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1
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Murray K, Oldfield L, Stefanova I, Gentiluomo M, Aretini P, O'Sullivan R, Greenhalf W, Paiella S, Aoki MN, Pastore A, Birch-Ford J, Rao BH, Uysal-Onganer P, Walsh CM, Hanna GB, Narang J, Sharma P, Campa D, Rizzato C, Turtoi A, Sever EA, Felici A, Sucularli C, Peduzzi G, Öz E, Sezerman OU, Van der Meer R, Thompson N, Costello E. Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer. Semin Cancer Biol 2025; 111:76-88. [PMID: 39986585 DOI: 10.1016/j.semcancer.2025.02.009] [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: 11/29/2024] [Revised: 02/13/2025] [Accepted: 02/17/2025] [Indexed: 02/24/2025]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.
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Affiliation(s)
- Kate Murray
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - Lucy Oldfield
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - Irena Stefanova
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | | | | | - Rachel O'Sullivan
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - William Greenhalf
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - Salvatore Paiella
- Pancreatic Surgery Unit, Department of Surgery, Dentistry, Paediatrics and Gynaecology, University of Verona, Italy
| | - Mateus N Aoki
- Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Oswaldo Cruz Foundation (Fiocruz), Brazil
| | - Aldo Pastore
- Fondazione Pisana per la Scienza, Scuola Normale Superiore di Pisa, Italy
| | - James Birch-Ford
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - Bhavana Hemantha Rao
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Czech Republic
| | - Pinar Uysal-Onganer
- School of Life Sciences, Cancer Mechanisms and Biomarkers Group, The University of Westminster, United Kingdom
| | - Caoimhe M Walsh
- Department of Surgery and Cancer, Imperial College London, United Kingdom
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College London, United Kingdom
| | | | | | | | | | - Andrei Turtoi
- Tumor Microenvironment and Resistance to Treatment Lab, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, France
| | - Elif Arik Sever
- Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Turkiye
| | | | | | | | - Elif Öz
- Department of Biostatistics and Bioinformatics, Acibadem Mehmet Ali Aydinlar University, Turkiye
| | - Osman Uğur Sezerman
- Department of Biostatistics and Bioinformatics, Acibadem Mehmet Ali Aydinlar University, Turkiye
| | | | | | - Eithne Costello
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom.
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Rouzbahani AK, Khalili-Tanha G, Rajabloo Y, Khojasteh-Leylakoohi F, Garjan HS, Nazari E, Avan A. Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration. Pathol Res Pract 2024; 263:155602. [PMID: 39357184 DOI: 10.1016/j.prp.2024.155602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 09/21/2024] [Accepted: 09/24/2024] [Indexed: 10/04/2024]
Abstract
PURPOSE Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes. METHODS The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment. RESULTS Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes. CONCLUSIONS The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.
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Affiliation(s)
- Arian Karimi Rouzbahani
- Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran; USERN Office, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Yasamin Rajabloo
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Hassan Shokri Garjan
- Department of Health Information Technology, School of Management University of Medical Sciences, Tabriz, Iran
| | - Elham Nazari
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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Cheng Y, Xu SM, Santucci K, Lindner G, Janitz M. Machine learning and related approaches in transcriptomics. Biochem Biophys Res Commun 2024; 724:150225. [PMID: 38852503 DOI: 10.1016/j.bbrc.2024.150225] [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: 02/25/2024] [Revised: 05/18/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
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Affiliation(s)
- Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Grace Lindner
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
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Wei M, Zhou G, Chen L, Zhang Y, Ma W, Gao L, Gao G. The prognostic and immune significance of PLBD1 in pan-cancer and its roles in proliferation and invasion of glioma. J Cancer 2024; 15:3857-3872. [PMID: 38911364 PMCID: PMC11190780 DOI: 10.7150/jca.96365] [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: 03/18/2024] [Accepted: 05/09/2024] [Indexed: 06/25/2024] Open
Abstract
Cancer is a destructive disease and is currently the leading cause of major threats to human health. PLBD1 is a transcription factor that regulates phospholipid metabolism, but its role in tumors is unknown. We assessed pan-cancer expression, methylation, and mutation data of PLBD1 by multiple databases to investigate its clinical prognostic value. In addition, we examined the pan-cancer immunological signature of PLBD1, particularly in gliomas. Furthermore, we assessed the impact of PLBD1 knockdown on the proliferation and invasive capacity of glioma cells by in vitro experiments. Our results suggest that PLBD1 is highly expressed in multiple types of cancers, and it can serve as an independent prognostic factor for gliomas. In addition, we found that the epigenetic alterations of PLBD1 were highly heterogeneous in a variety of cancers, including gliomas, and that its high methylation was associated with poor prognosis in a broad range of cancers. Immunological profiling demonstrated that PLBD1 was significantly associated with immune cell infiltration and multiple immune checkpoints in gliomas and is a potential biomarker for gliomas. Furthermore, cellular experiments showed that knockdown of PLBD1 significantly inhibited the proliferation and invasive ability of glioma cells. In conclusion, PLBD1 is a potential tumor prognostic biomarker and immunotherapeutic target that plays a crucial role in glioma cell proliferation, invasion and immunotherapy.
