Autophagy-Associated Exosomal microRNAs in Triple-Negative Breast Cancer: A Discovery-Phase Study

Autophagy-Associated Exosomal microRNAs in Triple-Negative Breast Cancer: A Discovery-Phase Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autophagy-Associated Exosomal microRNAs in Triple-Negative Breast Cancer: A Discovery-Phase Study Ananya Choudhary, Adhiraj Roy, Simran Tandon, Bhudev Das This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9298344/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Triple-negative breast cancer (TNBC) is characterised by aggressive pathobiology and chemoresistance, yet the mechanisms underpinning treatment failure remain incompletely understood. Autophagy, induced by cytotoxic chemotherapy, is increasingly implicated in exosome biogenesis and cargo remodelling; however, whether it systematically reprogrammes the exosomal microRNA (exomiR) landscape in TNBC has not been established. Here, we report that chemotherapy-induced autophagy significantly alters sEV-associated miRNA profiles in two TNBC cell lines (MDA-MB-231, MDA-MB-468) relative to basal and normal mammary epithelial (MCF-10A) controls. Small extracellular vesicles (sEVs), confirmed by TEM, NTA, and CD81/CD63 western blotting, were isolated under doxorubicin induced autophagy conditions. Small RNA sequencing (DESeq; nominal p value < 0.01) identified 10 differentially expressed exomiRs in MDA-MB-231 and 7 in MDA-MB-468. Multi-database in silico target prediction revealed convergent targeting of core autophagy regulators, most prominently Beclin-1 (BECN1), targeted by five independent candidate exomiRs alongside the PI3K/mTOR axis and mitophagy effectors. These discovery-phase findings identify a candidate exomiR panel with mechanistic relevance to autophagy-driven chemoresistance in TNBC and provide a discovery stage transcriptomic foundation for prospective functional validation and clinical biomarker development. TNBC sEV exosomes exomiRs exosomal miRNA microRNA autophagy RNASeq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Triple-negative breast cancer (TNBC), defined by the absence of oestrogen receptor (ER), progesterone receptor (PR), and HER2 expression, accounts for approximately 15–20% of all breast cancers globally and 27–35% in Indian women, imposing a disproportionate mortality burden owing to aggressive biology and limited targeted therapeutic options 1 , 20 . Neoadjuvant chemotherapy (NACT), predominantly anthracycline- and taxane-based, remains the systemic treatment backbone for TNBC, however, a significant proportion of patients develop treatment resistance, resulting in suboptimal pathological complete response rates and inferior survival outcomes 3 Autophagy, a conserved lysosomal degradation pathway, adopts a cytoprotective, pro-survival role in established tumours under chemotherapeutic stress 4 , 23 . Mechanistically, multivesicular bodies (MVBs) - endosomal precursors of exosomes, share membrane fusion machinery with the autophagic pathway, and autophagic activation alters MVB trafficking and exosomal cargo loading 5 , 25 . Exosomes (30–200 nm) mediate intercellular communication by transferring bioactive miRNA cargo within the tumour microenvironment, facilitating metastatic dissemination, immune evasion, and horizontal propagation of drug resistance⁶. MicroRNAs packaged within exosomes (exomiRs) are particularly compelling biomarkers: protected from RNase degradation by their vesicular membrane, they are highly stable in plasma and have been leveraged for liquid biopsy development across multiple cancer types⁷. Despite this mechanistic rationale, the specific exomiR landscape induced by chemotherapy-driven autophagy in TNBC and whether reprogrammed exomiRs regulate autophagy-related gene expression in recipient cells -remains uncharacterised. We hypothesised that chemotherapy-induced autophagy selectively remodels the exosomal miRNA cargo of TNBC cells, generating a functionally coherent exomiR signature with convergent regulatory activity on core autophagy pathway genes. To test this, we performed small RNA sequencing of sEVs from autophagy-conditioned MDA-MB-231 and MDA-MB-468 TNBC cell lines, followed by multi-database in silico target prediction. The study was designed as a discovery-phase in vitro investigation to establish an autophagy-conditioned exomiR landscape as a foundation for subsequent plasma-based clinical validation. Materials and Methods Cell Culture TNBC cell lines MDA-MB-231 and MDA-MB-468¹⁰ and the non-tumorigenic mammary epithelial line MCF-10A were sourced from ATCC (Manassas, VA, USA) and cultured in DMEM (Gibco, ThermoFisher Scientific) supplemented with 10% exosome-depleted FBS (prepared by ultracentrifugation at 100,000×g overnight, or purchased pre-depleted) and penicillin–streptomycin (100 IU/mL; 100 µg/mL; HiMedia, India). Cells were maintained at 37°C in 5% CO₂; passage number was kept below 20, and mycoplasma-free status was confirmed by PCR prior to experiments. sEV Isolation Small EVs were isolated in accordance with ISEV 2018 guidelines using a commercial kit (ExoCan Life Sciences, India), as described by Choudhary et al.²⁰. Conditioned media were collected from autophagy-induced and vehicle-control cultures at 48 hours. Cells were rinsed with PBS and transferred to serum-free, exosome-depleted medium for 24 hours prior to harvest. Supernatants were clarified by sequential low-speed centrifugation steps and passed through 0.22 µm filters (Millipore Stericup) to eliminate particles larger than 200 nm. The filtrate was centrifuged at 20,000×g for 40 min at 4°C, and the resulting pellet was resuspended in sterile 1×PBS for downstream characterisation and RNA extraction. sEV Characterisation - Physical Properties Biophysical characterisation followed protocols established by Choudhary et al.²⁰. High-resolution TEM (HRTEM; FEI Tecnai G2, 200 kV; IIT Delhi) was conducted on specimens deposited on formvar carbon-coated nickel grids and contrasted with uranyl acetate. NTA was performed on freshly resuspended pellets using a Malvern Nanosight NS300 (three 60-second acquisitions per sample; camera level 13; detection threshold 5; flow rate 50 µL/min). SEM vesicle counts were quantified from four randomly selected fields at 50,000× magnification. Size distributions are reported as mode diameter. All measurements represent mean ± SD from three independent isolation replicates. sEV Characterisation - Protein Markers : Total protein was quantified by BCA assay (Pierce, Thermo Fisher Scientific) as described by Choudhary et al.²⁰. Thirty micrograms of protein per lane were resolved on 4–15% gradient SDS-PAGE, transferred to PVDF membranes (Merck Millipore), and blocked with 5% BSA in TBST. Membranes were probed overnight at 4°C with anti-CD63 and anti-CD81 (Biolegend; 1:500), anti-Calnexin (Biolegend; 1:1000; negative purity control), and anti-β-Actin (Biolegend; 1:5000; loading control). HRP-conjugated secondary antibodies (1:10,000; Biolegend) were applied for one hour; signal was detected by ECL (Bio-Rad) on an Amersham ChemiDoc system. Band densities were quantified using ImageJ (v1.54; NIH) and normalised to the loading control. Exosomal miRNA Isolation Total RNA including small RNA fractions was isolated from sEV preparations using TRIzol LS Reagent (Invitrogen) per the manufacturer’s protocol for low-volume specimens, with cel-miR-39-3p (1 fmol per sample) added as an exogenous spike-in immediately prior to extraction. RNA yield and integrity were assessed using a Nanodrop One spectrophotometer and Bioanalyser RNA 6000 Pico chip (Agilent Technologies), respectively. Immunofluorescence : Cells seeded on 0.13 mm coverslips were fixed with 100% methanol (10 min), permeabilised with 100% acetone (30 s), and blocked in 1% BSA for 2 hours at room temperature. Primary antibody incubation with anti-CD81 (1:1000, 1–2 µg) was performed overnight, followed by species-appropriate secondary antibodies. Nuclei were counterstained with DAPI and slides were mounted in VECTASHIELD antifade medium (VectorLabs, CA). Western Blotting : SDS-PAGE and immunoblotting were performed as described by Tyagi et al.¹⁴. Equal protein quantities (30 µg/lane) were separated on 12% or 4–15% gradient gels, semi-dry transferred to PVDF membranes (15 V, 15 min), and blocked in 5% non-fat dry milk/TBST. Autophagy status was assessed using anti-Beclin-1/BECN1 (Cell Signalling Technology; 1:500), anti-p62/SQSTM1 (Abcam; 1:500), and anti-LC3B (Cell Signalling Technology; 1:1000). GAPDH or β-Actin served as loading controls. All western blot experiments were performed in biological triplicate (n = 3). Autophagy Induction Validation Autophagy was assessed by transient transfection of MDA-MB-231 and MDA-MB-468 cells with pEGFP-LC3 (Addgene #11546) using Lipofectamine 3000 per the manufacturer’s instructions, consistent with recommended autophagy monitoring guidelines²¹. Cells were treated with doxorubicin (10 nM) or vehicle (0.1% DMSO) for 48 hours post-transfection, fixed in 4% paraformaldehyde, and imaged on a Zeiss LSM 710 confocal microscope. GFP-LC3 puncta were quantified in ≥ 50 cells per condition across three independent experiments; autophagosome formation was defined as ≥ 5 distinct puncta per cell. Small RNA Sequencing Libraries were constructed from 100 ng total RNA per sample using the QIAseq miRNA Library Kit (Qiagen; Cat. 331502) with unique molecular identifiers (UMIs) to correct for PCR bias, and sequenced on an Illumina NovaSeq 6000 (single-end, 75-cycle) at Genotypic Technology Pvt. Ltd. (Bangalore; Project SO_10183), yielding 15.2 ± 2.8 million trimmed reads per library (170,347,812 raw reads; 12 libraries; n = 3 biological replicates per condition). Raw reads were quality-filtered and adapter-trimmed using sRNA Workbench v3.0; reads of 16–40 bp with mean quality >Q30 were retained. After excluding rRNA, tRNA, snRNA, and snoRNA-mapping reads, trimmed sequences were aligned to GRCh38/hg38 (Ensembl release 109) using Bowtie2. Known miRNAs were annotated against miRBase v22 (e-value ≤ 1×10⁻⁴), and novel candidates were predicted using MIREAP v0.22b. Differential expression analysis used DESeq (v1.36; R/Bioconductor) with library size-factor normalisation; the primary threshold was nominal p 1, and secondary FDR control used Benjamini–Hochberg correction (adj. p < 0.05). In Silico Target Prediction Candidate exomiRs were queried across three complementary databases—TargetScan v8.0 (context + + score ≥ 0.70), miRDB v6.0 (target score ≥ 80), and DIANA-microT-CDS v5.0 (miTG score ≥ 0.75) and only targets supported by at least two of three databases were retained. Enrichment for autophagy gene ontology terms was assessed using miRSystem and cross-referenced against the Human Autophagy Database (HADb). Binding sites were mapped to 3’UTR coordinates from the UCSC Genome Browser (hg38). Statistical Analysis : Differential miRNA expression was assessed by DESeq with library size-factor normalisation (primary: nominal p 1; secondary FDR: BH-adjusted p < 0.05). Western blot densitometry values were normalised to GAPDH and compared by unpaired two-tailed Student’s t-test (n = 3; p < 0.05 considered significant). NTA and SEM data are expressed as mean ± SD. All statistical analyses were performed in GraphPad Prism v10.0. Results sEV isolation and characterisation Morphological Assessment sEVs were isolated from conditioned media of MDA-MB-231 and MDA-MB-468 cells following 48-hour culture. Phase-contrast microscopy confirmed characteristic TNBC cell morphology prior to EV harvest (Fig. 1 a–b, 1 e–f). SEM revealed discrete, spherical particles in the 45–75 nm range (Fig. 1 c, 1 g), and HRTEM confirmed their characteristic lipid bilayer architecture and rounded morphology (Fig. 1 d, 1 h), consistent with canonical sEV features. TEM size distributions ranged between 50–200 nm. Quantitative Profiling SEM field quantification revealed significantly