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Urabe F. The relevance of circRNAs in serum of patients undergoing prostate biopsy. Int J Urol 2024; 31:581. [PMID: 38469670 DOI: 10.1111/iju.15452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Affiliation(s)
- Fumihiko Urabe
- Department of Urology, The Jikei University School of Medicine, Tokyo, Japan
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2
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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [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] [Indexed: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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Guo S, Mao C, Peng J, Xie S, Yang J, Xie W, Li W, Yang H, Guo H, Zhu Z, Zheng Y. Improved lung cancer classification by employing diverse molecular features of microRNAs. Heliyon 2024; 10:e26081. [PMID: 38384512 PMCID: PMC10878959 DOI: 10.1016/j.heliyon.2024.e26081] [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: 01/12/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-mir-182_48u (an edited miRNA), from 316 training samples. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples.
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Affiliation(s)
- Shiyong Guo
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
- College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
- College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
| | - Chunyi Mao
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Jun Peng
- Department of Thoracic Surgery, The First People's Hospital of Yunnan Province, i.e., The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650032, China
| | - Shaohui Xie
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Jun Yang
- School of Criminal Investigation, Yunnan Police College, Kunming, Yunnan 650223, China
| | - Wenping Xie
- College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
- College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
| | - Wanran Li
- College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
- College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
| | - Huaide Yang
- College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
- College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
| | - Hao Guo
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Zexuan Zhu
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Yun Zheng
- College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
- College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
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Inagaki M, Uchiyama M, Yoshikawa-Kawabe K, Ito M, Murakami H, Gunji M, Minoshima M, Kohnoh T, Ito R, Kodama Y, Tanaka-Sakai M, Nakase A, Goto N, Tsushima Y, Mori S, Kozuka M, Otomo R, Hirai M, Fujino M, Yokoyama T. Comprehensive circulating microRNA profile as a supersensitive biomarker for early-stage lung cancer screening. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04728-9. [PMID: 37076642 PMCID: PMC10115369 DOI: 10.1007/s00432-023-04728-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE Â Less-invasive early diagnosis of lung cancer is essential for improving patient survival rates. The purpose of this study is to demonstrate that serum comprehensive miRNA profile is high sensitive biomarker to early-stage lung cancer in direct comparison to the conventional blood biomarker using next-generation sequencing (NGS) technology combined with automated machine learning (AutoML). METHODS We first evaluated the reproducibility of our measurement system using Pearson's correlation coefficients between samples derived from a single pooled RNA sample. To generate comprehensive miRNA profile, we performed NGS analysis of miRNAs in 262 serum samples. Among the discovery set (57 patients with lung cancer and 57 healthy controls), 1123 miRNA-based diagnostic models for lung cancer detection were constructed and screened using AutoML technology. The diagnostic faculty of the best performance model was evaluated by inspecting the validation samples (74 patients with lung cancer and 74 healthy controls). RESULTS The Pearson's correlation coefficients between samples derived from the pooled RNA sampleââ„â0.98. In the validation analysis, the best model showed a high AUC score (0.98) and a high sensitivity for early stage lung cancer (85.7%, nâ=â28). Furthermore, in comparison to carcinoembryonic antigen (CEA), a conventional blood biomarker for adenocarcinoma, the miRNA-based model showed higher sensitivity for early-stage lung adenocarcinoma (CEA, 27.8%, nâ=â18; miRNA-based model, 77.8%, nâ=â18). CONCLUSION The miRNA-based diagnostic model showed a high sensitivity for lung cancer, including early-stage disease. Our study provides the experimental evidence that serum comprehensive miRNA profile can be a highly sensitive blood biomarker for early-stage lung cancer.
