Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease

Table 1 Summary of datasets used for training and validation in GC, CRC, and IBD

Datasets	Source	Healthy	Diseased	Total features	Source of extraction	Technology used
Training data
GC
Microbiome	Erawijantari et al. [178]	54	42	10,528	Fecal	Shotgun metagenomics sequencing
Metabolome	Erawijantari et al. [178]	54	42	525	Fecal	*CE-TOFMS
CRC
Microbiome	Yachida et al. [179]	127	150	11,942	Fecal	Whole genome sequencing
Metabolome	Yachida et al. [179]	127	150	450	Fecal	CE-TOFMS
IBD
Microbiome	Franzosa et al. [180]	56	164	11,720	Fecal	Whole genome shotgun sequencing
Metabolome	Franzosa et al. [180]	56	164	466	Fecal	*LC–MS
Validation data
GC
Microbiome	Jaeyun Sung et al. [181]	10	40	470	Gastric antrum	16S rRNA sequencing
Metabolome	UK BioBank	44,378	2,436	168	Plasma	NMR spectroscopy
CRC
Microbiome	Kim et al. [182]	102	36	499	Fecal	16S rRNA sequencing
Metabolome	Kim et al. [182]	102	36	462	Fecal	*UPLC-MS/MS
IBD
Microbiome	iHMP/HMP2 [183]	104	278	9694	Fecal	Shotgun metagenomic sequencing
Metabolome	iHMP/HMP2 [183]	104	278	596	Fecal	LC–MS

This table includes the total number of healthy and diseased patients, the number of microbiome and metabolome features, the source of extraction for microbes and metabolites, and the sequencing and analytical tools employed, respectively
*CE-TOFMS: Capillary electrophoresis time-of-flight mass spectrometry, *LC–MS: liquid chromatography-mass spectrometry, *UPLC-MS/MS: ultra-performance liquid chromatography-mass spectrometry

ISSN: 1479-5876