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pmid:40977852

An immunometabolism-related signature for renal clear cell carcinoma diagnosis and therapeutic target.

Journal of cell communication and signaling · 2025 doi:10.1002/ccs3.70047

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:40977852
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science-record
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2026-11-01T10:25:53Z
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paper object · 10 fields
state
partial
title
An immunometabolism-related signature for renal clear cell carcinoma diagnosis and therapeutic target.
doi
10.1002/ccs3.70047
pmid
40977852
authors
null
journal
Journal of cell communication and signaling
year
2025
abstract
null
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the headline numerical result the paper states for its main finding
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abstract
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the headline numerical result the paper states for its main finding
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read_published_estimate
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The number a replication must land on, and the sentence it came from.
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source:pmid:40977852
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the published estimate, its unit and its interval
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The published result cannot be recomputed without the data the paper analysed.
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the source data with its provenance
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no owned computational capability is bound to this experiment
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Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40977852
parameters object · 0 fields
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source:pmid:40977852
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read_published_estimate
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minimax
The diagnostic model showed strong discrimination, with AUC 0.976 in TCGA, 0.902 in GSE126964, and 0.916 in GSE36895.
minimax_alt
The diagnostic model achieved AUC 0.976 in TCGA, with external-validation AUCs of 0.902 in GSE126964 and 0.916 in GSE36895.
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Functional assays were performed in vitro and in vivo.
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Silencing ABCB1 or AKR1C3, or overexpressing LDHB, suppressed KIRC cell proliferation and migration in vitro
[2]
LDHB overexpression combined with sorafenib significantly reduced tumor growth in vivo.
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external cohort validation
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receiver operating characteristic (ROC) analysis
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[8]
clusterProfiler Gene Ontology and KEGG enrichment analysis
[9]
single-cell RNA sequencing analysis using the Seurat package
[10]
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[11]
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differential expression analysis using the Limma package
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least absolute shrinkage and selection operator
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Support Vector Machine–Recursive Feature Elimination (SVM‐RFE)
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random forest
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ROC analysis
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external cohort validation
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target_materials
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independent_readings
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minimax
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null
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minimax_alt
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HTTP Error 429: Too Many Requests
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Complete file
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