scholarly journals Online database for brain cancer-implicated genes: exploring the subtype-specific mechanisms of brain cancer

2021 ◽  
Author(s):  
Min Zhao ◽  
Yining Liu ◽  
Guiqiong Ding ◽  
Dacheng Qu ◽  
Hong Qu
Keyword(s):  
Brain Cancer ◽  
Differential Expression Analysis ◽  
Survival Rates ◽  
Brain Regions ◽  
Solid Base ◽  
Cancer Subtypes ◽  
Human Genes ◽  
Subtype Identification ◽  
Genetic Mechanisms ◽  
Gene Database

Abstract Background: Brain cancer is one of the eight most common cancers occurring in people aged 40+ and is the fifth-leading cause of cancer-related deaths for males aged 40-59. Accurate subtype identification is crucial for precise therapeutic treatment, which largely depends on understanding the biological pathways and regulatory mechanisms associated with different brain cancer subtypes. Unfortunately, the subtype-implicated genes that have been identified are scattered in thousands of published studies. So, systematic literature curation and cross-validation could provide a solid base for comparative genetic studies about major subtypes.Results: Here, we constructed just such a literature-based brain cancer gene database (BCGene). In the current release, we have a collection of 1,421 unique human genes gathered through an extensive manual examination of over 6,000 PubMed abstracts. We comprehensively annotated those curated genes to facilitate biological pathway identification, cancer genomic comparison, and differential expression analysis in various anatomical brain regions. When we compared those implicated genes between different subtypes, we found subtype-specific genetic events that had high mutational frequencies. Finally, gene prioritization helps users determine the relative importance of the curated genes, and top-ranked genes were significantly associated with survival rates in a combined dataset of more than 2,000 cancer cases. Conclusion: BCGene provides a useful tool for exploring the genetic mechanisms of and gene priorities in brain cancer. BCGene is freely available to academic users at http://soft.bioinfo-minzhao.org/bcgene/.

scholarly journals Online database for brain cancer-implicated genes: exploring the subtype-specific mechanisms of brain cancer

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Min Zhao ◽  
Yining Liu ◽  
Guiqiong Ding ◽  
Dacheng Qu ◽  
Hong Qu
Keyword(s):  
Brain Cancer ◽  
Differential Expression Analysis ◽  
Survival Rates ◽  
Brain Regions ◽  
Solid Base ◽  
Cancer Subtypes ◽  
Human Genes ◽  
Subtype Identification ◽  
Genetic Mechanisms ◽  
Gene Database

Abstract Background Brain cancer is one of the eight most common cancers occurring in people aged 40+ and is the fifth-leading cause of cancer-related deaths for males aged 40–59. Accurate subtype identification is crucial for precise therapeutic treatment, which largely depends on understanding the biological pathways and regulatory mechanisms associated with different brain cancer subtypes. Unfortunately, the subtype-implicated genes that have been identified are scattered in thousands of published studies. So, systematic literature curation and cross-validation could provide a solid base for comparative genetic studies about major subtypes. Results Here, we constructed a literature-based brain cancer gene database (BCGene). In the current release, we have a collection of 1421 unique human genes gathered through an extensive manual examination of over 6000 PubMed abstracts. We comprehensively annotated those curated genes to facilitate biological pathway identification, cancer genomic comparison, and differential expression analysis in various anatomical brain regions. By curating cancer subtypes from the literature, our database provides a basis for exploring the common and unique genetic mechanisms among 40 brain cancer subtypes. By further prioritizing the relative importance of those curated genes in the development of brain cancer, we identified 33 top-ranked genes with evidence mentioned only once in the literature, which were significantly associated with survival rates in a combined dataset of 2997 brain cancer cases. Conclusion BCGene provides a useful tool for exploring the genetic mechanisms of and gene priorities in brain cancer. BCGene is freely available to academic users at http://soft.bioinfo-minzhao.org/bcgene/.

scholarly journals A Guide to the Generation of a 6-Hydroxydopamine Mouse Model of Parkinson’s Disease for the Study of Non-Motor Symptoms

Biomedicines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 598
Author(s):  
Débora Masini ◽  
Carina Plewnia ◽  
Maëlle Bertho ◽  
Nicolas Scalbert ◽  
Vittorio Caggiano ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mouse Model ◽  
Survival Rates ◽  
Dorsal Striatum ◽  
Brain Regions ◽  
Motor Symptoms ◽  
Operative Care ◽  
6 Hydroxydopamine ◽  
Non Motor Symptoms

