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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Steele JD, Kernohan N, Nabi G. Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging. Int J Mol Sci 2024; 25:5379. [PMID: 38791417 PMCID: PMC11121591 DOI: 10.3390/ijms25105379] [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: 04/13/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.
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Affiliation(s)
- Chidozie N. Ogbonnaya
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | | | - Abeer J. Alhussaini
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - J. Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Neil Kernohan
- Department of Pathology, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK;
| | - Ghulam Nabi
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
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2
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Wang Y, Zhang J, Wan Y, Mi B, Li M, Xie X. Identification and validation of a novel apoptosis-related prognostic risk score model for lung adenocarcinoma. J Cancer 2024; 15:3381-3393. [PMID: 38817872 PMCID: PMC11134425 DOI: 10.7150/jca.92616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 04/11/2024] [Indexed: 06/01/2024] Open
Abstract
The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.
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Affiliation(s)
- Yan Wang
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P.R. China
| | - Jiaojiao Zhang
- Department of Pathology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P.R. China
| | - Yong Wan
- Department of Geriatric Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P710061, P.R. China
| | - Baibing Mi
- Department of Epidemiology and Biostatistic School of Public Health & Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, P.R. China
| | - Manxiang Li
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P.R. China
| | - Xinming Xie
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P.R. China
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3
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An Y, Lu W, Li S, Lu X, Zhang Y, Han D, Su D, Jia J, Yuan J, Zhao B, Tu M, Li X, Wang X, Fang N, Ji S. Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients. Discov Oncol 2023; 14:234. [PMID: 38112859 PMCID: PMC10730790 DOI: 10.1007/s12672-023-00847-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.
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Affiliation(s)
- Yang An
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Wenyuan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Shijia Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoyan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Yuanyuan Zhang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dongcheng Han
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dingyuan Su
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Jia
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Yuan
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Binbin Zhao
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Mengjie Tu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xinyu Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoqing Wang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Na Fang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Shaoping Ji
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
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Yedjou CG, Tchounwou SS, Aló RA, Elhag R, Mochona B, Latinwo L. Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification. INTERNATIONAL JOURNAL OF SCIENCE ACADEMIC RESEARCH 2021; 2:3081-3086. [PMID: 34825131 PMCID: PMC8612371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.
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Affiliation(s)
- Clement G Yedjou
- Department of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, United States
| | - Solange S Tchounwou
- Department of Pathology and Laboratory Medicine. School of Medicine, Tulane University, 1430 Tulane Avenue, New Orleans, LA, 70112, United States
| | - Richard A Aló
- Department of Computer and Information Science, College of Science and Technology, Florida Agricultural & Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 3230, United States
| | - Rashid Elhag
- Department of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, United States
| | - BereKet Mochona
- Department of Chemistry, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, United States
| | - Lekan Latinwo
- Department of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, United States
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