3.1. The characteristics and quality of the included studies
After duplicate removal, title and abstract assessment and full-text evaluation, we finally included 46 studies involving 4,326 PC patients and 4,277 non-PC controls. The characteristics of included studies were listed in Table 1. Among these original studies, 34 studies were conducted in Asia[13–46], 6 studies in Europe[47–52], 4 studies in North America[53–56], 1 study in Africa[57], and 1 study in South America[58]. The publication year were 2019 (n = 2), 2018 (n = 5), 2017 (n = 4), 2016 (n = 6), 2015 (n = 7), 2014 (n = 11), 2013 (n = 4), 2012 (n = 2), 2011 (n = 4), 2009 (n = 1). The flow diagram of literature search and study selection was detailed in Fig. 1 (A).
Table 1
Characteristics of included studies
author | year | region | specimen | conference test | markers and expression level in PC patients | normalization controls | PC patients | non-PC controls |
No. | population | No. | population |
Xuan Zou | 2019 | China | serum | Histopathology | let-7b-5p ↑、miR-192-5p ↑、19a-3p ↑、19b-3p ↑、223-3p ↑、25-3p ↑ | cel-miR-34 | 159 | PC | 137 | HC |
Zebo Huang | 2019 | China | serum | Histopathology | miR-16 ↑ | cel-miR-39 | 155 | PC | 137 | HC |
Takuma Goto | 2018 | Japan | serum | Imaging | miR-191 ↑、21 ↑、451a ↑、CEA ↑、CA19-9 ↑ | unclear | 32 | PC | 22 | GBP(4)、Chronic gastritis(3)、Gallbladder stone(2)、ADM(2)、Liver cyst(1)、IBS(1)、Accessory spleen(1)、Only symptom(7) |
Francesca Tavano | 2018 | Italy | plasma | Histopathology or Imaging | miR-1290 ↑、CA19-9 ↑ | unclear | 167 | PC | 267 | HC |
Rei Suzuki | 2018 | Japan | serum | Histopathology | miR-let-7d ↓、CEA ↑、CA19-9 ↑ | unclear | 45 | PC | 42 | CP(18)、Biliary stone(20)、others(4) |
Jin Wang | 2018 | USA | plasma | Histopathology | miR-21 ↑、210 ↑、155 ↑、196a ↑ | miR-16 | 49 | PC | 36 | HC |
Xin Zhou | 2018 | China | plasma | Histopathology | miR-122-5p ↑、125b-5p ↑、192-5p ↑、193b-3p ↑、221-3p ↑、27b-3p ↑ | miR-103a | 216 | PC | 220 | HC |
ARZUGUL Ablet | 2018 | China | plasma | Histopathology | miR-21 ↑、155 ↑ | U6 | 42 | PC | 84 | CP(42)、HC(42) |
Xianyin Lai | 2017 | China | plasma | Histopathology | miR-10b ↑、20a ↑、21 ↑、30c ↑、106b ↑、181a ↑、let-7a ↓、122 ↑ | miR-425-5p | 29 | PC | 6 | HC |
Kai Qu | 2017 | China | serum | Histopathology | miR-21-5p ↑ | cel-miR-39 | 56 | PC | 15 | HC |
Yongqiang Hua | 2017 | China | serum | Unclear | miR-373 ↓ | U6 | 103 | PC | 50 | HC |
Neveen Abd EI Moneim Hussein | 2017 | Egypt | plasma | Histopathology | miR-22-3p ↑、643b-3p ↑、885-5p ↑、CA19-9 ↑ | miR-3196 | 35 | PC | 15 | HC |
