Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

The Persistence of Hypertriglyceridemia and the Risk of Early Onset Colorectal Cancer According to Tumor Subsites: A Nationwide Population-Based Study: Young Hoon Chang, Cheol Min Shin, Kyungdo Han, Jin Hyung Jung, Eun Hyo Jin, Joo Hyun Lim, Seung Joo Kang, Yoon Jin Choi, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee; Cancer Res Treat. 2024;56(3):825-837. Published online December 20, 2023; DOI: https://doi.org/10.4143/crt.2023.753

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Purpose
The incidence of early-onset colorectal cancer (EoCRC) is increasing worldwide. The association between hypertriglyceridemia (HTG) and EoCRC risk remains unclear.
Materials and Methods
We conducted a nationwide cohort study of 3,340,635 individuals aged 20-49 years who underwent health checkups between 2009 and 2011 under the Korean National Health Insurance Service. HTG was defined as serum triglyceride (TG) level ≥ 150 mg/dL. According to the change in TG status, participants were categorized into persistent normotriglyceridemia (NTG; group 1), NTG to HTG (group 2), HTG to NTG (group 3), and persistent HTG (group 4) groups. The EoCRC incidence was followed up until 2019.
Results
In total, 7,492 EoCRC cases developed after a mean of 6.05 years of follow-up. Group 4 had the highest risk of EoCRC (adjusted hazard ratio [aHR], 1.097; 95% confidence interval [CI], 1.025 to 1.174). While the risk of rectal cancer was significantly increased in groups 3 and 4 (aHR [95% CI], 1.236 [1.076 to 1.419] and 1.175 [1.042-1.325], respectively), no significant risk differences were observed in right colon cancer. In group 4, male sex and diabetes were associated with a further increased risk of EoCRC (aHR [95% CI], 1.149 [1.082 to 1.221] and 1.409 [1.169 to 1.699], respectively). In addition, there was a dose-response relationship between serum TG levels and the risk of EoCRC (p for trends < 0.0001).
Conclusion
Persistent HTG increased the risk of EoCRC, which was significantly higher only for rectal cancer and marginally higher for other colonic subsites.

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Obesity-Associated Colorectal Cancer
Lucia Gonzalez-Gutierrez, Omar Motiño, Daniel Barriuso, Juan de la Puente-Aldea, Lucia Alvarez-Frutos, Guido Kroemer, Roberto Palacios-Ramirez, Laura Senovilla
International Journal of Molecular Sciences.2024; 25(16): 8836. CrossRef

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Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods: Hae Dong Lee, Kyung Han Nam, Cheol Min Shin, Hye Seung Lee, Young Hoon Chang, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee, Sang-Hoon Ahn, Hyung-Ho Kim; Cancer Res Treat. 2023;55(4):1240-1249. Published online March 21, 2023; DOI: https://doi.org/10.4143/crt.2022.1330

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Purpose
To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.
Materials and Methods
The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.
Results
LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.
Conclusion
The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

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Establishment of a machine learning model for predicting splenic hilar lymph node metastasis
Kenichi Ishizu, Satoshi Takahashi, Nobuji Kouno, Ken Takasawa, Katsuji Takeda, Kota Matsui, Masashi Nishino, Tsutomu Hayashi, Yukinori Yamagata, Shigeyuki Matsui, Takaki Yoshikawa, Ryuji Hamamoto
npj Digital Medicine.2025;[Epub] CrossRef
The artificial intelligence revolution in gastric cancer management: clinical applications
Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang
Cancer Cell International.2025;[Epub] CrossRef
Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach
Y. Zou, Q. Mao, Z. Zhao, X. Zhou, Y. Pan, Z. Zuo, W. Zhang
Clinical Radiology.2024; 79(2): e211. CrossRef
eCura and W-eCura: different scores, different populations, same goal
Rui Morais, Diogo Libanio, João Santos-Antunes
Gut.2024; 73(11): e29. CrossRef
A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
Minoru Kato, Yoshito Hayashi, Ryotaro Uema, Takashi Kanesaka, Shinjiro Yamaguchi, Akira Maekawa, Takuya Yamada, Masashi Yamamoto, Shinji Kitamura, Takuya Inoue, Shunsuke Yamamoto, Takashi Kizu, Risato Takeda, Hideharu Ogiyama, Katsumi Yamamoto, Kenji Aoi,
Gastric Cancer.2024; 27(5): 1069. CrossRef
The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide
Amin Zadeh Shirazi, Morteza Tofighi, Alireza Gharavi, Guillermo A. Gomez
Technology in Cancer Research & Treatment.2024;[Epub] CrossRef
Computed Tomography-Based Radiomics Analysis of Different Machine Learning Approaches for Differentiating Pulmonary Sarcomatoid Carcinoma and Pulmonary Inflammatory Pseudotumor
An-Lin Zhang, Yan-Mei Fu, Zhi-Yang He
Iranian Journal of Radiology.2024;[Epub] CrossRef
Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations
Ahao Wu, Tengcheng Hu, Chao Lai, Qingwen Zeng, Lianghua Luo, Xufeng Shu, Pan Huang, Zhonghao Wang, Zongfeng Feng, Yanyan Zhu, Yi Cao, Zhengrong Li
Cancer Medicine.2024;[Epub] CrossRef

