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Vellan CJ, Islam T, De Silva S, Mohd Taib NA, Prasanna G, Jayapalan JJ. Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review. Clin Biochem 2024; 129:110776. [PMID: 38823558 DOI: 10.1016/j.clinbiochem.2024.110776] [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: 02/16/2024] [Revised: 04/26/2024] [Accepted: 05/29/2024] [Indexed: 06/03/2024]
Abstract
This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.
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Affiliation(s)
- Christina Jane Vellan
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Tania Islam
- Department of Surgery, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Sumadee De Silva
- Institute of Biochemistry, Molecular Biology and Biotechnology, University of Colombo, Colombo 03, Sri Lanka
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Galhena Prasanna
- Institute of Biochemistry, Molecular Biology and Biotechnology, University of Colombo, Colombo 03, Sri Lanka
| | - Jaime Jacqueline Jayapalan
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia; Universiti Malaya Centre for Proteomics Research (UMCPR), Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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Zadeh Shirazi A, Tofighi M, Gharavi A, Gomez GA. The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide. Technol Cancer Res Treat 2024; 23:15330338241250324. [PMID: 38775067 PMCID: PMC11113055 DOI: 10.1177/15330338241250324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.
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Affiliation(s)
- Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
| | - Morteza Tofighi
- Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
| | - Alireza Gharavi
- Department of Computer Science, Azad University, Mashhad Branch, Mashhad, Iran
| | - Guillermo A. Gomez
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
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Khalid A, Mehmood A, Alabrah A, Alkhamees BF, Amin F, AlSalman H, Choi GS. Breast Cancer Detection and Prevention Using Machine Learning. Diagnostics (Basel) 2023; 13:3113. [PMID: 37835856 PMCID: PMC10572157 DOI: 10.3390/diagnostics13193113] [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: 09/04/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.
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Affiliation(s)
- Arslan Khalid
- Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan; (A.K.); (A.M.)
| | - Arif Mehmood
- Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan; (A.K.); (A.M.)
| | - Amerah Alabrah
- Department of Information Systems, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Bader Fahad Alkhamees
- Department of Information Systems, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Farhan Amin
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | - Hussain AlSalman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Gyu Sang Choi
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea;
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Albadr MAA, Ayob M, Tiun S, AL-Dhief FT, Arram A, Khalaf S. Breast cancer diagnosis using the fast learning network algorithm. Front Oncol 2023; 13:1150840. [PMID: 37434975 PMCID: PMC10332166 DOI: 10.3389/fonc.2023.1150840] [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: 02/02/2023] [Accepted: 04/10/2023] [Indexed: 07/13/2023] Open
Abstract
The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.
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Affiliation(s)
- Musatafa Abbas Abbood Albadr
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Masri Ayob
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Sabrina Tiun
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Fahad Taha AL-Dhief
- Department of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, (UTM), Johor Bahru, Johor, Malaysia
| | - Anas Arram
- Department of Computer Science, Birzeit University, Birzeit, Palestine
| | - Sura Khalaf
- Department of Communication Technology Engineering, College of Information Technology, Imam Ja’afer Al-Sadiq University, Baghdad, Iraq
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Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver. Diagnostics (Basel) 2022; 12:diagnostics12112870. [PMID: 36428930 PMCID: PMC9689917 DOI: 10.3390/diagnostics12112870] [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: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
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Issa GF, Shaalan K, Shaalan Y, Saeed HA, Fatima N, Rehman AU. Brain Stroke Prediction Using ANN. 2022 INTERNATIONAL CONFERENCE ON CYBER RESILIENCE (ICCR) 2022. [DOI: 10.1109/iccr56254.2022.9995885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Ghassan F. Issa
- Skyline University College, University City Sharjah,School of Information Technology,Sharjah,UAE,1797
| | - Khaled Shaalan
- The British University in Dubai,Faculty of Engineering and IT,United Arab Emirates
| | | | - Hafiza Afia Saeed
- Agriculture University,Department of Computer Science,Faisalabad,Pakistan
| | - Noor Fatima
- Fatima memorial hospital college of medicine and dentistry Lahore,Lahore,Pakistan,54000
| | - Abd Ur Rehman
- Riphah international University,Riphah School of Computing,Lahore,Pakistan
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Accuracy Enhancement for Breast Cancer Detection using Classification and Feature Selection. INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH 2022. [DOI: 10.4018/ijirr.299931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.
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Rasool A, Bunterngchit C, Tiejian L, Islam MR, Qu Q, Jiang Q. Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063211. [PMID: 35328897 PMCID: PMC8949437 DOI: 10.3390/ijerph19063211] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 12/24/2022]
Abstract
Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.
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Affiliation(s)
- Abdur Rasool
- University of Chinese Academy of Sciences, Beijing 101408, China; (A.R.); (C.B.)
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Chayut Bunterngchit
- University of Chinese Academy of Sciences, Beijing 101408, China; (A.R.); (C.B.)
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Luo Tiejian
- University of Chinese Academy of Sciences, Beijing 101408, China; (A.R.); (C.B.)
- Correspondence: (L.T.); (Q.J.); Tel.: +86-137-0127-2380 (L.T.); +86-755-8639-2340 (Q.J.)
| | - Md. Ruhul Islam
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4044 Stavanger, Norway;
| | - Qiang Qu
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Qingshan Jiang
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Correspondence: (L.T.); (Q.J.); Tel.: +86-137-0127-2380 (L.T.); +86-755-8639-2340 (Q.J.)