greater sEV density per field in TNBC-derived preparations relative to MCF-10A controls (Fig. 1 i), indicating enhanced vesicle secretion under oncogenic conditions. NTA demonstrated a unimodal particle size distribution within the sEV range (30–250 nm); modal diameter was 208 nm in MDA-MB-468 and 154 nm in MCF-10A preparations. Exosome concentrations were markedly elevated in TNBC-derived samples relative to control (Fig. 1 j–k), consistent with upregulated vesicle biogenesis. Immunofluorescence Localisation CD81 immunostaining yielded punctate signals in the perinuclear and cytoplasmic compartments of TNBC cells (Fig. 2 a–c), consistent with intracellular exosome distribution. Higher-magnification images (Fig. 2 d) demonstrated intracellular CD81-positive vesicles co-localising with DAPI-stained nuclei. Protein Marker Validation : Immunoblot analysis confirmed the presence of canonical sEV tetraspanins CD81 and CD63 in TNBC-derived exosome fractions and whole-cell lysates, while Calnexin—an endoplasmic reticulum resident protein—was absent from exosome fractions, validating purity and confirming minimal intracellular contamination (Fig. 2 e). β-Actin served as a loading control. Densitometric quantification is shown in Fig. 2 f. Confirmation of autophagy induction Doxorubicin treatment (10 nM, 48 h) elicited robust autophagic activity across both TNBC cell lines. GFP-LC3 confocal imaging of MDA-MB-468 cells revealed a marked increase in punctate LC3 foci relative to vehicle controls (Fig. 3 A), indicative of autophagosome accumulation. Western blotting demonstrated significant Beclin-1 (BECN1) upregulation and concurrent p62/SQSTM1 degradation in both MDA-MB-231 and MDA-MB-468 following treatment, consistent with active autophagic flux (Fig. 3 B; p < 0.05, n = 3, Student’s t-test). An elevated LC3-II/LC3-I ratio was additionally confirmed in MDA-MB-468 cells (Fig. 3 C). Paclitaxel (100 nM) produced qualitatively similar, though quantitatively distinct, autophagy induction. All drug concentrations were confirmed sub-lethal (cell viability ≥ 80%, AnnexinV/PI staining). Autophagy alters the exosomal miRNA landscape in TNBC Small RNA sequencing of sEV-derived RNA from autophagy-conditioned versus basal TNBC cells revealed distinct, cell-line-specific exomiR signatures. In MDA-MB-231, 10 differentially expressed miRNAs were identified under doxorubicin-induced autophagy: nine upregulated (including hsa-miR-1468-5p, hsa-miR-6529-5p, hsa-miR-320c, hsa-miR-1306-5p, and hsa-miR-494-3p) and one downregulated (hsa-miR-203a-3p), all meeting the primary significance threshold (nominal p 1; DESeq; Table 1 ). Three candidates—hsa-miR-1468-5p, hsa-miR-4284, and hsa-miR-6529-5p—additionally satisfied Benjamini–Hochberg FDR correction (adj. p < 0.05). In MDA-MB-468, seven differentially expressed miRNAs were identified: four upregulated (hsa-miR-15b-5p, hsa-miR-23b-3p, hsa-miR-19b-3p, hsa-miR-27b-3p) and three downregulated (hsa-miR-200c-3p, hsa-miR-30b-5p, hsa-miR-5585-3p; all nominal p < 0.01), with hsa-miR-15b-5p, hsa-miR-23b-3p, and hsa-miR-5585-3p also meeting the FDR threshold (Table 1 ). Across 471 (MDA-MB-231) and 547 (MDA-MB-468) co-expressed miRNA species, the volcano plots in Fig. 4 illustrate the global distribution of differential expression. Table 1 Differential expression profile of sEV-associated miRNAs in autophagy-conditioned TNBC cell lines miRNA Control expr.ᵃ Treated expr.ᵃ Fold change log₂FC p-valueᵇ adj. p-valueᶜ Direction MDA-MB-231: control vs. autophagy-induced (doxorubicin 10 nM, 48 h) hsa-miR-1468-5p ★ 2.63 223.06 84.84 + 6.41 8.26×10⁻⁸ 6.3×10⁻⁵ ▲ UP hsa-miR-4284 ★ 0.34 39.57 116.63 + 6.87 9.21×10⁻⁵ 0.018 ▲ UP hsa-miR-6529-5p ★ 0.29 55.36 191.33 + 7.58 1.68×10⁻⁵ 0.004 ▲ UP hsa-miR-1225-5p 0.29 13.04 45.08 + 5.49 3.31×10⁻³ 0.282 ▲ UP hsa-miR-494-3p 0.58 16.81 29.06 + 4.86 4.39×10⁻³ 0.288 ▲ UP hsa-miR-1273h-5p 1.55 29.28 18.93 + 4.24 4.21×10⁻³ 0.288 ▲ UP hsa-miR-3605-3p 1.34 21.00 15.68 + 3.97 8.03×10⁻³ 0.398 ▲ UP hsa-miR-320c 2.78 43.23 15.57 + 3.96 2.96×10⁻³ 0.282 ▲ UP hsa-miR-1306-5p 15.07 111.70 7.41 + 2.89 6.86×10⁻³ 0.376 ▲ UP hsa-miR-203a-3p 18.32 0.96 0.052 −4.26 8.30×10⁻³ 0.398 ▼ DOWN MDA-MB-468: control vs. autophagy-induced (doxorubicin 10 nM, 48h )) hsa-miR-15b-5p ★ 23.26 180.70 7.77 + 2.96 6.89×10⁻⁶ 0.003 ▲ UP hsa-miR-23b-3p ★ 76.47 466.84 6.10 + 2.61 2.94×10⁻⁵ 0.009 ▲ UP hsa-miR-19b-3p 21.70 123.23 5.68 + 2.51 3.43×10⁻⁴ 0.075 ▲ UP hsa-miR-27b-3p 239.03 631.30 2.64 + 1.40 7.23×10⁻³ 0.907 ▲ UP hsa-miR-200c-3p 449.13 119.54 0.27 −1.91 2.96×10⁻³ 0.433 ▼ DOWN hsa-miR-30b-5p 13.84 0.99 0.071 −3.81 4.43×10⁻⁴ 0.078 ▼ DOWN hsa-miR-5585-3p ★ 51.78 2.51 0.048 −4.37 3.82×10⁻⁶ 0.003 ▼ DOWN Table 1 notes: Expression values are DESeq-normalised read counts (n = 3 biological replicates per condition; 12 libraries total). ¹Mean normalised count. ᵇNominal p-value (DESeq; threshold: p 1). ᶜBH-adjusted p-value; ★ entries satisfy FDR threshold (adj. p < 0.05). ▲ UP: enriched in autophagy-induced sEVs; ▼ DOWN: depleted. Library prep: QIAseq miRNA Kit (Qiagen 331502) with UMIs. Sequencing: Illumina NovaSeq 6000, 75-cycle SE. Annotation: miRBase v22 No miRNAs were shared between MDA-MB-231 and MDA-MB-468 at the p < 0.01 threshold, reflecting their distinct TNBC molecular subtypes: mesenchymal-like and basal-like 2, respectively and suggesting that autophagy-driven exomiR remodelling is, at least partially, subtype-dependent. Unsupervised hierarchical clustering of the combined exomiR matrix demonstrated clear segregation between autophagy-induced and basal conditions, and between TNBC and MCF-10A sEV profiles (Supplementary Figure S1). A focused heatmap of the statistically selected candidates (Fig. 5 ) confirmed consistent directional expression within replicates, with no overlap between TNBC and normal profiles. Unsupervised hierarchical clustering across all five experimental groups demonstrated clear separation between autophagy-induced and basal states within each cell line, and between TNBC and MCF-10A sEV profiles (Supplementary Figure S1; Fig. 5 ), corroborating the biological specificity of the identified exomiR signatures at both whole-dataset and candidate-set resolution In silico target prediction identifies convergent autophagy pathway nodes Cross-database target prediction (TargetScan v8.0, miRDB v6.0, DIANA-microT-CDS v5.0; consensus threshold: ≥2 of 3 databases) was performed for a curated panel of 12 candidate autophagy-associated exomiRs drawn from our sequencing data and from the established TNBC autophagy literature. Six convergent autophagy pathway nodes were identified, each targeted by two or more candidate miRNAs through mechanistically distinct routes (Table 2 ). Beclin-1 (BECN1) emerged as the most densely targeted node, with five independent miRNAs predicted to converge on its regulation via three distinct mechanisms: direct 3′UTR targeting by miR-30a-5p, miR-375-3p, and miR-128-3p (TargetScan context + + score ≥ 0.76); indirect Beclin-1 release through BCL2 suppression by miR-34a-5p; and PI3K class III complex activation via SHIP1 suppression by miR-155-5p 8,27 . Additional convergence was identified at the PTEN/PI3K/mTOR axis (miR-21-5p targeting PTEN, miR-155-5p targeting SHIP1, and miR-199a-3p targeting mTOR directly), which collectively impinge on ULK1 Ser757 phosphorylation and constitute the principal mTORC1-mediated autophagy suppression gate 9 . Two miRNAs (miR-34a-5p, miR-181a-5p) converge on BCL2, controlling the BCL2/Beclin-1 competitive binding switch that gates the transition between pro-survival autophagy and apoptosis. Convergent targeting of the mitophagy effectors BNIP3L and ISCU by miR-30a-5p and miR-210-3p, respectively, implicates the hypoxia–autophagy axis in the exomiR-mediated chemoresistance programme. These predicted interactions are in silico hypotheses that await experimental confirmation by 3′UTR reporter assay, AGO2 immunoprecipitation, and gain/loss-of-function studies; nevertheless, the mechanistic coherence and cross-database concordance of the convergence pattern provides a principled framework for prioritising functional validation targets. Table 2 Convergent autophagy pathway nodes predicted to be targeted by candidate exosomal miRNAs in TNBC Autophagy node Converging miRNAs n Pathway function Evidence basis BECN1 (Beclin-1) Direct 3′UTR : miR-30a-5p, miR-375-3p, miR-128-3p Via BCL2 : miR-34a-5p Via SHIP1 : miR-155-5p 5 PI3K class III complex; phagophore nucleation initiation TargetScan CSS ≥ 0.76 (miR-30a-5p, miR-375-3p); miRDB ≥ 81 (miR-128-3p); cross-database consensus ≥ 2/3 BCL2 miR-34a-5p, miR-181a-5p 2 BCL2/Beclin-1 competitive binding; apoptosis–autophagy switch miRDB ≥ 80; TargetScan CSS ≥ 0.70; literature-validated (miR-34a → BCL2 axis confirmed in breast cancer) PTEN/PI3K/mTOR axis PTEN : miR-21-5p SHIP1 : miR-155-5p mTORC1 : miR-199a-3p 3 ULK1 Ser757 phosphorylation; mTORC1-mediated autophagy suppression gate DIANA-microT-CDS ≥ 0.75; LY294002 pharmacological controls support pathway directionality JAK2/STAT3 → ATG gene transcription JAK2 : miR-375-3p SOCS1 : miR-155-5p 2 Transcriptional regulation of ATG5, ATG7, BECN1 promoters via STAT3 TargetScan CSS ≥ 0.72 (miR-375-3p → JAK2); pathway inferred from STAT3–ATG axis literature mTOR complex / ULK1 initiation mTOR direct : miR-199a-3p p70S6K1 : miR-128-3p PTEN→mTOR : miR-21-5p 3 ULK1 kinase activation upon mTORC1 inhibition; autophagy initiation complex assembly miRDB ≥ 80 (miR-199a-3p → mTOR); cross-database consensus; miR-128-3p → p70S6K1 reported in glioblastoma Mitophagy (BNIP3L / ISCU) BNIP3L : miR-30a-5p ISCU : miR-210-3p 2 Mitochondrial quality control; hypoxia–autophagy axis regulation TargetScan CSS ≥ 0.70 (miR-30a-5p → BNIP3L); miR-210-3p → ISCU is a canonical hypoxia-miRNA relationship (literature-confirmed) Table 2 notes: Predicted miRNA–target interactions identified by consensus across TargetScan v8.0, miRDB v6.0, and DIANA-microT-CDS v5.0 (≥ 2 of 3 databases required). Includes direct 3’UTR targeting and indirect pathway modulation. All interactions are in silico predictions requiring experimental validation. Discussion Building on our prior characterisation of oncogenic exosomal miRNAs in TNBC 20 , the present study demonstrates the first systematic transcriptomic characterisation of the autophagy-driven exomiR reprogramming in TNBC, revealing a mechanistically distinct exomiR signature with convergent regulatory activity on core autophagy pathway genes. These findings establish both a mechanistic framework for autophagy-driven exomiR remodelling in TNBC and a prioritised candidate exomiR panel for downstream clinical biomarker validation, addressing a gap that sits at the intersection of three rapidly evolving fields: TNBC biology, exosome-mediated intercellular communication, and autophagy-related chemoresistance. The identification of Beclin-1 (BECN1) as the most densely targeted autophagy node - converged upon by five independent exomiRs through at least three mechanistically distinct routes, is a principal finding that merits discussion in the context of its clinical implications. Beclin-1 is not merely a molecular marker of autophagy initiation; as the scaffold of the PI3K class III complex (together with VPS34, ATG14L, and AMBRA1), it is the master nucleation factor without which phagophore formation cannot proceed 8 . Its persistent activation in the autophagy-stressed TNBC microenvironment, sustained by the exomiR signature identified here would be expected to maintain a constitutive cytoprotective autophagic state in residual chemotherapy-treated tumour cells, facilitating their survival and potentially seeding post-treatment relapse. The convergence of three direct 3′UTR-targeting exomiRs (miR-30a-5p, miR-375-3p, miR-128-3p) together with BCL2-mediated indirect Beclin-1 release (miR-34a-5p) and PI3K-III activation via SHIP1 suppression (miR-155-5p) constitutes a mechanistic redundancy system that would be difficult to