Metabolite information |
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| HMDB ID | HMDB0062251 |
| Synonyms |
2-Aminopropanoate2-Aminopropanoic acid2-Aminopropionate2-Aminopropionic acidAALAAbufèneAlaninAlaninaAlanineAlanine doms-adrian brandAlanine, L isomerAlanine, L-isomerDoms adrian brand OF alanineDoms-adrian brand OF alanineL AlanineL-AlanineL-Isomer alanine |
| Chemical formula | C3H7NO2 |
| IUPAC name | 2-aminopropanoic acid |
| CAS registry number | 302-72-7 |
| Monisotopic molecular weight | 89.047678473 |
Chemical taxonomy |
|
| Super class | Organic acids and derivatives |
| Class | Carboxylic acids and derivatives |
| Sub class | Amino acids, peptides, and analogues |
Biological properties |
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| Pahtways |
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| Author-emphasized biomarker in the paper(s) |
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| Reference | Country | Specimen | Marker function | Participants (Case) | Participants (Control) | |||||||||
| Cancer type | Stage | Number | Gender (M,F) | Age mean (range) (M/F) | Smoking status | Type | Number | Gender (M,F) | Age mean (range) (M/F) | Smoking status | ||||
| Chen et al. 2015 | – | serum | – | lung cancer | – | 30 | – | 61.58 ± 10.67 | – | healthy | 30 | – | 60.35 ± 12.48 | – |
| Chen et al. 2015 | – | serum | – | lung cancer (postoperative) | – | 30 | – | 61.58 ± 10.67 | – | healthy | 30 | – | 60.35 ± 12.48 | – |
| Ni et al. 2019 | – | serum | diagnosis | lung cancer | – | 40 | 26, 14 | 66.7 (49-83) | – | healthy | 100 | 65, 35 | 64.1 (41-90) | – |
| Fahrmann et al. 2015 | – | plasma | diagnosis | adenocarcinoma | I, II, III, IV | 52 | 17, 35 | 65.9 ± 9.66 | – | healthy | 31 | 11, 20 | 64.1 ± 8.97 | – |
| Fahrmann et al. 2015 | – | serum | diagnosis | adenocarcinoma | I, II, III, IV | 49 | 17, 32 | 65.9 ± 9.87 | – | healthy | 31 | 11, 20 | 64.1 ± 8.97 | – |
| Ni et al. 2019 | – | serum | diagnosis | NSCLC, SCLC | II, III, IV | 17 | 13, 4 | 66.3 (53-77) | former, current, non-smoker | healthy | 30 | 23, 7 | 62.8 (34-85) | former, current, non-smoker |
| Ni et al. 2016 | – | serum | diagnosis | lung cancer | – | 40 | 14, 26 | 67 | – | healthy | 100 | – | – | – |
| Fahrmann et al. 2015 | – | serum | diagnosis | adenocarcinoma | I, II, III, IV | 43 | 21, 22 | 67.3 ± 10.10 | – | healthy | 43 | 21, 22 | 65.9 ± 8.05 | – |
| Fahrmann et al. 2015 | – | plasma | diagnosis | adenocarcinoma | I, II, III, IV | 43 | 21, 22 | 67.3 ± 10.10 | – | healthy | 43 | 21, 22 | 65.9 ± 8.05 | – |
| Miyamoto et al. 2015 | – | blood | diagnosis | adenocarcinoma | unknown (mostly late stage) | 18 | 10, 8 | 67 (50-85) / 62 (53-72) | former, current | healthy | 20 | 8, 12 | 64 (49-80) / 66 (58-82) | former, current |
| Klupczynska et al. 2016a | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma | I, II, III | 90 | 58, 32 | 64 (48-86) | current, non-smoker, unknown | healthy | 63 | 41, 22 | 62 (43-78) | smoker, non-smoker, unknown |
| Miyamoto et al. 2015 | – | blood | diagnosis | NSCLC, SCLC, mesothelioma, secondary metastasis to lung | I, II, III, IV | 11 | 4, 7 | 67 (61-73) / 67 (47-76) | smoker, non-smoker | healthy | 11 | 5, 6 | 69 (61-83) / 54 (44-61) | unknown |
| Moreno et al. 2018 | – | tissue | therapy, diagnosis | adenocarcinoma | I, II, III | 33 | 24, 9 | 62.11 ± 9.73 | – | tumor vs. adjacent normal tissue | 33 | 24, 9 | 62.11 ± 9.73 | – |
| Wen et al. 2013 | – | plasma | diagnosis | adenocarcinoma | I | 31 | 15, 16 | median: 63 (40-81) | smoker, non-smoker | healthy | 28 | 20, 8 | median: 37 (29-50) | smoker, non-smoker |
| Mazzone et al. 2016 | – | serum | – | adenocarcinoma, squamous cell carcinoma | I, II, III | 94 | 55.3%, 44.7% | 68.7 | – | at-risk controls | 190 | 50.5%, 49.5% | 66.2 | – |
| Moreno et al. 2018 | – | tissue | therapy, diagnosis | squamous cell carcinoma | I, II, III | 35 | 35, 0 | 68.71 ± 7.46 | – | tumor vs. adjacent normal tissue | 35 | 35, 0 | 68.71 ± 7.46 | – |
| Wikoff et al. 2015b | – | tissue | diagnosis | adenocarcinoma | I | 39 | 15, 24 | 72.33 ± 8.78 | smoker, non-smoker | tumor vs. adjacent normal tissue | 39 | 15, 24 | 72.33 ± 8.78 | smoker, non-smoker |
| Reference | Chromatography | Ion source | Positive/Negative mode | Mass analyzer | Identification level |
| Chen et al. 2015 | GC | EI | – | quadrupole | – |
| Chen et al. 2015 | GC | EI | – | quadrupole | – |
| Ni et al. 2019 | LC | ESI | positive | triple quadrupole | MS/MS |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Ni et al. 2019 | LC | ESI | positive | triple quadrupole | MS/MS |
