Association of Pro-inflammatory Cytokines with Gastric Cancer Risk: A Case-Cohort Study

Seungju Baek; Eunjung Park; Eun Young Park

doi:10.4143/crt.2024.718

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Original Article Gastrointestinal cancer Association of Pro-inflammatory Cytokines with Gastric Cancer Risk: A Case-Cohort Study: Seungju Baek¹, Eunjung Park², Eun Young Park^1,3,; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2025;57(3):760-769.
DOI: https://doi.org/10.4143/crt.2024.718
Published online: November 19, 2024

¹Graduate School of Public Health, Korea University, Seoul, Korea

²Department of Cancer Control and Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Korea

³Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea

Correspondence: Eun Young Park, Department of Preventive Medicine, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea
Tel: 82-2-2286-1411 E-mail: eunyoungpark2022@gmail.com

*Seungju Baek and Eunjung Park contributed equally to this work.

• Received: July 29, 2024 • Accepted: November 18, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Purpose
This study aimed to assess the association between inflammatory cytokines and the risk of gastric cancer (GC).
Materials and Methods
We conducted a case-cohort study using Korean National Cancer Center Community (KNCCC) cohort data to investigate the associations between pro-inflammatory, anti-inflammatory cytokines, inflammatory mediators, and GC risk in the Korean general population (GC cases: n=159, subcohort: n=822). Serum levels of inflammatory cytokines were measured using Quantikine enzyme-linked immunosorbent assay and analyzed using a Cox proportional hazards regression model.
Results
Compared to those with the lowest serum interleukin 6 (IL-6) levels, the risk of GC significantly increased in the second (hazard ratio [HR], 3.48; 95% confidence interval [CI], 1.73 to 6.99), third (HR, 3.74; 95% CI, 1.91 to 7.29), and fourth quartiles (HR, 3.79; 95% CI, 1.93 to 7.48). Elevated levels of interleukin 1β (IL-1β) (HR, 1.57; 95% CI, 1.12 to 2.21) and interferon-γ (IFN-γ) (HR, 2.49; 95% CI, 1.73 to 3.58) were also associated with an increased risk of GC.
Conclusion
The findings of this study indicate associations between pro-inflammatory cytokines (IL-6, IL-1β, and IFN-γ) and the risk of GC, suggesting that regulating these cytokine levels may aid in GC prevention.
Key words: Stomach neoplasms, Pro-inflammatory cytokines, Case-cohort

Introduction

Gastric cancer (GC) ranks as the fourth most prevalent cause of global cancer mortality, demonstrating an alarming incidence of over one million newly diagnosed cases in the year 2020 (5.6%), accompanied by an estimated 768,793 mortalities (7.7%) [1]. In 2020, there were 247,952 newly diagnosed cases of cancer in South Korea, with GC accounting for 26,662 cases (10.8%) when combined for both sexes, ranking fourth among all cancer types. Furthermore, the mortality associated with GC in the same year amounted to 7,510 deaths, resulting in a mortality rate of 14.6% per 100,000 population [2]. According to the latest data, the total number of cancer cases in 2021 was 277,523, with GC cases accounting for 29,361 [3]. The incidence rate of GC was 10.6%, showing a decreasing trend compared to the previous year. The mortality rate of GC in 2021 demonstrated a parallel decline with the incidence rate, registering at 14.1% [4]. Although there is an overall declining trend in the incidence and mortality rates of GC, it still has relatively higher rates compared to other types of cancer. Eastern Asia has recently reported over 60% of GC cases, which is significantly higher than in other countries, indicating a higher incidence and mortality rate of the disease in Asian countries compared to the rest of the world [5].

The high incidence and mortality rates of GC can be attributed to various factors, including dietary habits, smoking behaviors, alcohol consumption, and other environmental factors. Additionally, genetic factors also play a role in the etiology of GC. Therefore, the development of GC is influenced by a complex interplay of both environmental and genetic factors [6]. Furthermore, Helicobacter pylori infection is a major risk factor for the development of GC, and the International Agency for Research on Cancer (IARC) has classified H. pylori infection as group 1 carcinogen for GC [7]. Globally, there are approximately 2 million cancer cases related to infections each year, with H. pylori infection-associated GC accounting for one-third of these cases [8]. While there is a correlation between H. pylori infection and the incidence of GC, it is important to note that not all infected individuals will develop cancer. Only a subset of infected individuals will develop GC. Chronic inflammation is a significant factor in the development of various diseases, including GC [9]. In the process of inflammation influencing tumor formation, various mediators are involved, including proinflammatory cytokines (interleukin 6 [IL-6], tumor necrosis factor-α [TNF-α], interleukin 1β [IL-1β], interferon-γ [IFN-γ]), anti-inflammatory cytokines (interleukin 10 [IL-10]), inflammatory mediators (reactive oxygen species [ROS], nitric oxide [NO]), and immune cells. These mediators interact to regulate tumor cell proliferation, invasion, angiogenesis, and immune response, ultimately affecting tumor formation and progression [10].

The incidence rate of GC is significantly high among East Asian countries, including Korea [5]. However, there is a lack of research analyzing the association between inflammation and GC. Therefore, the aim of this study is to investigate the association between inflammatory indices—such as IL-6, TNF-α, IL-1β, IFN-γ, IL-10, ROS, and NO—and GC. We conducted a case-cohort study using serum samples collected before cancer diagnosis, based on the Korean National Cancer Center Community (KNCCC) cohort.

