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Li L, Huang F, Zhang YH, Cai YD. Identifying allergic-rhinitis-associated genes with random-walk-based method in PPI network. Comput Biol Med 2024; 175:108495. [PMID: 38697003 DOI: 10.1016/j.compbiomed.2024.108495] [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: 01/19/2024] [Revised: 03/21/2024] [Accepted: 04/21/2024] [Indexed: 05/04/2024]
Abstract
Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.
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Affiliation(s)
- Lin Li
- Department of Otolaryngology and Head&neck, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, 214023, China; Department of Otolaryngology and Head&neck, China-Japan Union Hospital, Jilin University, Changchun, 130033, China.
| | - FeiMing Huang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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2
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Kim YH, Chung JS, Lee HH, Park JH, Kim MK. Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study. Nutrients 2024; 16:1265. [PMID: 38732512 PMCID: PMC11085891 DOI: 10.3390/nu16091265] [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: 03/29/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.
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Affiliation(s)
- Yeon-Hee Kim
- Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, National Cancer Center, 323 Ilsandong-gu, Goyang-si 10408, Republic of Korea; (Y.-H.K.); (J.-H.P.)
| | - Jin-Soo Chung
- Department of Urology, Center for Urologic Cancer, Research Institute, Hospital of National Cancer Center, 323 Ilsandong-gu, Goyang-si 10408, Republic of Korea; (J.-S.C.); (H.-H.L.)
| | - Hyung-Ho Lee
- Department of Urology, Center for Urologic Cancer, Research Institute, Hospital of National Cancer Center, 323 Ilsandong-gu, Goyang-si 10408, Republic of Korea; (J.-S.C.); (H.-H.L.)
| | - Jin-Hee Park
- Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, National Cancer Center, 323 Ilsandong-gu, Goyang-si 10408, Republic of Korea; (Y.-H.K.); (J.-H.P.)
| | - Mi-Kyung Kim
- Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, National Cancer Center, 323 Ilsandong-gu, Goyang-si 10408, Republic of Korea; (Y.-H.K.); (J.-H.P.)
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3
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Ghanem M, Ghaith AK, Zamanian C, Bon-Nieves A, Bhandarkar A, Bydon M, Quiñones-Hinojosa A. Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs. World Neurosurg 2023; 175:e1089-e1109. [PMID: 37088416 DOI: 10.1016/j.wneu.2023.04.072] [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: 01/26/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study aims to assess the accuracy of different novel deep learning models trained on a set of simple clinical, demographic, and surgical variables to assist in clinical practice, even in areas with constrained health care infrastructure. METHODS Our study included 37,095 patients with GBM from the SEER (Surveillance Epidemiology and End Results) database. All predictors were based on demographic, clinicopathologic, and treatment information of the cases. Our outcomes of interest were months of survival and vital status. Concordance index (C-index) and integrated Brier scores (IBS) were used to evaluate the performance of the models. RESULTS The patient characteristics and the statistical analyses were consistent with the epidemiologic literature. The models C-index and IBS ranged from 0.6743 to 0.6918 and from 0.0934 to 0.1034, respectively. Probabilistic matrix factorization (0.6918), multitask logistic regression (0.6916), and logistic hazard (0.6916) had the highest C-index scores. The models with the lowest IBS were the probabilistic matrix factorization (0.0934), multitask logistic regression (0.0935), and logistic hazard (0.0936). These models had an accuracy (1-IBS) of 90.66%; 90.65%, and 90.64%, respectively. The deep learning algorithms were deployed on an interactive Web-based tool for practical use available via https://glioblastoma-survanalysis.herokuapp.com/. CONCLUSIONS Novel deep learning algorithms can better predict GBM prognosis than do baseline methods and can lead to more personalized patient care regardless of extensive electronic health record availability.
