Back

Atypical MDM2 p53 Regulation and Chemosensitivity Induced by Proximal PAS Deletion

Kim, M.; Yoon, C.; Jun, J.; Lee, Y.; Chung, H.; Kim, Y.

2026-08-24 cancer biology
10.64898/2026.08.23.746494 bioRxiv
Show abstract

This study proposes a novel therapeutic strategy to suppress cancer growth by modulating the MDM2-p53 axis via Alternative Polyadenylation (APA). MDM2 normally promotes tumorigenesis by ubiquitinating and degrading the tumor suppressor p53. In cancer cells, preferential use of proximal polyadenylation signals (PAS) results in shortened 3'UTRs, allowing oncogenic transcripts like MDM2 to evade nuclear sequestration mediated by Inverted Alu (IRAlu) double-stranded RNA structures. We hypothesized that forcing distal PAS usage would elongate the MDM2 mRNA, promoting its nuclear retention and reducing protein translation, thereby restoring p53 activity. Using CRISPR-Cas9, we targeted and deleted the most frequent proximal PAS in the MDM2 3'UTR of A549 cells. Successful genome editing was confirmed via PCR. As expected, Western blot analysis showed a significant reduction in MDM2 expression in PAS-edited cells. However, experimental outcomes contradicted our initial hypothesis: edited cells exhibited higher viability under doxorubicin treatment compared to wild-type cells. Furthermore, despite decreased MDM2 levels, a concurrent reduction in phosphorylated p53 (p-p53) was observed. These unexpected results suggest that MDM2 3'UTR elongation may trigger a non-canonical regulatory mechanism that bypasses the traditional MDM2-p53 interaction. This study highlights the complexity of post-transcriptional regulation and suggests that APA-mediated gene modulation can induce unforeseen compensatory survival pathways in cancer cells, necessitating further investigation into the broader functional landscape of elongated 3'UTRs.

Matching journals

The top 10 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.