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

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6980 ◽  
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.

scholarly journals A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yujia Zheng ◽  
He Tian ◽  
Zheng Zhou ◽  
Chu Xiao ◽  
Hengchang Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cell Death ◽  
Programmed Cell Death ◽  
Lung Adenocarcinoma ◽  
Regression Model ◽  
Immune Cells ◽  
Cox Regression ◽  
Consensus Clustering ◽  
Cox Regression Model ◽  
Immune Infiltration

Lung adenocarcinoma is one of the most malignant diseases worldwide. The immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and programmed cell death-ligand 1 (PD-L1) have changed the paradigm of lung cancer treatment; however, there are still patients who are resistant. Further exploration of the immune infiltration status of lung adenocarcinoma (LUAD) is necessary for better clinical management. In our study, the CIBERSORT method was used to calculate the infiltration status of 22 immune cells in LUAD patients from The Cancer Genome Atlas (TCGA). We clustered LUAD based on immune infiltration status by consensus clustering. The differentially expressed genes (DEGs) between cold and hot tumor group were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Last, we constructed a Cox regression model. We found that the infiltration of M0 macrophage cells and follicular helper T cells predicted an unfavorable overall survival of patients. Consensus clustering of 22 immune cells identified 5 clusters with different patterns of immune cells infiltration, stromal cells infiltration, and tumor purity. Based on the immune scores, we classified these five clusters into hot and cold tumors, which are different in transcription profiles. Hot tumors are enriched in cytokine–cytokine receptor interaction, while cold tumors are enriched in metabolic pathways. Based on the hub genes and prognostic-related genes, we developed a Cox regression model to predict the overall survival of patients with LUAD and validated in other three datasets. In conclusion, we developed an immune-related signature that can predict the prognosis of patients, which might facilitate the clinical application of immunotherapy in LUAD.

scholarly journals Glioblastoma Distance From the Subventricular Neural Stem Cell Niche Does Not Correlate With Survival

2020 ◽  
Vol 10 ◽  
Author(s):  
Akshitkumar M. Mistry ◽  
Nishit Mummareddy ◽  
Sanjana Salwi ◽  
Larry T. Davis ◽  
Rebecca A. Ihrie
Keyword(s):  
Overall Survival ◽  
Stem Cell ◽  
Regression Model ◽  
Neural Stem Cell ◽  
Stem Cell Niche ◽  
Tumor Volume ◽  
Cox Regression ◽  
Cox Regression Model ◽  
Cell Niche ◽  
The Relationship

ObjectiveTo determine the relationship between survival and glioblastoma distance from the ventricular-subventricular neural stem cell niche (VSVZ).Methods502 pre-operative gadolinium-enhanced, T1-weighted MRIs with glioblastoma retrieved from an institutional dataset (n = 252) and The Cancer Imaging Atlas (n=250) were independently reviewed. The shortest distance from the tumor contrast enhancement to the nearest lateral ventricular wall, the location of the VSVZ, was measured (GBM-VSVZDist). The relationship of GBM-VSVZDist with the proportion of glioblastomas at each distance point and overall survival was explored with a Pearson’s correlation and Cox regression model, respectively, adjusting for the well-established glioblastoma prognosticators.Results244/502 glioblastomas had VSVZ contact. The proportion of non-VSVZ-contacting glioblastomas correlated inversely with GBM-VSVZDist (partial Pearson’s correlation adjusted for tumor volume R=-0.79, p=7.11x10-7). A fit of the Cox regression model adjusted for age at diagnosis, Karnofsky performance status score, post-operative treatment with temozolomide and/or radiotherapy, IDH1/2 mutation status, MGMT promoter methylation status, tumor volume, and extent of resection demonstrated a significantly decreased overall survival only when glioblastoma contacted the VSVZ. Overall survival did not correlate with GBM-VSVZDist.ConclusionsIn the two independent cohorts analyzed, glioblastomas at diagnosis were found in close proximity or in contact with the VSVZ with a proportion that decreased linearly with GBM-VSVZDist. Patient survival was only influenced by the presence or absence of a gadolinium-enhanced glioblastoma contact with the VSVZ. These results may guide analyses to test differential effectiveness of VSVZ radiation in VSVZ-contacting and non-contacting glioblastomas and/or inform patient selection criteria in clinical trials of glioblastoma radiation.

scholarly journals Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis

2011 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Tania Dottorini ◽  
Nicola Senin ◽  
Giorgio Mazzoleni ◽  
Kalle Magnusson ◽  
Andrea Crisanti
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Software Tool

Large-scale gene expression analysis of human skeletal myoblast differentiation

2004 ◽  
Vol 14 (8-9) ◽  
pp. 507-518 ◽  
Author(s):  
Ellen Sterrenburg ◽  
Rolf Turk ◽  
Peter A.C. 't Hoen ◽  
Judith C.T. van Deutekom ◽  
Judith M. Boer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Myoblast Differentiation ◽  
Skeletal Myoblast

scholarly journals Large-scale gene expression analysis of osteoblasts cultured on three different Ti–6Al–4V surface treatments

Biomaterials ◽  
2002 ◽  
Vol 23 (21) ◽  
pp. 4193-4202 ◽  
Author(s):  
Ching-Hsin Ku ◽  
Martin Browne ◽  
Peter J Gregson ◽  
Jacques Corbeil ◽  
Dominique P Pioletti
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Surface Treatments

scholarly journals A unified censored normal regression model for qPCR differential gene expression analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0182832 ◽  
Author(s):  
Peter Pipelers ◽  
Lieven Clement ◽  
Matthijs Vynck ◽  
Jan Hellemans ◽  
Jo Vandesompele ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Differential Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Differential Gene Expression Analysis ◽  
Normal Regression ◽  
Differential Gene

scholarly journals Long Oligonucleotide Arrays on Nylon for Large-Scale Gene Expression Analysis

BioTechniques ◽  
2002 ◽  
Vol 33 (3) ◽  
pp. 612-618 ◽  
Author(s):  
M. El Atifi ◽  
I. Dupré ◽  
B. Rostaing ◽  
E.M. Chambaz ◽  
A.L. Benabid ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Oligonucleotide Arrays

scholarly journals Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysis

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Patrick Deelen ◽  
Sipko van Dam ◽  
Johanna C. Herkert ◽  
Juha M. Karjalainen ◽  
Harm Brugge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Exome Sequencing ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Diagnostic Yield
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