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. 2020 Sep 17;10:1466. doi: 10.3389/fonc.2020.01466

Prognostic Biomarkers for Pancreatic Ductal Adenocarcinoma: An Umbrella Review

Yizhi Wang 1,†, Xi Zhong 2,†, Li Zhou 1, Jun Lu 1, Bolun Jiang 1, Chengxi Liu 1, Junchao Guo 1,*
PMCID: PMC7527774  PMID: 33042793

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) leads to the majority of cancer-related deaths due to its morbidity with similar mortality. Lack of effective prognostic biomarkers are the main reason for belated post-operative intervention of recurrence which causes high mortality. Numerous systematic reviews and meta-analyses have explored the prognostic value of biomarkers in PDAC so far. In this article, we performed an umbrella review analyzing these studies to provide an overview of associations between prognostic biomarkers and PDAC survival outcome and synthesized these results to guide better clinical practice.

Methods: Systematic reviews and meta-analyses investigating the associations between PDAC survival outcomes and prognostic biomarkers were acquired via the PubMed and Embase databases from inception till February 1, 2020. Associations supported by nominally statistically significant results were classified into strong, highly suggestive, suggestive, and weak based on several critical factors such as the statistical significance of summary estimates, the number of events, the estimate of the largest study included, interstudy heterogeneity, small-study effects, 95% predictive interval (PI), excess significance bias, and the results of credibility ceiling sensitivity analyses.

Results: We included 41 meta-analyses containing 63 associations between PDAC survival outcomes and prognostic biomarkers. Although, none was supported by strong evidence among these associations, an association between C-reactive protein to albumin ratio (CAR) and PDAC overall survival (OS) and an association between neutrophil-lymphocyte ratio (NLR) and PDAC OS were supported by highly suggestive evidence. Otherwise, the association between lactate dehydrogenase (LDH) and PDAC OS was supported by suggestive evidence. The remaining 60 associations were supported by weak or not suggestive evidence.

Conclusion: Associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. And the association between LDH and PDAC OS was supported by suggestive evidence. Although the methodological quality of the included systematic reviews and meta-analyses which were evaluated by AMSTAR2.0 is generally poor, the identification of the relatively robust prognostic biomarkers of PDAC may guide better post-operative intervention and follow-up to prolong patients' survival.

Keywords: pancreatic ductal adenocarcinoma, biomarkers, umbrella review, prognosis, survival

Introduction

Pancreatic ductal adenocarcinoma (PDAC) leads to major cancer-related death due to its morbidity with similar mortality. The mortality remains unchanged and may even have increased during the last few decades (1). The current estimated 5-year overall survival (OS) of PDAC is <8% and PDAC ranks fourth among cancer-related deaths in the US both in men and women for consecutive years, and the 5-year OS reduces to a dismal 3% when distant metastases occurs (2–5). In China, PDAC ranks sixth among the most lethal cancers and an increasing number of younger patients have been subjected to this disease (6). To raise the rate of survival in PDAC patients, more financial costs have been made around the world (7, 8). Currently, surgical treatment is still the only radical therapy for PDAC (9, 10). Although adjuvant chemotherapy may prolong the survival of PDAC patients, the prognosis of PDAC patients remains poor due to the lack of early diagnostic methods and accurate recurrence and prognostic biomarkers (11–13).

Biomarkers are biological molecules or molecular combinations involved in tumor progression by modulating various signaling pathways which can be used in early diagnosis and prognostic evaluation for cancers (14, 15). Detecting efficient prognostic biomarkers preoperatively can ensure a correct and individual evaluation of the OS and recurrence potential. By doing this, clinical practitioners can perform specific follow-up strategies and timely intervention of possible recurrence to prolong patients' 5-year OS and decrease the mortality (16). Of note, the most frequently evaluated serum biomarker for prognostic evaluation after operation is carbohydrate antigen 19-9 (CA19-9) for PDAC (17, 18). It was reported that CA19-9 could precede radiological evidence for about 3–6 months predicting tumor recurrence (19). However, CA19-9 may not be so effective or accurate in prognostic evaluation. Other serum biomarkers have been proven to have better sensitivity and specificity than CA19-9 in PDAC but without widespread clinical application, such as circulating tumor DNA, miRNAs, lncRNAs, and even circRNAs (20–23). There have been numerous systematic reviews and meta-analyses focusing on prognostic biomarkers of PDAC so far. However, to the best of our knowledge, the results of these systematic reviews and meta-analyses have not been synthetically evaluated. To provide an overview of associations between PDAC survival outcomes and prognostic biomarkers, and to find robust prognostic biomarkers to guide clinical practice, an umbrella review analyzing currently available meta-analyses and systematic reviews and rating the evidence depending on the credibility of these associations was performed.

Methods

Literature Retrieval and Eligibility Criteria

A literature search of the PubMed and Embase databases was conducted for systematic reviews and meta-analyses investigating the associations between PDAC survival outcomes and prognostic biomarkers independently by two experienced researchers (YW and XZ) from inception till February 1, 2020. No filters regarding language or publication time were applied. The “related function” was used to include more articles. Then manual retrieval from the citations of included studies was also applied to supplement the potentially missing literature. The relevant search terms are presented below: (pancreatic cancer OR PDAC OR pancreatic ductal adenocarcinoma OR pancreatic carcinoma OR pancreatic neoplasm) AND (biomarkers OR prognosis OR prognostic biomarkers) AND (systematic reviews OR meta-analyses). The titles and abstracts of these studies were independently browsed by the same two experienced reviewers (YW and XZ). Then full texts of the potentially relevant ones were carefully read by both reviewers. The studies which met the inclusion criteria were finally included in the umbrella review.

Inclusion and Exclusion Criteria

The inclusive and exclusive criterion of the evaluated meta-analyses was shown below: (1) Studies including associations between prognostic biomarkers, rather than diagnostic or pharmacodynamic biomarkers, and PDAC survival outcomes such as overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and cancer-specific survival (CSS) were included. (2) Studies investigating PDAC risk factors and genetic polymorphism and studies focusing on benign pancreatic lesions, such as solid pseudopapillary tumors of the pancreas, pancreatic cystic tumors, and intraductal papillary mucinous neoplasm were excluded. (3) Meta-analyses containing just one original study or not providing sufficient data were excluded in our present review. When two or more meta-analyses discussed the same association, we only included the meta-analyses with the latest or the largest primary studies.

Data Extraction

Two investigators (YW and XZ) independently extracted the data from included meta-analyses and contradictions between the two reviewers were resolved through discussion with a third reviewer (JG). The name of first author, country, biomarker name, cases number, population size, and relative risk estimation, including risk ratio (RR), odds ratio (OR), and hazard ratio (HR), and the corresponding 95% confidential interval (CI) were retrieved from each of the included meta-analysis.

Quality Assessment

AMSTAR (A MeaSurement Tool to Assess Systematic Reviews) version 2.0 was used to evaluate the methodological quality of each of the included systematic reviews (24). AMSTAR is an important tool used in umbrella reviews to conduct methodological quality assessment of the included systematic reviews and meta-analyses. AMSTAR version 2.0 is an updated version of AMSTAR and contains 16 items to create a more comprehensive and rational evidence evaluation. According to the comprehensive evaluation of the 16 items, the methodological quality of the included studies can be graded as high, moderate, low, or critically low rather than obtaining an overall score.

