Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35292033", "kind": "science-record", "revision": 4, "content_hash": "sha256:b53af138bf041f8a66fd77d29abccd7b6b205eb02bb938be75cfda6fbb06f083", "created_at": "2026-11-01T18:14:04Z", "updated_at": "2026-11-01T22:06:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer.", "doi": "10.1186/s12935-022-02502-4", "pmid": "35292033", "authors": ["Leilei Liang", "Jian Li", "Jing Yu", "Jing Liu", "Lin Xiu", "Jia Zeng", "Tiantian Wang", "Ning Li", "Lingying Wu"], "journal": "Cancer cell international", "year": 2022, "abstract": "BACKGROUND: Ovarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC.\nMETHODS: We extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC.\nRESULTS: Based on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells.\nCONCLUSION: We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.", "claims": [{"id": "claim-method", "statement": "Using the training set data, a univariate Cox proportional hazards regression model was performed using the R package survival with the coxph function; p < 0.05 was selected as the threshold for filtering. According to previously identified DEGs, we used the R software package glmnet for least absolute shrinkage and selection operator (lasso) Cox regression to further compress the screened genes and to reduce the number of genes in the risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35292033", "locator": "sections[5] \"Construction of a prognostic risk model\" paragraph 2", "quote": "Using the training set data, a univariate Cox proportional hazards regression model was performed using the R package survival with the coxph function; p < 0.05 was selected as the threshold for filtering. According to previously identified DEGs, we used the R software package glmnet for least absolute shrinkage and selection operator (lasso) Cox regression to further compress the screened genes and to reduce the number of genes in the risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data and materials can be obtained from the first author and corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35292033", "locator": "sections[33] \"notes\" paragraph 1", "quote": "The data and materials can be obtained from the first author and corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35292033", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features.", "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:35292033"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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