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In Silico Approach to Cancer Therapy
Published in Anjana Pandey, Saumya Srivastava, Recent Advances in Cancer Diagnostics and Therapy, 2022
Anjana Pandey, Saumya Srivastava
Another prevalent tool is MANTRA (Iorio et al., 2010). It is a network-based analytic tool that permits the exploration of drug locality based on the similarity among induced transcriptional reactions. The latest version also enables the user-provided profiles for the network growth in a collaborative manner (Carrella et al., 2014). MANTRA was used to identify anthelmintic drugs and play an essential role in inhibiting oncogenic PI3K/AKT/P70S6K-dependent signaling pathways (Carrella et al., 2016). Furthermore, MANTRA also supported identifying growth inhibitors in pancreatic tumors depending on the K-RAS oncogene activation (Mottini et al., 2019). The MANTRA model is created on symmetric Gene Set Enrichment Analysis (GSEA) of the highest and lowest expressed genes amid two drugs.
Big data statistical methods for radiation oncology
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Yu Jiang, Vojtech Huser, Shuangge Ma
The first is to conduct marginal analysis and identify functional groups that are marginally associated with the outcome and can serve as markers. With omics data, the most popular functional-group–based analysis is perhaps the gene set enrichment analysis (GSEA) (Subramanian et al., 2005), which is needed to identify sets of genes (e.g., methylation loci) that are enriched with (i.e., they have unproportionally high percentages of) genes that are associated with the outcome. A large number of alternative methods are also available (Kim and Volsky, 2005; Backes et al., 2007). Unfortunately, the statistics involved is complicated and cannot be easily described. For comprehensive reviews and discussions, see Irizarry et al. (2009) and Hung et al. (2011). Luckily, for the purpose of routine data analysis, there are some well-developed software tools. For example, the GSEA software is freely available at software.broadinstitute.org/gsea/.
Breast cancer
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Mapping association studies were performed between radiomic features and genomic features downloaded from the TCGA (including DNA mutation, miRNA expression, protein expression, pathway gene expression, and copy number variation) in order to map the genetic mechanisms that regulate the imaging presentation of specific MRI-based tumor phenotypes (102,103). Mappings were obtained using gene-set enrichment analysis (GSEA) and linear regression analysis on the radiomic and genomic features, yielding relationships with transcriptional activities of pathways and miRNA expressions. Associations were discovered between pathway transcriptional activities and various image-based phenotypes, indicating that they may be regulating various aspects of the MRI-based characteristics (phenotypes) (Figure 14.12). Ultimately, it may be possible in the future to clinically use radiomic phenotypes to predict miRNA activities, augmenting the medical practice of tumor biopsy and miRNA profiling. Such “virtual digital biopsies” will have the benefit of assessing the entire tumor to assess heterogeneity, being basically noninvasive, and being repeatable over time, such as in the case of monitoring treatment.
An immune cell infiltration landscape classification to predict prognosis and immunotherapy effect in oral squamous cell carcinoma
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
All statistical analysis processes were carried out using R software (version 4.2.0). The Kruskal–Wallis test was used to compare whether there is a significant statistical difference between more than two groups of data, whereas the Wilcoxon test was used to compare the significant statistical difference between the two groups of data. The X-tile software was used to differentiate the optimal cutoff values across subgroups with different survival data (Camp et al. 2004). The Kaplan–Meier technique was used to generate survival curves for different subgroups. The Log-rank test was used to test whether there is a significant difference between subgroups, and the univariate and multivariate Cox regression analyses were used to determine if they were independent prognostic risk factors. The correlation coefficient and the correlation between variables were calculated using Spearman correlation analysis. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to define the molecular functions that the gene set may perform, the cellular environment in which it was located, and the biological processes involved. The threshold of statistical difference was set as p < 0.05.
Systems toxicology approach explores target-pathway relationship and adverse health impacts of ubiquitous environmental pollutant bisphenol A
Published in Journal of Toxicology and Environmental Health, Part A, 2022
Manigandan Nagarajan, Gobichettipalayam Balasubramaniam Maadurshni, Jeganathan Manivannan
In order to understand the system wide molecular effects and disease relationship of BPA targets, current study employed an enrichment analysis through EnrichR (http://amp.pharm.mssm.edu/Enrichr) tool, a comprehensive gene set enrichment analysis web server (Kuleshov et al. 2016). In the current study, the top 100 potential targets were subjected to pathway analysis. For suitable format, the top 100 protein identifiers were converted into their corresponding gene identifiers using ID mapping function of UniProt (https://www.uniprot.org/uploadlists/). For enrichment analysis, databases such as KEGG 2019 Human and OMIM disease modules were selected. Enriched terms were ordered based upon the combined score and considered as significant if adjusted P < .05.
MicroRNAs diagnostic and prognostic value as predictive markers for malignant mesothelioma
Published in Archives of Environmental & Occupational Health, 2020
Elena Sturchio, Maria Grazia Berardinelli, Priscilla Boccia, Miriam Zanellato, Silvia Gioiosa
Many studies have focused on the identification of a single therapeutic candidate, while others have attempted to interpret the data using bioinformatics methodologies such as the Gene Set Enrichment Analysis (GSEA). Thanks to an ever-increasing amount of questionable data, it will be possible to obtain increasingly adequate statistical results oriented to the selection of biomarkers of the highest quality suitable for the diagnosis of numerous pathological conditions, including the MM.