Statistical Analysis

Random-effect models were used to evaluate the synthesized summary effects for the included meta-analyses. The summary RR estimates, the 95% CI, and the corresponding P-values were calculated. P < 0.05 was deemed as significant.

Cochran's Q-test and I2 statistic were applied to assess interstudy heterogeneity. And I2 > 50% indicates significant heterogeneity (25). Ninety-five percent prediction interval (95% PI), which predicts the likely effect in an individual setting rather than just an average effect across all included studies in a meta-analysis, was used to facilitate the application of the results to clinical practice (26, 27).

Small-study effects were assessed using Egger's regression asymmetry test. Small-study effects were considered detected when a P < 0.1 was reached (27).

Potential reporting selection biases or publication biases can be detected by the excess significance test. A Chi-square test with a two-tailed P < 0.1 as the statistical significance threshold was used to assess whether the actual observed number (O) was different from the expected number (E) (28). The E number which was the sum of the statistical power estimates of the component studies in the included meta-analyses expecting to be statistically significant was calculated using an algorithm of a non-central t distribution with the relative risk estimate of the largest study set as the plausible effect size. The excess significance test was considered positive in cases where both O>E and P < 0.1.

Credibility ceiling sensitivity analyses were performed to account for the inherent methodological limits of observational studies. The level of the credibility ceiling was set at 10% for the present study which indicates that the weight of an observational study in the summary effect is limited to 10% no matter how well the study was conducted (29).

Strength of Existing Evidence

Based on the results of a series of aforementioned analyses, the strength of the statistically significant (P < 0.05) associations between risk factors and risk of PDAC can be categorized into four levels. To reach the “strong evidence” level, the included meta-analysis were expected to show a P-value of random-effect model smaller than 10−6, include more than 1000 PDAC cases, the 95% PI should exclude the null value, have a smaller heterogeneity with I2 < 50%, have no evidence of small-study effect, and an excess significance bias and also survive the 10% credibility ceiling test. “Highly suggestive” evidences refer to associations with a P-value of random-effect model smaller than 10−6 and covering more than 1,000 cases. A “suggestive” association was required to reach a P < 10−3 and include more than 1,000 cases. The rest of the associations where P-value was statistically significant were graded as “weak” evidence (30, 31).

Results

Characteristics of the Included Systematic Reviews and Meta-Analyses

In total, 2259 records were extracted from the literature search and manual screening using the PubMed and Embase databases. A total of 2,083 records were excluded due to an irrelevant theme after browsing titles and abstracts from the acquired records. Finally, 41 of the remaining 176 studies which met the inclusion criteria were included in the present umbrella review after a full-text review (32–72). The search flowchart is shown in Figure 1. And Supplementary Table 1 displays the full list of 176 meta-analyses and the exclusive reasons for the 135 studies. A total of 63 different associations between PDAC survival outcomes and prognostic biomarkers were covered in the included 41 meta-analyses which contained more than 31,000 subjects and over 300 primary studies. Characteristics of the 63 associations in the included meta-analyses were presented in Table 1. Data of the included primary studies in the 41 meta-analyses were extracted, processed, and coded in order to conduct further analysis.

Figure 1.

Figure 1

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The flow diagram of the study selection.

Table 1.

Characteristics of the 63 associations in the included 41 systematic reviews and meta-analyses.

References Biomarker Association between biomarker and pancreatic cancer Effect metrics Country No. of study estimates No. of cases/total population Summary relative risk estimate (95%CI)
Rezende et al. (30) AEG-1 OS HR China 2 82#/194 2.41 (1.63–3.57)
Markozannes et al. (31) B7-H4 OS HR China 5 349/372 3.00 (2.20–4.10)
Luo et al. (32) Bax OS HR UK 5 185#/274 0.63 (0.48–0.83)
Bcl-2 OS HR UK 5 267/314 0.51 (0.38–0.68)
EGFR OS HR UK 4 178#/250 1.35 (0.80–2.27)
P16 OS HR UK 3 28#/229 0.63 (0.43–0.92)
P53 OS HR UK 17 510#/933 1.22 (0.96–1.56)
VEGF OS HR UK 11 537#/767 1.51 (1.81–1.92)
Chen et al. (33) CAR OS HR China 11 1,666/1934 1.86 (1.53–2.26)
Smith et al. (34) CD133 5-year OS HR China 11 552#/723 1.22 (0.98–1.52)
Fu et al. (35) CD44 5-year OS OR China 5 284/318 0.52 (0.30–0.91)
Li et al. (36) CXCL12 OS HR UK 4 354/439 1.54 (1.21–1.97)
PFS HR UK 2 84/104 1.79 (1.05–3.04)
Liu et al. (37) CXCR4 OS HR China 8 1,131/1262 1.27 (1.00–1.61)
Samarendra et al. (38) CXCR7 OS HR China 2 503/584 2.72 (1.11–6.66)
Ding et al. (39) COX-2 OS HR China 6 498#/712 1.48 (1.12–1.85)
Fan et al. (40) DTCs/CTCs OS HR Australia 12 561#/829 1.84 (1.37–2.45)
PFS HR Australia 4 272/292 1.93 (1.19–3.11)
Wang et al. (41) DLL4 OS HR China 2 105/125 2.13 (1.37–2.32)
Notch3 OS HR China 3 53#/166 2.05 (1.49–2.82)
Stephenson et al. (42) E-cadherin OS HR UK 2 172/197 1.58 (1.22–2.22)
Ki-67 OS HR UK 4 196/252 2.42 (1.87–3.14)
pAkt OS HR UK 2 74/104 0.59 (0.33–1.07)
P21 OS HR UK 3 154/163 0.49 (0.33–0.73)
P27 OS HR UK 5 309/356 1.11 (0.84–1.47)
Survivin OS HR UK 2 105/114 0.46 (0.29–0.73)
TP OS HR UK 2 120/144 2.03 (1.22–3.38)
TS OS HR UK 2 176/204 1.05 (0.73–1.51)
Ye et al. (43) FGFR2 3-year OS OR China 3 135/177 1.66 (0.42–6.53)
5-year OS OR China 3 148/177 1.04 (0.37–2.91)
Jamieson et al. (44) FoxM1 OS HR China 3 237/266 1.73 (1.05–2.86)
Liu et al. (45) GLUT1 OS HR China 8 451/538 1.79 (1.19–2.70)
Dai et al. (46) HDAC1 OS HR China 4 208/244 1.43 (0.71–2.88)
Sharen et al. (47) hENT1 OS HR UK 7 679/770 0.52 (0.38–0.72)
DFS HR UK 6 313#/536 0.58 (0.42–0.79)
Cao et al. (48) HER2 OS HR China 4 132#/676 1.87 (0.64–5.46)
Bird et al. (49) HIF-1α OS HR China 6 262#/422 1.88 (1.39–2.56)
Li et al. (50) HIF-2α OS HR China 3 411/443 1.97 (1.42–2.75)
Ye et al. (51) HK2 OS HR China 3 110#/235 1.11 (0.58–2.11)
Luo et al. (52) HMGB1 OS HR China 2 101/123 2.61 (1.48–4.59)
Wu et al. (53) LDH OS HR China 18 3,137/3345 1.58 (1.31–1.91)
Wu et al. (54) LMR OS HR China 5 668/748 0.59 (0.41–0.85)
Gan et al. (55) LncRNA loc285194 OS HR Iran 2 108/179 1.97 (1.05–3.68)
Mao et al. (56) LncRNA UCA1 OS HR China 2 160/208 1.58 (1.01–2.15)
Mehrad-Majd et al. (57) L1CAM OS HR China 3 257/311 0.96 (0.42–2.21)
Liu et al. (58) MiRNA-203 OS HR China 3 142#/295 1.19 (1.09–1.31)
Hua et al. (59) NLR OS HR China 43 6,479#/8252 1.81 (1.59–2.05)
DFS HR China 8 1,141/1236 1.66 (1.17–2.35)
Shao et al. (60) PD-L1 OS HR China 8 766/912 1.63 (1.34–1.98)
CSS HR China 3 381/490 1.86 (1.34–2.57)
Zhou et al. (61) Plasma fibrinogen OS HR China 6 601/800 1.56 (1.13–2.15)
Hu et al. (62) PLR OS HR China 3 591/632 1.00 (0.92–1.09)
Ji et al. (63) PKM2 OS HR China 4 259/313 1.41 (0.68–2.93)
Yin et al. (64) Podoplanin+fbroblast OS HR China 3 184/231 2.20 (1.40–3.46)
DFS HR China 2 165/200 1.97 (1.37–2.84)
Zhu et al. (65) RKIP OS HR China 3 256/276 0.76 (0.51–1.01)
DFS HR China 3 237/276 0.71 (0.28–1.13)
Hu et al. (66) RRM1 OS HR China 9 666/733 1.56 (0.95–2.17)
DFS HR China 3 185/220 1.37 (0.25–2.48)
Yu et al. (67) Smad4 OS HR China 10 1,446/1734 0.61 (0.38–0.99)
Zhang et al. (68) SPARC OS HR China 7 908/1128 1.55 (1.11–2.17)
Wang et al. (69) STAT3 5-year OS OR China 3 17/243 9.71 (1.80–52.41)
Han et al. (70) ZEB1 OS HR China 2 120/188 1.49 (1.07–2.06)
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#

