Data from: Targeting UAP1 with rabeprazole: A novel repurposed therapeutic strategy for multiple myeloma
Data files
Sep 16, 2026 version files 78.79 MB
-
README.md
3.06 KB
-
tumor_metabolomics.csv
152.30 KB
-
tumor_transcriptomics.csv
78.64 MB
Abstract
Background: Despite significant advancements in proteasome inhibitors and immunomodulatory drugs, multiple myeloma (MM) remains largely incurable due to persistent drug resistance and frequent relapse, highlighting an urgent need for novel therapeutic targets.
Methods: We analyzed MMRF and GEO datasets to identify genes associated with disease progression, prioritizing UDP-N-acetylglucosamine pyrophosphorylase 1 (UAP1) for functional validation. The impact of UAP1 on MM cell growth was evaluated through both in vitro and in vivo models. Integrated metabolomics and transcriptomics were employed to elucidate the underlying regulatory mechanisms. Furthermore, virtual screening of an FDA-approved drug library was performed to identify potential UAP1 expression suppressors, followed by experimental validation of the lead compound’s efficacy via molecular docking, combination therapy analysis, and gene expression intervention.
Results: UAP1 was found to be overexpressed in MM patients and positively correlated with disease severity. UAP1 knockdown significantly suppressed MM cell proliferation, and promoted apoptosis. Mechanistically, integrated multi-omics analysis revealed that this growth inhibition was driven by the dysregulation of protein biosynthesis, degradation, and amino acid metabolism. Virtual screening identified Rabeprazole (RAB) as a potent expression suppressor candidate. RAB effectively inhibited MM cell growth by downregulating UAP1 expression and exhibited synergistic anti-myeloma effects when combined with the proteasome inhibitor bortezomib. Knockdown and overexpression experiments confirmed that UAP1 is a functional target of RAB.
Conclusions: Our findings establish UAP1 as a critical driver of MM progression and a promising therapeutic target. Genetic or pharmacological inhibition of UAP1 expression via RAB disrupts essential metabolic pathways, thereby stunting tumor growth. This study provides preclinical evidence for repurposing RAB as a novel UAP1-targeted strategy, offering a potential avenue to sensitize the therapeutic efficacy of bortezomib and improve clinical outcomes for MM patients.
Dataset DOI: 10.5061/dryad.g79cnp66t
Description of the data and file structure
This dataset contains untargeted and transcriptomic metabolomic data generated to investigate the role of UDP-N-acetylglucosamine pyrophosphorylase 1 (UAP1) in multiple myeloma (MM) tumor growth in vivo. Human MM H929 cells were transduced with lentivirus to stably knock down UAP1 (shUAP1), with non-targeting shRNA used as the negative control (shNC). After validation of knockdown efficiency, shUAP1 and shNC H929 cells were subcutaneously transplanted into NOG immunodeficient mice to establish xenograft tumor models and evaluate the effect of UAP1 on MM tumor growth. Two complementary omics analyses were conducted:
(1) Untargeted metabolomics. Tumor tissues were extracted with methanol and analyzed by ultra-performance liquid chromatography coupled with Q-Exactive Orbitrap high-resolution mass spectrometry (UPLC-HRMS). The metabolomics file (tumor_metabolomics.csv) contains relative peak intensities of 1078 metabolites across 12 tumor samples (six NC and six UAP1-KD). An empty cell indicates that the corresponding metabolite was not detected, or that its signal fell below the limit of detection, in that particular sample. No "0" was entered for these cells. Empty cells are not the result of data loss, file corruption, or processing errors. They are an intrinsic property of untargeted LC-MS data.
(2) RNA sequencing (RNA-seq). Total RNA was extracted and mRNA libraries were constructed and sequenced on an Illumina NovaSeq 6000 platform. The transcriptome file (tumor_transcriptomics.csv) contains gene-level raw read counts and FPKM values for 12 samples (six UAP1-KD and six NC biological replicates), together with gene annotations including chromosomal location, GO terms, KEGG pathways, eggNOG classification, Entrez Gene ID and UniProt accession.
These data were used to identify differentially expressed genes and differential metabolites and to perform GO/KEGG enrichment and integrated transcriptome–metabolome pathway analyses, revealing the pathways through which UAP1 knockdown suppresses MM tumor growth in vivo.
Code/software
The submitted data files (tumor_metabolomics.csv and tumor_transcriptomics.csv) are standard comma-separated values (CSV) tables. Both files can be opened and viewed using any free spreadsheet software, including Microsoft Office Excel, LibreOffice Calc, or WPS Office Spreadsheet.
Statistical analysis of the processed expression data was performed using GraphPad Prism 10, a commercial scientific graphing and statistics software. No custom code or scripts are included in this submission.
Access information
Other publicly accessible locations of the data:
- All raw-measurement data generated in this study are original, and no external-sourced datasets were adopted.
Data was derived from the following sources:
- None.