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Affiliation(s)
- Minghao Wei
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
- Department of Neurosurgery Ward II, the Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, 712046, China
| | - Gaoyang Zhou
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
| | - Lian Chen
- Department of Neurosurgery, the Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110001 China
| | - Yufu Zhang
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
| | - Wei Ma
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
| | - Li Gao
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
| | - Guodong Gao
- Department of Neurosurgery, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
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He Q, Zheng Y, Lu L, Shen H, Gu W, Yang J, Zhang X, Jin H. Hyperthermia improves gemcitabine sensitivity of pancreatic cancer cells by suppressing the EFNA4/β-catenin axis and activating dCK. Heliyon 2024; 10:e28488. [PMID: 38590861 PMCID: PMC10999932 DOI: 10.1016/j.heliyon.2024.e28488] [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: 12/12/2023] [Revised: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background Previously, our investigations have underscored the potential of hyperthermia to improve the therapeutic efficacy of gemcitabine (GEM) in pancreatic cancer (PC). Nonetheless, the precise underlying mechanisms remain elusive. Methods We engineered two GEM-resistant PC cell lines (BxPC-3/GEM and PANC-1/GEM) and treated them with GEM alongside hyperthermia. The impact of hyperthermia on the therapeutic potency of GEM was ascertained through MTT assay, assessment of the concentration of its active metabolite dFdCTP, and evaluation of deoxycytidine kinase (dCK) activity. Lentivirus-mediated dCK silencing was further employed to validate its involvement in mediating the GEM-sensitizing effect of hyperthermia. The mechanism underlying hyperthermia-mediated dCK activation was explored using bioinformatics analyses. The interplay between hyperthermia and the ephrin A4 (EFNA4)/β-catenin/dCK axis was investigated, and their roles in GEM resistance was further explored via the establishment of xenograft tumor models in nude mice. Results Hyperthermia restored dCK expression in GEM-resistant cell lines, concurrently enhancing GEM sensitivity and fostering DNA damage and cell death. These observed effects were negated by dCK silencing. Regarding the mechanism, hyperthermia activated dCK by downregulating EFNA4 expression and mitigating β-catenin activation. Overexpression of EFNA4 activated the β-catenin while suppressing dCK, thus diminishing cellular GEM sensitivity-a phenomenon remediated by the β-catenin antagonist MSAB. Consistently, in vivo, hyperthermia augmented the therapeutic efficacy of GEM on xenograft tumors through modulation of the ephrin A4/β-catenin/dCK axis. Conclusion This study delineates the role of hyperthermia in enhancing GEM sensitivity of PC cells, primarily mediated through the suppression of the EFNA4/β-catenin axis and activation of dCK.
