Abstract
Background:
Previous prospective studies of associations between circulating inflammation-related molecules and pancreatic cancer risk have included limited numbers of markers.
Methods:
We conducted a case-cohort study nested within the Japan Public Health Center-based Prospective Study Cohort II. We selected a random subcohort (n=774) from a total of 23,335 participants aged 40 to 69 years who returned a questionnaire and provided blood samples at baseline. During the follow-up period from 1993 through 2010, we identified 111 newly diagnosed pancreatic cancer cases including one case within the subcohort. Plasma concentrations of 62 inflammatory markers of chemokines, cytokines and growth factors were measured by a Luminex fluorescent bead-based assay. Cox regression models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pancreatic cancer risk for quartiles of marker levels adjusted for potential confounders.
Results:
The HR (95% CI) for the highest vs. the lowest category of C-C motif ligand chemokine 8 /monocyte chemoattractant protein 2 (CCL8/MCP2) was 2.03 (1.05 – 3.93) (P for trend = 0.048). After we corrected for multiple comparisons, none of the examined biomarkers were associated with pancreatic cancer risk at p-value <0.05.
Conclusions:
We found no significant associations between 62 inflammatory markers and pancreatic cancer risk.
Impact:
The suggestive association with circulating levels of leukocyte recruiting cytokine CCL8/MCP2 may warrant further investigation.
Keywords: pancreatic cancer, inflammation, cytokine, chemokine, case-cohort study
Introduction
Pancreatic cancer is a highly fatal disease and accounts for more than 200,000 deaths worldwide every year (1). In Japan, pancreatic cancer is the 4th leading cause of cancer deaths with a crude mortality rate that increased from 21.3 in 2009 to 29.4/100,000 in 2019 (2). Age-standardized mortality rates are similar in Eastern and Western countries (3). Chronic pancreatitis, smoking, diabetes, and obesity are established risk factors for pancreatic cancer (1) which may implicate variant inflammatory processes in its etiology (4).
The tissue microenvironment impacts pancreatic tumor development, progression and metastasis by both intra-cellular and extra-cellular components (5–7). Acellular influences include collagen, fibronectin, and multiple soluble factors such as cytokines, chemokines, and growth factors secreted by pancreatic stellate cells residing within the tumor and surrounding stroma (5, 8, 9).
Previous prospective studies of associations between circulating inflammation-related molecules and pancreatic cancer risk have included limited numbers of markers. Two nested case-control studies found that high C-reactive protein (CRP) concentrations were not associated with incident pancreatic cancer (10). Similarly, the EPIC studies did not find any significant associations of CRP and interleukin-6 (IL-6) with pancreatic cancer (4). A pooled study of five US cohorts observed that associations between the risk of pancreatic cancer and pre-diagnostic levels of CRP, IL-6, and tumour necrosis factor receptor 2 (TNF-R2) were not significant (11). A recent publication of the Swedish AMORIS cohort study reported an increased risk of pancreatic cancer with high pre-diagnostic serum levels of haptoglobin, CRP, and leukocytes (12). However, to our knowledge, no study has comprehensively evaluated a wide range of circulatory inflammation markers for associations with pancreatic cancer risk.
When a patient is diagnosed with pancreatic cancer, it is often too late for treatment. Pancreatic cancer is generally asymptomatic until it becomes advanced (9), at which point available curative treatment is limited. To probe potential mechanisms of carcinogenesis and evaluate utility for screening, we investigated the association of pancreatic cancer with inflammatory markers of chemokines, cytokines, and growth factors in a population-based prospective cohort in Japan.
Materials and Methods
Study population
The Japan Public Health Center-based Prospective Study (JPHC) Cohort II was initiated in 1993–1994. We defined our target population as Japanese inhabitants of six areas (Niigata, Ibaraki, Osaka, Kochi, Nagasaki, and Okinawa) across Japan aged 40–69 years at the baseline survey (n = 78,825). Details of the study design had been described elsewhere (13). This study conformed to the ethical guidelines of the Declaration of Helsinki. Although informed written consent was not obtained from each subject, the Institutional Review Board of the National Cancer Center, Japan (IRB No. 2004–059) and Fukushima Medical University, Japan (IRB No. Generic 2019–325) approved the study protocol.
