Biological data: The use of -omics in outcome models
Issam El Naqa in A Guide to Outcome Modeling in Radiotherapy and Oncology, 2018
Gene expression profiling entails the use of techniques to investigate the relative rate of expression of many mRNA transcripts at a single time with respect to some baseline genes (called housing keeping genes) or across different time points. Microarray techniques using fluorophores and unbiased sequencing approaches (RNA-seq) are the most commonly used methods for determining such expression levels. The advent of high-throughput expression profiling techniques has meant that mRNA levels can be readily quantified and integrated into outcome models. For example, several mRNA signatures have been developed to predict the efficacy of neoadjuvant chemotherapy in breast cancer patients. Several signatures have been derived that reflect tumor hypoxia and predict benefit from combining hypoxia-modifying treatment with radiotherapy [217, 218].
Tapping into the Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine for Individualization of Breast Cancer Treatment
Brian Leyland-Jones in Pharmacogenetics of Breast Cancer, 2020
Gene expression profiling has become an important aspect in prediction, prognosis, and cancer modeling. Gene signatures resulting from expression profiling studies have the potential to define cancer subtypes, predict the clinical outcome (recurrence of disease) and response to specific therapies, and analyze oncogenic pathways (20–23). Investigations of gene pathways and interactions indicated by gene signatures that are truly predictive of the clinical endpoints are necessary to understand the biology underlying this predictive value. More importantly, when combined with clinical and demographic factors, multiple forms of molecular (protein- and gene-based) data can provide information that has the potential to identify unique characteristics of individuals and so lead to individualized treatment strategies (24,25).
Gene Expression Profiling to Detect New Treatment Targets in Leukemia and Lymphoma: A Future Perspective
Gertjan J. L. Kaspers, Bertrand Coiffier, Michael C. Heinrich, Elihu Estey in Innovative Leukemia and Lymphoma Therapy, 2019
Another outstanding investigation was conducted by Holleman et al. (36) who identified a set of differentially expressed genes in B-lineage ALL being sensitive or resistant to several drugs such as prednisolone (33 genes), vincristine (40 genes), asparaginase (35 genes), and daunorubicin (20 genes). A score of genes combined to define overall sensitivity or resistance to all four drugs was tested in a multivariate analysis and predicted outcome of 173 children investigated (p = 0.027). Although these genes do not per se define new targets of treatment, gene expression profiling clearly demonstrated in a prospective setting which treatment may or may not be successful. This may serve as an example for the application of gene expression profiling to improve treatment and to define targets and drugs against these targets in ALL. The authors further point to the aspect that it may be important to determine whether specific modulation of proteins encoded by genes that were found may describe treatment response best. These proteins may also point to previously unrecognized potential targets and new agents to augment the efficacy of current chemotherapy for ALL.
Overview of gene expression techniques with an emphasis on vitamin D related studies
Published in Current Medical Research and Opinion, 2023
Jeffrey Justin Margret, Sushil K. Jain
During cell development, certain sets of genes express proteins that allow them to communicate with neighboring cells to coordinate development in multicellular organisms. All living organisms make use of this process, known as gene expression, to create the building blocks of life from genetic information1. The exceedingly complex process of gene expression enables cells to control their size, shape, and functions as it involves the interactions among DNA, RNA, and proteins, as well as with the environment. The phenotype of an organism is determined by how its genes are expressed2 and regulated at many levels. The protein expressed determines the function of the cell, and each cell type has a unique gene expression profile. Thus, gene expression profiling is a fundamental tool with which to investigate changes in the expression at a cellular level, thus unraveling the complexity of biological systems and the effects of mutations that result in disease states or pathobiology.
Systematic identification of the interventional mechanism of Qingfei Xiaoyan Wan (QFXYW) in treatment of the cytokine storm in acute lung injury using transcriptomics-based system pharmacological analyses
Published in Pharmaceutical Biology, 2022
Jing-Yi Hou, Jia-Rong Wu, Yi-Bing Chen, Dong Xu, Shu Liu, Dan-dan Shang, Guan-Wei Fan, Yuan-Lu Cui
Transcriptomics, also known as gene expression profiling, might explain the gene regulation mechanism of complex diseases, screen potential target genes and pathways through a variety of computational methods, and maybe successfully applied to reveal potential biomarkers or further explore the treatment of complex diseases (Kersch et al. 2020; Shockley et al. 2020). System pharmacology perfectly integrates the concepts and methods of system biology, bioinformatics, and pharmacology, focussing on a combination of multiple components, targets, and pathways to investigate the systematic mechanism of TCM (Zhao et al. 2018; Shu et al. 2019). The combination of transcriptomics and system pharmacology reveals the inheritable and genetic changes throughout the life process system, which is consistent with the principles of TCM that utilises multiple ingredients and targets, as a whole system in the treatment of diseases (Ding et al. 2020; Chen et al. 2020b).
Effect of thrombopoietin receptor agonists on markers of coagulation and P-selectin in patients with immune thrombocytopenia
Published in Platelets, 2019
Lamya Garabet, Waleed Ghanima, Christine Monceyron Jonassen, Vibe Skov, René Holst, Marie-Christine Mowinckel, Hans C. Hasselbalch, Torben A. Kruse, Mads Thomassen, Howard Liebman, James B Bussel, Per Morten Sandset
The limited number of patients, lack of a control group treated with other ITP therapies, and the fact that venous thrombosis was not encountered during the study represent limitations of our study. However, the number of patients included in this study, 44, was by far the largest number studied thus far. Similarly, the samples for gene expression profiling were acquired from a very limited number of patients but nonetheless achieved statistical significance. These findings will be pursued in future studies. One additional limitation was that the median age in controls was slightly lower than median age in ITP cohort due to the difficulty in acquiring controls above the age of 70 years.
Related Knowledge Centers
- DNA Microarray
- Gene Expression
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- Transcriptomics Technologies
- Rna-Seq