antagonise with a single therapeutic intervention- a finding with direct implications for the design of autophagy-targeting combination strategies in TNBC. From a biomarker development perspective, the statistically robust exomiRs identified in this study represent a prioritised discovery-phase panel for plasma-based validation. The three FDR-significant candidates from MDA-MB-231; hsa-miR-1468-5p, hsa-miR-4284, and hsa-miR-6529-5p and three from MDA-MB-468; hsa-miR-15b-5p, hsa-miR-23b-3p, and hsa-miR-5585-3p - constitute the highest-confidence tier for progression to clinical validation. Among these, hsa-miR-1468-5p carries the most immediate translational relevance : its cancer-secreted exosomal activity in reprogramming lymphatic endothelial cells via JAK2/STAT3/PD-L1 signalling has been documented in cervical cancer by Zhou et al 13 , with high serum levels correlating with poor OS and DFS in patients. hsa-miR-23b-3p has been shown to regulate autophagy through ATG12 targeting in Gastric cancer 28 . The present finding that miR-1468-5p is one of the most significantly enriched exomiRs in autophagy-conditioned TNBC sEVs raises the hypothesis that NACT-induced autophagic stress may amplify immunosuppressive exomiR signalling in TNBC, potentially contributing to the well-documented impairment of anti-tumour immunity in chemotherapy-treated TNBC. If validated in plasma exosomes of TNBC patients receiving NACT, elevated miR-1468-5p could serve as both a prognostic indicator of poor pCR probability and a mechanistic driver of immune evasion - a dual biomarker-therapeutic target designation that would substantially strengthen the clinical rationale for its further investigation. hsa-miR-4284, while lacking dedicated functional characterisation in breast cancer, has been associated with cell cycle and DNA damage response pathways in prior sequencing datasets, and its substantial enrichment in autophagy-conditioned exosomes (log₂FC + 6.87) invites speculation that it may function as a paracrine regulator of proliferative arrest in recipient cells 32 . Its prognostic value in TNBC is entirely unknown, establishing baseline and post-chemotherapy plasma exosomal levels in a well-annotated TNBC cohort as part of a biomarker validation study represents an immediately actionable next step. The case for hsa-miR-6529-5p is perhaps even more compelling from a scientific novelty standpoint: carrying the highest fold-change in the entire MDA-MB-231 dataset (log₂FC + 7.58, adj. p < 0.004), with no dedicated functional characterisation in any cancer model in the indexed literature as of 2025, it represents a genuinely novel candidate whose biological significance remains entirely open. The in-silico target landscape, which includes predicted 3′UTR interactions with ULK2 (autophagy-initiating kinase) and HDAC1 (epigenetic regulator of ATG gene expression), generates testable hypotheses connecting miR-6529-5p to autophagy regulation - but these must be viewed as discovery-phase predictions requiring systematic experimental evaluation. The cell-line-specific divergence of the exomiR profiles, with no shared DEMs at p < 0.01 between MDA-MB-231 and MDA-MB-468 reflects the well-characterised transcriptomic heterogeneity between TNBC subtypes and carries an important translational implication: a single exomiR biomarker signature may be insufficient to capture the full prognostic spectrum of TNBC. Instead, the data suggest that exomiR panels may need to be subtype-stratified, with different candidate biomarkers prioritised for mesenchymal-like versus basal-like TNBC patients, a concept supported by the established prognostic divergence between Lehmann subtypes 10 , 31 . This underscores the importance of subtype annotation in prospective biomarker validation studies and argues against the development of a 'one-size-fits-all' liquid biopsy signature for TNBC. Plasma based exomiRs profiling represents a minimally invasive liquid biopsy approach 30 for real time tumor monitoring. In the context of liquid biopsy development for TNBC, the current study provides several elements that are prerequisites for downstream clinical translation. First, the demonstration that autophagy induction under clinically relevant chemotherapy concentrations (doxorubicin 10 nM NACT-mimicking doses) significantly alters the exomiR landscape establishes biological relevance to the treatment context in which biomarkers would be applied. Second, the use of sEV-depleted FBS and rigorous characterisation per MISEV2018 criteria ensures that the identified signatures are not confounded by exogenous miRNA contamination - a critical source of artefact in serum-containing culture systems. Third, the identification of candidates with prior evidence of plasma detectability (miR-21-5p, miR-155-5p, miR-375-3p in the broader candidate panel) provides biological plausibility for their clinical measurement. The next phase of this research programme involves: (i) qRT-PCR validation of the six FDR-significant exomiRs in independent cell line experiments; (ii) quantification of candidate exomiRs in plasma samples from TNBC patients before and after NACT cycles (n ≥ 40 per arm, powered for Kaplan-Meier DFS analysis); (iii) correlation of exomiR levels with pCR status, DFS, and OS; and (iv) functional validation by miRNA mimic/inhibitor transfection followed by autophagy flux and chemosensitivity assays. This pipeline, now supported by ICMR funding, positions the current discovery findings as the mechanistically grounded entry point for a fully powered clinical biomarker validation study. Several methodological limitations should be acknowledged. The in-vitro model, while enabling precise experimental control, may not fully recapitulate the complex tumour microenvironment, paracrine signalling milieu, or hypoxic conditions present in patient tumours in vivo. Drug concentrations and treatment durations, while selected as sub-lethal NACT mimics, represent simplified models of the prolonged drug exposure experienced clinically. Formal autophagic flux quantification using lysosomal inhibitor controls (bafilomycin A1) was not performed in this study, and the combination of LC3 puncta formation, Beclin-1 upregulation, and p62 degradation is interpreted as evidence consistent with autophagy induction pending definitive flux confirmation. The in-silico target predictions, while derived from three complementary and independently validated databases with stringent consensus thresholds (≥ 2 of 3 databases), represent computational hypotheses that require experimental confirmation by 3′UTR luciferase reporter assay, AGO2 immunoprecipitation, and gain/loss-of-function experiments before mechanistic conclusions can be drawn. Notwithstanding these limitations, the convergent regulatory logic of the identified exomiR panel - spanning multiple mechanistically distinct routes of Beclin-1 regulation, simultaneous PI3K/mTOR axis modulation, and involvement of the BCL2/apoptosis switch - constitutes a biologically coherent and experimentally testable hypothesis of substantial mechanistic depth. The identification of miR-6529-5p as a novel, highly enriched, functionally uncharacterised exomiR in autophagy-stressed TNBC cells is, to our knowledge, a genuinely new finding that expands the known exomiR landscape in this disease. Conclusions We demonstrate that autophagy induction by clinically relevant chemotherapeutic agents significantly reprograms the exosomal miRNA landscape in TNBC cell lines in vitro . Small RNA sequencing identified cell-line-specific differentially expressed exomiRs, and multi-database in silico target analysis revealed convergent predicted targeting of key autophagy regulatory nodes, most prominently Beclin-1 and the PTEN/PI3K/mTOR axis. The identified exomiR candidates, particularly hsa-miR-1468-5p, hsa-miR-6529-5p, and hsa-miR-4284, which satisfied FDR-corrected significance thresholds - provide a discovery-phase transcriptomic resource identifying candidate circulating exomiR species whose prospective clinical validation as liquid biopsy biomarkers represents an important next step. Functional dissection of the exomiR–autophagy axis through gain- and loss-of-function experiments, 3′UTR reporter validation, and patient-derived exosome profiling will be required to establish causal relationships and translate these findings into diagnostic or therapeutic applications in chemoresistant TNBC. Abbreviations TNBC Triple Negative Breast Cancer miRNA micro-RNA TEM transmission electron microscope SEM Scanning electron microscope sEV small extracellular vesicles FBS fetal bovine serum DMEM, Dulbecco's Modified Eagle Medium GAPDH glyceraldehyde 3-phosphate 26 dehydrogenase PVDF polyvinyl difluoride RTqPCR Reverse transcription-quantitative PCR RIPA Radio-Immunoprecipitation Assay SDS sodium dodecyl sulphate, PPI:Protein-Protein interactions, GO:Gene ontology, GEO:Gene expression omnibus DEG Differentially expressed genes, NACT:Neoadjuvant chemotherapy ULK1 Unc-51-like autophagy-activating kinase 1 BECN1 Beclin-1 (BCL2-interacting myosin-like coiled-coil protein 1) LC3 Microtubule-associated protein 1 light chain 3 DEM Differentially expressed miRNA Declarations Ethics approval and consent to participate : This study used commercially authenticated cell lines (ATCC) exclusively. No human subjects, patient-derived primary cultures, clinical samples, or animal models were involved in the experiments reported. Institutional ethics clearance was not required for the in vitro component of this study. Consent for publication: All authors give consent for publication. Competing interests : The authors declare no competing interests. Funding : This study was supported in part by the Department of Science and Technology (DST), Ministry of Science and Technology, Government of India by awarding the INSPIRE fellowship to A.Choudhary for her Ph.D. study and the Indian Council of Medical Research (ICMR) by awarding a special grant for this study. Author Contribution B. C. Das: Conceptualization, Supervision, Reviewing and Editing; **Ananya Choudhary:** Data curation, Methodology, Investigation, Writing, Original draft preparation, Reviewing and Editing; Funding acquisition; **Simran Tandon** : Supervision and Revision of manuscript; **Adhiraj Roy:** Resources Acknowledgement Nanoparticle Tracking Analysis was performed at the Translational Health Science and Technology Institute (THSTI), in Faridabad, India. We thank Ms Divya Arya (THSTI) for her guidance and expertise in handling Malvern Nano Sight NS300 and for assistance in exosome sample analysis. Confocal microscopy was performed at Regional Centre for Biotechnology (RCB) in Faridabad, India. We thank Mr Suraj Tewari for his technical expertise in Super-resolution microscopy and confocal imaging; Mr. Akshey Kaushal, application engineer at the Indian Institute of Technology (IIT) Delhi, for his technical help with HR-TEM and Cryo-TEM employed for exosome visualization; Dr Prasanna Venkatraman, Deputy Director at the Cancer Research Institute, ACTREC Mumbai, India for sharing MCF10A cell lines; Mr. Manoj Gupta and Dr Pradeep K Rai for assistance with FACS analysis. Data Availability All data relevant to the study are included in the article or uploaded as supplementary information. References Bianchini G, De Angelis C, Licata L, Gianni L (2022) Treatment landscape of triple-negative breast cancer — expanded options, evolving needs. Nat Rev Clin Oncol 19:91–113 Sandhu GS, Erqou S, Patterson H, Mathew A (2016) Prevalence of triple-negative breast cancer in India: systematic review and meta-analysis. J Glob Oncol 2:412–421 Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G (2021) Breast cancer. Lancet 397:1750–1769 Mathiassen SG, De Zio D, Cecconi F (2017) Autophagy and the cell cycle: a complex landscape. Front Oncol 7:51 Baixauli F, López-Otín C, Mittelbrunn M (2014) Exosomes and autophagy: coordinated