Ting Deng | 2016 | China | serum | Histopathology | miR-25 ↑ | unclear | 303 | PC | 760 | HC(600)、CP(40)、gastric cancer(20)、breast cancer(20)、lung cancer(20)、liver cancer(20)、esophageal cancer(20)、colorectal cancer(20) |
Bárbara Alemar | 2016 | Brazil | serum | Histopathology | miR-21 ↑、34a ↑ | cel-miR-39 | 24 | PC | 9 | HC |
Zhe Cao | 2016 | China | plasma | Histopathology | miR-486-5p ↓、126-3p ↓、106-3p ↓、938 ↓、26b-3p ↓、1285 ↓、CA19-9 ↑ | U6 | 185 | PC | 158 | CP(73)、OPN(85) |
Pavel Skrha | 2016 | Czech Republic | serum | Histopathology | miR196 ↑、200 ↑、CA19-9 ↑ | miR-191、454 | 77 | PC | 64 | HC |
Manabu Akamatsu | 2016 | Japan | serum | Histopathology | miR-7 ↑、34a ↑、181d ↑、193b ↑ | cel-miR-39 | 69 | PC | 15 | AIP |
Julia S. Johansen | 2016 | Denmark | serum | Histopathology | miR-16 ↑、18a ↓、24 ↓、25 ↓、27a ↓、30a-5p ↓、323-3p ↓、20a ↑、29c ↓、191 ↓、345 ↓、483-5p ↑、CA19-9 ↑ | unclear | 417 | PC | 340 | PAC(33)、CP(59)、HC(248) |
Bindhu Madhavan | 2015 | Germany | serum | Histopathology | miR-1246 ↑、4644 ↑、3976 ↑、4306 ↑ | U43、U6、18S and 5S rRNA | 87 | PC | 51 | CP(17)、BPT(14)、HC(20) |
Shuhei Komatsu | 2015 | Japan | plasma | Histopathology | miR-223 ↑ | cel-miR-39 | 71 | PC | 67 | HC |
Mahito Miyamae | 2015 | Japan | plasma | Histopathology | miR-744 ↑ | cel-miR-39 | 94 | PC | 68 | HC |
Hu Yingxia | 2015 | China | plasma | Histopathology | miR-196a ↑、210 ↑、CA19-9 ↑ | U6 | 60 | PC | 30 | CP(20)、HC(10) |
Hu Yingxia | 2015 | China | plasma | Histopathology | miR-210 ↑、CA19-9 ↑、CA242 ↑、CEA ↑ | U6 | 60 | PC | 30 | CP(20)、HC(10) |
Wang Xiaogang | 2015 | China | serum | Histopathology or Imaging | miR-155 ↑、CA19-9 ↑ | cel-miR-39 | 110 | PC | 70 | CP |
Wang Shanbing | 2015 | China | plasma | Histopathology or Imaging | miR-21 ↑、483-3p ↑、155 ↑、CA19-9 ↑ | miR-16 | 43 | PC | 21 | HC |
Ling Gao | 2014 | China | plasma | Histopathology | CA19-9 ↑、miR-16 ↑ | cel-miR-39 | 70 | PC | 120 | HC(50)、CP(70) |
Gregory A. Cote | 2014 | USA | plasma | Histopathology | miR-10b ↑、30c ↑、106b ↑、155 ↑、212 ↑ | miR-425-5p | 40 | PC | 54 | CP(30)、BBD(24) |
Maosong Lin | 2014 | China | serum | Histopathology | miR-492 ↓、663a ↓ | cel-miR-39 | 49 | PC | 27 | HC |
Qiulan Chen | 2014 | China | plasma | Histopathology | miR-182 ↑、CA19-9 ↑ | U6 | 109 | PC | 38 | CP |
Ang Li | 2014 | USA | serum | Histopathology | miR-1290 ↑、628-3p ↑、550 ↑、1825 ↑、24 ↑、134 ↑、146a ↑、200c ↑、378 ↑、484 ↑、625 ↑、22 ↑、210 ↑、744 ↑、CA19-9 ↑ | miR-16 | 41 | PC | 72 | HC(19)、CP(35)、pNET(18) |
Nicolai A. Schultz | 2014 | Denmark | serum | Histopathology | miR-145 ↑、150 ↓、223 ↑、636 ↓、26b ↑、34a ↑、122 ↑、126 ↑、145 ↑、150 ↑、223 ↑、505 ↑、636 ↑、885-5p ↑、CA19-9 ↑ | ath-miR-159a | 409 | PC | 347 | HC(322)、CP(25) |
