Table 2.
Diagnostic models using microbiome data derived from EVs.
| Patient group | Sample type | Genera | Diagnostics | Ref. | ||
|---|---|---|---|---|---|---|
| Enriched in case | Enriched in control | Method | Performance | |||
| Colorectal cancer | Feces | Faecalibacterium, Eubacterium, Ruminococcus, Bifidobacterium | Escherichia-Shigella, Pseudomonas, Methylobacterium, Molicutes, Proteus | Logistic regression using age, sex and metagenomic biomarkers selected by statistical analysis | AUC: 0.95, Sen: 0.90, Spe: 1.00, Acc: 0.93 | Kim et al.90 |
| Logistic regression using age, sex and metagenomic and metabolomic biomarkers selected by statistical analysis | AUC: 1.00, Sen: 1.00, Spe: 1.00, Acc: 1.00 | Kim et al.90 | ||||
| Atopic dermatitis | Blood | Escherichia-Shigella, Enterococcus, Alistipes, Klebsiella, Veillonella, Bifidobacterium, Akkermansia, Bacteroides | Acinetobacter, Pseudomonas, Parabacteroides, Proteus, Prevotella, Dialister, Rhizobium, Sphingomonas, Staphylococcus | Logistic regression using biomarkers selected by LEfSe | AUC: 1.00, Sen: 1.00, Spe: 1.00, Acc: 1.00 | Yang et al.89 |
| Urine | Pseudomonas, Alicyclobacillus, Propionibacterium, Corynebacterium | Lactobacillus, Leuconostoc, Lactococcus, Bradyrhizobium | – | – | Kim et al.107 | |
| Skin washing fluid | Staphylococcus, Pseudomonas, Streptococcus, Acinetobacter | Alcaligenaceae, Sediminibacterium, Lactococcus, Phaeospirillum, Rhodococcus, Lactobacillus, Methylobacterium | – | – | Kim et al.122 | |
| Asthma | Blood | Klebsiella, Bacteroides, Alistipes, Subdoligranulum, Bifidobacterium, Faecalibacterium, Veilonella, Eubacterium, Parabacteroides, Prevotella | Pseudomonas, Akkermansia, Citrobacter, Staphylococcus, Micrococcus, Acinetobacter, Lactobacillus, Corynebacterium, Sphingomonas, Propionibacterium, Cupriavidus, Streptococcus | Logistic regression using biomarkers selected by LEfSe with age and sex as covariates | AUC: 0.97, Sen: 0.92, Spe: 0.93, Acc: 0.92 | Lee et al.88 |
| – | – | Logistic regression using antibacterial EV IgG, IgG1, and IgG4 with smoking status as a covariate | AUC: 0.78, Sen: 0.65, Spe: 0.88, Acc: 0.71 | Yang et al.96 | ||
| Autism | Urine | Halomonas, Streptococcus, Rhodococcus, Bacteroidales S24-7, Akkermansia, | Pseudomonas, Sphingomonas, Agrobacterium, Achromobacter | – | – | Lee et al.123 |
| Bipolar depressive disorder | Blood | Faecalibacterium, Dialister, Klebsiella, Bacteroidales S24-7, Escherichia-Shigella, Ruminococcus, Alistipes, Prevotella | – | – | Rhee et al.124 | |
| Major depressive disorder | Blood | Dialister, Faecalibacterium, Prevotella, Alistipes, Bacteroidales S24-7, Corynebacteriaceae, Escherichia-Shigella | Pseudomonas | – | – | Rhee et al.124 |
| Brain tumor | Blood | Turicibacter, Lactococcus, Lactobacillus, Staphylococcus, Peptoclostridium, Diaphorobacter, Klebsiella, Propionibacterium, Acinetobacter, Salmonella | Stenotrophomonas, Sphingomonas, Actinomyces, Streptococcus, Bifidobacterium, Knoellia, Pseudomonas, Corynebacterium, Veillonella | Logistic regression using biomarkers selected by LEfSe | AUC: 0.97, Sen: 0.93, Spe: 0.90, Acc: 0.91 | Yang et al.82 |
