Table 2.
Performance characteristics of single metabolites and panels of potential biomarkers.
| First Author, Year | Metabolites | Diagnostic Performance | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Outcomes | Am A/ | FA | CH | Others | Sn | Sp | AUC-No | AUC with Validation | p-Value | ||||
| Pep | Validation | SS | CV | BS | EV | ||||||||
| Biomarker panels | |||||||||||||
| Dried blood spot | |||||||||||||
| Jing, 2017 [18] | CRC | 4 | 4 | 0 | 0 | 81.2 | 84.0 | 0.91 | <0.05 | ||||
| Serum | |||||||||||||
| Zhang, 2018 [22] | CRC | 0 | 2 | 0 | 0 | n.a. | n.a. | 0.90 | <0.05 | ||||
| Guo, 2017 [24] | CRC ♂ CRC ♀ |
0 0 |
5 2 |
0 0 |
0 0 |
77.3 80.8 |
92.4 85.9 |
0.90 0.90 |
n.a. n.a. |
||||
| Farshidfar, 2016 [14] | CRC | 9 | 7 | 12 | 13 | 85.0 | 86.0 | 0.91 | 0.91 | <0.00001 | |||
| Y. Zhang, 2016 [26] | CRC | 0 | 6 | 0 | 0 | 93.8 | 92.2 | 0.98 | <0.001 | ||||
| H. Gu, 2015 [27] | CRC | 8 | 0 | 0 | 0 | 65.0 | 95.0 | 0.91 | <0.05 | ||||
| Zhu, 2014 [28] | CRC | 7 | 3 | 3 | 0 | 96.0 | 80.0 | 0.93 | 0.93 1 | <0.05 | |||
| F. Li, 2013 [29] | CRC | 0 | 9 | 0 | 0 | 86.5 | 96.2 | 0.96 | <0.05 | ||||
| Tan, 2013 [31] | CRC | 6 | 1 | 3 | 0 | 83.7 | 91.7 | n.a. | <0.05 | ||||
| Ma, 2012 [34] | CRC | 3 | 0 | 3 | 0 | 93.3 2 | 96.7 2 | n.a. | <0.05 | ||||
| Nishiumi, 2012 [35] | CRC | 3 | 0 | 1 | 0 | 83.1 | 81.0 | n.a. | <0.05 | ||||
| Ritchie, 2010 [36] | CRC | 0 | 3 | 0 | 0 | 75.0 | 90.0 | 0.91 | <0.05 | ||||
| Ludwig, 2009 [37] | CRC | 0 | 1 | 4 | 0 | 70.0 | 95.0 | n.a. | n.a. | ||||
| Plasma | |||||||||||||
| Nishiumi, 2017 [39] | Stage 0/I/II | 3 | 3 | 2 | 0 | 99.3 | 93.8 | 1.00 | 0.000781 | ||||
| S. Li, 2013 [43] | CRC | 0 | 3 | 0 | 0 | 88.3 | 80.0 | n.a. | <0.05 | ||||
| Miyagi, 2011 [44] | CRC | 10 | 0 | 0 | 0 | n.a. | n.a. | 0.87 3 | <0.001 | ||||
| Okamoto, 2009 [45] | CRC | 6 | 0 | 0 | 0 | n.a. | n.a. | 0.91 | <0.05 | ||||
| Zhao, 2007 [46] | CRC | 0 | 4 | 0 | 0 | 82.0 | 93.0 | n.a. | <0.001 | ||||
| Urine | |||||||||||||
| Nakajima, 2018 [47] | CRC | 2 | 0 | 0 | 0 | n.a. | n.a. | 0.79 | <0.0001 | ||||
| Deng, Chang, 2017 [48] | AP | 0 | 1 | 2 | 0 | 82.4 4 | 36.0 4 | 0.69 | <0.05 | ||||
| Deng, Fang, 2017 [19] | AP | 7 | 2 | 8 | 0 | 82.6 | 42.4 | 0.72 | n.a. | ||||
| Wang, 2017 [49] | CRC I/II | 3 | 0 | 1 | 0 | 87.5 | 91.3 | 0.93 | <0.01 | ||||
| Rozalski, 2015 [50] | CRC | 0 | 0 | 3 | 0 | 78.6 | 75.0 | 0.78 | <0.0001 | ||||
| Wang, 2014 [51] | AP | 7 | 2 | 8 | 0 | 82.7 | 51.2 | n.a. | n.a. | <0.05 | |||
| Eisner, 2013 [16] | P | 2 | 0 | 2 | 0 | 64.0 | 65.0 | 0.72 | <0.01 | ||||
| Hsu, 2013 [52] | CRC | 0 | 0 | 6 | 0 | 69.0 | 98.0 | n.a. | <0.01 | ||||
| Yue, 2013 [17] | CRC | 0 | 9 | 0 | 1 | 100.0 | 80.0 | n.a. | <0.05 | ||||
| Chen, 2012 [53] | CRC | 8 | 0 | 4 | 0 | n.a. | n.a. | 1.00 | <0.01 | ||||
| Cheng, 2012 [54] | CRC | 4 | 1 | 2 | 0 | 97.5 | 100.0 | 1.00 | 1.00 | <0.001 | |||
| Wang, 2010 [21] | CRC | 4 0 |
5 0 |
0 7 |
0 0 |
n.a. n.a |
n.a n.a |
0.96 0.89 |
<0.05 <0.05 |
||||