Materials and Methods

1. Case-cohort study design and study population

The KNCCC cohort, conducted between 1993 and 2010, was a community-based prospective study aimed at exploring the risk factors for cancer and protective factors associated with its prevention [11]. It involved 16,304 participants aged 30 years and older from Changwon, Chuncheon, Chungju, Sancheong, and Haman. Detailed information on demographics, lifestyle, and environmental factors was obtained through a structured questionnaire. Additionally, physical examinations and laboratory assessments were conducted, and blood and urine samples were collected at cohort enrollment. The collected serum samples were stored at ‒70°C (between 1993 and 2008) and were preserved at ‒140°C (between 2009 and 2010). The obtained data were linked to the cancer registry data acquired from the Korea Central Cancer Registry and death records obtained from Statistics Korea, with follow-up until 31 December 2017.

A case-cohort study was designed to investigate the associations between serum levels of pro-inflammatory cytokines and the risk of GC. Of 16,304 men and women, participants with a follow-up period of less than 1 year for exclusion of undiagnosed cancer (n=446) and those who did not provide a serum sample (n=2,059) and information on other covariates (e.g., education achievement, cigarette smoking status, alcohol consumption status, body mass index [BMI]) (n=4,628) were excluded.

A representative subcohort of 822 participants was randomly selected by survey year, region, age, and sex from the eligible population who participated in the cohort between 2001 and 2010 (n=9,171). As a result, this study included 159 cases (148 cases plus 11 cases in the subcohort) and 822 participants in the subcohort (11 cases in the subcohort) (Fig. 1).

This case-cohort study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

2. Outcome definition

The primary GC was ascertained by the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes.

3. Measurements of serum pro-inflammatory cytokines

Serum levels of pro-inflammatory cytokines were measured using Quantikine enzyme-linked immunosorbent assay. The intra- and inter-assay precision were reported as follows: coefficient of variation (%): IL-6 (1.7-4.4/2.0-3.7), TNF-α (4.2-5.2/6.8-8.7), IL-1β (2.8-8.5/4.1-8.4), IFN-γ (2.6-4.7/2.7-7.8), IL-10 (1.7-5.0/5.6-7.6). The average recoveries were 93% (IL-6), 107% (TNF-α), 95% (IL-1β), 102% (IFN-γ), and 100% (IL-10), respectively. The limits of detection (LODs) were 0.01 pg/mL (IL-6), 0.01 pg/mL (TNF-α), 0.01 pg/mL (IL-1β), 0.01 pg/mL (IFN-γ), and 0.01 pg/mL (IL-10), respectively.

4. Statistical analyses

The follow-up for this study started on January 1, 2001, and conducted until the first cancer event, cancer death, or right-censoring, whichever came first. Right-censoring occurred at the earliest of diagnosis of any cancer (except melanoma), a noncancerous death, or the end of the study period (December 31, 2017).

We compared differences in demographic characteristics between GC cases and subcohort at baseline: age, sex, region, year of enrollment, education achievement (elementary school or less, middle school, high school, or college or higher), cigarette smoking status (never smokers, former smokers, current smokers), alcohol consumption status (non-drinkers, ≤ 24 g per day, > 24 g per day), BMI (continuous variable), physical activity (low, metabolic equivalent of tasks [METs] < 600; moderate, 600 ≤ METs < 3,000; high, METs ≥ 3,000), meat consumption (almost never, < 3 times per week, ≥ 4 times per week), and vegetable/fruit consumption (6 times per week or less, and every day).

The serum concentrations of pro-inflammatory cytokines were natural log-transformed to account for their skewed distribution and then categorized into quartiles, aligning with the distribution of pro-inflammatory cytokines within the subcohort. Alternatively, if values exceeded or were equal to 50% of the LOD, they were split into two groups using the LOD value. This approach helped derive hazard ratios (HRs) that are easier to interpret and allowed for the identification of potential nonlinear associations.

HRs with 95% confidence intervals (CIs) were estimated using a Cox proportional hazards regression model with attained age as the underlying time scale [12]. The weighted Barlow method was employed for our case-cohort design [13]. Crude, age-adjusted, and multivariable-adjusted HRs (aHRs) were estimated. The aHRs were adjusted for sex, year of entry into the cohort, region, education achievement, cigarette smoking status, alcohol consumption status, BMI, physical activity, meat consumption, and vegetable/fruit consumption. We did not detect any evidence indicating a violation of the proportional-hazards assumption for any exposure-outcome associations, confirming its validity through Schoenfeld residuals.

All statistical analyses were performed using the PHREG procedures in SAS ver. 9.4 (SAS Institute Inc.). All statistical tests were 2-sided, with a significance level of p < 0.05.

Results

1. Baseline characteristics of the study participants

A total of 981 participants were included in this case-cohort study, consisting of 159 GC cases (age [mean±standard deviation (SD)], 64.75±7.36 years) and the 822 subcohort cases (age [mean±SD], 60.11±10.80 years). The median follow-up periods were 5.82 years for GC cases (interquartile range [IQR], 3.57 to 8.63) and 11.43 years for the subcohort (IQR, 8.48 to 13.88), and the person-times were 997.33 years for GC cases and 9,004.12 years for the subcohort. GC cases had higher proportions of men, the elderly, low-educated persons, cigarette smokers, and drinkers than the subcohort. However, difference in proportions of obesity, meat consumption, and vegetable/fruit consumption was not observed. The detailed sociodemographic characteristics and lifestyle behaviors of the study participants are presented in Table 1.