In Parkinson’s disease (PD), a large number of symptoms affecting the peripheral and central nervous system precede, develop in parallel to, the cardinal motor symptoms of the disease. The study of these conditions, which are often refractory to and may even be exacerbated by standard dopamine replacement therapies, relies on the availability of appropriate animal models. Previous work in rodents showed that injection of the neurotoxin 6-hydroxydopamine (6-OHDA) in discrete brain regions reproduces several non-motor comorbidities commonly associated with PD, including cognitive deficits, depression, anxiety, as well as disruption of olfactory discrimination and circadian rhythm. However, the use of 6-OHDA is frequently associated with significant post-surgical mortality. Here, we describe the generation of a mouse model of PD based on bilateral injection of 6-OHDA in the dorsal striatum. We show that the survival rates of males and females subjected to this lesion differ significantly, with a much higher mortality among males, and provide a protocol of enhanced pre- and post-operative care, which nearly eliminates animal loss. We also briefly discuss the utility of this model for the study of non-motor comorbidities of PD.

scholarly journals Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Omneya Attallah ◽  
Maha Sharkas
Keyword(s):  
Skin Cancer ◽  
Binary Classification ◽  
Survival Rates ◽  
Skin Lesions ◽  
Accurate Diagnosis ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Cancer Subtypes ◽  
Low Level ◽  
High Level

The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

scholarly journals qSVA framework for RNA quality correction in differential expression analysis

2017 ◽  
Vol 114 (27) ◽  
pp. 7130-7135 ◽  
Author(s):  
Andrew E. Jaffe ◽  
Ran Tao ◽  
Alexis L. Norris ◽  
Marc Kealhofer ◽  
Abhinav Nellore ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Brain Regions ◽  
Rna Degradation ◽  
Rna Seq ◽  
Postmortem Human Brain ◽  
Surrogate Variable Analysis ◽  
Expression Levels ◽  
Rna Quality

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a method—quality surrogate variable analysis (qSVA)—as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.

scholarly journals Her2-Positive Cancers and Antibody-Based Treatment: State of the Art and Future Developments

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5771
Author(s):  
Serafin Morales ◽  
Ariadna Gasol ◽  
Douglas Rene Sanchez
Keyword(s):  
Breast Cancer ◽  
Survival Rates ◽  
Her2 Positive ◽  
Her2 Protein ◽  
Cancer Subtypes ◽  
Highly Active ◽  
The Status ◽  
Cure Rates ◽  
Very High

HER2 positive breast cancer represent about 20% of all breast cancer subtypes and it was considered the subtype with the worst prognosis until the discovery of therapies directed against the HER2 protein. The determination of the status of the HER2 must be very precise and well managed to identify this subtype, and there are very specific and updated guides that allow its characterization to be adjusted. Treatment in local disease has been considerably improved with less aggressive and highly effective approaches and very high cure rates. In metastatic disease, average median survival rates of 5 years have been achieved. New highly active molecules have also been discovered that allow disease control in very complicated situations. This article reviews all these options that can be used for the management of this disease.

scholarly journals Cellular Models for Primary CoQ Deficiency Pathogenesis Study

2021 ◽  
Vol 22 (19) ◽  
pp. 10211
Author(s):  
Carlos Santos-Ocaña ◽  
María V. Cascajo ◽  
María Alcázar-Fabra ◽  
Carmine Staiano ◽  
Guillermo López-Lluch ◽  
...  
Keyword(s):  
Mitochondrial Diseases ◽  
Cellular Models ◽  
New Genes ◽  
Whole Exome ◽  
Human Genes ◽  
Genetic Mechanisms ◽  
Induced Pluripotent ◽  
Next Generation Sequencing Ngs ◽  
The Impact ◽  
Excellent Tool

Primary coenzyme Q10 (CoQ) deficiency includes a heterogeneous group of mitochondrial diseases characterized by low mitochondrial levels of CoQ due to decreased endogenous biosynthesis rate. These diseases respond to CoQ treatment mainly at the early stages of the disease. The advances in the next generation sequencing (NGS) as whole-exome sequencing (WES) and whole-genome sequencing (WGS) have increased the discoveries of mutations in either gene already described to participate in CoQ biosynthesis or new genes also involved in this pathway. However, these technologies usually provide many mutations in genes whose pathogenic effect must be validated. To functionally validate the impact of gene variations in the disease’s onset and progression, different cell models are commonly used. We review here the use of yeast strains for functional complementation of human genes, dermal skin fibroblasts from patients as an excellent tool to demonstrate the biochemical and genetic mechanisms of these diseases and the development of human-induced pluripotent stem cells (hiPSCs) and iPSC-derived organoids for the study of the pathogenesis and treatment approaches.

