scholarly journals Age-Related Gene Expression Signature in Rats Demonstrate Early, Late, and Linear Transcriptional Changes from Multiple Tissues

Cell Reports ◽  
2019 ◽  
Vol 28 (12) ◽  
pp. 3263-3273.e3 ◽  
Author(s):  
Tea Shavlakadze ◽  
Melody Morris ◽  
Jian Fang ◽  
Sharon X. Wang ◽  
Jiang Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Related Gene ◽  
Expression Signature ◽  
Age Related ◽  
Transcriptional Changes ◽  
Early Late

scholarly journals Age-related Gene Expression Signatures (AGES) in rats demonstrate early, late, and linear transcriptional changes from multiple tissues

2019 ◽  
Author(s):  
Tea Shavlakadze ◽  
Melody Morris ◽  
Jian Fang ◽  
Sharon X. Wang ◽  
Weihua Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Chronological Age ◽  
Related Gene ◽  
Age Related ◽  
Number Of Genes ◽  
Organ Specific ◽  
Transcriptional Changes ◽  
Effects Of Aging ◽  
Early Late

SUMMARYIn order to understand changes in gene expression that occur as a result of age, which might create a permissive or causal environment for age-related diseases, we produced a multi-timepoint Age-related Gene Expression Signature (AGES) from liver, kidney, skeletal muscle and hippocampus of rats, comparing 6, 9, 12, 18, 21, 24 and 27-month old animals. We focused on genes that changed in one direction throughout the lifespan of the animal, either early in life (early logistic changes); at mid-age (mid-logistic); late in life (late-logistic); or linearly, throughout the lifespan. The pathways perturbed as a result of chronological age demonstrate organ-specific and more global effects of aging, and point to mechanisms that might be counter-regulated pharmacologically in order to treat age-associated diseases. A small number of genes were regulated by aging in the same manner in every tissue, suggesting they may be more universal markers of aging.

scholarly journals Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4468
Author(s):  
Seokjin Haam ◽  
Jae-Ho Han ◽  
Hyun Woo Lee ◽  
Young Wha Koh
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Expression Profiling ◽  
Predictive Power ◽  
Gene Expression Signature ◽  
Learning Approach ◽  
Related Gene ◽  
Ecm Remodeling ◽  
Expression Signature ◽  
Machine Learning Approach

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

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