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Affiliation(s)
- Marc Ghanem
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
| | - Abdul Karim Ghaith
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Cameron Zamanian
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Antonio Bon-Nieves
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Archis Bhandarkar
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
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Chen WW, Chu TSM, Xu L, Zhao CN, Poon WS, Leung GKK, Kong FM(S. Immune related biomarkers for cancer metastasis to the brain. Exp Hematol Oncol 2022; 11:105. [PMID: 36527157 PMCID: PMC9756766 DOI: 10.1186/s40164-022-00349-z] [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: 04/06/2022] [Accepted: 07/14/2022] [Indexed: 12/23/2022] Open
Abstract
Brain metastasis accounts for a large number of cancer-related deaths. The host immune system, involved at each step of the metastatic cascade, plays an important role in both the initiation of the brain metastasis and their treatment responses to various modalities, through either local and or systemic effect. However, few reliable immune biomarkers have been identified in predicting the development and the treatment outcome in patients with cancer brain metastasis. Here, we provide a focused perspective of immune related biomarkers for cancer metastasis to the brain and a thorough discussion of the potential utilization of specific biomarkers such as tumor mutation burden (TMB), genetic markers, circulating and tumor-infiltrating immune cells, cytokines, in predicting the brain disease progression and regression after therapeutic intervention. We hope to inspire the field to extend the research and establish practical guidelines for developing and validating immune related biomarkers to provide personalized treatment and improve treatment outcomes in patients with metastatic brain cancers.
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Affiliation(s)
- Wei-Wei Chen
- grid.194645.b0000000121742757Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, SAR China
| | - Timothy Shun Man Chu
- grid.419334.80000 0004 0641 3236Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road, Newcastle Upon Tyne, NE1 4LP UK ,grid.1006.70000 0001 0462 7212Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE1 7RU UK
| | - LiangLiang Xu
- grid.440671.00000 0004 5373 5131Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Cai-Ning Zhao
- grid.194645.b0000000121742757Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, SAR China
| | - Wai-Sang Poon
- grid.440671.00000 0004 5373 5131Neuro-Medical Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China ,grid.194645.b0000000121742757Department of Surgery, School of Clinical Medicine,LKS Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, SAR China
| | - Gilberto Ka-Kit Leung
- grid.194645.b0000000121742757Department of Surgery, School of Clinical Medicine,LKS Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, SAR China
| | - Feng-Ming (Spring) Kong
- grid.194645.b0000000121742757Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, SAR China ,grid.440671.00000 0004 5373 5131Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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Bhandari N, Walambe R, Kotecha K, Khare SP. A comprehensive survey on computational learning methods for analysis of gene expression data. Front Mol Biosci 2022; 9:907150. [PMID: 36458095 PMCID: PMC9706412 DOI: 10.3389/fmolb.2022.907150] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.
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Affiliation(s)
- Nikita Bhandari
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Rahee Walambe
- Electronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Ketan Kotecha
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Satyajeet P. Khare
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
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Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review. JOURNAL OF OTORHINOLARYNGOLOGY, HEARING AND BALANCE MEDICINE 2022. [DOI: 10.3390/ohbm3040007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors.
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Chu SS, Nguyen HA, Zhang J, Tabassum S, Cao H. Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders. SENSORS (BASEL, SWITZERLAND) 2022; 22:5200. [PMID: 35890880 PMCID: PMC9323394 DOI: 10.3390/s22145200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.
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Affiliation(s)
- Sung Sik Chu
- Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California Irvine, Irvine, CA 92697, USA; (S.S.C.); (J.Z.)
| | - Hung Anh Nguyen
- Department of Electrical Engineering and Computer Science, Henry Samueli School of Engineering, University of California Irvine, Irvine, CA 92697, USA;
| | - Jimmy Zhang
- Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California Irvine, Irvine, CA 92697, USA; (S.S.C.); (J.Z.)
| | - Shawana Tabassum
- Department of Electrical Engineering, College of Engineering, The University of Texas at Tyler, Tyler, TX 75799, USA
| | - Hung Cao
- Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California Irvine, Irvine, CA 92697, USA; (S.S.C.); (J.Z.)
