Skip to content

The molecular interplay between USP32 and ERP44 in driving multiple myeloma progression and tumor growth.

Sep 2026 · Cellular Signalling · Vol 149, pp. 112904 · 0 citations · 26 references
Medicine

TL;DR

Findings identify ERP44 as a pro-proliferative factor in MM and establish a USP32-ERP44-PI3K-AKT regulatory axis.

Abstract

Multiple myeloma (MM) is a malignancy of plasma cells with complex pathogenesis and unmet clinical needs. ERP44 is an Endoplasmic Reticulum (ER)-resident protein involved in protein folding, but its role in MM remains unclear. Kaplan-Meier survival analysis showed that high ERP44 expression correlated with poor prognosis in MM patients, prompting us to investigate its biological function and regulatory mechanisms. Using lentiviral-mediated overexpression and knockdown strategies in MM cell lines, we demonstrated that ERP44 significantly promoted cell proliferation in vitro, and ERP44 knockdown suppressed tumor growth in a xenograft mouse model. Mechanistically, we identified USP32 as an upstream regulator that interacted with and stabilized ERP44 through deubiquitination. Functional rescue experiments showed that ERP44 overexpression partially reversed USP32 knockdown-induced proliferation inhibition, confirming that USP32 promoted MM cell proliferation through ERP44. RNA sequencing combined with Western blotting revealed that ERP44 activated the PI3K-AKT signaling pathway, leading to upregulation of CDK2 and Cyclin D1 and driving cell cycle progression. Notably, the PI3K-AKT inhibitor LY294002 significantly attenuated the proliferative advantage conferred by ERP44 overexpression, suggesting this pathway as a critical downstream effector. Furthermore, ERP44 overexpression upregulated UPR markers (GRP78, XBP1, and CHOP) as well as osteolytic factors (DKK1, RANKL, and MIP-1α), indicating its broader role in UPR and bone destruction. Collectively, these findings identify ERP44 as a pro-proliferative factor in MM and establish a USP32-ERP44-PI3K-AKT regulatory axis.

View source

Similar papers

Open access Sep 2026

HSP90AA1 inhibits autophagy to promote tumor growth: A novel therapeutic target in endometrial cancer.

HSP90AA1 was significantly overexpressed in tumor tissues, and its high expression correlated with advanced International Federation of Gynecology and Obstetrics stage, lymphovascular space invasion, lymph node metastasis, and higher tumor grade.

L. Xue, Peng-Fei Wang, Chenlingzi Huang et al. · 0 citations
Open access Aug 2026

TET1-mediated DNA demethylation and transcription activation of MRPS17 induces lung adenocarcinoma through PI3K-AKT-mTOR pathway

This study investigates the role of mitochondrial ribosomal protein S17 in the progression of LUAD, with a specific focus on its interaction with the PI3K-AKT-mTOR signaling pathway and highlights the potential of MRPS17 as a prognostic marker and therapeutic target.

Nan Zhou, Lei Song, Yang Wu et al. · 0 citations
Open access Sep 2026

HIST1H4L promotes small cell lung cancer tumorigenesis by regulating the PI3K/Akt pathway and serves as a potential diagnostic biomarker

HIST1H4L was significantly upregulated in SCLC tumors versus normal tissues and enriched in DNA transcription-related pathways and correlated with poor patient survival; critically, high HIST1H4L expression correlated with poor patient survival.

Shi-Cheng Feng, Min Feng, Zhi-Qiang Lu et al. · 0 citations
Review Sep 2026

SET Oncoprotein in Gastric Cancer: A Master Regulator of PP2A Suppression and Tumor Progression

Therapeutic modulation of the SET-PP2A axis may provide a novel strategy for suppressing tumor progression and improving treatment outcomes, and further mechanistic studies and welldesigned clinical investigations are required to establish the therapeutic relevance of SET targeting and facilitate its integration into p...

Rohit Das, Shikha Yadav, Sudeshna Das et al. · 0 citations

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.