Evidence map›Paper›PMID 40500522›Full record

ArticleJournal of cancer research and clinical oncology2025

Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.

Yijun Shao, Harmony S Cen, Anu Dhananjay, S J Pawan, Xiaomeng Lei, Inderbir S Gill, Anishka D'souza, Vinay A Duddalwar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. TCIA Radiology Image Processing for AI and Radiomics.medRxiv : the preprint server for health sciences · 2026
    Article
  3. Review
  4. Review
  5. Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yijun ShaoKeck School of Medicine, University of Southern California, Los Angeles, CA, USA. yijunsha@usc.edu.
Harmony S CenKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Anu DhananjayNewport High School, Bellevue, WA, USA.
S J PawanRadiomics Lab, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Xiaomeng LeiRadiomics Lab, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Inderbir S GillInstitute of Urology, University of Southern California, Los Angeles, CA, USA.
Anishka D'souzaDepartment of Medical Oncology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Vinay A DuddalwarRadiomics Lab, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to evaluate radiomic models' ability to predict hypoxia-related biomarker expression in clear cell renal cell carcinoma (ccRCC).

methodsClinical and molecular data from 190 patients were extracted from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma dataset, and corresponding CT imaging data were manually segmented from The Cancer Imaging Archive. A panel of 2,824 radiomic features was analyzed, and robust, high-interscanner-reproducibility features were selected. Gene expression data for 13 hypoxia-related biomarkers were stratified by tumor grade (1/2 vs. 3/4) and stage (I/II vs. III/IV) and analyzed using Wilcoxon rank sum test. Machine learning modeling was conducted using the High-Performance Random Forest (RF) procedure in SAS Enterprise Miner 15.1, with significance at P < 0.05.

resultsDescriptive univariate analysis revealed significantly lower expression of several biomarkers in high-grade and late-stage tumors, with KLF6 showing the most notable decrease. The RF model effectively predicted the expression of KLF6, ETS1, and BCL2, as well as PLOD2 and PPARGC1A underexpression. Stratified performance assessment showed improved predictive ability for RORA, BCL2, and KLF6 in high-grade tumors and for ETS1 across grades, with no significant performance difference across grade or stage.

conclusionThe RF model demonstrated modest but significant associations between texture metrics derived from clinical CT scans, such as GLDM and GLCM, and key hypoxia-related biomarkers including KLF6, BCL2, ETS1, and PLOD2. These findings suggest that radiomic analysis could support ccRCC risk stratification and personalized treatment planning by providing non-invasive insights into tumor biology.

Indexed as

Biomarkers, TumorCarcinoma, Renal CellKidney NeoplasmsAgedFemaleHumansMaleMiddle AgedTomography, X-Ray ComputedBiomarkers, TumorBiomarkersClear cell renal cell carcinomaHypoxiaRadiogenomicsRadiomics

Identifiers

PMID40500522
PMCPMC12159112

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Registered trials

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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.