- Department of Electrical Engineering and Computer Science, Henry Samueli School of Engineering, University of California Irvine, Irvine, CA 92697, USA;
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Ward M, Yeganegi A, Baicu CF, Bradshaw AD, Spinale FG, Zile MR, Richardson WJ. Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers. Am J Physiol Heart Circ Physiol 2022; 322:H798-H805. [PMID: 35275763 PMCID: PMC8993521 DOI: 10.1152/ajpheart.00497.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/22/2022]
Abstract
Arterial hypertension can lead to structural changes within the heart including left ventricular hypertrophy (LVH) and eventually heart failure with preserved ejection fraction (HFpEF). The initial diagnosis of HFpEF is costly and generally based on later stage remodeling; thus, improved predictive diagnostic tools offer potential clinical benefit. Recent work has shown predictive value of multibiomarker plasma panels for the classification of patients with LVH and HFpEF. We hypothesized that machine learning algorithms could substantially improve the predictive value of circulating plasma biomarkers by leveraging more sophisticated statistical approaches. In this work, we developed an ensemble classification algorithm for the diagnosis of HFpEF within a population of 480 individuals including patients with HFpEF, patients with LVH, and referent control patients. Algorithms showed strong diagnostic performance with receiver-operating-characteristic curve (ROC) areas of 0.92 for identifying patients with LVH and 0.90 for identifying patients with HFpEF using demographic information, plasma biomarkers related to extracellular matrix remodeling, and echocardiogram data. More impressively, the ensemble algorithm produced an ROC area of 0.88 for HFpEF diagnosis using only demographic and plasma panel data. Our findings demonstrate that machine learning-based classification algorithms show promise as a noninvasive diagnostic tool for HFpEF, while also suggesting priority biomarkers for future mechanistic studies to elucidate more specific regulatory roles.NEW & NOTEWORTHY Machine learning algorithms correctly classified patients with heart failure with preserved ejection fraction with over 90% area under receiver-operating-characteristic curves. Classifications using multidomain features (demographics and circulating biomarkers and echo-based ventricle metrics) proved more accurate than previous studies using single-domain features alone. Excitingly, HFpEF diagnoses were generally accurate even without echo-based measurements, demonstrating that such algorithms could provide an early screening tool using blood-based measurements before sophisticated imaging.
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Affiliation(s)
- Michael Ward
- Department of Bioengineering, Clemson University, Clemson, South Carolina
| | - Amirreza Yeganegi
- Department of Bioengineering, Clemson University, Clemson, South Carolina
| | - Catalin F Baicu
- Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina
| | - Amy D Bradshaw
- Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina
| | - Francis G Spinale
- School of Medicine, University of South Carolina, Columbia, South Carolina
- Columbia Veterans Affairs Health Care System, Columbia, South Carolina
| | - Michael R Zile
- Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina
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Steiner CA, Berinstein JA, Louissaint J, Higgins PDR, Spence JR, Shannon C, Lu C, Stidham RW, Fletcher JG, Bruining DH, Feagan BG, Jairath V, Baker ME, Bettenworth D, Rieder F. Biomarkers for the Prediction and Diagnosis of Fibrostenosing Crohn's Disease: A Systematic Review. Clin Gastroenterol Hepatol 2022; 20:817-846.e10. [PMID: 34089850 PMCID: PMC8636551 DOI: 10.1016/j.cgh.2021.05.054] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/20/2021] [Accepted: 05/23/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIMS Intestinal strictures are a common complication of Crohn's disease (CD). Biomarkers of intestinal strictures would assist in their prediction, diagnosis, and monitoring. Herein we provide a comprehensive systematic review of studies assessing biomarkers that may predict or diagnose CD-associated strictures. METHODS We performed a systematic review of PubMed, EMBASE, ISI Web of Science, Cochrane Library, and Scopus to identify citations pertaining to biomarkers of intestinal fibrosis through July 6, 2020, that used a reference standard of full-thickness histopathology or cross-sectional imaging or endoscopy. Studies were categorized based on the type of biomarker they evaluated (serum, genetic, histopathologic, or fecal). RESULTS Thirty-five distinct biomarkers from 3 major groups were identified: serum (20 markers), genetic (9 markers), and histopathology (6 markers). Promising markers include cartilage oligomeric matrix protein, hepatocyte growth factor activator, and lower levels of microRNA-19-3p (area under the curves were 0.805, 0.738, and 0.67, respectively), and multiple anti-flagellin antibodies (A4-Fla2 [odds ratio, 3.41], anti Fla-X [odds ratio, 2.95], and anti-CBir1 [multiple]). Substantial heterogeneity was observed and none of the markers had undergone formal validation. Specific limitations to acceptance of these markers included failure to use a standardized definition of stricturing disease, lack of specificity, and insufficient relevance to the pathogenesis of intestinal strictures or incomplete knowledge regarding their operating properties. CONCLUSIONS There is a lack of well-defined studies on biomarkers of intestinal stricture. Development of reliable and accurate biomarkers of stricture is a research priority. Biomarkers can support the clinical management of CD patients and aid in the stratification and monitoring of patients during clinical trials of future antifibrotic drug candidates.
