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Nazari E, Naderi H, Tabadkani M, ArefNezhad R, Farzin AH, Dashtiahangar M, Khazaei M, Ferns GA, Mehrabian A, Tabesh H, Avan A. Breast cancer prediction using different machine learning methods applying multi factors. J Cancer Res Clin Oncol 2023; 149:17133-17146. [PMID: 37773467 DOI: 10.1007/s00432-023-05388-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/01/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVE Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.
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Affiliation(s)
- Elham Nazari
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Naderi
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Tabadkani
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reza ArefNezhad
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | | | - Majid Khazaei
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Amin Mehrabian
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Hamed Tabesh
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
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Beihaghi M, Sahebi R, Beihaghi MR, Nessiani RK, Yarasmi MR, Gholamalizadeh S, Shahabnavaie F, Shojaei M. Evaluation of rs10811661 polymorphism in CDKN2A / B in colon and gastric cancer. BMC Cancer 2023; 23:985. [PMID: 37845622 PMCID: PMC10577985 DOI: 10.1186/s12885-023-11461-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023] Open
Abstract
One of the causes of colon and gastric cancer is the dysregulation of carcinogenic genes, tumor inhibitors, and micro-RNA. The purpose of this study is to apply rs10811661 polymorphism in CDKN2A /B gene as an effective biomarker of colon cancer and early detection of gastric cancer. As a result,400 blood samples, inclusive of 200 samples from healthy individuals and 200 samples (100 samples from intestinal cancer,100 samples from stomach cancer) from the blood of someone with these cancers, to determine the genotype of genes in healthful and ill people through PCR-RFLP approach and Allelic and genotypic tests of SPSS software. To observe the connection between gastric cancer and bowel cancer risk and genotypes, the t-student test for quantitative variables and Pearson distribution for qualitative variables have been tested and the results have been evaluated using the Chi-square test. The effects confirmed that the highest frequency of TT genotypes is in affected individuals and CC genotype is in healthful individuals. In addition, it confirmed that women were more inclined than men to T3 tumor invasion and most grade II and III colon cancers, and in older sufferers with gastric cancer, the grade of tumor tended to be grade I. Among genetic variety and rs10811661, with invasiveness, there is a tumor size and degree in the affected person. In summary, our findings suggest that the rs10811661 polymorphism of the CDKN2A / B gene is strongly associated with the occurrence of intestinal cancer and stomach is linked to its potential role as a prognostic biomarker for the management of bowel cancer and stomach.
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Affiliation(s)
- Maria Beihaghi
- Department of Biology, Kavian Institute of Higher Education, Mashhad, Iran.
- School of Science and Technology, The University of Georgia, Tbilisi, Georgia.
| | - Reza Sahebi
- Department of Modern Sciences and Technologies, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Reza Beihaghi
- Department of Public Health, Sheffield Hallam University, Sheffield, South Yorkshire, England
| | | | | | | | | | - Mitra Shojaei
- Department of Modern Sciences and Technologies, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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3
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Sanchez A, Lhuillier J, Grosjean G, Ayadi L, Maenner S. The Long Non-Coding RNA ANRIL in Cancers. Cancers (Basel) 2023; 15:4160. [PMID: 37627188 PMCID: PMC10453084 DOI: 10.3390/cancers15164160] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome 9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.
