Background Advanced-stage non-small cell lung cancer (NSCLC) poses treatment challenges,
with immune checkpoint inhibitors (ICIs) as the main therapy. Emerging evidence suggests
the gut microbiome significantly influences ICI efficacy. This study explores the
link between the gut microbiome and ICI outcomes in NSCLC patients, using metatranscriptomic
(MTR) signatures. Methods We utilized a de novo assembly-based MTR analysis on fecal
samples from 29 NSCLC patients undergoing ICI therapy, segmented according to progression-free
survival (PFS) into long (> 6 months) and short (<= 6 months) PFS groups. Through
RNA sequencing, we employed the Trinity pipeline for assembly, MMSeqs2 for taxonomic
classification, DESeq2 for differential expression (DE) analysis. We constructed Random
Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)
machine learning (ML) algorithms and comprehensive microbial profiles. Results We
detected no significant differences concerning alpha-diversity, but we revealed a
biologically relevant separation between the two patient groups in beta-diversity.
Actinomycetota was significantly overrepresented in patients with short PFS (vs long
PFS, 36.7% vs. 5.4%, p < 0.001), as was Euryarchaeota (1.3% vs. 0.002%, p = 0.009),
while Bacillota showed higher prevalence in the long PFS group (66.2% vs. 42.3%, p
= 0.007), when comparing the abundance of corresponding RNA reads. Among the 120 significant
DEGs identified, cluster analysis clearly separated a large set of genes more active
in patients with short PFS and a smaller set of genes more active in long PFS patients.
Protein Domain Families (PFAMs) were analyzed to identify pathways enriched in patient
groups. Pathways related to DNA synthesis and Translesion were more enriched in short
PFS patients, while metabolism-related pathways were more enriched in long PFS patients.
E. coli-derived PFAMs dominated in patients with long PFS. RF, SVM and XGBoost ML
models all confirmed the predictive power of our selected RNA-based microbial signature,
with ROC AUCs all greater than 0.84. Multivariate Cox regression tested with clinical
confounders PD-L1 expression and chemotherapy history underscored the influence of
n = 6 key RNA biomarkers on PFS. Conclusion According to ML models specific gut microbiome
MTR signatures' associate with ICI treated NSCLC outcomes. Specific gene clusters
and taxa MTR gene expression might differentiate long vs short PFS.