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Affiliation(s)
- Qiaoxian He
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
| | - Yangyang Zheng
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
| | - Lei Lu
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
| | - Hongzhang Shen
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
| | - Weigang Gu
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
| | - Jianfeng Yang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
- Hangzhou Institute of Digestive Diseases, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
| | - Xiaofeng Zhang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
- Hangzhou Institute of Digestive Diseases, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
| | - Hangbin Jin
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
- Hangzhou Institute of Digestive Diseases, Hangzhou, 310006, Zhejiang, PR China
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou, 310006, Zhejiang, PR China
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Mumme H, Thomas BE, Bhasin SS, Krishnan U, Dwivedi B, Perumalla P, Sarkar D, Ulukaya GB, Sabnis HS, Park SI, DeRyckere D, Raikar SS, Pauly M, Summers RJ, Castellino SM, Wechsler DS, Porter CC, Graham DK, Bhasin M. Single-cell analysis reveals altered tumor microenvironments of relapse- and remission-associated pediatric acute myeloid leukemia. Nat Commun 2023; 14:6209. [PMID: 37798266 PMCID: PMC10556066 DOI: 10.1038/s41467-023-41994-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/25/2023] [Indexed: 10/07/2023] Open
Abstract
Acute myeloid leukemia (AML) microenvironment exhibits cellular and molecular differences among various subtypes. Here, we utilize single-cell RNA sequencing (scRNA-seq) to analyze pediatric AML bone marrow (BM) samples from diagnosis (Dx), end of induction (EOI), and relapse timepoints. Analysis of Dx, EOI scRNA-seq, and TARGET AML RNA-seq datasets reveals an AML blasts-associated 7-gene signature (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH), which we validate on independent datasets. The analysis reveals distinct clusters of Dx relapse- and continuous complete remission (CCR)-associated AML-blasts with differential expression of genes associated with survival. At Dx, relapse-associated samples have more exhausted T cells while CCR-associated samples have more inflammatory M1 macrophages. Post-therapy EOI residual blasts overexpress fatty acid oxidation, tumor growth, and stemness genes. Also, a post-therapy T-cell cluster associated with relapse samples exhibits downregulation of MHC Class I and T-cell regulatory genes. Altogether, this study deeply characterizes pediatric AML relapse- and CCR-associated samples to provide insights into the BM microenvironment landscape.
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Affiliation(s)
- Hope Mumme
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Beena E Thomas
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Swati S Bhasin
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Upaasana Krishnan
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Bhakti Dwivedi
- Department of Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Pruthvi Perumalla
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Debasree Sarkar
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Gulay B Ulukaya
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Himalee S Sabnis
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sunita I Park
- Department of Pathology, Children's Healthcare of Atlanta, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Deborah DeRyckere
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sunil S Raikar
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Melinda Pauly
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Ryan J Summers
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sharon M Castellino
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Daniel S Wechsler
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Christopher C Porter
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Douglas K Graham
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Manoj Bhasin
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA.
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Burger B, Sagiorato RN, Cavenaghi I, Rodrigues HG. Abnormalities of Sphingolipids Metabolic Pathways in the Pathogenesis of Psoriasis. Metabolites 2023; 13:metabo13020291. [PMID: 36837912 PMCID: PMC9968075 DOI: 10.3390/metabo13020291] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Psoriasis is immune-mediated skin disorder affecting thousands of people. Sphingolipids (SLs) are bioactive molecules present in the epidermis, involved in the following cellular processes: proliferation, differentiation, and apoptosis of keratinocytes. Alterations in SLs synthesis have been observed in psoriatic skin. To investigate if the imbalance in lipid skin metabolism could be related to psoriasis, we analyzed the gene expression in non-lesioned and lesioned skin of patients with psoriasis available in two datasets (GSE161683 and GSE136757) obtained from National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched for using NCBI analysis, and Gene Ontology (GO) biological process analyses were performed using the Database of Annotation, Visualization, and Integrated Discovery (DAVID) platform. Venn diagrams were done with InteractiVenn tool and heatmaps were constructed using Morpheus software. We observed that the gene expression of cytoplasmic phospholipase A2 (PLA2G4D), glycerophosphodiester phosphodiesterase domain containing 3 (GDP3), arachidonate 12-lipoxygenase R type (ALOX12B), phospholipase B-like 1 (PLBD1), sphingomyelin phosphodiesterase 3 (SMPD3), ganglioside GM2 activator (GM2A), and serine palmitoyltransferase long chain subunit 2 (SPTLC2) was up-regulated in lesioned skin psoriasis when compared with the non-lesioned skin. These genes are related to lipid metabolism and more specifically to sphingolipids. So, in the present study, the role of sphingolipids in psoriasis pathogenesis is summarized. These genes could be used as prognostic biomarkers of psoriasis and could be targets for the treatment of patients who suffer from the disease.
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8
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Lin KW, Ang TL, Li JW. Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma. Artif Intell Med Imaging 2022; 3:21-32. [DOI: 10.35711/aimi.v3.i2.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.
