Monitoring One-carbon metabolism by mass spectrometry for early diagnosis of cirrhosis and HCC
Bibliographic record
Abstract
Introduction: Liver cancer represents one of the most frequent causes of death by cancer, ranked as the sixth most prevalent and the second in lethality. Hepatocellular carcinoma (HCC) is the predominant type of liver cancer and presents an increasing incidence during the last years. Late diagnosis is one of the reasons explaining the low survival rate of HCC patients (5 years survival after diagnosis below 20%). Remarkably, 80% of HCC cases develop in cirrhotic tissue (1). Main risk factors for HCC are well known and include hepatitis B and C viral infections or abusive alcohol consumption. However, the underlying molecular mechanisms remain unknown and its research will lead to the characterization of new biomolecular markers for the early diagnosis, prognosis and therapy of HCC. Metabolic remodeling is a common feature among several hepatic disorders, from steatosis to HCC (2). Tumoral hepatocytes modify their metabolism to satisfy cancer’s proliferative requirements. One-carbon metabolism (OCM) plays a fundamental role maintaining the differentiation and quiescent state of hepatocytes, and is recognized as the link between intermediate metabolism and epigenetic regulation. Owing to these reasons, it might be a potential source of biomarkers for early diagnosis and prognosis of HCC. Accordingly, it has been demonstrated that some OCM enzymes are differentially expressed in murine HCC models (3). Materials and methods: We have developed a robust targeted mass spectrometry-based method, using SRM mode (Selected Reaction Monitoring), for the systematic quantification of 13 enzymes that participate in OCM. For this purpose, purified synthetic heavy standard peptides, as well as OCM recombinant proteins have been used. Sixty-four human liver samples, including 28 control samples, 21 tumoral samples and15 cirrhotic samples have been used. Results and conclusions: We have demonstrated that there is a profound remodeling of the OCM cycle in HCC versus control samples, while cirrhotic samples tend to show intermediate expression levels between both physiological situations. Machine learning- based analysis of our results suggests that monitoring a panel of functionally related proteins might be useful for future clinical developments and improve the management of HCC patients. However, further experiments with larger cohorts are required to confirm these findings. References 1) Ryerson, A. B., et al (2016). Annual Report to the Nation on the Status of Cancer, 1975-2012, featuring the increasing incidence of liver cancer. Cancer, 122(9), 1312-1337. 2) Vander Heiden, et al (2009). Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science, 324(5930), 1029-1033 3) Mora, M. I., Corrales, F. J., et al (2017). Prioritizing popular proteins in liver cancer: remodelling one-carbon metabolism. Journal of proteome research, 16(12), 4506-4514.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".