A total of 23,335 participants answered the study questionnaire and provided 10 mL of blood sample during health checkups. Excluding those individuals with insufficient blood volume, missing information on cancer, fasting status or blood collection time, or those who had diagnosed cancer or moved out before blood collection, we followed 20,900 study participants until 31 December 2010. We identified 111 pancreatic cancer cases in total. We identified incident pancreatic cancer by voluntary reports from the local hospitals and by data linkage with population-based cancer registries, with permission. We used death certificates from the Ministry of Health, Labor and Welfare as supplementary information to identify cancer incidence. Changes in residential and vital status were obtained annually from the population registry of each study area. Cases were coded by using the 3rd edition of International Classification of Diseases for Oncology (C25.0-C25.9) (14).
A subcohort (n = 774) from a total of 20,900 participants was randomly selected and matched to the age and sex distribution of cancer cases. We had a total of 884 participants for analysis in this study including the subcohort participants and 110 additional pancreatic cancer cases from the rest of the cohort (Figure 1). Diagnosis of pancreatic cancer in 82% (n = 91) was implemented by histologic or cytologic examination or clinical findings (images, tumor markers, etc.).
Figure 1.
Flow diagram of selection of 20,900 eligible individuals from a total of 78,825 participants and the case-cohort sample design nested within the JPHC Study. The stratified random sample had a subcohort of 774 individuals with similar age and gender distribution as cancer cases. There were a total of 111 pancreatic cases, of which 1 was from the subcohort.
Laboratory assays
We collected blood specimens into vacutainer tubes with heparin when participants attended health checkups. These specimens were centrifuged within 12 hours and divided into plasma and buffy coat layers, preserved at −80°C until analysis.
We measured plasma levels of 67 inflammatory and immunity markers using a previously developed and validated assay with Luminex fluorescent bead-based multiplex panel (Millipore Inc, Billerica, MA) (15). We estimated the concentrations by interpolation on a four- or five-parameter curve fitted to those of seven calibration standards provided by the manufacturer. Samples of case and subcohort were randomly distributed on test plates. To monitor the reproducibility, each plate included masked samples were tested in triplicate (total n = 56), in two random wells on one plate and the third well on another. We calculated the intraclass correlation coefficients using sum of squares from repeated measures analysis of variance as the ratio of between subject-variance over total variance. Two markers (IL3, TNFB) were excluded from the analysis due to levels detectable in less than 10% of samples. Three markers (IL7, CXCL8/IL8, and CCL20/MIP3A) were estimated both by conventional and high-sensitivity assays in different panels. We retained for analysis the respective assays with greater frequency of detection, which were the high-sensitivity result for IL7 and the conventional results for CXCL8/IL8 and for CCL20/MIP3A (16). Of the 62 remaining markers, we log-transformed the marker values and then computed coefficients of variations (CVs). The CVs within batch were median 11% (all <30%) and between batch were median 29% (all but three < 50%); intraclass correlation coefficients were median 96% (all but nine > 80%) (16).
Statistical analysis
Cox proportional hazards regression with the robust variance was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between each marker and pancreatic cancer risk. Variance was estimated using the sandwich variance estimate computed using the observed information matrix and the matrix product of the dfbeta residuals; that is, the estimated change in model coefficients due to the deletion of each subject, calculated for all study subjects (17–19). Age was used as a time metric in models. For individuals in the subcohort, follow-up started from the date of blood draw until the diagnosis of pancreatic cancer or censoring, defined as movement out of the study area, death from any cause, or the end of follow-up on 31 December 2010, whichever came first. For cases that were not subcohort members, follow-up time was the 60-day period prior to pancreatic cancer diagnosis, an arbitrarily selected short period to ensure inclusion in only one risk set (17).
Markers were analyzed as quantiles based on the distribution among the subcohort, with the lowest quantile as the reference. Measurements of markers were categorized according to proportions of individuals below the lower limit of detection (LLOD). Markers with less than 25% of individuals below the LLOD were categorized into quartiles; markers with 25% to 50% of individuals below the LLOD were categorized as less than LLOD and tertiles of detectable levels; markers with 50% to 75% of individuals below the LLOD were categorized as less than LLOD and below and above the median; markers with 75–90% of individuals below the LLOD were categorized as undetectable and detectable.
