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pmid:34103941
Development and Validation of an Autophagy-Stroma-Based Microenvironment Gene Signature for Risk Stratification in Colorectal Cancer.
Lin Chen · Kunzi Zhang · Jian Sun · Jingtong Tang · Jianping Zhou
OncoTargets and therapy · 2021 doi:10.2147/OTT.S312003
Paper
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Experiment
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Data
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Analysis
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Complete file
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The aim of this study was to establish a tumor gene signature based on tumor stromal cell and autophagy for predicting the risk of recurrence in patients with colorectal cancer.\nMETHODS: We used \"Rtsne\" and \"xCell\" R packages to estimate autophagy and stroma status, respectively. The discovery cohort used microarray gene expression data retrieved from the GSE39582 dataset. The Cox regression model and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify prognostic genes and to construct an autophagy-stroma-based gene signature. Moreover, external validation was conducted using GSE17538, GSE38832, TCGA database, and patient data obtained from the First Hospital of China Medical University (CMU).\nRESULTS: The LASSO model identified three genes (TNS1, TAGLN, and SFRP4) which were used to develop a risk stratification gene signature. The autophagy-stroma-based gene signature was identified as an independent prognostic factor by multivariate analysis (p = 0.0023). The results were validated in GSE17538 (p=0.0062), GSE38832 (p=0.028), TCGA (p=0.046) database, and patient data obtained from the First Hospital of China Medical University (CMU) (p=0.027).\nCONCLUSION: We have established and verified a feasible prognostic model of colorectal cancer based on autophagy and stromal cell characteristics of patients. The model can be used to evaluate recurrence risk of cancer patients, and the hub genes in the model provide potential targets for targeted colorectal cancer treatment.", "claims": [{"id": "claim-method", "statement": "In our study, we screened the most prognostic gene marker using the LASSO-Cox regression model of glmnet25 R package from identified prognostic factors associated with autophagy and stroma in the discovery cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34103941", "locator": "sections[7] \"Derivation of Autophagy-Stroma-Based Gene Classifier and Prognosis Classifier\" paragraph 2", "quote": "In our study, we screened the most prognostic gene marker using the LASSO-Cox regression model of glmnet25 R package from identified prognostic factors associated with autophagy and stroma in the discovery cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34103941", "locator": "sections[22] \"Data Sharing Statement\" paragraph 2", "quote": "The data used or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34103941"], "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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read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "In our study, we screened the most prognostic gene marker using the LASSO-Cox regression model of glmnet25 R package from identified prognostic factors associated with autophagy and stroma in the discovery cohort."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", 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