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Affiliation(s)
- Calen A Steiner
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.
| | - Jeffrey A Berinstein
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jeremy Louissaint
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Peter D R Higgins
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jason R Spence
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan; Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan
| | - Carol Shannon
- Taubman Health Sciences Library, University of Michigan, Ann Arbor, Michigan
| | - Cathy Lu
- Division of Gastroenterology, Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Ryan W Stidham
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | | | - David H Bruining
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Brian G Feagan
- Alimentiv Inc, London, Ontario, Canada; Department of Medicine, Western University, London, Ontario, Canada; Department of Biostatistics and Epidemiology, Western University, London, Ontario, Canada
| | - Vipul Jairath
- Alimentiv Inc, London, Ontario, Canada; Department of Medicine, Western University, London, Ontario, Canada; Department of Biostatistics and Epidemiology, Western University, London, Ontario, Canada
| | - Mark E Baker
- Section of Abdominal Imaging, Imaging Institute, Digestive Diseases and Surgery Institute and Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio
| | - Dominik Bettenworth
- Department of Medicine B, Gastroenterology and Hepatology, University of Münster, Münster, Germany
| | - Florian Rieder
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio; Department of Gastroenterology, Hepatology, and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, Ohio
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10
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Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
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Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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11
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Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021; 159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Barbara R Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom; School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia.
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12
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Toroghi MK, Cluett WR, Mahadevan R. A Personalized Multiscale Modeling Framework for Dose Selection in Precision Medicine. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Masood Khaksar Toroghi
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - William R. Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
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13
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Miskovic L, Béal J, Moret M, Hatzimanikatis V. Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties. PLoS Comput Biol 2019; 15:e1007242. [PMID: 31430276 PMCID: PMC6716680 DOI: 10.1371/journal.pcbi.1007242] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 08/30/2019] [Accepted: 07/03/2019] [Indexed: 11/18/2022] Open
Abstract
A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions. Kinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.
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Affiliation(s)
- Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), EPFL, CH, Lausanne, Switzerland
| | - Jonas Béal
- Master's Program in Life Sciences and Technology, EPFL, CH, Lausanne, Switzerland
| | - Michael Moret
- Master's Program in Life Sciences and Technology, EPFL, CH, Lausanne, Switzerland
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14
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Chung D, Zhang K, Yang J. Method for Identifying Cancer-Related Genes Using Gene Similarity-Based Collaborative Filtering. J Comput Biol 2019; 26:875-881. [PMID: 31120387 DOI: 10.1089/cmb.2018.0115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The aim of this study is to diagnose the stage of renal cell carcinoma and to predict the prognosis of breast cancer by using RNA sequencing and microarray data that are representative gene expression data. To identify biomarkers for prediction, top-N genes of each class of cancer or noncancer are recommended by collaborative filtering method based on three gene similarity coefficients. We then construct a machine learning model for classification using the union of the recommended genes as the final feature set. The optimal genetic markers were used to identify the set with the highest classification performance in the model. Experiments conducted by the proposed method showed higher performance than those conducted by the machine learning model using all the gene features without performing feature selection. In addition, it showed better performance than other studies based on existing correlation-based feature selection.