Contain missing values; CI, confidence interval; OS, overall survival; CSS, cancer-specific survival; DFS, disease free survival; PFS, progression free survival; AEG-1, astrocyte elevated gene-1; CAR, C-reactive protein to albumin ratio; COX-2, cyclooxygenase-2; DLL4, delta like ligand 4; DTCs/CTCs, disseminated tumor cells/circulating tumor cells; EGFR, epidermal growth factor receptor; VEGF, vascular endothelial growth factor; FGFR2, fibroblast growth factor receptors; FoxM1, Forkhead Box M1; GLUT1, glucose transporter type 1; HDAC1, histone deacetylase 1; hENT1, human equilibrative nucleoside transporter 1; HER2, human epidermal growth factor receptor-2; HIF-1α, hypoxia inducible factor-1α; HIF-2α, hypoxia inducible factor-2α; HK2, hexokinase 2; HMGB1, high mobility group box 1; LDH, lactate dehydrogenase; LMR, lymphocyte-to-monocyte ratio; LncRNA, long non-coding RNA; L1CAM, L1 cell adhesion molecule; MiRNA, MicroRNA; NLR, neutrophil-to-lymphocyte ratio; pAkt, phosphorylated protein kinase B; PD-L1, programmed cell death ligand 1; PLR, platelet-to-lymphocyte ratio; PKM2, pyruvate kinase M2; RKIP, Raf kinase inhibitor protein; RRM1, ribonucleotide reductase M1; SPARC, secreted protein acidic and rich in cysteine; STAT3, signal transducer and activator of transcription proteins 3; TS, thymidylate synthase; TP, thymidylate phosphorylase; VEGF, vascular endothelial growth factor; ZEB1, zinc finger E-box binding homeobox 1.

Quality Assessment Methodology Using AMSTAR 2.0

The 16-item AMSTAR 2.0 tool was recruited to assess the methodological quality of the 41 included meta-analyses. The results showed that qualities of all the included studies were considered as critically low. All of these meta-analyses had more than two critical flaws [usually in items 2 (41/41,100%), 7 (41/41,100%), and 13 (41/41,100%)] and several non-critical flaws [usually in items 3 (41/41,100%), 10 (41/41,100%), and 12 (41/41,100%)]. It is worth noting that studies with more than one critical flaw were considered as critically low-quality studies regardless of non-critical flaws. Considering the critically low quality of all the included systematic reviews, the results should be interpreted cautiously. The detailed results and rating criteria are shown in Supplementary Table 2.

Summary Effect Size

The quantitative syntheses of the 63 associations were conducted using a random-effect model to provide more conservative estimates. A total of 44 of the 63 associations in the included meta-analyses were statistically significant with P < 0.05, while the remaining 19 associations showing P > 0.05 (Table 2 and Supplementary Table 3). Of the statistically significant associations, four reached P < 10−6, namely, associations between C-reactive protein to albumin ratio (CAR), neutrophil-lymphocyte ratio (NLR), or B7-H4 and PDAC OS and the association between CD133 and 5-year OS of PDAC. And 20 other associations reached moderate statistical significance (P < 10−3). More than half of the associations that reached statistical significance reported an increased risk of mortality of PDAC, indicating a potential biomarker role in PDAC prognostic prediction. However, the remaining statistically significant associations reported a decreased risk of mortality of PDAC, such as B7-H4 and PDAC OS, and Bax and PDAC OS.

Table 2.

Evidence-rating results based on the results of statistical analyses of the 63 associations.

