Fungal microbiota signatures anticipate neoadjuvant immunochemotherapy outcomes in esophageal cancer

Background Predicting neoadjuvant immunochemotherapy (NICT) response remains a critical challenge in esophageal squamous cell carcinoma (ESCC) management. While the gut bacteriome’s role in immunotherapy has been established, the mycobiome’s predictive potential remains largely unexplored. This study investigated whether gut fungal signatures could serve as reliable biomarkers for NICT response prediction in patients with ESCC. Methods We performed internal transcribed spacer 2 sequencing on 155 fecal samples from 68 patients with ESCC (pre-NICT and post-NICT) and 19 healthy controls. Patients were stratified by tumor regression grade scores. We analyzed mycobiome-immune marker correlations and developed multilayer perceptron (MLP) models using Boruta feature selection. Performance was validated in 37 independent pretreatment patients. Functional causality was confirmed using Candidaboidinii in syngeneic mouse experiments with anti-programmed cell death protein-1 (PD-1) therapy. Results Patients with ESCC exhibited significant mycobiome dysbiosis compared with healthy controls, characterized by reduced alpha diversity and enrichment of pathogenic fungi including sRhodotorulaₘinuta, sActinomucorₑlegans, and sCandidaᵦeylanoides. Baseline mycobiome profiles distinguished treatment responders from non-responders before therapy initiation. Responders demonstrated higher fungal diversity, more stable co-occurrence networks, and enrichment of beneficial taxa (sCandidaboidinii, gMeyerozyma, sMeyerozymaguilliermondii, sTrichosporondermatis) that correlated with Th1-polarized immunity and elevated cytotoxic markers (interferon-γ, interleukin (IL) -12p70, IL-2). Non-responders harbored immunosuppressive fungi (sCandidaₐlbicans, sCandidaₚarapsilosis, sCandidaglabrata, gSaccharomyces) associated with Th2 skewing and regulatory cytokines (IL-4, IL-10, IL-13). Functional analysis revealed responders exhibited enhanced catabolic pathways and phospholipase activities, while non-responders showed upregulated nucleotide biosynthesis. The MLP model achieved robust discriminative performance (genus-level: training area under the receiver operating characteristic curve (AUC) 98. 0%, test AUC 82. 9%; species-level: training AUC 87. 1%, test AUC 87. 4%). Candidaboidinii administration enhanced anti-PD-1 efficacy in mice, validating predicted metabolomic and immune changes. Conclusions Baseline gut mycobiome signatures predict NICT response in ESCC with high accuracy. Experimental validation confirms functional causality, enabling precision medicine approaches for patient stratification and identifying therapeutic targets.