ncbi.nlm.nih.gov/geo/) [[58]22]. This dataset consisted of miRNA expression profiles obtained by Illumina MiSeq v2 miRNA sequencing of samples resistant to oxaliplatin colorectal carcinoma HCT116 sublines and parental sensitive HCT116 cell lines. The data were divided into two case/control groups, including oxaliplatin chemotherapy resistance and sensitive groups. The Galaxy server ([59]https://usegalaxy.org/) was utilized to analyze the sequencing data.First, HISAT2 was employed for the alignment of each sample, followed by the use of StringTie for transcript assembly and quantification. The DESeq2 was used for determining differentially expressed miRNAs (DEmiRNAs) from count tables between case and control groups, and miRNAs with log fold change |FC| ≥ 2 and P-value < 0.05 were considered for further evaluation. Next, the combioROC package in R was utilized to perform an analysis called the generalized linear model (GLM) and combined receiver operating characteristic (ROC) curve analysis. This analysis aimed to evaluate the effectiveness of diagnostics and establish diagnostic models. Various indicators, such as sensitivity, specificity, cutoff value, positive predictive value, negative predictive value, and area under the ROC curve, were examined to determine the ability of individual or combined biomarkers to distinguish between groups. For the next step, target miRNAs were identified as the best-selected miRNAs from the previous step using the miRDB databases ([60]http://www.mirdb.org/) with a target score above 98. The significant enrichment analysis of common target genes between selected miRNAs was assessed based on Gene Ontology (GO), Reactome, and Human Disease Ontology (DO). Additionally, the gene interactions were displayed using the STRING database ([61]https://string-db.org/) to show the relationship between selected target proteins that are directly or indirectly involved in the development of cancer chemotherapy resistance. Cell culture and development of resistance cell line The HCT116 human colon cancer cell was obtained from Pasteur Institute, Tehran, Iran (Code: C570). This cell line was cultured in Dulbecco’s Modified Eagle’s Medium F12 (DMEM F12) (Biosera, France) supplemented with antibiotics (100 units/mL of penicillin-streptomycin) (Biosera, France) and 10% Fetal Bovine Serum (FBS) (Biosera, Cholet, France) and was maintained in an incubator supplied with 5% CO2 at 37°C. Drug-resistant HCT116 cells were established by exposing cells to the IC[50] dose of oxaliplatin (Nanoalvand Pharmaceuticals, Tehran, Iran) (10 µM) for 72 h. Then, the rescued cells were allowed to recover and rested in drug-free medium for 3–4 weeks until they reached the trypsinization stage. Afterward, the same dose of the drug was administered again, and this cycle continued by continuously culturing HCT116 cells in a medium containing increasing concentrations of oxaliplatin (10–30 µM) for approximately nine months until the cells acquired stable drug resistance, as described previously [[62]23]. The oxaliplatin-resistant HCT116 subline was labeled HCT116-OXA. The cells were maintained in drug-free medium for at least two weeks before initiating experiments. Cell proliferation assay and doubling time analysis The cell proliferation and doubling rates were determined by counting the number of cells using trypan blue exclusion (Sigma-Aldrich, USA, CAS No. 72-57-1). In 24-well plates, HCT116 and HCT116-OXA cells were seeded at a density of 5 × 10^4 cells per well and incubated at 37°C in 5% CO[2] and 95% humidity. The cells were trypsinized using trypsin-EDTA (Biosera, France), harvested, and counted at 1, 2, 3, 4, and 7 days. The formula to calculate the percent of whole viable cells is: [MATH: TotalCells=(Averagecountpersquare×Totaloriginalvolumeofcellsuspension×Dilutionfactor)104< /msup> :MATH] Cell viability assay Cell viability was determined using 3-(4, 5-dimethylthiazol-2-yl)−2, 5-diphenyl-2 H-tetrazolium bromide (MTT) assay (Sigma-Aldrich, CAS No. 298-93-1) and the IC[50] of oxaliplatin in HCT116 and HCT116-OXA cells were calculated. HCT116 and HCT116-OXA cells were seeded in 96-well plates (10^4 cells per well) and incubated overnight. The following