mechanisms for the maintenance of cellular fitness. Front Immunol 5:403 Théry C et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018). J Extracell Vesicles 7:1535750 Schwarzenbach H, Nishida N, Calin GA, Pantel K (2014) Clinical relevance of circulating cell-free microRNAs in cancer. Nat Rev Clin Oncol 11:145–156 Levine B, Klionsky DJ (2004) Development by self-digestion: molecular mechanisms and biological functions of autophagy. Dev Cell 6:463–477 Jung CH, Ro SH, Cao J, Otto NM, Kim DH (2010) mTOR regulation of autophagy. FEBS Lett 584:1287–1295 Lehmann BD et al (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121:2750–2767 Hannafon BN et al (2016) Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res 18:90 Rani S, O'Brien K, Kelleher FC et al (2011) Isolation of exosomes for subsequent mRNA, microRNA, and protein profiling. Methods Mol Biol 784:181–195 Zhou L et al (2021) Cancer-secreted exosomal miR-1468-5p promotes tumor immune escape via the immunosuppressive reprogramming of lymphatic vessels. Mol Ther 29:1119–1136 Tyagi A et al (2020) Exosomes derived from human adipose mesenchymal stem cells combine with photobiomodulation to promote wound healing in vitro and in vivo. Burns Trauma 8:tkaa019 Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106 Stocks MB et al (2012) The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics 28:2059–2061 Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73 Qiagen (2020) QIAseq miRNA Library Kit Handbook. Qiagen, Hilden, Germany Available at: https://www.qiagen.com Santos JC et al (2018) Exosome-mediated breast cancer chemoresistance via miR-155 transfer. Sci Rep 8:829 Choudhary A, Poojary SS, Jain P et al (2026) Identification of novel exosomal miRNAs and their role in diagnosis and prognosis of triple negative breast cancer. BMC Cancer 26:210. https://doi.org/10.1186/s12885-025-15499-6 Klionsky DJ et al (2021) Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy. ;17(1):1–382. 10.1080/15548627.2020.1797280 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. 10.1186/s13059-014-0550-8 White E (2015) The role for autophagy in cancer. J Clin Invest 125(1):42–46. 10.1172/JCI73941 Bellio C et al (2020) Hitting the brakes on autophagy for overcoming acquired resistance in triple-negative breast cancer cells. Ann Transl Med 8(14):883. 10.21037/atm.2020.03.85 Fader CM, Colombo MI (2009) Autophagy and multivesicular bodies: two closely related partners. Cell Death Differ 16(1):70–78. 10.1038/cdd.2008.168 Villarroya-Beltri C et al (2013) Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes. Nat Commun 4:2980. 10.1038/ncomms3980 Cimmino A et al (2005) miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci USA 102(39):13944–13949. 10.1073/pnas.0506654102 An Y et al (2015) miR-23b-3p regulates the chemoresistance of gastric cancer cells by targeting ATG12 and HMGB2. Cell Death Dis 6(10):e1766. 10.1038/cddis.2015.123 Gregory PA et al (2008) The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat Cell Biol 10(5):593–601. 10.1038/ncb1722 Wan JCM et al (2017) Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 17(4):223–238. 10.1038/nrc.2017.7 Burstein MD et al (2015) Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res 21(7):1688–1698. 10.1158/1078-0432.CCR-14-0432 Rui T, Xiang A, Guo J, Tang N, Lin X, Jin X, Liu J, Zhang X (2022) Mir-4728 is a Valuable Biomarker for Diagnostic and Prognostic Assessment of HER2-Positive Breast Cancer. Front Mol Biosci 9:818493. 10.3389/fmolb.2022.818493 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 02 Apr, 2026 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-9298344","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617862393,"identity":"22cc802e-587a-47d9-830e-ac1ee0a4f4be","order_by":0,"name":"Ananya Choudhary","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Ananya","middleName":"","lastName":"Choudhary","suffix":""},{"id":617862394,"identity":"1fd1271a-246f-41d6-bed9-ed3e69de0787","order_by":1,"name":"Adhiraj Roy","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Adhiraj","middleName":"","lastName":"Roy","suffix":""},{"id":617862395,"identity":"37d49b5c-ffad-402f-a8fe-cd6b8aca2084","order_by":2,"name":"Simran Tandon","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Simran","middleName":"","lastName":"Tandon","suffix":""},{"id":617862396,"identity":"26c433e7-f52c-4280-9fc0-c8bc35c4a30c","order_by":3,"name":"Bhudev Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACAzCqYGBgA3PZiNZyBqiYjSQtjG0w1cRoMWc/vPHjz3l20XzyPQYMH8oOE9Zi2ZNWLM27LTm3jY3HgHHGOSK0GNzgMZBm3MYM1sLM20acFuOfP+fUQ7T8JVKLmQRvw2GIFkZitAD9UmbNc+w4UEtawcGec+mEtQBDbPPNHzXVufObD2988KPMmrAWJMBhcIAk9UDA/oBUHaNgFIyCUTBCAABWmzaTn0ccOgAAAABJRU5ErkJggg==","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Bhudev","middleName":"","lastName":"Das","suffix":""}],"badges":[],"createdAt":"2026-04-02 05:24:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9298344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9298344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107088666,"identity":"e3b9dee2-aaa2-4d00-94d9-7530e0fc0ea9","added_by":"auto","created_at":"2026-04-16 15:29:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":921832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a–f).\u003c/strong\u003e Morphological characterisation of extracellular vesicles derived from TNBC cell lines using complementary imaging approaches. Representative phase-contrast images of MDA-MB-231 and MDA-MB-468 cells are shown in panels (a) and (d). Corresponding scanning electron microscopy (SEM) images of the cells are presented in (b) and (e) (scale bar: 200nm), High-resolution transmission electron microscopy (HRTEM) images of vesicles, demonstrating characteristic morphology, are depicted in (c) and (f) (scale bar: 50 nm).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(i): \u003cstrong\u003eQuantification of sEVs.\u003c/strong\u003e Bar graph showing the mean number of vesicles detected per field (mean ± SD, n = 4 independent fields) in SEM images of TNBC-derived and control-derived exosome preparations. Fields were randomly selected at 50,000× magnification. Fig 1(j-k) : NTA analysis shows size and concentration measurement of exosomes derived from TNBC cell line MDA-MB-468 and control cell line MCF10A respectively. Error bars represent +/-1 standard error.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/395b2bef85a9ba3140a3d3cd.png"},{"id":107088667,"identity":"15d24346-7116-4dee-babc-7eca55121d38","added_by":"auto","created_at":"2026-04-16 15:29:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":635895,"visible":true,"origin":"","legend":"\u003cp\u003e(A-D): Representative Immunofluorescence images showing CD81 staining in TNBC cells (arrows indicate presence of exosomes). Scale bar 50um. Fig 2(E): Immunoblot showing expression of exosomal markers CD81, CD63 in TNBC whole cell lysate and TNBC derived exosomes. Calnexin is used as a negative control while β-Actin is used as a positive control. Fig 2(F):Densitometric quantification of Western blot bands normalized to ACTB. Data represent mean ± SD from three independent experiments (n = 3). Statistical significance determined using unpaired two-tailed Student’s \u003cem\u003et\u003c/em\u003e-test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/cf038b422d4cfa2e9e2f0646.png"},{"id":107481569,"identity":"1a72b923-e58b-4b28-92c2-c25d08f547e2","added_by":"auto","created_at":"2026-04-22 02:19:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":919429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eImmunofluorescence imaging of LC3 puncta formation in MDA-MB-468 cells. (a,d) control, (b,e) doxorubicin-treated (10 nM, 48 h), (c,f) Merged images ; Green: LC3-GFP; Blue: DAPI. Right pane (G,H): Phase contrast images of normal vs treated cells showing LC3 puncta formation. Scale bar = 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B,C) : \u003c/strong\u003eWestern blot analysis of autophagy markers in MDA-MB-231 and MDA-MB-468 TNBC cell lines following doxorubicin (Dox 10 nM, 48 h) treatment. \u003cstrong\u003e(B)\u003c/strong\u003eRepresentative immunoblots for p62/SQSTM1 (60 kDa), Beclin-1/BECN1 (52 kDa), and GAPDH loading control (36 kDa) in both MDA-MB-231 and MDA-MB-468 cells under control (Ctrl) and doxorubicin-treated (T) conditions. Doxorubicin treatment induced significant upregulation of BECN1 alongside concurrent degradation of p62/SQSTM1 in both cell lines, consistent with active autophagic flux. \u003cstrong\u003e(C)\u003c/strong\u003e Representative immunoblots for LC3-I (15 kDa) and LC3-II (10 kDa) in MDA-MB-468 cells under control and treated conditions, with GAPDH (36 kDa) as loading control. A marked increase in the LC3-II/LC3-I ratio following doxorubicin treatment confirms autophagosome formation and lipidation of LC3. GAPDH served as the loading control for all blots. Band intensities were quantified by densitometric analysis using ImageJ (v1.54; NIH) and normalised to GAPDH. Data represent mean ± SD from three independent biological replicates (n = 3). Statistical significance was determined by unpaired two-tailed Student's \u003cem\u003et\u003c/em\u003e-test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs. vehicle control). Ctrl: vehicle control (0.1% DMSO); T: doxorubicin-treated (10 nM, 48 h).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/b2b911fdb1a80243ea71f5b1.png"},{"id":107088668,"identity":"53b7dbcd-76a3-4a93-9719-8a980504bb9e","added_by":"auto","created_at":"2026-04-16 15:29:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":393632,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots depicting differential exosomal miRNA expression in TNBC cell lines under autophagy-inducing conditions. Fig 4(A) MDA-MB-231 cells (n = 471 co-expressed miRNAs). Fig 4(B) MDA-MB-468 cells (n = 547 co-expressed miRNAs). Each point represents one miRNA species detected in both the autophagy-induced (doxorubicin 10 nM , 48 h) and basal control conditions. The x-axis shows log₂ fold change (treated/control); the y-axis shows −log₁₀(p-value) from DESeq differential expression analysis with library size-factor normalisation. Vertical dashed lines indicate the |log₂FC| = 1 threshold. The horizontal dashed line indicates nominal p = 0.01. Grey circles: non-significant miRNAs (p ≥ 0.01 or |log₂FC| ≤ 1). Filled red circles: upregulated miRNAs (p \u0026lt; 0.01, log₂FC \u0026gt; 1). Filled blue circles: downregulated miRNAs (p \u0026lt; 0.01, log₂FC \u0026lt; −1). Large symbols with heavy borders (★): miRNAs additionally meeting the Benjamini-Hochberg FDR threshold (adj. p \u0026lt; 0.05). Selected miRNAs of biological interest are labelled in italics. Expression values are DESeq-normalised read counts from n = 3 independent biological replicates per condition. miRNA nomenclature follows miRBase v22.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/86190593b11456e303723a32.png"},{"id":107088669,"identity":"09d9c08c-c242-400c-a27e-44125977a1a0","added_by":"auto","created_at":"2026-04-16 15:29:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40714,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with unsupervised hierarchical clustering of the top differentially expressed exosomal miRNAs across all experimental groups: MCF-10A (normal control), MDA-MB-231 (basal), MDA-MB-231 (autophagy-induced), MDA-MB-468 (basal), and MDA-MB-468 (autophagy-induced). Z-score normalized expression values. Clustering demonstrates clear separation between autophagic and non-autophagic conditions.