Abstract
Background:
Gastric cancer (GC) lacks specific symptoms, resulting in diagnosis at later stages and high mortality. Serum pepsinogen is a biomarker for atrophic gastritis, a GC precursor, and may be useful to detect persons at increased risk of GC.
Methods:
The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial was conducted in the US between 1993 and 2001. ELISA-based pepsinogen tests were conducted on pre-diagnostic serum samples of 105 PLCO participants who developed GC and 209 age, sex, and race-matched controls. Pepsinogen positive (PG+) was defined as pepsinogen I ≤ 70μg/L and pepsinogen I/II ratio ≤3.0. Results of conditional logistic regression models, and sensitivity and specificity, of PG+ for GC are reported.
Results:
GC cases were more likely to be PG+ (31.4% vs 5.5%, p<0.001) at baseline than controls. Compared to PG-, PG+ was associated with an 8.5-fold increased risk for GC (95%CI=3.8–19.4). This risk remained significant after adjusting for Helicobacter pylori, family history of GC, education, smoking, and BMI (aOR 10.6; 95%CI=4.3–26.2). In subgroup analysis, PG+ individuals were 11-fold more like to develop non-cardia GC (OR 11.1; 95%CI=4.3–28.8); conversely, they were not significantly more likely to develop cardia GC (OR, 2.0; 95%CI=0.3–14.2). PG+ status yielded low sensitivity but high specificity for both non-cardia [44.3%; 93.6%] and cardia GC [5.7%; 97.2%].
Conclusions:
Pre-diagnostic serum pepsinogen levels from a large, prospective cohort study were associated with risk of GC, particularly non-cardia GC.
Impact:
PG status may identify individuals at higher risk of non-cardia GC for targeted screening or interventions.
Introduction
Gastric cancer (GC) mortality is unduly high in the United States; only 28% of gastric cancers are diagnosed at earlier, localized stages of cancer, resulting in an overall 5-year survival of 32%.(1) When GC is diagnosed at an early stage, 5-year survival can exceed 90%.(2,3) GC lacks specific symptoms, and, without screening, is generally discovered in later stages of cancer. Thus, GC mortality could be improved if biomarkers to detect individuals at higher risk of GC could be used to guide GC screening and prevention strategies.
Gastric carcinogenesis follows a multi-step histopathological pathway known as the Correa cascade, which involves the following steps: chronic active gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, and, ultimately, cancer.(4,5) Serum pepsinogen (PG) is a plausible serum biomarker candidate. It is a pro-enzyme of the digestive enzyme pepsin, and is mainly produced by the chief cells of the fundic glands of the stomach.(6) PG reflects the functional and morphological status of the gastric mucosa and serves as a marker of atrophic gastritis. Low serum PG I and PGI/II ratio have been associated with severe atrophic gastritis and gastric cancer due to loss of cells in the corpus and fundus.(7–9) Given that PG identifies atrophic gastritis, a precursor of GC, PG has great potential to be used as a tool for identifying individuals at increased GC risk. PG has been examined extensively in Asia and Europe and found to be predictive of GC,(10–16) however, very few studies are available of US populations, and are mainly limited to select high-risk groups, including one study of Alaskans(17) and another limited to Hawaiian men of Japanese descent.(18) There have been no studies that have examined PG as a predictor of GC in a broader US population.
In this study, we conducted a nested case-control analysis of GC patients and 2:1 matched controls from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) cohort to examine the association between serum PG and subsequent GC development. Our aim was to examine PG as a biomarker of GC risk in the US population.
Methods
Study Population
PLCO was a large randomized trial that was conducted over 10 centers in the US between 1993 and 2001. The study was designed to determine the effectiveness of prostate, lung, colorectal and ovarian cancer screening on cancer-related mortality.(19) Subjects were enrolled at baseline and followed until the development of cancer or death. Approximately 155,000 men and women aged 55 to 74 years were enrolled in the study. Cancer diagnoses were ascertained though annual study update forms and subsequent medical record verification. Information on subjects’ demographic, lifestyle, and medical history were collected through a survey at the time of enrollment. Serum samples were collected at time of enrollment and during follow up. Only individuals with a complete baseline questionnaire and serum sample available from the enrollment visit were considered for matching.
Analyses reported here are based on 2:1 matched nested case-control sample. Cases were defined as those who were diagnosed with GC (ICD-O-2 codes C160-C169) after enrollment in the trial and controls were PLCO participants who had not developed GC by follow up date of September 26, 2008. Controls were 2:1 frequency matched to the cases by age (<=59, 60–64, 65–69, >=70 years), sex, and race/ethnicity (White, Black, Hispanic, Asian). Serum samples of cases and matched controlled were used to conduct PG tests and Helicobacter pylori (H. pylori) IgG tests using commercial ELISA kits (Biohit Healthcare, Helsinki, Finland). The assay kit used for testing is based on sandwich enzyme immunoassay technique with a pepsinogen I/pepsinogen II/H. pylori antibody specific capture antibody adsorbed on a microplate and a detection antibody labeled with horseradish peroxidase (HRP). Testing was conducted in 2013. The testing was performed by National Cancer Institute investigators under the study titled “Gastric Cancer Cohort Consortium Study,” which aimed to develop a large, nested case-control study by pooling prospectively collected specimens from multiple cohort studies. Data generated for the above study, including the test results of PG and H. pylori, were provided by PLCO by special request for the conduct of this research.
The original data provided by PLCO included 109 GC and 217 controls. Upon further examination we found 4 cases that did not have confirmed GC- 3 subjects were found to be “erroneous reports of cancer” while the 4th case was an esophageal cancer that had been misclassified as gastric cancer. Removal of those cases as well as their matched controls resulted in 105 cases and 209 controls. Among the 105 cases and 209 controls there was one match with one case and a single corresponding control, and three matches which had 2 cases and 4 corresponding controls, due to identical matching characteristics. This explains why our sample size did not result in an exact 1:2 ratio of cases to controls. For subset analyses by site, subgroups of non-cardia and cardia cancers were examined separately with their respective controls. One of the matches with 2 cases contained a non-cardia and a cardia case, hence this match was included as a single case with 4 controls each for both the non-cardia and cardia groups. This resulted in 70 cases and 141 matched controls for the non-cardia GC subset and 35 cases and 72 matched controls for cardia GC subset.