At baseline, the serum levels of pro-inflammatory cytokines are presented in Table 2. The median IL-6, TNF-α, IL-1 β, IFNγ, and IL-10 values were higher in GC cases than in the subcohort.

2. Risk of gastric cancer by serum levels of pro-inflammatory cytokines

The associations between serum levels of pro-inflammatory cytokines and the GC risk were shown in Fig. 2. For 2.74-fold increase, the risk of GC increased by 32% in IL-6 and 15% in IFNγ (HR, 1.32; 95% CI, 1.16 to 1.50), and IFNγ (HR, 1.15; 95% CI, 1.08 to 1.22).

In the Cox hazards models with categorized variables, the GC risk increased in the second, third, and fourth quartiles of serum IL-6 (HR [95% CI], 3.48 [1.73 to 6.99], 3.74 [1.91 to 7.29], 3.79 [1.93 to 7.48], respectively) compared with the lowest quartile. In addition, the GC risk was associated with higher levels of IL-1β and IFN-γ than the LOD value (HR [95% CI], 1.57 [1.12 to 2.21], 2.49 [1.73 to 3.58], respectively) (Table 3).

Discussion

We identified potential biomarkers for identifying GC patients by analyzing five cytokines (IL-6, TNF-α, IL-1β, IFN-γ, and IL-10) and two inflammatory mediators (ROS, NO) associated with gastric inflammation. Our research elucidates a correlation between the serum concentrations of IL-6, IL-1β, and IFN-γ and the susceptibility to GC.

Previous studies have investigated various factors that influence the development and progression of GC and reported that increased levels of certain cytokines may play a role in the diagnosis of GC, which is partially consistent with our findings [14,15]. In a controlled study, it has been reported that the increase in levels of IL-6, IFN-γ, and IL-10 can be useful as diagnostic biomarkers for GC, particularly noting that the concentrations of IFN-γ and IL-10 vary according to the tumor-node-metastasis (TNM) staging of GC [16]. Several studies have reported differences in the genetic background between Asian and Caucasian populations concerning the epidemiology of different GC subtypes. Specifically, studies have indicated that variances in IL1B and IL10 gene polymorphisms could have varying impacts on the development of GC across different populations [17,18]. Although various studies have investigated the association between inflammatory cytokines and GC risk, our study was initiated due to the scarcity of research among Koreans and the inconsistent results stemming from population variances in gene polymorphisms.

GC, like other cancers, results from the complex interaction of various risk and protective factors. In its pathogenesis, genetic/epigenetic alterations and environmental factors, particularly H. pylori infection and diet, are recognized as significant contributors [19]. Immune cytokines produced during chronic inflammation have been shown to influence GC progression, though the exact roles of many cytokines remain unclear. Understanding the factors that affect cytokine levels is critical to elucidating their association with inflammation and GC. Previous studies have identified key factors that influence pro-inflammatory cytokine levels, such as age, BMI, smoking, alcohol consumption, physical activity, and dietary habits like red meat consumption [20- 22]. In this study, these factors were statistically adjusted to minimize their potential confounding effects, ensuring the association between pro-inflammatory cytokines and GC was not distorted, thereby increasing the validity and significance of the findings. In particular, smoking and alcohol consumption are well-known contributors to chronic inflammation, significantly raising pro-inflammatory cytokine levels. However, beyond smoking and alcohol, physiological and environmental factors like age, obesity, physical activity, and diet also play critical roles in regulating cytokine levels. Additionally, the mechanisms of inflammation can directly affect cytokine levels. Thus, while adjusting for these variables could lead to over-adjustment, potentially underestimating the association between cytokines and GC, the significant risks identified in this study highlight the importance of inflammation regulation in cancer management. These adjustments contribute to a more accurate reflection of the relationship between cytokines and GC, emphasizing inflammation control as a key aspect of cancer prevention.

Over the years, extensive research has been conducted on the correlation between inflammation and cancer, revealing that chronic inflammation and infections account for roughly 25% of all cancer incidences [23]. Specifically in GC, it has been demonstrated that chronic inflammation resulting from Helicobacter pylori infection and autoimmune gastritis escalates the susceptibility of an individual to gastric carcinogenesis [24]. GC mainly consists of adenocarcinomas (about 90%-95%) [25]. The two predominant subtypes of gastric adenocarcinomas are the intestinal type, distinguished by a more favorable prognosis attributed to distinct genetic mutations that may facilitate targeted pharmacotherapy, and the diffuse type, recognized for its aggressive growth pattern and advanced metastasis, posing greater therapeutic challenges compared to the intestinal counterpart [26]. Over 90% of adenocarcinomas are regarded as the culmination of prolonged mucosal inflammation. Intestinal type gastric adenocarcinomas are characterized by a series of events known as the “Correa pathway,” which initiates with chronic gastritis and advances through stages of gastric atrophy, intestinal metaplasia (IM), dysplasia, and cancer [27]. Preneoplastic lesions for GC, such as atrophic gastritis, IM, and dysplasia, occur in an inflamed environment influenced by a complex network of cytokines [28].

