A robustness-first cross-disease framework supports a candidate shared microbial redox axis and disease-specific metabolic divergence in colorectal cancer, Crohn’s disease, and liver cirrhosis
- 1School of Biological Sciences, National Institute of Science Education and Research (NISER), Khurda, Odisha, India
- 2Homi Bhabha National Institute, Mumbai, Maharashtra, India
Abstract
Gut microbiome dysbiosis is associated with colorectal cancer (CRC), Crohn’s disease (CD), and liver cirrhosis (LC), yet whether these diseases share conserved microbial vulnerabilities remains unresolved. Analytical pipeline choices alone can shift apparent performance from near-zero to near-perfect on identical data, rendering cross-disease comparison unreliable. Here, in a secondary cross-sectional analysis of public data sets, maximin optimization is introduced as a proposed pipeline-selection criterion guaranteeing worst-case performance across all tasks simultaneously. Benchmarking 1,152 preprocessing-model configurations across six tasks using gut metagenomics and serum metabolomics from CRC, CD, and LC reveals a candidate 19-species microbiome signature in which Firmicutes bacterium CAG:41 is the sole threshold-stable cross-disease taxon, depleted in all three diseases. Microbiome pathway enrichment converges on sulfur-selenium redox metabolism and B-vitamin biosynthesis, while host metabolomic responses are predominantly disease-specific. CD and LC are dominated by single discriminative taxa; CRC requires community-level integration. Exploratory external evaluation was adequately powered only for CRC (AUC = 0.769, 95% bootstrap CI 0.678–0.849); CD and LC assessments were exploratory only.
IMPORTANCE: Cross-disease microbiome comparison has lacked a principled analytical foundation: arbitrary preprocessing choices can shift apparent classification performance from near-random to near-perfect on identical data, making biological conclusions unreliable when pooled across diseases. Maximin optimization addresses this by providing a decision-theoretic guarantee that every classification task contributes valid signal, enabling the first analytically controlled cross-disease comparison of gut metagenomics and serum metabolomics across three major gut-associated diseases. The identification of Firmicutes bacterium CAG:41 as the sole threshold-stable cross-disease taxon, harboring predicted functions in sulfur-selenium metabolism, offers a concrete target for experimental characterization and prospective screening validation. The two-layer dysbiosis architecture—universal microbial vulnerability with disease-specific host metabolic responses—provides a conceptual template for cross-disease microbiome study design in other gut-associated conditions, pending prospective confirmation.
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BibTeX
@article{sahu2026robustnessfirst,
title = {{A robustness-first cross-disease framework supports a candidate shared microbial redox axis and disease-specific metabolic divergence in colorectal cancer, Crohn’s disease, and liver cirrhosis}},
author = {Kartika Sahu and Palok Aich},
journal = {mSystems},
year = {2026},
month = sep,
doi = {10.1128/msystems.00836-26},
url = {https://doi.org/10.1128/msystems.00836-26},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A robustness-first cross-disease framework supports a candidate shared microbial redox axis and disease-specific metabolic divergence in colorectal cancer, Crohn’s disease, and liver cirrhosis
AU - Kartika Sahu
AU - Palok Aich
PY - 2026
DA - 2026/09/14
JO - mSystems
DO - 10.1128/msystems.00836-26
UR - https://doi.org/10.1128/msystems.00836-26
AB - Gut microbiome dysbiosis is associated with colorectal cancer (CRC), Crohn’s disease (CD), and liver cirrhosis (LC), yet whether these diseases share conserved microbial vulnerabilities remains unresolved. Analytical pipeline choices alone can shift apparent performance from near-zero to near-perfect on identical data, rendering cross-disease comparison unreliable. Here, in a secondary cross-sectional analysis of public data sets, maximin optimization is introduced as a proposed pipeline-selection criterion guaranteeing worst-case performance across all tasks simultaneously. Benchmarking 1,152 preprocessing-model configurations across six tasks using gut metagenomics and serum metabolomics from CRC, CD, and LC reveals a candidate 19-species microbiome signature in which Firmicutes bacterium CAG:41 is the sole threshold-stable cross-disease taxon, depleted in all three diseases. Microbiome pathway enrichment converges on sulfur-selenium redox metabolism and B-vitamin biosynthesis, while host metabolomic responses are predominantly disease-specific. CD and LC are dominated by single discriminative taxa; CRC requires community-level integration. Exploratory external evaluation was adequately powered only for CRC (AUC = 0.769, 95% bootstrap CI 0.678–0.849); CD and LC assessments were exploratory only. IMPORTANCE: Cross-disease microbiome comparison has lacked a principled analytical foundation: arbitrary preprocessing choices can shift apparent classification performance from near-random to near-perfect on identical data, making biological conclusions unreliable when pooled across diseases. Maximin optimization addresses this by providing a decision-theoretic guarantee that every classification task contributes valid signal, enabling the first analytically controlled cross-disease comparison of gut metagenomics and serum metabolomics across three major gut-associated diseases. The identification of Firmicutes bacterium CAG:41 as the sole threshold-stable cross-disease taxon, harboring predicted functions in sulfur-selenium metabolism, offers a concrete target for experimental characterization and prospective screening validation. The two-layer dysbiosis architecture—universal microbial vulnerability with disease-specific host metabolic responses—provides a conceptual template for cross-disease microbiome study design in other gut-associated conditions, pending prospective confirmation.
SN - 2379-5077
ER -