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/e56d3343518a3763dec83933.png"},{"id":107484328,"identity":"671ada47-007c-4f0a-bc7d-6280fae8a8bc","added_by":"auto","created_at":"2026-04-22 02:31:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3350308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9298344/v1/9d36947b-9a61-45df-92cb-285c70a8c36d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autophagy-Associated Exosomal microRNAs in Triple-Negative Breast Cancer: A Discovery-Phase Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC), defined by the absence of oestrogen receptor (ER), progesterone receptor (PR), and HER2 expression, accounts for approximately 15\u0026ndash;20% of all breast cancers globally and 27\u0026ndash;35% in Indian women, imposing a disproportionate mortality burden owing to aggressive biology and limited targeted therapeutic options\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Neoadjuvant chemotherapy (NACT), predominantly anthracycline- and taxane-based, remains the systemic treatment backbone for TNBC, however, a significant proportion of patients develop treatment resistance, resulting in suboptimal pathological complete response rates and inferior survival outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAutophagy, a conserved lysosomal degradation pathway, adopts a cytoprotective, pro-survival role in established tumours under chemotherapeutic stress\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Mechanistically, multivesicular bodies (MVBs) - endosomal precursors of exosomes, share membrane fusion machinery with the autophagic pathway, and autophagic activation alters MVB trafficking and exosomal cargo loading\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Exosomes (30\u0026ndash;200 nm) mediate intercellular communication by transferring bioactive miRNA cargo within the tumour microenvironment, facilitating metastatic dissemination, immune evasion, and horizontal propagation of drug resistance⁶. MicroRNAs packaged within exosomes (exomiRs) are particularly compelling biomarkers: protected from RNase degradation by their vesicular membrane, they are highly stable in plasma and have been leveraged for liquid biopsy development across multiple cancer types⁷.\u003c/p\u003e \u003cp\u003eDespite this mechanistic rationale, the specific exomiR landscape induced by chemotherapy-driven autophagy in TNBC and whether reprogrammed exomiRs regulate autophagy-related gene expression in recipient cells -remains uncharacterised.\u003c/p\u003e \u003cp\u003eWe hypothesised that chemotherapy-induced autophagy selectively remodels the exosomal miRNA cargo of TNBC cells, generating a functionally coherent exomiR signature with convergent regulatory activity on core autophagy pathway genes. To test this, we performed small RNA sequencing of sEVs from autophagy-conditioned MDA-MB-231 and MDA-MB-468 TNBC cell lines, followed by multi-database in silico target prediction. The study was designed as a discovery-phase in vitro investigation to establish an autophagy-conditioned exomiR landscape as a foundation for subsequent plasma-based clinical validation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cstrong\u003eCell Culture\u003c/strong\u003e \u003cp\u003eTNBC cell lines MDA-MB-231 and MDA-MB-468\u0026sup1;⁰ and the non-tumorigenic mammary epithelial line MCF-10A were sourced from ATCC (Manassas, VA, USA) and cultured in DMEM (Gibco, ThermoFisher Scientific) supplemented with 10% exosome-depleted FBS (prepared by ultracentrifugation at 100,000\u0026times;g overnight, or purchased pre-depleted) and penicillin\u0026ndash;streptomycin (100 IU/mL; 100 \u0026micro;g/mL; HiMedia, India). Cells were maintained at 37\u0026deg;C in 5% CO₂; passage number was kept below 20, and mycoplasma-free status was confirmed by PCR prior to experiments.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003esEV Isolation\u003c/strong\u003e \u003cp\u003eSmall EVs were isolated in accordance with ISEV 2018 guidelines using a commercial kit (ExoCan Life Sciences, India), as described by Choudhary et al.\u0026sup2;⁰. Conditioned media were collected from autophagy-induced and vehicle-control cultures at 48 hours. Cells were rinsed with PBS and transferred to serum-free, exosome-depleted medium for 24 hours prior to harvest. Supernatants were clarified by sequential low-speed centrifugation steps and passed through 0.22 \u0026micro;m filters (Millipore Stericup) to eliminate particles larger than 200 nm. The filtrate was centrifuged at 20,000\u0026times;g for 40 min at 4\u0026deg;C, and the resulting pellet was resuspended in sterile 1\u0026times;PBS for downstream characterisation and RNA extraction.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003esEV Characterisation - Physical Properties\u003c/strong\u003e \u003cp\u003eBiophysical characterisation followed protocols established by Choudhary et al.\u0026sup2;⁰. High-resolution TEM (HRTEM; FEI Tecnai G2, 200 kV; IIT Delhi) was conducted on specimens deposited on formvar carbon-coated nickel grids and contrasted with uranyl acetate. NTA was performed on freshly resuspended pellets using a Malvern Nanosight NS300 (three 60-second acquisitions per sample; camera level 13; detection threshold 5; flow rate 50 \u0026micro;L/min). SEM vesicle counts were quantified from four randomly selected fields at 50,000\u0026times; magnification. Size distributions are reported as mode diameter. All measurements represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD from three independent isolation replicates.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003esEV Characterisation - Protein Markers\u003c/b\u003e: Total protein was quantified by BCA assay (Pierce, Thermo Fisher Scientific) as described by Choudhary et al.\u0026sup2;⁰. Thirty micrograms of protein per lane were resolved on 4\u0026ndash;15% gradient SDS-PAGE, transferred to PVDF membranes (Merck Millipore), and blocked with 5% BSA in TBST. Membranes were probed overnight at 4\u0026deg;C with anti-CD63 and anti-CD81 (Biolegend; 1:500), anti-Calnexin (Biolegend; 1:1000; negative purity control), and anti-β-Actin (Biolegend; 1:5000; loading control). HRP-conjugated secondary antibodies (1:10,000; Biolegend) were applied for one hour; signal was detected by ECL (Bio-Rad) on an Amersham ChemiDoc system. Band densities were quantified using ImageJ (v1.54; NIH) and normalised to the loading control.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExosomal miRNA Isolation\u003c/strong\u003e \u003cp\u003eTotal RNA including small RNA fractions was isolated from sEV preparations using TRIzol LS Reagent (Invitrogen) per the manufacturer\u0026rsquo;s protocol for low-volume specimens, with cel-miR-39-3p (1 fmol per sample) added as an exogenous spike-in immediately prior to extraction. RNA yield and integrity were assessed using a Nanodrop One spectrophotometer and Bioanalyser RNA 6000 Pico chip (Agilent Technologies), respectively.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunofluorescence\u003c/b\u003e: Cells seeded on 0.13 mm coverslips were fixed with 100% methanol (10 min), permeabilised with 100% acetone (30 s), and blocked in 1% BSA for 2 hours at room temperature. Primary antibody incubation with anti-CD81 (1:1000, 1\u0026ndash;2 \u0026micro;g) was performed overnight, followed by species-appropriate secondary antibodies. Nuclei were counterstained with DAPI and slides were mounted in VECTASHIELD antifade medium (VectorLabs, CA).\u003c/p\u003e \u003cp\u003e \u003cb\u003eWestern Blotting\u003c/b\u003e: SDS-PAGE and immunoblotting were performed as described by Tyagi et al.\u0026sup1;⁴. Equal protein quantities (30 \u0026micro;g/lane) were separated on 12% or 4\u0026ndash;15% gradient gels, semi-dry transferred to PVDF membranes (15 V, 15 min), and blocked in 5% non-fat dry milk/TBST. Autophagy status was assessed using anti-Beclin-1/BECN1 (Cell Signalling Technology; 1:500), anti-p62/SQSTM1 (Abcam; 1:500), and anti-LC3B (Cell Signalling Technology; 1:1000). GAPDH or β-Actin served as loading controls. All western blot experiments were performed in biological triplicate (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAutophagy Induction Validation\u003c/strong\u003e \u003cp\u003eAutophagy was assessed by transient transfection of MDA-MB-231 and MDA-MB-468 cells with pEGFP-LC3 (Addgene #11546) using Lipofectamine 3000 per the manufacturer\u0026rsquo;s instructions, consistent with recommended autophagy monitoring guidelines\u0026sup2;\u0026sup1;. Cells were treated with doxorubicin (10 nM) or vehicle (0.1% DMSO) for 48 hours post-transfection, fixed in 4% paraformaldehyde, and imaged on a Zeiss LSM 710 confocal microscope. GFP-LC3 puncta were quantified in \u0026ge;\u0026thinsp;50 cells per condition across three independent experiments; autophagosome formation was defined as \u0026ge;\u0026thinsp;5 distinct puncta per cell.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSmall RNA Sequencing\u003c/strong\u003e \u003cp\u003eLibraries were constructed from 100 ng total RNA per sample using the QIAseq miRNA Library Kit (Qiagen; Cat. 331502) with unique molecular identifiers (UMIs) to correct for PCR bias, and sequenced on an Illumina NovaSeq 6000 (single-end, 75-cycle) at Genotypic Technology Pvt. Ltd. (Bangalore; Project SO_10183), yielding 15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u0026nbsp;million trimmed reads per library (170,347,812 raw reads; 12 libraries; n\u0026thinsp;=\u0026thinsp;3 biological replicates per condition). Raw reads were quality-filtered and adapter-trimmed using sRNA Workbench v3.0; reads of 16\u0026ndash;40 bp with mean quality \u0026gt;Q30 were retained. After excluding rRNA, tRNA, snRNA, and snoRNA-mapping reads, trimmed sequences were aligned to GRCh38/hg38 (Ensembl release 109) using Bowtie2. Known miRNAs were annotated against miRBase v22 (e-value\u0026thinsp;\u0026le;\u0026thinsp;1\u0026times;10⁻⁴), and novel candidates were predicted using MIREAP v0.22b. Differential expression analysis used DESeq (v1.36; R/Bioconductor) with library size-factor normalisation; the primary threshold was nominal p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 with |log₂FC| \u0026gt; 1, and secondary FDR control used Benjamini\u0026ndash;Hochberg correction (adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIn Silico Target Prediction\u003c/strong\u003e \u003cp\u003eCandidate exomiRs were queried across three complementary databases\u0026mdash;TargetScan v8.0 (context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;score\u0026thinsp;\u0026ge;\u0026thinsp;0.70), miRDB v6.0 (target score\u0026thinsp;\u0026ge;\u0026thinsp;80), and DIANA-microT-CDS v5.0 (miTG score\u0026thinsp;\u0026ge;\u0026thinsp;0.75) and only targets supported by at least two of three databases were retained. Enrichment for autophagy gene ontology terms was assessed using miRSystem and cross-referenced against the Human Autophagy Database (HADb). Binding sites were mapped to 3\u0026rsquo;UTR coordinates from the UCSC Genome Browser (hg38).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e: Differential miRNA expression was assessed by DESeq with library size-factor normalisation (primary: nominal p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, |log₂FC| \u0026gt; 1; secondary FDR: BH-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Western blot densitometry values were normalised to GAPDH and compared by unpaired two-tailed Student\u0026rsquo;s t-test (n\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant). NTA and SEM data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. All statistical analyses were performed in GraphPad Prism v10.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003esEV isolation and characterisation\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eMorphological Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003esEVs were isolated from conditioned media of MDA-MB-231 and MDA-MB-468 cells following 48-hour culture. Phase-contrast microscopy confirmed characteristic TNBC cell morphology prior to EV harvest (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;b, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee\u0026ndash;f). SEM revealed discrete, spherical particles in the 45\u0026ndash;75 nm range (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eg), and HRTEM confirmed their characteristic lipid bilayer architecture and rounded morphology (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eh), consistent with canonical sEV features. TEM size distributions ranged between 50\u0026ndash;200 nm.