Inflammation, a crucial feature of both the innate and adaptive immune systems, is regulated by a variety of alternative mechanisms that aid in tissue restructuring and maintaining tissue balance, initiated by internal or external infections or injuries. Cells of the innate immune system, including macrophages, mast cells, dendritic cells, and natural killer cells, modulate the inflammatory response through the secretion of an array of molecules such as growth factors, cytokines, chemokines, proteases involved in matrix remodeling, as well as reactive oxygen and nitrogen species, thereby exerting control over the inflammatory cascade [10]. In a previous study, a comprehensive analysis was conducted on over 300 primary gastric carcinomas from distinct patient cohorts. The results revealed that most gastric malignancies exhibit dysregulation of key oncogenic pathways, including nuclear factor-κB (NF-κB) (39% of GCs), Wnt/beta-catenin (46% of GCs), and proliferation/stem cell (40% of GCs) cascades [29]. NF-κB pathway is well known for regulating pro-inflammatory cytokines such as TNF, IL-1, and IL-6, and previous study has demonstrated a significant correlation between NF-κB activation and IL-6 expression in human GC tissues [30]. Furthermore, previous research findings provide evidence indicating that the upregulation of integrin β3–mediated NF-κB signaling is partially responsible for the induction of GC cell proliferation and metastasis by IFN-γ [31].

Although our research revealed the association between serum levels of IL-6, IL-1β, and IFN-γ, and susceptibility to GC, there are several limitations. First, in this study, adjustment for H. pylori infection was not made due to a lack of data. Epidemiological studies conducted in Korea in 1998, 2005, and 2011 reported a prevalence of approximately 60%-70% among individuals aged 60 and above [32]. Given that the participants in this study have an average age in their 60s, where there is a high prevalence of H. pylori, the lack of adjustment for H. pylori is unlikely to distort the research findings. However, the lack of adjustment for H. pylori infection remains a limitation. Second, due to serum concentrations being measured only at the time of cohort enrollment, it is challenging to assess changes in serum concentrations after the time of measurement. Additionally, measurements taken at specific time points may not accurately reflect an individual’s average value. Single-point measurements may obscure the dynamic nature of cytokine levels and their relationships with disease processes, limiting the assessment of long-term exposure effects on disease onset. Cytokine levels fluctuate in various conditions, and not capturing their average may lead to misinterpretations regarding cancer progression and treatment response. Third, unlike general cohort studies, case-cohort studies focus on comparisons between cases and non-case groups, which can lead to selection bias. Additionally, the potential for bias inherent in the case-cohort design cannot be overlooked. Since this study was conducted on a specific subcohort, there is a risk of introducing biases that may affect the results. Therefore, the findings of this study have limitations in terms of generalizability and need to be replicated in a larger population or cohort. Fourth, like many observational studies, our research includes several potential confounding factors. To mitigate this limitation, we accounted for variables such as smoking, alcohol use, BMI, and dietary habits, all of which are strongly associated with the development of GC [6]. Nevertheless, our findings may still be subject to bias from other confounders (e.g., job story, previous medical conditions), due to self-reported data and the presence of unmeasured or omitted confounders (e.g., family history of cancer, exposure to stressful events).

Despite these limitations, our findings indicate that proinflammatory cytokines (IL-6, IL-1β, and IFN-γ) are associated with the risk of GC and suggest that their regulation could offer potential for GC prevention and treatment. The abnormal activation of pro-inflammatory cytokines plays a critical role in tumor growth and progression by influencing cancer development through modulation of immune responses and inflammation. Specifically, IL-6 is recognized for its role in promoting tumor growth, cell survival, proliferation, and angiogenesis, making elevated IL-6 levels a potential indicator of cancer development. This is supported by evidence demonstrating that drugs designed to block IL-6 signaling have shown positive effects in cancer patients [33]. Conversely, IL-1β and IFN-γ also impact the tumor microenvironment, but their effects are complex and vary depending on the cancer type [34,35]. Further research is needed to determine if elevated levels of IL-1β, and IFN-γ reliably indicate cancer development. While previous research suggests that smoking cessation, alcohol withdrawal, dietary changes, and lifestyle modifications such as exercise can reduce inflammatory cytokine levels, more investigation is required to establish the impact of these approaches on gastric cancer prevention [36-39]. The role of inflammatory cytokines can vary depending on factors such as the tumor microenvironment, gene expression, and interactions with other immune cells [40], highlighting the need for more in-depth research to fully understand their effects.

In this study, pro-inflammatory cytokines (IL-6, IL-1β, and IFN-γ) were found to be associated with the risk of GC. Our research findings can provide valuable insights for the development of GC prevention strategies and policies. The results suggest that these cytokines may serve as important indicators of cancer development, potentially aiding in the identification of high-risk populations and the formulation of personalized prevention strategies. Furthermore, given that GC is acknowledged as a major health concern worldwide, understanding the mechanisms of inflammation and cancer can be a valuable tool in comprehending cancer development and prognosis.

NOTES

Ethical Statement

This study was approved by the Institutional Review Board of the National Cancer Center, Korea (no. NCC2020-0203). The study was conducted in accordance with the principles of the Declaration of Helsinki. All participants provided written informed consent.

Author Contributions

Conceived and designed the analysis: Park EY.

Collected the data: Baek S, Park E.

Contributed data or analysis tools: Park E.

Performed the analysis: Park E.

Wrote the paper: Baek S, Park E.

Design the study and supervised entire process: Park EY.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Fig. 1.

Study participant selection process.

Fig. 2.

Associations between serum pro-inflammatory cytokines and risk of gastric cancer in linear Cox proportional-hazards models. Gastric cancer risk for each unit increases in the natural log-transformed pro-inflammatory cytokines. Model 1 was unadjusted; Model 2 was adjusted for sex, year of entry into the cohort, and region; Model 3 was further adjusted for education, cigarette smoking (never, former, and current), alcohol consumption status (non-drinkers, < 24 g per day, and ≥ 24 g per day), and body mass index, physical activity, meat consumption, and vegetable/fruit consumption). CI, confidence interval; HR, hazard ratio; IFN-γ, interferon γ; IL-1β, interleukin 1β; IL-6, interleukin 6; IL-10, interleukin 10; NO, nitric oxide; ROS, reactive oxygen species; TNF-α, tumor necrosis factor-α.

