scholarly journals Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

2003 ◽  
Vol 19 (9) ◽  
pp. 1079-1089 ◽  
Author(s):  
G. Getz ◽  
H. Gal ◽  
I. Kela ◽  
D. A. Notterman ◽  
E. Domany
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Colon Cancer ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Cancer Gene ◽  
Expression Data

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity

2020 ◽  
Vol 36 (9) ◽  
pp. 2755-2762
Author(s):  
Jia-Juan Tu ◽  
Le Ou-Yang ◽  
Hong Yan ◽  
Xiao-Fei Zhang ◽  
Hong Qin
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Tumor Heterogeneity ◽  
Supplementary Information ◽  
Cancer Type ◽  
Cancer Gene ◽  
Expression Data ◽  
Cancer Subtypes

Abstract Motivation Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account. Results In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification. Availability and implementation The source code is available at https://github.com/Zhangxf-ccnu/NETI2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Immune microenvironment and intrinsic subtyping in hormone receptor-positive/HER2-negative breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Gaia Griguolo ◽  
Maria Vittoria Dieci ◽  
Laia Paré ◽  
Federica Miglietta ◽  
Daniele Giulio Generali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Hormone Receptor ◽  
Tumor Biology ◽  
Expression Data ◽  
Immune Microenvironment ◽  
Immune Infiltrate ◽  
Hormone Receptor Positive ◽  
Her2 Breast Cancer

AbstractLittle is known regarding the interaction between immune microenvironment and tumor biology in hormone receptor (HR)+/HER2− breast cancer (BC). We here assess pretreatment gene-expression data from 66 HR+/HER2− early BCs from the LETLOB trial and show that non-luminal tumors (HER2-enriched, Basal-like) present higher tumor-infiltrating lymphocyte levels than luminal tumors. Moreover, significant differences in immune infiltrate composition, assessed by CIBERSORT, were observed: non-luminal tumors showed a more proinflammatory antitumor immune infiltrate composition than luminal ones.

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