Serum pepsinogen I levels ≤ 70μg/L and pepsinogen I to II ratio ≤ 3.0 were classified as PG-seropositive (PG+) and all others were considered PG-seronegative (PG-). These cut-offs are widely accepted and are the most commonly used.(20) H. pylori IgG >=10 EIU/ml was considered H. pylori positive.(21,22)
Statistical Analysis
Descriptive statistics were used to summarize baseline characteristics of the study sample. Conditional logistic regression of the matched sets was used to estimate the odds ratio (OR) and associated 95% confidence intervals (95%CI) for GC comparing PG+ to PG- patients. Adjusted models included variables with univariate associations of p < 0.2 in addition to variables that were a priori deemed relevant such as family history of GC and education. The utility of the pepsinogen biomarker was based on its estimated sensitivity and specificity to identify gastric cancer cases versus controls. Confidence intervals for sensitivity and specificity were calculated based on the normal approximation to the binomial distribution. Stability of PG as a biomarker of GC by time was examined by comparing the OR of PG in patients where the time interval between blood draw and cancer diagnosis was greater than the median follow-up time with those were less. Stability of PG irrespective of time from cancer diagnosis would indicate that PG is more a reliable biomarker for risk of development of GC, but may be less useful as a marker of the presence of GC. Lastly, given that the addition of H. pylori antibody information has been shown to further stratify risk above using PG alone in the literature, we also examined the risk of GC of 3 groups; Group A (HP-/PG-), Group B (HP+/PG-) and Group C (PG+). We pooled Group C (HP+/PG+) and Group D (HP-/PG+) together as Group C (PG+) as there were only 5 participants in group D.
All analyses were conducted using SAS 9.4 (SAS Institute, Inc., Cary, NC, USA).
Results
Participant characteristics at the time of enrollment are provided in Table 1. Median and range of time from the baseline pepsinogen measurement to incident GC diagnosis was 6.7 years and 0.5 to 12.8 years, respectively. For controls, the median and range of duration of follow-up was 13.1 years and 2.3 to 16.1 years, respectively, up to follow up date of September 26, 2008. GC participants were more likely to be PG+ (31.4% vs 5.5%, p < 0.001) and current smokers (20.0% vs 7.7%, p = 0.009) at baseline than controls. No difference was observed for H. pylori IgG positivity between GC cases and controls (70% vs 67%, p=0.59).
Table 1.
Participant characteristics
| Variable | Categories | Cases N=105 n (%) | Controls N=209 n (%) | p-value |
|---|---|---|---|---|
| Age | 55–59 | 16 (15) | 31 (15) | 0.99 |
| 60–64 | 34 (32) | 69 (33) | ||
| 65–69 | 30 (29) | 62 (30) | ||
| 70–74 | 25 (24) | 47 (22) | ||
| Sex | Male | 85 (81) | 169 (81) | 0.98 |
| Female | 20 (19) | 40 (19) | ||
| Race | White | 84 (80) | 167 (80) | 1.00 |
| Black | 7 (7) | 14 (7) | ||
| Hispanic | 5 (5) | 10 (5) | ||
| Asian | 9 (9) | 18 (9) | ||
| Family History of GC | Yes | 8 (8) | 10 (5) | 0.31 |
| No/Possibly | 96 (93) | 197 (95) | ||
| Smoking | Never | 33 (31) | 81 (39) | 0.009 |
| Former Smoker (>6m) | 51 (49) | 111 (53) | ||
| Current Smoker | 21 (20) | 17 (8) | ||
| Education | Less than HS | 16 (15) | 27 (13) | 0.31 |
| HS | 23 (22) | 46 (22) | ||
| Less than College | 42 (40) | 68 (33) | ||
| College or more | 24 (23) | 68 (33) | ||
| BMI >= 30 | Yes | 25 (24) | 33 (16) | 0.09 |
| (missing =3) | No | 80 (76) | 174 (84) | |
| HP IgG | Yes | 73 (70) | 139 (67) | 0.59 |
| No | 32 (30) | 70 (33) | ||
| Pepsinogen positivity * | PG+ | 33 (31) | 11 (5) | <.0001 |
| PG- | 72 (69) | 198 (95) | ||
| Location, cases only | Cardia | 35 (33) | ||
| Fundus, Body, Antrum | 42 (40) | |||
| Stomach, NOS | 28 (27) | |||
| Histology (ICD-O-3 codes), cases only | Carcinoma NOS (8010) | 5 (5) | ||
| Adenocarcinoma NOS (8140) | 75 (71) | |||
| Adenocarcinoma, Intestinal type (8144) | 4 (4) | |||
| Mucinous Adenocarcinoma (8480) | 3 (3) | |||
| Signet ring cell carcinoma (8490) | 18 (17) |
HS; High School, BMI; body mass index, HP; H. pylori, PGI; pepsinogen-I, PGR; Pepsinogen-I to Pepsinogen–II ratio
Pepsinogen positivity defined as PGI≤ 70μg/L and PGR≤ 3
In a univariable conditional logistic regression model, PG+ was associated with an increased risk for GC compared to PG- (OR=8.5; 95%CI=3.8–19.4). Risk of GC for PG+ remained significant after adjusting for H. pylori, family history of GC, education, smoking, and BMI (aOR=10.6; 95%CI=4.3–26.2). A complete summary of these analyses is presented in Table 2.
Table 2.
