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Citation Information
J Proteome Res. 2021 Jul 2;20(7):3444-3454. doi: 10.1021/acs.jproteome.0c01019. Epub 2021 May 30.
Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway.
Zheng Y, He Z, Kong Y, Huang X, Zhu W, Liu Z, Gong L.
Analytical methods | ||
|---|---|---|
| Comparative study | #1 | |
| Chromatography | GC | |
| Ion source | EI | |
| Positive/Negative mode | – | |
| Mass analyzer | quadrupole | |
| Identification level | – | |
Sample information | ||
|---|---|---|
| Comparative study | #1 | |
| Country | China | |
| Specimen | Serum | |
| Marker function | diagnosis | |
| Participants(Case) | Cancer type | lung cancer |
| Stage | I, II, III, IV | |
| Number | 57 | |
| Gender (M,F) | 38, 19 | |
| Mean age (range) (M,F) | Median: 62 (52-69) | |
| Smoking status | smoker, non-smoker | |
| Participants(Control) | Type | healthy |
| Number | 59 | |
| Gender (M,F) | 48, 11 | |
| Mean age (range) (M,F) | Median: 60 (59-62) | |
| Smoking status | smoker, non-smoker | |
Data processing and metabolite identification | |
|---|---|
| Data processing software | MassHunter Workstation software, Mass Profiler Professional software |
| Database search | NIST14, HMDB, Golm Metabolome Database |
Statistical analysis | |
|---|---|
| Differential analysis method | Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA |
| Classification method | ROC analysis |
| Survival analysis method | – |
Lung cancer-related metabolites identified in the paper
| Metabolite | Author-emphasized biomarkers | Mean concentration (case) | Mean concentration (control) | Fold change (case/control) | P-value | FDR | VIP |
|---|---|---|---|---|---|---|---|
| L-lactic acid | – | – | – | 0.91 | 3.21e-17 | 1.16e-16 | 1.27 |
| 2-hydroxybutyric acid | – | – | – | 1.02 | 3.57e-10 | 4.02e-10 | 1.03 |
| ketoleucine | – | – | – | 0.89 | 4.73e-13 | 8.51e-13 | 1.08 |
| 4-hydroxybutyric acid | – | – | – | 0.89 | 2.02e-22 | 1.21e-21 | 1.23 |
| pelargonic acid | – | – | – | 0.90 | 1.89e-12 | 2.62e-12 | 1.11 |
| threonic acid | – | – | – | 0.89 | 3.61e-18 | 1.62e-17 | 1.20 |
| palmitoleic acid | – | – | – | 0.91 | 1.97e-06 | 1.97e-06 | 1.00 |
| palmitic acid | – | – | – | 0.91 | 8.06e-16 | 2.42e-15 | 1.16 |
| myo-inositol | – | – | – | 0.93 | 3.65e-13 | 7.31e-13 | 1.06 |
| oleic acid | – | – | – | 0.91 | 1.53e-14 | 3.95e-14 | 1.07 |
| stearic acid | – | – | – | 0.93 | 5.61e-13 | 8.42e-13 | 1.11 |
| arachidic acid | – | – | – | 0.91 | 5.32e-13 | 8.70e-13 | 1.03 |
| MG(0:0/16:0/0:0) | – | – | – | 0.86 | 3.98e-29 | 3.58e-28 | 1.30 |
| cholesterol | – | – | – | 0.84 | 9.76e-31 | 1.76e-29 | 1.34 |
| Metabolite | Author-emphasized biomarkers | Cutoff value | AUROC (95%CI) | Sensitivity (%) | Specificity (%) | Accuracy (%) |
|---|---|---|---|---|---|---|
| L-lactic acid | – | – | – | – | – | – |
| 2-hydroxybutyric acid | – | – | 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) | – | – | – |
| ketoleucine | – | – | – | – | – | – |
| 4-hydroxybutyric acid | – | – | 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) | – | – | – |
| pelargonic acid | – | – | – | – | – | – |
| threonic acid | – | – | – | – | – | – |
| palmitoleic acid | – | – | – | – | – | – |
| palmitic acid | – | – | – | – | – | – |
| myo-inositol | – | – | 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) | – | – | – |
| oleic acid | – | – | 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) | – | – | – |
| stearic acid | – | – | – | – | – | – |
| arachidic acid | – | – | – | – | – | – |
| MG(0:0/16:0/0:0) | – | – | – | – | – | – |
| cholesterol | – | – | 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) | – | – | – |
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