1
|
Alanezi ST, Kraśny MJ, Kleefeld C, Colgan N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters. Cancers (Basel) 2024; 16:2163. [PMID: 38893281 PMCID: PMC11171700 DOI: 10.3390/cancers16112163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/25/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.
Collapse
Affiliation(s)
- Saleh T. Alanezi
- Department of Physics, College of Science, Northern Border University, Arar P.O. Box 1321, Saudi Arabia
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
| | - Marcin Jan Kraśny
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
- Translational Medical Device Lab (TMDLab), Lambe Institute for Translational Research, University of Galway, H91 V4AY Galway, Ireland
| | - Christoph Kleefeld
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
| | - Niall Colgan
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
- Faculty of Engineering & Informatics, Technological University of the Shannon, N37 HD68 Athlone, Ireland
| |
Collapse
|
2
|
Duderstadt EL, Samuelson DJ. Rat Mammary carcinoma susceptibility 3 (Mcs3) pleiotropy, socioenvironmental interaction, and comparative genomics with orthologous human 15q25.1-25.2. G3 (BETHESDA, MD.) 2022; 13:6782958. [PMID: 36315068 PMCID: PMC9836357 DOI: 10.1093/g3journal/jkac288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022]
Abstract
Genome-wide association studies of breast cancer susceptibility have revealed risk-associated genetic variants and nominated candidate genes; however, the identification of causal variants and genes is often undetermined by genome-wide association studies. Comparative genomics, utilizing Rattus norvegicus strains differing in susceptibility to mammary tumor development, is a complimentary approach to identify breast cancer susceptibility genes. Mammary carcinoma susceptibility 3 (Mcs3) is a Copenhagen (COP/NHsd) allele that confers resistance to mammary carcinomas when introgressed into a mammary carcinoma susceptible Wistar Furth (WF/NHsd) genome. Here, Mcs3 was positionally mapped to a 7.2-Mb region of RNO1 spanning rs8149408 to rs107402736 (chr1:143700228-150929594, build 6.0/rn6) using WF.COP congenic strains and 7,12-dimethylbenz(a)anthracene-induced mammary carcinogenesis. Male and female WF.COP-Mcs3 rats had significantly lower body mass compared to the Wistar Furth strain. The effect on female body mass was observed only when females were raised in the absence of males indicating a socioenvironmental interaction. Furthermore, female WF.COP-Mcs3 rats, raised in the absence of males, did not develop enhanced lobuloalveolar morphologies compared to those observed in the Wistar Furth strain. Human 15q25.1-25.2 was determined to be orthologous to rat Mcs3 (chr15:80005820-82285404 and chr15:83134545-84130720, build GRCh38/hg38). A public database search of 15q25.1-25.2 revealed genome-wide significant and nominally significant associations for body mass traits and breast cancer risk. These results support the existence of a breast cancer risk-associated allele at human 15q25.1-25.2 and warrant ultrafine mapping of rat Mcs3 and human 15q25.1-25.2 to discover novel causal genes and variants.
Collapse
Affiliation(s)
- Emily L Duderstadt
- Present address for Emily L. Duderstadt: Procter and Gamble (P&G), 8700 Mason-Montgomery Road, Mason, OH 45040, USA
| | - David J Samuelson
- Corresponding author: Department of Biochemistry & Molecular Genetics, University of Louisville School of Medicine, 319 Abraham Flexner Way, Louisville, KY 40202, USA.
| |
Collapse
|
3
|
Wang X, Li X, Chen H, Peng Y, Li Y. Pulmonary MRI Radiomics and Machine Learning: Effect of Intralesional Heterogeneity on Classification of Lesion. Acad Radiol 2022; 29 Suppl 2:S73-S81. [PMID: 33495072 DOI: 10.1016/j.acra.2020.12.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/09/2020] [Accepted: 12/30/2020] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the effect of intralesional heterogeneity on differentiating benign and malignant pulmonary lesions, quantitative magnetic resonance imaging (MRI) radiomics, and machine learning methods were adopted. MATERIALS AND METHODS A total of 176 patients with multiparametric MRI were involved in this exploratory study. To investigate the effect of intralesional heterogeneity on lesion classification, a radiomics model called tumor heterogeneity model was developed and compared to the conventional radiomics model based on the entire tumor. In tumor heterogeneity model, each lesion was divided into five sublesions depending on the spatial location through clustering algorithm. From the five sublesions in multi-parametric MRI sequences, 1100 radiomics features were extracted. The recursive feature elimination method was employed to select features and support vector machine classifier was used to distinguish benign and malignant lesion. The performance of classification was evaluated with the receiver operating characteristic curve and the area under the curve (AUC) was the figure of merit. The 3-fold cross-validation (CV) with and without nesting was used to validate the model, respectively. RESULTS The tumor heterogeneity model (AUC = 0.74 ± 0.04 and 0.90 ± 0.03, CV with and without nesting, respectively) outperforms conventional model (AUC = 0.68 ± 0.04 and 0.87 ± 0.03). The difference between the two models is statistically significant (p = 0.03) for lesions greater than 18.80 cm3. CONCLUSION Intralesional heterogeneity influences the classification of pulmonary lesions. The tumor heterogeneity model tends to perform better than conventional radiomics model.
