Analysis of Differentially Expressed Proteins in Hepatocellular Carcinoma
Bibliographic record
Abstract
Introduction Hepatocellular carcinoma (HCC), the third leading cause of cancer-related mortality worldwide, has limited treatment options; therefore, the importance of developing new and innovative therapeutic strategies. The field of proteomics has emerged as a powerful tool to identify changes in protein expression in HCC patients. Objectives Identify differentially expressed proteins in tissue biopsies of HCC patients who underwent liver transplantation. Material & Methods The protein expression in paraffin-embedded liver biopsies of 24 HCC liver transplant patients (12 Poor Differentiated/Vascular Invasion -Poor-, 12 Well/Moderate Differentiated -Well-) was assessed by Mass Spectrometry (LC/MS). Five non-tumor liver biopsies were used as Controls (Ctr). The expression ratios of abundances were calculated (1 ± 0.25) and p-values were determined using two-tailed Student's t-test. Relevant demographics and clinical characteristics of the patients were also collected (age, sex, total tumor volume, number of tumors, peak AFP pre-surgery, vascular invasion, MELD, tumor differentiation, Child-Pugh, tumor distribution). Results The preliminary results are shown in Table 1. The explorative, quantitative proteome analyses identified over 4,000 proteins in HCC biopsies. Although we didn't achieve statistically significance differences given the small number of patients, we identify 56 proteins overexpressed (>1.5 fold) in poor differentiated HCC compare to Controls. These proteins were differentially expressed in well differentiated HCC vs poor differentiated HCC.Conclusions We identified proteins differentially expressed in poor differentiated HCC. These results may have implications as prognostic biomarkers and future targets for immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".