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Aberrant DNA Methylation Maker for Predicting Metachronous Recurrence After Endoscopic Resection of Gastric Neoplasms: Cheol Min Shin, Nayoung Kim, Hyuk Yoon, Yoon Jin Choi, Ji Hyun Park, Young Soo Park, Dong Ho Lee; Cancer Res Treat. 2022;54(4):1157-1166. Published online January 18, 2022; DOI: https://doi.org/10.4143/crt.2021.997

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Purpose
This study aimed to investigate whether MOS methylation can be useful for the prediction of metachronous recurrence after endoscopic resection of gastric neoplasms.
Materials and Methods
From 2012 to 2017, 294 patients were prospectively enrolled after endoscopic resection of gastric dysplasia (n=171) or early gastric cancer (n=123). When Helicobacter pylori was positive, eradication therapy was performed. Among them, 124 patients completed the study protocol (follow-up duration > 3 years or development of metachronous recurrence during the follow-up). Methylation levels of MOS were measured at baseline using quantitative MethyLight assay from the antrum.
Results
Median follow-up duration was 49.9 months. MOS methylation levels at baseline were not different by age, sex, and current H. pylorii infection, but they showed a weak correlation with operative link on gastritis assessment (OLGA) or operative link on gastric intestinal metaplasia assessment (OLGIM) stages (Spearman’s ρ=0.240 and 0.174, respectively; p < 0.05). During the follow-up, a total of 20 metachronous gastric neoplasms (13 adenomas and 7 adenocarcinomas) were developed. Either OLGA or OLGIM stage was not useful in predicting the risk for metachronous recurrence. In contrast, MOS methylation high group (≥ 34.82%) had a significantly increased risk for metachronous recurrence compared to MOS methylation low group (adjusted hazard ratio, 4.76; 95% confidence interval, 1.54 to 14.79; p=0.007).
Conclusion
MOS methylation can be a promising marker for predicting metachronous recurrence after endoscopic resection of gastric neoplasms. To confirm the usefulness of MOS methylation, validation studies are warranted in the future (ClinicalTrials No. NCT04830618).

Citations

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MIR124-3 and NKX6-1 hypermethylation profiles accurately predict metachronous gastric lesions in a Caucasian population
Catarina Lopes, Tatiana C. Almeida, Catarina Macedo-Silva, João Costa, Sofia Paulino, Carmen Jerónimo, Diogo Libânio, Mário Dinis-Ribeiro, Carina Pereira
Clinical Epigenetics.2024;[Epub] CrossRef
The methylation signature of hepatocellular carcinoma trajectory based on pseudotime and chronological time for predicting precancerous patients
Kang Li, Chaoran Zang, Yanan Zhao, Dandan Guo, Wanting Shi, Tingting Mei, Ang Li, Yonghong Zhang
The Oncologist.2024;[Epub] CrossRef
Risk assessment of metachronous gastric cancer development using OLGA and OLGIM systems after endoscopic submucosal dissection for early gastric cancer: a long-term follow-up study
Yun Suk Na, Sang Gyun Kim, Soo-Jeong Cho
Gastric Cancer.2023; 26(2): 298. CrossRef

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Association between the Persistence of Obesity and the Risk of Gastric Cancer: A Nationwide Population-Based Study: Joo Hyun Lim, Cheol Min Shin, Kyung-Do Han, Seung Woo Lee, Eun Hyo Jin, Yoon Jin Choi, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee; Cancer Res Treat. 2022;54(1):199-207. Published online May 4, 2021; DOI: https://doi.org/10.4143/crt.2021.130