HRs were adjusted for the potential confounding variables: age (years), gender, study area, family history of pancreatic cancer (yes or no), history of diabetes mellitus (yes or no), body mass index (<21, 21-<23, 23-<27, and ≥27 kg/m2), smoking habits (never, past, current <20, and current ≥20 cigarettes/day), alcohol drinking (none or occasional, regular <300, and regular ≥300 g ethanol/week) and quartiles of energy expenditure in metabolic equivalents/day. Missing data were treated as a separate category for all covariates. The quantiles were included as ordinal variables in the regression models to examine the linear trend of pancreatic cancer risk per quantile increase of marker levels. We only present results in detail for those markers with P for trends < 0.05. For multiple testing adjustment, we also calculated false discovery rate (FDR) corrected P-values considering < 0.05 as statistically significant. We repeated analyses stratified by latency (i.e., time from blood specimen collection to diagnosis) of <5 vs. ≥5 years. SAS statistical software, version 9.4 (SAS Institute Inc, Cary, NC), was used for all analyses.
Results
Table 1 shows the baseline characteristics of pancreatic cancer patients and subcohort participants of the JPHC study.
Table 1.
Baseline characteristics of pancreatic cancer patients and subcohort participants of the JPHC study
| Pancreatic cancer (n = 111) | Subcohort (n = 774) | |||
|---|---|---|---|---|
| Age at enrollment, mean ± SD, years | 61.7 ± 7.7 | 60.1 ± 7.5 | ||
| Male, % | 49 | (44.1%) | 413 | (53.4%) |
| Family history of pancreatic cancera, % | 5 | (4.5%) | 7 | (0.9%) |
| Smoking | ||||
| Never | 69 | (62.2%) | 452 | (58.4%) |
| Former | 13 | (11.7%) | 134 | (17.3%) |
| Current < 20 cigarettes/day | 20 | (18.0%) | 129 | (16.7%) |
| Current ≥ 20 cigarettes/day | 7 | (6.3%) | 47 | (6.1%) |
| Unknown | 2 | (1.8%) | 12 | (1.6%) |
| Alcohol drinking, % | ||||
| Never/occasional | 81 | (73.0%) | 552 | (71.3%) |
| Regular < 300 g/week | 20 | (18.0%) | 135 | (17.4%) |
| Regular ≥ 300 g/week | 10 | (9.0%) | 64 | (8.3%) |
| Unknown | 0 | (0.0%) | 23 | (3.0%) |
| BMI, % | ||||
| <21kg/m2 | 19 | (17.1%) | 155 | (20.0%) |
| 21-<23kg/m2 | 34 | (30.6%) | 189 | (24.4%) |
| 23-<27 kg/m2 | 45 | (40.5%) | 331 | (42.8%) |
| >=27 kg/m2 | 12 | (10.8%) | 87 | (11.2%) |
| Unknown | 1 | (0.9%) | 12 | (1.6%) |
| Physical activity (METs/day), % | ||||
| >=42.7 | 40 | (36.0%) | 160 | (20.7%) |
| 34.3–42.6 | 23 | (20.7%) | 148 | (19.1%) |
| 31.9–34.2 | 13 | (11.7%) | 178 | (23.0%) |
| < 31.8 | 32 | (28.8%) | 259 | (33.5%) |
| Unknown | 3 | (2.7%) | 29 | (3.7%) |
| History of diabetes, % | ||||
| No | 44 | (39.6%) | 311 | (40.2%) |
| Yes | 14 | (12.6%) | 46 | (5.9%) |
| Unknown | 53 | (47.8%) | 417 | (53.9%) |
| Clinical staging of cancerb, % | ||||
| Early | 8 | (7.2%) | ||
| Advanced | 70 | (63.1%) | ||
| Unknown | 33 | (29.7%) | 1 | (100%) |
Information regarding pancreatic cancer family history was acquired at 5 year follow-up visit.
