scholarly journals Protein Structure Readouts of Cancer Drivers for Precision Medicine

2021 ◽  
Author(s):  
Jaspreet Kaur Dhanjal ◽  
Rajkumar Singh Kalra
Keyword(s):  
Expression Profiles ◽  
Protein Structures ◽  
Gene Expression Profiles ◽  
Routine Clinical Practice ◽  
Driver Mutations ◽  
Structural Alterations ◽  
Disease States ◽  
Relevant Evidence ◽  
Cancer Drivers ◽  
Genetic Signatures

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:

scholarly journals Genetic Signatures From RNA Sequencing of Pediatric Localized Scleroderma Skin

2021 ◽  
Vol 9 ◽  
Author(s):  
Emily Mirizio ◽  
Christopher Liu ◽  
Qi Yan ◽  
Julia Waltermire ◽  
Roosha Mandel ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Functional Disability ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Localized Scleroderma ◽  
Clinical Activity ◽  
Cellular Expression ◽  
Genetic Signatures

The purpose of this study was to explore the skin transcriptional profile in pediatric localized scleroderma (LS) to provide a better understanding of the altered immune and fibrotic pathways promoting disease. LS is a progressive disease of the skin and underlying tissue that causes significant functional disability and disfigurement, especially in developing children. RNA sequencing (RNAseq) technology allows for improved understanding of relevant cellular expression through transcriptome analysis of phases during LS disease progression (more active/inflammatory vs. inactive/fibrotic) and also permits the use of RNA extracted from existing paraffin-embedded skin tissue, which is important in pediatrics. A strong correlation was observed between the comparison of genes expressed between fresh (RNAlater) and paraffinized skin in healthy and LS subjects, supporting the use of paraffinized tissue. LS gene signatures compared to healthy controls showed a distinct expression of an inflammatory response gene signature (IRGS) composed of IFNγ-, IFNα-, and TNFα-associated genes. GSEA© enrichment analysis showed that the IRGS, including interferon-inducible chemokines such as CXCL9, CXCL10, CXCL11, and IFNγ itself, was more highly expressed in LS patients with more inflammatory lesions. The use of paraffinized skin for sequencing was proven to be an effective substitute for fresh skin by comparing gene expression profiles. The prevalence of the IFNγ signature in the lesion biopsies of active LS patients indicates that these genes reflect clinical activity parameters and may be the promoters of early, inflammatory disease.

scholarly journals Transcriptional dysregulation study reveals a core network involving the genesis for Alzheimer’s disease

2017 ◽  
Author(s):  
Guofeng Meng ◽  
Hongkang Mei
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Synaptic Function ◽  
Core Network ◽  
Disease Etiology ◽  
Transcriptional Dysregulation ◽  
Disease States ◽  
Human And Mouse

AbstractBackgroundThe pathogenesis of Alzheimer’s disease is associated with dysregulation at different levels from transcriptome to cellular functioning. Such complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes.MethodsApplying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states.Results97 genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimer’s disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the evolutionary conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles.ConclusionOverall, our study reveals a profile of transcriptional dysregulation in the genesis of Alzheimer’s disease by forming a core network with altered regulation; the core network is associated with Alzheimer’s diseases by affecting the aging and synaptic functions related genes; the gene regulation in brain may not be conservative between human and mouse.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

scholarly journals Multiclass cancer classification based on gene expression comparison

2014 ◽  
Vol 0 (0) ◽  
Author(s):  
Sitan Yang ◽  
Daniel Q. Naiman
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Classification Problems ◽  
New Approach ◽  
Class Discrimination ◽  
Disease States ◽  
Genomic Analyses ◽  
Multiclass Classification Problems

AbstractAs the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an

scholarly journals Genomic Landscape of Young-Onset Bladder Cancer and Its Prognostic Implications on Adult Bladder Cancer

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 307
Author(s):  
Sun-Wha Im ◽  
Chang Ohk Sung ◽  
Kun Suk Kim ◽  
Nam Hoon Cho ◽  
Young Min Kim ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Expression Profiles ◽  
Point Mutations ◽  
Gene Expression Profiles ◽  
Good Prognosis ◽  
Driver Mutations ◽  
Low Grade ◽  
Kras Mutations ◽  
Young Onset ◽  
Fgfr3 Gene

Due to the rare occurrence of young-onset bladder cancer (YBC), its genomic characteristics remain largely unknown. Twenty-nine biopsy-proven YBC cases were collected using a nation-wide search for bladder cancer diagnosed at 20 years or younger. Whole exome sequencing and RNA sequencing were carried out in 21 and 11 cases, respectively, and compared with those of adult bladder cancer (ABC) cases obtained from public databases. Almost all YBCs were low grade, non-invasive papillary tumors. YBC had a low mutation burden and less complex copy number alterations. All cases harbored putative driver mutations. Mutations were most commonly found in HRAS (10 cases), with a preference for exon 5. FGFR3 gene fusions were noted with various partner genes (7 cases). The alterations on HRAS and FGFR3 occurred in a mutually exclusive manner. Others included KRAS mutations (2 cases), chromosomes 4p and 10q arm-level deletions (1 case), and ERCC2 mutation (1 case). There were no point mutations in TP53 and FGFR3. The gene expression profiles of YBC were similar to those of the ABC group with good prognosis. None of the YBCs and ABCs with YBC-like mutations showed progression to muscle-invasive tumors. Our results suggest that bladder cancer with YBC-like mutations represents an indolent bladder tumor, regardless of age.

scholarly journals Identification of microRNAs and their target genes in the placenta as biomarkers of inflammation

