A Duplex qPCR Assay Targeting the fadA Gene Enables Robust Detection of Fusobacterium in Clinical Samples

<i>Fusobacterium nucleatum</i> (<i>Fn</i>) is increasingly recognized as a cancer-associated bacterium, yet reliable quantification in human specimens is challenging due to low bacterial burden and abundant host DNA. We analyzed 145 <i>Fusobacterium</i> genomes to design primers targeting conserved regions of the <i>fadA</i> adhesin gene and developed a duplex quantitative real-time PCR (qPCR) assay for simultaneous detection of <i>fadA</i> and a human <i>PGT</i> as an internal control. Analytical sensitivity, specificity, precision, and reproducibility were evaluated using serially diluted <i>Fn</i> DNA, spike-in experiments with human DNA, and cross-platform/operator validation. Clinical performance was assessed in colorectal cancer patient tissues, including fresh tissue (<i>n</i> = 24) and formalin-fixed paraffin-embedded (FFPE) samples (<i>n</i> = 22), using 16S rRNA-based methods as references. The assay successfully detected all four major <i>Fn</i> subspecies (<i>nucleatum</i>, <i>animalis</i>, <i>polymorphum</i>, and <i>vincentii</i>). The limit of detection was ≤0.1 pg, with no interference between duplex targets. Spike-in experiments demonstrated consistent target detection in human-DNA-rich samples, with strong linearity (<i>R</i>² = 0.998) across dilutions. High precision (coefficient of variations < 5%) was observed across intra-day, inter-day, inter-instrument, and inter-operator evaluations. In fresh tissues, the assay yielded 86% sensitivity, 94% specificity, and 92% accuracy. Using the FFPE samples, the assay achieved 91% sensitivity and 100% specificity, confirming robust classification in both clinical samples. This duplex qPCR assay enables broad detection of <i>Fn</i> with high analytical performance in both fresh and FFPE tissues. Its simplicity, reproducibility, and compatibility with pathology workflows support deployment in multi-center studies and downstream applications in diagnostic studies and prognostic modeling.