Prognostic score model based on six m6A-related autophagy genes for predicting survival in esophageal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35611939", "kind": "science-record", "revision": 3, "content_hash": "sha256:cad48778615fdcbcec74c087fb50ac95a580469f7a7c6eafc4e92791ad10972b", "created_at": "2026-11-01T18:14:31Z", "updated_at": "2026-11-01T21:27:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic score model based on six m6A-related autophagy genes for predicting survival in esophageal squamous cell carcinoma.", "doi": "10.1002/jcla.24507", "pmid": "35611939", "authors": ["Funan Chen", "Erxiu Gong", "Jun Ma", "Jiehuan Lin", "Canxing Wu", "Shanshan Chen", "Shuqiao Hu"], "journal": "Journal of clinical laboratory analysis", "year": 2022, "abstract": "BACKGROUND: Prognostic signatures based on autophagy genes have been proposed for esophageal squamous cell carcinoma (ESCC). Autophagy genes are closely associated with m6A genes. Our purpose is to identify m6A-related autophagy genes in ESCC and develop a survival prediction model.\nMETHODS: Differential expression analyses for m6A genes and autophagy genes were performed based on TCGA and HADd databases followed by constructing a co-expression network. Uni-variable Cox regression analysis was performed for m6A-related autophagy genes. Using the optimal combination of feature genes by LASSO Cox regression model, a prognostic score (PS) model was developed and subsequently validated in an independent dataset.\nRESULTS: The differential expression of 13 m6A genes and 107 autophagy genes was observed between ESCC and normal samples. The co-expression network contained 13 m6A genes and 96 autophagy genes. Of the 12 m6A-related autophagy genes that were significantly related to survival, DAPK2, DIRAS3, EIF2AK3, ITPR1, MAP1LC3C, and TP53 were used to construct a PS model, which split the training set into two risk groups with significant different survival ratios (p = 0.015, 1-year, 3-year, and 5-year AUC = 0.873, 0.840, and 0.829). Consistent results of GSE53625 dataset confirmed predictive ability of the model (p = 0.024, 1-year, 3-year, and 5-year AUC = 0.793, 0.751, and 0.744). The six-gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390-7.064; p-value = 0.012).\nCONCLUSION: Our study recommends 6 m6A-related autophagy genes as promising prognostic biomarkers and develops a PS model to predict survival in ESCC.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) Cox regression model 21 along with 1000‐fold cross‐validation likelihood was employed to identify the optimal combination of feature autophagy genes using penalized package (https://cran.r‐project.org/web/packages/penalized/index.html) in R. 22", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35611939", "locator": "sections[5] \"Development and validation of PS model based on feature autophagy genes\" paragraph 3", "quote": "Least absolute shrinkage and selection operator (LASSO) Cox regression model 21 along with 1000‐fold cross‐validation likelihood was employed to identify the optimal combination of feature autophagy genes using penalized package (https://cran.r‐project.org/web/packages/penalized/index.html) in R. 22"}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression data analyzed in this study are available from The Cancer Genome Atlas (TCGA) data portal (https://gdc‐portal.nci.nih.gov/) and Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) with accession number of GSE53625.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35611939", "locator": "sections[16] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "Gene expression data analyzed in this study are available from The Cancer Genome Atlas (TCGA) data portal (https://gdc‐portal.nci.nih.gov/) and Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) with accession number of GSE53625."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene prognostic score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p‐value = 0.012).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The six‐gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p‐value = 0.012)."}, "evidence": [{"asset": "source:pmid:35611939", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The six‐gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p‐value = 0.012)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene prognostic score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p‐value = 0.012).", "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:35611939"], "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:GSE53625"], "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:GSE53625"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35611939", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/jcla.24507", "sha256": 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