scholarly journals SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics

2021 ◽  
Author(s):  
Satwik Acharyya ◽  
Xiang Zhou ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Breast Cancer ◽  
Cognitive Abilities ◽  
Single Gene ◽  
Expression Patterns ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Cancer Data ◽  
Cellular Environment ◽  
Depth Analysis ◽  
Network Estimation

Motivation: The analysis of spatially-resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. Results: We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

scholarly journals TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer

2012 ◽  
Vol 11 ◽  
pp. CIN.S9983 ◽  
Author(s):  
Xi Chen ◽  
Jiang Li ◽  
William H. Gray ◽  
Brian D. Lehmann ◽  
Joshua A. Bauer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Genomic Research ◽  
Data Matrix ◽  
Cancer Data ◽  
Subtyping Tool

Motivation Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer group, and identification of molecular subtypes is essential for understanding the biological characteristics and clinical behaviors of TNBC as well as for developing personalized treatments. Based on 3,247 gene expression profiles from 21 breast cancer data sets, we discovered six TNBC subtypes from 587 TNBC samples with unique gene expression patterns and ontologies. Cell line models representing each of the TNBC subtypes also displayed different sensitivities to targeted therapeutic agents. Classification of TNBC into subtypes will advance further genomic research and clinical applications. Result We developed a web-based subtyping tool TNBCtype for candidate TNBC samples using our gene expression meta data and classification methods. Given a gene expression data matrix, this tool will display for each candidate sample the predicted subtype, the corresponding correlation coefficient, and the permutation P-value. We offer a user-friendly web interface to predict the subtypes for new TNBC samples that may facilitate diagnostics, biomarker selection, drug discovery, and the more tailored treatment of breast cancer.

scholarly journals MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

2019 ◽  
Author(s):  
Rafsan Ahmed ◽  
Ilyes Baali ◽  
Cesim Erten ◽  
Evis Hoxha ◽  
Hilal Kazan
Keyword(s):  
Random Walk ◽  
Mutual Exclusion ◽  
Risk Scores ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

AbstractMotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at:https://github.com/abu-compbio/[email protected]

scholarly journals Identification of cancer genes that are independent of dominant proliferation and lineage programs

2017 ◽  
Vol 114 (52) ◽  
pp. E11276-E11284 ◽  
Author(s):  
Laura M. Selfors ◽  
Daniel G. Stover ◽  
Isaac S. Harris ◽  
Joan S. Brugge ◽  
Jonathan L. Coloff
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Genetic Alterations ◽  
Breast Cancer Cell Lines ◽  
Cancer Genes ◽  
Linear Modeling ◽  
Cancer Subtypes ◽  
Multiple Tumor ◽  
Cancer Data ◽  
Normal Tissues

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

Bayesian Frailty Model for Time to Event Breast Cancer Data

2011 ◽  
Vol 4 (2) ◽  
pp. 8-12
Author(s):  
Leo Alexander T Leo Alexander T ◽  
◽  
Pari Dayal L Pari Dayal L ◽  
Valarmathi S Valarmathi S ◽  
Ponnuraja C Ponnuraja C ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frailty Model ◽  
Breast Cancer Data ◽  
Time To Event ◽  
Cancer Data

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

AGE FEATURES OF MOLECULAR-BIOLOGICAL SUBTYPES OF BREAST CANCER IN THE REPUBLIC OF UZBEKISTAN

2018 ◽  
Vol 64 (2) ◽  
pp. 196-199
Author(s):  
Gulya Miryusupova ◽  
G. Khakimov ◽  
N. Shayusupov
Keyword(s):  
Breast Cancer ◽  
Cancer Care ◽  
Indigenous Women ◽  
Morbidity And Mortality ◽  
Breast Cancer Data ◽  
Female Population ◽  
Cancer Data ◽  
Age Related ◽  
The Republic ◽  
Age Features

According to the results of breast cancer data in the Republic of Uzbekistan in addition to the increase in morbidity and mortality from breast cancer among women the presence of age specific features among indigenous women in the direction of “rejuvenating” of the disease with all molecular-biological (phenotypic) subtypes of breast cancer were marked. Within the framework of age-related features the prevalence of the least favorable phenotypes of breast cancer was found among indigenous women: Her2/neu hyperexpressive and three times negative subtype of breast cancer. The data obtained made it possible to build a so-called population “portrait” of breast cancer on the territory of the Republic, which in turn would contribute to further improvement of cancer care for the female population of the country.

Dysregulation of HOX as a Potential Therapeutic Target in Breast Cancer

2020 ◽  
Vol 27 ◽  
Author(s):  
Ji-Yeon Lee ◽  
Myoung Hee Kim
Keyword(s):  
Breast Cancer ◽  
Hox Genes ◽  
Therapeutic Potential ◽  
Expression Patterns ◽  
Genome Structure ◽  
Control Cell ◽  
Three Dimensional ◽  
Cellular Processes ◽  
Hox Protein

: HOX genes belong to the highly conserved homeobox superfamily, responsible for the regulation of various cellular processes that control cell homeostasis, from embryogenesis to carcinogenesis. The abnormal expression of HOX genes is observed in various cancers, including breast cancer; they act as oncogenes or as suppressors of cancer, according to context. In this review, we analyze HOX gene expression patterns in breast cancer and examine their relationship, based on the three-dimensional genome structure of the HOX locus. The presence of non-coding RNAs, embedded within the HOX cluster, and the role of these molecules in breast cancer have been reviewed. We further evaluate the characteristic activity of HOX protein in breast cancer and its therapeutic potential.

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