Study Association between biomarkers and pancreatic cancer Summary relative risk estimate (random-effect P)* Cases >1,000 Largest study relative risk estimate P < 0.05 I2 < 50% Small study effects 95% prediction interval exclude the null value Excess significance 10% credibility ceiling survival
Associations supported by highly suggestive evidence (2)
Chen et al. (33) CAR OS +++ + + – – + + +
Hua et al. (59) NLR OS +++ + + – + – + +
Associations supported by suggestive evidence (1)
Wu et al. (53) LDH OS ++ + – – – – + +
Associations supported by weak evidence (41)
Rezende et al. (30) AEG-1 OS ++ – + + NA NA – –
Markozannes et al. (31) B7-H4 OS +++ – + + – + + +
Luo et al. (32) Bax OS ++ – + + – + + +
Bcl-2 OS ++ – + + – + + +
P16 OS + – – + + – – –
VEGF OS ++ – + – – – – +
Smith et al. (34) CD133 5-year OS +++ – + + + + + +
Fu et al. (35) CD44 5-year OS + – – + – – – –
Li et al. (36) CXCL12s OS ++ – – + – – – +
CXCL12s PFS + – + + NA NA – –
Liu et al. (37) CXCR4 PFS + + – + + – – –
Samarendra et al. (38) CXCR7 OS + – – + – – – –
Ding and Du (39) COX-2 OS ++ – + + – + – +
Fan et al. (40) DTCs/CTCs OS ++ – – + – – + +
DTCs/CTCs PFS + – – – – – + +
Wang et al. (41) DLL4 OS ++ – + + NA NA – –
Notch3 OS ++ – + + + – + +
Stephenson et al. (42) E-cadherin OS + – + + NA NA – –
Ki-67 OS + – + – – – – +
P21 OS + – + – – – + –
TP OS + – + + NA NA – –
Liu et al. (45) GLUT1OS + – – – + – + –
Sharen et al. (47) hENT1 OS ++ – + – – – + +
hENT1 DFS ++ – + – + – + –
Bird et al. (49) HIF-1α OS ++ – + + – + – +
Li et al. (50) HIF-2α OS ++- – + + – – + +
Luo et al. (52) HMGB1 OS ++- – + + NA NA + –
Wu et al. (54) LMR OS + – + – – – + –
Gan et al. (55) LncRNA loc285194 OS + – + + NA NA – –
Mao et al. (56) LncRNA UCA1 OS + – + + NA NA + –
Hua et al. (59) NLR DFS + + + – – – + –
Shao et al. (60) PD-L1 OS ++ – + + – + + +
PD-L1 CSS ++ – + + – – – +
Zhou et al. (61) Plasma fibrinogen OS + – + – – – + –
Yin et al. (64) Podoplanin+fbroblast OS ++ – + + – – – –
Podoplanin+fbroblast DFS ++ – + + NA NA + –
Hu et al. (66) RRM1 DFS ++ – + – – – + +
Yu et al. (67) Smad4 OS + + + – – – – +
Zhang et al. (68) SPARC OS + – – – – – + –
Wang et al. (69) STAT3 5-year OS + – – + – – – +
Han et al. (70) ZEB1 OS + – – + NA NA – –
Associations supported by not suggestive evidence (19)
Luo et al. (32) EGFR OS – – – – – – – –
P53 OS – – – – – – – –
Ye et al. (43) FGFR2 3-year OS – – + – – – – +
FGFR2 5-year OS – – – + – – – –
Jamieson et al. (44) FoxM1 OS – – – + – – + –
Dai et al. (46) HIDAC1 OS – – – – – – + –
Cao et al. (48) HER2 OS – – – – + – – –
Ye et al. (51) HK2 OS – – + – – – – –
Mehrad-Majd et al. (57) LICAM OS – – – – – – + –
Liu et al. (58) MiRNA-203 OS – – + + – – – –
Stephenson et al. (42) pAkt OS – – + – NA NA – –
P27 OS – – – – – – + –
Survivin OS – – + – NA NA + –
Thymidylate synthase OS – – + – NA NA + –
Hu et al. (62) PLR OS – – – + – – – –
Ji et al. (63) PKM2 OS – – + – – – + –
Zhu et al. (65) RKIP OS – – – + – – + –
RKIP DFS – – – – – – + –
Hu et al. (66) RRM1 DFS – – + – – – + –
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*

P-value calculated using random-effect model: +++P < 10−6; ++P < 10−3; +P < 0.05; −P > 0.05. For other items, +, yes; –, no.

CI, confidence interval; NA, not available for meta-analysis that included less than three studies; OS, overall survival; CSS, cancer-specific survival; DFS, disease free survival; PFS, progression free survival; AEG-1, astrocyte elevated gene-1; CAR, C-reactive protein to albumin ratio; COX-2, cyclooxygenase-2; DLL4, delta like ligand 4; DTCs/CTCs, disseminated tumor cells/circulating tumor cells; EGFR, epidermal growth factor receptor; VEGF, vascular endothelial growth factor; FGFR2, fibroblast growth factor receptors; FoxM1, Forkhead Box M1; GLUT1, glucose transporter type 1; HDAC1, histone deacetylase 1; hENT1, human equilibrative nucleoside transporter 1; HER2, human epidermal growth factor receptor-2; HIF-1α, hypoxia inducible factor-1α; HIF-2α, hypoxia inducible factor-2α; HK2, hexokinase 2; HMGB1, high mobility group box 1; LDH, lactate dehydrogenase; LMR, lymphocyte-to-monocyte ratio; LncRNA, long non-coding RNA; L1CAM, L1 cell adhesion molecule; MiRNA, MicroRNA; NLR, neutrophil-to-lymphocyte ratio; pAkt, phosphorylated protein kinase B; PD-L1, programmed cell death ligand 1; PLR, platelet-to-lymphocyte ratio; PKM2, pyruvate kinase M2; RKIP, Raf kinase inhibitor protein; RRM1, ribonucleotide reductase M1; SPARC, secreted protein acidic and rich in cysteine; STAT3, signal transducer and activator of transcription proteins 3; TS, thymidylate synthase; TP, thymidylate phosphorylase; VEGF, vascular endothelial growth factor; ZEB1, Zinc finger E-box binding homeobox 1.

Heterogeneity

Of the 63 associations, there were 17 associations with moderate to high heterogeneity (I2 = 50–75%) and 13 associations with high heterogeneity (I2 > 75%). When we further evaluated the interstudy heterogeneity using 95% PI estimates, we found eight associations with the null value excluded (Table 2 and Supplementary Table 3).

Small-Study Effects

Among the 63 associations between PDAC survival outcomes and prognostic biomarkers, small-study effects were detected in eight associations [NLR OS, P16 OS, CD133 5-year OS, CXCR4 PFS, Notch3 OS, glucose transporter type 1(GLUT1) OS, human equilibrative nucleoside transporter 1 (hENT1) DFS, and Human epidermal growth factor receptor-2 (HER2) OS] according to the Egger's test (P < 0.1) as shown in Table 2 and Supplementary Table 3.

Excess Significance

Significant excess significance (O>E and P < 0.1) was shown in 35 of the 63 associations (Table 2 and Supplementary Table 3).

10% Credibility Ceiling

There were 37 of the 63 associations that survived the 10% credibility ceiling (P < 0.1) which included all associations graded as highly suggestive, or suggestive and most of the associations categorized as weak evidence. Table 2 and Supplementary Table 3 show the detailed information.

Robustness of Evidence

Among the 63 associations between PDAC survival outcomes and prognostic biomarkers, none was supported by strong evidence. However, associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. Besides, association between lactate dehydrogenase (LDH) and PDAC OS. The remaining 60 associations were supported by weak or not suggestive evidence. Detailed results of these analysis processes are shown in Supplementary Table 3.

Discussion

Principal Finding and Existing Evidence Interpretation

Both diagnostic and prognostic biomarkers are vital to PDAC treatment and can prolong the survival of PDAC patients. However, it is difficult to find valuable diagnostic biomarkers of PDAC due to its insidious onset and retroperitoneal localized lesions. Therefore, in this umbrella review, we mainly focused on prognostic biomarkers evaluation in order to guide clinical practice and the timely detection of early post-operative recurrence to prolong the survival of the patients (73).

To the best of our knowledge, the present umbrella review is the first to comprehensively collect the existing and available meta-analyses and assess the robustness of evidence to provide an overview of associations between PDAC survival outcomes and prognostic biomarkers. Generally, 41 meta-analyses containing 54 different prognostic biomarkers which consisted of both tissue biomarkers and serum biomarkers were included in this umbrella review. None of the associations were supported by strong evidence. However, two associations were supported by highly suggestive evidence, namely, an association between CAR and PDAC OS and an association between NLR and PDAC OS. Only one association was supported by suggestive evidence, an association between LDH and PDAC OS. And the remaining 60 associations were supported by either weak or not suggestive evidence. Nevertheless, the results should be cautiously interpreted given the relatively poor quality and small number of samples of the meta-analyses included in our umbrella review evaluated by AMSTAR2.0.