Table 1.

Demographic characteristics of gastric cancer cases and the subcohort

	Gastric cancer cases (n=159)	Subcohort (n=822)
Subcohort person-time (yr)	997.33	9,004.12
Age (yr)	64.75±7.36	60.11±10.80
Sex
Men	99 (62.3)	329 (40.0)
Women	60 (37.7)	493 (60.0)
Region
Sancheong	68 (42.8)	441 (53.6)
Uiryeong	17 (10.7)	27 (3.3)
Changwon	9 (5.7)	121 (14.7)
Chuncheon	9 (5.7)	62 (7.5)
Chungju	25 (15.7)	76 (9.2)
Haman	31 (19.5)	95 (11.6)
Year of entry into the cohort
2001	34 (21.4)	72 (8.8)
2002	2 (1.3)	16 (1.9)
2003	27 (17.0)	107 (13.0)
2004	34 (21.4)	147 (17.9)
2005	16 (10.1)	103 (12.5)
2006	19 (11.9)	129 (15.7)
2008	13 (8.2)	104 (12.7)
2009	9 (5.7)	73 (8.9)
2010	5 (3.1)	71 (8.6)
Educational achievement
None	40 (25.2)	208 (25.3)
Middle school	99 (62.3)	472 (57.4)
High school	15 (9.4)	109 (13.3)
College or more	5 (3.1)	33 (4.0)
Cigarette smoking
Never smokers	70 (44.0)	520 (63.3)
Former smokers	32 (20.1)	140 (17.0)
Current smokers	57 (35.8)	162 (19.7)
Alcohol consumption status
Non-drinkers	70 (44.0)	477 (58.0)
≤ 24 g per day	54 (34.0)	211 (25.7)
> 24 g per day	35 (22.0)	134 (16.3)
Obesity (BMI)	23.32±3.17	23.82±3.29
Yes (BMI ≥ 25 kg/m²)	108 (68.0)	543 (66.1)
No (BMI < 25 kg/m²)	51 (32.1)	279 (33.9)
Physical activity
Low, METs < 600	86 (54.1)	414 (50.4)
Moderate, 600 ≤ METs < 3,000	23 (14.5)	152 (18.5)
High, METs ≥ 3,000	50 (31.4)	256 (31.1)
Meat consumption
Almost never	50 (31.4)	311 (37.8)
< 3 times per week	69 (43.4)	246 (29.9)
≥ 4 times per week	40 (25.2)	265 (32.2)
Vegetable/Fruit consumption
6 times per week or less	52 (32.7)	260 (31.6)
Everyday	107 (67.3)	562 (68.4)

Values are presented as mean±SD or number (%). BMI, body mass index; METs, metabolic equivalent of tasks; SD, standard deviation.

Table 2.

Serum levels of pro-inflammatory cytokines stratified by gastric cancer cases and the subcohort

	Gastric cancer cases							Subcohort							< LOD (%)
	GM (95% CI)	Distribution						GM (95% CI)	Distribution
	GM (95% CI)	No.	Min	25th	Median	75th	Max	GM (95% CI)	No.	Min	25th	Median	75th	Max
IL-6 (pg/mL)	2.4902 (2.05-3.02)	157	LOD	1.82	2.92	4.24	28.73	0.97 (0.84-1.12)	811	LOD	0.82	1.95	3.49	158.33	11.47
TNF-α (pg/mL)	1.6441 (1.17-2.30)	158	LOD	1.51	3.685	5.49	42.33	1.22 (1.04-1.44)	816	LOD	1.04	3.23	5.58	25.51	14.99
IL-1β (pg/mL)	0.0748 (0.05-0.11)	151	LOD	LOD	0.07	0.7	8.96	0.06 (0.05-0.07)	799	LOD	LOD	LOD	0.68	47.89	54.11
IFN-γ (pg/mL)	0.1819 (0.11-0.29)	148	LOD	LOD	0.625	2.375	43.38	0.06 (0.05-0.07)	792	LOD	LOD	LOD	1.39	63.06	59.04
IL-10 (pg/mL)	0.1867 (0.14-0.25)	138	LOD	0.1	0.33	0.72	2.97	0.14 (0.12-0.16)	769	LOD	LOD	0.29	0.70	11.61	25.14
ROS (U/L)	700.8 (582.60-84)	159	LOD	598.2	699.7	1,049.3	3,062.2	778.00 (705.90-857.50)	817	LOD	612.90	840.20	1,464.20	8,761.50	2.66
NO (μmol/L)	53.34 (47.52-59.88)	157	7.43	33.09	50.42	87.84	341.93	51.10 (48.83-53.47)	807	3.63	31.86	49.28	77.31	459.36	0.00

LOD: 0.01 pg/mL (IL-6), 0.01 pg/mL (TNF-α), 0.01 pg/mL (IL-1β), 0.01 pg/mL (IFN-γ), 0.01 pg/mL (IL-10), 0.50 U/L (ROS), 0.01 μmol/L (NO). CI, confidence interval; GM, geometric mean; IFN-γ, interferon gamma; IL-1β, interleukin 1β; IL-6, interleukin 6; IL-10, interleukin 10; LOD, limit of detection; NO, nitric oxide; ROS, reactive oxygen species; TNF-α, tumor necrosis factor-α.

Table 3.