| Ni et al. 2016 | LC | ESI | positive | Triple quadrupole | MS/MS |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Miyamoto et al. 2015 | GC | EI | – | TOF | MS/MS |
| Klupczynska et al. 2016a | LC | – | – | QTRAP | MS/MS |
| Miyamoto et al. 2015 | GC | EI | – | TOF | MS/MS |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Wen et al. 2013 | GC | EI | – | – | – |
| Mazzone et al. 2016 | GC | EI | – | quadrupole | MS/MS |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Wikoff et al. 2015b | GC | EI | – | TOF | – |
| Reference | Data processing software | Database search |
| Chen et al. 2015 | ChemStation | NIST |
| Chen et al. 2015 | ChemStation | NIST |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Ni et al. 2016 | – | – |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Miyamoto et al. 2015 | ChromaTOF software (Leco) | UC Davis Metabolomics BinBase database |
| Klupczynska et al. 2016a | – | – |
| Miyamoto et al. 2015 | ChromaTOF software (Leco) | UC Davis Metabolomics BinBase database |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Wen et al. 2013 | MassHunter, Mass Profiler Professional software (Agilent) | NIST 08, HMDB, METLIN, LIPID MAPS |
| Mazzone et al. 2016 | Metabolon LIMS system | Metabolon LIMS system |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Wikoff et al. 2015b | BinBase | NIST11, BinBase |
| Reference | Difference method | Mean concentration (case) | Mean concentration (control) | Fold change (case/control) | P-value | FDR | VIP |
| Chen et al. 2015 | PCA, PLS-DA, independent t test | – | – | 0.687770909069872 | <0.001 | – | 1.28 |
| Chen et al. 2015 | PCA, PLS-DA, independent t test | – | – | 0.732042847972813 | <0.001 | – | 1.14 |
| Ni et al. 2019 | Mann-Whitney U test, Student's t-test, Welch's F test | 146.5 | 135.48 | – | 0.934 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 124011 ± 34021 | 124422 ± 35741 | 1 | 0.795 | 0.91 | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 155793 ± 36291 | 161536 ± 43045 | 0.96 | 0.769 | 0.898 | – |
| Ni et al. 2019 | Mann-Whitney U test, Student's t-test, Welch's F test | 126.29 | 127.9 | – | 0.704 | – | – |
| Ni et al. 2016 | one‐way ANOVA | 146.50 ± 53.53 μmol/L | 141.98 ± 32.66 μmol/L | – | 0.5434 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 157690 ± 42761 | 152525 ± 48794 | 1.034 | 0.492 | 0.785 | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 67220 ± 21747 | 73742 ± 30099 | 0.91 | 0.46 | 0.695 | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 494183.5 | 529623.45 | 0.933084628333583 | 0.263749686358265 | – | – |
| Klupczynska et al. 2016a | t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA | 424.82±101.12 ?M | 473.15±133.12 ?M | 0.9 | 0.0166 | – | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 449419.272727273 | 576252.636363636 | 0.779899725167891 | 0.00765422852270164 | – | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 1.22665985752527 | 0.00239929705863565 | 0.00545746843907279 | – |
| Wen et al. 2013 | Mann–Whitney–Wilcoxon test, OPLS-DA | – | – | 0.38156480224014 | 0.000712 | – | 1.39 |
| Mazzone et al. 2016 | two- sample independent t test | 0.9644947± 0.2833716 | 1.0890479± 0.2653567 | 0.885631109522364 | 0.0003222 | 0.015006832 | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 1.54272457017363 | 0.00000556876518898235 | 0.0000126246438091134 | – |
| Wikoff et al. 2015b | OPLS-DA | – | – | 1.2 | – | 0.013 | – |
| Reference | Classification method | Cutoff value | AUROC 95%CI | Sensitivity (%) | Specificity (%) | Accuracy (%) |
| Chen et al. 2015 | – | – | – | – | – | – |
| Chen et al. 2015 | – | – | – | – | – | – |
| Ni et al. 2019 | ROC analysis | – | 0.505 | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Ni et al. 2019 | ROC analysis | – | 0.467 | – | – | – |
| Ni et al. 2016 | – | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Klupczynska et al. 2016a | ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis | – | 0.62 | alanine+histidine+ornithine+isoleucine+tryptophan+valine=84.4 alanine+histidine+ornithine+glutamine+lysine+serine=82.2 | alanine+histidine+ornithine+isoleucine+tryptophan+valine=52.4 alanine+histidine+ornithine+glutamine+lysine+serine=58.7 | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Wen et al. 2013 | ROC curve analysis | – | 0.76 | – | – | – |
| Mazzone et al. 2016 | – | – | – | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Wikoff et al. 2015b | – | – | – | – | – | – |