Proportion shown for cancer cases. Early stage = localized, Advanced stage = regional lymph node, adjacent organ, distant metastasis
Figure 2 shows associations of pancreatic cancer risk per quantile increase for all the markers examined. Higher quantile of C-C motif ligand-8/monocyte chemoattractant protein-2 (CCL8/MCP2) was marginally associated with increased risk of pancreatic cancer while other markers had no associations. The coefficient of variation was 9.4% and the intraclass correlation coefficient was 0.9 for CCL8/MCP2. In addition, results stratified by gender, age group, or smoking habits were consistent showing no significant associations for each inflammatory marker. A complete list of pancreatic cancer associations for all 62 detectable markers is presented in Supplementary materials (Table S1).
Figure 2.
Association of per quantile increase of immune-related markers with risk of pancreatic cancer in the JPHC Study. Associations were estimated with adjustment for age (years), gender, study area, family history of pancreatic cancer (yes or no), history of diabetes mellitus (yes or no), body mass index (<21, 21-<23, 23-<27, and ≥27 kg/m2), smoking habits (never, past, current <20, and current ≥20 cigarettes/day), alcohol drinking (none or occasional, regular <300, and regular ≥300 g ethanol/week) and quartiles of energy expenditure in metabolic equivalents/day. * p<0.05
Per quartile HR and 95% CI is shown for CCL8/MCP2 in Table 2. In the fully adjusted model, the HR in the highest quartile was statistically significantly doubled compared to the lowest quartile. Similar significant associations were observed for cases diagnosed <5 years but not for cases diagnosed ≥5 years after blood draw (Table 2).
Table 2.
Hazard ratios for quartiles of CCL8/MCP2a among pancreatic cancer cases stratified by time between blood sample collection and diagnosis
| All cases (n = 111) | < 5 years (n = 21) | ≥ 5 years (n = 90 ) | |||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| CCL8/MCP2 level, pg/mL | Case (n) | HR1b (95% CI) | HR2c (95% CI) | Case (n) | HR2c (95% CI) | Case (n) | HR2c (95% CI) |
| ≤ 8.98 | 18 | 1.00 | 1.00 | 2 | 1.00 | 16 | 1.00 |
| 8.99–16.79 | 35 | 1.96 (1.08–3.56) | 1.74 (0.92–3.29) | 9 | 3.16 (1.22–8.21) | 26 | 1.51 (0.77–2.97) |
| 16.84–23.33 | 28 | 1.60 (0.85–3.01) | 1.75 (0.92–3.32) | 4 | 2.01 (0.63–6.44) | 24 | 1.71 (0.88–3.33) |
| ≥ 23.34 | 30 | 1.94 (1.04–3.60) | 2.03 (1.05–3.93) | 6 | 3.92 (1.36–11.34) | 24 | 1.82 (0.92–3.61) |
| P trend | 0.085 | 0.048 | 0.011 | 0.096 | |||
Cox proportional hazards model stratified by gender and age group was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).
Abbreviations: CCL=chemokine (C-C motif) ligand, MCP=monocyte chemoattractant protein
HR1 was adjusted for age (years), gender, study site.
HR2 was adjusted for covariates of HR1 and family history of pancreatic cancer (yes or no), history of diabetes mellitus (yes or no), body mass index (<21, 21-<23, 23-<27, and ≥27 kg/m2), smoking habits (never, past, current <20, and current ≥20 cigarettes/day), alcohol drinking (none or occasional, regular <300, and regular ≥300 g ethanol/week) and quartiles of energy expenditure in metabolic equivalents/day.
We did not find any statistically significant associations between the markers and pancreatic cancer risk after correction for multiple testing.
Discussion
This is the first comprehensive study to explore associations of a wide range of circulating inflammation markers with pancreatic cancer risk. We observed a suggestive positive association between CCL8/MCP2 levels and risk of pancreatic cancer. Although the CCL8/MCP2 association was not statistically significant after correction of multiple testing, this marker might be a candidate for future research.
Acute inflammatory responses produce a protective microenvironment in tissue to recognize and repair cell damage, but chronic inflammation may boost tumor formation (20). In response to tissue injury, mononuclear phagocytes are guided by chemotactic factors (21). Pro-inflammatory cytokines secreted by immune and inflammatory cells are functionally important components of the tumor microenvironment (22). Chemokines represent the largest family of cytokines and the interacting signaling networks influence tumor cell growth, survival and migration and also angiogenesis and immune-cell infiltration (23, 24).