Abstract PDF Supplementary Material PubReader ePub

Purpose
There remains controversy about relationship between obesity and gastric cancer. We aimed to examine the association using obesity-persistence.
Materials and Methods
We analyzed a nationwide population-based cohort which underwent health check-up between 2009 and 2012. Among them, those who had annual examinations during the last 5 years were selected. Gastric cancer risk was compared between those without obesity during the 5 years (never-obesity group) and those with obesity diagnosis during the 5 years (non-persistent obesity group; persistent obesity group).
Results
Among 2,757,017 individuals, 13,441 developed gastric cancer after median 6.78 years of follow-up. Gastric cancer risk was the highest in persistent obesity group (incidence rate [IR], 0.89/1,000 person-years; hazard ratio [HR], 1.197; 95% confidence interval [CI], 1.117 to 1.284), followed by non-persistent obesity group (IR, 0.83/1,000 person-years; HR, 1.113; 95% CI, 1.056 to 1.172) compared with never-obesity group. In subgroup analysis, this positive relationship was true among those < 65 years old and male. Among heavy-drinkers, the impact of obesity-persistence on the gastric cancer risk far increased (non-persistent obesity: HR, 1.297; 95% CI, 1.094 to 1.538; persistent obesity: HR, 1.351; 95% CI, 1.076 to 1.698).
Conclusion
Obesity-persistence is associated with increased risk of gastric cancer in a dose-response manner, especially among male < 65 years old. The risk raising effect was much stronger among heavy-drinkers.

Citations

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Diagnosis of gastric cancer in role of endoscopic imaging techniques in artificial intelligence and machine learning applications: An overview
Pooja K., Kishore Kanna R., G. Li, U. Subramaniam, M. Sekar
E3S Web of Conferences.2024; 491: 03016. CrossRef
Joint association of drinking alcohol and obesity in relation to cancer risk: A systematic review and data synthesis
Graeme A. Macdonald, James A. Thomas, Christine Dalais, Bradley J. Kendall, Aaron P. Thrift
Cancer Epidemiology.2024; 91: 102596. CrossRef
Roles of long non‑coding RNA SNHG16 in human digestive system cancer (Review)
Lujie Zhao, Yuling Kan, Lu Wang, Jiquan Pan, Yun Li, Haiyan Zhu, Zhongfa Yang, Lin Xiao, Xinhua Fu, Fujun Peng, Haipeng Ren
Oncology Reports.2024;[Epub] CrossRef
Adiposity and risks of gastrointestinal cancers: A 10‐year prospective study of 0.5 million Chinese adults
Wing Ching Chan, Iona Millwood, Christiana Kartsonaki, Huaidong Du, Daniel Schmidt, Rebecca Stevens, Junshi Chen, Pei Pei, Canqing Yu, Dianjianyi Sun, Jun Lv, Xianyong Han, Liming Li, Zhengming Chen, Ling Yang
International Journal of Cancer.2024;[Epub] CrossRef
Gastric Cancer Risk in Association with Underweight, Overweight, and Obesity: A Systematic Review and Meta-Analysis
Narges Azizi, Moein Zangiabadian, Golnoosh Seifi, Afshan Davari, Elham Yekekhani, Seyed Amir Ahmad Safavi-Naini, Nathan A. Berger, Mohammad Javad Nasiri, Mohammad-Reza Sohrabi
Cancers.2023; 15(10): 2778. CrossRef
Recent research progress on the correlation between metabolic syndrome and Helicobacter pylori infection
Qinli Xie, Yangjun He, Danni Zhou, Yi Jiang, Ying Deng, Ruoqing Li
PeerJ.2023; 11: e15755. CrossRef
Risk of gastric cancer in relation with serum cholesterol profiles: A nationwide population-based cohort study
Mi Jin Oh, Kyungdo Han, Bongseong Kim, Joo Hyun Lim, Bokyung Kim, Sang Gyun Kim, Soo-Jeong Cho
Medicine.2023; 102(48): e36260. CrossRef
Decreasing Incidence of Gastric Cancer with Increasing Time after Helicobacter pylori Treatment: A Nationwide Population-Based Cohort Study
Taewan Kim, Seung In Seo, Kyung Joo Lee, Chan Hyuk Park, Tae Jun Kim, Jinseob Kim, Woon Geon Shin
Antibiotics.2022; 11(8): 1052. CrossRef
Integration of clinical and transcriptomics reveals programming of the lipid metabolism in gastric cancer
Yanyan Li, Jungang Zhao, Renpin Chen, Shengwei Chen, Yilun Xu, Weiyang Cai
BMC Cancer.2022;[Epub] CrossRef
Body fatness associations with cancer: evidence from recent epidemiological studies and future directions
Susanna C. Larsson, Nikolaos Spyrou, Christos S. Mantzoros
Metabolism.2022; 137: 155326. CrossRef
FOXC2-AS1 stabilizes FOXC2 mRNA via association with NSUN2 in gastric cancer cells
Jijun Yan, Juntao Liu, Zhengbin Huang, Wenwei Huang, Jianfa Lv
Human Cell.2021; 34(6): 1755. CrossRef

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