Negative-control-anchored urinary microbiome profiling with absolute 16S quantification: a pilot study in newly diagnosed, treatment-naive bladder cancer and healthy individuals

Recent studies utilizing 16S rRNA amplicon sequencing have challenged the notion of urine sterility, yet urine is a low-biomass specimen in which apparent community profiles can be strongly influenced by background signal from reagents and processing. To address this interpretability gap, we integrate culture-independent absolute 16S rRNA gene quantification with urinary 16S amplicon sequencing in a negative-control-anchored workflow. Bacterial load provides a biomass-aware quality control gate that defines interpretable low-biomass thresholds and objective exclusion criteria. As a pilot application, we compared midstream urine collected prior to instrumentation from healthy volunteers and newly diagnosed bladder cancer (BC) patients, quality filtering retained 29 controls and 5 BC cases. Samples > 106 copies/mL typically produced > 10 000 reads; near 105 copies/mL read counts dropped sharply yet remained distinguishable from background. Thirteen negative controls (V3–V4 PCR and stabilization buffer; median 90, mean 124 reads) supported excluding samples with < 1 000 reads. Median bacterial load was lower in BC than in controls (7.0×103 vs 1.07 × 106 copies/mL), although not significant in this underpowered cohort (p = 0.07). This cohort-size-independent framework enables load-based triage for sequencing, reduces background-driven over-interpretation in low-biomass urine datasets, and supports modelling bacterial load as a covariate or stratifier in future studies of the bladder cancer microbiome.