| Machine learning algorithm based on the gradient boosting machine (GBM) model | AUC: 0.99, Sen: 1.00, Spe: 0.94 | Yang et al.82 | ||||
| Tissue | Bacteroides, Erysipelatoclostridium | Bactroidales S24-7, Prevotella | – | – | Yang et al.82 | |
| Chronic rhinitis | Urine | Propionibacterium, Methylobacterium, Enhydrobacter | Achromobacter, Enterobacteriaceae | – | – | Samra et al.125 |
| Allergic rhinitis | Urine | Methylobacterium | Agrobacterium, Achromobacter, Enterobacteriaceae | – | – | Samra et al.125 |
| Atopic asthma | Urine | Methylobacterium, Sphingomonadaceae | Enterobacteriaceae | – | – | Samra et al.125 |
| Hepatocellular carcinoma | Blood | Staphylococcus, Acinetobacter | Pseudomonas, Streptococcus | Logistic regression using age, sex and biomarkers selected by statistical analysis | AUC: 0.88, Sen: 0.73, Spe: 0.85, Acc: 0.82 | Cho et al.91 |
| Biliary tract cancer | Blood | Ralstonia | Corynebacterium, Comamonas | Logistic regression using stepwise selection with age and sex as covariates | AUC: 1.00, Sen: 1.00, Spe: 1.00, Acc: 1.00 | Lee et al.94 |
| Preterm birth | Blood | Bacteroides, Lactobacillus, Sphingomonas, Rhizobium | Delftia, Pseudomonas, Stenotrophomonas | – | – | You et al.126 |
| Alcoholic hepatitis | Feces | Veillonella, Lactobacillales | Eubacterium, Oscillibacter, Christensenellaceae | – | – | Kim et al.127 |
| COPD | Blood | – | – | Logistic regression using antibacterial EV IgG, IgG1, and IgG4 with smoking status as a covariate | AUC: 0.79, Sen: 0.90, Spe: 0.61, Acc: 0.81 | Yang et al.96 |
| Gastric cancer | Urine | Corynebacterium 1, Neisseria, Fusobacterium, Diaphorobacter, Actinomyces, Porphyromonas, Cloacibacterium, Peptoniphilus | Acinetobacter, Staphylococcus, Bifidobacterium, Sphingomonas | Logistic regression using metagenomic biomarkers selected by statistical analysis | AUC: 0.82, Sen: 0.68, Spe: 0.85, Acc: 0.76 | Park et al.92 |
| Lung cancer | Blood | – | – | Logistic regression using antibacterial EV IgG, IgG1, and IgG4 with smoking status as a covariate | Auc: 0.81, Sen: 0.85, Spe: 0.61, Acc: 0.80 | Yang et al.96 |
| Lung cancer (from COPD) | Blood | – | – | Logistic regression using antibacterial EV IgG, IgG1, and IgG4 with smoking status as a covariate | AUC: 0.74, Sen 0.69, Spe: 0.69, Acc: 0.69 | Yang et al.96 |
| Ovarian cancer (from benign ovarian tumor) | Blood | Acinetobacter | Logistic regression using biomarkers, age, serum CA-125 levels, and Acinetobacter EVs selected by statistical analysis | AUC: 0.85, Sen: 0.82, Spe: 0.68 | Kim et al.95 | |
| Pancreatic cancer | Blood | Ruminococcaceae UCG-014, Lachnospiraceae NK4A136 group, Akkermansia, Turicibacter, Ruminiclostridium, Lachnospiraceae UCG-001 | Stenotrophomonas, Sphingomonas, Propionibacterium, Corynebacterium 1 | Logistic regression using age, sex and biomarkers selected by statistical analysis | AUC: 1.00, Sen: 1.00, Spe: 0.92 | Kim et al.93 |
AUC area under curve, Sen sensitivity, Spe specificity, Acc accuracy.