In the current study, we found a non-significant positive association of circulating CCL8/MCP2 level with pancreatic cancer risk. CCL8/ MCP2 binds CC receptor 2 and CCR5 in common with other chemokines (23). A recent in vitro study demonstrated that through directly targeting CCL8, miR-345–5p plays a critical role in suppressing cell proliferation and metastasis of pancreatic cancer (25). Thus CCL8/MCP2 might be an appropriate candidate for future studies.
In cross-sectional studies comparing with healthy controls significant higher levels of TNF-α (26, 27), IL-6 (26–28), and IL-8 (26, 28) have been observed in patients with chronic pancreatitis or pancreatic cancer. The CRP level was reported to be an indicator of the aggressiveness of advanced pancreatic cancer (29).
These findings led to epidemiologic studies investigating whether the levels of these inflammation markers among healthy individuals could be a predictor for incidence of pancreatic cancer. In previous prospective studies, pre-diagnostic circulating CRP (4, 10, 11), IL-6 (4, 11), and TNF-αR2 (11) were not associated with pancreatic cancer risk, as seen in our study, except for one study reporting an association of CRP (12). The roles of inflammation markers in pancreatic carcinogenesis are still not clear.
One strength in this study is the use of a multiplex analytical method that has been extensively validated (30–32). Also, our blood samples collected long before diagnosis are unlikely to reflect occult pancreatic cancer. Further, the population-based design and multivariate analysis incorporating established risk factors make the study results more robust.
One of the limitations in our study was that we had only a single plasma sample from each study participant, and it is uncertain to what extent an individual’s concentrations vary over time (16) during the long lag between blood sample collection and pancreatic cancer incidence. If the stability of the concentration was low, we may have underestimated the association with pancreatic cancer risk. Second, the number of pancreatic cancer cases is limited for this study despite the large number of markers evaluated. Furthermore, due to lack of relevant information, our analysis could not adjust for any nonsteroidal anti-inflammatory drug therapies or other medications that might affect levels of circulating markers and/or pancreatic cancer risk (33, 34).
In conclusion, we observed no association between the concentration of 62 inflammatory markers and incident pancreatic cancer in older Japanese adults. Similar studies in other racial/ethnic populations are needed to further explore the roles of local and systematic inflammation in tumor development and evaluate profiles of inflammatory markers, individually or synergistically, in pancreatic cancer risk.
Supplementary Material
Acknowledgements
We are indebted to the Aomori, Iwate, Ibaraki, Niigata, Osaka, Kochi, Nagasaki, and Okinawa Cancer Registries for providing their incidence data. JPHC members as of May 2021 are listed at the following site: https://epi.ncc.go.jp/en/jphc/781/8510.html.
This study was supported by the Japanese National Cancer Center Research and Development Fund (Grant No. 23-A-31 [toku], 26-A-2, and 29-A-4 (since 2011)) (Principal Investigator, S. Tsugane), and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan (from 1989 to 2010) (Principal Investigator, S. Tsugane). This study was also supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, US National Cancer Institute, National Institutes of Health, Department of Health and Human Services (Grant No. ZIA-CP010212, Principal Investigator, C.S. Rabkin). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funding and Manuscript Deposition
Grant No.: ZIA-CP010212
Grant recipient: C.S. Rabkin
Abbreviations:
- BMI
body mass index
- CCL
C-C motif ligand
- CI
confidence interval
- CRP
C-reactive protein
- HR
hazard ratio
- IL
interleukin
- JPHC Study
Japan Public Health Center-based Prospective Study
- LLOD
lower limit of detection
- MCP
monocyte chemoattractant protein
- SD
standard deviation
- TME
tumor microenvironment
- TNFR
tumour necrosis factor receptor
Footnotes
Conflict of Interest Statement:
The authors declare no potential conflicts of interest.
Data Availability Statement: For information on how to submit an application for gaining access to JPHC data and/or biospecimens, please follow the instructions at https://epi.ncc.go.jp/en/jphc/805/8155.html.
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