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Year 2023, Volume: 8 Issue: 2, 37 - 40

Şeyma Yaşar Büşra Nur Fındık

https://doi.org/10.52876/jcs.1394024

Abstract

References

  • REFERENCES [1] M.H.S. Jong, S.S. Gisbertz, M. I. Berge Henegouwen, W. A. Draaisma (2023). Prevalence of nodal metastases in the individual lymph node stations for different T-stages in gastric cancer: a systematic review. Updates in Surgery, 75(2), 281-290.
  • [2] J. Machlowska, J. Baj, M. Sitarz, R. Maciejewski, R. Sitarz (2020) Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies. International journal of molecular sciences, vol. 21, p. 4012.
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  • [5] H. B. El–Serag, H. Hampel, and F. Javadi (2006) The association between diabetes and hepatocellular carcinoma: a systematic review of epidemiologic evidence. Clinical Gastroenterology and Hepatology, vol. 4, pp. 369-380.
  • [6] E. Friberg, N. Orsini, C. Mantzoros, and A. Wolk (2007) Diabetes mellitus and risk of endometrial cancer: a meta-analysis. Diabetologia, vol. 50, pp. 1365-1374.
  • [7] S. C. Larsson, N. Orsini, and A. Wolk (2005) Diabetes mellitus and risk of colorectal cancer: a meta-analysis. Journal of the National Cancer Institute, vol. 97, pp. 1679-1687.
  • [8] A. Sekikawa, H. Fukui, T. Maruo, T. Tsumura, Y. Okabe, and Y. Osaki (2014) Diabetes mellitus increases the risk of early gastric cancer development. European journal of cancer, vol. 50, pp. 2065-2071.
  • [9] H.-J. Yang, D. Kang, Y. Chang, J. Ahn, S. Ryu, J. Cho, et al. (2020) Diabetes mellitus is associated with an increased risk of gastric cancer: a cohort study. Gastric Cancer, vol. 23, pp. 382-390.
  • [10] Y. W. Kwon, H.-S. Jo, S. Bae, Y. Seo, P. Song, M. Song, et al. (2021) Application of proteomics in cancer: recent trends and approaches for biomarkers discovery. Frontiers in Medicine, vol. 8, p. 747333.
  • [11] H. Desaire, E. P. Go, and D. Hua (2022) Advances, obstacles, and opportunities for machine learning in proteomics. Cell Reports Physical Science, vol. 3.
  • [12] E. A. Freeman, G. G. Moisen, J. W. Coulston, and B. T. Wilson (2016) Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, vol. 46, pp. 323-339.
  • [13] H. Duan, Z. Deng, F. Deng, and D. Wang (2016) Assessment of groundwater potential based on multicriteria decision making model and decision tree algorithms. Mathematical Problems in Engineering, vol. 2016, pp. 1-11.
  • [14] S.-W. Lin, N. D. Freedman, A. R. Hollenbeck, A. Schatzkin, and C. C. Abnet (2011) Prospective study of self-reported diabetes and risk of upper gastrointestinal cancers. Cancer epidemiology, biomarkers & prevention, vol. 20, pp. 954-961.
  • [15] K. Mansori, Y. Moradi, S. Naderpour, R. Rashti, A. B. Moghaddam, L. Saed, et al. (2020) Helicobacter pylori infection as a risk factor for diabetes: a meta-analysis of case-control studies. BMC gastroenterology, vol. 20, pp. 1-14.
  • [16] T. Shimamura, H. Ito, J. Shibahara, A. Watanabe, Y. Hippo, H. Taniguchi, et al. (2005) Overexpression of MUC13 is associated with intestinal‐type gastric cancer. Cancer science, vol. 96, pp. 265-273.
  • [17] S.-S. Cui, P. Zhang, L. Sun, Y.-L.-L. Yuan, J. Wang, F.-X. Zhang, et al. (2023) Mucin1 induced trophoblast dysfunction in gestational diabetes mellitus via Wnt/β-catenin pathway. Biological Research, vol. 56, p. 48.

Biomarkers for predicting diabetes in gastric cancer patients with machine learning methods based on proteomic data

Year 2023, Volume: 8 Issue: 2, 37 - 40

Şeyma Yaşar Büşra Nur Fındık

https://doi.org/10.52876/jcs.1394024

Abstract

Gastric cancer is a type of cancer that occurs when cells in the stomach tissue grow and multiply abnormally. Gastric cancer usually starts in the inner layer of the stomach wall and can spread to other layers over time. This type of cancer is most common in people over the age of 50, but it can also occur in younger people. Symptoms of gastric cancer include indigestion and stomach pain, nausea and vomiting, loss of appetite and weight loss, bloody stools, fatigue and weakness. Although the exact cause of stomach cancer is not known, several risk factors have been identified. These risk factors include infection with the bacterium Helicobacter pylori, a family history of stomach cancer, consumption of excessively salty foods, smoking, heavy alcohol use and some genetic factors. Diabetes, on the other hand, is a hormonal disorder that regulates the body's blood sugar levels. Normally, an organ called the pancreas controls blood sugar by producing a hormone called insulin. Insulin helps glucose (sugar) enter the cells so that they can make energy. In diabetes, this regulation is disrupted, which can lead to high blood sugar and various health problems. The relationship between stomach cancer and diabetes is not yet fully understood. In this study, machine learning models (Stochastic Gradient Boosting, Bagged Classification and Regression Trees) based on proteomic data were used to predict the diabetes risk of 40 gastric cancer patients, 21 with DM and 19 with non-DM. Performance metrics for the optimal model (Stochastic Gradient Boosting) the accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1-score values are 0.86, 0.83, 0.67, 1.00, 1.00, 0.80, 0.80, respectively. According to the variable importance values obtained as a result of the model, Mucin-13 protein has a positive predictive value in predicting the diabetes risk of gastric cancer patients in the clinic.

