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pmid:34671675

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis.

Yu Wang · Sibo Hu · Xianguang Bai · Ke Zhang · Ruixue Yu · Xichao Xia · Xinhua Zheng

BioMed research international · 2021 doi:10.1155/2021/5516218

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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title
Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis.
doi
10.1155/2021/5516218
pmid
34671675
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[0]
Yu Wang
[1]
Sibo Hu
[2]
Xianguang Bai
[3]
Ke Zhang
[4]
Ruixue Yu
[5]
Xichao Xia
[6]
Xinhua Zheng
journal
BioMed research international
year
2021
abstract
BACKGROUND: The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. METHODS: We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log2 fold change (FC) | >2. RESULTS: A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. CONCLUSIONS: We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.
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The Kaplan-Meier survival chart was conducted to compare the two patient cohorts, and the 95% confidence interval and logrank value hazard ratio were calculated.
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The Kaplan-Meier survival chart was conducted to compare the two patient cohorts, and the 95% confidence interval and logrank value hazard ratio were calculated.
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Five genes were associated with overall survival in nonsmoking women with lung cancer: upregulated GREM1, SPP1, and MMP11 were associated with lower survival in the high-expression groups, while downregulated FOSB and IL33 showed the opposite pattern.
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sections[12] "3.4. Overall Survival Analysis Validation and Gene Ontology" paragraph 2
quote
The upregulated genes of GREM1 (HR = 3.82) (Figure 6(c)), SPP1 (HR = 3.6) (Figure 6(d)), and MMP11 (HR = 4.93) (Figure 6(e)) had lower survival rate in the high expression group compared with the low expression group, while the downregulated genes of FOSB (HR = 0.45) (Figure 6(a)) and IL33 (HR = 0.35) (Figure 6(b)) were the opposite.
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Five genes were associated with overall survival in nonsmoking women with lung cancer: upregulated GREM1, SPP1, and MMP11 were associated with lower survival in the high-expression groups, while downregulated FOSB and IL33 showed the opposite pattern.
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Genome-wide screening of transcriptional modulation in non-smoking female lung cancer in Taiwan
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34671675", "kind": "science-record", "revision": 5, "content_hash": "sha256:0dec5232bbbd00c8653500f06f46f7d7f2c813bc478d3335692fa7b245758c23", "created_at": "2026-11-01T17:36:20Z", "updated_at": "2026-11-01T22:47:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis.", "doi": "10.1155/2021/5516218", "pmid": "34671675", "authors": ["Yu Wang", "Sibo Hu", "Xianguang Bai", "Ke Zhang", "Ruixue Yu", "Xichao Xia", "Xinhua Zheng"], "journal": "BioMed research international", "year": 2021, "abstract": "BACKGROUND: The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.\nMETHODS: We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log2 fold change (FC) | >2.\nRESULTS: A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.\nCONCLUSIONS: We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier survival chart was conducted to compare the two patient cohorts, and the 95% confidence interval and logrank value hazard ratio were calculated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34671675", "locator": "sections[7] \"2.5. 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