day, cells were exposed to different concentrations of oxaliplatin (0.01–1000 µM) for 48 and 72 h. After treatment, the culture media was removed and, 20 µL of MTT (0.5% in PBS) was added to each well and incubated at 37°C for four hours. The resulting formazan crystals were dissolved in 100 µL of DMSO (Sigma-Aldrich, USA, CAS No. 67–68-5), and the color intensity was measured (570 nm) using a Synergy Microplate Spectrophotometer (BioTek Instruments). Cell viability data were expressed as a percentage of viable cells compared to controls, and the IC[50] value was calculated using GraphPad Prism^® 8.2.1 software. Each concentration was tested in triplicate and presented as mean ± SD. Annexin-V FITC-PI double staining apoptosis assay The Annexin V-FITC/propidium iodide (PI) assay was used to determine the amount of necrotic, early, and late apoptotic cells. HCT116 and HCT116-OXA cells (7 × 10 ^4 cells per well) were seeded in 12-well plates and treated with oxaliplatin (75 and 90 µM) for 48 h. The concentrations of 75 µM and 90 µM were selected based on the IC[50] value of the HCT116 and HCT116-OXA cells. High drug concentrations were necessary to induce a significant apoptotic response within 48 h, ensuring a quantifiable apoptotic effect while reducing non-specific cytotoxicity.Then, the cells were then trypsinized, harvested, and washed with 1X binding buffer before resuspending in 50 µL of 1X binding buffer containing Annexin V-FITC (ab14085, AbCam, UK) and PI (Sigma-Aldrich, USA, CAS No. 25535-16-4). The cells were added to a flow cytometric tube and incubated for 10 min at room temperature in the dark. The final volume was fixed at 200 µL with 1X binding buffer, and the resulting sample was analyzed using a BD FACSCalibur™ flow cytometer (Becton Dickinson, USA) to quantify necrosis, early and late apoptosis, and viable cells. The FlowJo V10 software (Flowjo, OH, USA) was used to assess the flow cytometry data. Cell cycle analysis To conduct the cell cycle analysis, 12-well plates were seeded with 7 × 10^4 cells per well of HCT116 and HCT116-OXA for 24 h, and then the cells were treated with oxaliplatin (10 and 30 µM) for 48 h. After treatment, the cells were trypsinized and fixed with 70% ethanol at 4°C for 4 hours. Then, the cells were exposed to 1% RNase solution in PBS for 30 min at room temperature. Following this, PI solution was added to the cells, which were then quantified using a BD FACSCalibur™ flow cytometer (Becton Dickinson, USA). The cell cycle distribution data was analyzed using FlowJo V10 software (Flowjo, OH, USA). Exosome isolation To prepare the conditioned medium for exosome isolation, the HCT116 and HCT116-OXA cells were separately seeded into 175-cm^2 cell culture flasks with a complete medium and incubated at 37°C in 5% CO[2] and 95% humidity. When the cell confluence reached 70–80%, they were washed twice with PBS, and the medium was replaced with serum-free medium. After 48 h, the supernatants of these cells were collected and centrifuged sequentially at 500 ×g for 20 min, 2500 ×g for 25 min, and 14,600 ×g for 45 min at 4°C for removing cells, apoptotic bodies, and microvesicles, respectively. Collected conditioned media was concentrated to ~ 25 mL using an Amicon Ultra-Centrifugal filter (100 kDa cutoff). Exosomes were isolated from the concentrated conditioned media according to the manufacturer’s instructions using an exosome isolation kit, ExoCib (Cib Biotech, Iran). Characterization of extracted exosomes Protein quantification and Transmission Electron Microscopy (TEM) To evaluate the isolated exosomes, the BCA protein assay kit (DNABioTech, Tehran, Iran, Cat No. DB9684) was used to measure the total protein content of the extracted exosomes, following the manufacturer’s instructions. Then, transmission electron microscopy (TEM) was employed to examine the morphology of the isolated exosomes. To do this, a small amount (20 µL) of isolated exosomes was placed on a 300 mesh carbon-coated TEM grid for 2 min. Afterward, it was negatively stained using a uranyl acetate (SPI-Chem, PA, USA, Cat No. 02624-AB). The grid was then left to dry naturally and observed using a TEM microscope (Zeiss, EM10C) operating at a voltage of 