Univariable and Multivariable Conditional Logistic Regression Analyses of Gastric Cancer risk
| Univariable model | Multivariable model* | ||
|---|---|---|---|
| Variables | Reference | OR (95% CI) | aOR (95% CI) |
| PG+ | PG- | 8.5 (3.8–19.4) | 10.6 (4.3–26.2) |
| HP+ | HP- | 0.8 (0.4–1.5) | |
| Confirmed Family history of GC | none | 2.3 (0.8–7.0) | |
| Less than HS | HS or more | 1.2 (0.5–3.0) | |
| Former smoker | Never smoker | 1.5 (0.8– 3.0) | |
| Current smoker | Never smoker | 4.1 (1.7–9.9) | |
| BMI>=30 | BMI<30 | 2.0 (0.9–4.5) |
Model includes – HP, Family hx of GC, education, smoking, BMI
Sub-group analyses were performed separately for non-cardia GC (70 cases and 141 matched controls) and cardia GC (35 cases and 72 matched controls). This was done due to known heterogeneity in the etiology of these two GC sub-types. We were unable to perform separate analyses by diffuse- versus intestinal-type adenocarcinoma due to small numbers in these categories (Supplementary Table 1). We observed a substantially larger risk of GC for PG+ individuals for non-cardia GC (OR=11.1; 95%CI=4.3–28.8) than for cardia GC (OR=2.0; 95%CI=0.3–14.2) (Table 3). Risk of non-cardia GC for PG+ remained significant after adjusting for H. pylori, family history of GC, education, smoking, and BMI (aOR=14.3; 95%CI=4.8–42.0). For these analyses, the definition of non-cardia included all tumor locations that were not cardia, including stomach not otherwise specified (NOS). When stomach NOS was removed and magnitude of risk for non-cardia GC was limited to tumors documented as being in fundus, body, antrum or pre-pylorus locations (42 non-cardia GC, and 85 matched controls), associations of PG+ and non-cardia GC were non-significantly greater (OR=12.9; 95%CI=3.8–43.6 and aOR=17.4; 95%CI=3.5–85.9) (Supplementary Table 2).
Table 3.
Univariable and Multivariable Conditional Logistic Regression Analysis of Gastric Cancer risk, by subtype
| Non Cardia Gastric Cancer (70 non-cardia, 141 matched controls) |
Cardia Gastric cancer (35 cardia, 72 matched controls) |
||||
|---|---|---|---|---|---|
| Univariable | Multivariable* | Univariable | Multivariable* | ||
| Variables | Reference | OR (95% CI) | aOR (95% CI) | OR (95% CI) | aOR (95% CI) |
| PG+ | PG- | 11.1 (4.3–28.8) | 14.3 (4.8–42.0) | 2.0 (0.3–14.2) | 5.3 (0.6–48.7) |
| HP+ | HP- | 1.1 (0.4–2.8) | 0.5 (0.1–1.3) | ||
| Family history of GC | none | 3.6 (1.0–12.6) | 0.6 (0.1–7.7) | ||
| Less than HS | HS or more | 1.5 (0.4–5.1) | 0.99 (0.2–4.2) | ||
| Former smoker | Never smoker | 1.9 (0.8–4.9) | 1.2 (0.4–3.3) | ||
| Current smoker | Never smoker | 4.5 (1.4–14.8) | 3.0 (0.7–13.7) | ||
| BMI>=30 | BMI<30 | 1.23 (0.4–3.8) | 3.95 (1.1–14.3) | ||
Model includes – HP, Family hx of GC, education, smoking, BMI
Among the 209 controls enrolled in this nested case-control study, 198 were determined to be PG seronegative, yielding a specificity of 94.7% (95%CI=90.8%−97.3%). Among the 105 cases, 33 were determined to be seropositive yielding an estimated sensitivity of 31.4% (95%CI=22.7%−41.2%). In a subgroup analysis, PG yielded low sensitivity and high specificity for both non-cardia (44.3% [95%CI=32.4% −56.7%] and 93.6% [95%CI=88.2–97.0%], respectively) and cardia GC (5.7% [95%CI=0.7%−19.2%] and 97.2% [95%CI=90.3–99.7%] respectively) (Table 4). Single variable and multivariable models, as well as sensitivity and specificity using different definitions of PG sensitivity were also examined and found to not differ significantly (Supplementary Table 3). Additionally, no clear difference was observed by signet ring cell type as compared to non-signet cell ring type (Supplementary Table 4).
Table 4.
Examination by groups according to ABC method
| Cases n | Controls n | Single variable model HR (95% CI) | Multivariable model* aHR (95% CI) | ||
|---|---|---|---|---|---|
| All GC (105 GC, 209 controls) | Group A (HP-/PG-) | 27 (26%) | 70 (33%) | ref | ref |
| Group B (HP+/PG-) | 45 (43%) | 128 (61%) | 1.04 (0.6–1.9) | 0.9 (0.5–1.6) | |
| Group C (PG+) | 33 (31%) | 11 (5%) | 8.8 (3.5–22.1) | 9.3 (3.4–25.2) | |
| Non-Cardia GC (70 non-cardia, 141 matched controls) | Group A (HP-/PG-) | 8 (11%) | 40 (28%) | ref | ref |
| Group B (HP+/PG-) | 31 (44%) | 92 (65%) | 1.7 (0.7–4.2) | 1.44 (0.5–3.8) | |
| Group C (PG+) | 31 (44%) | 9 (6%) | 17.0 (5.2–55.6) | 18.8 (5.1–68.7) | |
| Cardia GC (35 cardia, 72 matched controls) | Group A (HP-/PG-) | 19 (54%) | 32 (44%) | ref | ref |
| Group B (HP+/PG-) | 14 (40%) | 38 (53%) | 0.7 (0.3–1.5) | 0.5 (0.2–1.3) | |
| Group C (PG+) | 2 (6%) | 2 (3%) | 1.5 (0.2–11.4) | 2.8 (0.3–25.7) |
Model includes – HP, Family hx of GC, education, smoking, BMI
The association of PG with GC was stratified by the median interval of time (6.7 years) between baseline to diagnosis; 53 cases (with the corresponding 106 controls) with the shorter interval and 52 GC (with the corresponding 103 controls) with the longer interval. The association of PG+ with GC at a shorter interval (OR=8.02; 95%CI=2.69–23.95) and longer interval (OR=9.23; 95%CI=2.66–32.03) was similar, suggesting that the association of PG with GC was relatively stable over an extended period of time.
