ArticleBMC cancer2022
Prescreening of tumor samples for tumor-centric transcriptome analyses of lung adenocarcinoma.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction.PLoS computational biology · 2026Article
- Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma.Scientific reports · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) enables the systemic assessment of intratumoral heterogeneity within tumor cell populations and in diverse stromal cells of the tumor microenvironment. Gain of treatment resistance during tumor progression or drug treatment are important subjects of tumor-centric scRNA-seq analyses, which are hampered by scarce tumor cell portions. To guarantee the inclusion of tumor cells in the data analysis, we developed a prescreening strategy for lung adenocarcinoma.
methodsWe obtained candidate genes that were differentially expressed between normal and tumor cells, excluding stromal cells, from the scRNA-seq data. Tumor cell-specific expression of the candidate genes was assessed via real-time reverse transcription-polymerase chain reaction (RT-PCR) using lung cancer cell lines, normal vs. lung cancer tissues, and lymph node biopsy samples with or without metastasis.
resultsWe found that CEA cell adhesion molecule 5 (CEACAM5) and high mobility group box 3 (HMGB3) were reliable markers for RT-PCR-based prescreening of tumor cells in lung adenocarcinoma.
conclusionsThe prescreening strategy using CEACAM5 and HMGB3 expression facilitates tumor-centric scRNA-seq analyses of lung adenocarcinoma.
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