Associations between serum levels of pro-inflammatory cytokines and the gastric cancer risk by quartiles

	Level	Case/Subcohort	Subcohort person-years	Model 1			Model 2			Model 3
	Level	Case/Subcohort	Subcohort person-years	HR (95% CI)	p-value	p for trend	HR (95% CI)	p-value	p for trend	HR (95% CI)	p-value	p for trend
IL-6 (pg/mL)
1st quartile	< 0.82	11/203	2,391	Reference		< 0.001	Reference		< 0.001	Reference		< 0.001
2nd quartile	0.82-1.95	35/203	2,158	3.13 (1.59-6.18)	< 0.001		3.28 (1.65-6.54)	< 0.001		3.48 (1.73-6.99)	< 0.001
3rd quartile	1.95-3.49	56/204	2,191	4.36 (2.28-8.34)	< 0.001		3.88 (2.00-7.50)	< 0.001		3.74 (1.91-7.29)	< 0.001
4th quartile	3.49-158.33	55/201	2,135	4.21 (2.19-8.08)	< 0.001		3.83 (1.95-7.52)	< 0.001		3.79 (1.93-7.48)	< 0.001
TNF-α (pg/mL)
1st quartile	< 1.04	36/204	2,194	Reference		0.939			0.188	Reference		0.209
2nd quartile	1.04-3.23	36/204	2,250	0.97 (0.61-1.54)	0.885		0.70 (0.43-1.14)	0.155		0.70 (0.43-1.14)	0.152
3rd quartile	3.23-5.58	48/204	2,191	1.22 (0.79-1.88)	0.373		0.92 (0.58-1.45)	0.709		0.91 (0.58-1.45)	0.703
4th quartile	5.58-25.51	38/204	2,302	0.94 (0.60-1.49)	0.794		0.63 (0.39-1.03)	0.065		0.64 (0.39-1.05)	0.077
IL-1β (pg/mL)
< LOD	< 0.01	73/441	4,912	Reference		0.066			0.019	Reference		0.010
≥ LOD	≥ 0.01	78/358	3,832	1.35 (0.98-1.86)	0.066		1.49 (1.07-2.08)	0.019		1.57 (1.12-2.21)	0.010
IFN-γ (pg/mL)
< LOD	< 0.01	61/494	5,607	Reference		< 0.001			< 0.001	Reference		< 0.001
≥ LOD	≥ 0.01	87/298	3,060	2.41 (1.74-3.35)	< 0.001		2.50 (1.75-3.55)	< 0.001		2.49 (1.73-3.58)	< 0.001
IL-10 (pg/mL)
1st quartile	< 0.01	25/203	2,113	Reference		0.392			0.718	Reference		0.901
2nd quartile	0.01-0.29	40/184	2,050	1.59 (0.96-2.62)	0.071		1.40 (0.84-2.33)	0.193		1.41 (0.84-2.36)	0.193
3rd quartile	0.29-0.70	38/190	2,119	1.48 (0.89-2.45)	0.132		1.53 (0.91-2.58)	0.107		1.60 (0.94-2.71)	0.082
4th quartile	0.70-11.61	35/192	2,131	1.34 (0.80-2.24)	0.266		1.13 (0.66-1.92)	0.659		1.04 (0.61-1.78)	0.891
ROS (U/L)
1st quartile	< 612.90	44/205	2,287	Reference		0.037			0.479	Reference		0.281
2nd quartile	612.90-840.20	57/204	2,129	1.25 (0.85-1.86)	0.259		1.72 (1.12-2.63)	0.013		1.82 (1.17-2.83)	0.008
3rd quartile	840.20-1,464.2	32/204	2,275	0.78 (0.50-1.24)	0.297		1.11 (0.68-1.80)	0.682		1.16 (0.70-1.91)	0.566
4th quartile	1,464.2-8,761.5	26/204	2,262	0.68 (0.42-1.10)	0.114		0.94 (0.56-1.57)	0.811		0.86 (0.51-1.46)	0.574
NO (μmol/L)
1st quartile	3.63-31.86	38/202	2,104	Reference		0.785			0.603	Reference		0.829
2nd quartile	31.86-49.28	37/202	2,134	0.91 (0.58-1.43)	0.678		0.94 (0.60-1.49)	0.794		0.84 (0.53-1.34)	0.466
3rd quartile	49.28-77.31	35/202	2,236	0.95 (0.60-1.51)	0.841		1.15 (0.72-1.85)	0.560		1.02 (0.63-1.65)	0.942
4th quartile	77.31-459.36	47/201	2,351	1.04 (0.68-1.59)	0.861		1.07 (0.68-1.68)	0.762		0.99 (0.63-1.57)	0.972

Model 1 was unadjusted; Model 2 was adjusted for age, sex, year of entry into the cohort, and region; Model 3 was further adjusted for education, cigarette smoking (never, former, and current), alcohol consumption status (non-drinkers, < 24 g per day, and ≥ 24 g per day), and body mass index, physical activity, meat consumption, and vegetable/fruit consumption). CI, confidence interval; HR, hazard ratio; IFN-γ, interferon γ; IL-1β, interleukin 1β; IL-6, interleukin 6; IL-10, interleukin 10; LOD, limit of detection; NO, nitric oxide; ROS, reactive oxygen species; TNF-α, tumor necrosis factor-α.