Lastly, risk of GC was examined as Group A (HP-/PG-), Group B (HP+/PG-) and Group C (PG+). We found no difference for Group B compared to Group A (reference group) (OR=0.9; 95%CI=0.5–1.6). Risk of GC in Group C, incorporating PG status, was significantly different compared to Group A both in the univariable model (Group C: OR=8.8; 95%CI=3.5–22.1), as well as the adjusted model (Group C: aOR=9.3; 95%CI=3.4–25.2). Among the non-cardia GC cohort alone, again no difference was found for Group B compared to Group A, however Group C showed a substantial increase in risk (aOR=18.8; 95%CI=5.1–68.7). For cardia GC, no difference in risk was found by Group categorization. (Table 5).
Table 5.
Test Characteristics of PG
| All GC (105 cases, 209 matched controls) | Non-Cardia GC (70 non-cardia, 141 matched controls) | Cardia GC (35 cardia, 72 matched controls) | |
|---|---|---|---|
| Sensitivity (95% CI) | 31.4% (22.7%−41.2%) | 44.3% (32.4%−56.7%) | 5.7% (0.7%−19.2%) |
| Specificity (95% CI) | 94.7% (90.8%−97.3%) | 93.6% (88.2%−97.0%) | 97.2% (90.3%−99.7%) |
Discussion
Our nested case-control study from a large prospective study was the first to examine the utility of PG as a predictor of GC in a broad, generalizable US population. Despite the low incidence of GC in the US, the findings in our study are similar to prior studies conducted in Asia and Europe,(7,10,14–16,20,23–29) and corroborate the predictive role of PG for GC. In our examination of the PLCO cohort, PG+ consistently conferred higher risk for GC. Stratified analyses for cardia and non-cardia GC demonstrated an association of increased risk for PG+ individuals for non-cardia GC but not for cardia GC, similar to what other studies have found.(7,17,30) Additionally, we found high specificity of PG for both non-cardia and cardia GC, suggesting that PG may offer a method to non-invasively identify individuals at high risk for GC.
Extensive research on GC screening has been conducted in East Asia where there is a higher prevalence of GC.(31) Given the importance of the Correa pathway in the development of GC, changes in pepsinogen levels, signaling atrophy of the stomach mucosa, are potentially well suited to be biomarkers for the development GC precursors (e.g., intestinal metaplasia and dysplasia) as well as GC. Numerous studies have used the PG I and PGR combination to determine the utility of pepsinogen as a biomarker for GC. Based on cut-offs used, ranging between 10–70ng/mL for PG I and 2–4.5 for PGR, studies have shown differing sensitivity and specificity.(7,10,14–16,23–28) The most widely accepted cut-off for PG-seropositive, and the one used in the present study – serum PG I levels ≤ 70μg/L and PG I/II ratio ≤ 3.0 – was most predictive in a meta-analysis of PG+ and gastric cancer in population-based studies.(20) Using these cutoffs, a meta-analysis of 27 studies, including 18 cohort studies, reported a pooled sensitivity of 59%, and specificity of 73% for GC,(27) and a meta-analysis of 8 studies, including 1 cohort study, reported pooled sensitivity of 59% and specificity of 89% for the GC precursor chronic atrophic gastritis.(32)
The examination of the utility of PG+ for non-cardia GC compared with cardia GC has produced mixed results. Some studies have found PG levels to be inversely associated with the detection of cardia GC,(30) while others were unable to find any significant association.(7,17) This difference is thought to be attributed to the fact that most non-cardia gastric cancers are related to H. pylori infection, and follow the Correa pathway,(33–35) while cardia cancers have at least two separate etiologies. The first is associated with H. pylori and resembles non-cardia cancers(36) while the second is not associated with H. pylori and resembles esophageal adenocarcinoma.(36) As cardia GC does not always correlate to H. pylori exposure and the Correa pathway, PG+ would not be expected to be associated with these types of GC. This would especially be true for Western populations in which the incidence of cardia GC has been thought to be increasing,(37–39) with GERD and obesity playing an important role.(40–42)
One unexpected finding of this study was the lack of an association of H. pylori antibodies at baseline and GC incidence. The role of H. pylori in increasing the risk for gastric cancer has been well established in the literature.(35,43) It was officially declared a human carcinogen in 1994.(44,45) Over 4 billion people, more than half of the world’s population, are affected by H. pylori infections.(46) In the US, H. pylori infection rates vary greatly by race; for example, in a national dataset, while the prevalence was found to be 26.2% in non-Hispanic Whites, it was 61.6% for non-Hispanic Blacks and 61.6% for Mexican Americans.(47) Recent literature suggests that 90% of GC is associated with H. pylori.(48) Using a combination of H. pylori and pepsinogen to determine risk for GC began in 1993,(49) as the “ABC” model, where GC risk was stratified into 3 levels from lowest to highest risk (Group A: HP-/PG-, Group B: HP+/PG-, Group C: PG+). Later, the “ABCD” model was created whereby Group C was further divided by H. pylori status into Group C (HP+/PG+) and Group D (HP-/PG+). HP- in the setting of PG+ reflects highest risk, in which the extreme loss of gastric mucosa no longer makes it a viable environment for H. pylori to survive. Since serum PG levels and H. pylori antibody titers are relatively stable, the study investigators suggested this to be an effective means of stratifying persons into risk categories.(21) Multiple studies have evaluated this method with various degrees of success in risk stratification based on a person’s biomarker results.(22,50–53)
It is unclear why H. pylori was not predictive in our study. One possibility may be the virulence factors expressed by the bacterial stains represented in this cohort of patients.(54) Cytotoxin-associated gene pathogenicity island-encoded protein (CagA) and vacuolating cytotoxin (VacA) contribute to differences in disease severity and have been used as individual markers for determining the risk of H. pylori infection progressing to cancer.(55) Distinct variation of H. pylori CagA and VacA subtypes are found globally.(56) For example, over 90–95% of H. pylori-positive individuals in East Asian countries such as Japan, Korea, and China are CagA-positive, while approximately 40–60% of strains in Western nations are CagA-positive.(57,58) Within the US, CagA-positive strains have been found more commonly among people of non-white race,(59) and the PLCO population is predominantly white. These differences along with the dramatically decreased prevalence of H. pylori infections over time are possible reasons GC rates are so much lower in Western nations.(57,58) Thus, the lower rates of virulent strains found in the West may be part of the explanation for the lack of association between H. pylori and GC in this US cohort of patients.
