ArticleCancers2021
Genetic Analysis of Multiple Myeloma Identifies Cytogenetic Alterations Implicated in Disease Complexity and Progression.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- A bortezomib resistance-related gene signature predicts prognosis, with ARID5B downregulation associated with poor overall survival in multiple myeloma.Discover oncology · 2026Article
- UAP1 as a prognostic biomarker regulating malignant biological functions in multiple myeloma cells.Frontiers in oncology · 2026Article
- The oligosaccharyltransferase complex is an essential component of multiple myeloma plasma cells.Molecular therapy. Oncology · 2025Article
- Unraveling the Role of Ubiquitin-Conjugating Enzyme UBE2T in Tumorigenesis: A Comprehensive Review.Cells · 2024Review
- Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.Journal of cellular and molecular medicine · 2024Article
- Dihydrolipoamide dehydrogenase (DLD) is a novel molecular target of bortezomib.Cell death & disease · 2024Article
- The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities.Nature cancer · 2024Article
- pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods.BMC bioinformatics · 2023Article
- Dependency of B-Cell Acute Lymphoblastic Leukemia and Multiple Myeloma Cell Lines on MEN1 Extends beyond MEN1-KMT2A Interaction.International journal of molecular sciences · 2023Article
- Diverse roles of UBE2T in cancer (Review).Oncology reports · 2023Article
- A novel human multiple myeloma cell line with a 1q21 gain genetic abnormality and CKS1B overexpression.Annals of translational medicine · 2023Article
- Article
- Gene expression profiling and in vitro functional studies reveal RAD54L as a potential therapeutic target in multiple myeloma.Genes & genomics · 2022Article
- Prognostic Value of Association of Copy Number Alterations and Cell-Surface Expression Markers in Newly Diagnosed Multiple Myeloma Patients.International journal of molecular sciences · 2022Article
- The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications.International journal of molecular sciences · 2021Review
- A Comprehensive Multiomics Analysis Identified Ubiquilin 4 as a Promising Prognostic Biomarker of Immune-Related Therapy in Pan-Cancer.Journal of oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Multiple myeloma (MM) is a genetically heterogeneous disease characterized by genomic chaos making it difficult to distinguish driver from passenger mutations. In this study, we integrated data from whole genome gene expression profiling (GEP) microarrays and CytoScan HD high-resolution genomic arrays to integrate GEP with copy number variations (CNV) to more precisely define molecular alterations in MM important for disease initiation, progression and poor clinical outcome. We utilized gene expression arrays from 351 MM samples and CytoScan HD arrays from 97 MM samples to identify eight CNV events that represent possible MM drivers. By integrating GEP and CNV data we divided the MM into eight unique subgroups and demonstrated that patients within one of the eight distinct subgroups exhibited common and unique protein network signatures that can be utilized to identify new therapeutic interventions based on pathway dysregulation. Data also point to the central role of 1q gains and the upregulated expression of ANP32E, DTL, IFI16, UBE2Q1, and UBE2T as potential drivers of MM aggressiveness. The data presented here utilized a novel approach to identify potential driver CNV events in MM, the creation of an improved definition of the molecular basis of MM and the identification of potential new points of therapeutic intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.