scholarly journals The Impact of Gene Expression Patterns in Breast Cancer

2016 ◽  
Vol 62 (8) ◽  
pp. 1150-1151 ◽  
Author(s):  
Therese Sørlie
Author(s):  
Michael V. Lombardo ◽  
Elena Maria Busuoli ◽  
Laura Schreibman ◽  
Aubyn C. Stahmer ◽  
Tiziano Pramparo ◽  
...  

AbstractEarly detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., <24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predict 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predict the rate of skill growth, accounting for 13% of the variance in treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism postmortem cortical tissue, and are normatively highly expressed in a variety of subcortical and cortical areas important for social communication and language development. This work suggests that pre-treatment biological and clinical behavioral characteristics are important for predicting developmental change in the context of early intervention and that individualized pre-treatment biology related to histone acetylation may be key.


2021 ◽  
Author(s):  
Graham L. Cromar ◽  
Jonathan Epp ◽  
Ana Popovic ◽  
Yusing Gu ◽  
Violet Ha ◽  
...  

ABSTRACTToxoplasma gondii is a single celled parasite thought to infect 1 in 3 worldwide. During chronic infection, T. gondii can migrate to the brain where it promotes low-grade neuroinflammation with the capacity to induce changes in brain morphology and behavior. Consequently, infection with T. gondii has been linked with a number of neurocognitive disorders including schizophrenia (SZ), dementia, and Parkinson’s disease. Beyond neuroinflammation, infection with T. gondii can modulate the production of neurotransmitters, such as dopamine. To further dissect these pathways and examine the impact of altered dopaminergic sensitivity in T. gondii-infected mice on both behavior and gene expression, we developed a novel mouse model, based on stimulant-induced (cocaine) hyperactivity. Employing this model, we found that infection with T. gondii did not alter fear behavior but did impact motor activity and neuropsychiatric-related behaviurs. While both behaviors may help reduce predator avoidance, consistent with previous studies, the latter finding is reminiscent of neurocognitive disorders. Applying RNASeq to two relevant brain regions, striatum and hippocampus, we identified a broad upregulation of immune responses. However, we also noted significant associations with more meaningful neurologically relevant terms were masked due to the sheer number of terms incorporated in multiple testing correction. We therefore performed a more focused analysis using a curated set of neurologically relevant terms revealing significant associations across multiple pathways. We also found that T. gondii and cocaine treatments impacted the expression of similar functional pathways in the hippocampus and striatum although, as indicated by the low overlap among differentially expressed genes, largely via different proteins. Furthermore, while most differentially expressed genes reacted to a single condition and were mostly upregulated, we identified gene expression patterns indicating unexpected interactions between T. gondii infection and cocaine exposure. These include sets of genes which responded to cocaine exposure but not upon cocaine exposure in the context of T. gondii infection, suggestive of a neuroprotective effect advantageous to parasite persistence. Given its ability to uncover such complex relationships, we propose this novel model offers a new perspective to dissect the molecular pathways by which T. gondii infection contributes to neuropsychiatric disorders such as schizophrenia.


2007 ◽  
Vol 108 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Xuesong Lu ◽  
Xin Lu ◽  
Zhigang C. Wang ◽  
J. Dirk Iglehart ◽  
Xuegong Zhang ◽  
...  

2019 ◽  
Vol 35 (22) ◽  
pp. 4830-4833 ◽  
Author(s):  
Seyed Ali Madani Tonekaboni ◽  
Venkata Satya Kumar Manem ◽  
Nehme El-Hachem ◽  
Benjamin Haibe-Kains

Abstract Motivation High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. Results We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. Availability and implementation An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).


2004 ◽  
Vol 36 (8) ◽  
pp. 1043-1057 ◽  
Author(s):  
Cheol-Koo Lee ◽  
Thomas D Pugh ◽  
Roger G Klopp ◽  
Jode Edwards ◽  
David B Allison ◽  
...  

2005 ◽  
Vol 11 (17) ◽  
pp. 6226-6232 ◽  
Author(s):  
Sherry X. Yang ◽  
Richard M. Simon ◽  
Antoinette R. Tan ◽  
Diana Nguyen ◽  
Sandra M. Swain

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