Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36703911", "kind": "science-record", "revision": 4, "content_hash": "sha256:632ce23f753a7450db975cc89f5411a9bc1fb1027fad9a5ebf7bf9de660cdbb2", "created_at": "2026-11-01T19:01:15Z", "updated_at": "2026-11-01T22:51:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma.", "doi": "10.1155/2023/6811625", "pmid": "36703911", "authors": ["Kai Lei", "Ruihao Liang", "Binghua Tan", "Lin Li", "Yingcheng Lyu", "Kexi Wang", "Wenjian Wang", "Kefeng Wang", "Xueting Hu", "Duoguang Wu", "Huayue Lin", "Minghui Wang"], "journal": "Oxidative medicine and cellular longevity", "year": 2023, "abstract": "Background: Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC).\nMethods: In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis.\nResults: 191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort.\nConclusions: PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.", "claims": [{"id": "claim-method", "statement": "We first used the univariate Cox regression analysis to screen DEGs associated with LUSC prognosis. LASSO regression analysis was then performed using the R package “glmnet” to eliminate genes with similar prognostic values. Finally, the multivariate Cox regression analysis was used to determine independent prognostic genes in LUSC. The Kaplan-Meier survival analysis was performed with the R package “survival” to study the effect of independent prognostic genes on the overall survival (OS) of LUSC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36703911", "locator": "sections[5] \"2.3. Determination of Independent Prognostic Genes\" paragraph 2", "quote": "We first used the univariate Cox regression analysis to screen DEGs associated with LUSC prognosis. LASSO regression analysis was then performed using the R package “glmnet” to eliminate genes with similar prognostic values. Finally, the multivariate Cox regression analysis was used to determine independent prognostic genes in LUSC. The Kaplan-Meier survival analysis was performed with the R package “survival” to study the effect of independent prognostic genes on the overall survival (OS) of LUSC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were used in this study. These data can be found in three databases: The Cancer Genome Atlas (TCGA), the Gene Expression Overview (GEO), and the Genotype-Tissue Expression (GTEx). The rest of the datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36703911", "locator": "sections[34] \"Data Availability\" paragraph 2", "quote": "Publicly available datasets were used in this study. These data can be found in three databases: The Cancer Genome Atlas (TCGA), the Gene Expression Overview (GEO), and the Genotype-Tissue Expression (GTEx). The rest of the datasets used and analyzed during the current study are available from the corresponding authors on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36703911", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.", "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:36703911"], "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:GSE4573"], "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:GSE4573"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36703911", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2023/6811625", "sha256": "sha256:2ceba0707a944e227ecf067aa1510caef13567e7600ee2736ba473a71c964811", "bytes": 41958, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2023/6811625", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9873467", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE4573", "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=GSE4573", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE4573", "version": null, "note": "Publicly available datasets were used in this study. These data can be found in three databases: The Cancer Genome Atlas (TCGA), the Gene Expression Overview (GEO), and the Genotype-Tissue Expression (GTEx). The rest of the datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were used in this study. These data can be found in three databases: The Cancer Genome Atlas (TCGA), the Gene Expression Overview (GEO), and the Genotype-Tissue Expression (GTEx). 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All cells were cultured in the 1640 (Gibco, Carlsbad, CA, USA) medium w"], "order_basis": "paper_text_order:3795", "id": "assay:5"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We first used the univariate Cox regression analysis to screen DEGs associated with LUSC prognosis. LASSO regression analysis was then performed using the R package “glmnet” to eliminate genes with similar prognostic values. Finally, the multivariate Cox regression analysis was used to determine independent prognostic genes in LUSC. The Kaplan-Meier survival analysis was performed with the R package “survival” to study the effect of independent prognostic genes on the overall survival (OS) of LUSC."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}