Novel diagnostic biomarkers of oxidative stress, immune- infiltration characteristics and experimental validation of SERPINE1 in colon cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37980291", "kind": "science-record", "revision": 3, "content_hash": "sha256:8add6944b69a7f36dab77f97d55fe56327d741d5d0df207c8fbdd772a6e56136", "created_at": "2026-11-01T19:02:53Z", "updated_at": "2026-11-01T22:10:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel diagnostic biomarkers of oxidative stress, immune- infiltration characteristics and experimental validation of SERPINE1 in colon cancer.", "doi": "10.1007/s12672-023-00833-w", "pmid": "37980291", "authors": ["Kaisheng Yuan", "Di Hu", "Xiaocong Mo", "Ruiqi Zeng", "Bing Wu", "Zunhao Zhang", "Ruixiang Hu", "Cunchuan Wang"], "journal": "Discover oncology", "year": 2023, "abstract": "BACKGROUND: Colon cancer (CC) is a prevalent malignant tumor that affects the colon in the gastrointestinal tract. Its aggressive nature, strong invasiveness, and rapid progression make it a significant health concern. In addition, oxidative stress can lead to the production of reactive oxygen species (ROS) that surpass the body's antioxidant defense capacity, causing damage to proteins, lipids, and DNA, potentially promoting tumor development. However, the relationship between CC and oxidative stress requires further investigation.\nMETHODS: We collected gene expression data and clinical data from 473 CC patients from The Cancer Genome Atlas (TCGA) dataset. Additionally, we obtained 433 oxidative stress genes from Genecards ( https://www.genecards.org/ ). Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients. To validate the models, we utilized data from the Gene Expression Omnibus (GEO) database. We assessed the accuracy of the models through various techniques, including the creation of a nomogram, receiver operating characteristic curve (ROC) analysis, and principal component analysis (PCA). The Cytoscape program was utilized to identify hub genes among differentially expressed genes (DEGs) in tumor patients using the TCGA dataset. Subsequently, we conducted survival analysis, clinical relevance analysis, and immune cell relevance analysis for the intersected genes obtained by combining the hub genes with the genes from the predictive models. Moreover, we investigated the mRNA expression and potential functions of these intersected genes using a range of experimental approaches.\nRESULTS: In both the TCGA and GSE17538 datasets, patients classified as high-risk had significantly shorter overall survival compared to those in the low-risk group (TCGA: p < 0.001; GSE17538: p = 0.010). As a result, we decided to further investigate the role of SERPINE1. Our survival analysis revealed that patients with high expression of SERPINE1 had a significantly lower probability of survival compared to those with low expression (p < 0.05). Additionally, our clinical correlation analysis showed a significant relationship between SERPINE1 expression and T, N, and M stages, as well as tumor grade. Furthermore, our immune infiltration correlation analysis demonstrated notable differences in multiple immune cells between the high- and low-expression groups of SERPINE1. To validate our findings, we conducted experimental tests and observed that knocking down SERPINE1 in colon cancer cells resulted in significant reductions in cell viability and proliferation. Interestingly, we also noticed an increase in oxidative stress parameters, such as ROS and MDA levels, while the levels of reduced GSH decreased upon SERPINE1 knockdown. These findings suggest that the antineoplastic effect of silencing SERPINE1 may be associated with the induction of oxidative stress.\nCONCLUSION: In conclusion, this study introduces a new approach for the early diagnosis and treatment of CC, and further exploration of SERPINE1 could potentially lead to a significant advancement.", "claims": [{"id": "claim-method", "statement": "Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37980291", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw transcriptome data of this study are available in the TCGA database (TCGA cohorts) (https://portal.gdc.cancer.gov/) and the GEO database (GSE17538) (https://www.ncbi.nlm.nih.gov/gds/). Oxidative stress genes were downloaded and collated from Genecards (https://www.genecards.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37980291", "locator": "sections[41] \"Data availability\" paragraph 2", "quote": "The raw transcriptome data of this study are available in the TCGA database (TCGA cohorts) (https://portal.gdc.cancer.gov/) and the GEO database (GSE17538) (https://www.ncbi.nlm.nih.gov/gds/). Oxidative stress genes were downloaded and collated from Genecards (https://www.genecards.org/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through LASSO regression analysis, we identified 16 genes (NGF, IL13, RPS6KA5, TERT, CDKN2A, SERPINE1, CD36, PPARGC1A, ACADL, ACOX1, POMC, BDNF, MSRA, DDIT3, GSTM1, CPT2) based on the best λ value to construct a prognostic model (Supplementary Figure S4A–B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37980291", "locator": "sections[26] \"Construction and evaluation of a prognostic model for CC based on oxidative stress-related genes\" paragraph 2", "quote": "Through LASSO regression analysis, we identified 16 genes (NGF, IL13, RPS6KA5, TERT, CDKN2A, SERPINE1, CD36, PPARGC1A, ACADL, ACOX1, POMC, BDNF, MSRA, DDIT3, GSTM1, CPT2) based on the best λ value to construct a prognostic model (Supplementary Figure S4A–B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through LASSO regression analysis, we identified 16 genes (NGF, IL13, RPS6KA5, TERT, CDKN2A, SERPINE1, CD36, PPARGC1A, ACADL, ACOX1, POMC, BDNF, MSRA, DDIT3, GSTM1, CPT2) based on the best λ value to construct a prognostic model (Supplementary Figure S4A–B).", "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:37980291"], "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:GSE17538"], "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:GSE17538"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37980291", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-023-00833-w", "sha256": "sha256:6f696a5fbe24ffc37907f437d921cec06afbf5b7d6ba61e2fd4294f6140b687d", "bytes": 64529, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-023-00833-w", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10657345", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17538", "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=GSE17538", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17538", "version": null, "note": "The raw transcriptome data of this study are available in the TCGA database (TCGA cohorts) (https://portal.gdc.cancer.gov/) and the GEO database (GSE17538) (https://www.ncbi.nlm.nih.gov/gds/). 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These findings suggest that the antineoplas"], "order_basis": "paper_text_order:2936", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["ned from Dr. Yunlong Chen. The CC cells were cultured in DMEN medium (Gibco). From August 2021 to Oct"], "order_basis": "paper_text_order:5621", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["truction of the prognostic model for CC. D–E Immunohistochemical staining of SERPINE1 gene in normal tissue (", "KB)—Figure S9: (A-B) Three other samples of immunohistochemical analysis of SERPINE1 expression in CC patien", "PA, https://www.proteinatlas.org/) offers an IHC-based assay for relative protein abundance ["], "order_basis": "paper_text_order:9786", "id": "assay:3"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["itional file10 (PDF 164 KB)—Figure S10: Full western blot images. Additional file11 (XLS 10 KB)—Table"], "order_basis": "paper_text_order:17507", "id": "assay:4"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}