Emily P. Slater | 2014 | Germany | serum | Histopathology | miR-196a ↑、196b ↑ | miR-24 | 24 | PC | 20 | CP(10)、HC(10) |
Ganepola AP Ganepola | 2014 | USA | plasma | Histopathology | miR-885-5p ↑、22-3p ↑、642b-3p ↑、CA19-9 ↑ | miR-3196 | 11 | PC | 22 | HC |
Jing Zhang | 2014 | China | serum | Histopathology | miR-192 ↑、194 ↑ | U6 | 70 | PC | 40 | HC |
Wenzheng Pan | 2014 | China | plasma | Histopathology | miR-210 ↑、25 ↑、CA19-9 ↑ | cel-miR-39 | 30 | PC | 26 | HC |
Wei Shi | 2014 | China | plasma | Histopathology or Imaging | miR-155 ↑、196a ↑、CA19-9 ↑、CA242 ↑、CEA ↑ | U6 | 60 | PC | 30 | CP(20)、HC(10) |
Risheng Que | 2013 | China | serum | Histopathology | miR-17-5p ↑、21 ↑ | U6 | 22 | PC | 27 | AC(6)、BPN(7)、CP(6)、HC(8) |
T Kawaguchi | 2013 | Japan | plasma | Histopathology | miR-221 ↑ | U6 | 47 | PC | 9 | BPN |
Wansheng Wang | 2013 | China | serum | Unclear | miR-27a-3p ↑、CA19-9 ↑ | U6 | 129 | PC | 163 | BPD(103)、HC(60) |
Chenyan Zhao | 2013 | China | serum | Histopathology | miR-192 ↑ | U6 | 80 | PC | 40 | HC |
Rui Liu | 2012 | China | serum | Histopathology | miR-20a ↑、21 ↑、24 ↑、25 ↑、99a ↑、185 ↑、191 ↑、CA19-9 ↑、CEA ↑ | serum volume | 123 | PC | 61 | HC(52)、CP(9) |
Feng Pan | 2012 | China | plasma | Histopathology | miR-451 ↑、409-3p ↑ | cel-miR-39 | 24 | PC | 24 | HC |
Jianqiang Liu | 2011 | China | plasma | Histopathology or Imaging | miR-16 ↑、196a ↑、CA19-9 ↑ | cel-miR-39 | 138 | PC | 175 | HC(68)、CP(107) |
Jianqiang Liu | 2011 | China | plasma | Histopathology | miR-181a ↑、181b ↑、210 ↑、CA19-9 ↑ | cel-miR-39 | 55 | PC | 96 | HC(39)、CP(57) |
Jianqiang Liu | 2011 | China | plasma | Histopathology | miR-21 ↑ | cel-miR-39 | 45 | PC | 75 | HC(30)、CP(45) |
Jianqiang Liu | 2011 | China | plasma | Histopathology | miR-155 ↑ | cel-miR-39 | 62 | PC | 97 | HC(36)、CP(61) |
We found that there was a high risk of bias in the domain of "Patient Selection" after the process of quality assessment using the QUADAS-2 tool. According to the statement of the QUADAS-2 group, an ideal diagnostic study should enroll a proportion of suspected patients ("difficult-to-diagnose patients") to reduce the risk of bias[12]. But all the included studies included patients with a definitive diagnosis, which resulted in a high risk of bias in this domain. Besides, there is a large proportion of the unclear risk of bias in the domain of "Index Test", because the researchers of these included studies did not describe how they determined the threshold. The risk of bias was low in the domain of "Reference test" and "Flow and Timing". All domains exhibited low concerns of applicability. The results of the quality assessment were shown in Fig. 1 (B-C).