100 kV. Evaluating the size and zeta potential of isolated exosomes The dynamic light scattering (DLS) technique was utilized to evaluate the size and the surface charge of samples of the isolated exosomes. Exosome samples were diluted in particle-free PBS before injection into the device chamber at a ratio of 1:1000. Then DLS measurements were performed using the HORIBA SZ-100 Nanoparticle Analyzer (Kyoto, Japan) at 25°C. The size distribution was determined by signal intensity and Z-average diameter obtained from the autocorrelation function using general-purpose mode. Western blot analysis Western blot analysis was performed to verify the presence of exosomes with identifying membrane exosomal markers. The extracted exosome was mixed with 6X SDS sample buffer containing protease inhibitors and boiled at 95°C for 5 min. Samples were then separated by 12% SDS-PAGE and transferred to PVDF paper membrane (Roche Life Science, Germany). After blocking with skim milk (Sigma, Germany), the membrane was incubated with primary antibodies against CD9 (Santa Cruz Biotechnology, USA, sc-13118), CD63 (Santa Cruz Biotechnology, USA, sc-5275) and HSP70 (Cell Signaling Technology, USA, 4872) for 24 h at 4°C. The membrane was washed three times with TBS-Tween-20 (0.1%) and incubated with a secondary antibody m-IgGκBP-HRP (Santa Cruz Biotechnology, USA, sc-516102) for 1 h at room temperature. The ECL advanced kit (Amersham Biosciences, USA) was used to detect each band, following the manufacturer’s instructions. Evaluation of miRNA expression using qRT-PCR According to manufacturer instructions, total RNA was isolated from the exosome and HCT116 and HCT116-OXA cells using the miRNeasy Micro Kit (Qiagen, Germany, Cat No. 217084). UV spectrophotometry (NanoDrop 1000TM, USA) and agarose gel electrophoresis were applied to assess the concentration and quality of the extracted RNA, respectively. In the next step, the total RNA was transcribed into cDNA using a miRNA cDNA synthesis kit (BiomiR high sensitivity microRNA kit, Anacell, Tehran, Iran) according to the manufacturer’s protocol. Next, RealQ Plus 2X-MasterMix Green high RoxTM (Amplicon, Denmark) was utilized for the qRT-PCR, and amplification was done using the Step One Plus Real-time PCR system (Applied Biosystems, USA). SNORD 47 snRNA was used as the endogenous control. The relative expression of miRNAs was evaluated using the 2^−ΔΔCt method. Statistical analysis GraphPad Prism^® 8.2.1 software was employed for statistical data analysis (GraphPad Software, USA). One-way analysis of variance (ANOVA) followed by Tukey post hoc test was used for differences between three or more groups, and the Student’s t-test was used for two-group analyses. Nonlinear regression was employed for IC[50] calculation. Statistical significance was determined by p-value < 0.05. The data were presented as the mean ± standard deviation. Results A combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and mir-454-3p is related to chemotherapy resistance The Galaxy server analysis of the [63]GSE119481 identified a total of 173 DEmiRNAs (Supplementary Table 1). ROC curve data was obtained by plotting the rate of sensitivity versus specificity. As shown in Fig. [64]1 and Table [65]1, a combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p was able to discriminate CRC resistance to chemotherapy with a higher area under the curve (AUC) of 1. At the cutoff values of 0.5, the sensitivities of the combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p were 100% with a specificity of 100%. The combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p also showed higher AUC and sensitivity than each of the other candidate gene combinations. The results in Table [66]2 also revealed that the combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p gives the lowest Akaike’s information criterion (AIC), which shows a higher and better diagnostic and predictive performance quality compared to others. Fig. 1. [67]Fig. 1 [68]Open in a new tab ROC curve analysis. This analysis revealed the biomarker potency of the combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p using the R combioROC package. (a: hsa-mir-130b-3p, b: hsa-mir-454-3p, c: hsa-miR-7974, d: hsa-miR-3615, e: hsa-miR-4326) Table 1. Results for the ROC curve for the combination of miR-4326, miR-3615, miR-7974, miR-130b, and miR-454 Name Selected miRNAs AUC Sensitivity Specificity Cut off NPV PPV Combination 3: a + d hsa-miR-130b-3p + hsa-miR-3615 0.867 70 100 0.869 50 100 Combination 12: a + b + d hsa-miR-130b-3p+ has-miR-454-3p+ has-miR-3615 0.9 80 100 0.858 60 100 Combination 21: a + b + c + d has-miR-130b-3p+ has-miR-454-3p+ has-miR-7974+ has-miR-3615 0.9 70 100 0.878 50 100 Combination 26 : a + b + c + d + e has-miR-130b-3p+ has-miR-454-3p+ has-miR-7974+ has-miR-3615+ has-miR-4326 1 100 100 0.5 100 100 [69]Open in a new tab AUC Area under the curve, NPV Negative predictive value, PPV POSITIVE predictive value, a: hsa-mir-130b-3p, b: hsa-mir-454-3p, c: hsa-miR-7974, d: hsa-miR-3615, e: hsa-miR-4326 Table 2. Result of AIC for the combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p Diagnosis Biomarkers and their combinations Intercept Coefficients Degrees of Freedom Null Deviance AIC Combination 3: a + d 8.454 a= −7.248 d = 6.755 12 14.05 14.9 Combination 12: a + b + d 27.3 a= −8.214 b= −1.973 d = 6.371 12 14.05 16.48 Combination 21: a + b + c + d 20.70 a= −7.304 b= −0.035 c= −1.62 d = 6.02 12 14.05 18.41 Combination 26 a + b + c + d + e 321.10 a= −119.92 b= −157.02 c = 99.51 d = 231.38 e= −82.78 12 14.05 12 [70]Open in a new tab a: hsa-mir-130b-3p, b: hsa-mir-454-3p, c: hsa-miR-7974, d: hsa-miR-3615, e: hsa-miR-4326 Pathway enrichment analysis and Protein–Protein Interaction (PPI) network At first, the target of selected miRNAs from the previous step was identified, and molecular and Reactome pathway enrichment was displayed to show the important functional and molecular cellular pathways. Enrichment analysis showed that selected common target genes by miRNAs were significantly enriched in biological processes related to colorectal cancer chemotherapy resistance. Based on GO analysis, the main biological processes involving differentially expressed genes (DEGs) included the mTOR signaling pathway, endocrine resistance, breast cancer, and proteoglycans in cancer (Fig. [71]2A). In terms of molecular functions, miRNAs’ selected common target genes were mostly enriched in the regulation of MECP2 expression and activity (Fig. [72]2B). In addition, according to the PPI network, MECP2 interacts with TBL1XR1, ESR1, FMR1, AGO4, and IGF1 (Fig. [73]2C). Fig. 2. [74]Fig. 2 [75]Open in a new tab Pathway Enrichment Analysis and Protein–Protein Interaction (PPI) network. A Reactome functional pathways and (B) Gene Ontology (GO) of selected common gene targets of miRNAs in colorectal cancer resistance to chemotherapy agents. The p-value is less than 0.05 and is shown by the color. C Protein–protein interaction (PPI) network identified by survival analysis from STRING Characterization of the resistant HCT116-OXA cells Multiple experiments were conducted to characterize the resistant HCT116-OXA cells developed from the HCT116 cell line. A growth curve was plotted to investigate the growth rate and doubling time of cells. The doubling time of the HCT-116 cell was determined to be 26 h, while the resistant HCT116-OXA cells had a longer doubling time, which was calculated as 35 h (Fig. [76]3A). These results revealed that HCT116-OXA cells grew slower than their parental cell lines. The cell viability assay showed that oxaliplatin reduced the viability of HCT-116 and HCT116-OXA cells in a dose- and time-dependent manner (Fig. [77]3B). The IC[50] value of oxaliplatin for HCT-116 cells was calculated to be 13 µM and 8 µM after 48 and 72 h, respectively, whereas for HCT116-OXA cells, IC[50 ]values were 152 µM and 69 µM. Analysis of cell cycle distribution revealed that oxaliplatin caused S and G2/M phase arrest in HCT116 cells. In untreated cells, there was no significant difference in the cell count across various cell cycle stages between the HCT116 and HCT116-OXA groups (Fig. [78]3C). When HCT116 cells were treated with oxaliplatin, the proportion of the cells in the S and G2/M phases significantly increased compared to the control HCT-116 cells. Treatment with oxaliplatin (10 µM) in HCT116-OXA indicated a slight increase in S phase, which