Triple-negative breast most cancers (TNBC) stays one of the vital aggressive and clinically difficult subtypes of breast carcinoma [1]. Characterised by the absence of estrogen receptor, progesterone receptor, and HER2 amplification, TNBC accounts for about 15–20% of breast cancers and is related to poor prognosis, early metastasis, and restricted therapeutic choices [2], [3], [4]. Tumor heterogeneity inside breast most cancers additional complicates prognosis and customized remedy [5], [6], highlighting the pressing want for exact and minimally invasive diagnostic methods that may be straight coupled with therapeutic interventions.
Current advances in nucleic acid nanotechnology have supplied highly effective instruments for most cancers diagnostics [7], [8], enabling delicate detection of disease-associated biomarkers with programmable specificity. DNA probes, specifically, have emerged as versatile platforms resulting from their predictable base-pairing, facile synthesis, and capability for sign amplification via enzyme-free circuits akin to catalytic hairpin meeting (CHA) [9] and hybridization chain response (HCR) [10], [11]. These designs have demonstrated nice promise in detecting low-abundance nucleic acid signatures in residing programs with excessive spatial and temporal decision [12], [13].
Parallel to those technological advances, microRNAs (miRNAs) have been acknowledged as key regulators of TNBC biology, with distinct expression patterns distinguishing TNBC from luminal and HER2-enriched subtypes [14], [15], [16]. Harnessing miRNA signatures for most cancers subtype discrimination represents a promising technique for advancing precision oncology. Nonetheless, tumor heterogeneity presents a serious barrier to correct breast most cancers classification [17], as single biomarkers are sometimes inadequate to tell apart between intently associated subtypes. Though gene expression profiles and miRNA signatures have been broadly investigated [18], [19], [20], [21], [22], their medical translation is hindered by the problem of decoding a number of markers concurrently. Subsequently, there’s a urgent want for a strong molecular classifier able to integrating subtype-specific biomarkers and enabling dependable discrimination throughout heterogeneous tumor populations.
To handle this unmet want, we designed a DNA-based molecular classifier that distinguishes breast most cancers subtypes via sequential miRNA consumption whereas offering direct steering for downstream remedy. The classifier operates via a two-stage recognition cascade. Within the first stage, a nucleic acid probe performs broad-spectrum screening for oncogenic genetic signatures. Within the second stage, the DNA classifier decodes the expression profiles of two clinically related miRNAs- oncogenic miR-21 and tumor-suppressive miR-339. The relative expression ranges of miR-339 and miR-21 discriminate TNBC from different breast most cancers subtypes and regular tissue. In contrast to standard signal-activation classifiers, our system operates via sequential consumption: binding of miR-339 exposes a hidden binding website for miR-21, and subsequent seize of each miRNAs suppresses fluorescence output, enabling subtype-specific discrimination (Scheme 1). Critically, this diagnostic output is straight coupled to therapeutic actuation, offering steering for photothermal remedy and siRNA-mediated gene silencing inside a unified DNA framework. By integrating recognition, decision-making, and remedy right into a single platform, our system exemplifies a theranostic technique for precision oncology.
