PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38614981", "kind": "science-record", "revision": 3, "content_hash": "sha256:e1ed5a6f4caa62eee5316b74948eeaaa5a7bb2bbecfbc2e216d8c4a3dea783db", "created_at": "2026-11-01T19:20:35Z", "updated_at": "2026-11-01T21:32:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma.", "doi": "10.1186/s12885-024-12183-z", "pmid": "38614981", "authors": ["Jun Wang", "Xiaoming He", "Yuanyuan Mi", "Yong Q Chen", "Jie Li", "Rong Wang"], "journal": "BMC cancer", "year": 2024, "abstract": "BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.\nMETHODS: We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.\nRESULTS: The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.\nCONCLUSION: The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.", "claims": [{"id": "claim-method", "statement": "The Cox proportional hazard regression model was used to estimate the hazard ratio and its 95% confidence interval (CI) for each potential risk factor, and data were visualized through Forest plots. The stepwise multivariate Cox regression analysis included inclusion and exclusion criteria of type I error = 0.1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38614981", "locator": "sections[11] \"Statistical analyses\" paragraph 3", "quote": "The Cox proportional hazard regression model was used to estimate the hazard ratio and its 95% confidence interval (CI) for each potential risk factor, and data were visualized through Forest plots. The stepwise multivariate Cox regression analysis included inclusion and exclusion criteria of type I error = 0.1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38614981", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, age [HR: 1.032, 95% CI: 1.018 to 1.046, p < 0.001], grade (HR: 1.491, 95% CI: 1.022 to 2.175, p = 0.038), and PSAT1 expression (HR: 1.855, 95% CI: 1.322 to 2.604, p < 0.001) were identified as independent risk factors of OS in the two groups, whereas neoadjuvant therapy (HR: 0.432, 95% CI: 0.221 to 0.827, p = 0.01) was identified as a protective factor (Fig. 3B and Table 2).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Finally, age [HR: 1.032, 95% CI: 1.018 to 1.046, p < 0.001], grade (HR: 1.491, 95% CI: 1.022 to 2.175, p = 0.038), and PSAT1 expression (HR: 1.855, 95% CI: 1.322 to 2.604, p < 0.001) were identified as independent risk factors of OS in the two groups, whereas neoadjuvant therapy (HR: 0.432, 95% CI: 0.221 to 0.827, p = 0.01) was identified as a protective factor (Fig. 3B and Table 2)."}, "evidence": [{"asset": "source:pmid:38614981", "locator": "sections[15] \"Confirm risk/protect factors of prognosis\" paragraph 3", "quote": "Finally, age [HR: 1.032, 95% CI: 1.018 to 1.046, p < 0.001], grade (HR: 1.491, 95% CI: 1.022 to 2.175, p = 0.038), and PSAT1 expression (HR: 1.855, 95% CI: 1.322 to 2.604, p < 0.001) were identified as independent risk factors of OS in the two groups, whereas neoadjuvant therapy (HR: 0.432, 95% CI: 0.221 to 0.827, p = 0.01) was identified as a protective factor (Fig. 3B and Table 2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, age [HR: 1.032, 95% CI: 1.018 to 1.046, p < 0.001], grade (HR: 1.491, 95% CI: 1.022 to 2.175, p = 0.038), and PSAT1 expression (HR: 1.855, 95% CI: 1.322 to 2.604, p < 0.001) were identified as independent risk factors of OS in the two groups, whereas neoadjuvant therapy (HR: 0.432, 95% CI: 0.221 to 0.827, p = 0.01) was identified as a protective factor (Fig. 3B and Table 2).", "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:38614981"], "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. 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METHODS: We first analyzed the microarray expression data of ccRCC patients from the G"], "order_basis": "paper_text_order:753", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["RCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis. RESULTS:", "l tissues were selectively used for qRT-PCR, Western blotting, and immunohistochemical analysis. The diagn"], "order_basis": "paper_text_order:1654", "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": ["r verified by qRT-PCR, Western blotting, and immunohistochemical analysis. RESULTS: The datasets GSE73731, GS", "vely used for qRT-PCR, Western blotting, and immunohistochemical analysis. The diagnosis for each patient was"], "order_basis": "paper_text_order:1676", "id": "assay:3"}, {"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": ["The Cox proportional hazard regression model was used to estimate the hazard ratio and its 95% confidence interval (CI) for each potential risk factor, and data were visualized through Forest plots. The stepwise multivariate Cox regression analysis included inclusion and exclusion criteria of type I error = 0.1."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}