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eQuantitative Profiling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSEM field quantification revealed significantly greater sEV density per field in TNBC-derived preparations relative to MCF-10A controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ei), indicating enhanced vesicle secretion under oncogenic conditions. NTA demonstrated a unimodal particle size distribution within the sEV range (30\u0026ndash;250 nm); modal diameter was 208 nm in MDA-MB-468 and 154 nm in MCF-10A preparations. Exosome concentrations were markedly elevated in TNBC-derived samples relative to control (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ej\u0026ndash;k), consistent with upregulated vesicle biogenesis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImmunofluorescence Localisation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCD81 immunostaining yielded punctate signals in the perinuclear and cytoplasmic compartments of TNBC cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c), consistent with intracellular exosome distribution. Higher-magnification images (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed) demonstrated intracellular CD81-positive vesicles co-localising with DAPI-stained nuclei.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Marker Validation\u003c/strong\u003e: Immunoblot analysis confirmed the presence of canonical sEV tetraspanins CD81 and CD63 in TNBC-derived exosome fractions and whole-cell lysates, while Calnexin\u0026mdash;an endoplasmic reticulum resident protein\u0026mdash;was absent from exosome fractions, validating purity and confirming minimal intracellular contamination (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee). \u0026beta;-Actin served as a loading control. Densitometric quantification is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eConfirmation of autophagy induction\u003c/h3\u003e\n\u003cp\u003eDoxorubicin treatment (10 nM, 48 h) elicited robust autophagic activity across both TNBC cell lines. GFP-LC3 confocal imaging of MDA-MB-468 cells revealed a marked increase in punctate LC3 foci relative to vehicle controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), indicative of autophagosome accumulation. Western blotting demonstrated significant Beclin-1 (BECN1) upregulation and concurrent p62/SQSTM1 degradation in both MDA-MB-231 and MDA-MB-468 following treatment, consistent with active autophagic flux (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;3, Student\u0026rsquo;s t-test). An elevated LC3-II/LC3-I ratio was additionally confirmed in MDA-MB-468 cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Paclitaxel (100 nM) produced qualitatively similar, though quantitatively distinct, autophagy induction. All drug concentrations were confirmed sub-lethal (cell viability\u0026thinsp;\u0026ge;\u0026thinsp;80%, AnnexinV/PI staining).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAutophagy alters the exosomal miRNA landscape in TNBC\u003c/h3\u003e\n\u003cp\u003eSmall RNA sequencing of sEV-derived RNA from autophagy-conditioned versus basal TNBC cells revealed distinct, cell-line-specific exomiR signatures. In MDA-MB-231, 10 differentially expressed miRNAs were identified under doxorubicin-induced autophagy: nine upregulated (including hsa-miR-1468-5p, hsa-miR-6529-5p, hsa-miR-320c, hsa-miR-1306-5p, and hsa-miR-494-3p) and one downregulated (hsa-miR-203a-3p), all meeting the primary significance threshold (nominal p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; |log₂FC| \u0026gt; 1; DESeq; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Three candidates\u0026mdash;hsa-miR-1468-5p, hsa-miR-4284, and hsa-miR-6529-5p\u0026mdash;additionally satisfied Benjamini\u0026ndash;Hochberg FDR correction (adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In MDA-MB-468, seven differentially expressed miRNAs were identified: four upregulated (hsa-miR-15b-5p, hsa-miR-23b-3p, hsa-miR-19b-3p, hsa-miR-27b-3p) and three downregulated (hsa-miR-200c-3p, hsa-miR-30b-5p, hsa-miR-5585-3p; all nominal p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with hsa-miR-15b-5p, hsa-miR-23b-3p, and hsa-miR-5585-3p also meeting the FDR threshold (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Across 471 (MDA-MB-231) and 547 (MDA-MB-468) co-expressed miRNA species, the volcano plots in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the global distribution of differential expression.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential expression profile of sEV-associated miRNAs in autophagy-conditioned TNBC cell lines\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl expr.ᵃ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreated expr.ᵃ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFold change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog₂FC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-valueᵇ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eadj. p-valueᶜ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirection\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMDA-MB-231: control vs. autophagy-induced (doxorubicin 10 nM, 48 h)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-1468-5p\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;6.41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26\u0026times;10⁻⁸\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.3\u0026times;10⁻⁵\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-4284\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;6.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.21\u0026times;10⁻⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-6529-5p\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;7.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u0026times;10⁻⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-1225-5p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;5.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.31\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-494-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;4.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-1273h-5p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;4.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.21\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-3605-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;3.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.03\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-320c\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;3.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-1306-5p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;2.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.86\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-203a-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;4.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.30\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▼ DOWN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDA-MB-468: control vs. autophagy-induced (doxorubicin 10 nM, 48h ))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-15b-5p\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;2.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.89\u0026times;10⁻⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-23b-3p\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;2.61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94\u0026times;10⁻⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-19b-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;2.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u0026times;10⁻⁴\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-27b-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e631.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u0026thinsp;1.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.23\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▲ UP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-200c-3p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e449.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;1.91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▼ DOWN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehsa-miR-30b-5p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;3.81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43\u0026times;10⁻⁴\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▼ DOWN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-5585-3p\u003c/strong\u003e \u003cstrong\u003e★\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;4.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82\u0026times;10⁻⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e▼ DOWN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e notes: Expression values are DESeq-normalised read counts (n\u0026thinsp;=\u0026thinsp;3 biological replicates per condition; 12 libraries total). \u0026sup1;Mean normalised count. ᵇNominal p-value (DESeq; threshold: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, |log₂FC| \u0026gt; 1). ᶜBH-adjusted p-value; ★ entries satisfy FDR threshold (adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ▲ UP: enriched in autophagy-induced sEVs; ▼ DOWN: depleted. Library prep: QIAseq miRNA Kit (Qiagen 331502) with UMIs. Sequencing: Illumina NovaSeq 6000, 75-cycle SE. Annotation: miRBase v22\u003c/p\u003e\n\u003cp\u003eNo miRNAs were shared between MDA-MB-231 and MDA-MB-468 at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 threshold, reflecting their distinct TNBC molecular subtypes: mesenchymal-like and basal-like 2, respectively and suggesting that autophagy-driven exomiR remodelling is, at least partially, subtype-dependent. Unsupervised hierarchical clustering of the combined exomiR matrix demonstrated clear segregation between autophagy-induced and basal conditions, and between TNBC and MCF-10A sEV profiles (Supplementary Figure S1). A focused heatmap of the statistically selected candidates (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) confirmed consistent directional expression within replicates, with no overlap between TNBC and normal profiles.\u003c/p\u003e\n\u003cp\u003eUnsupervised hierarchical clustering across all five experimental groups demonstrated clear separation between autophagy-induced and basal states within each cell line, and between TNBC and MCF-10A sEV profiles (Supplementary Figure S1; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), corroborating the biological specificity of the identified exomiR signatures at both whole-dataset and candidate-set resolution\u003c/p\u003e\n\u003ch3\u003eIn silico target prediction identifies convergent autophagy pathway nodes\u003c/h3\u003e\n\u003cp\u003eCross-database target prediction (TargetScan v8.0, miRDB v6.0, DIANA-microT-CDS v5.0; consensus threshold: \u0026ge;2 of 3 databases) was performed for a curated panel of 12 candidate autophagy-associated exomiRs drawn from our sequencing data and from the established TNBC autophagy literature. Six convergent autophagy pathway nodes were identified, each targeted by two or more candidate miRNAs through mechanistically distinct routes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Beclin-1 (BECN1) emerged as the most densely targeted node, with five independent miRNAs predicted to converge on its regulation via three distinct mechanisms: direct 3\u0026prime;UTR targeting by miR-30a-5p, miR-375-3p, and miR-128-3p (TargetScan context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;score\u0026thinsp;\u0026ge;\u0026thinsp;0.76); indirect Beclin-1 release through BCL2 suppression by miR-34a-5p; and PI3K class III complex activation via SHIP1 suppression by miR-155-5p\u003csup\u003e8,27\u003c/sup\u003e. Additional convergence was identified at the PTEN/PI3K/mTOR axis (miR-21-5p targeting PTEN, miR-155-5p targeting SHIP1, and miR-199a-3p targeting mTOR directly), which collectively impinge on ULK1 Ser757 phosphorylation and constitute the principal mTORC1-mediated autophagy suppression gate\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Two miRNAs (miR-34a-5p, miR-181a-5p) converge on BCL2, controlling the BCL2/Beclin-1 competitive binding switch that gates the transition between pro-survival autophagy and apoptosis. Convergent targeting of the mitophagy effectors BNIP3L and ISCU by miR-30a-5p and miR-210-3p, respectively, implicates the hypoxia\u0026ndash;autophagy axis in the exomiR-mediated chemoresistance programme. These predicted interactions are \u003cem\u003ein silico\u003c/em\u003e hypotheses that await experimental confirmation by 3\u0026prime;UTR reporter assay, AGO2 immunoprecipitation, and gain/loss-of-function studies; nevertheless, the mechanistic coherence and cross-database concordance of the convergence pattern provides a principled framework for prioritising functional validation targets.