3.2. The diagnostic performance of circulating miRNAs
Circulating single miRNAs, which means that only one miRNA was used for diagnosis, distinguished PC patients from non-PC controls with a SEN of 0.78 (0.76–0.81), a SPE of 0.78 (0.75–0.80), and the PLR, NLR, DOR AUC were 3.55 (3.13–4.02), 0.28 (0.25–0.31), 12.78 (10.19–16.03) and 0.85 (0.82–0.88), respectively. The circulating miRNAs panel, which means multiple miRNAs were applied for diagnosis, discriminated cases with PC from case of non-PC with a SEN of 0.79 (0.76–0.82), a SPE of 0.75 (0.72–0.78), a PLR of 3.16 (2.74–3.65), a NLR of 0.28 (0.23–0.33), a DOR of 11.40 (8.55–15.20), and an AUC of 0.84 (0.80–0.87). There is no significant difference in the diagnostic efficacy between single miRNAs and miRNAs panels. Overall, the SEN, SPE, PLR, NLR, DOR, AUC of circulating miRNAs (including single miRNAs and miRNAs panel) in differentiating patients with PC from non-PC controls were 0.79 (0.77–0.81), 0.77 (0.75–0.79), 3.38 (3.08–3.72), 0.28 (0.25–0.31), 12.22 (10.23–14.60) and 0.85 (0.81–0.87), respectively. The results were shown in Table 2 and Fig. 2 (A-C).
Table 2
The results of meta-analysis
| SEN(95%CI) | SPE(95%CI) | PLR(95%CI) | NLR(95%CI) | DOR(95%CI) | AUC(95%CI) | number of data sets | number of PC | number of control |
1 miRNAs | | | | | | | | |
PC vs non-PC | 0.79(0.77–0.81) | 0.77(0.75–0.79) | 3.38(3.08–3.72) | 0.28(0.25–0.31) | 12.22(10.23–14.60) | 0.85(0.81–0.87) | 228 | 13554 | 14474 |
PC vs CP | 0.77(0.74–0.80) | 0.67(0.62–0.71) | 2.32(2.01–2.69) | 0.35(0.30–0.40) | 6.72(5.10–8.86) | 0.79(0.75–0.82) | 48 | 2554 | 1435 |
PC vs HC | 0.83(0.80–0.85) | 0.81(0.78–0.83) | 4.29(3.67–5.02) | 0.22(0.18–0.26) | 19.94(14.73–26.98) | 0.88(0.85–0.91) | 102 | 5828 | 5983 |
1.1 single miRNAs | | | | | | | | | |
PC vs non-PC | 0.78(0.76–0.81) | 0.78(0.75–0.80) | 3.55(3.13–4.02) | 0.28(0.25–0.31) | 12.78(10.19–16.03) | 0.85(0.82–0.88) | 148 | 7107 | 6426 |
PC vs CP | 0.73(0.68–0.78) | 0.68(0.63–0.73) | 2.28(1.94–2.69) | 0.39(0.32–0.48) | 5.80(4.18–8.03) | 0.76(0.82 − 0.80) | 26 | 1081 | 824 |
PC vs HC | 0.81(0.77–0.85) | 0.81(0.77–0.84) | 4.21(3.46–5.12) | 0.23(0.19–0.29) | 17.98(12.38–26.10) | 0.88(0.85–0.90) | 72 | 3756 | 2686 |
1.2 miRNAs panel | | | | | | | | | |
PC vs non-PC | 0.79(0.76–0.82) | 0.75(0.72–0.78) | 3.16(2.74–3.65) | 0.28(0.23–0.33) | 11.40(8.55–15.20) | 0.84(0.80–0.87) | 80 | 6447 | 8048 |
PC vs CP | 0.80(0.77–0.83) | 0.65(0.56–0.73) | 2.30(1.79–2.95) | 0.30(0.25–0.37) | 7.58(4.91–11.70) | 0.82(0.78–0.85) | 22 | 1473 | 611 |