2020 ◽  
Vol 47 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Hee Yeon Jang ◽  
Seung Mook Lim ◽  
Hyun Jung Lee ◽  
Joon-Seok Hong ◽  
Gi Jin Kim
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Western Blots ◽  
Tissue Samples ◽  
Disease States ◽  
Polymerase Chain ◽  
Lps Treatment

Objective: Recently, microRNA (miRNA) has been identified both as a powerful regulator involved in various biological processes through the regulation of numerous genes and as an effective biomarker for the prediction and diagnosis of various disease states. The objective of this study was to identify and validate miRNAs and their target genes involved in inflammation in placental tissue.Methods: Microarrays were utilized to obtain miRNA and gene expression profiles from placentas with or without inflammation obtained from nine normal pregnant women and 10 preterm labor patients. Quantitative real-time polymerase chain reaction and Western blots were performed to validate the miRNAs and differentially-expressed genes in the placentas with inflammation. Correlations between miRNA and target gene expression were confirmed by luciferase assays in HTR-8/SVneo cells.Results: We identified and validated miRNAs and their target genes that were differentially expressed in placentas with inflammation. We also demonstrated that several miRNAs (miR-371a-5p, miR-3065-3p, miR-519b-3p, and miR-373-3p) directly targeted their target genes (<i>LEF1, LOX, ITGB4</i>, and <i>CD44</i>). However, some miRNAs and their direct target genes showed no correlation in tissue samples. Interestingly, miR-373-3p and miR-3065-3p were markedly regulated by lipopolysaccharide (LPS) treatment, although the expression of their direct targets <i>CD44</i> and <i>LOX</i> was not altered by LPS treatment.Conclusion: These results provide candidate miRNAs and their target genes that could be used as placental biomarkers of inflammation. These candidates may be useful for further miRNA-based biomarker development.

scholarly journals Noninvasive Analysis of High-Risk Driver Mutations and Gene Expression Profiles in Primary Cutaneous Melanoma

2019 ◽  
Vol 139 (5) ◽  
pp. 1127-1134 ◽  
Author(s):  
Laura K. Ferris ◽  
Ronald L. Moy ◽  
Pedram Gerami ◽  
James E. Sligh ◽  
Burkhard Jansen ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Primary Cutaneous Melanoma ◽  
Noninvasive Analysis

scholarly journals Universal cancer tasks, evolutionary tradeoffs, and the functions of driver mutations

2018 ◽  
Author(s):  
Jean Hausser ◽  
Pablo Szekely ◽  
Noam Bar ◽  
Anat Zimmer ◽  
Hila Sheftel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Diversity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Data ◽  
The Body ◽  
Driver Mutations ◽  
Passenger Mutations ◽  
Tumor Gene Expression ◽  
Tumor Gene

AbstractRecent advances have led to an appreciation of the vast molecular diversity of cancer. Detailed data has enabled powerful methods to sort tumors into groups with benefits for prognosis and treatment. We are still missing, however, a general theoretical framework to understand the diversity of tumor gene-expression and mutations. To address this, we present a framework based on multi-task evolution theory, using the fact that tumors evolve in the body, and that tumors are faced with multiple tasks that contribute to their fitness. In accordance with the theory, we find that tradeoff between tasks constrains tumor gene-expression to a continuum bounded by a polyhedron. The vertices of the polyhedron are gene-expression profiles each specializing in one task, allowing the tasks to be identified. We find five universal cancer tasks across tissue-types: cell-division, biomass & energy, lipogenesis, immune-interaction and invasion & tissue remodeling. Tumors whose gene-expression lies close to a vertex are task specialists. We find evidence that such specialists are more sensitive to drugs that interfere with this task. We find that driver mutations, but not passenger mutations, tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a theoretically-based framework for understanding tumor diversity.

scholarly journals An emerging cyberinfrastructure for biodefense pathogen and pathogen–host data

2007 ◽  
Vol 36 (Supplement_1) ◽  
pp. D884-D891 ◽  
Author(s):  
C. Zhang ◽  
O. Crasta ◽  
S. Cammer ◽  
R. Will ◽  
R. Kenyon ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Expression Profiles ◽  
Protein Structures ◽  
Gene Expression Profiles ◽  
Structural Data ◽  
Data Types ◽  
Individual Gene

Abstract The NIAID-funded Biodefense Proteomics Resource Center (RC) provides storage, dissemination, visualization and analysis capabilities for the experimental data deposited by seven Proteomics Research Centers (PRCs). The data and its publication is to support researchers working to discover candidates for the next generation of vaccines, therapeutics and diagnostics against NIAID's Category A, B and C priority pathogens. The data includes transcriptional profiles, protein profiles, protein structural data and host–pathogen protein interactions, in the context of the pathogen life cycle in vivo and in vitro. The database has stored and supported host or pathogen data derived from Bacillus, Brucella, Cryptosporidium, Salmonella, SARS, Toxoplasma, Vibrio and Yersinia, human tissue libraries, and mouse macrophages. These publicly available data cover diverse data types such as mass spectrometry, yeast two-hybrid (Y2H), gene expression profiles, X-ray and NMR determined protein structures and protein expression clones. The growing database covers over 23 000 unique genes/proteins from different experiments and organisms. All of the genes/proteins are annotated and integrated across experiments using UniProt Knowledgebase (UniProtKB) accession numbers. The web-interface for the database enables searching, querying and downloading at the level of experiment, group and individual gene(s)/protein(s) via UniProtKB accession numbers or protein function keywords. The system is accessible at http://www.proteomicsresource.org/.

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