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Record W3213048077 · doi:10.1186/s12014-021-09333-x

Combined proteomic/transcriptomic signature of recurrence post-liver transplantation for hepatocellular carcinoma beyond Milan

2021· article· en· W3213048077 on OpenAlexafffund
Mamatha Bhat, Sergi Clotet‐Freixas, Cristina Baciu, Elisa Pasini, Ahmed Hammad, Tommy Ivanics, Shelby Reid, Amirhossein Azhie, Marc Angeli, Anand Ghanekar, Sandra E. Fischer, Gonzalo Sapisochín, Ana Konvalinka

Bibliographic record

VenueClinical Proteomics · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanadian Liver FoundationCanada Foundation for InnovationToronto General and Western Hospital FoundationAmerican Society of Transplantation
KeywordsHepatocellular carcinomaMilan criteriaGene signatureUnivariate analysisTranscriptomeLiver transplantationCirrhosisContext (archaeology)Internal medicineMedicineTransplantationOncologyCancer researchGastroenterologyBiologyGeneMultivariate analysisGene expressionGenetics

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Liver transplantation (LT) can be offered to patients with Hepatocellular carcinoma (HCC) beyond Milan criteria. However, there are currently limited molecular markers on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to derive a combined proteomic/transcriptomic signature on HCC explant predictive of recurrence post-transplant using unbiased, high-throughput approaches. METHODS: Patients who received a LT for HCC beyond Milan criteria in the context of hepatitis B cirrhosis were identified. Tumor explants from patients with post-transplant HCC recurrence (N = 7) versus those without recurrence (N = 4) were analyzed by mass spectrometry and gene expression array. Univariate analysis was used to generate a combined proteomic/transcriptomic signature linked to recurrence. Significantly predictive genes and proteins were verified and internally validated by immunoblotting and immunohistochemistry. RESULTS: Seventy-nine proteins and 636 genes were significantly differentially expressed in HCC tumors with subsequent recurrence (p < 0.05). Univariate survival analysis identified Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) gene (HR = 0.084, 95%CI 0.01-0.68, p = 0.0152), ALDH1A1 protein (HR = 0.039, 95%CI 0.16-0.91, p = 0.03), Galectin 3 Binding Protein (LGALS3BP) gene (HR = 7.14, 95%CI 1.20-432.96, p = 0.03), LGALS3BP protein (HR = 2.6, 95%CI 1.1-6.1, p = 0.036), Galectin 3 (LGALS3) gene (HR = 2.89, 95%CI 1.01-8.3, p = 0.049) and LGALS3 protein (HR = 2.6, 95%CI 1.2-5.5, p = 0.015) as key dysregulated analytes in recurrent HCC. In concordance with our proteome findings, HCC recurrence was linked to decreased ALDH1A1 and increased LGALS3 protein expression by Western Blot. LGALS3BP protein expression was validated in 29 independent HCC samples. CONCLUSIONS: Significantly increased LGALS3 and LGALS3BP gene and protein expression on explant were associated with post-transplant recurrence, whereas increased ALDH1A1 was associated with absence of recurrence in patients transplanted for HCC beyond Milan criteria. This combined proteomic/transcriptomic signature could help in predicting HCC recurrence risk and guide post-transplant surveillance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.301
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2021
Admission routes2
Has abstractyes

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