| Literature DB >> 35284729 |
Hanaa M Morad1, Mohamed M Abou-Elzahab2, Salah Aref3, Ahmed M A El-Sokkary1.
Abstract
Cancer refers to a massive number of diseases distinguished by the development of abnormal cells that divide uncontrollably and have the capability of infiltration and destroying the normal body tissue. It is critical to detect biomarkers that are early detectable and noninvasive to save millions of lives. The aim of the present work is to use NMR as a noninvasive diagnostic tool for cancer diseases. This study included 30 plasma and 21 urine samples of patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), 25 plasma and 17 urine samples of patients diagnosed with breast cancer (BC), and 9 plasma and urine samples obtained from healthy individuals as controls. They were prepared for NMR measurements; then, the metabolites were identified and the data were analyzed using multivariate statistical procedures. The OPLS-DA score plots clearly discriminated ALL, AML, and BC from healthy controls. Plots of the PLS-DA loadings and S-line plots showed that all metabolites in plasma were greater in BC than in the healthy controls, whereas lactate, O-acetylcarnitine, pyruvate, trimethylamine-N-oxide (TMAO), and glucose were higher in healthy controls than in ALL and AML. On the other hand, urine samples showed lower amounts of lactate, melatonin, pyruvate, and succinate in all of the studied types of cancer when compared to those of healthy controls. 1H NMR can be a successful and noninvasive tool for the diagnosis of different types of cancer.Entities:
Year: 2022 PMID: 35284729 PMCID: PMC8908535 DOI: 10.1021/acsomega.2c00083
Source DB: PubMed Journal: ACS Omega ISSN: 2470-1343
Patients’ Characteristics (WBC = White Blood Cells, HGB = Hemoglobin, PLT = Platelets)
| no | sex | age | diagnosis | grade/stage | WBC (K/μL) | HGB (g/dL) | PLT (K/μL) |
|---|---|---|---|---|---|---|---|
| 1 | F | 60 | infiltrating ductal carcinoma | II | 9.1 | 10.4 | 215.3 |
| 2 | F | 33 | invasive ductal carcinoma | II | 7.027 | 13.48 | 507.2 |
| 3 | F | 34 | invasive ductal carcinoma | III | 8.751 | 12.13 | 442.2 |
| 4 | F | 38 | invasive ductal carcinoma | II | 4 | 12.2 | 415 |
| 5 | F | 36 | invasive ductal carcinoma | II | 7.836 | 11.34 | 402.7 |
| 6 | F | 58 | invasive ductal carcinoma | III | 8.349 | 13.23 | 139.7 |
| 7 | F | 38 | infiltrating ductal carcinoma | II | 4.714. | 11.94 | 240.3 |
| 8 | F | 37 | invasive ductal carcinoma | III | 2.579 | 12.06 | 255.8 |
| 9 | F | 61 | invasive ductal carcinoma | II | 10.1 | 12 | 243 |
| 10 | F | 59 | invasive ductal carcinoma | III | 5.5 | 12.9 | 229 |
| 11 | F | 45 | invasive ductal carcinoma | III | 9.516 | 11.33 | 299.6 |
| 12 | F | 44 | infiltrating ductal carcinoma | III | 3.8 | 12.3 | 180 |
| 13 | F | 48 | metastatic poorly differentiated carcinoma | IV | 5.1 | 13.5 | 324 |
| 14 | F | 66 | invasive mammary carcinoma | II | 6.9 | 11.8 | 373 |
| 15 | F | 78 | infiltrating ductal carcinoma | II | 8.955 | 9.396 | 413.6 |
