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pmid:35966313
Development and validation of endoplasmic reticulum stress-related eight-gene signature for predicting the overall survival of lung adenocarcinoma.
Lin Lin · Wei Zhang
Translational cancer research · 2022 doi:10.21037/tcr-22-106
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Experiment
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Complete file
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Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD.\nMethods: The mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms.\nResults: A prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways.\nConclusions: The 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.", "claims": [{"id": "claim-method", "statement": "The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[4] \"Construction overall survival (OS)-related gene signature in TCGA-LUAD\" paragraph 2", "quote": "The “lambda.1se” value, which was determined by tenfold cross-validation, was set as the lambda for model fitting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[25] \"Footnotes\" paragraph 2", "quote": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-106/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35966313", "locator": "sections[13] \"The internal validation data sets in TCGA verifies the robustness of the prognostic model\" paragraph 2", "quote": "In time-dependent ROC curve analyses, the AUC for the eight -mRNA signature prognostic model was 0.749 at one year, 0.737 at two years and 0.707 at four years of OS, this suggests that the model had good discrimination (AUCmax =0.749) (Figure 3B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The eight-mRNA prognostic model showed good discrimination in TCGA-LUAD, with a maximum time-dependent AUC of 0.749.", "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:35966313"], "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:35966313", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-22-106", "sha256": 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