scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38355636", "kind": "science-record", "revision": 3, "content_hash": "sha256:53aa5d6408c646a8039def65c71b09ec3da00bc4b4b0ff315b9c357dc2f1f290", "created_at": "2026-11-01T19:20:18Z", "updated_at": "2026-11-01T22:11:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma.", "doi": "10.1038/s41598-024-54135-4", "pmid": "38355636", "authors": ["GuoYong Lin", "ZhiSen Gao", "Shun Wu", "JianPing Zheng", "XiangQiong Guo", "XiaoHong Zheng", "RunNan Chen"], "journal": "Scientific reports", "year": 2024, "abstract": "Lung adenocarcinoma (LUAD) is one of the sole causes of death in lung cancer patients. This study combined with single-cell RNA-seq analysis to identify tumor stem-related prognostic models to predict the prognosis of lung adenocarcinoma, chemotherapy agents, and immunotherapy efficacy. mRNA expression-based stemness index (mRNAsi) was determined by One Class Linear Regression (OCLR). Differentially expressed genes (DEGs) were detected by limma package. Single-cell RNA-seq analysis in GSE123902 dataset was performed using Seurat package. Weighted Co-Expression Network Analysis (WGCNA) was built by rms package. Cell differentiation ability was determined by CytoTRACE. Cell communication analysis was performed by CellCall and CellChat package. Prognosis model was constructed by 10 machine learning and 101 combinations. Drug predictive analysis was conducted by pRRophetic package. Immune microenvironment landscape was determined by ESTIMATE, MCP-Counter, ssGSEA analysis. Tumor samples have higher mRNAsi, and the high mRNAsi group presents a worse prognosis. Turquoise module was highly correlated with mRNAsi in TCGA-LUAD dataset. scRNA analysis showed that 22 epithelial cell clusters were obtained, and higher CSCs malignant epithelial cells have more complex cellular communication with other cells and presented dedifferentiation phenomenon. Cellular senescence and Hippo signaling pathway are the major difference pathways between high- and low CSCs malignant epithelial cells. The pseudo-temporal analysis shows that cluster1, 2, high CSC epithelial cells, are concentrated at the end of the differentiation trajectory. Finally, 13 genes were obtained by intersecting genes in turquoise module, Top200 genes in hdWGCNA, DEGs in high- and low- mRNAsi group as well as DEGs in tumor samples vs. normal group. Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model. The high-risk group samples had immunosuppressive status, higher tumor malignancy and low benefit from immunotherapy. This work found that malignant tumors and malignant epithelial cells have high CSC characteristics, and identified a model that could predict the prognosis, immune microenvironment, and immunotherapy of LUAD, based on CSC-related genes. These results provided reference value for the clinical diagnosis and treatment of LUAD.", "claims": [{"id": "claim-method", "statement": "The prognostic genes were then combined with 101 algorithms to fit the prediction model based on the leave-one cross-validation (LOOCV) framework in the TCGA-LUAD cohort; (c) All models were detected in two validation datasets (GSE31210, GSE50081); (d) For each model, the Harrell Consistency Index (C-index) is calculated on all validation datasets, and the model with the highest average C-index is considered to be the optimal.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38355636", "locator": "sections[21] \"Construction and validation of prognostic model\" paragraph 2", "quote": "The prognostic genes were then combined with 101 algorithms to fit the prediction model based on the leave-one cross-validation (LOOCV) framework in the TCGA-LUAD cohort; (c) All models were detected in two validation datasets (GSE31210, GSE50081); (d) For each model, the Harrell Consistency Index (C-index) is calculated on all validation datasets, and the model with the highest average C-index is considered to be the optimal."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38355636", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38355636", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38355636"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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