ArticleHereditas2025
Metabolic heterogeneity and survival outcomes in papillary renal cell carcinoma: insights from multi-datasets and machine learning analyses.
Article in Hereditas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- [Research Progress on the Role and Mechanisms of PYCR1 in Tumorigenesis and Progression].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundRenal cell carcinoma is characterized by immune and metabolic alterations. These metabolic reprogramming processes enhance tumor cell proliferation and infiltration. The purpose of this study was to investigate the characteristics of metabolism-related molecules and to identify potential prognostic biomarkers in kidney renal papillary renal cell carcinoma (KIRP).
methodsWe conducted a comprehensive analysis of metabolism-related genes using weighted gene co-expression network analysis and differential expression analysis. Subsequently, we constructed a metabolism-related signature (MRS) by integrating 90 machine learning algorithms. Based on Cox regression analyses, we developed a predictive nomogram. Functional enrichment analysis, genomic variant analysis, chemotherapy response evaluation, and immune cell infiltration profiling were then performed among the MRS subtypes. Finally, the MRS was further examined at the single-cell level, and quantitative PCR and immunohistochemical staining were conducted to validate the key genes.
resultsWe identified 16 differentially expressed metabolic genes. The random survival forest (RSF) emerged as the optimal machine learning model in the TCGA-KIRP and GSE2748 cohorts. The MRS demonstrated robust predictive performance, with an AUC of 0.989 for 5-year survival predictions. The risk score was significantly correlated with T stage and pathological stage and was identified as an independent prognostic factor. Patients in the high-risk group exhibited higher tumor mutation burdens and derived greater benefits from sunitinib, pazopanib, lenvatinib, and temsirolimus. A four-genes nomogram was then constructed to predict overall survival. PYCR1, INMT, and KIF20A were highly expressed in KIRP according to scRNA-seq analysis and were validated in vitro.
conclusionThis study revealed the heterogeneity of metabolic molecules in KIRP and established a prognostic machine learning model that enhances risk stratification and may optimize chemotherapy strategies in the management of KIRP.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.