Identification of Specific Gene Modules in Mouse Lung Tissue Exposed to Cigarette Smoke

Xing, Yonghua; Zhang, Junling; Lu, Lu; Li, Deguan; Wang, Yueying; Huang, Song; Li, Chengcheng; Zhang, Zhubo; Li, Jianguo; Xu, Guoshun; Meng, Aimin

doi:10.7314/apjcp.2015.16.10.4251

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…It is extremely useful for data mining to identify and evaluate new hypotheses. Various publications using such databases have been reported to identify novel pathway(s) or molecule(s) as new targets for treatment and prediction of cancer (20)(21)(22). One limitation of microarray database is that the use of cross platform analysis is restricted.…”

Section: Introductionmentioning

confidence: 99%

Expression and influence of Notch signaling in oral squamous cell carcinoma

Osathanon

Nowwarote

Pavasant

2016

Journal of Oral Science

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Section: Introductionmentioning

confidence: 99%

Expression and influence of Notch signaling in oral squamous cell carcinoma

Osathanon

Nowwarote

Pavasant

2016

Journal of Oral Science

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“…Yadav et al had con rmed that UPR play a role in oncogenic transformation and involved in the network of oncogene and tumor suppressor genes to affect the progression of lung cancer [30]. Protein misfolding took place in endoplasmic reticulum (ER) can trigger ER stress and result in UPR to ER, which was took part in the development of various cancers [31]. KEGG and GO function analysis for smoking module also exhibited pathway or function about ER, for example protein processing in ER, retrograde protein transport, ER to cytosol, and ER chaperone complex, which might indicate tobacco smoking could promote the development of cancer by affect UPR and ER associated process.…”

Section: Discussionmentioning

confidence: 99%

Computational Gene Expression Profiling in Non-Small Cell Lung Cancer Reveals Network-Based Immune Signatures Associated With Smoking and Overall Survival

Song

Cheng

Zhang

et al. 2020

Preprint

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Background Lung cancer, a common risky disease, has been the leading cause of death since 2010. In this study, the prognosis of immune genes and its targeted miRNA of non-small lung cancer (NSCLC) was explored. Methods Based on microarray GSE13213 (lung adenocarcinoma (LAD) patient mRNA expression profiles), microarray GSE4573 (lung squamous cell carcinoma (LSC) patient mRNA expression profiles), and microarray GSE102286 (NSCLC patient miRNA profiles), the gene co-expression network was constructed with weight gene co-expression network analysis (WGCNA). KEGG and Gene Ontology were enriched. The binding site was predicted on miRanda. The genes corresponding to immune as well as smoking and its targeted miRNA which was also related to smoking was discovered. And then the prognostic value of them was estimated and confirmed in another data cohort. Results Smoking-related module from LSC had a remarkable association with immune gene set (P = 0.0001), which was not observed in LAD. KEGG and GO enrichment analysis in LSC also exhibited immunity-related pathways and functions, such as the B cell receptor signaling pathway, which was still not found in LAD. High expression of nine immune genes (CD27, CD38, CD79A, MZB1, IGHM, IGKC, IGLL3P, GUSBP11, and IGHD) from the purple module in LSC had better overall survival. In an independent LSC microarray, six of them (CD27, CD38, GUSBP11, IGKC, IGLL3P, and IGHD) were validated. IGHD related to plasma cell regulatory showed better overall survival (OS) in two datasets both. Low expression of IGHD targeted miR-29a and low infiltration of its regulated plasma cells was both associated with better OS. Conclusion Our study revealed that, in LSC, smoking might trigger a more severe immune response, compared to LAD. There were six immune genes related to prognosis being discovered. And miR-29a might bind to IGHD affect OS by regulating plasma cells in LSC.

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“…Moreover, the concept of network biology has been widely applied to a variety of disease. Xing et al (27) indicated that specific gene modules derived from gene coexpression networks and may provide better understanding of molecular mechanisms in disease. In co-expression networks, two genes are connected and assumed to functionally interact if their expression profiles are correlated across multiple conditions.…”

Section: Discussionmentioning

confidence: 99%

Determination of Biomarkers for Neonatal Sepsis Based on Differential Modules

Wang

Luan

et al. 2016

Iran Red Crescent Med J

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Background: The exact interacting factor that response to the infection for neonatal sepsis is still needed to urgently to be disclosed. Objectives: This research was aimed to explore the potential biomarkers and illuminate the underlying molecular mechanisms associated with neonatal sepsis via identifying differential modules (DMs). Methods: This is a case-control bioinformatics analysis using already published microarray data of neonatal sepsis. This study was conducted in Qingdao, China from September 2015 to May 2016. We recruited the gene expression profile of neonatal sepsis from the Array Express database (http://www.ebi.ac.uk/arrayexpress) under the accessing number of E-GEOD-25504, which included 27 neonatal samples with a confirmed blood culture-positive test for sepsis (bacterial infected cases) as well as 35 matched controls. Meanwhile, the human protein-protein interaction (PPI) data was collected from the database of Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, http://string-db.org). All of the data was preprocessed. Then, the differential co-expression network (DCN) was constructed by integrating co-expression analysis and differential expression analysis. Next, a systemic module searching strategy, which contained seed genes selection, module searching and refinement of modules, was performed by select DMs. Results: Starting from the gene expression data and PPI data, the DCN that included 430 edges (covering 324 nodes) was constructed, in which each edge was assigned a weight value. From the DCN, we selected a total of 16 seed genes. Starting from these seed genes, a total of 3 modules were identified from the DCN based on the systemic module algorithm. Of them, only one module (Module 3) was considered as DM under P < 0.05. This DM was involved in the progress of ribosome biogenesis in eukaryotes. Conclusions:In the present study, we identified a key gene RPS16 and a significant module involved in ribosome biogenesis in eukaryotes that were related to neonatal sepsis, which might be potential biomarkers for early detection and therapy for neonatal sepsis.

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Identification of Specific Gene Modules in Mouse Lung Tissue Exposed to Cigarette Smoke

Cited by 6 publications

References 40 publications

Expression and influence of Notch signaling in oral squamous cell carcinoma

Expression and influence of Notch signaling in oral squamous cell carcinoma

Computational Gene Expression Profiling in Non-Small Cell Lung Cancer Reveals Network-Based Immune Signatures Associated With Smoking and Overall Survival

Determination of Biomarkers for Neonatal Sepsis Based on Differential Modules

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