PC vs HC | 0.86(0.83–0.88) | 0.81(0.76–0.85) | 4.47(3.43–5.81) | 0.18(0.14–0.22) | 25.43(16.02–40.37) | 0.90(0.88–0.93) | 30 | 2072 | 3297 |
2 miRNAs combine CA19-9 | | | | | | | | |
PC vs non-PC | 0.84(0.80–0.87) | 0.91(0.89–0.93) | 9.77(7.65–12.47) | 0.17(0.14–0.22) | 56.01(37.70–83.20) | 0.94(0.92–0.96) | 65 | 6121 | 8124 |
PC vs CP | 0.82(0.76–0.87) | 0.82(0.73–0.89) | 4.61(2.87–7.40) | 0.22(0.15–0.32) | 21.12(9.59–46.51) | 0.89(0.86–0.91) | 16 | 1280 | 562 |
PC vs HC | 0.86(0.81–0.91) | 0.96(0.94–0.97) | 19.52(14.92–25.53) | 0.14(0.10–0.20) | 136.75(91.16-205.15) | 0.97(0.96–0.98) | 20 | 1725 | 3106 |
2.1 single miRNAs combine CA19-9 | | | | | | | | |
PC vs non-PC | 0.88(0.85–0.91) | 0.92(0.88–0.95) | 10.80(7.12–16.38) | 0.13(0.10–0.17) | 84.16(47.15-150.25) | 0.95(0.92–0.96) | 12 | 965 | 830 |
PC vs CP | 0.83(0.79–0.87) | 0.88(0.83–0.92) | 7.18(4.87–10.58) | 0.19(0.14–0.24) | 38.29(22.55-65.00) | 0.92(0.90–0.94) | 4 | 349 | 198 |
PC vs HC | 0.92(0.87–0.96) | 0.94(0.87–0.97) | 15.30(6.88–34.01) | 0.08(0.05–0.14) | 189.00(89.48-399.17) | 0.97(0.95–0.98) | 5 | 357 | 379 |
2.2 miRNAs panel combine CA19-9 | | | | | | | | |
PC vs non-PC | 0.83(0.78–0.86) | 0.91(0.89–0.93) | 9.49(7.14–12.61) | 0.19(0.15–0.25) | 49.60(31.15–78.98) | 0.94(0.92–0.96) | 53 | 5156 | 7294 |
PC vs CP | 0.81(0.70–0.88) | 0.79(0.66–0.88) | 3.88(2.12–7.08) | 0.24(0.14–0.42) | 16.04(5.41–47.54) | 0.87(0.84–0.90) | 12 | 931 | 364 |
PC vs HC | 0.83(0.75–0.89) | 0.96(0.94–0.97) | 20.40(15.17–27.45) | 0.17(0.12–0.26) | 116.62(71.44-190.38) | 0.97(0.95–0.98) | 15 | 1368 | 2727 |
3 Conventional biomarker ( PC vs non-PC ) | | | | | | | | |
CA19-9 | 0.78(0.75–0.80) | 0.90(0.85–0.94) | 7.90(5.14–12.13) | 0.25(0.22–0.28) | 31.89(18.96–53.62) | 0.85(0.82–0.88) | 51 | 3787 | 4508 |
CEA | 0.79(0.39–0.96) | 0.32(0.08–0.72) | 1.17(0.82–1.65) | 0.65(0.26–1.60) | 1.80(0.55–5.88) | 0.59(0.54–0.63) | 10 | 500 | 237 |
CA242 | 0.79(0.52–0.93) | 0.46(0.21–0.74) | 1.47(0.95–2.27) | 0.45(0.21–0.97) | 3.25(1.14–9.32) | 0.68(0.63–0.71) | 5 | 300 | 90 |
CA19-9、CEA、CA242 | 0.77(0.61–0.88) | 0.66(0.42–0.85) | 2.29(1.15–4.58) | 0.35(0.18–0.67) | 6.61(1.92–22.77) | 0.79(0.75–0.82) | 5 | 300 | 90 |
In addition, we also summarized the SEN, SPE, PLR, NLR, DOR, and AUC of miRNAs in distinguishing PC patients from healthy control (HC) or patients with chronic pancreatitis (CP). The data was listed in Table 2. In general, the diagnostic accuracy of miRNAs in discriminating PC from HC was higher than that of discriminating PC from CP.