High-burden countries like Korea and Japan have experienced a 30–60% decrease in mortality rates following the implementation of population-based endoscopic screening and treatment,(60–67) with a concomitant stage shift from late- to early-stage GC and better overall 5-year survival. However, GC is not as common in the US, making the use of endoscopy as a population-wide screening tool unacceptable in terms of population benefits and harms. When screening for less common cancers, the screening test to identify high-risk individuals must be non-invasive and inexpensive for widespread applicability, and have high specificity (hence low false-positive rates) to reduce the number of people subjected unnecessarily to costly diagnostic procedures and psychologic stress.(68) Pepsinogen is a well-developed ELISA-based blood test that is simple, cheap, and non-invasive and thus, we propose, has great potential to be a cost-effective method to identify individuals at increased risk for non-cardia GC who should undergo further testing. Despite the modest sensitivity that was found in this sample of US patients, we believe PG deserves to continue to be explored in prospective US studies,, in combination with other biomarkers, as a potential screening tool for gastric cancer in the US.
Strengths of this study include the use of pre-diagnostic samples from a large prospective cohort study representative of the US population. It is particularly encouraging that the use of PG+ was found to be statistically significant for the detection of non-cardia GC, which is in line with more extensive studies of higher incidence populations.
A limitation of using serum PG is that it does not detect the existence of cancer per se, but is a measure of the mucosal changes that lead to GC. However, as it is believed that most non-cardia GC is associated with H. pylori and follows the Correa pathway, PG offers both a chance to detect GC as well as an opportunity to find precursors of GC whereby local treatment (such as endoscopic excision of dysplasia) can prevent the development of GC. Another limitation of the present study is that the analyses were unable to examine risk associated with age, sex and race, since the controls were matched on these factors to the cases. No data regarding patient history of H. pylori was recorded, and we were unable to include information about previous or current infections or treatment for H. pylori. Lastly, the PLCO was a randomized control trial, despite it being a relatively large study, it reflects only people in the study, not the entirety of the US population, including less representation of minorities with only about 14% of the sample being minorities (6% non-Hispanic blacks; 2% Hispanics; and 6% Asian or Pacific Islanders).(69)
Conclusions
Pre-diagnostic serum pepsinogen levels from a large prospective cohort study were strongly associated with development of non-cardia GC but not cardia GC in a low-risk US population. PG shows promise as a potential risk biomarker to identify individuals at higher risk of non-cardia GC for targeted screening or interventions in the U.S.
Supplementary Material
Acknowledgements:
The authors thank the National Cancer Institute for access to NCI’s data collected by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
Funding:
Effort by HI was supported by The Society for Surgery of the Alimentary Tract (SSAT) Health Care Disparities Research Award and NIH/NCATS grant 5UL1TR002556-05 (Clinical and Translational Science Award).
Footnotes
Disclaimer: The opinions expressed by the authors are their own and this material should not be interpreted as representing the official viewpoint of the U.S. Department of Health and Human Services, the National Institutes of Health, or the National Cancer Institute.
Disclosure: The authors have no relevant disclosures to report. The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.
References
- 1.Pasechnikov VD, Chukov SZ, Kotelevets SM, Mostovov AN, Mernova VP, Polyakova MB. Possibility of non-invasive diagnosis of gastric mucosal precancerous changes. World journal of gastroenterology 2004;10(21):3146–50 doi 10.3748/wjg.v10.i21.3146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kikuchi S, Katada N, Sakuramoto S, Kobayashi N, Shimao H, Watanabe M, et al. Survival after surgical treatment of early gastric cancer: surgical techniques and long-term survival. Langenbecks Arch Surg 2004;389(2):69–74 doi 10.1007/s00423-004-0462-2. [DOI] [PubMed] [Google Scholar]
- 3.Onodera H, Tokunaga A, Yoshiyuki T, Kiyama T, Kato S, Matsukura N, et al. Surgical outcome of 483 patients with early gastric cancer: prognosis, postoperative morbidity and mortality, and gastric remnant cancer. Hepato-gastroenterology 2004;51(55):82–5. [PubMed] [Google Scholar]
- 4.Correa P. Human gastric carcinogenesis: a multistep and multifactorial process--First American Cancer Society Award Lecture on Cancer Epidemiology and Prevention. Cancer Res 1992;52(24):6735–40. [PubMed] [Google Scholar]
- 5.Correa P, Shiao YH. Phenotypic and genotypic events in gastric carcinogenesis. Cancer Res 1994;54(7 Suppl):1941s-3s. [PubMed] [Google Scholar]
- 6.Stemmermann GN, Samloff IM, Hayashi T. Pepsinogens I and II in carcinoma of the stomach: an immunohistochemical study. Appl Pathol 1985;3(3):159–63. [PubMed] [Google Scholar]