Collapse
Affiliation(s)
- Xinhui Wang
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Houjin Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China.
| | - Yahui Peng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China
| | - Yanfeng Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China
| |
Collapse
|
4
|
Wu Z, Shou L, Wang J, Huang T, Xu X. The Methylation Pattern for Knee and Hip Osteoarthritis. Front Cell Dev Biol 2020; 8:602024. [PMID: 33240895 PMCID: PMC7677303 DOI: 10.3389/fcell.2020.602024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 01/08/2023] Open
Abstract
Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.
Collapse
Affiliation(s)
- Zhen Wu
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Lu Shou
- Departmemt of Pneumology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Jian Wang
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Xinwei Xu
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
| |
Collapse
|
5
|
Li M, Chen F, Zhang Y, Xiong Y, Li Q, Huang H. Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies. Front Physiol 2020; 11:483. [PMID: 32581823 PMCID: PMC7287215 DOI: 10.3389/fphys.2020.00483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/20/2020] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.
Collapse
Affiliation(s)
- Ming Li
- Department of Cardiology, Eastern Hospital, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Fuli Chen
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Yaling Zhang
- Department of Nephrology, Eastern Hospital, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Yan Xiong
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Qiyong Li
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Hui Huang
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| |
Collapse
|
6
|
Yan Q, Hu D, Li M, Chen Y, Wu X, Ye Q, Wang Z, He L, Zhu J. The Serum MicroRNA Signatures for Pancreatic Cancer Detection and Operability Evaluation. Front Bioeng Biotechnol 2020; 8:379. [PMID: 32411694 PMCID: PMC7201024 DOI: 10.3389/fbioe.2020.00379] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/06/2020] [Indexed: 12/19/2022] Open
Abstract
Pancreatic cancer (PC) has high morbidity and mortality. It is the fourth leading cause of cancer death. Its diagnosis and treatment are difficult. Liquid biopsy makes early diagnosis of pancreatic cancer possible. We analyzed the expression profiles of 2,555 serum miRNAs in 100 pancreatic cancer patients and 150 healthy controls. With advanced feature selection methods, we identified 13 pancreatic cancer signature miRNAs that can classify the pancreatic cancer patients and healthy controls. For pancreatic cancer treatment, operation is still the first choice. But many pancreatic cancer patients are already inoperable. Therefore, we compared the 79 inoperable and 21 operable patients and identified 432 miRNAs that can predict whether a pancreatic cancer patient was operable. The functional analysis of the 13 pancreatic cancer signatures and the 432 operability miRNAs revealed the molecular mechanisms of pancreatic cancer and shield light on the diagnosis and therapy of pancreatic cancer in clinical practice.
Collapse
Affiliation(s)
- Qiuliang Yan
- Department of General Surgery, Jinhua People's Hospital, Jinhua, China
| | - Dandan Hu
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Maolan Li
- Department of General Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangsong Wu
- Department of General Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhijiang Wang
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingzhe He
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
7
|
Wang X, Wan Q, Chen H, Li Y, Li X. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods. Eur Radiol 2020; 30:4595-4605. [PMID: 32222795 DOI: 10.1007/s00330-020-06768-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 01/23/2020] [Accepted: 02/20/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. MATERIALS AND METHODS This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results. RESULTS For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set. CONCLUSION Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods. KEY POINTS • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.
Collapse
Affiliation(s)
- Xinhui Wang
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China
| | - Qi Wan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Yanjiangxilu No 151 in Yuexiu, Guangzhou, China
| | - Houjin Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China.
| | - Yanfeng Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Yanjiangxilu No 151 in Yuexiu, Guangzhou, China.
| |
Collapse
|
8
|
Zhang J, Hu H, Xu S, Jiang H, Zhu J, Qin E, He Z, Chen E. The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer. Front Genet 2020; 11:17. [PMID: 32117436 PMCID: PMC7010953 DOI: 10.3389/fgene.2020.00017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/07/2020] [Indexed: 12/11/2022] Open
Abstract
Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11–16% of lung adenocarcinomas (p.G12C accounts for 45–50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.
Collapse
Affiliation(s)
- Jisong Zhang
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - Huihui Hu
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - Shan Xu
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - Hanliang Jiang
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - Jihong Zhu
- Department of Anesthesiology, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - E Qin
- Department of Respiratory Medicine, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Zhengfu He
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| | - Enguo Chen
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China
| |
Collapse
|
9
|
Chen L, Li D, Shao Y, Wang H, Liu Y, Zhang Y. Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine. Front Genet 2019; 10:1146. [PMID: 31803234 PMCID: PMC6872643 DOI: 10.3389/fgene.2019.01146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.
Collapse
Affiliation(s)
- Lijuan Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Daojie Li
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Ye Shao
- School of Medicine, Huaqiao University, Quanzhou, China
| | - Hui Wang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Yuqing Liu
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| | - Yunhua Zhang
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| |
Collapse
|
10
|
Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes. Int J Mol Sci 2019; 20:ijms20174269. [PMID: 31480430 PMCID: PMC6747348 DOI: 10.3390/ijms20174269] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 08/19/2019] [Accepted: 08/29/2019] [Indexed: 12/18/2022] Open
Abstract
Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.
Collapse
|