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Affiliation(s)
- Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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Maruyama SR, Fuzo CA, Oliveira AER, Rogerio LA, Takamiya NT, Pessenda G, de Melo EV, da Silva AM, Jesus AR, Carregaro V, Nakaya HI, Almeida RP, da Silva JS. Insight Into the Long Noncoding RNA and mRNA Coexpression Profile in the Human Blood Transcriptome Upon Leishmania infantum Infection. Front Immunol 2022; 13:784463. [PMID: 35370994 PMCID: PMC8965071 DOI: 10.3389/fimmu.2022.784463] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 02/11/2022] [Indexed: 12/13/2022] Open
Abstract
Visceral leishmaniasis (VL) is a vector-borne infectious disease that can be potentially fatal if left untreated. In Brazil, it is caused by Leishmania infantum parasites. Blood transcriptomics allows us to assess the molecular mechanisms involved in the immunopathological processes of several clinical conditions, namely, parasitic diseases. Here, we performed mRNA sequencing of peripheral blood from patients with visceral leishmaniasis during the active phase of the disease and six months after successful treatment, when the patients were considered clinically cured. To strengthen the study, the RNA-seq data analysis included two other non-diseased groups composed of healthy uninfected volunteers and asymptomatic individuals. We identified thousands of differentially expressed genes between VL patients and non-diseased groups. Overall, pathway analysis corroborated the importance of signaling involving interferons, chemokines, Toll-like receptors and the neutrophil response. Cellular deconvolution of gene expression profiles was able to discriminate cellular subtypes, highlighting the contribution of plasma cells and NK cells in the course of the disease. Beyond the biological processes involved in the immunopathology of VL revealed by the expression of protein coding genes (PCGs), we observed a significant participation of long noncoding RNAs (lncRNAs) in our blood transcriptome dataset. Genome-wide analysis of lncRNAs expression in VL has never been performed. lncRNAs have been considered key regulators of disease progression, mainly in cancers; however, their pattern regulation may also help to understand the complexity and heterogeneity of host immune responses elicited by L. infantum infections in humans. Among our findings, we identified lncRNAs such as IL21-AS1, MIR4435-2HG and LINC01501 and coexpressed lncRNA/mRNA pairs such as CA3-AS1/CA1, GASAL1/IFNG and LINC01127/IL1R1-IL1R2. Thus, for the first time, we present an integrated analysis of PCGs and lncRNAs by exploring the lncRNA–mRNA coexpression profile of VL to provide insights into the regulatory gene network involved in the development of this inflammatory and infectious disease.
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Affiliation(s)
- Sandra Regina Maruyama
- Department of Genetics and Evolution, Center for Biological Sciences and Health, Federal University of São Carlos, São Carlos, Brazil
| | - Carlos Alessandro Fuzo
- Department of Clinical Analyses, Toxicology and Food Sciences, Ribeirão Preto School of Pharmaceutics Sciences, University of São Paulo, Ribeirão Preto, Brazil
| | - Antonio Edson R Oliveira
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Luana Aparecida Rogerio
- Department of Genetics and Evolution, Center for Biological Sciences and Health, Federal University of São Carlos, São Carlos, Brazil
| | - Nayore Tamie Takamiya
- Department of Genetics and Evolution, Center for Biological Sciences and Health, Federal University of São Carlos, São Carlos, Brazil
| | - Gabriela Pessenda
- Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Enaldo Vieira de Melo
- Department of Medicine, University Hospital-Empresa Brasileira de Serviços Hospitalares (EBSERH), Federal University of Sergipe, Aracaju, Brazil
| | - Angela Maria da Silva
- Department of Medicine, University Hospital-Empresa Brasileira de Serviços Hospitalares (EBSERH), Federal University of Sergipe, Aracaju, Brazil
| | - Amélia Ribeiro Jesus
- Department of Medicine, University Hospital-Empresa Brasileira de Serviços Hospitalares (EBSERH), Federal University of Sergipe, Aracaju, Brazil
| | - Vanessa Carregaro
- Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Roque Pacheco Almeida
- Department of Medicine, University Hospital-Empresa Brasileira de Serviços Hospitalares (EBSERH), Federal University of Sergipe, Aracaju, Brazil
| | - João Santana da Silva
- Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.,Fiocruz-Bi-Institutional Translational Medicine Platform, Ribeirão Preto, Brazil
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10
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Supino D, Minute L, Mariancini A, Riva F, Magrini E, Garlanda C. Negative Regulation of the IL-1 System by IL-1R2 and IL-1R8: Relevance in Pathophysiology and Disease. Front Immunol 2022; 13:804641. [PMID: 35211118 PMCID: PMC8861086 DOI: 10.3389/fimmu.2022.804641] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 12/11/2022] Open
Abstract
