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Record W4366465782 · doi:10.1080/02648725.2023.2204692

Significance of PBRM1 mutation in disease progress and drug selection in clear cell renal cell carcinoma

2023· article· en· W4366465782 on OpenAlexaff
Donghua He, Tianyan Ma, Yi Ni, Sijie Zhang, Guanxiong Ding

Bibliographic record

VenueBiotechnology and Genetic Engineering Reviews · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMutationClear cell renal cell carcinomaBiologyGeneCancer researchGeneticsMedicineRenal cell carcinomaOncology

Abstract

fetched live from OpenAlex

Clear cell renal cell carcinoma (ccRCC) is the predominant type of kidney cancer, and the mutation of PBRM1 (Polybromo 1) gene is a commonly observed genetic alteration. The high frequency of PBRM1 mutation in ccRCC suggests its potential use as a biomarker for personalized therapy. In this study, we aimed to investigate the significance of PBRM1 mutation in disease progression and drug sensitivity in ccRCC. Additionally, we analyzed the critical pathways and genes associated with PBRM1 mutation to understand its potential mechanisms. Our findings show that PBRM1 mutation was observed in 38% of ccRCC patients and correlated with advanced disease stages. We also identified selective inhibitors for ccRCC with PBRM1 mutation using online databases such as PD173074 and AGI-6780. Furthermore, we identified 1253 genes as differentially expressed genes (DEGs) that were significantly enriched in categories such as metabolic progression, cell proliferation, and development. Although PBRM1 mutation did not show an association with ccRCC prognosis, a lower PBRM1 expression level correlated with worsened prognosis. Our study provides insights into the association of PBRM1 mutation with disease progression in ccRCC and suggests potential gene and signaling pathways for personalized treatment in ccRCC with PBRM1 mutation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.230
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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