scholarly journals A Meta-Analysis of Alzheimer’s Disease Brain Transcriptomic Data

2018 ◽  
Author(s):  
Hamel Patel ◽  
Richard J.B Dobson ◽  
Stephen J Newhouse
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontal Lobe ◽  
Temporal Lobe ◽  
Parietal Lobe ◽  
Meta Analysis ◽  
Differential Expression Analysis ◽  
Brain Regions ◽  
Biological Pathways ◽  
Transcriptomic Changes

ABSTRACTBackgroundMicroarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer’s disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations. We are yet to identify robust transcriptomic changes specific to AD brains.Methods and ResultsTwenty-two AD, eight Schizophrenia, five Bipolar Disorder, four Huntington's disease, two Major Depressive Disorder and one Parkinson’s disease dataset totalling 2667 samples and mapping to four different brain regions (Temporal lobe, Frontal lobe, Parietal lobe and Cerebellum) were analysed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction. This identified 323, 435, 1023 and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe and cerebellum brain regions respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. Biological pathways involved in the “metabolism of proteins” and viral components were significantly enriched across AD brains.ConclusionThis study solely relied on publicly available microarray data, which too often lacks appropriate phenotypic information for robust data analysis and needs to be addressed by future studies. Nevertheless, with the information available, we were able to identify specific transcriptomic changes in AD brains which could make a significant contribution towards the understanding of AD disease mechanisms and may also provide new therapeutic targets.

scholarly journals Genomic Copy Number Signatures Based Classifiers for Subtype Identification in Cancer

2020 ◽  
Author(s):  
Bo Gao ◽  
Michael Baudis
Keyword(s):  
Copy Number ◽  
Cancer Type ◽  
Driver Genes ◽  
Bioinformatic Pipeline ◽  
Cancer Subtypes ◽  
The Past ◽  
Source Data ◽  
Subtype Identification ◽  
Limited Application ◽  
Cancer Types

AbstractCopy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies has been focused on the association of CNA to pre-selected “driver” genes with limited application to rare drivers and other genomic elements.In this study, we developed a bioinformatic pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

Access to Pediatric Cancer Care by Age, Race, and Diagnosis, and Outcomes of Cancer Treatment in Pediatric and Adolescent Patients in the State of Georgia

2007 ◽  
Vol 25 (29) ◽  
pp. 4610-4615 ◽  
Author(s):  
Della L. Howell ◽  
Kevin C. Ward ◽  
Harland D. Austin ◽  
John L. Young ◽  
William G. Woods
Keyword(s):  
Pediatric Cancer ◽  
Cancer Care ◽  
Lymphoblastic Leukemia ◽  
Survival Rates ◽  
Minority Populations ◽  
Actuarial Survival ◽  
Aged Patients ◽  
Cancer Centers ◽  
Cancer Subtypes ◽  
Treatment Data

Purpose There have been concerns among pediatric oncologists that adolescent and minority patients are not getting adequate access to care. This study examines access to cancer care and survival outcomes based on age, race, and type of cancer in patients in Georgia. Patients and Methods We performed a retrospective review of 1,751 cancer patients aged 0 to 19 years, diagnosed between 1998 and 2002, in the Georgia Comprehensive Cancer Registry, which identified patients who were treated at one of five Georgia pediatric cancer centers (Children's Oncology Group [COG] members) at any point in their treatment. Data were further analyzed for age at diagnosis, race, county of residence, and 5-year survival. Results Eighty-seven percent of patients aged 0 to 14 years and 36% of those aged 15 to 19 years were treated at a COG institution. Twenty-five percent of all patients were of African descent, with 75.4% of black versus 70.3% of white patients (age 0 to 19 years) treated at a COG institution (P < .01); 97.1% of other minorities were treated at a COG institution (P < .05). The 5-year actuarial survival rates for more pediatric-specific cancers were significantly lower in all leukemias (75.1% v 46.4%; P = .0015), and acute lymphoblastic leukemia specifically (86.3% v 53.3%; P < .05) for patients not treated at a COG institution. Actuarial survival rates were much lower for blacks than whites in all cancers as a whole (70% v 82%; P < .001) and for many specific subtypes. Conclusion Adolescent-aged patients are less likely to be referred to a COG institution, potentially exposing them to worse outcomes in some cancer subtypes. Reassuringly, minority populations are receiving adequate access to pediatric cancer care; unfortunately their survival rates are lower.

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