Keywords

Diabetes mellitus Gastric Cancer Stochastic Gradient Boosting Bagged CART Classification

Ethical Statement

An ethics committee decision is not required.

Supporting Institution

There is no institutional support.

References

  • REFERENCES [1] M.H.S. Jong, S.S. Gisbertz, M. I. Berge Henegouwen, W. A. Draaisma (2023). Prevalence of nodal metastases in the individual lymph node stations for different T-stages in gastric cancer: a systematic review. Updates in Surgery, 75(2), 281-290.
  • [2] J. Machlowska, J. Baj, M. Sitarz, R. Maciejewski, R. Sitarz (2020) Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies. International journal of molecular sciences, vol. 21, p. 4012.
  • [3] D. Tomic, J. E. Shaw, and D. J. Magliano (2022) The burden and risks of emerging complications of diabetes mellitus. Nature Reviews Endocrinology, vol. 18, pp. 525-539.
  • [4] I. Satman, T. Yilmaz, A. Sengul, S. Salman, F. Salman, S. Uygur, et al. (2002) Population-based study of diabetes and risk characteristics in Turkey: results of the turkish diabetes epidemiology study (TURDEP). Diabetes care, vol. 25, pp. 1551-1556.
  • [5] H. B. El–Serag, H. Hampel, and F. Javadi (2006) The association between diabetes and hepatocellular carcinoma: a systematic review of epidemiologic evidence. Clinical Gastroenterology and Hepatology, vol. 4, pp. 369-380.
  • [6] E. Friberg, N. Orsini, C. Mantzoros, and A. Wolk (2007) Diabetes mellitus and risk of endometrial cancer: a meta-analysis. Diabetologia, vol. 50, pp. 1365-1374.
  • [7] S. C. Larsson, N. Orsini, and A. Wolk (2005) Diabetes mellitus and risk of colorectal cancer: a meta-analysis. Journal of the National Cancer Institute, vol. 97, pp. 1679-1687.
  • [8] A. Sekikawa, H. Fukui, T. Maruo, T. Tsumura, Y. Okabe, and Y. Osaki (2014) Diabetes mellitus increases the risk of early gastric cancer development. European journal of cancer, vol. 50, pp. 2065-2071.
  • [9] H.-J. Yang, D. Kang, Y. Chang, J. Ahn, S. Ryu, J. Cho, et al. (2020) Diabetes mellitus is associated with an increased risk of gastric cancer: a cohort study. Gastric Cancer, vol. 23, pp. 382-390.
  • [10] Y. W. Kwon, H.-S. Jo, S. Bae, Y. Seo, P. Song, M. Song, et al. (2021) Application of proteomics in cancer: recent trends and approaches for biomarkers discovery. Frontiers in Medicine, vol. 8, p. 747333.
  • [11] H. Desaire, E. P. Go, and D. Hua (2022) Advances, obstacles, and opportunities for machine learning in proteomics. Cell Reports Physical Science, vol. 3.
  • [12] E. A. Freeman, G. G. Moisen, J. W. Coulston, and B. T. Wilson (2016) Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, vol. 46, pp. 323-339.
  • [13] H. Duan, Z. Deng, F. Deng, and D. Wang (2016) Assessment of groundwater potential based on multicriteria decision making model and decision tree algorithms. Mathematical Problems in Engineering, vol. 2016, pp. 1-11.
  • [14] S.-W. Lin, N. D. Freedman, A. R. Hollenbeck, A. Schatzkin, and C. C. Abnet (2011) Prospective study of self-reported diabetes and risk of upper gastrointestinal cancers. Cancer epidemiology, biomarkers & prevention, vol. 20, pp. 954-961.
  • [15] K. Mansori, Y. Moradi, S. Naderpour, R. Rashti, A. B. Moghaddam, L. Saed, et al. (2020) Helicobacter pylori infection as a risk factor for diabetes: a meta-analysis of case-control studies. BMC gastroenterology, vol. 20, pp. 1-14.
  • [16] T. Shimamura, H. Ito, J. Shibahara, A. Watanabe, Y. Hippo, H. Taniguchi, et al. (2005) Overexpression of MUC13 is associated with intestinal‐type gastric cancer. Cancer science, vol. 96, pp. 265-273.
  • [17] S.-S. Cui, P. Zhang, L. Sun, Y.-L.-L. Yuan, J. Wang, F.-X. Zhang, et al. (2023) Mucin1 induced trophoblast dysfunction in gestational diabetes mellitus via Wnt/β-catenin pathway. Biological Research, vol. 56, p. 48.
There are 17 citations in total.

Details

Primary Language English
Subjects Machine Learning (Other)
Journal Section Articles
Authors

Şeyma Yaşar 0000-0003-1300-3393

Büşra Nur Fındık 0000-0002-1811-3164

Early Pub Date January 22, 2024
Publication Date
Submission Date November 22, 2023
Acceptance Date November 23, 2023
Published in Issue Year 2023 Volume: 8 Issue: 2

Cite

APA Yaşar, Ş., & Fındık, B. N. (2024). Biomarkers for predicting diabetes in gastric cancer patients with machine learning methods based on proteomic data. The Journal of Cognitive Systems, 8(2), 37-40. https://doi.org/10.52876/jcs.1394024
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