| Feng, 2005 [55] | CRC | 0 | 0 | 2 | 0 | 71.2 | 93.3 | n.a. | <0.01 | ||||
| Zheng, 2005 [57] | CRC | 0 | 0 | 14 | 0 | 71.0 | 96.0 | n.a. | <0.05 | ||||
| Feces | |||||||||||||
| Amiot, 2015 [59] | ACN | 2 | 4 | 1 | 0 | n.a. | n.a. | 0.94 | <0.0001 | ||||
| Phua, 2014 [15] | CRC | 0 | 1 | 2 | 0 | n.a. | n.a. | 1.00 | <0.05 | ||||
| Bezabeh, 2009 [60] | CRC | 3 | 2 | 0 | 0 | 85.2 | 86.9 | 0.92 | 0.92 3 | n.a. | |||
| Single markers | |||||||||||||
| Serum | |||||||||||||
| Hata, 2017 [25] | CRC | 0 | 1 | 0 | 0 | 83.3 | 84.8 | 0.91 | <0.05 | ||||
| Uchiyama, 2017 [23] | CRC |
0 0 0 1 His |
1 C7 1 C8 1 C10 0 |
0 0 0 0 |
0 0 0 0 |
89.0 76.0 71.0 63.0 |
82.0 71.0 75.0 82.0 |
0.89 0.83 0.79 0.74 |
<0.01 <0.01 <0.01 <0.01 |
||||
| Ritchie, 2013 [30] | CRC | 0 | 1 | 0 | 0 | 85.7 | ~52.1 5 | n.a. | <0.05 | ||||
| Ikeda, 2012 [32] | CRC | 1 Ala 0 1 Gln |
0 0 0 |
0 1 GluL 0 |
0 0 0 |
54.5 75.0 81.8 |
91.6 75.0 66.7 |
n.a. | <0.05 | ||||
| Leichtle, 2012 [33] | CRC | 1 | 0 | 0 | 0 | n.a. | n.a. | 0.71 | <0.001 | ||||
| Plasma | |||||||||||||
| Liu, 2018 [38] | RC/A | 1 | 0 | 0 | 0 | 43.5 | 98.8 | 0.71 | <0.05 | ||||
| Shen, 2017 [40] | CRC | 0 0 |
1 PG 1 SM |
0 0 |
0 0 |
1.00 1.00 |
1.00 1.00 |
1.00 1.00 |
<0.05 <0.05 |
||||
| Crotti, 2016 [41] | CRC | 0 | 1 | 0 | 0 | 87.8 | 80.0 | 0.82 | <0.01 | ||||
| Cavia-Saiz, 2014 [42] | CRC | 1 | 0 | 0 | 0 | 85.2 | 100.0 | 0.92 | <0.001 | ||||
| Urine | |||||||||||||
| Johnson, 2006 [20] | CRC | 0 | 1 | 0 | 0 | 90.0 | 45.0 | 0.64 | <0.05 | ||||
| Hiramatsu, 2005 [56] | CRC | 1 | 0 | 0 | 0 | 75.8 | 96.0 | n.a. | <0.0001 | ||||
| Feces | |||||||||||||
| Lin, 2016 [58] | Early stage | 0 0 |
1 Ace 1 Suc |
0 0 |
0 0 |
94.7 91.2 |
92.3 93.5 |
0.99 0.94 |
0.99 0.94 |
<0.001 <0.001 |
|||
The numbers in the column of the metabolites indicate how many metabolites were used for the biomarker panel from each biochemical subclass. In case of single markers, the biochemical subclass of the marker is listed. Abbreviations: (A)A, (advanced) adenomas; Ace, acetate; ACN, advanced colorectal neoplasms; Ala, alanine; Am A, amino acids, AP, adenomatous polyps; AUC, area under the curve; BS, bootstrapping; C7, benzoic acid; C8, octanoic acid; C10, decanoic acid; CH, carbohydrates; CV, cross validation; EV, external validation; FA, fatty acids; Gln, glutamine; GluL, glucuronic lactone; His, histidine; LOOCV, leave one out cross validation; MCCV, Monte Carlo cross validation; P, polyps; pep, peptides; PG, phosphatidylglycerol (34:0); RC, rectal cancer; SM, sphingomyelin (38:8); Sn, sensitivity; Sp, specificity; SS, subsampling; Suc, succinate. 1 Monte Carlo cross validation (MCCV). 2 Sensitivity and specificity calculated from available data. 3 Leave-one-out cross validation (LOOCV). 4 Additional results for different cut-off values can be read from the original article. 5 Specificity was calculated for the intended to screening population (40–74 years olds in the colonoscopy population).