Histone acetylation modification regulator-mediated tumor microenvironment infiltration characteristics and prognostic model of lung adenocarcinoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36389327", "kind": "science-record", "revision": 4, "content_hash": "sha256:bde516a5a1a461202c19ad090f53f5f945fa89d8d0c9e162b089b1aca90ee760", "created_at": "2026-11-01T18:15:49Z", "updated_at": "2026-11-01T22:08:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Histone acetylation modification regulator-mediated tumor microenvironment infiltration characteristics and prognostic model of lung adenocarcinoma patients.", "doi": "10.21037/jtd-22-1000", "pmid": "36389327", "authors": ["Wenmiao Wang", "Yao Shen", "Peng Zhang", "Lei Liu", "Xinyu Sha", "Houqiang Li", "Silin Wang", "Haijian Zhang", "Youlang Zhou", "Jiahai Shi"], "journal": "Journal of thoracic disease", "year": 2022, "abstract": "Background: The incidence rate of lung adenocarcinoma (LUAD) is rapidly increasing. Recent studies have reported that histone acetylation modification plays an important role in the occurrence and development of tumors. However, the potential role of modification of histone acetylation modification in the development of tumor immune microenvironment is still unclear.\nMethods: In this study, we comprehensively evaluated the acetylation modification patterns of LUAD samples obtained from various different databases based on 36 histone modification regulators, and constructed a prognostic model based on The Cancer Genome Atlas (TCGA) LUAD cohort using the Cox regression method. The close relationship between histone acetylation and tumor immune characteristics was further studied, including immune infiltration, immune escape and immunotherapy. Finally, we combined three cohort (GSE30219, GSE72094 and GSE50081) from Gene Expression Omnibus (GEO) database to verify the above results.\nResults: We analyzed the expression, mutation and interaction of 36 histone acetylation regulated genes. After Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO), 5 genes (KAT2B, SIRT2, HDAC5, KAT8, HDAC2) were screened to establish the prognosis model and calculate the risk score. Then, patients in the TCGA cohort were divided into high- and low-risk groups based on the risk scores. Further analysis indicated that patients in the high-risk group exhibited significantly reduced overall survival (OS) compared with those in the low-risk group. The high- and low-risk groups exhibited significant differences in terms of tumor immune characteristics, such as immune infiltration, immune escape and immunotherapy. The high-risk group had lower immune score, less immune cell infiltration and higher clinical stage. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. In addition, drugs sensitive for this classification were identified. Finally, the efficacy of the prognostic model was validated by cohort (GSE30219, GSE72094 and GSE50081) from GEO database.\nConclusions: Our study provided a robust signature for predicting changing prognosis of patients with LUAD. Thus, it appears to be a potentially useful prognostic tool. Moreover, the important relationship between histone acetylation and tumor immune microenvironment was revealed.", "claims": [{"id": "claim-method", "statement": "After Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO), 5 genes (KAT2B, SIRT2, HDAC5, KAT8, HDAC2) were screened to establish the prognosis model and calculate the risk score.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36389327", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO), 5 genes (KAT2B, SIRT2, HDAC5, KAT8, HDAC2) were screened to establish the prognosis model and calculate the risk score."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-Seq data and corresponding clinical information of patients with LUAD were collected from The Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36389327", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "The RNA-Seq data and corresponding clinical information of patients with LUAD were collected from The Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In this study, we comprehensively evaluated the acetylation modification patterns of LUAD samples obtained from various different databases based on 36 histone modification regulators, and constructed a prognostic model based on The Cancer Genome Atlas (TCGA) LUAD cohort using the Cox regression method.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36389327", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "In this study, we comprehensively evaluated the acetylation modification patterns of LUAD samples obtained from various different databases based on 36 histone modification regulators, and constructed a prognostic model based on The Cancer Genome Atlas (TCGA) LUAD cohort using the Cox regression method."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In this study, we comprehensively evaluated the acetylation modification patterns of LUAD samples obtained from various different databases based on 36 histone modification regulators, and constructed a prognostic model based on The Cancer Genome Atlas (TCGA) LUAD cohort using the Cox regression method.", "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:36389327"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36389327", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-22-1000", "sha256": 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