Back

Sex-dependent pathways in hepatocarcinogenesis triggered by deregulated cholesterol synthesis

Rozman, D.; Cokan, K. B.; Urlep, Z.; Lorbek, G.; Soja, M. M.; Skubic, C.; Perse, M.; Jeruc, J.; Juvan, P.; Rezen, T.

2020-09-04 cancer biology
10.1101/2020.09.03.280974 bioRxiv
Show abstract

We uncover novel pathways of sex-dependent hepatocarcinogenesis due to chronic repression of cholesterol synthesis at the lanosterol 14-demethylase (CYP51) step. The response to metabolic insult determined by global liver transcriptome, qPCR and sterol metabolite analysis, together with blood parameters, revealed molecular signatures that differ between females and males. The data deduced from the mouse model are highly relevant for humans. The dampened hepatic metabolism presents a hallmark of carcinogenesis, particularly in ageing females, with increased plasma cholesterol and HDL, and a substantial negative enrichment of transcription factors from lipid metabolism, such as NR1B1, LXR, LRH1, and FXR. Importantly, the carcinogenic signalling pathways (ECM-receptor interaction and PI3K/Akt) are positively enriched, albeit with sex-dependent gene targets. The activated TGF-{beta}, mTOR, Wnt, and estrogen signalling worsen the phenotype, with NFATC1/2 being central to the female phenotype. This collectively leads to activated cell death and diminished basal metabolism. In conclusion, our data underline sex as an important biological variable of hepatocarcinogenesis. We uncover novel cholesterol-dependent transcription factors and signalling pathways as cancer markers in the ageing females. SignificanceChronic repression of the late part of cholesterol synthesis provokes hepatocarcinogenesis with sex-dependent modulation of signalling pathways and transcription factors. Aging females show specific metabolic signatures and a more aggrevated phenotype of metabolism-related HCC.

Matching journals

The top 14 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.