ArticleTranslational oncology2023
Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches.
Article in Translational oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 48 citations in OpenAlex.
- Identification of key genes related to macrophages and metabolic reprogramming in myocardial infarct based on single-cell and bulk transcriptomics data and experimental validation.BMC cardiovascular disorders · 2026Article
- MicroRNA-regulated signaling and tumor microenvironment modulation in lung cancer: mechanistic insights and translational opportunities.Cancer cell international · 2026Review
- Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.Cell reports. Medicine · 2026Article
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Article
- CYP27A1 suppresses brain metastasis via ferroptosis in lung adenocarcinoma: a six-gene signature predicting the immunotherapy response and clinical outcomes.Cancer cell international · 2025Article
- MS4A1 regulates M1-polarized tumor-associated macrophage infiltration, angiogenesis, and cancer progression through the HIPPO pathway in lung adenocarcinoma.Cancer immunology, immunotherapy : CII · 2025Article
- Two inflammation-related genes model could predict risk in prognosis of patients with lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Relationship between preoperative body mass index and overall mortality in patients who have undergone lobectomy for lung cancer.World journal of surgical oncology · 2025Article
- Single-Cell Sequencing: An Emerging Tool for Biomarker Development in Nuclear Emergencies and Radiation Oncology.Cancers · 2025Review
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Network-Based Integrative Analysis to Identify Key Genes and Corresponding Reporter Biomolecules for Triple-Negative Breast Cancer.Cancer medicine · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- Epigenetic modifications in early stage lung cancer: pathogenesis, biomarkers, and early diagnosis.MedComm · 2025Review
- Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating High-Throughput Sequencing, Machine Learning, and Validation.Journal of inflammation research · 2025Article
- Single-cell sequencing in molecular diagnostics: Transformative yet untapped potential.Frontiers in genetics · 2025Review
- Precision Medicine for Pulmonary Vascular Disease: The Future Is Now (2023 Grover Conference Series).Pulmonary circulation · 2025Article
- DDR1 as a key prognostic biomarker in non-small cell lung cancer: identification, validation, and potential therapeutic implications.Frontiers in immunology · 2025Article
- Tumor heterogeneity assessment using single-cell RNA sequencing (scRNA-seq): applications in lung cancer for diagnosis and treatment.Frontiers in immunology · 2025Review
- MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway.Cancer biology & therapy · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.