Evidence map›Paper›PMID 40119186›Full record

ArticleJournal of cancer research and clinical oncology2025

Genomic analysis of small renal masses reveals mutations linked with renal cell carcinoma and fast-growing tumors.

Ieva Vaicekauskaitė, Algirdas Žalimas, Rasa Sabaliauskaitė, Kristina Žukauskaitė, Mantas Trakymas, Jurgita Ušinskienė, Albertas Ulys, Sonata Jarmalaitė

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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 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ieva VaicekauskaitėNational Cancer Institute, Vilnius, Lithuania.
Algirdas ŽalimasNational Cancer Institute, Vilnius, Lithuania.
Rasa SabaliauskaitėNational Cancer Institute, Vilnius, Lithuania.
Kristina ŽukauskaitėNational Cancer Institute, Vilnius, Lithuania.
Mantas TrakymasNational Cancer Institute, Vilnius, Lithuania.
Jurgita UšinskienėNational Cancer Institute, Vilnius, Lithuania.
Albertas UlysNational Cancer Institute, Vilnius, Lithuania.
Sonata JarmalaitėNational Cancer Institute, Vilnius, Lithuania. sonata.jarmalaite@gf.vu.lt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSmall renal masses (SRMs) SRMs are a heterogeneous group of small kidney lesions. Currently, the genomic landscape of SRMs is understudied, and clinically relevant tools for malignancy detection and fast tumor growth prediction are lacking. The aim of the study was to evaluate whether mutations in SRMs are associated with increased risk of renal cell carcinoma (RCC) or aggressive tumors.

methodsIn this pilot study, 52 patients with SRMs were divided based on tumor histology into RCC and benign tumors, while RCC cases were divided into fast-growing and slow-growing tumor groups. Tissue biopsy samples evaluated for mutations in 51 cancer hotspot genes using next generation sequencing and qPCR. Non-benign mutations were tested for associations with RCC and clinical features. Receiver operating curve analysis used for evaluation of mutation biomarker models prediction of RCC and fast-growing tumors.

results75% of SRMs harbored non-synonymous alterations in 16/51 genes. 38.5% of detected mutations were listed in ClinVar and correlated with smaller SRM volume (p = 0.023). KRAS, VHL, HNF1A, TP53, and ATM mutations were predominantly detected in RCC rather than benign SRMs (p = 0.046). SRMs with pathogenic mutations were at three times higher risk of being RCC and four times higher risk of fast growth.

conclusionGenomic biomarkers may improve risk stratification and management of patients with SRMs, however a more extensive genomic analysis of SRMs is still needed.

Indexed as

Biomarkers, TumorCarcinoma, Renal CellKidney NeoplasmsMutationAdultAgedAged, 80 and overFemaleGenomicsHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedPilot ProjectsBiomarkers, TumorKidney tumorNext generation sequencingRenal cell carcinomaSmall renal massSomatic mutations

Identifiers

PMID40119186
PMCPMC11928375

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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.