Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37942473", "kind": "science-record", "revision": 3, "content_hash": "sha256:e10fc5a4e2ed3e33db180a341cf26ed6ecaa6ed529ac9073343fb7f57819cf30", "created_at": "2026-11-01T19:02:50Z", "updated_at": "2026-11-01T22:10:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma.", "doi": "10.2147/IJGM.S430408", "pmid": "37942473", "authors": ["Ke Mi", "Lizhong Zeng", "Yang Chen", "Shuanying Yang"], "journal": "International journal of general medicine", "year": 2023, "abstract": "Background: Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.\nMethods: The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.\nResults: 424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.\nConclusion: Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.", "claims": [{"id": "claim-method", "statement": "In this study, univariate Cox regression analysis was performed using the TCGA-LUAD dataset, and 13 genes were found to be significantly associated with OS (Figure 5A). Next, a LASSO algorithm was used to screen for genes to construct a model (Figure 5B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37942473", "locator": "sections[17] \"Construction and Validation of a Seven-Characteristic Gene-Based Prognostic Risk Model\" paragraph 2", "quote": "In this study, univariate Cox regression analysis was performed using the TCGA-LUAD dataset, and 13 genes were found to be significantly associated with OS (Figure 5A). Next, a LASSO algorithm was used to screen for genes to construct a model (Figure 5B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary Materials.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37942473", "locator": "sections[26] \"Data Sharing Statement\" paragraph 2", "quote": "The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary Materials."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified seven macrophage polarization-related prognostic genes for lung adenocarcinoma and constructed a seven-gene prognostic risk model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37942473", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified seven macrophage polarization-related prognostic genes for lung adenocarcinoma and constructed a seven-gene prognostic risk 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:37942473"], "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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Methods: The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene E", "or prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["cers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq", "e combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can ident", "ion from a broader perspective. Methods: The scRNA-seq data and bulk RNA-seq data of LUAD were down", "ncer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and"], "order_basis": "paper_text_order:216", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["(TFs) that affect signature genes. Finally, qPCR were employed to validate the expression lev", "ssion of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognos"], "order_basis": "paper_text_order:1885", "id": "assay:3"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ion of cysteine-type endopeptidase activity, apoptosis process, and leukocyte aggregation. In terms", "also enriched in pathways such as IL-17 and Apoptosis (Figure 4C). The final PPI network of the in"], "order_basis": "paper_text_order:6623", "id": "assay:4"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In this study, univariate Cox regression analysis was performed using the TCGA-LUAD dataset, and 13 genes were found to be significantly associated with OS (Figure 5A). Next, a LASSO algorithm was used to screen for genes to construct a model (Figure 5B)."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}