Cancer cachexia, a debilitating syndrome characterized by weight loss, muscle wasting, and anorexia, significantly impacts the survival and quality of life of cancer patients. Its prevalence is particularly high in gastrointestinal and lung cancers, reaching up to 80% and 60%, respectively. While the etiology remains unclear, inflammation and metabolic abnormalities play crucial roles. Cytokine imbalances, including decreased insulin-like growth factor-1 and increased pro-inflammatory cytokines like IL-6, IFN-γ, and TNF-α, have been implicated. Elevated serum lipopolysaccharide-binding protein (LBP) is also considered a potential biomarker. This study aimed to investigate the association between gut microbiota composition and function and the development of cachexia in lung cancer patients. Understanding this relationship could provide new avenues for therapeutic intervention.

The existing literature highlights the complex interplay of factors contributing to cancer cachexia. Studies have established a strong correlation between cachexia and reduced survival rates in various cancer types. The involvement of cytokines in the pathogenesis of cachexia has been extensively explored, with studies in both animal models and human patients indicating disruptions in the balance between anabolic and catabolic processes. Furthermore, alterations in metabolic pathways, particularly those related to amino acid metabolism, have been observed in cachectic individuals. However, the specific role of the gut microbiome in the development and progression of cachexia remains an area of active investigation, with limited studies directly exploring its impact in a clinical setting. The present study aims to address this gap by investigating the detailed composition and functional aspects of the gut microbiome to establish a relationship with cachexia in lung cancer patients.

This study enrolled 31 lung cancer patients (12 women, 19 men) categorized into cachexia (n=12, aPG-SGA scores >4) and non-cachexia (n=19, aPG-SGA scores 0–4) groups based on the abridged Patient-Generated Subjective Global Assessment (aPG-SGA). Plasma samples underwent untargeted metabolomics analysis using ultra-high-performance liquid chromatography-quadruple time-of-flight mass spectrometry (UHPLC-QTOF-MS), identifying over 5000 metabolite features. Fecal samples were subjected to shotgun metagenomic sequencing to characterize the gut microbiome. Statistical analyses included ANOSIM, betadisper, Wilcoxon rank-sum test, Student's t-test, and Spearman's rank correlation to compare metabolite and microbiome profiles between groups. Survival analysis was performed using the Log-rank test. A machine-learning model was developed to predict cachexia based solely on gut microbial features. The data was compared to a previously published dataset of healthy individuals for additional context.

Cachexia patients showed significantly lower survival probability compared to non-cachectic patients (p = 0.0051). Plasma metabolomics revealed significant differences between groups. BCAAs (isoleucine, leucine, valine), methylhistamine, and vitamins were significantly depleted in cachectic patients (p<0.05, Student's t-test). Pipecolic acid was significantly enriched. Gut microbiome analysis showed no significant differences in alpha-diversity or phylum abundance. However, beta-diversity analysis revealed a significantly distinct microbiota composition in cachexia patients (p = 0.001). Fifty-one differentially abundant bacterial species were identified, 44 remaining significant after FDR correction (p<0.05). Specifically, *Prevotella copri* was significantly less abundant in cachectic patients (FDR-corrected p = 0.006). Correlation analysis showed associations between specific bacterial species and plasma metabolites. Notably, *Prevotella copri* abundance correlated positively with plasma BCAA levels, and *Lactobacillus gasseri* with 3-oxocholic acid levels. Finally, a machine learning model successfully predicted cachexia using gut microbial data.

The findings demonstrate a strong association between the gut microbiome and cachexia in lung cancer patients. The observed alterations in plasma metabolites, particularly the depletion of BCAAs and vitamins, align with the metabolic disturbances characteristic of cachexia. The significant differences in gut microbial composition and function suggest that specific microbial species or pathways may play a causal role in cachexia development. The positive correlations between certain microbial species (e.g., *Prevotella copri*) and plasma BCAAs suggest potential mechanistic links between gut microbiota and host metabolism. The enrichment of lipopolysaccharide biosynthesis in cachectic patients highlights the potential contribution of gut inflammation. Future studies should investigate causal relationships and explore the potential for gut microbiome modulation as a therapeutic strategy for cachexia.

This study establishes a significant link between gut microbiome composition and metabolic dysfunction in lung cancer cachexia. The distinct microbial profiles and altered plasma metabolites highlight the potential of targeted interventions, such as dietary or microbial therapies, for improving outcomes in cachectic patients. Future research should focus on mechanistic studies to understand the causal relationships and larger clinical trials to evaluate the efficacy of microbiome-based therapies for cachexia management.

The relatively small sample size limits the generalizability of the findings. Further studies with larger cohorts and diverse patient populations are needed to validate the results. The cross-sectional nature of the study prevents conclusions about causality. Longitudinal studies are required to determine the temporal relationship between gut microbiome changes and the development of cachexia. Finally, functional studies are needed to fully elucidate the mechanisms through which the identified microbial species influence cachexia pathogenesis.