- 7.Agkoc M, Dursun H, Albayrak F, Yilmaz O, Kiziltunc A, Yilmaz A, et al. Usefulness of serum pepsinogen levels as a screening test for atrophic gastritis and gastric cancer. The Eurasian journal of medicine 2010;42(1):15–8 doi 10.5152/eajm.2010.05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Samloff IM, Varis K, Ihamaki T, Siurala M, Rotter JI. Relationships Among Serum Pepsinogen I, Serum Pepsinogen II, and Gastric Mucosal Histology: A Study in Relatives of Patients with Pernicious Anemia. Gastroenterology 1982;83(1):204–9 doi 10.1016/0016-5085(82)90176-7. [DOI] [PubMed] [Google Scholar]
- 9.Nasrollahzadeh D, Aghcheli K, Sotoudeh M, Shakeri R, Persson EC, Islami F, et al. Accuracy and cut-off values of pepsinogens I, II and gastrin 17 for diagnosis of gastric fundic atrophy: influence of gastritis. PLoS One 2011;6(10):e26957 doi 10.1371/journal.pone.0026957. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Chiang TH, Chiu SY, Chen SL, Yen AM, Fann JC, Liu CY, et al. Serum Pepsinogen as a Predictor for Gastric Cancer Death: A 16-Year Community-based Cohort Study. Journal of clinical gastroenterology 2019;53(5):e186–e93 doi 10.1097/mcg.0000000000000992. [DOI] [PubMed] [Google Scholar]
- 11.Abnet CC, Zheng W, Ye W, Kamangar F, Ji BT, Persson C, et al. Plasma pepsinogens, antibodies against Helicobacter pylori, and risk of gastric cancer in the Shanghai Women’s Health Study Cohort. Br J Cancer 2011;104(9):1511–6 doi 10.1038/bjc.2011.77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Yoshihara M, Sumii K, Haruma K, Kiyohira K, Hattori N, Kitadai Y, et al. Correlation of ratio of serum pepsinogen I and II with prevalence of gastric cancer and adenoma in Japanese subjects. The American journal of gastroenterology 1998;93(7):1090–6 doi 10.1111/j.1572-0241.1998.00335.x. [DOI] [PubMed] [Google Scholar]
- 13.Kang JM, Kim N, Yoo JY, Park YS, Lee DH, Kim HY, et al. The role of serum pepsinogen and gastrin test for the detection of gastric cancer in Korea. Helicobacter 2008;13(2):146–56 doi 10.1111/j.1523-5378.2008.00592.x. [DOI] [PubMed] [Google Scholar]
- 14.Lomba-Viana R, Dinis-Ribeiro M, Fonseca F, Vieira AS, Bento MJ, Lomba-Viana H. Serum pepsinogen test for early detection of gastric cancer in a European country. European journal of gastroenterology & hepatology 2012;24(1):37–41 doi 10.1097/MEG.0b013e32834d0a0a. [DOI] [PubMed] [Google Scholar]
- 15.Castro C, Dinis-Ribeiro M, Rodrigues ANG, Calheiros T, Santos J, Pereira P, et al. Western long-term accuracy of serum pepsinogen-based gastric cancer screening. European journal of gastroenterology & hepatology 2018;30(3):274–7 doi 10.1097/meg.0000000000001040. [DOI] [PubMed] [Google Scholar]
- 16.Shikata K, Ninomiya T, Yonemoto K, Ikeda F, Hata J, Doi Y, et al. Optimal cutoff value of the serum pepsinogen level for prediction of gastric cancer incidence: the Hisayama Study. Scandinavian journal of gastroenterology 2012;47(6):669–75 doi 10.3109/00365521.2012.658855. [DOI] [PubMed] [Google Scholar]
- 17.Keck JW, Miernyk KM, Bulkow LR, Kelly JJ, McMahon BJ, Sacco F, et al. Helicobacter pylori infection and markers of gastric cancer risk in Alaska Native persons: a retrospective case-control study. Can J Gastroenterol Hepatol 2014;28(6):305–10 doi 10.1155/2014/892084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Nomura A, Stemmermann GN, Chyou PH, Kato I, Perez-Perez GI, Blaser MJ. Helicobacter pylori infection and gastric carcinoma among Japanese Americans in Hawaii. N Engl J Med 1991;325(16):1132–6 doi 10.1056/nejm199110173251604. [DOI] [PubMed] [Google Scholar]
- 19.Gohagan JK, Prorok PC, Hayes RB, Kramer BS. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial of the National Cancer Institute: history, organization, and status. Control Clin Trials 2000;21(6 Suppl):251s–72s doi 10.1016/s0197-2456(00)00097-0. [DOI] [PubMed] [Google Scholar]
- 20.Dinis-Ribeiro M, Yamaki G, Miki K, Costa-Pereira A, Matsukawa M, Kurihara M. Meta-analysis on the validity of pepsinogen test for gastric carcinoma, dysplasia or chronic atrophic gastritis screening. J Med Screen 2004;11(3):141–7 doi 10.1258/0969141041732184. [DOI] [PubMed] [Google Scholar]
- 21.Yamaguchi Y, Nagata Y, Hiratsuka R, Kawase Y, Tominaga T, Takeuchi S, et al. Gastric Cancer Screening by Combined Assay for Serum Anti-Helicobacter pylori IgG Antibody and Serum Pepsinogen Levels--The ABC Method. Digestion 2016;93(1):13–8 doi 10.1159/000441742. [DOI] [PubMed] [Google Scholar]
- 22.Miki K. Gastric cancer screening by combined assay for serum anti-Helicobacter pylori IgG antibody and serum pepsinogen levels - “ABC method”. Proc Jpn Acad Ser B Phys Biol Sci 2011;87(7):405–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kikuchi R, Abe Y, Iijima K, Koike T, Ara N, Uno K, et al. Low serum levels of pepsinogen and gastrin 17 are predictive of extensive gastric atrophy with high-risk of early gastric cancer. Tohoku J Exp Med 2011;223(1):35–44. [DOI] [PubMed] [Google Scholar]
- 24.Juan Cai W, Yin L, Kang Q, Chen Zeng Z, Liang Wang S, Cheng J. The Serum Pepsinogen Test as a Predictor of Kazakh Gastric Cancer. Scientific Reports 2017;7:43536 doi 10.1038/srep43536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hattori Y, Tashiro H, Kawamoto T, Kodama Y. Sensitivity and specificity of mass screening for gastric cancer using the measurment of serum pepsinogens. Jpn J Cancer Res 1995;86(12):1210–5 doi 10.1111/j.1349-7006.1995.tb03317.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Oishi Y, Kiyohara Y, Kubo M, Tanaka K, Tanizaki Y, Ninomiya T, et al. The serum pepsinogen test as a predictor of gastric cancer: the Hisayama study. American journal of epidemiology 2006;163(7):629–37 doi 10.1093/aje/kwj088. [DOI] [PubMed] [Google Scholar]