A total of 58 different single miRNAs and 23 miRNAs panels were involved in the 46 inclusion studies. For the single miRNAs and miRNAs panels being studied in one data set, we extracted the diagnostic SEN, SPE, PLR, NLR, and DOR from the original literature. For those being studied in more than 2 data sets, we performed a meta-analysis and obtained pooled diagnostic SEN, SPE, PLR, NLR, DOR. The results were listed in Table S1 and Table S2. Among these single miRNAs, miR-122, 212, 22-3p, 483-3p, 642b-3p, 885-5p yield a high SEN of more than 90%, while the SPE of miR-25, 223, 17-5p, 223-3p, 30c and 409-3p were greater than 90%. The SEN and SPE of miR-451, 106b, 10b, 181a, 196b, 20a, let-7a were both greater than 90%. For miRNAs panels, the SEN of the combination of let-7b-5p, miR-192-5p, 19a-3p, 19b-3p, 223-3p, 25-3p exceeded 90% while the SPE of the combination of miR-1246, 4464, 3976, 4306 was over 90%. The combination of miR-196a, 196b, the combination of miR-451, 409-3p, as well as the combination of 885-5p, 22-3p, 642b-3p exhibited high diagnostic accuracy, with a SEN and SPE both greater than 90%.
3.3. Circulating miRNAs for the diagnosis of early-stage PC
Early-stage PC was defined as PC patient with a TNM stage of 0-IIa. For this group of patients, the SEN, SPE, PLR, NLR, DOR and AUC of circulating miRNAs were 0.79 (0.76–0.82), 0.74 (0.68–0.79), 2.60 (2.19–3.10), 0.35 (0.30–0.41), 8.14 (5.85–11.33) and 0.81 (0.77–0.84), respectively. MiR-196b and the combination of miR-196a and 196b exhibited high diagnostic accuracy with a SEN and SPE both greater than 90%. The results were listed in Fig. 2 (D) and Table 3.
Table 3
The diagnostic performance of circulating miRNAs for early-stage PC
miRNAs | TNM stage | number of data sets | number of PC | number of non-PC | SEN(95%CI) | SPE(95%CI) | PLR(95%CI) | NLR(95%CI) | DOR(95%CI) |
miR-196a | 0 | 2 | 10 | 20 | 1.00(0.69-1.00) | 0.60(0.36–0.81) | 2.24(1.32–3.81) | 0.14(0.02–0.95) | 15.89(1.73-145.79) |
miR-196b | 0 | 2 | 10 | 20 | 0.90(0.56-1.00) | 1.00(0.83-1.00) | 18.26(2.64-126.12) | 0.21(0.06–0.71) | 107.46(7.99-1444.70) |
miR-196a、196b | 0 | 2 | 10 | 20 | 0.90(0.56-1.00) | 1.00(0.83-1.00) | 18.26(2.64-126.12) | 0.21(0.06–0.71) | 107.46(7.99-1444.70) |
miR-1290 | I | 5 | 30 | 198 | 0.83(0.65–0.94) | 0.78(0.71–0.83) | 3.45(2.39–4.99) | 0.22(0.10–0.49) | 18.21(6.31–52.56) |
miR-191 | I-IIa | 1 | 9 | 22 | 0.67 | 0.84 | 4.22 | 0.40 | 10.67 |
miR-21 | I-IIa | 1 | 9 | 22 | 0.67 | 0.81 | 3.51 | 0.41 | 8.54 |
miR-451a | I-IIa | 1 | 9 | 22 | 0.67 | 0.86 | 4.66 | 0.39 | 12.00 |
miR-145、150、223、636 | I-IIa | 9 | 420 | 2082 | 0.77(0.73–0.81) | 0.64(0.62–0.66) | 1.83(1.54–2.18) | 0.40(0.33–0.48) | 4.65(3.26–6.64) |
miR-26b、34a、122、126、145、150、223、505、636、885-5p | I-IIa | 9 | 420 | 2082 | 0.80(0.76–0.84) | 0.80(0.79–0.82) | 3.23(2.55–4.09) | 0.33(0.23–0.48) | 10.20(6.03–17.26) |
Overall | 0-IIa | 32 | 927 | 4488 | 0.79(0.76–0.82) | 0.74(0.68–0.79) | 2.60(2.19–3.10) | 0.35(0.30–0.41) | 8.14(5.85–11.33) |
3.4. The diagnostic performance of conventional biomarkers
Besides circulating miRNAs, some researchers have also evaluated the diagnostic efficacy of conventional biomarkers, such as CA19-9, CEA, and CA242.