was greater at 30 µM oxaliplatin. Similarly, the percent of cells in G1 was lower in both treatments. The sub G1 and G2/M phases did not greatly change following treatment with oxaliplatin in HCT116-OXA cells. The apoptosis experiment demonstrated that the HCT116 cell line displayed a significantly higher rate of apoptotic response to oxaliplatin compared to HCT116-OXA cells (Fig. [79]3D). Treatment of HCT-116 with 75 and 90 µM of oxaliplatin significantly increased the early and late apoptosis up to 34.1 and 42.2%, respectively. However, in HCT116-OXA cells after oxaliplatin treatment (75 and 90 µM), the total apoptotic cells were ~ 3.5%, indicating no significant difference in comparison to untreated cells .These data together validate the successful development of the resistant HCT116-OXA cells from the HCT-116 cell line. Fig. 3. [80]Fig. 3 [81]Open in a new tab Characterization of the resistant HCT116-OXA cells. A The doubling time of HCT-116 and HCT116-OXA cell was determined by counting the total viable cells using the trypan blue exclusion assay. B Effects of oxaliplatin treatment at different doses on HCT116 and HCT116-OXA cell viability were evaluated after 48 and 72 h using the MTT assay. C The cell cycle assessment was conducted on both HCT116 and HCT116-OXA cells following treatment with oxaliplatin at concentrations of 10 and 30 µM. The DNA content is plotted on the x-axis using propidium iodide (PI), while the number of cells is displayed on the y-axis. The plot displays the cell cycle phases Sub-G1 (white), G1 (green), S (yellow), and G2/M (blue). The bar graph illustrates the relative percentages of the cell cycle phases in comparison to the control group, 48 hours after oxaliplatin treatment. D Double-staining with Annexin V-FITC PI and flow cytometry analysis were utilized for apoptosis evaluation. The x-axis represented annexin V-FITC, while the y-axis represented PI. The diagram utilized the Q4 to Q1 quadrants to represent live cells, early apoptotic, late apoptotic, and necrotic cells, respectively. The bar graph represents the percentage of early and late apoptotic cells in the treated samples in comparison to the control group, 48 hours after treatment. The data are presented as means ± SD, n = 3. (*p<0.05, **p<0.01) Characterization of isolated exosomes Western blotting, DLS, and TEM were utilized to characterize exosomes and verify successful exosome isolation. The isolated exosomes were expressing notable abundance of CD9, CD63, and HSP70, which are specific markers of exosomes (Figs. [82]4C and Supplementary Fig. 1). Additionally, dynamic light scattering (DLS) measurements indicated that the average size of the exosomes was approximately 168.3 ± 65.8 nm with a negative surface charge of −27.8mV (Fig. [83]4A and B). Moreover, transmission electron microscopy (TEM) images showed the characteristic cup-shaped structure of the exosomes with diameters ranging from 120 to 200 nm (Fig. [84]4D). Fig. 4. [85]Fig. 4 [86]Open in a new tab Characterization of isolated exosomes. A Using dynamic light scattering, it was determined that the exosomes had an average size of 168.3 ± 65.8 nm and (B) a negative surface charge of -27.8Mv. C Western blotting analysis of CD9, CD63, and HSP70 as exosome-specific markers in exosomes isolated from HCT-116 (A) and HCT116-OXA cells (B). D Transmission electron microscopy captured an image of the exosomes, clearly highlighting their distinctive cup-shaped structure Expression levels of exosomal and cellular miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p Expression levels of 5 miRNAs, including miR-454-3p, miR-130b-3p, miR-7974, miR-3615, and miR-4326, were investigated in HCT-116 and HCT116-OXA cells using real-time PCR. The results showed that the amount of miR-454-3p, miR-3615, and miR-7974 expression was significantly lower in HCT116-OXA cells compared to HCT-116 cells (Fig. [87]5A). On the other hand, in isolated exosomes, the miR-130b-3p and miR-4326 had a significantly high expression while miR-3615 was significantly lower in exosomes isolated from HCT116-OXA cells compared to HCT-116 cells (Fig. [88]5B). Fig. 5. [89]Fig. 5 [90]Open in a new tab Real-time PCR analysis was used for the