- 27.Kitahara F, Kobayashi K, Sato T, Kojima Y, Araki T, Fujino MA. Accuracy of screening for gastric cancer using serum pepsinogen concentrations. Gut 1999;44(5):693–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kodoi A, Yoshihara M, Sumii K, Haruma K, Kajiyama G. Serum pepsinogen in screening for gastric cancer. Journal of gastroenterology 1995;30(4):452–60 doi 10.1007/BF02347560. [DOI] [PubMed] [Google Scholar]
- 29.Liu L, Lang J, Jin Y, Chen Y, Chang W, Yao Y, et al. The Value of Pepsinogen in GC Screening: A Systematic Review and Meta-Analysis. Gastroenterology research and practice 2019;2019:7087232 doi 10.1155/2019/7087232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Ren JS, Kamangar F, Qiao YL, Taylor PR, Liang H, Dawsey SM, et al. Serum pepsinogens and risk of gastric and oesophageal cancers in the General Population Nutrition Intervention Trial cohort. Gut 2009;58(5):636–42 doi 10.1136/gut.2008.168641. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA: a cancer journal for clinicians 2015;65(2):87–108 doi 10.3322/caac.21262. [DOI] [PubMed] [Google Scholar]
- 32.Huang Y-k, Yu J-c, Kang W-m, Ma Z-q, Ye X, Tian S-b, et al. Significance of Serum Pepsinogens as a Biomarker for Gastric Cancer and Atrophic Gastritis Screening: A Systematic Review and Meta-Analysis. PloS one 2015;10(11):e0142080 doi 10.1371/journal.pone.0142080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kinoshita H, Hayakawa Y, Koike K. Metaplasia in the Stomach-Precursor of Gastric Cancer? International journal of molecular sciences 2017;18(10):2063 doi 10.3390/ijms18102063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Uemura N, Okamoto S, Yamamoto S, Matsumura N, Yamaguchi S, Yamakido M, et al. Helicobacter pylori infection and the development of gastric cancer. The New England journal of medicine 2001;345(11):784–9 doi 10.1056/NEJMoa001999. [DOI] [PubMed] [Google Scholar]
- 35.Plummer M, Franceschi S, Vignat J, Forman D, de Martel C. Global burden of gastric cancer attributable to Helicobacter pylori. International journal of cancer Journal international du cancer 2015;136(2):487–90 doi 10.1002/ijc.28999. [DOI] [PubMed] [Google Scholar]
- 36.Mukaisho K-I, Nakayama T, Hagiwara T, Hattori T, Sugihara H. Two distinct etiologies of gastric cardia adenocarcinoma: interactions among pH, Helicobacter pylori, and bile acids. Frontiers in microbiology 2015;6:412- doi 10.3389/fmicb.2015.00412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Steevens J, Botterweck AA, Dirx MJ, van den Brandt PA, Schouten LJ. Trends in incidence of oesophageal and stomach cancer subtypes in Europe. European journal of gastroenterology & hepatology 2010;22(6):669–78 doi 10.1097/MEG.0b013e32832ca091. [DOI] [PubMed] [Google Scholar]
- 38.Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality Rates and Trends--An Update. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2016;25(1):16–27 doi 10.1158/1055-9965.Epi-15-0578. [DOI] [PubMed] [Google Scholar]
- 39.Camargo MC, Anderson WF, King JB, Correa P, Thomas CC, Rosenberg PS, et al. Divergent trends for gastric cancer incidence by anatomical subsite in US adults. Gut 2011;60(12):1644 doi 10.1136/gut.2010.236737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism 2019;92:121–35 doi 10.1016/j.metabol.2018.11.001. [DOI] [PubMed] [Google Scholar]
- 41.Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body Fatness and Cancer--Viewpoint of the IARC Working Group. The New England journal of medicine 2016;375(8):794–8 doi 10.1056/NEJMsr1606602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chen Y, Liu L, Wang X, Wang J, Yan Z, Cheng J, et al. Body mass index and risk of gastric cancer: a meta-analysis of a population with more than ten million from 24 prospective studies. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2013;22(8):1395–408 doi 10.1158/1055-9965.Epi-13-0042. [DOI] [PubMed] [Google Scholar]
- 43.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 2021;71(3):209–49 doi 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
- 44.Shiotani A, Cen P, Graham DY. Eradication of gastric cancer is now both possible and practical. Seminars in cancer biology 2013;23(6 Pt B):492–501 doi 10.1016/j.semcancer.2013.07.004. [DOI] [PubMed] [Google Scholar]
- 45.Schistosomes, liver flukes and Helicobacter pylori. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Lyon, 7–14 June 1994. IARC monographs on the evaluation of carcinogenic risks to humans 1994;61:1–241. [PMC free article] [PubMed] [Google Scholar]
- 46.Hooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology 2017;153(2):420–9 doi 10.1053/j.gastro.2017.04.022. [DOI] [PubMed] [Google Scholar]
- 47.Everhart JE, Kruszon-Moran D, Perez-Perez GI, Tralka TS, McQuillan G. Seroprevalence and Ethnic Differences in Helicobacter pylori Infection among Adults in the United States. The Journal of Infectious Diseases 2000;181(4):1359–63 doi 10.1086/315384. [DOI] [PubMed] [Google Scholar]