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Affiliation(s)
| | | | | | - Lilia Ayadi
- CNRS, Université de Lorraine, IMoPA, F-54000 Nancy, France
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Sadegh-Khorrami M, Hatami H, Bakhshani A, Bagherikashouk S, Sadabadi F, Ghazizadeh H, Amerizadeh F, Esmaeily H, Moohebati M, Heidari-Bakavoli A, Ferns GA, Pasdar A, Ghayour-Mobarhan M, Avan A. The association between a variant of the cyclin-dependent kinase inhibitor 2A/B gene and risk of cardiovascular disease. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2021.101480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chen YX, Chen J, Yin JY, Zhou HH, He BM, Liu ZQ. Non-Coding RNA Polymorphisms (rs2910164 and rs1333049) Associated With Prognosis of Lung Cancer Under Platinum-Based Chemotherapy. Front Pharmacol 2021; 12:709528. [PMID: 34603024 PMCID: PMC8481925 DOI: 10.3389/fphar.2021.709528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/06/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose: Lung cancer is the largest cause of cancer deaths in the world. Platinum-based chemotherapy is a foundation of first-line chemotherapy. However, the prognosis of lung cancer treated with platinum-based chemotherapy is still a challenge. Single nucleotide polymorphism of non-coding RNA has the potential to be a biomarker, but its effectiveness has yet to be comprehensively assessed. In this study, we explored the association between polymorphisms of non-coding RNA and prognosis of lung cancer patients receiving platinum-based chemotherapy. Materials and Methods: For 446 lung cancer patients receiving platinum-based chemotherapy, 22 single nucleotide polymorphisms of microRNA and long noncoding RNA were genotyped by MALDI-TOF mass spectrometry. Cox regression analysis, Kaplan-Meier method, and long-rank test have been performed to assess the association of overall and progression-free survival with polymorphisms. Results: In the additive and dominant models, genetic polymorphism of ANRIL rs1333049 (G > C) was significantly associated with progression-free survival. Additive model: CC vs GC vs GG [HR = 0.84, p = 0.021, 95% CI (0.73–0.97)]; Recessive model: CC vs GG + GC [HR = 0.77, p = 0.026, 95% CI (0.61–0.97)]. In the dominant model, compared with the CC genotype patients, lower risk of death [HR = 0.81, p = 0.036, 95% CI (0.66–0.99)] and lower risk of progression [HR = 0.81, p = 0.040, 95% CI (0.67–0.99)] have been observed on the patients with CG or GG genotype in miR-146A rs2910164. Conclusion: Our research demonstrated the potential of using ANRIL rs1333049 (G > C) and miR-146A rs2910164 (C > G) as biomarkers to support the prediction of a better prognosis for lung cancer patients receiving platinum-based chemotherapy.
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Affiliation(s)
- Yi-Xin Chen
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Central South University, Changsha, China
| | - Juan Chen
- Departments of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Ji-Ye Yin
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Central South University, Changsha, China
| | - Hong-Hao Zhou
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Central South University, Changsha, China
| | - Bai-Mei He
- Departments of Gerontology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhao-Qian Liu
- Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Central South University, Changsha, China
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Qiu C, Wang B, Wang P, Wang X, Ma Y, Dai L, Shi J, Wang K, Sun G, Ye H, Zhang J. Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer. Cancer Sci 2021; 112:3388-3400. [PMID: 34115421 PMCID: PMC8353906 DOI: 10.1111/cas.15021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/06/2021] [Accepted: 06/08/2021] [Indexed: 12/24/2022] Open
Abstract
Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.
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Affiliation(s)
- Cuipeng Qiu
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Bofei Wang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Peng Wang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Xiao Wang
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Yan Ma
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Liping Dai
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Jianxiang Shi
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Keyan Wang
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Guiying Sun
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Hua Ye
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Jianying Zhang
- BGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
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Rahmani F, Avan A, Amerizadeh F, Ferns GA, Talebian S, Shahidsales S. The association of a genetic variant in CDKN2A/B gene and the risk of colorectal cancer. EXCLI JOURNAL 2020; 19:1316-1321. [PMID: 33122978 PMCID: PMC7588726 DOI: 10.17179/excli2020-2051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 09/14/2020] [Indexed: 11/10/2022]
Abstract
Colorectal cancer is among the most aggressive tumors, and its development involves an interplay between various genetic and environmental familial risk factors. Several genetic polymorphisms have been reported to be associated with colorectal cancer in recent studies. In this current study, we aimed to evaluate the possible relationship between a CDKN2A/B, single nucleotide polymorphisms (SNP) (rs10811661), with the risk of colorectal cancer. A total of 541 individuals with, or without cancer were recruited. DNA was extracted, and genotyped using a Taq-Man based real-time PCR method. The rs10811661 SNP was associated with an increased risk of colorectal cancer (additive model: OR=3.46, CI= 1.79-6.69, p<0.0001 and recessive model: 5.72, CI= 3.12-10.49, p<0.0001). The distribution of minor alleles in the total population for homozygote allele was 9.2 %, while this was 20.1 % for heterozygotes. In summary, our findings indicate that the rs10811661 polymorphism of the CDKN2A/B gene was strongly related to the occurrence of colorectal cancer suggesting its potential role as a prognostic biomarker for the management of colorectal cancer.
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Affiliation(s)
- Farzad Rahmani
- Iranshahr University of Medical Sciences, Iranshahr, Iran.,Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Forouzan Amerizadeh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK
| | - Sahar Talebian
- Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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