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Serum pro-inflammatory cytokines as potential biomarkers for the diagnosis of gastric carcinoma
Le Ren, Jun Liu, Ya-Yun Xu, Zhen-Wang Shi
World Journal of Clinical Oncology.2025;[Epub] CrossRef
Inflammation and detection: Rethinking the biomarker landscape in gastric cancer
Keykavous Parang, Koosha Paydary
World Journal of Clinical Oncology.2025;[Epub] CrossRef

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Association of Pro-inflammatory Cytokines with Gastric Cancer Risk: A Case-Cohort Study

Cancer Res Treat. 2025;57(3):760-769. Published online November 19, 2024

DOI: https://doi.org/10.4143/crt.2024.718

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Abstract
Introduction
Materials and Methods
Results
Discussion
NOTES
REFERENCES

Association of Pro-inflammatory Cytokines with Gastric Cancer Risk: A Case-Cohort Study

Fig. 1. Study participant selection process.

Fig. 2. Associations between serum pro-inflammatory cytokines and risk of gastric cancer in linear Cox proportional-hazards models. Gastric cancer risk for each unit increases in the natural log-transformed pro-inflammatory cytokines. Model 1 was unadjusted; Model 2 was adjusted for sex, year of entry into the cohort, and region; Model 3 was further adjusted for education, cigarette smoking (never, former, and current), alcohol consumption status (non-drinkers, < 24 g per day, and ≥ 24 g per day), and body mass index, physical activity, meat consumption, and vegetable/fruit consumption). CI, confidence interval; HR, hazard ratio; IFN-γ, interferon γ; IL-1β, interleukin 1β; IL-6, interleukin 6; IL-10, interleukin 10; NO, nitric oxide; ROS, reactive oxygen species; TNF-α, tumor necrosis factor-α.

Fig. 1.

Fig. 2.

Association of Pro-inflammatory Cytokines with Gastric Cancer Risk: A Case-Cohort Study

	Gastric cancer cases (n=159)	Subcohort (n=822)
Subcohort person-time (yr)	997.33	9,004.12
Age (yr)	64.75±7.36	60.11±10.80
Sex
Men	99 (62.3)	329 (40.0)
Women	60 (37.7)	493 (60.0)
Region
Sancheong	68 (42.8)	441 (53.6)
Uiryeong	17 (10.7)	27 (3.3)
Changwon	9 (5.7)	121 (14.7)
Chuncheon	9 (5.7)	62 (7.5)
Chungju	25 (15.7)	76 (9.2)
Haman	31 (19.5)	95 (11.6)
Year of entry into the cohort
2001	34 (21.4)	72 (8.8)
2002	2 (1.3)	16 (1.9)
2003	27 (17.0)	107 (13.0)
2004	34 (21.4)	147 (17.9)
2005	16 (10.1)	103 (12.5)
2006	19 (11.9)	129 (15.7)
2008	13 (8.2)	104 (12.7)
2009	9 (5.7)	73 (8.9)
2010	5 (3.1)	71 (8.6)
Educational achievement
None	40 (25.2)	208 (25.3)
Middle school	99 (62.3)	472 (57.4)
High school	15 (9.4)	109 (13.3)
College or more	5 (3.1)	33 (4.0)
Cigarette smoking
Never smokers	70 (44.0)	520 (63.3)
Former smokers	32 (20.1)	140 (17.0)
Current smokers	57 (35.8)	162 (19.7)
Alcohol consumption status
Non-drinkers	70 (44.0)	477 (58.0)
≤ 24 g per day	54 (34.0)	211 (25.7)
> 24 g per day	35 (22.0)	134 (16.3)
Obesity (BMI)	23.32±3.17	23.82±3.29
Yes (BMI ≥ 25 kg/m²)	108 (68.0)	543 (66.1)
No (BMI < 25 kg/m²)	51 (32.1)	279 (33.9)
Physical activity
Low, METs < 600	86 (54.1)	414 (50.4)
Moderate, 600 ≤ METs < 3,000	23 (14.5)	152 (18.5)
High, METs ≥ 3,000	50 (31.4)	256 (31.1)
Meat consumption
Almost never	50 (31.4)	311 (37.8)
< 3 times per week	69 (43.4)	246 (29.9)
≥ 4 times per week	40 (25.2)	265 (32.2)
Vegetable/Fruit consumption
6 times per week or less	52 (32.7)	260 (31.6)
Everyday	107 (67.3)	562 (68.4)

	Gastric cancer cases							Subcohort							< LOD (%)
	GM (95% CI)	Distribution						GM (95% CI)	Distribution
	GM (95% CI)	No.	Min	25th	Median	75th	Max	GM (95% CI)	No.	Min	25th	Median	75th	Max
IL-6 (pg/mL)	2.4902 (2.05-3.02)	157	LOD	1.82	2.92	4.24	28.73	0.97 (0.84-1.12)	811	LOD	0.82	1.95	3.49	158.33	11.47
TNF-α (pg/mL)	1.6441 (1.17-2.30)	158	LOD	1.51	3.685	5.49	42.33	1.22 (1.04-1.44)	816	LOD	1.04	3.23	5.58	25.51	14.99
IL-1β (pg/mL)	0.0748 (0.05-0.11)	151	LOD	LOD	0.07	0.7	8.96	0.06 (0.05-0.07)	799	LOD	LOD	LOD	0.68	47.89	54.11
IFN-γ (pg/mL)	0.1819 (0.11-0.29)	148	LOD	LOD	0.625	2.375	43.38	0.06 (0.05-0.07)	792	LOD	LOD	LOD	1.39	63.06	59.04
IL-10 (pg/mL)	0.1867 (0.14-0.25)	138	LOD	0.1	0.33	0.72	2.97	0.14 (0.12-0.16)	769	LOD	LOD	0.29	0.70	11.61	25.14
ROS (U/L)	700.8 (582.60-84)	159	LOD	598.2	699.7	1,049.3	3,062.2	778.00 (705.90-857.50)	817	LOD	612.90	840.20	1,464.20	8,761.50	2.66
NO (μmol/L)	53.34 (47.52-59.88)	157	7.43	33.09	50.42	87.84	341.93	51.10 (48.83-53.47)	807	3.63	31.86	49.28	77.31	459.36	0.00

