| Literature DB >> 29043299 |
Baoyan Ji1, Xiongfei Cheng2, Xiaojun Cai2, Chuiyan Kong3, Qingyan Yang4, Ting Fu2, Yahang Wang5, Ying Song2.
Abstract
The aim of this study was to evaluate the diagnostic value of serumCK20 mRNA as a biomarker for colorectal cancer diagnosis by meta-analysis. Clinical studies related to serum CK20 mRNA expression for colorectal cancer diagnosis were searched in the databases of Pubmed, Cochrane Library, Embase, ISI Web of Knowledge, CNKI and Wanfang. The number of true positive (tp), false positive (fp), false negative (fn) and true negative (tn) of the original included publications were extracted by two reviewers independently. The diagnostic sensitivity, specificity, positive likely hood ratio (+LR), negative likelyhood ratio (-LR), diagnostic odds ratio (DOR) and area under the symmetric ROC curve (AUC) were pooled by random or fixed effect method according to the statistical heterogeneity among the studies. After screening the databases, nineteen publications met the inclusion criteria and were finally included in this meta-analysis. The diagnostic sensitivity and specificity were pooled by random effect model(I2>50%). The pooled diagnostic sensitivity and specificity of CK20 mRNA in serum as biomarker for colorectal cancer were 0.49 (95% CI:0.46 to 0.51) and 0.94 (95%CI:0.92-0.96) respectively. The pooled +LR and -LR were 10.90 (95%CI:5.78 to 20.55) and 0.51 (95%CI:0.45 to 0.57) respectively by random-effect method. The pooled DOR was 22.31 with the 95% CI of 11.65 to 42.71. The pooled area under the ROC curve (AUC) was 0.72for CK20 mRNA in serum as a biomarker for colorectal cancer diagnosis. Conclusion Serum CK20 mRNA expression was significantly elevated in colorectal cancer patients which could be a promising serum biomarker for colorectal cancer diagnosis with high specificity.Entities:
Keywords: Biomarker; CK20; Colorectal cancer; Diagnosis; Meta-analysis
Year: 2017 PMID: 29043299 PMCID: PMC5639391 DOI: 10.1515/med-2017-0050
Source DB: PubMed Journal: Open Med (Wars)
Figure 1The publication searching flow chart
The general characters of the included 19 publications
| Author | Year | Sample size | Distribution | Control type | Country | Methods | |||
|---|---|---|---|---|---|---|---|---|---|
| TP | FP | FN | TN | ||||||
| Richard Q[ | 1999 | 170 | 35 | 1 | 65 | 69 | Population based | Britain | RT-qPCR |
| Chausovsky G[ | 1999 | 57 | 15 | 0 | 20 | 22 | Population based | Israel | RT-qPCR |
| Lin GL[ | 2002 | 67 | 23 | 0 | 24 | 20 | Mixed | China | RT-qPCR |
| Sun JW[ | 2004 | 62 | 19 | 0 | 23 | 20 | Population based | China | RT-qPCR |
| Cui M[ | 2004 | 122 | 62 | 0 | 40 | 20 | Population based | China | RT-qPCR |
| Zeng QG[ | 2004 | 89 | 41 | 6 | 12 | 30 | Hospital based | China | RT-qPCR |
| Yin HZ[ | 2005 | 57 | 34 | 0 | 13 | 10 | Population based | China | FQ-PCR |
| Yin HZ[ | 2005 | 57 | 34 | 0 | 13 | 10 | Hospital based | China | FQ-PCR |
| Dandachi N[ | 2005 | 134 | 46 | 17 | 36 | 35 | Mixed | Austria | RT-qPCR |
| Guo J[ | 2005 | 50 | 31 | 0 | 9 | 10 | Hospital based | China | FQ-PCR |
| Xu D[ | 2006 | 198 | 46 | 2 | 122 | 28 | Population based | China | RT-qPCR |
| Katsumata K[ | 2006 | 54 | 18 | 0 | 22 | 14 | Population based | Japan | RT-qPCR |
| Wang ZC[ | 2007 | 211 | 35 | 0 | 101 | 73 | Mixed | China | FQ-PCR |
| Shen C[ | 2008 | 281 | 74 | 21 | 82 | 104 | Mixed | China | RT-qPCR |
| Lagoudianakis EE[ | 2009 | 58 | 28 | 0 | 14 | 14 | Population based | Greece | RT-qPCR |
| Wong SC[ | 2009 | 342 | 62 | 3 | 70 | 207 | Mixed | China | FQ-PCR |
| Wu F[ | 2009 | 142 | 44 | 0 | 48 | 50 | Population based | China | RT-qPCR |
| Chen P[ | 2009 | 56 | 18 | 0 | 18 | 20 | Population based | China | RT-qPCR |
| Chen P[ | 2009 | 57 | 18 | 1 | 18 | 10 | Hospital based | China | RT-qPCR |
| He Q[ | 2010 | 151 | 49 | 0 | 52 | 50 | Population based | China | RT-qPCR |
| Li LH[ | 2012 | 110 | 48 | 3 | 32 | 27 | Hospital based | China | RT-qPCR |
| Li LH[ | 2012 | 110 | 48 | 0 | 32 | 30 | Population based | China | RT-qPCR |
Figure 2The forest plot of diagnostic sensitivity for serum CK20 mRNA in diagnosis of colorectal cancer
Figure 3The forest plot of diagnostic specificity for serum CK20 mRNA in diagnosis of colorectal cancer
Figure 4The forest plot of diagnostic +LR and −LR for serum CK20 mRNA in diagnosis of colorectal cancer
Figure 5The forest plot of DOR for serum CK20 mRNA in diagnosis of colorectal cancer.
Figure 6ROC cure of CK20 mRNA in serum as biomarker for colorectal cancer diagnosis.
Subgroup analysis according to the control type and CK20 mRNA detection methods
| Factors | Sen(95%CI) | Spe(95%CI) | +LR(95%CI) | -LR(95%CI) | DOR(95%CI) | AUC |
|---|---|---|---|---|---|---|
| Control type | ||||||
| Population based | 0.47(0.44-0.50) | 0.99(0.98-1.00) | 15.51(7.81-30.83) | 0.51(0.44-0.60) | 29.37(14.30-60.34) | 0.86 |
| Hospital based | 0.67(0.61-0.73) | 0.90(0.82-0.95) | 5.61(3.21-9.82) | 0.36(0.26-0.49) | 17.44(8.46-35.94) | 0.88 |
| Methods | ||||||
| RT-qPCR | 0.49(0.46-0.52) | 0.92(0.89-0.94) | 8.30(4.37-15.60) | 0.53(0.47-0.60) | 16.60(8.42-32.75) | 0.71 |
| FQ-PCR | 0.49(0.44-0.54) | 0.99(0.77-1.00) | 26.77(11.17-64.15) | 0.41(0.27-0.61) | 59.21(23.64-148.29) | 0.95 |
Figure 7Publication bias evaluated by Deeks’ funnel plot