Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Taguchi, Y-h.; Wang, Hsiuying

doi:10.3390/ijms19051318

Cited by 40 publications

(31 citation statements)

References 45 publications

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“…Interestingly, the miRNA expression profiles derived from the study performed by Freischmidt and colleagues [ 68 ] were re-elaborated applying principal component analysis (PCA)-based unsupervised feature extraction (FE), another analysis approach [ 81 ]. The authors identified a total of 51 deregulated miRNAs, 27 down-regulated and 24 up-regulated in ALS patients in comparison with healthy controls.…”

Section: Mirnas As Biomarkers For Alsmentioning

confidence: 99%

MicroRNAs as Biomarkers in Amyotrophic Lateral Sclerosis

Ricci

Marzocchi

Battistini

2018

Cells

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Amyotrophic lateral sclerosis (ALS) is an incurable and fatal disorder characterized by the progressive loss of motor neurons in the cerebral cortex, brain stem, and spinal cord. Sporadic ALS form accounts for the majority of patients, but in 1–13.5% of cases the disease is inherited. The diagnosis of ALS is mainly based on clinical assessment and electrophysiological examinations with a history of symptom progression and is then made with a significant delay from symptom onset. Thus, the identification of biomarkers specific for ALS could be of a fundamental importance in the clinical practice. An ideal biomarker should display high specificity and sensitivity for discriminating ALS from control subjects and from ALS-mimics and other neurological diseases, and should then monitor disease progression within individual patients. microRNAs (miRNAs) are considered promising biomarkers for neurodegenerative diseases, since they are remarkably stable in human body fluids and can reflect physiological and pathological processes relevant for ALS. Here, we review the state of the art of miRNA biomarker identification for ALS in cerebrospinal fluid (CSF), blood and muscle tissue; we discuss advantages and disadvantages of different approaches, and underline the limits but also the great potential of this research for future practical applications.

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Section: Mirnas As Biomarkers For Alsmentioning

confidence: 99%

MicroRNAs as Biomarkers in Amyotrophic Lateral Sclerosis

Ricci

Marzocchi

Battistini

2018

Cells

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“…In addition to cancer, miRNAs may also contribute to neurological diseases and inflammation. The miR-34a and the miR-504, as well as other miRNAs, have been identified to be associated with amyotrophic lateral sclerosis (ALS) [ 48 , 49 ]. The miRNAs are also related to the development of Parkinson’s disease (PD) [ 49 , 50 ], frontotemporal dementia [ 51 ], Alzheimer’s disease [ 52 ], and spinal muscular atrophy [ 53 ].…”

Section: Micrornamentioning

confidence: 99%

“…The miR-34a and the miR-504, as well as other miRNAs, have been identified to be associated with amyotrophic lateral sclerosis (ALS) [ 48 , 49 ]. The miRNAs are also related to the development of Parkinson’s disease (PD) [ 49 , 50 ], frontotemporal dementia [ 51 ], Alzheimer’s disease [ 52 ], and spinal muscular atrophy [ 53 ]. Additionally, let-7b is a miRNA biomarker for anti-NMDA receptor encephalitis [ 54 , 55 , 56 ].…”

Section: Micrornamentioning

confidence: 99%

MicroRNAs and Apoptosis in Colorectal Cancer

Wang

2020

IJMS

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Colorectal cancer (CRC) is the third leading cause of cancer death in the world, and its incidence is rising in developing countries. Treatment with 5-Fluorouracil (5-FU) is known to improve survival in CRC patients. Most anti-cancer therapies trigger apoptosis induction to eliminate malignant cells. However, de-regulated apoptotic signaling allows cancer cells to escape this signaling, leading to therapeutic resistance. Treatment resistance is a major challenge in the development of effective therapies. The microRNAs (miRNAs) play important roles in CRC treatment resistance and CRC progression and apoptosis. This review discusses the role of miRNAs in contributing to the promotion or inhibition of apoptosis in CRC and the role of miRNAs in modulating treatment resistance in CRC cells.

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“…The various unsupervised gene selection methods were invented, e.g., highly variable genes, bimodal genes and dpFeature and principal component analysis (PCA) based unsupervised feature extraction (FE) (50, 34, 49, 42, 25, 31, 27, 40, 47, 4, 5, 44, 45, 13, 12, 11, 51, 39, 43, 22, 23, 24, 26, 41, 48). Chen et al (2) recently compared genes selected by these methods and concluded that the genes selected are very diverse and have their own (unique) biological features.…”

Section: Introductionmentioning

confidence: 99%

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Taguchi

Turki

2019

Preprint

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3Although single cell RNA sequencing (scRNA-seq) technology is newly invented and promising 4 one, because of lack of enough information that labels individual cells, it is hard to interpret 5 the obtained gene expression of each cell. Because of this insufficient information available, 6 unsupervised clustering, e.g., tSNE and UMAP, is usually employed to obtain low dimensional 7 embedding that can help to understand cell-cell relationship. One possible drawback of this 8 strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. 9 In order to fulfill this requirements, there are many methods that performed unsupervised gene 10 selection. In this study, a tensor decomposition (TD) based unsupervised feature extraction 11 (FE) was applied to the integration of two scRNA-seq expression profiles that measure human 12 and mouse midbrain development. TD based unsupervised FE could not only select coincident 13 genes between human and mouse, but also biologically reliable genes. Coincidence between two 14 species as well as biological reliability of selected genes is increased compared with principal 15 component analysis (PCA) based FE applied to the same data set in the previous study. Since 16 PCA based unsupervised FE outperformed other three popular unsupervised gene selection 17 methods, highly variable genes, bimodal genes and dpFeature, TD based unsupervised FE can 18 do so as well. In addition to this, ten transcription factors (TFs) that might regulate selected genes 19 and might contribute to midbrain development are identified. These ten TFs, BHLHE40, EGR1, 20 GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to 21 be related to brain functions and diseases. TD based unsupervised FE is promising method to 22 integrate two scRNA-seq profiles effectively. 23 Keywords: tensor decomposition, enrichment analysis, sngle cell RNA-sequencing, midbrain development, inter-species analysis 24 INTRODUCTIONSingle cell RNA sequencing (scRNA-seq) (17) is a newly invented technology that enables us to measure 25 amount of RNA in single cell basis. In spite of its promising potential, it is not easy to interpret the 26 measurements. The primary reason of this difficulty is the lack of sufficient information that characterizes 27 1 Y-h. Taguchi TD based FE to single-cell individual cells. In contrast to the huge number of cells measured, which is often as many as several 28 thousands, the number of labeling is limited, e.g., measurement of conditions as well as the amount of 29 expression of key genes measured by fluorescence-activated cell sorting, whose number is typically as little 30 as tens. This prevents us from selecting genes that characterize the individual cell properties. 31In order to deal with samples without suitable number of labelling, unsupervised method is frequently 32 used, since it does not make use of labeling information directly. K-means clustering as well as hierarchical 33 clustering are the popular methodology tha...

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Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Cited by 40 publications

References 45 publications

MicroRNAs as Biomarkers in Amyotrophic Lateral Sclerosis

MicroRNAs as Biomarkers in Amyotrophic Lateral Sclerosis

MicroRNAs and Apoptosis in Colorectal Cancer

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

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