Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36849551", "kind": "science-record", "revision": 3, "content_hash": "sha256:1882363de7cc45873689930e33fdf754c5a4d05fc95b1f71c735b53af18dc1b1", "created_at": "2026-11-01T19:01:29Z", "updated_at": "2026-11-01T22:08:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.", "doi": "10.1038/s41598-023-30020-4", "pmid": "36849551", "authors": ["Yujie Weng", "Pengfei Ning"], "journal": "Scientific reports", "year": 2023, "abstract": "Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.", "claims": [{"id": "claim-method", "statement": "Twenty-four prognostic key factors, including three clinical traits (age, grade, stage) and 21 genes, were screened by univariate Cox regression analysis", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36849551", "locator": "sections[17] \"Univariate Cox regression analysis for key prognostic factors\" paragraph 2", "quote": "Twenty-four prognostic key factors, including three clinical traits (age, grade, stage) and 21 genes, were screened by univariate Cox regression analysis"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study are public. The dataset downloaded from the GEO database is GSE29609. TCGA Database: https://portal.gdc.cancer.gov/. GEO Database: https://www.ncbi.nlm.nih.gov/geo/. GSE29609: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29609.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36849551", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The data used in this study are public. The dataset downloaded from the GEO database is GSE29609. TCGA Database: https://portal.gdc.cancer.gov/. GEO Database: https://www.ncbi.nlm.nih.gov/geo/. 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