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Abstract Despite advances in diagnostic and therapeutic strategies, the prognosis of lung squamous cell carcinoma (LUSC) patients remains poor, and the potential of microbiome-based prognostic biomarkers and therapeutic targets remains largely unexplored. LUSC patient data from The Cancer Genome Atlas (TCGA), including microbial genus level abundance data and RNA sequencing (RNA-Seq) data, were used as a training dataset. Two other independent datasets GSE19188 and GSE157009 serve as validation datasets. A microbiome-based risk score (RS) model was constructed by univariate Cox regression analysis combined with the least absolute contraction and selection operator (LASSO) regression. 18 microbial genera were found to be significantly associated with RFS in LUSC patients. The microbial signature built with these microbial genera, exhibited robust predictive accuracy in both the training and validation datasets. Furthermore, hub mRNA between high- and low-risk groups were selected by XGBOOST and intersect with mRNAs screened by univariate Cox regression analysis, finally identifying four mRNA significantly associated with LUSC prognosis. This study reveals a complex interplay between the lung microbiome and genetic biomarkers, and identifies specific microbial-based and mRNA associated with prognosis in LUSC. These findings provide a basis for future studies aimed to elucidate the mechanisms underlying these associations and provide potential biomarkers for guiding treatment decisions and improving patient outcomes.