ReviewFrontiers in immunology2022
Decoding Lung Cancer at Single-Cell Level.
Review in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- E2F7 targets S100A2 to suppress CD8Journal of molecular medicine (Berlin, Germany) · 2026Article
- C15ORF48 serves as a potential biomarker and therapeutic target in pan-cancer with implications for lung cancer.Scientific reports · 2025Article
- Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations.Journal of advanced research · 2025Review
- Identification and validation of hypoxia-associated genes positively correlated withTranslational cancer research · 2025Article
- Comprehensive multi-omics analysis identifies NUSAP1 as a potential prognostic and immunotherapeutic marker for lung adenocarcinoma.International journal of medical sciences · 2025Article
- Applications and Prospects of Single-Cell RNA Sequencing and Spatial Transcriptomics in Cervical Cancer.BioMed research international · 2025Review
- Knockdown of IGF2BP2 overcomes cisplatin-resistance in lung cancer through downregulating Spon2 gene.Hereditas · 2024Article
- Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine.Genes & diseases · 2024Review
- A new exploration: characterization of the differentiation trajectory of prostate cancer cells.Discover oncology · 2024Article
- Article
- Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing.Therapeutic advances in medical oncology · 2024Review
- Tumor necrosis factor-related lncRNAs predict prognosis and immunotherapy response for patients with lung adenocarcinoma.Journal of thoracic disease · 2023Article
- How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy.Frontiers in immunology · 2023Review
- Application of single-cell sequencing to the research of tumor microenvironment.Frontiers in immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is the leading cause of cancer death due to its high degree of malignancy, rapid growth, and early metastasis. Recent studies have found that lung cancer has a high degree of heterogeneity which is characterized by the mixture of different tumor cell types. However, the driving genetic/epigenetic mechanism of lung cancer heterogeneity, how different types of cells interact, and the relationship between heterogeneity and drug resistance have been poorly understood. Single-cell technology can decompose high throughput sequencing information into each cell and provide single-cell information in high resolution. By using single-cell analysis, researchers can not only fully understand the molecular characteristics of different cell types in the same tissue, but also define completely new cell types. Thus, single-cell analysis has been widely utilized in systems biology, drug discovery, disease diagnosis and precision medicine. We review recent exploration of the mechanism of heterogeneity, tumor microenvironment and drug resistance in lung cancer by using single-cell analysis. We propose that the recent findings may pave new ways for the treatment strategies of lung cancer.
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Registered trials
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.