Detection of Urinary Biomarkers for Early Diagnosis of Pancreatic Cancer by Data Analysis

Chi Le, Yucheng Liu, Fangyi Tian, Yang Xu

2022

Abstract

In this sample-structured document, neither the cross-linking of float elements and bibliography nor metadata/copyright information is available. The sample document is provided in “Draft” mode and to view it in the final layout format, applying the required template is essential with some standard steps. According to data released by the American Cancer Society in 2019, the mortality rate caused by pancreatic cancer ranks fourth among malignant tumors. By 2030, the incidence of Pancreatic ductal adenocarcinoma (PDAC) will continue to increase and may become the second leading cause of death among all tumor diseases. If the tumor could be detected and resectted at an early stage, the survival rate of PDAC patients will be greatly improved. However, symptoms rarely show until the cancer reaches its advanced stage and most of the available treatments are palliative. Therefore, most patients have reached the advanced stage of cancer when they are diagnosed and thus having poor prognoses. Therefore, we are interested in the early detection, prediction and diagnosis of pancreatic cancer, and we will discusse which factors are related to pancreatic cancer in the following parts. We collected a total of 590 samples in which 7 attributes, age, CA 19–9 (Carbohydrate antigen199), creatinine, LYVE1 (Lymphatic Vessel Endothelial Hyaluronic Acid Receptor 1), REG1B (regenerating islet-derived 1 beta), TFF1 (Recombinant Trefoil Factor 1) and REG1A (Recombinant Human Regenerating Islet-Derived Protein 1-alpha) were selected as our independent variables. The dependent variable Y is diagnosis which indicates whether a participant has pancreatic cancer. Logistic regression and lasso regression were used to construct a model for the prediction of pancreatic cancer. All analyses above were performed using R software, version 4.1.1. We finally found that the distributions of Blood plasma levels of CA 19–9 monoclonal antibody, creatine, LYVE1, REG1B, TFF1 and REG1A are all positive skewed and asymmetrical. In addition, people’s illness is significantly related to age, creatine, LYVE1, REG1B, TFF1 and REG1A. However, the level of CA 19-9 monoclonal antibody in the human body is not so significantly correlated with the corresponding human disease. After selecting appropriate methods and analyzing a large amount of data, according to the regression results, etc., we can conclude that the incidence of PDAC disease is significantly related to age and gender. Based on this, in the follow-up research, it has provided the possibility for early prediction and disease prevention and control of PDAC based on age and gender, and also provided new ideas for the pharmaceutical, treatment and daily care of the disease.

Paper Citation

in Harvard Style

Le C., Liu Y., Tian F. and Xu Y. (2022). Detection of Urinary Biomarkers for Early Diagnosis of Pancreatic Cancer by Data Analysis. In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH, ISBN 978-989-758-596-8, pages 56-61. DOI: 10.5220/0011228500003438

in Bibtex Style

@conference{ichih22,
author={Chi Le and Yucheng Liu and Fangyi Tian and Yang Xu},
title={Detection of Urinary Biomarkers for Early Diagnosis of Pancreatic Cancer by Data Analysis},
booktitle={Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH,},
year={2022},
pages={56-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011228500003438},
isbn={978-989-758-596-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH,
TI - Detection of Urinary Biomarkers for Early Diagnosis of Pancreatic Cancer by Data Analysis
SN - 978-989-758-596-8
AU - Le C.
AU - Liu Y.
AU - Tian F.
AU - Xu Y.
PY - 2022
SP - 56
EP - 61
DO - 10.5220/0011228500003438