Prognostic signature of lipid metabolism associated LncRNAs predict prognosis and treatment of lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36387240", "kind": "science-record", "revision": 4, "content_hash": "sha256:0f308f4ba7e78e7c5c7e1d1f548a5fee7281818d3bc0b153e359101e9eba773a", "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": "Prognostic signature of lipid metabolism associated LncRNAs predict prognosis and treatment of lung adenocarcinoma.", "doi": "10.3389/fonc.2022.986367", "pmid": "36387240", "authors": ["Jie Zhao", "Guangjian Li", "Guangqiang Zhao", "Wei Wang", "Zhenghai Shen", "Yantao Yang", "Yunchao Huang", "Lianhua Ye"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Lung adenocarcinoma (LUAD) is the most predominant histological subtype of lung cancer. Abnormal lipid metabolism is closely related to the development of LUAD. LncRNAs are involved in the regulation of various lipid metabolism-related genes in various cancer cells including LUAD. Here, we aimed to identify lipid metabolism-related lncRNAs associated with LUAD prognosis and to propose a new prognostic signature.\nMethods: First, differentially expressed lncRNAs (DE-lncRNAs) from the TCGA-LUAD and the GSE31210 dataset were identified. Then the correlation analysis between DE-lncRNAs and lipid metabolism genes was performed to screen lipid metabolism-related lncRNAs. Cox regression analyses were performed in the training set to establish a prognostic model and the model was validated in the testing set and the validation set. Moreover, The role of this model in the underlying molecular mechanisms, immunotherapy, and chemotherapeutic drug sensitivity analysis was predicted by methods such as Gene Set Enrichment Analysis, immune infiltration, tumor mutational burden (TMB), neoantigen, Tumor Immune Dysfunction and Exclusion, chemosensitivity analysis between the high- and low-risk groups. The diagnostic ability of prognostic lncRNAs has also been validated. Finally, we validated the expression levels of selected prognostic lncRNAs by quantitative real-time polymerase chain reaction (qRT-PCR).\nResults: The prognostic model was constructed based on four prognostic lncRNAs (LINC00857, EP300-AS1, TBX5-AS1, SNHG3) related to lipid metabolism. The receiver operating characteristic curve (ROC) and Kaplan Meier (KM) curves of the risk model showed their validity. The results of Gene Set Enrichment Analysis suggested that differentially expressed genes in high- and low-risk groups were mainly enriched in immune response and cell cycle. There statistical differences in TMB and neoantigen between high- and low-risk groups. Drug sensitivity analysis suggested that patients with low risk scores may have better chemotherapy outcomes. The results of qRT-PCR were suggesting that compared with the normal group, the expressions of EP300-AS1 and TBX5-AS1 were down-regulated in the tumor group, while the expressions of LINC00857 and SNHG3 were up-regulated. The four prognostic lncRNAs had good diagnostic capabilities, and the overall diagnostic model of the four prognostic lncRNAs was more effective.\nConclusion: A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.", "claims": [{"id": "claim-method", "statement": "The independent prognostic Lipid metabolism-related DE-lncRNAs were screened by Cox regression analysis (univariate and multivariate) to construct the prognostic features (30).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36387240", "locator": "sections[5] \"Construction and validation of the prognostic signature\" paragraph 2", "quote": "The independent prognostic Lipid metabolism-related DE-lncRNAs were screened by Cox regression analysis (univariate and multivariate) to construct the prognostic features (30)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36387240", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36387240", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.", "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:36387240"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36387240", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.986367", "sha256": "sha256:a142d8fb7877f363b28198c0a7d48a9ff17da23bff1ad616dd762e62aab92e56", "bytes": 45602, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2022.986367", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9664164", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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