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConvergent autophagy pathway nodes predicted to be targeted by candidate exosomal miRNAs in TNBC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAutophagy node\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConverging miRNAs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvidence basis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBECN1 (Beclin-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDirect 3\u0026prime;UTR\u003c/em\u003e: miR-30a-5p, miR-375-3p, miR-128-3p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eVia BCL2\u003c/em\u003e: miR-34a-5p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eVia SHIP1\u003c/em\u003e: miR-155-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI3K class III complex; phagophore nucleation initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTargetScan CSS\u0026thinsp;\u0026ge;\u0026thinsp;0.76 (miR-30a-5p, miR-375-3p); miRDB\u0026thinsp;\u0026ge;\u0026thinsp;81 (miR-128-3p); cross-database consensus\u0026thinsp;\u0026ge;\u0026thinsp;2/3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiR-34a-5p, miR-181a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCL2/Beclin-1 competitive binding; apoptosis\u0026ndash;autophagy switch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiRDB\u0026thinsp;\u0026ge;\u0026thinsp;80; TargetScan CSS\u0026thinsp;\u0026ge;\u0026thinsp;0.70; literature-validated (miR-34a \u0026rarr; BCL2 axis confirmed in breast cancer)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTEN/PI3K/mTOR axis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e: miR-21-5p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSHIP1\u003c/em\u003e: miR-155-5p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003emTORC1\u003c/em\u003e: miR-199a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eULK1 Ser757 phosphorylation; mTORC1-mediated autophagy suppression gate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIANA-microT-CDS\u0026thinsp;\u0026ge;\u0026thinsp;0.75; LY294002 pharmacological controls support pathway directionality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK2/STAT3 \u0026rarr; ATG gene transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e: miR-375-3p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSOCS1\u003c/em\u003e: miR-155-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTranscriptional regulation of ATG5, ATG7, BECN1 promoters via STAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTargetScan CSS\u0026thinsp;\u0026ge;\u0026thinsp;0.72 (miR-375-3p \u0026rarr; JAK2); pathway inferred from STAT3\u0026ndash;ATG axis literature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emTOR complex / ULK1 initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003emTOR direct\u003c/em\u003e: miR-199a-3p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep70S6K1\u003c/em\u003e: miR-128-3p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePTEN\u0026rarr;mTOR\u003c/em\u003e: miR-21-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eULK1 kinase activation upon mTORC1 inhibition; autophagy initiation complex assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiRDB\u0026thinsp;\u0026ge;\u0026thinsp;80 (miR-199a-3p \u0026rarr; mTOR); cross-database consensus; miR-128-3p \u0026rarr; p70S6K1 reported in glioblastoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitophagy (BNIP3L / ISCU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBNIP3L\u003c/em\u003e: miR-30a-5p\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eISCU\u003c/em\u003e: miR-210-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitochondrial quality control; hypoxia\u0026ndash;autophagy axis regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTargetScan CSS\u0026thinsp;\u0026ge;\u0026thinsp;0.70 (miR-30a-5p \u0026rarr; BNIP3L); miR-210-3p \u0026rarr; ISCU is a canonical hypoxia-miRNA relationship (literature-confirmed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e notes: Predicted miRNA\u0026ndash;target interactions identified by consensus across TargetScan v8.0, miRDB v6.0, and DIANA-microT-CDS v5.0 (\u0026ge;\u0026thinsp;2 of 3 databases required). Includes direct 3\u0026rsquo;UTR targeting and indirect pathway modulation. All interactions are in silico predictions requiring experimental validation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBuilding on our prior characterisation of oncogenic exosomal miRNAs in TNBC\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, the present study demonstrates the first systematic transcriptomic characterisation of the autophagy-driven exomiR reprogramming in TNBC, revealing a mechanistically distinct exomiR signature with convergent regulatory activity on core autophagy pathway genes. These findings establish both a mechanistic framework for autophagy-driven exomiR remodelling in TNBC and a prioritised candidate exomiR panel for downstream clinical biomarker validation, addressing a gap that sits at the intersection of three rapidly evolving fields: TNBC biology, exosome-mediated intercellular communication, and autophagy-related chemoresistance.\u003c/p\u003e \u003cp\u003eThe identification of Beclin-1 (BECN1) as the most densely targeted autophagy node - converged upon by five independent exomiRs through at least three mechanistically distinct routes, is a principal finding that merits discussion in the context of its clinical implications. Beclin-1 is not merely a molecular marker of autophagy initiation; as the scaffold of the PI3K class III complex (together with VPS34, ATG14L, and AMBRA1), it is the master nucleation factor without which phagophore formation cannot proceed\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Its persistent activation in the autophagy-stressed TNBC microenvironment, sustained by the exomiR signature identified here would be expected to maintain a constitutive cytoprotective autophagic state in residual chemotherapy-treated tumour cells, facilitating their survival and potentially seeding post-treatment relapse. The convergence of three direct 3\u0026prime;UTR-targeting exomiRs (miR-30a-5p, miR-375-3p, miR-128-3p) together with BCL2-mediated indirect Beclin-1 release (miR-34a-5p) and PI3K-III activation via SHIP1 suppression (miR-155-5p) constitutes a mechanistic redundancy system that would be difficult to antagonise with a single therapeutic intervention- a finding with direct implications for the design of autophagy-targeting combination strategies in TNBC.\u003c/p\u003e \u003cp\u003eFrom a biomarker development perspective, the statistically robust exomiRs identified in this study represent a prioritised discovery-phase panel for plasma-based validation. The three FDR-significant candidates from MDA-MB-231; \u003cb\u003ehsa-miR-1468-5p, hsa-miR-4284, and hsa-miR-6529-5p\u003c/b\u003e and three from MDA-MB-468; \u003cb\u003ehsa-miR-15b-5p, hsa-miR-23b-3p, and hsa-miR-5585-3p\u003c/b\u003e - constitute the highest-confidence tier for progression to clinical validation. Among these, \u003cb\u003ehsa-miR-1468-5p carries the most immediate translational relevance\u003c/b\u003e: its cancer-secreted exosomal activity in reprogramming lymphatic endothelial cells via JAK2/STAT3/PD-L1 signalling has been documented in cervical cancer by Zhou et al\u003csup\u003e13\u003c/sup\u003e, with high serum levels correlating with poor OS and DFS in patients. hsa-miR-23b-3p has been shown to regulate autophagy through ATG12 targeting in Gastric cancer\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The present finding that miR-1468-5p is one of the most significantly enriched exomiRs in autophagy-conditioned TNBC sEVs raises the hypothesis that NACT-induced autophagic stress may amplify immunosuppressive exomiR signalling in TNBC, potentially contributing to the well-documented impairment of anti-tumour immunity in chemotherapy-treated TNBC. If validated in plasma exosomes of TNBC patients receiving NACT, elevated miR-1468-5p could serve as both a prognostic indicator of poor pCR probability and a mechanistic driver of immune evasion - a dual biomarker-therapeutic target designation that would substantially strengthen the clinical rationale for its further investigation.\u003c/p\u003e \u003cp\u003ehsa-miR-4284, while lacking dedicated functional characterisation in breast cancer, has been associated with cell cycle and DNA damage response pathways in prior sequencing datasets, and its substantial enrichment in autophagy-conditioned exosomes (log₂FC\u0026thinsp;+\u0026thinsp;6.87) invites speculation that it may function as a paracrine regulator of proliferative arrest in recipient cells\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Its prognostic value in TNBC is entirely unknown, establishing baseline and post-chemotherapy plasma exosomal levels in a well-annotated TNBC cohort as part of a biomarker validation study represents an immediately actionable next step. The case for hsa-miR-6529-5p is perhaps even more compelling from a scientific novelty standpoint: carrying the highest fold-change in the entire MDA-MB-231 dataset (log₂FC\u0026thinsp;+\u0026thinsp;7.58, adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.004), with no dedicated functional characterisation in any cancer model in the indexed literature as of 2025, it represents a genuinely novel candidate whose biological significance remains entirely open. The in-silico target landscape, which includes predicted 3\u0026prime;UTR interactions with ULK2 (autophagy-initiating kinase) and HDAC1 (epigenetic regulator of ATG gene expression), generates testable hypotheses connecting miR-6529-5p to autophagy regulation - but these must be viewed as discovery-phase predictions requiring systematic experimental evaluation.\u003c/p\u003e \u003cp\u003eThe cell-line-specific divergence of the exomiR profiles, with no shared DEMs at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 between MDA-MB-231 and MDA-MB-468 reflects the well-characterised transcriptomic heterogeneity between TNBC subtypes and carries an important translational implication: a single exomiR biomarker signature may be insufficient to capture the full prognostic spectrum of TNBC. Instead, the data suggest that exomiR panels may need to be subtype-stratified, with different candidate biomarkers prioritised for mesenchymal-like versus basal-like TNBC patients, a concept supported by the established prognostic divergence between Lehmann subtypes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This underscores the importance of subtype annotation in prospective biomarker validation studies and argues against the development of a 'one-size-fits-all' liquid biopsy signature for TNBC. Plasma based exomiRs profiling represents a minimally invasive liquid biopsy approach\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e for real time tumor monitoring.