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Rehman AU, Haroon HM, Malik JA, Saeed A, Ali A, Ateeq K. Fuzzy System For Covid-19 Disease Detection. 2022 INTERNATIONAL CONFERENCE ON BUSINESS ANALYTICS FOR TECHNOLOGY AND SECURITY (ICBATS) 2022. [DOI: 10.1109/icbats54253.2022.9759014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Abd Ur Rehman
- Riphah International university,Department of Computer Science,Lahore,Pakistan
| | | | | | - Afia Saeed
- Agriculture University,Department of Computer Science,Faislabad,Pakistan
| | - Asad Ali
- NCBA&E,School of Computer Science,Lahore,Pakistan
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Cassim N, Mapundu M, Olago V, Celik T, George JA, Glencross DK. Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa. BMC Med Inform Decis Mak 2021; 21:330. [PMID: 34823522 PMCID: PMC8614040 DOI: 10.1186/s12911-021-01697-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background Prostate cancer (PCa) is the leading male neoplasm in South Africa with an age-standardised incidence rate of 68.0 per 100,000 population in 2018. The Gleason score (GS) is the strongest predictive factor for PCa treatment and is embedded within semi-structured prostate biopsy narrative reports. The manual extraction of the GS is labour-intensive. The objective of our study was to explore the use of text mining techniques to automate the extraction of the GS from irregularly reported text-intensive patient reports. Methods We used the associated Systematized Nomenclature of Medicine clinical terms morphology and topography codes to identify prostate biopsies with a PCa diagnosis for men aged > 30 years between 2006 and 2016 in the Gauteng Province, South Africa. We developed a text mining algorithm to extract the GS from 1000 biopsy reports with a PCa diagnosis from the National Health Laboratory Service database and validated the algorithm using 1000 biopsies from the private sector. The logical steps for the algorithm were data acquisition, pre-processing, feature extraction, feature value representation, feature selection, information extraction, classification, and discovered knowledge. We evaluated the algorithm using precision, recall and F-score. The GS was manually coded by two experts for both datasets. The top five GS were reported, with the remaining scores categorised as “Other” for both datasets. The percentage of biopsies with a high-risk GS (≥ 8) was also reported. Results The first output reported an F-score of 0.99 that improved to 1.00 after the algorithm was amended (the GS reported in clinical history was ignored). For the validation dataset, an F-score of 0.99 was reported. The most commonly reported GS were 5 + 4 = 9 (17.6%), 3 + 3 = 6 (17.5%), 4 + 3 = 7 (16.4%), 3 + 4 = 7 (14.7%) and 4 + 4 = 8 (14.2%). For the validation dataset, the most commonly reported GS were: (i) 3 + 3 = 6 (37.7%), (ii) 3 + 4 = 7 (19.4%), (iii) 4 + 3 = 7 (14.9%), (iv) 4 + 4 = 8 (10.0%) and (v) 4 + 5 = 9 (7.4%). A high-risk GS was reported for 31.8% compared to 17.4% for the validation dataset. Conclusions We demonstrated reliable extraction of information about GS from narrative text-based patient reports using an in-house developed text mining algorithm. A secondary outcome was that late presentation could be assessed.
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Affiliation(s)
- Naseem Cassim
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand and National Health Laboratory Service (NHLS), 7 York Road, Parktown, Johannesburg, South Africa.
| | - Michael Mapundu
- School of Public Health, Faculty of Health Sciences, University of Witwatersrand, 7 York Road, Parktown, Johannesburg, South Africa
| | - Victor Olago
- National Health Laboratory Service (NHLS), National Cancer Registry (NCR), 1 Modderfontein Road, Sandringham, Johannesburg, South Africa
| | - Turgay Celik
- School of Electrical & Information Engineering and Wits Institute of Data Science, University of Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, South Africa
| | - Jaya Anna George
- Department of Chemical Pathology, Faculty of Health Sciences, University of Witwatersrand and National Health Laboratory Service (NHLS), 7 York Road, Parktown, Johannesburg, South Africa
| | - Deborah Kim Glencross
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand and National Health Laboratory Service (NHLS), 7 York Road, Parktown, Johannesburg, South Africa
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Basic Pan-Cancer Analysis of the Carcinogenic Effects of Cyclin-Dependent Kinase 4 (CDK4) in Human Surface Tumors. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8493572. [PMID: 34422248 PMCID: PMC8371625 DOI: 10.1155/2021/8493572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 07/23/2021] [Indexed: 11/23/2022]
Abstract
Although the evidence based on current human, animal, or molecular biology can explain some of the relationships between CDK4 and cancer, there is no pan-cancer analysis of the gene CDK4 in human skin tumors. Therefore, the potential carcinogenic effects of CDK4 in 33 tumors were initially explored in the datasets of the GEO (Gene Expression Omnibus) and the CGA (Cancer Genome Atlas). We found that CDK4 was highly expressed in most cancers and that CDK4 performance levels significantly correlated with the prognosis of cancer patients. These were found in our preliminary exploration. In addition, we used the dataset in tumors such as cutaneous melanoma or lung adenocarcinoma and found increased levels of phosphorylation of r24 l/C/h/s. In addition, fibroblast infiltration associated with CDK4 cancer was observed in head and neck, sarcoma, and melanoma skin. Using this pan-cancer study, our group has provided a comprehensive preliminary demonstration of the oncogenic effects of the CDK4 gene on different human skin tumors.
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Abstract
Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.
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Affiliation(s)
- Mohammed A Alaidarous
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Kingdom of Saudi Arabia. E-mail.
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