	Level	Case/Subcohort	Subcohort person-years	Model 1			Model 2			Model 3
	Level	Case/Subcohort	Subcohort person-years	HR (95% CI)	p-value	p for trend	HR (95% CI)	p-value	p for trend	HR (95% CI)	p-value	p for trend
IL-6 (pg/mL)
1st quartile	< 0.82	11/203	2,391	Reference		< 0.001	Reference		< 0.001	Reference		< 0.001
2nd quartile	0.82-1.95	35/203	2,158	3.13 (1.59-6.18)	< 0.001		3.28 (1.65-6.54)	< 0.001		3.48 (1.73-6.99)	< 0.001
3rd quartile	1.95-3.49	56/204	2,191	4.36 (2.28-8.34)	< 0.001		3.88 (2.00-7.50)	< 0.001		3.74 (1.91-7.29)	< 0.001
4th quartile	3.49-158.33	55/201	2,135	4.21 (2.19-8.08)	< 0.001		3.83 (1.95-7.52)	< 0.001		3.79 (1.93-7.48)	< 0.001
TNF-α (pg/mL)
1st quartile	< 1.04	36/204	2,194	Reference		0.939			0.188	Reference		0.209
2nd quartile	1.04-3.23	36/204	2,250	0.97 (0.61-1.54)	0.885		0.70 (0.43-1.14)	0.155		0.70 (0.43-1.14)	0.152
3rd quartile	3.23-5.58	48/204	2,191	1.22 (0.79-1.88)	0.373		0.92 (0.58-1.45)	0.709		0.91 (0.58-1.45)	0.703
4th quartile	5.58-25.51	38/204	2,302	0.94 (0.60-1.49)	0.794		0.63 (0.39-1.03)	0.065		0.64 (0.39-1.05)	0.077
IL-1β (pg/mL)
< LOD	< 0.01	73/441	4,912	Reference		0.066			0.019	Reference		0.010
≥ LOD	≥ 0.01	78/358	3,832	1.35 (0.98-1.86)	0.066		1.49 (1.07-2.08)	0.019		1.57 (1.12-2.21)	0.010
IFN-γ (pg/mL)
< LOD	< 0.01	61/494	5,607	Reference		< 0.001			< 0.001	Reference		< 0.001
≥ LOD	≥ 0.01	87/298	3,060	2.41 (1.74-3.35)	< 0.001		2.50 (1.75-3.55)	< 0.001		2.49 (1.73-3.58)	< 0.001
IL-10 (pg/mL)
1st quartile	< 0.01	25/203	2,113	Reference		0.392			0.718	Reference		0.901
2nd quartile	0.01-0.29	40/184	2,050	1.59 (0.96-2.62)	0.071		1.40 (0.84-2.33)	0.193		1.41 (0.84-2.36)	0.193
3rd quartile	0.29-0.70	38/190	2,119	1.48 (0.89-2.45)	0.132		1.53 (0.91-2.58)	0.107		1.60 (0.94-2.71)	0.082
4th quartile	0.70-11.61	35/192	2,131	1.34 (0.80-2.24)	0.266		1.13 (0.66-1.92)	0.659		1.04 (0.61-1.78)	0.891
ROS (U/L)
1st quartile	< 612.90	44/205	2,287	Reference		0.037			0.479	Reference		0.281
2nd quartile	612.90-840.20	57/204	2,129	1.25 (0.85-1.86)	0.259		1.72 (1.12-2.63)	0.013		1.82 (1.17-2.83)	0.008
3rd quartile	840.20-1,464.2	32/204	2,275	0.78 (0.50-1.24)	0.297		1.11 (0.68-1.80)	0.682		1.16 (0.70-1.91)	0.566
4th quartile	1,464.2-8,761.5	26/204	2,262	0.68 (0.42-1.10)	0.114		0.94 (0.56-1.57)	0.811		0.86 (0.51-1.46)	0.574
NO (μmol/L)
1st quartile	3.63-31.86	38/202	2,104	Reference		0.785			0.603	Reference		0.829
2nd quartile	31.86-49.28	37/202	2,134	0.91 (0.58-1.43)	0.678		0.94 (0.60-1.49)	0.794		0.84 (0.53-1.34)	0.466
3rd quartile	49.28-77.31	35/202	2,236	0.95 (0.60-1.51)	0.841		1.15 (0.72-1.85)	0.560		1.02 (0.63-1.65)	0.942
4th quartile	77.31-459.36	47/201	2,351	1.04 (0.68-1.59)	0.861		1.07 (0.68-1.68)	0.762		0.99 (0.63-1.57)	0.972

Table 1. Demographic characteristics of gastric cancer cases and the subcohort

Values are presented as mean±SD or number (%). BMI, body mass index; METs, metabolic equivalent of tasks; SD, standard deviation.

Table 2. Serum levels of pro-inflammatory cytokines stratified by gastric cancer cases and the subcohort

Table 3. Associations between serum levels of pro-inflammatory cytokines and the gastric cancer risk by quartiles