\u003c/p\u003e \u003cp\u003eIn the context of liquid biopsy development for TNBC, the current study provides several elements that are prerequisites for downstream clinical translation. First, the demonstration that autophagy induction under clinically relevant chemotherapy concentrations (doxorubicin 10 nM NACT-mimicking doses) significantly alters the exomiR landscape establishes biological relevance to the treatment context in which biomarkers would be applied. Second, the use of sEV-depleted FBS and rigorous characterisation per MISEV2018 criteria ensures that the identified signatures are not confounded by exogenous miRNA contamination - a critical source of artefact in serum-containing culture systems. Third, the identification of candidates with prior evidence of plasma detectability (miR-21-5p, miR-155-5p, miR-375-3p in the broader candidate panel) provides biological plausibility for their clinical measurement. The next phase of this research programme involves: (i) qRT-PCR validation of the six FDR-significant exomiRs in independent cell line experiments; (ii) quantification of candidate exomiRs in plasma samples from TNBC patients before and after NACT cycles (n\u0026thinsp;\u0026ge;\u0026thinsp;40 per arm, powered for Kaplan-Meier DFS analysis); (iii) correlation of exomiR levels with pCR status, DFS, and OS; and (iv) functional validation by miRNA mimic/inhibitor transfection followed by autophagy flux and chemosensitivity assays. This pipeline, now supported by ICMR funding, positions the current discovery findings as the mechanistically grounded entry point for a fully powered clinical biomarker validation study.\u003c/p\u003e \u003cp\u003eSeveral methodological limitations should be acknowledged. The in-vitro model, while enabling precise experimental control, may not fully recapitulate the complex tumour microenvironment, paracrine signalling milieu, or hypoxic conditions present in patient tumours in vivo. Drug concentrations and treatment durations, while selected as sub-lethal NACT mimics, represent simplified models of the prolonged drug exposure experienced clinically. Formal autophagic flux quantification using lysosomal inhibitor controls (bafilomycin A1) was not performed in this study, and the combination of LC3 puncta formation, Beclin-1 upregulation, and p62 degradation is interpreted as evidence consistent with autophagy induction pending definitive flux confirmation. The in-silico target predictions, while derived from three complementary and independently validated databases with stringent consensus thresholds (\u0026ge;\u0026thinsp;2 of 3 databases), represent computational hypotheses that require experimental confirmation by 3\u0026prime;UTR luciferase reporter assay, AGO2 immunoprecipitation, and gain/loss-of-function experiments before mechanistic conclusions can be drawn. Notwithstanding these limitations, the convergent regulatory logic of the identified exomiR panel - spanning multiple mechanistically distinct routes of Beclin-1 regulation, simultaneous PI3K/mTOR axis modulation, and involvement of the BCL2/apoptosis switch - constitutes a biologically coherent and experimentally testable hypothesis of substantial mechanistic depth. The identification of miR-6529-5p as a novel, highly enriched, functionally uncharacterised exomiR in autophagy-stressed TNBC cells is, to our knowledge, a genuinely new finding that expands the known exomiR landscape in this disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe demonstrate that autophagy induction by clinically relevant chemotherapeutic agents significantly reprograms the exosomal miRNA landscape in TNBC cell lines \u003cem\u003ein vitro\u003c/em\u003e. Small RNA sequencing identified cell-line-specific differentially expressed exomiRs, and multi-database \u003cem\u003ein silico\u003c/em\u003e target analysis revealed convergent predicted targeting of key autophagy regulatory nodes, most prominently Beclin-1 and the PTEN/PI3K/mTOR axis. The identified exomiR candidates, particularly hsa-miR-1468-5p, hsa-miR-6529-5p, and hsa-miR-4284, which satisfied FDR-corrected significance thresholds - provide a discovery-phase transcriptomic resource identifying candidate circulating exomiR species whose prospective clinical validation as liquid biopsy biomarkers represents an important next step. Functional dissection of the exomiR\u0026ndash;autophagy axis through gain- and loss-of-function experiments, 3\u0026prime;UTR reporter validation, and patient-derived exosome profiling will be required to establish causal relationships and translate these findings into diagnostic or therapeutic applications in chemoresistant TNBC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriple Negative Breast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emicro-RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransmission electron microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScanning electron microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall extracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efetal bovine serum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMEM, Dulbecco's Modified Eagle Medium\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAPDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglyceraldehyde 3-phosphate 26 dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolyvinyl difluoride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRTqPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReverse transcription-quantitative PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRIPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadio-Immunoprecipitation Assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esodium dodecyl sulphate, PPI:Protein-Protein interactions, GO:Gene ontology, GEO:Gene expression omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes, NACT:Neoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eULK1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnc-51-like autophagy-activating kinase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBECN1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeclin-1 (BCL2-interacting myosin-like coiled-coil protein 1)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLC3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrotubule-associated protein 1 light chain 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed miRNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate :\u003c/h2\u003e \u003cp\u003eThis study used commercially authenticated cell lines (ATCC) exclusively. No human subjects, patient-derived primary cultures, clinical samples, or animal models were involved in the experiments reported. Institutional ethics clearance was not required for the in vitro component of this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eAll authors give consent for publication.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding :\u003c/h2\u003e \u003cp\u003eThis study was supported in part by the Department of Science and Technology (DST), Ministry of Science and Technology, Government of India by awarding the INSPIRE fellowship to A.Choudhary for her Ph.D. study and the Indian Council of Medical Research (ICMR) by awarding a special grant for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB. C. Das: Conceptualization, Supervision, Reviewing and Editing; **Ananya Choudhary:** Data curation, Methodology, Investigation, Writing, Original draft preparation, Reviewing and Editing; Funding acquisition; **Simran Tandon** : Supervision and Revision of manuscript; **Adhiraj Roy:** Resources\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNanoparticle Tracking Analysis was performed at the Translational Health Science and Technology Institute (THSTI), in Faridabad, India. We thank Ms Divya Arya (THSTI) for her guidance and expertise in handling Malvern Nano Sight NS300 and for assistance in exosome sample analysis. Confocal microscopy was performed at Regional Centre for Biotechnology (RCB) in Faridabad, India. We thank Mr Suraj Tewari for his technical expertise in Super-resolution microscopy and confocal imaging; Mr. Akshey Kaushal, application engineer at the Indian Institute of Technology (IIT) Delhi, for his technical help with HR-TEM and Cryo-TEM employed for exosome visualization; Dr Prasanna Venkatraman, Deputy Director at the Cancer Research Institute, ACTREC Mumbai, India for sharing MCF10A cell lines; Mr. Manoj Gupta and Dr Pradeep K Rai for assistance with FACS analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data relevant to the study are included in the article or uploaded as supplementary information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBianchini G, De Angelis C, Licata L, Gianni L (2022) Treatment landscape of triple-negative breast cancer \u0026mdash; expanded options, evolving needs. 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Front Mol Biosci 9:818493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmolb.2022.818493\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2022.818493\" 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":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"TNBC, sEV, exosomes, exomiRs, exosomal miRNA, microRNA, autophagy, RNASeq","lastPublishedDoi":"10.21203/rs.3.rs-9298344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9298344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriple-negative breast cancer (TNBC) is characterised by aggressive pathobiology and chemoresistance, yet the mechanisms underpinning treatment failure remain incompletely understood. Autophagy, induced by cytotoxic chemotherapy, is increasingly implicated in exosome biogenesis and cargo remodelling; however, whether it systematically reprogrammes the exosomal microRNA (exomiR) landscape in TNBC has not been established. Here, we report that chemotherapy-induced autophagy significantly alters sEV-associated miRNA profiles in two TNBC cell lines (MDA-MB-231, MDA-MB-468) relative to basal and normal mammary epithelial (MCF-10A) controls. Small extracellular vesicles (sEVs), confirmed by TEM, NTA, and CD81/CD63 western blotting, were isolated under doxorubicin induced autophagy conditions. Small RNA sequencing (DESeq; nominal p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) identified 10 differentially expressed exomiRs in MDA-MB-231 and 7 in MDA-MB-468. Multi-database in silico target prediction revealed convergent targeting of core autophagy regulators, most prominently Beclin-1 (BECN1), targeted by five independent candidate exomiRs alongside the PI3K/mTOR axis and mitophagy effectors. These discovery-phase findings identify a candidate exomiR panel with mechanistic relevance to autophagy-driven chemoresistance in TNBC and provide a discovery stage transcriptomic foundation for prospective functional validation and clinical biomarker development.\u003c/p\u003e","manuscriptTitle":"Autophagy-Associated Exosomal microRNAs in Triple-Negative Breast Cancer: A Discovery-Phase Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 15:28:55","doi":"10.21203/rs.3.rs-9298344/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-05T18:39:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T13:05:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T13:05:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2026-04-02T05:20:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff25465e-bc02-4f92-bbde-4b91967f03e4","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T12:46:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 15:28:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9298344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9298344","identity":"rs-9298344","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}