Cancer Bioinformatics [Working Title]

2022 ◽  
Keyword(s):  
Cancer Bioinformatics

Cancer Bioinformatics for Updating Anticancer Drug Developments and Personalized Therapeutics

2017 ◽  
Vol 12 (2) ◽  
pp. 101-110 ◽  
Author(s):  
Da-Yong Lu ◽  
Rong-Xin Qu ◽  
Ting-Ren Lu ◽  
Hong-Ying Wu
Keyword(s):  
Anticancer Drug ◽  
Cancer Bioinformatics

scholarly journals Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

2021 ◽  
Author(s):  
Anders Berglund ◽  
Ryan M. Putney ◽  
Imene Hamaidi ◽  
Sungjune Kim
Keyword(s):  
Patient Outcome ◽  
Immune Responses ◽  
Cancer Cells ◽  
Immune Evasion ◽  
Epigenetic Regulation ◽  
Current Evidence ◽  
Immune Recognition ◽  
Bioinformatics Tools ◽  
Visualization Techniques ◽  
Cancer Bioinformatics

AbstractCancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

2020 ◽  
Vol 18 (03) ◽  
pp. 2050016 ◽  
Author(s):  
Jorge Francisco Cutigi ◽  
Adriane Feijo Evangelista ◽  
Adenilso Simao
Keyword(s):  
Computational Methods ◽  
Complex Disease ◽  
Cancer Genomics ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Easy Access ◽  
Computational Perspective ◽  
High Level ◽  
Passenger Mutations ◽  
Cancer Bioinformatics

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual’s life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

scholarly journals Chromatic: WebAssembly-Based Cancer Genome Viewer

2018 ◽  
Vol 17 ◽  
pp. 117693511877197 ◽  
Author(s):  
Richard Finney ◽  
Daoud Meerzaman
Keyword(s):  
Ease Of Use ◽  
Data Sets ◽  
Web Browsers ◽  
Web Browser ◽  
Cancer Data ◽  
Genomic Variations ◽  
Genome Viewer ◽  
Code Format ◽  
Generation Sequencing ◽  
Cancer Bioinformatics

Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics.

scholarly journals Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

2021 ◽  
Author(s):  
Xi Yu ◽  
Xiaofei Lv
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Tongue Cancer ◽  
Differential Expression Analysis ◽  
Marker Genes ◽  
Data Sets ◽  
Expression Data ◽  
Oral Cancers ◽  
Limma Package ◽  
Cancer Bioinformatics

Abstract Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

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