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      Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

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          Abstract

          Background

          Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

          Methods

          In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

          Results

          Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

          Conclusion

          Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

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          Most cited references67

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            MicroRNAs in cancer: biomarkers, functions and therapy.

            The emergence of microRNAs has been one of the defining developments in cancer biology over the past decade, and the explosion of knowledge in this area has brought forward new diagnostic and therapeutic opportunities. The importance of microRNAs in cancer has been underlined by the identification of alterations in microRNA target binding sites and the microRNA processing machinery in tumor cells. Clinical trials utilizing microRNA profiling for patient prognosis and clinical response are now underway, and the first microRNA mimic entered the clinic for cancer therapy in 2013. In this article we review the potential applications of microRNAs for the clinical assessment of patient outcome in cancer, as well as in cancer monitoring and therapy. Copyright © 2014 Elsevier Ltd. All rights reserved.
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              Pancreatic Cancer : A Review

              Pancreatic ductal adenocarcinoma (PDAC) is a relatively uncommon cancer, with approximately 60 430 new diagnoses expected in 2021 in the US. The incidence of PDAC is increasing by 0.5% to 1.0% per year, and it is projected to become the second-leading cause of cancer-related mortality by 2030.
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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                03 August 2023
                2023
                : 13
                : 1244578
                Affiliations
                [1] 1 Clinical Medical College, Southwest Medical University , Luzhou, China
                [2] 2 Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University , Luzhou, China
                [3] 3 Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province , Luzhou, China
                [4] 4 Academician (Expert) Workstation of Sichuan Province , Luzhou, China
                [5] 5 First Teaching Hospital of Tianjin University of Traditional Chinese Medicine , Tianjin, China
                [6] 6 National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion , Tianjin, China
                [7] 7 Beijing University of Chinese Medicine , Beijing, China
                [8] 8 Beijing University of Chinese Medicine Second Affiliated DongFang Hospital , Beijing, China
                [9] 9 Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich , Munich, Germany
                [10] 10 Shenzhen Frontiers in Chinese Medicine Research Co., Ltd. , Shenzhen, China
                [11] 11 Department of Specialty Medicine, Ohio University , Athens, OH, United States
                Author notes

                Edited by: Zhendong Jin, Second Military Medical University, China

                Reviewed by: Feng Zhu, Jincheng People’s Hospital, China; Xiaying Han, Zhejiang Chinese Medical University, China

                †These authors have contributed equally to this work

                Article
                10.3389/fonc.2023.1244578
                10437932
                37601672
                ad6aea9a-d6ec-4cec-82d7-1f2189884d6b
                Copyright © 2023 Chi, Chen, Wang, Zhang, Jiang, Zhang, Jiang, Huang, Quan, Liu, Zhang and Yang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 June 2023
                : 18 July 2023
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 67, Pages: 13, Words: 5060
                Categories
                Oncology
                Original Research
                Custom metadata
                Surgical Oncology

                Oncology & Radiotherapy
                pancreatic cancer,artificial intelligence,early diagnosis,serum mirna,machine learning,therapy

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