Comparison With Other Studies and Possible Explanations

In our study, some inflammation-based prognostic biomarkers acquired stronger evidence grading rather than tissue biomarkers. For example, associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. PDAC can be a deeline in its advanced stage, even at a local advanced stage, which can cause cachexia of patients leading to high mortality (74). Systemic inflammation response (SIR) seems to play an important role in cachexia, such as weight loss and functional decline (75, 76). Elevated C-reactive protein (CRP) is one of the most frequently used biomarkers indicating systemic inflammation and extreme hypoalbuminemia is common in advanced PDAC patients with refractory cachexia due to severe malnutrition. According to what we have mentioned above, preoperative level of CAR implies the systemic inflammation extent of patients, and a higher preoperative CAR level may relate to more severe cachexia and a poorer prognosis in patients. Actually, several studies have indicated the prognostic value of CAR in PDAC. A study by Ikuta et al. indicated that in 136 PDAC patients, preoperative CAR was an independent poor OS predictor using multivariate analysis. Moreover, the prognostic evaluation power of CAR was significantly higher than other inflammation-based factors such as NLR, platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) (77). Besides, the study by Arima et al. also confirmed this result through using CAR at day 14 after an operation (78). NLR is the other prognostic biomarker supported by highly suggestive evidence. In the process of SIR, the level of neutrophil increases to secrete various cytokines. As SIR mitigates, the level of NLR decreases which may explain why NLR can be used as a prognostic biomarker in PDAC patients. Several studies suggested that the prognostic accuracy of the combination of post-operative NLR and AJCC 8th edition is much better than that of the combination of the TNM staging system and AJCC 8th edition (79, 80). Moreover, a recent study unveiled that preoperative NLR may also be a useful prognostic biomarker in PDAC patients undergoing surgical resection (79).

The association between LDH and PDAC OS was supported by suggestive evidence in our umbrella review. LDH is a vital enzyme in glycolysis which can facilitate the conversion of pyruvate to lactate. And the serum LDH level is higher in patients than in healthy individuals which indicates a tumor-promoting role (81). Therefore, LDH may play an important role in tumor progression, especially in PDAC whose microenvironment is usually hypoxic. A meta-analysis showed that high level of glycolysis-related proteins including LDH5 were associated with shorter OS of various cancer patients including PDAC patients (82). Moreover, LDH combined with other biomarkers can also be prognostic in PDAC. For instance, the ratio of LDH to albumin (LAR) can be a prognostic indicator for unresectable PDAC patients. Besides, the combination of LAR, CEA, and the TNM staging system can improve prognosis predictive power compared with the TNM staging system alone (83).

Several studies have revealed the prognostic role of CA19-9 in PDAC. Palmquist et al. indicated that patients with a high level of CA19-9, interleukin-6, and human cartilage glycoprotein-40 had a shorter OS than patients with lower levels (84). Besides, the study by Gu et al. analyzed 109 Chinese PDAC patients and indicated that the preoperative level of CA19-9 was independently correlated with PDAC OS (85). However, the role of CA19-9 in PDAC post-operative recurrence and prognosis has not been systematically reviewed (86). As far as we know, no systematic reviews or meta-analyses discussed the prognostic value of CA19-9 in PDAC patients. And it is the reason why CA19-9 was not included in the present study. Therefore, the potential of CA19-9 as a potential prognostic biomarker should be comprehensively explored in future studies.

Limitations

The present umbrella review is the first to make a comprehensive survey about the associations between PDAC survival outcomes and prognostic biomarkers. However, several limitations still exist in our study. Firstly, the methodological quality of the included systematic reviews is generally poor, thus the interpretation of the results in this umbrella review should be questioned. It may be the reason why no strong evidence exists in our study. Besides, some prognostic biomarkers of PDAC including CA19-9 were not comprehensively evaluated by systematic reviews or meta-analyses. Therefore, these biomarkers were not included in this study. Secondly, most of the eligible meta-analyses include <10 studies, which weakened the power of the statistical tests to identify small-study effects and excess significance. Thirdly, diagnostic biomarkers of PDAC were not evaluated in our study which should be further assessed in the future. Fourthly, more than half of the included meta-analyses were conducted in one country (China) rather than evenly distributed around the world which may cause some bias and be unrepresentative.

Conclusion

The associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. And the association between LDH and PDAC OS was supported by suggestive evidence. However, these results should be cautiously interpreted due to the relatively inferior methodological quality of the included meta-analyses evaluated by AMSTAR2.0.

Data Availability Statement

All datasets generated for this study are included in the article/Supplementary Material.

Author Contributions

JG and YW conceived and designed the study. YW and XZ performed the literature search, and acquired and collated the data, which were analyzed by LZ, JL, BJ and CL. JG was guarantor, and attests that all listed authors meet authorship criteria and that no authors meeting the criteria have been omitted. All authors drafted and critically revised the manuscript for important intellectual content, and gave final approval of the version to be published and contributed to the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Funding. This study was supported by the National Natural Science Foundation of China (Grant No. 81972324), the China Academy of Medical Sciences Innovation Fund for Medical Sciences (Grant No. 2016-I2M-3-019), and the non-profit Central Research Institute Fund of Chinese of Academy of Medical Sciences (Grant No. 2018PT32014).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2020.01466/full#supplementary-material

