Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

Purpose The high 5-year disease relapse rate in patients with stage I lung adenocarcinoma indicates the need for additional risk stratification parameters. This study assessed whether gene signatures translate into a pathologic feature for better disease stratification. Materials and Methods The mutual interdependence and risk stratification power of three gene signatures, cell cycle, hypoxia, and mammalian target of rapamycin (mTOR), were investigated in nine cohorts of patients with lung adenocarcinoma and five cohorts of patients with lung squamous cell carcinoma. The translation from gene signatures to a pathologic feature, tumor necrosis, was validated in The Cancer Genome Atlas lung adenocarcinoma cohort. The translation of signature score to pathway activity was further investigated by integrative analyses using The Cancer Genome Atlas and The Cancer Protein Atlas lung adenocarcinoma data sets. Results The results showed that the three gene signatures were mutually interdependent in lung adenocarcinoma but not in lung squamous cell carcinoma. The signature activities were higher in necrosis-positive tumors than in necrosis-negative tumors. The signature score correlated with the expression level of the representative protein that implicated the activity of each pathway. These signatures stratified patients with operable and stage I lung adenocarcinomas into different risk groups independent of age and stage. Furthermore, the signatures translated to a pathologic feature, tumor necrosis, which predicted shorter overall and relapse-free survival in patients with operable and stage I lung adenocarcinomas. Conclusion This study showed that gene signatures could translate into a pathologic feature, tumor necrosis, with risk stratification ability in patients with operable and stage I lung adenocarcinomas.

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Neuropathological and transcriptomic characteristics of the aged brain

eLife ◽

10.7554/elife.31126 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 24

Author(s):

Jeremy A Miller ◽

Angela Guillozet-Bongaarts ◽

Laura E Gibbons ◽

Nadia Postupna ◽

Anne Renz ◽

...

Keyword(s):

Neurodegenerative Diseases ◽

Amyloid Beta ◽

Individual Variation ◽

Cognitive Abilities ◽

Inflammation Markers ◽

Gene Signatures ◽

Common Gene ◽

Rna Quality ◽

Age Related

As more people live longer, age-related neurodegenerative diseases are an increasingly important societal health issue. Treatments targeting specific pathologies such as amyloid beta in Alzheimer’s disease (AD) have not led to effective treatments, and there is increasing evidence of a disconnect between traditional pathology and cognitive abilities with advancing age, indicative of individual variation in resilience to pathology. Here, we generated a comprehensive neuropathological, molecular, and transcriptomic characterization of hippocampus and two regions cortex in 107 aged donors (median = 90) from the Adult Changes in Thought (ACT) study as a freely-available resource (http://aging.brain-map.org/). We confirm established associations between AD pathology and dementia, albeit with increased, presumably aging-related variability, and identify sets of co-expressed genes correlated with pathological tau and inflammation markers. Finally, we demonstrate a relationship between dementia and RNA quality, and find common gene signatures, highlighting the importance of properly controlling for RNA quality when studying dementia.

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Comprehensive Analysis of Differential Gene Expression to Identify Common Gene Signatures in Multiple Cancers

Medical Science Monitor ◽

10.12659/msm.919953 ◽

2020 ◽

Vol 26 ◽

Cited By ~ 2

Author(s):

Jin-min Xue ◽

Yi Liu ◽

Ling-hong Wan ◽

Yu-xi Zhu

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Comprehensive Analysis ◽

Gene Signatures ◽

Common Gene ◽

Multiple Cancers ◽

Differential Gene

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Gene signatures and potential therapeutic targets of Middle East respiratory syndrome coronavirus (MERS-CoV)-infected human lung adenocarcinoma epithelial cells

Journal of Microbiology Immunology and Infection ◽

10.1016/j.jmii.2021.03.007 ◽

2021 ◽

Author(s):

Yen-Hung Wu ◽

I-Jeng Yeh ◽

Nam Nhut Phan ◽

Meng-Chi Yen ◽

Jui-Hsiang Hung ◽

...

Keyword(s):

Middle East ◽

Epithelial Cells ◽

Lung Adenocarcinoma ◽

Human Lung ◽

Therapeutic Targets ◽

Middle East Respiratory Syndrome ◽

Gene Signatures ◽

Human Lung Adenocarcinoma

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Signaling pathway-based identification of extensive prognostic gene signatures for lung adenocarcinoma

Lung Cancer ◽

10.1016/j.lungcan.2011.09.016 ◽

2012 ◽

Vol 76 (1) ◽

pp. 98-105 ◽

Cited By ~ 15

Author(s):

Ying-Wooi Wan ◽

David G. Beer ◽

Nancy Lan Guo

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathway ◽

Gene Signatures ◽

Prognostic Gene

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In silico Therapeutic Drug Screening for Reversing the Lung Adenocarcinoma Overexpressed Gene Signatures

Journal of Cancer Science & Therapy ◽

10.4172/scientificreports.101 ◽

2012 ◽

Vol 01 (01) ◽

Author(s):

Yu-Lun Kuo ◽

Peter Mu-Hsin Chang ◽

Yu-Wen Liu ◽

Po-Hsu Chen ◽

Pei-Ying Lee

Keyword(s):

Lung Adenocarcinoma ◽

Drug Screening ◽

In Silico ◽

Therapeutic Drug ◽

Gene Signatures ◽

Overexpressed Gene

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Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

PeerJ ◽

10.7717/peerj.6980 ◽

2019 ◽

Vol 7 ◽

pp. e6980 ◽

Cited By ~ 2

Author(s):

Yiyan Songyang ◽

Wei Zhu ◽

Cong Liu ◽

Lin-lin Li ◽

Wei Hu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Regression Model ◽

Expression Analysis ◽

Large Scale ◽

Gene Expression Analysis ◽

Cox Regression ◽

Cox Regression Model ◽

Gene Signatures

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

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