- 48.Poorolajal J, Moradi L, Mohammadi Y, Cheraghi Z, Gohari-Ensaf F. Risk factors for stomach cancer: a systematic review and meta-analysis. Epidemiol Health 2020:e2020004 doi 10.4178/epih.e2020004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Parsonnet J, Samloff IM, Nelson LM, Orentreich N, Vogelman JH, Friedman GD. Helicobacter pylori, pepsinogen, and risk for gastric adenocarcinoma. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 1993;2(5):461–6. [PubMed] [Google Scholar]
- 50.Kwak MS, Chung GE, Chung SJ, Kang SJ, Yang JI, Kim JS. Predicting the Development of Gastric Neoplasms in a Healthcare Cohort by Combining Helicobacter pylori Antibodies and Serum Pepsinogen: A 5-Year Longitudinal Study. Gastroenterology research and practice 2018;2018:8796165 doi 10.1155/2018/8796165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Ohata H, Kitauchi S, Yoshimura N, Mugitani K, Iwane M, Nakamura H, et al. Progression of chronic atrophic gastritis associated with Helicobacter pylori infection increases risk of gastric cancer. Int J Cancer 2004;109(1):138–43 doi 10.1002/ijc.11680. [DOI] [PubMed] [Google Scholar]
- 52.Mizuno S, Miki I, Ishida T, Yoshida M, Onoyama M, Azuma T, et al. Prescreening of a high-risk group for gastric cancer by serologically determined Helicobacter pylori infection and atrophic gastritis. Digestive diseases and sciences 2010;55(11):3132–7 doi 10.1007/s10620-010-1154-0. [DOI] [PubMed] [Google Scholar]
- 53.M Y Nomura A, N Kolonel L, Miki K, N Stemmermann G, R Wilkens L, T Goodman M, et al. Helicobacter pylori, Pepsinogen, and Gastric Adenocarcinoma in Hawaii. 2005. 2075–81 p. [DOI] [PubMed] [Google Scholar]
- 54.Kusters JG, van Vliet AHM, Kuipers EJ. Pathogenesis of Helicobacter pylori infection. Clin Microbiol Rev 2006;19(3):449–90 doi 10.1128/CMR.00054-05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Jones KR, Whitmire JM, Merrell DS. A Tale of Two Toxins: Helicobacter Pylori CagA and VacA Modulate Host Pathways that Impact Disease. Frontiers in microbiology 2010;1:115- doi 10.3389/fmicb.2010.00115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.van Doorn LJ, Figueiredo C, Sanna R, Blaser MJ, Quint WG. Distinct variants of Helicobacter pylori cagA are associated with vacA subtypes. J Clin Microbiol 1999;37(7):2306–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Hatakeyama M. Helicobacter pylori CagA -- a bacterial intruder conspiring gastric carcinogenesis. Int J Cancer 2006;119(6):1217–23 doi 10.1002/ijc.21831. [DOI] [PubMed] [Google Scholar]
- 58.Hatakeyama M, Higashi H. Helicobacter pylori CagA: a new paradigm for bacterial carcinogenesis. Cancer science 2005;96(12):835–43 doi 10.1111/j.1349-7006.2005.00130.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Varga MG, Butt J, Blot WJ, Le Marchand L, Haiman CA, Chen Y, et al. Racial Differences in Helicobacter pylori CagA Sero-prevalence in a Consortium of Adult Cohorts in the United States. Cancer Epidemiology Biomarkers & Prevention 2020;29(10):2084–92 doi 10.1158/1055-9965.Epi-20-0525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Hamashima C, Ogoshi K, Okamoto M, Shabana M, Kishimoto T, Fukao A. A community-based, case-control study evaluating mortality reduction from gastric cancer by endoscopic screening in Japan. PLoS One 2013;8(11):e79088 doi 10.1371/journal.pone.0079088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Mizoue T, Yoshimura T, Tokui N, Hoshiyama Y, Yatsuya H, Sakata K, et al. Prospective study of screening for stomach cancer in Japan. International journal of cancer Journal international du cancer 2003;106(1):103–7 doi 10.1002/ijc.11183. [DOI] [PubMed] [Google Scholar]
- 62.Miyamoto A, Kuriyama S, Nishino Y, Tsubono Y, Nakaya N, Ohmori K, et al. Lower risk of death from gastric cancer among participants of gastric cancer screening in Japan: a population-based cohort study. Preventive medicine 2007;44(1):12–9 doi 10.1016/j.ypmed.2006.07.016. [DOI] [PubMed] [Google Scholar]
- 63.Inaba S, Hirayama H, Nagata C, Kurisu Y, Takatsuka N, Kawakami N, et al. Evaluation of a screening program on reduction of gastric cancer mortality in Japan: preliminary results from a cohort study. Preventive medicine 1999;29(2):102–6 doi 10.1006/pmed.1999.0507. [DOI] [PubMed] [Google Scholar]
- 64.Hamashima C, Shabana M, Okada K, Okamoto M, Osaki Y. Mortality reduction from gastric cancer by endoscopic and radiographic screening. Cancer science 2015;106(12):1744–9 doi 10.1111/cas.12829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Jun JK, Choi KS, Lee HY, Suh M, Park B, Song SH, et al. Effectiveness of the Korean National Cancer Screening Program in Reducing Gastric Cancer Mortality. Gastroenterology 2017;152(6):1319–28.e7 doi 10.1053/j.gastro.2017.01.029. [DOI] [PubMed] [Google Scholar]
- 66.Kim GH, Liang PS, Bang SJ, Hwang JH. Screening and surveillance for gastric cancer in the United States: Is it needed? Gastrointestinal endoscopy 2016;84(1):18–28 doi 10.1016/j.gie.2016.02.028. [DOI] [PubMed] [Google Scholar]
- 67.Jaffee EM, Dang CV, Agus DB, Alexander BM, Anderson KC, Ashworth A, et al. Future cancer research priorities in the USA: a Lancet Oncology Commission. Lancet Oncology;18(11):e653–e706 doi 10.1016/S1470-2045(17)30698-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001;93(14):1054–61 doi 10.1093/jnci/93.14.1054. [DOI] [PubMed] [Google Scholar]
- 69.Stallings FL, Ford ME, Simpson NK, Fouad M, Jernigan JC, Trauth JM, et al. Black participation in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials 2000;21(6 Suppl):379s–89s doi 10.1016/s0197-2456(00)00093-3. [DOI] [PubMed] [Google Scholar]
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