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References

  • 1.Lucas AL, Malvezzi M, Carioli G, Negri E, La Vecchia C, Boffetta P, et al. Global trends in pancreatic cancer mortality from 1980 through 2013 and predictions for 2017. Clin Gastroenterol Hepatol. (2016) 14:1452–62. 10.1016/j.cgh.2016.05.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. (2017) 67:7–30. 10.3322/caac.21387 [DOI] [PubMed] [Google Scholar]
  • 3.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. (2018) 68:7–30. 10.3322/caac.21442 [DOI] [PubMed] [Google Scholar]
  • 4.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. (2019) 69:7–34. 10.3322/caac.21551 [DOI] [PubMed] [Google Scholar]
  • 5.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. (2020) 70:7–30. 10.3322/caac.21590 [DOI] [PubMed] [Google Scholar]
  • 6.Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. (2016) 66:115–32. 10.3322/caac.21338 [DOI] [PubMed] [Google Scholar]
  • 7.Peery AF, Crockett SD, Murphy CC, Lund JL, Dellon ES, Williams JL, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2018. Gastroenterology. (2019) 156:254–72. 10.1053/j.gastro.2018.08.063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Carrato A, Falcone A, Ducreux M, Valle JW, Parnaby A, Djazouli K, et al. A systematic review of the burden of pancreatic cancer in Europe: real-world impact on survival, quality of life and costs. J Gastrointest Cancer. (2015) 46:201–11. 10.1007/s12029-015-9724-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Kamarajah SK, Bundred JR, Marc OS, Jiao LR, Hilal MA, Manas DM, et al. A systematic review and network meta-analysis of different surgical approaches for pancreaticoduodenectomy. HPB. (2019) 22:329–39. 10.1016/j.hpb.2019.09.016 [DOI] [PubMed] [Google Scholar]
  • 10.Wu WM, Jin G, Wang CY, Miao Y, Wang HZ, Lou WH, et al. Pancreatic surgery Study Group of Chinese Society of Surgery of Chinese Medical Association. The current surgical treatment of pancreatic cancer in China: a national wide cross-sectional study. J Pancreatol. (2019) 2:16–21. 10.1097/JP9.0000000000000012 [DOI] [Google Scholar]
  • 11.Maeda S, Unno M, Yu J. Adjuvant and neoadjuvant therapy for pancreatic cancer. J Pancreatol. (2019) 2:100–6. 10.1097/JP9.0000000000000028 [DOI] [Google Scholar]
  • 12.Dimastromatteo J, Houghton JL, Lewis JS, Kelly KA. Challenges of pancreatic cancer. Cancer J. (2015) 21:188–93. 10.1097/PPO.0000000000000109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Zhang Q, Zeng L, Chen Y, Lian G, Qian C, Chen S, et al. Pancreatic cancer epidemiology, detection, and management. Gastroenterol Res Pract. (2016) 2016:8962321. 10.1155/2016/8962321 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Biomarkers Definitions Working Group . Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. (2001) 69:89–95. 10.1067/mcp.2001.113989 [DOI] [PubMed] [Google Scholar]
  • 15.Füzéry AK, Levin J, Chan MM, Chan DW. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteomics. (2013) 10:13. 10.1186/1559-0275-10-13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Daamen LA, Groot VP, Intven MPW, Besselink MG, Busch OR, Koerkamp BG. Postoperative surveillance of pancreatic cancer patients. Eur J Surg Oncol. (2019) 45:1770–7. 10.1016/j.ejso.2019.05.031 [DOI] [PubMed] [Google Scholar]
  • 17.Distler M, Pilarsky E, Kersting S, Grutzmann R. Preoperative CEA and CA 19-9 are prognostic markers for survival after curative resection for ductal adenocarcinoma of the pancreas-a retrospective tumor marker prognostic study. Int J Surg. (2013) 11:1067.e72. 10.1016/j.ijsu.2013.10.005 [DOI] [PubMed] [Google Scholar]
  • 18.Boeck S, Stieber P, Holdenrieder S, Wilkowski R, Heinemann V. Prognostic and therapeutic significance of carbohydrate antigen 19-9 as tumor marker in patients with pancreatic cancer. Oncology. (2006) 70:255–64. 10.1159/0000948885 [DOI] [PubMed] [Google Scholar]
  • 19.Li J, Li Z, Kan H, Sun Z, Xing J, Cheng Y, et al. CA19-9 elevation as an indication to start salvage treatment in surveillance after pancreatic cancer resection. Pancreatology. (2019) 19:302–6. 10.1016/j.pan.2019.01.023 [DOI] [PubMed] [Google Scholar]
  • 20.Cheng H, Luo G, Jin K, Fan Z, Huang Q, Gong Y, et al. Kras mutation correlating with circulating regulatory T cells predicts the prognosis of advanced pancreatic cancer patients. Cancer Med. (2020) 9:2153–9. 10.1002/cam4.2895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Huang J, Liu J, Chen-Xiao K, Zhang X, Lee WN, Go VL, et al. Advance in microRNA as a potential biomarker for early detection of pancreatic cancer. Biomark Res. (2016) 4:20. 10.1186/s40364-016-0074-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Han T, Hu H, Zhuo M, Wang L, Cui JJ, Jiao F, et al. Long non-coding RNA: an emerging paradigm of pancreatic cancer. Curr Mol Med. (2016) 16:702–9. 10.2174/1566524016666160927095812 [DOI] [PubMed] [Google Scholar]
  • 23.Wang YZ, An Y, Li BQ, Lu J, Guo JC. Research progress on circularRNAs in pancreatic cancer: emerging but promising. Cancer Biol Ther. (2019) 20:1163–71. 10.1080/15384047.2019.1617563 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. (2017) 358: j4008 10.1136/bmj.j4008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. (2007) 335:914–6. 10.1136/bmj.39343.408449.80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. (2011) 342:d549. 10.1136/bmj.d549 [DOI] [PubMed] [Google Scholar]
  • 27.Patsopoulos NA, Evangelou E, Ioannidis JP. Heterogeneous views on heterogeneity. Int J Epidemiol. (2009) 38:1740–2. 10.1093/ije/dyn235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ioannidis JP, Trikalinos TA. An exploratory test for an excess of significant findings. Clin Trials. (2007) 4:245–53. 10.1177/1740774507079441 [DOI] [PubMed] [Google Scholar]
  • 29.Salanti G, Ioannidis JP. Synthesis of observational studies should consider credibility ceilings. J Clin Epidemiol. (2009) 62:115–22. 10.1016/j.jclinepi.2008.05.014 [DOI] [PubMed] [Google Scholar]
  • 30.Rezende LFM, Sá TH, Markozannes G, Rey-López JP, Lee IM, Tsilidis KK, et al. Physical activity and cancer: an umbrella review of the literature including 22 major anatomical sites and 770 000 cancer cases. Br J Sports Med. (2018) 52:826–33. 10.1136/bjsports-2017-098391 [DOI] [PubMed] [Google Scholar]
  • 31.Markozannes G, Tzoulaki I, Karli D, Evangelou E, Ntzani E, Gunter MJ, et al. Diet, body size, physical activity and risk of prostate cancer: an umbrella review of the evidence. Eur J Cancer. (2016) 69:61–9. 10.1016/j.ejca.2016.09.026 [DOI] [PubMed] [Google Scholar]