| 16 | F | 51 | infiltrating ductal carcinoma | II | 9.884 | 11.97 | 310.6 |
| 17 | F | 59 | ductal carcinoma | I | 7.7 | 11.1 | 223 |
| 18 | F | 38 | infiltrating ductal carcinoma | II | 10 | 12.86 | 390.7 |
| 19 | F | 35 | invasive ductal carcinoma | II | 9.078 | 8.61 | 283.9 |
| 20 | F | 53 | ductal carcinoma | III | 5.987 | 11.81 | 427.3 |
| 21 | F | 74 | infiltrating ductal carcinoma | II | 6.392 | 11.98 | 217 |
| 22 | F | 41 | infiltrating ductal carcinoma | II | 5.81 | 13.5 | 328 |
| 23 | F | 54 | invasive ductal carcinoma | II | 7.9 | 12.1 | 315 |
| 24 | F | 22 | invasive ductal carcinoma | II | 7.183 | 13.39 | 298.8 |
| 25 | F | 49 | invasive ductal carcinoma | III | 8.514 | 12.19 | 232.1 |
| 26 | F | 51 | AML | M(1–2) | 26.75 | 7.43 | 18.21 |
| 27 | F | 62 | AML | M(4–5) | 9.64 | 7.22 | 67.55 |
| 28 | F | 28 | AML | M(4–5) | 38.52 | 9.7 | 38.6 |
| 29 | F | 50 | AML | M4 | 41.4 | 6.2 | 159 |
| 30 | M | 47 | AML | M6 | 1.842 | 7.249 | 18.91 |
| 31 | F | 52 | AML | M2 | 3.8 | 6.8 | 47 |
| 32 | F | 37 | AML | M4 | 22.7 | 7.6 | 125 |
| 33 | M | 61 | AML | M4 | 34.6 | 7.84 | 38.6 |
| 34 | F | 38 | AML | M(1–2) | 28.22 | 10.29 | 72.17 |
| 35 | F | 31 | AML | M(1–2) | 2.1 | 8.1 | 169 |
| 36 | M | 42 | AML | M(1–2) | 2.1 | 7.1 | 241 |
| 37 | F | 63 | AML | M2 | 61.1 | 5.81 | 80.13 |
| 38 | F | 23 | AML | M(4–5) | 41.6 | 6.9 | 56 |
| 39 | F | 31 | AML | M3 | 2.55 | 6.9 | 16.1 |
| 40 | F | 42 | AML | M(1–2) | 14.41 | 11.54 | 11.96 |
| 41 | M | 59 | AML | M(1–2) | 153.8 | 7.58 | 9.6 |
| 42 | M | 21 | ALL | T-ALL | 116 | 9.1 | 35 |
| 43 | M | 34 | ALL | T-ALL | 142 | 10.9 | 104 |
| 44 | F | 44 | ALL | B-ALL | 12.5 | 6.4 | 5 |
| 45 | M | 22 | ALL | T-ALL | 205 | 7.6 | 40 |
| 46 | M | 58 | ALL | T-ALL | 93.9 | 6.9 | 9 |
| 47 | M | 18 | ALL | B-ALL | 0.9 | 6.9 | 28 |
| 48 | M | 26 | ALL | B-ALL | 35.2 | 11 | 33 |
| 49 | M | 41 | ALL | T-ALL | 151.2 | 12.77 | 30.56 |
| 50 | M | 68 | ALL | B-ALL | 119.4 | 7.41 | 148.2 |
| 51 | F | 42 | ALL | T-ALL | 4.16 | 11.71 | 188.8 |
| 52 | M | 37 | ALL | T-ALL | 8 | 12.1 | 53.7 |
| 53 | M | 26 | ALL | B-ALL | 3.1 | 3.8 | 35 |
| 54 | F | 54 | ALL | T-ALL | 89.37 | 11.29 | 22.86 |
| 55 | F | 53 | ALL | B-ALL | 36.2 | 7.5 | 35 |
Figure 1Comparison of blood plasma samples; (A) ALL vs healthy control, (B) AML vs healthy control, and (C); BC vs healthy control. The green color indicates the disease type, and the brown color indicates the control.
Figure 2Comparison of urine samples; (A) ALL vs healthy control, (B) AML vs healthy control, and (C) BC vs healthy control. The green color indicates the disease type, and the brown color indicates the control.
Figure 3(1) PCA, (2) PLS-DA, and (3) OPLS-DA score plots for healthy controls (green), ALL (blue), AML (red), and BC (yellow) in blood (A) and urine (B) samples.
Figure 4PLS-DA Loading plots of (A) blood and (B) urine samples.
Figure 5OPLS-DA S-line plots of the healthy controls versus (A) ALL, (B) AML, and (C) BC blood plasma samples.
Figure 6OPLS-DA S-line plots of the healthy controls versus (A) ALL, (B) AML, and (C) BC urine samples.