ArticleTranslational lung cancer research2024
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Article in Translational lung cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Single-Cell RNA Sequencing Revealed the Role of Interferon-Gamma Related Genes in Primary Sjögren's Syndrome.Journal of cellular and molecular medicine · 2026Article
- ATF3 and HNF4A: an oxidative phosphorylation and cholesterol homeostasis-associated diagnostic and therapeutic repurposing framework target for metabolic dysfunction-associated steatohepatitis patients.Frontiers in medicine · 2026Article
- Structure-based virtual screening identifies ethyl rosmarinate as a SLC1A6-targeted therapeutic candidate for bladder cancer.Frontiers in pharmacology · 2026Article
- Integrated transcriptome and single-cell RNA sequencing identifies small GTPase-associated biomarkers in ulcerative colitis.Frontiers in immunology · 2026Article
- Article
- Single-cell RNA sequencing technology was employed to construct a risk prediction model for genes associated with pyroptosis and ferroptosis in lung adenocarcinoma.Respiratory research · 2025Article
- KDM1A-mediated ZFP64 demethylation activates CENPL to promote epithelial ovarian cancer progression.Cytotechnology · 2025Article
- The Combined Prognostic Value ofCancer medicine · 2025Article
- Cancer phenomics research hotspots and development trends: a bibliometric analysis from 2000 to 2023.Discover oncology · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancers, and is the leading cause of tumor-related death. Lung adenocarcinoma (LUAD) is the most prevalent subtype of NSCLC. Although significant progress of LUAD treatment has been made under multimodal strategies, the prognosis of advanced LUAD is still poor due to recurrence and metastasis. There is still a lack of reliable markers to evaluate the LUAD prognosis. This study aims to explore novel biomarkers and construct a prognostic model to predict the prognosis of LUAD patients. Methods: The Genomic Data Commons-The Cancer Genome Atlas-Lung Adenocarcinoma (GDC-TCGA-LUAD) dataset was downloaded from the University of California, Santa Cruz (UCSC) Xena browser. The GSE72094 and GSE13213 datasets and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) database. By analyzing these datasets using DESeq2 R package and Limma R package, differentially expressed genes (DEGs) were found. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to analyze possible enrichment pathways. A protein-protein interaction (PPI) network was constructed to explore possible relationship among DEGs by using the STRING database. A survival analysis was performed to identify reliable prognostic genes using the Kaplan-Meier method. A multi-omics analysis was performed using the Gene Set Cancer Analysis (GSCA). The Tumor Immune Estimation Score (TIMER) database was used to analyze the association between prognostic genes and immune infiltration. A Spearman correlation analysis was conducted to examine the correlation between prognostic genes and drug sensitivity. A multivariate Cox regression was used to identify independent prognostic factors. Next, a nomogram was constructed using the rms R package. Finally, the expressions of aspartyl-tRNA synthetase 2 (DARS2) and phosphoribosyl aminoimidazole carboxylase (PAICS) were detected using immunohistochemistry (IHC). Results: We screened out 30 DEGs prior to functional enrichment and PPI network analysis revealing potential enrichment pathways and interactions of these DEGs. Then survival analysis revealed the Conclusions: The
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