  • 32.Luo Y, Zhang X, Tan Z, Wu P, Xiang X, Dang Y, et al. Astrocyte elevated gene-1 as a novel clinicopathological and prognostic biomarker for gastrointestinal cancers: a meta-analysis with 2999 patients. PLoS ONE. (2015) 10: e0145659. 10.1371/journal.pone.0145659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Chen X, Tao L, Yuan C, Xiu D. The prognostic value of B7-H4 in pancreatic cancer: systematic review and meta-analysis. Medicine. (2018) 97:e0088. 10.1097/MD.0000000000010088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Smith RA, Tang J, Tudur-Smith C, Neoptolemos JP, Ghaneh P. Meta-analysis of immunohistochemical prognostic markers in resected pancreatic cancer. Br J Cancer. (2011) 104:1440–51. 10.1038/bjc.2011.110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Fu YJ, Li KZ, Bai JH, Liang ZQ. C-reactive protein/albumin ratio is a prognostic indicator in Asians with pancreatic cancers: a meta-analysis. Medicine. (2019) 98:e18219. 10.1097/MD.0000000000018219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Li X, Zhao H, Gu J, Zheng L. Prognostic value of cancer stem cell marker CD133 expression in pancreatic ductal adenocarcinoma (PDAC): a systematic review and meta-analysis. Int J Clin Exp Pathol. (2015) 8:12084–92. [PMC free article] [PubMed] [Google Scholar]
  • 37.Liu Y, Wu T, Lu D, Zhen J, Zhang L. CD44 overexpression related to lymph node metastasis and poor prognosis of pancreatic cancer. Int J Biol Markers. (2018) 33:308–13. 10.1177/1724600817746951 [DOI] [PubMed] [Google Scholar]
  • 38.Samarendra H, Jones K, Petrinic T, Silva MA, Reddy S, Soonawalla Z, et al. A meta-analysis of CXCL12 expression for cancer prognosis. Br J Cancer. (2017) 117:124–35. 10.1038/bjc.2017.134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Ding Y, Du Y. Clinicopathological significance and prognostic role of chemokine receptor CXCR4 expression in pancreatic ductal adenocarcinoma, a meta-analysis and literature review. Int J Surg. (2019) 65:32–8. 10.1016/j.ijsu.2019.03.009 [DOI] [PubMed] [Google Scholar]
  • 40.Fan H, Wang W, Yan J, Xiao L, Yang L. Prognostic significance of CXCR7 in cancer patients: a meta-analysis. Cancer Cell Int. (2018) 18:212. 10.1186/s12935-018-0702-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Wang D, Guo XZ, Li HY, Zhao JJ, Shao XD, Wu CY. Prognostic significance of cyclooxygenase-2 protein in pancreatic cancer: a meta-analysis. Tumour Biol. (2014) 35:10301–7. 10.1007/s13277-014-2260-y [DOI] [PubMed] [Google Scholar]
  • 42.Stephenson D, Nahm C, Chua T, Gill A, Mittal A, de Reuver P, et al. Circulating and disseminated tumor cells in pancreatic cancer and their role in patient prognosis: a systematic review and meta-analysis. Oncotarget. (2017) 8:107223–36. 10.18632/oncotarget.19928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ye J, Wen J, Ning Y, Li Y. Higher notch expression implies poor survival in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. Pancreatology. (2018) 18:954–61. 10.1016/j.pan.2018.09.014 [DOI] [PubMed] [Google Scholar]
  • 44.Jamieson NB, Carter CR, McKay CJ, Oien KA. Tissue biomarkers for prognosis in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. Clin Cancer Res. (2011) 17:3316–31. 10.1158/1078-0432.CCR-10-3284 [DOI] [PubMed] [Google Scholar]
  • 45.Liu G, Xiong D, Xiao R, Huang Z. Prognostic role of fibroblast growth factor receptor 2 in human solid tumors: a systematic review and meta-analysis. Tumour Biol. (2017) 39. 10.1177/1010428317707424. [Epub ahead of print]. [DOI] [PubMed] [Google Scholar]
  • 46.Dai J, Yang L, Wang J, Xiao Y, Ruan Q. Prognostic value of FOXM1 in patients with malignant solid tumor: a meta-analysis and system review. Dis Markers. (2015) 2015:352478. 10.1155/2015/352478 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Sharen G, Peng Y, Cheng H, Liu Y, Shi Y, Zhao J. Prognostic value of GLUT-1 expression in pancreatic cancer: results from 538 patients. Oncotarget. (2017) 8:19760–7. 10.18632/oncotarget.15035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Cao LL, Yue Z, Liu L, Pei L, Yin Y, Qin L, et al. The expression of histone deacetylase HDAC1 correlates with the progression and prognosis of gastrointestinal malignancy. Oncotarget. (2017) 8:39241–53. 10.18632/oncotarget.16843 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Bird NT, Elmasry M, Jones R, Psarelli E, Dodd J, Malik H, et al. Immunohistochemical hENT1 expression as a prognostic biomarker in patients with resected pancreatic ductal adenocarcinoma undergoing adjuvant gemcitabine-based chemotherapy. Br J Surg. (2017) 104:328–36. 10.1002/bjs.10482 [DOI] [PubMed] [Google Scholar]
  • 50.Li X, Zhao H, Gu J, Zheng L. Prognostic role of HER2 amplification based on fluorescence in situ hybridization (FISH) in pancreatic ductal adenocarcinoma (PDAC): a meta-analysis. World J Surg Oncol. (2016) 14:38. 10.1186/s12957-016-0792-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Ye LY, Zhang Q, Bai XL, Pankaj P, Hu QD, Liang TB. Hypoxia-inducible factor 1α expression and its clinical significance in pancreatic cancer: a meta-analysis. Pancreatology. (2014) 14:391–7. 10.1016/j.pan.2014.06.008 [DOI] [PubMed] [Google Scholar]
  • 52.Luo D, Liu H, Lin D, Lian K, Ren H. The clinicopathological and prognostic value of hypoxia-inducible factor-2α in cancer patients: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. (2019) 28:857–66. 10.1158/1055-9965.EPI-18-0881 [DOI] [PubMed] [Google Scholar]
  • 53.Wu J, Hu L, Wu F, Zou L, He T. Poor prognosis of hexokinase 2 overexpression in solid tumors of digestive system: a meta-analysis. Oncotarget. (2017) 8:32332–44. 10.18632/oncotarget.15974 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Wu T, Zhang W, Yang G, Li H, Chen Q, Song R, et al. HMGB1 overexpression as a prognostic factor for survival in cancer: a meta-analysis and systematic review. Oncotarget. (2016) 7:50417–27. 10.18632/oncotarget.10413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Gan J, Wang W, Yang Z, Pan J, Zheng L, Yin L. Prognostic value of pretreatment serum lactate dehydrogenase level in pancreatic cancer patients: a meta-analysis of 18 observational studies. Medicine. (2018) 97:e13151. 10.1097/MD.0000000000013151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Mao Y, Chen D, Duan S, Zhao Y, Wu C, Zhu F, et al. Prognostic impact of pretreatment lymphocyte-to-monocyte ratio in advanced epithelial cancers: a meta-analysis. Cancer Cell Int. (2018) 18:201. 10.1186/s12935-018-0698-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Mehrad-Majd H, Ravanshad S, Moradi A, Khansalar N, Sheikhi M, Akhtari J. Decreased expression of lncRNA loc285194 as an independent prognostic marker in cancer: a systematic review and meta-analysis. Pathol Res Pract. (2019) 215:152426. 10.1016/j.prp.2019.04.018 [DOI] [PubMed] [Google Scholar]
  • 58.Liu FT, Dong Q, Gao H, Zhu ZM. The prognostic significance of UCA1 for predicting clinical outcome in patients with digestive system malignancies. Oncotarget. (2017) 8:40620–32. 10.18632/oncotarget.16534 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Hua T, Liu S, Xin X, Jin Z, Liu Q, Chi S, et al. Prognostic significance of L1 cell adhesion molecule in cancer patients: a systematic review and meta-analysis. Oncotarget. (2016) 7:85196–207. 10.18632/oncotarget.13236 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Shao Y, Gu W, Ning Z, Song X, Pei H, Jiang J. Evaluating the prognostic value of microRNA-203 in solid tumors based on a meta-analysis and the cancer genome atlas (TCGA) datasets. Cell Physiol Biochem. (2017) 41:1468–80. 10.1159/000470649 [DOI] [PubMed] [Google Scholar]