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Affiliation(s)
- Masayasu Inagaki
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Makoto Uchiyama
- Research and Development Division, ARKRAY, Inc., Yousuien-Nai, 59 Gansuin-Cho, Kamigyo-Ku, Kyoto, 602-0008, Japan.
| | - Kanae Yoshikawa-Kawabe
- Department of Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Masafumi Ito
- Department of Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Hideki Murakami
- Department of Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Masaharu Gunji
- Department of Cytology and Molecular Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Makoto Minoshima
- Department of Cytology and Molecular Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Takashi Kohnoh
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Ryota Ito
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Yuta Kodama
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Mari Tanaka-Sakai
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Atsushi Nakase
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Nozomi Goto
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Yusuke Tsushima
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan
| | - Shoich Mori
- Department of Respiratory Surgery, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Masahiro Kozuka
- Research and Development Division, ARKRAY, Inc., Yousuien-Nai, 59 Gansuin-Cho, Kamigyo-Ku, Kyoto, 602-0008, Japan
| | - Ryo Otomo
- Research and Development Division, ARKRAY, Inc., Yousuien-Nai, 59 Gansuin-Cho, Kamigyo-Ku, Kyoto, 602-0008, Japan
| | - Mitsuharu Hirai
- Research and Development Division, ARKRAY, Inc., Yousuien-Nai, 59 Gansuin-Cho, Kamigyo-Ku, Kyoto, 602-0008, Japan
| | - Masahiko Fujino
- Department of Pathology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Aichi, 453-8511, Japan
| | - Toshihiko Yokoyama
- Department of Respiratory Medicine, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35 Michishita-Cho, Nakamura-Ku, Nagoya, Aichi, 453-8511, Japan.
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Cygert S, Pastuszak K, GĂłrski F, SieczczyĆski M, Juszczyk P, Rutkowski A, Lewalski S, RĂłĆŒaĆski R, Jopek MA, Jassem J, CzyĆŒewski A, Wurdinger T, Best MG, Ć»aczek AJ, Supernat A. Platelet-Based Liquid Biopsies through the Lens of Machine Learning. Cancers (Basel) 2023; 15:cancers15082336. [PMID: 37190262 DOI: 10.3390/cancers15082336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.
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Affiliation(s)
- Sebastian Cygert
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
- Ideas NCBR, 00-801 Warsaw, Poland
| | - Krzysztof Pastuszak
- Department of Algorithms and System Modeling, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
| | - Franciszek GĂłrski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | - MichaĆ SieczczyĆski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | - Piotr Juszczyk
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | - Antoni Rutkowski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | - Sebastian Lewalski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | | | - Maksym Albin Jopek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
| | - Jacek Jassem
- Department of Oncology and Radiotherapy, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
| | - Andrzej CzyĆŒewski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 GdaĆsk, Poland
| | - Thomas Wurdinger
- Department of Neurosurgery, Amsterdam University Medical Center, 1081 Amsterdam, The Netherlands
| | - Myron G Best
- Department of Neurosurgery, Amsterdam University Medical Center, 1081 Amsterdam, The Netherlands
| | - Anna J ƻaczek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
| | - Anna Supernat
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of GdaĆsk, 80-210 GdaĆsk, Poland
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Urabe F, Yamamoto Y, Kimura T. miRNAs in prostate cancer: Intercellular and extracellular communications. Int J Urol 2022; 29:1429-1438. [PMID: 36122303 DOI: 10.1111/iju.15043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/25/2022] [Indexed: 12/23/2022]
Abstract
Prostate cancer is the most prevalent male cancer in Western Europe and North America. Although new drugs were recently approved, clinical challenges such as accurately predicting and screening drug-resistant prostate cancer remain. microRNAs are short noncoding RNA molecules that participate in gene regulation at the post-transcriptional level by targeting messenger RNAs. There is accumulating evidence that intracellular microRNAs play important roles as promoters or inhibitors of prostate cancer progression. Additionally, recent studies showed that microRNAs are encapsulated in extracellular vesicles and shuttled into the extracellular space. Transfer of extracellular microRNAs contributes to intercellular communication between prostate cancer cells and components of the tumor microenvironment, which can promote prostate cancer progression. Furthermore, due to their encapsulation in extracellular vesicles, extracellular microRNAs can be stably present in body fluids which contain high levels of RNase. Thus, circulating microRNAs have great potential as noninvasive diagnostic and prognostic biomarkers for prostate cancer. Here, we summarize the roles of intracellular and extracellular microRNAs in prostate cancer progression and discuss the potential of microRNA-based therapeutics as a novel treatment strategy for prostate cancer.
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Affiliation(s)
- Fumihiko Urabe
- Department of Urology, The Jikei University School of Medicine, Tokyo, Japan.,Laboratory of Integrative Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Yusuke Yamamoto
- Laboratory of Integrative Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Takahiro Kimura
- Department of Urology, The Jikei University School of Medicine, Tokyo, Japan
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