miRNA expression quantification. A The expression levels of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p were evaluated in HCT-116 cells compared with the resistant HCT-116 OXA cells. B The expression levels of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p were evaluated in isolated exosomes from HCT-116 and HCT-116 OXA cells. Data are presented as the mean ± SD. (*p<0.05) Discussion Drug resistance in colorectal cancer (CRC) remains a significant challenge despite therapeutic advancements. Recent studies suggesting that miRNAs could serve as biomarkers to predict drug response to chemotherapy and aid in the development of personalized therapy for CRC patients based on their interaction with chemoresistance proteins [[91]11, [92]24]. The results of the current study showed that a combination of miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p has the highest AUC, sensitivity, and specificity and better diagnostic and predictive performance quality. The results of pathway enrichment analysis and PPI network analysis in the current study revealed that the DEGs were involved in biological processes, including the mTOR signaling pathway, endocrine resistance breast cancer, and proteoglycans in cancer. This also accords with the previously published articles, which showed that several miRNAs, including miR-27a, miR-130b, miR-103, miR-454, and miR-107, enhance chemoresistance via modulating the AMP-activated protein kinase-mammalian target of the rapamycin (AMPK–mTOR) pathway [[93]25–[94]29]. Our results also showed that the selected common target genes were enriched in the regulation of MeCP2 expression and activity. These results reflect those of Guo et al. (2022), who also found that MeCP2 expression could cause chemotherapy resistance via activation of the AKT pathway [[95]30]. Previous research has shown that miR-454-3p and miR-130b played a role in enhancing drug resistance by targeting PTEN and activating the AKT signaling pathway. In addition, several reports have shown that MeCP2 expression is related to CRC cell proliferation, migration, and metastasis [[96]31, [97]32]. Another finding of our study is that MeCP2 interacts with TBL1XR1, ESR1, FMR1, AGO4, and IGF1 proteins. What is interesting that all TBL1XR1, ESR1, FMR1, and IGF1 gene expressions or mutations have been reported to be related to cancer progression and chemotherapy resistance [[98]33–[99]35]. To determine and evaluate the expression of selected miRNAs, a resistant HCT-116 cell line was developed (HCT116-OXA). The results showed that HCT116-OXA cells grew slower than their parental cell lines, and the IC[50] value of oxaliplatin for HCT116-OXA cells significantly increased after 48 and 72 h compared to HCT-116 cells. Following the present results, previous studies have demonstrated that oxaliplatin-resistant cell lines could be derived from exposure to continuous and increasing concentrations of oxaliplatin [[100]36–[101]38]. The cell cycle and apoptosis level evaluation revealed that treatment with oxaliplatin caused a higher significant S and G2/M arrest in the HCT-116 cells compared to the resistant HCT116-OXA cells. Moreover, analysis of the amount of apoptosis indicated that oxaliplatin treatment caused significant early and late apoptosis in HCT-116 cells compared to HCT116-OXA cells. These results are in agreement with previous reports of an abrupt transition from the G1 to the S phase and eventual G2/M arrest of HCT-116 cells treated with oxaliplatin [[102]39]; however, treatment of HCT116-OXA cells with oxaliplatin did not significantly change the percentage of the cells in G1, S, and G2/M phases [[103]40]. Furthermore, the molecular mechanism of oxaliplatin-resistant tumors is associated with apoptosis [[104]41, [105]42], and our results corroborate with previous findings that oxaliplatin induces apoptosis in CRC cells than resistant cells [[106]43–[107]45]. It has been suggested that miRNAs could affect the efficacy of chemotherapy drugs’ effectiveness by modulating several drug resistance mechanisms, as mentioned above. Moreover, miRNAs could affect the efficacy of chemotherapy compounds via modulating several signaling pathways, including