Age-related gene expression alterations by SARS-CoV-2 infection contribute to poor prognosis in elderly

Abstract Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.

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Genetic control of age-related gene expression and complex traits in the human brain

10.1101/125195 ◽

2017 ◽

Cited By ~ 1

Author(s):

Trevor Martin ◽

Hunter B. Fraser

Keyword(s):

Gene Expression ◽

Human Brain ◽

Neurodegenerative Diseases ◽

Genetic Variants ◽

Complex Traits ◽

Molecular Mechanisms ◽

Neurological Diseases ◽

Expression Patterns ◽

Related Gene ◽

Age Related

AbstractAge is the primary risk factor for many of the most common human diseases—particularly neurodegenerative diseases—yet we currently have a very limited understanding of how each individual’s genome affects the aging process. Here we introduce a method to map genetic variants associated with age-related gene expression patterns, which we call temporal expression quantitative trait loci (teQTL). We found that these loci are markedly enriched in the human brain and are associated with neurodegenerative diseases such as Alzheimer’s disease and Creutzfeldt-Jakob disease. Examining potential molecular mechanisms, we found that age-related changes in DNA methylation can explain some cis-acting teQTLs, and that trans-acting teQTLs can be mediated by microRNAs. Our results suggest that genetic variants modifying age-related patterns of gene expression, acting through both cis- and trans-acting molecular mechanisms, could play a role in the pathogenesis of diverse neurological diseases.

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Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer

BioMed Research International ◽

10.1155/2021/6057948 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Qianshi Zhang ◽

Zhen Feng ◽

Yongnian Zhang ◽

Shasha Shi ◽

Yu Zhang ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Poor Prognosis ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Prognostic Signature ◽

Discovery Cohort ◽

Immune Related Gene

Background. Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods. Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results. A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion. We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

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