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Record W4414478390 · doi:10.3390/onco5040044

Association of TP53 Arg72Pro (rs1042522) Polymorphism with Pancreatic Cancer Risk in a Patient Cohort

2025· article· en· W4414478390 on OpenAlexfundno aff
Laura Antolino, Germana de Nucci, Stefania Scarpino, Giuseppe Bianco, Gianluca Lopez, Paolo Aurello, Niccolò Petrucciani, Roberto Santoro, Giuseppe Nigri, S. Agnes, Gianpiero Manes, Francesco D’Angelo

Bibliographic record

VenueOnco · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsnot available
FundersSapienza Università di RomaMcMaster University
KeywordsPancreatic cancerPancreatic ductal adenocarcinomaCohortOdds ratioGenotypePopulationLogistic regression

Abstract

fetched live from OpenAlex

Pancreatic cancer is expected to become the second leading cause of death by 2030 in Western countries. There is a need to pinpoint high-risk populations since extensive screening would be economically impractical. Methods: This study, conducted on liquid biopsies of patients affected by pancreatic ductal adenocarcinoma (PDAC), sequenced, by NGS, the main genes involved in pancreatic carcinogenesis. Results: The study was discontinued due to a low recruitment rate. NGS analysis, conducted on included patients, revealed the TP53 variant rs1042522 in 30 out of 35 patients, with a cytosine (C) replaced by a guanine (G), hence inserting an Arginine in the final protein instead of a Proline. The presence of the rs1042522 variant confers an odds ratio of 6.11 for PaC and an OR of 20 for homozygosity G/G when comparing our cohort of PaC patients to a healthy population from the 1000GenomeProject. Conclusion: These findings could identify a very-high-risk population deserving of being screened for PDAC, even though a wider validation of rs1042522 as a risk factor is needed. Impact: These preliminary data may open the way for identification of a population more prone to developing pancreatic cancer.

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 score0.029
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.312
Teacher spread0.302 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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