Construction and Validation of a 9-Gene Signature for Predicting Prognosis in Stage III Clear Cell Renal Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30941304", "kind": "science-record", "revision": 6, "content_hash": "sha256:11e47f034007f2454a2554e295adbb58142bf86b62c8048cff70f439b54b63d3", "created_at": "2026-11-01T15:40:43Z", "updated_at": "2026-11-01T21:22:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and Validation of a 9-Gene Signature for Predicting Prognosis in Stage III Clear Cell Renal Cell Carcinoma.", "doi": "10.3389/fonc.2019.00152", "pmid": "30941304", "authors": ["Junlong Wu", "Shengming Jin", "Weijie Gu", "Fangning Wan", "Hailiang Zhang", "Guohai Shi", "Yuanyuan Qu", "Dingwei Ye"], "journal": "Frontiers in oncology", "year": 2019, "abstract": "Purpose: Aim of this study was to develop a multi-gene signature to help better predict prognosis for stage III renal cell carcinoma (RCC) patients. Methods: Fourteen pairs of stage III tumor and normal tissues mRNA expression data from GSE53757 and 16 pairs mRNA expression data from TCGA clear cell RCC database were used to analyze differentially expressed genes between tumor and normal tissues. Common different expressed genes in both datasets were used for further modeling. Lasso Cox regression analysis was performed to select and build prognostic multi-gene signature in TCGA stage III kidney cancer patients (N = 122). Then, the multi-gene signature was validated in stage III renal cancer cases in Fudan University Shanghai Cancer Center (N = 77). C-index and time-dependent ROC were used to test the efficiency of this signature in predicting overall survival. Results: In total, 1,370 common different expressed genes were found between tumor and normal tissues in both datasets. After Lasso Cox modeling, nine mRNAs were finally identified to build a classifier. Using this classifier, we could classify stage III clear cell RCC patients into high-risk group and low-risk group. Prognosis was significantly different between these groups in discovery TCGA cohort, validation FUSCC cohort and entire set (All P < 0.001). Multivariate cox regression in entire set (N = 199) revealed that risk group classified by 9-gene signature, age of diagnosis, pN stage and ISUP grade were independent prognostic factor of overall survival in stage III kidney cancer patients. Conclusion: We developed a robust multi-gene classifier that can effectively classify stage III RCC patients into groups with low and high risk of poor prognosis. This signature may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.", "claims": [{"id": "claim-method", "statement": "After identifying DEGs between tumor and normal tissues, we used LASSO (least absolute shrinkage and selection operator) Cox regression analysis in the discovery stage to select a panel of genes, and then constructed a multi-gene signature for predicting prognosis in stage III ccRCC patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30941304", "locator": "sections[7] \"Calculation of Risk Score and Statistical Analysis\" paragraph 3", "quote": "After identifying DEGs between tumor and normal tissues, we used LASSO (least absolute shrinkage and selection operator) Cox regression analysis in the discovery stage to select a panel of genes, and then constructed a multi-gene signature for predicting prognosis in stage III ccRCC patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) with an identifier GSE53757 and TCGA database obtained from UCSC Xena (https://xenabrowser.net/datapages/). Besides, The datasets from Fudan University Shanghai Cancer Center (FUSCC) are also available in supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30941304", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The datasets generated and analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) with an identifier GSE53757 and TCGA database obtained from UCSC Xena (https://xenabrowser.net/datapages/). Besides, The datasets from Fudan University Shanghai Cancer Center (FUSCC) are also available in supplementary material."}], "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:30941304"], "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:GSE53757"], "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:GSE53757"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30941304", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2019.00152", "sha256": "sha256:72a1fbf272a2fc8504ff3b97460f0363e00837425cfae0f2ab12c065c52f66b1", "bytes": 44408, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2019.00152", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6433707", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE53757", "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=GSE53757", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE53757", "version": null, "note": "The datasets generated and analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) with an identifier GSE53757 and TCGA database obtained from UCSC Xena (https://xenabrowser.net/datapages/). 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