phosphatidylinositol 3-kinase (PI3K)/serine/threonine kinase (AKT), Wnt/β-catenin, TGF-β, Hippo, NF-κB, Notch, and Raf/MEK/ERK signaling pathways [[108]24]. Some of these miRNAs can be utilized as reliable biomarkers to predict the response of chemotherapy drugs or as potential targets for developing personalized therapy for patients with CRC [[109]46]. Besides miRNAs, it has been reported that exosomes could also contribute to CRC development and promote CRC chemotherapy resistance. Since one of the most important cargoes of exosomes is miRNAs, in the present study the expression of all selected miRNAs was evaluated in both cells and exosomes that derived from them [[110]47]. To address the expression of selected miRNAs, i.e., miR-454-3p, miR-130b-3p, miR-7974, miR-3615, and miR-4326, we tested their level in cells and exosomes isolated from HCT116 and HCT116-OXA cells. The results showed that all selected miRNA expressions were decreased in the resistant HCT116-OXA cells compared to HCT-116 cells, and the changes in miR-454-3p, miR-3615, and miR-7974 levels were statistically significant. In contrast, in isolated exosomes, the expression of miR-130b-3p and miR-4326 was significantly increased in HCT116-OXA exosomes compared to HCT-116 exosomes. In addition, an increase in the expression of miR-7974 and a slight increase in the expression of miR-454-3p extracted from exosomes of HCT116-OXA compared to HCT-116 were observed; however, the differences were not statistically significant. These results indicated a higher packaging of these miRNAs in exosomes of drug-resistant cells than in sensitive cells. It has been previously reported that there was a significant increase in the expression of miR-454-3p in cells that developed resistance to oxaliplatin [[111]48]. In addition, Shao et al. indicate that high miR-454-3p expression in exosomes as well as low miR-454-3p expression in tissue is associated with glioma poor prognosis, suggesting miR-454-3p as an exosomal biomarker [[112]49]. High expression of miR-130b and miR-4326 is also reported to be related to chemotherapy resistance and cancer cell proliferation [[113]50–[114]52].One unanticipated result was that the expression of miR-3615 significantly decreased in oxaliplatin- resistant cells at both cellular and exosomal levels. Nevertheless, a study by Yuan et al. revealed that miR-3615 expression increased dramatically in tissue samples of hepatocellular carcinoma [[115]53]. Thus, further studies are needed to elucidate the exact mechanism and expression of miR-3615 in CRC cancer progression and response to chemotherapy agents. Taken together, the expression pattern of miRNA in cells can be indicative of resistance to oxaliplatin. Furthermore, the miRNAs in exosomes perhaps have the potential to transfer the resistant miRNA to the sensitive cells. However, additional investigations across multiple cancer cell lines, as well as the evaluation of these biomarkers for other chemotherapy drugs, are needed to better understand the specificity and applicability of these biomarkers. Conclusion In conclusion, our study provides evidence that specific miRNAs, particularly miR-4326, miR-3615, miR-7974, miR-130b-3p, and miR-454-3p, are closely associated with the induction of oxaliplatin resistance in CRC cells, particularly HCT116-OXA cells. These miRNAs may modulate key signaling pathways, including the AKT and mTOR pathways, which are pivotal in mediating cellular responses to chemotherapy. The reduced expression of these miRNAs in resistant cells, coupled with their elevated presence in exosomes, suggests a potential mechanism of intercellular communication that could perpetuate drug resistance. We propose that the miRNAs identified in this study could either serve as biomarkers for predicting oxaliplatin resistance or as therapeutic targets to overcome such resistance in colorectal cancer. However, further validation across various colorectal cancer cell lines and other chemotherapy agents is necessary to fully elucidate the specificity and broader implications of these findings. Supplementary Information [116]Supplementary Material 1.^ (24.6KB, xlsx) [117]Supplementary Material 2.^ (2.2MB, docx) Acknowledgements