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Affiliation(s)
- Dahye Chung
- Department of Computer Science and Engineering, Sogang University, Seoul, Korea
| | - Kaiyuan Zhang
- Department of Computer Science and Engineering, Sogang University, Seoul, Korea
| | - Jihoon Yang
- Department of Computer Science and Engineering, Sogang University, Seoul, Korea
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15
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Leclercq M, Vittrant B, Martin-Magniette ML, Scott Boyer MP, Perin O, Bergeron A, Fradet Y, Droit A. Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data. Front Genet 2019; 10:452. [PMID: 31156708 PMCID: PMC6532608 DOI: 10.3389/fgene.2019.00452] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 04/30/2019] [Indexed: 12/11/2022] Open
Abstract
The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.
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Affiliation(s)
- Mickael Leclercq
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Médecine Moléculaire, Université Laval, Québec City, QC, Canada
| | - Benjamin Vittrant
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Médecine Moléculaire, Université Laval, Québec City, QC, Canada
| | - Marie Laure Martin-Magniette
- Institute of Plant Sciences Paris Saclay IPS2, CNRS, INRA, Université Paris-Sud, Université Evry, Université Paris-Saclay, Paris Diderot, Sorbonne Paris-Cité, Orsay, France.,UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France
| | - Marie Pier Scott Boyer
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Médecine Moléculaire, Université Laval, Québec City, QC, Canada
| | - Olivier Perin
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Alain Bergeron
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Chirurgie, Oncology Axis, Université Laval, Québec City, QC, Canada
| | - Yves Fradet
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Chirurgie, Oncology Axis, Université Laval, Québec City, QC, Canada
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.,Département de Médecine Moléculaire, Université Laval, Québec City, QC, Canada
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16
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Hutchinson L, Steiert B, Soubret A, Wagg J, Phipps A, Peck R, Charoin J, Ribba B. Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level. CPT Pharmacometrics Syst Pharmacol 2019; 8:131-134. [PMID: 30549240 PMCID: PMC6430152 DOI: 10.1002/psp4.12377] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022] Open
Abstract
Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.
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Affiliation(s)
- Lucy Hutchinson
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Bernhard Steiert
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Antoine Soubret
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Jonathan Wagg
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Alex Phipps
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center WelwynWelwynUK
| | - Richard Peck
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Jean‐Eric Charoin
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
| | - Benjamin Ribba
- Roche Pharmaceutical Research and Early DevelopmentRoche Innovation Center BaselBaselSwitzerland
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17
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Tutino VM, Poppenberg KE, Li L, Shallwani H, Jiang K, Jarvis JN, Sun Y, Snyder KV, Levy EI, Siddiqui AH, Kolega J, Meng H. Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms. J Transl Med 2018; 16:373. [PMID: 30593281 PMCID: PMC6310942 DOI: 10.1186/s12967-018-1749-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 12/17/2018] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. METHODS Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls). RESULTS The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort. CONCLUSIONS Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.