Among these conventional biomarkers, CA19-9 was the most frequently studied. The SEN, SPE, PLR, NLR, DOR, and AUC of CA19-9 in discriminating PC from non-PC were 0.78 (0.75–0.80), 0.90 (0.85–0.94), 7.90 (5.14–12.13), 0.25 (0.22–0.28), 31.89 (18.96–53.62), and 0.85 (0.82–0.88), respectively. The sensitivity of CEA and CA242 was similar to that of CA19-9, but the specificity is significantly lower than that of CA19-9. CEA distinguished PC from non-PC with a SEN and SPE of 0.79 (0.39–0.96) and 0.32 (0.08–0.72), respectively. The PLR, NLR, DOR and AUC of CEA were 1.17 (0.82–1.65), 0.65 (0.26–1.60), 1.80 (0.55–5.88) and 0.59 (0.54–0.63), respectively. The SEN, SPE, PLR, NLR, DOR, and AUC of CA242 were 0.79 (0.52–0.93), 0.46 (0.21–0.74), 1.47 (0.95–2.27), 0.45 (0.21–0.97), 3.25 (1.14–9.32) and 0.68 (0.63–0.71), respectively. The results were listed in Fig. 2 (E) and Table 2.
3.5. The diagnostic performance of circulating miRNAs combined with CA19-9
The combination of circulating miRNAs and CA19-9 for the diagnosis of PC exhibited a significantly higher diagnostic accuracy than that of using of circulating miRNAs or CA19-9 alone. The SEN, SPE, PLR, NLR, DOR, AUC of miRNAs combined CA19-9 in differentiating PC from non-PC were 0.84 (0.80–0.87), 0.84 (0.80–0.87), 9.77 (7.65–12.47), 0.17 (0.14–0.22), 56.01 (37.70–83.20) and 0.94 (0.92–0.96), respectively. The results were listed in Fig. 2 (F-H) and Table 2.
The combination of miR-196, 200 and CA19-9 exhibited a high SEN of more than 90%. Combinations with a SPE greater than 90% including the combination of miR-1290, CA19-9, the combination of miR-16, CA19-9, the combination of miR-16, 196a, CA19-9, the combination of miR-145, 150, 223, 636, CA19-9, the combination of 26b, 34a, 122, 126, 145, 150, 223, 505, 636, 885-5p, CA19-9. There are 4 combinations with SEN and SPE exceeding 90%. They are the combination of miR-210, CA19-9, the combination of miR-25 and CA19-9, the combination of miR-196a, 210, CA19-9, the combination of 181a, 181b, 210, CA19-9. The results were listed in Table S3.
3.6. Subgroup analysis and threshold effect analysis
Since significant heterogeneity presented in our meta-analysis (I2 > 50%), random-effects model was applied for pooled analysis. Meanwhile, subgroup analysis of five potential sources of heterogeneity including region, conference test, miRNAs profiling, non-PC controls population and specimen, was conducted to identify the source of heterogeneity. However, the results suggested that the I2 value of most subgroups was still greater than 50%, indicating that these factors were not associated with heterogeneity. The results were listed in Table S4.
The value of the spearman correlation coefficient was − 0.276 (p = 0.000) in the threshold effect analysis, suggesting the existence of a threshold effect, which may be the main source of heterogeneity in the present meta-analysis.
3.7. Sensitivity analysis and publication bias
A sensitivity analysis was performed to validate the reliability of our results. Any removal of the original studies did not have a significant impact on the results and corresponding 95% CI, suggesting that the results were stable. Deeks' funnel plots provided no evidence for publication bias (P > 0.05).