System analysis based on the ER stress-related genes identifies WFS1 as a novel therapy target for colon cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36445321", "kind": "science-record", "revision": 3, "content_hash": "sha256:83d8e571f4353a1213590a3294438d96b73cf1a1053198f54f89ace2b9916025", "created_at": "2026-11-01T19:00:55Z", "updated_at": "2026-11-01T22:08:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "System analysis based on the ER stress-related genes identifies WFS1 as a novel therapy target for colon cancer.", "doi": "10.18632/aging.204404", "pmid": "36445321", "authors": ["Xianguang Yang", "Chaoyang Zhang", "Cheng Yan", "Liukai Ma", "Jiahao Ma", "Xiaoke Meng"], "journal": "Aging", "year": 2022, "abstract": "BACKGROUND: Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients.\nMETHODS: The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R \"limma\" package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments.\nRESULTS: We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells.\nCONCLUSIONS: The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.", "claims": [{"id": "claim-method", "statement": "Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36445321", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GSE40967). All authors read and approved the final manuscript.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36445321", "locator": "sections[18] \"Availability of data and material\" paragraph 2", "quote": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GSE40967). All authors read and approved the final manuscript."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed a 9-gene ERSRG prognostic model using univariate, lasso, and multivariate Cox regression, with a training-set ROC AUC of 0.669.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36445321", "locator": "sections[21] \"Construction of a prognostic model relied on ERSRGs\" paragraph 2", "quote": "Finally, 9 ERSRGs were determined to construct the prognostic model according to the multivariate Cox regression, including 3 risky genes and 6 protective genes (Figure 2D)."}, {"asset": "source:pmid:36445321", "locator": "sections[21] \"Construction of a prognostic model relied on ERSRGs\" paragraph 2", "quote": "The area under the receiver operating characteristic (AUC) curve was 0.669, showing that prognostic model we built had a good predictive performance (Figure 2I)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed a 9-gene ERSRG prognostic model using univariate, lasso, and multivariate Cox regression, with a training-set ROC AUC of 0.669.", "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:36445321"], "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:GSE40967"], "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:GSE40967"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36445321", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.204404", "sha256": "sha256:79edd884936551acf45797d7b055abdc45f7f677b3349867fe70a3b6e799b2b9", "bytes": 29458, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.204404", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9740360", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE40967", "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=GSE40967", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE40967", "version": null, "note": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GSE40967). All authors read and approved the final manuscript.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GSE40967). 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