Interleukin-1 (IL-1) is a primary cytokine of innate immunity and inflammation. IL-1 belongs to a complex family including ligands with agonist activity, receptor antagonists, and an anti-inflammatory cytokine. The receptors for these ligands, the IL-1 Receptor (IL-1R) family, include signaling receptor complexes, decoy receptors, and negative regulators. Agonists and regulatory molecules co-evolved, suggesting the evolutionary relevance of a tight control of inflammatory responses, which ensures a balance between amplification of innate immunity and uncontrolled inflammation. IL-1 family members interact with innate immunity cells promoting innate immunity, as well as with innate and adaptive lymphoid cells, contributing to their differentiation and functional polarization and plasticity. Here we will review the properties of two key regulatory receptors of the IL-1 system, IL-1R2, the first decoy receptor identified, and IL-1R8, a pleiotropic regulator of different IL-1 family members and co-receptor for IL-37, the anti-inflammatory member of the IL-1 family. Their complex impact in pathology, ranging from infections and inflammatory responses, to cancer and neurologic disorders, as well as clinical implications and potential therapeutic exploitation will be presented.
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Affiliation(s)
- Domenico Supino
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Luna Minute
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
| | - Andrea Mariancini
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
| | - Federica Riva
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Elena Magrini
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Cecilia Garlanda
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
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11
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Artificial Intelligence in Blood Transcriptomics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Establishment of Pancreatobiliary Cancer Zebrafish Avatars for Chemotherapy Screening. Cells 2021; 10:cells10082077. [PMID: 34440847 PMCID: PMC8393525 DOI: 10.3390/cells10082077] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Cancers of the pancreas and biliary tree remain one of the most aggressive oncological malignancies, with most patients relying on systemic chemotherapy. However, effective biomarkers to predict the best therapy option for each patient are still lacking. In this context, an assay able to evaluate individual responses prior to treatment would be of great value for clinical decisions. Here we aimed to develop such a model using zebrafish xenografts to directly challenge pancreatic cancer cells to the available chemotherapies. Methods: Zebrafish xenografts were generated from a Panc-1 cell line to optimize the pancreatic setting. Pancreatic surgical resected samples, without in vitro expansion, were used to establish zebrafish patient-derived xenografts (zAvatars). Upon chemotherapy exposure, zAvatars were analyzed by single-cell confocal microscopy. Results: We show that Panc-1 zebrafish xenografts are able to reveal tumor responses to both FOLFIRINOX and gemcitabine plus nanoparticle albumin-bound (nab)-paclitaxel in just 4 days. Moreover, we established pancreatic and ampullary zAvatars with patient-derived tumors representative of different histological types. Conclusion: Altogether, we provide a short report showing the feasibility of generating and analyzing with single-cell resolution zAvatars from pancreatic and ampullary cancers, with potential use for future preclinical studies and personalized treatment.
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13
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Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review. Cancers (Basel) 2021; 13:cancers13112722. [PMID: 34072842 PMCID: PMC8198035 DOI: 10.3390/cancers13112722] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use. Abstract Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010—August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.
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14
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Warnat-Herresthal S, Oestreich M, Schultze JL, Becker M. Artificial Intelligence in Blood Transcriptomics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_262-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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15
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Fu H, Mi W, Pan B, Guo Y, Li J, Xu R, Zheng J, Zou C, Zhang T, Liang Z, Zou J, Zou H. Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks. Front Oncol 2021; 11:665929. [PMID: 34249702 PMCID: PMC8267174 DOI: 10.3389/fonc.2021.665929] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/10/2021] [Indexed: 01/11/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
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Affiliation(s)
- Hao Fu
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Weiming Mi
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Boju Pan
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yucheng Guo
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Junjie Li
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Rongyan Xu
- Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai, China
| | - Jie Zheng
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Chunli Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Tao Zhang
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Zhiyong Liang
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Junzhong Zou
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Hao Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
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