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Affiliation(s)
- Vincent M. Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY USA
| | - Kerry E. Poppenberg
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY USA
| | - Lu Li
- Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY USA
| | - Hussain Shallwani
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - Kaiyu Jiang
- Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY USA
| | - James N. Jarvis
- Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY USA
- Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - Yijun Sun
- Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY USA
- Department of Microbiology and Immunology, University at Buffalo, Buffalo, NY USA
| | - Kenneth V. Snyder
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
- Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - Elad I. Levy
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
- Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - Adnan H. Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
- Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - John Kolega
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
| | - Hui Meng
- Canon Stroke and Vascular Research Center, University at Buffalo, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14214 USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY USA
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
- Department of Mechanical & Aerospace Engineering, University at Buffalo, Buffalo, NY USA
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18
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Sokolenko AP, Imyanitov EN. Molecular Diagnostics in Clinical Oncology. Front Mol Biosci 2018; 5:76. [PMID: 30211169 PMCID: PMC6119963 DOI: 10.3389/fmolb.2018.00076] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/25/2018] [Indexed: 12/12/2022] Open
Abstract
There are multiple applications of molecular tests in clinical oncology. Mutation analysis is now routinely utilized for the diagnosis of hereditary cancer syndromes. Healthy carriers of cancer-predisposing mutations benefit from tight medical surveillance and various preventive interventions. Cancers caused by germ-line mutations often require significant modification of the treatment strategy. Personalized selection of cancer drugs based on the presence of actionable mutations has become an integral part of cancer therapy. Molecular tests underlie the administration of EGFR, BRAF, ALK, ROS1, PARP inhibitors as well as the use of some other cytotoxic and targeted drugs. Tumors almost always shed their fragments (single cells or their clusters, DNA, RNA, proteins) into various body fluids. So-called liquid biopsy, i.e., the analysis of circulating DNA or some other tumor-derived molecules, holds a great promise for non-invasive monitoring of cancer disease, analysis of drug-sensitizing mutations and early cancer detection. Some tumor- or tissue-specific mutations and expression markers can be efficiently utilized for the diagnosis of cancers of unknown primary origin (CUPs). Systematic cataloging of tumor molecular portraits is likely to uncover a multitude of novel medically relevant DNA- and RNA-based markers.
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Affiliation(s)
- Anna P Sokolenko
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, St. Petersburg, Russia.,Department of Medical Genetics, St. Petersburg Pediatric Medical University, St. Petersburg, Russia
| | - Evgeny N Imyanitov
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, St. Petersburg, Russia.,Department of Medical Genetics, St. Petersburg Pediatric Medical University, St. Petersburg, Russia.,Department of Oncology, I.I. Mechnikov North-Western Medical University, St. Petersburg, Russia.,Department of Oncology, St. Petersburg State University, St. Petersburg, Russia
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19
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Sherafatian M. Tree-based machine learning algorithms identified minimal set of miRNA biomarkers for breast cancer diagnosis and molecular subtyping. Gene 2018; 677:111-118. [PMID: 30055304 DOI: 10.1016/j.gene.2018.07.057] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 06/19/2018] [Accepted: 07/23/2018] [Indexed: 12/13/2022]
Abstract
Breast cancer is a complex disease and its effective treatment needs affordable diagnosis and subtyping signatures. While the use of machine learning approach in clinical computation biology is still in its infancy, the prevalent approach in identifying molecular biomarkers remains to be screening of all biomarkers by differential expression analysis. Many of these attempts used miRNAs expression data in breast cancer and amounted to the multitude of differentially expressed miRNAs in this cancer; hence, the minimal set of miRNA biomarkers to classify breast cancer is yet to be identified. Availability of diverse and vast amount of cancer datasets like The Cancer Genome Atlas facilitated the molecular profiling of patients' tumors and introduced new challenges like clinical grade interpretations from big data. In this study, miRNA expression dataset of breast cancer patients from TCGA database was used to develop prediction models from which miRNA biomarkers were identified for diagnosis and molecular subtyping of this cancer. I took the advantage of interpretability of tree-based classification models to extract their rules and identify minimal set of biomarkers in this cancer. Empirical negative control miRNAs in breast cancer obtained and used to normalize the dataset. Tree-based machine learning models trained in my analysis used hsa-miR-139 with hsa-miR-183 to classify breast tumors from normal samples, and hsa-miR4728 with hsa-miR190b to further classify these tumors into three major subtypes of breast cancer. In addition to the proposed biomarkers, the most important miRNAs in breast cancer classification were also presented.
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Affiliation(s)
- Masih Sherafatian
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
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20
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RNA sequencing data from neutrophils of patients with cystic fibrosis reveals potential for developing biomarkers for pulmonary exacerbations. J Cyst Fibros 2018; 18:194-202. [PMID: 29941318 DOI: 10.1016/j.jcf.2018.05.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 05/01/2018] [Accepted: 05/22/2018] [Indexed: 01/16/2023]
Abstract
BACKGROUND There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses. METHODS RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CF patients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CF patients before and after therapy. RESULTS We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CF patients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy. CONCLUSIONS Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.
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21
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Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A. A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.10.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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