  • 61.Zhou Y, Wei Q, Fan J, Cheng S, Ding W, Hua Z. Prognostic role of the neutrophil-to-lymphocyte ratio in pancreatic cancer: a meta-analysis containing 8252 patients. Clin Chim Acta. (2018) 479:181–9. 10.1016/j.cca.2018.01.024 [DOI] [PubMed] [Google Scholar]
  • 62.Hu Y, Chen W, Yan Z, Ma J, Zhu F, Huo J. Prognostic value of PD-L1 expression in patients with pancreatic cancer: a PRISMA-compliant meta-analysis. Medicine. (2019) 98: e14006. 10.1097/MD.0000000000014006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Ji R, Ren Q, Bai S, Wang Y, Zhou Y. Prognostic significance of pretreatment plasma fibrinogen in patients with hepatocellular and pancreatic carcinomas: a meta-analysis. Medicine. (2018) 97:e10824. 10.1097/MD.0000000000010824 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Yin X, Wu L, Yang H, Yang H. Prognostic significance of neutrophil-lymphocyte ratio (NLR) in patients with ovarian cancer: a systematic review and meta-analysis. Medicine. (2019) 98:e17475. 10.1097/MD.0000000000017475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Zhu H, Luo H, Zhu X, Hu X, Zheng L, Zhu X. Pyruvate kinase M2 (PKM2) expression correlates with prognosis in solid cancers: a meta-analysis. Oncotarget. (2017) 8:1628–40. 10.18632/oncotarget.13703 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Hu G, Wang S, Xu F, Ding Q, Chen W, Zhong K, et al. Tumor-infiltrating podoplanin+ fibroblasts predict worse outcome in solid tumors. Cell Physiol Biochem. (2018) 51:1041–50. 10.1159/000495484 [DOI] [PubMed] [Google Scholar]
  • 67.Yu M, Wang Q, Ding JW, Yang Z, Xie C, Lu NH. Association between raf kinase inhibitor protein loss and prognosis in cancers of the digestive system: a meta-analysis. Cancer Biomark. (2014) 14:389–400. 10.3233/CBM-140410 [DOI] [PubMed] [Google Scholar]
  • 68.Zhang X, Jin FS, Zhang LG, Chen RX, Zhao JH, Wang YN, et al. Predictive and prognostic roles of ribonucleotide reductase M1 in patients with pancreatic cancer treated with gemcitabine: a meta-analysis. Asian Pac J Cancer Prev. (2013) 14:4261-5 [DOI] [PubMed] [Google Scholar]
  • 69.Wang JD, Jin K, Chen XY, Lv JQ, Ji KW. Clinicopathological significance of SMAD4 loss in pancreatic ductal adenocarcinomas: a systematic review and meta-analysis. Oncotarget. (2017) 8:16704–11. 10.18632/oncotarget.14335 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Han W, Cao F, Chen MB, Lu RZ, Wang HB, Yu M, et al. Prognostic value of SPARC in patients with pancreatic cancer: a systematic review and meta-analysis. PLoS ONE. (2016) 11: e0145803. 10.1371/journal.pone.0145803 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Wu P, Wu D, Zhao L, Huang L, Shen G, Huang J, et al. Prognostic role of STAT3 in solid tumors: a systematic review and meta-analysis. Oncotarget. (2016) 7:19863–83. 10.18632/oncotarget.7887 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Chen H, Lu W, Huang C, Ding K, Xia D, Wu Y, et al. Prognostic significance of ZEB1 and ZEB2 in digestive cancers: a cohort-based analysis and secondary analysis. Oncotarget. (2017) 8:31435–48. 10.18632/oncotarget.15634 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Kalisvaart M, Broadhurst D, Marcon F, et al. Recurrence patterns of pancreatic cancer after pancreatoduodenectomy: systematic review and a single-centre retrospective study. HPB. (2020). 10.1016/j.hpb.2020.01.005. [Epub ahead of print]. [DOI] [PubMed] [Google Scholar]
  • 74.Nosacka RL, Delitto AE, Delitto D, Patel R, Judge SM, Trevino JG, et al. Distinct cachexia profiles in response to human pancreatic tumours in mouse limb and respiratory muscle. J Cachexia Sarcopenia Muscle. (2020) 11:820–37. 10.1002/jcsm.12550 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Gulen ST, Karadag F, Karul AB, Kilicarslan N, Ceylan E, Kuman NK, et al. Adipokines and systemic inflammation in weight-losing lung cancer patients. Lung. (2012) 190:327–32. 10.1007/s00408-011-9364-6 [DOI] [PubMed] [Google Scholar]
  • 76.Roxburgh CS, McMillan DC. Cancer and systemic inflammation: treat the tumour and treat the host. Br J Cancer. (2014) 110:1409–12. 10.1038/bjc.2014.90 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Ikuta S, Aihara T, Yamanaka N. Preoperative C-reactive protein to albumin ratio is a predictor of survival after pancreatic resection for pancreatic ductal adenocarcinoma. Asia Pac J Clin Oncol. (2019) 15:e109–14. 10.1111/ajco.13123 [DOI] [PubMed] [Google Scholar]
  • 78.Arima K, Yamashita YI, Hashimoto D, Nakagawa S, Umezaki N, Yamao T, et al. Clinical usefulness of postoperative C-reactive protein/albumin ratio in pancreatic ductal adenocarcinoma. Am J Surg. (2018) 216:111–5. 10.1016/j.amjsurg.2017.08.016 [DOI] [PubMed] [Google Scholar]
  • 79.Hoshimoto S, Hishinuma S, Shirakawa H, Tomikawa M, Ozawa I, Ogata Y. Validation and clinical usefulness of pre- and postoperative systemic inflammatory parameters as prognostic markers in patients with potentially resectable pancreatic cancer. Pancreatology. (2019) 4:239–246. 10.1016/j.pan.2019.12.004 [DOI] [PubMed] [Google Scholar]
  • 80.Pu N, Yin H, Zhao G, Nuerxiati A, Wang D, Xu X, et al. Independent effect of postoperative neutrophil-to-lymphocyte ratio on the survival of pancreatic ductal adenocarcinoma with open distal pancreatosplenectomy and its nomogram-based prediction. J Cancer. (2019) 10:5935–43. 10.7150/jca.35856 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Markert CL. Lactate dehydrogenase isozymes: dissociation and recombination of subunits. Science. (1963) 140:1329–30. 10.1126/science.140.3573.1329 [DOI] [PubMed] [Google Scholar]
  • 82.Yu M, Chen S, Hong W, Gu Y, Huang B, Lin Y, et al. Prognostic role of glycolysis for cancer outcome: evidence from 86 studies. J Cancer Res Clin Oncol. (2019) 145:967–99. 10.1007/s00432-019-02847-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Gao S, Wu M, Chen Y, Lou W, Zhou G, Li J, et al. Lactic dehydrogenase to albumin ratio in prediction of unresectable pancreatic cancer with intervention chemotherapy. Future Oncol. (2018) 14:1377–86. 10.2217/fon-2017-0556 [DOI] [PubMed] [Google Scholar]
  • 84.Palmquist C, Dehlendorff C, Calatayud D, Hansen CP, Hasselby JP, Johansen JS. Prediction of unresectability and prognosis in patients undergoing surgery on suspicion of pancreatic cancer using carbohydrate antigen 19-9, interleukin 6, and YKL-40. Pancreas. (2020) 49:53–61. 10.1097/MPA.0000000000001466 [DOI] [PubMed] [Google Scholar]
  • 85.Gu X, Zhou R, Li C, Liu R, Zhao Z, Gao Y, et al. Preoperative maximum standardized uptake value and carbohydrate antigen 19-9 were independent predictors of pathological stages and overall survival in Chinese patients with pancreatic duct adenocarcinoma. BMC Cancer. (2019) 19:456. 10.1186/s12885-019-5691-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.van Manen L, Groen JV, Putter H, Vahrmeijer AL, Swijnenburg RJ, Bonsing BA, et al. Elevated CEA and CA19-9 serum levels independently predict advanced pancreatic cancer at diagnosis. Biomarkers. (2020) 25:186–93. 10.1080/1354750X.2020.1725786 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

All datasets generated for this study are included in the article/Supplementary Material.

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