Comprehensive Bioinformatic Analysis Reveals Survival-Associated Hub Genes and MicroRNAs in Multiple Myeloma Patients

Abstract

Background:

Multiple myeloma (MM) is a B-cell malignancy characterized by clonal plasma cell proliferation in the bone marrow. Although significant advances have been achieved in treatment, it remains largely incurable, and fundamental insights at the molecular level remain to be obtained.

Objecteves:

This study aimed to identify key genes and microRNA (miRNA) involved in multiple myeloma by re-analyzing transcriptomic datasets. We sought to determine differentially expressed genes and miRNAs, perform pathway and network analyses, and highlight their roles in disease progression, prognosis, and therapeutic resistance.

Materials and Methods:

We identified and characterized the hub genes and miRNAs associated with MM by re-analyzing three microarray datasets, GSE16558, GSE141260, and GSE146649, using high-throughput sequencing. We re-identified DEGs using a strict filtering criterion: |logFC| ≥ 1 and p-value < 0.05. The application of the Venn diagram analysis highlighted 13 common DEGs among the datasets. A total of 3211 differentially expressed genes (DEGs) and 25 differentially expressed microRNAs (DEMs) were screened out, Thereafter, GO and pathway enrichment of the DEGs were analyzed using FunRich software, involving biological processes, cellular components, and molecular functions. The PPI network was constructed using the Cytoscape software to determine the interactions among these DEGs.

Results:

Our analyses underlined several key biological processes, including the migration of immune cells, lymphocyte activation, and TGF-β signaling pathways, which play crucial roles in the progression of MM. The PPI network identified a number of hub genes; among these, CCND1, ITGB1, and CREB1 were significantly associated with patient survival outcomes. In addition, the interaction predictions indicated an important function of miR-34c-5p and miR-155-5p in governing apoptosis, thereby promoting drug resistance in MM cells. We identified 13 common DEGs across datasets, with key enrichments in immune cell migration, lymphocyte activation, and TGF-β signaling. PPI analysis revealed CCND1, ITGB1, and CREB1 as top hub genes, significantly linked to survival outcomes. MiRNA interactions, particularly miR-34c-5p and miR-155-5p, were implicated in apoptosis and drug resistance.

Conclusion:

These data highlight the complex interplay between genetic alterations and the immune microenvironment in MM, opening new prospects for biomarkers and therapeutic targets that may hopefully improve patient management, treatment strategies, prognosis, and therapeutic resistance.

1. Background

Multiple myeloma (MM) is an aggressive B-cell cancer that causes the development of clonal plasma cells in the bone marrow (BM), and is often incurable ( 1 ). Over the last 20 years, the global rate of MM has increased by 126% and the worldwide incidence of MM is 160,000, with a mortality rate of 106,000(A) ( 2 ). However, the cause of MM remains unclear. Most specialists believe that aberrant plasmacytes are derived from memory B lymphocytes or proplasmacytes that have undergone C-myc gene recombination and have elevated levels of expression of specific N-ras genes. This may lead to excessive growth of plasmacytes and an abnormal increase in IL-6 levels in the bone marrow. Clinical signs bone pain (especially in the spine/ribs), pathologic fractures, fatigue (due to anemia), recurrent infections (from immunosuppres-sion), hypercalcemia (causing confusion, nausea, or thirst), renal dysfunction (elevated creatinine), and neuropathy. Bleeding tendencies and weight loss may also occur due to disease progression and organ involvement and severe cases may result in mortality ( 3 ). Therefore, it is critical to investigate the genes associated with MM to provide a theoretical foundation for early detection and therapeutic targets. Unlike previous research methodologies, the widespread adoption and implementation of high-throughput sequencing as well as the construction of a worldwide gene database offer wider and more important evidence supporting MM etiology ( 4 ). Bioinformatics offers an efficient technique for identifying critical regulatory genes responsible for disease ( 5 ). Accordingly, an increasing number of genes and pathways related to MM have been identified, some of which have been verified to play a very important role in the occurrence and development of the disease ( 6 - 8 ). Among them, hub genes and microRNAs (miRNAs) are key regulators in the development of MM; they can influence the proliferation of tumor cells and the formation of the tumor microenvironment ( 9 , 10 ). These miRNAs are small non-coding RNA molecules ( 11 ), which function as critical post-transcriptional regulators and can affect the expression of hub genes that have a high degree of connectivity in gene networks; thus, they are potential biomarkers or therapeutic targets. Expression alterations driven by miRNAs in these hub genes can affect disease outcomes and lead to prognosis in patients ( 12 ).

This study aimed to systematically identify and characterize the hub genes and miRNAs involved in disease pathogenesis using GEO datasets, programming in R, and various online databases. This study aimed to explain the exact regulatory mechanism underlying MM through integrated high-throughput sequencing data with analyses of gene expression profiles.

2. Objectives

This would provide better insights into the molecular landscape of MM by establishing possible novel biomarkers and therapeutic targets, with the hope of improving patient management and treatment strategies. We also studied the functional interactions of the hub genes and miRNAs to identify new avenues for therapeutic intervention in MM.

3. Materials and Methods

3.1. Microarray Data Information and Differentially Expressed Genes (DEGs)

Identification In this study, we utilized three microarray datasets: GSE16558 ( 13 ), GSE141260, and GSE146649. Each dataset included both tumor samples and corresponding normal samples to ensure a comprehensive comparison of gene expression profiles. Importantly, none of the patients had received any drug treatment prior to sample collection. All samples were extracted from the bone marrow cells of the patients, specifically from CD, mononuclear cells, and mesenchymal stromal cells. The GSE141260 dataset comprised of 10 patients and 10 normal samples, utilizing the GPL23126 platform (Affymetrix Human Clariom D Assay). The GSE16558 dataset included 60 patients and five normal samples, which were analyzed using the GPL6244 platform (Affymetrix Human Gene 1.0 ST Array). Additionally, the GSE146649 dataset consisted of 32 patients and 10 normal samples, using the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). To identify DEGs, we applied stringent filtering criteria across all three studies, we selected GSE16558, GSE141260 and GSE146649 because there weren’t any treatment in MM and matched normal group, samples derived from bone marrow in two groups, Platform compatibility for cross-study comparison, and adequate sample size for robust DEG analysis (|logFC| ≥ 1, p < 0.05). The filtering process was visually represented using volcano plot diagrams for each study, illustrating the distribution of gene expression changes. Following the analysis conducted in R, a Venn diagram was generated to identify the shared DEGs among the three datasets. A Venn diagram was created using the tool available at (https://bioinformatics.psb.ugent.be/webtools/Venn/), which facilitated the identification of overlapping genes in the analysis.

3.2. Gene Ontology (GO) and Pathway Enrichment Analysis

For functional annotation of the identified genes, GO and pathway enrichment analyses were performed using the FunRich software. This analysis was conducted on genes located at the center of the Venn diagram, representing the most relevant shared DEGs. Enrichment analysis included various biological processes, cellular components, and molecular functions. Four distinct diagrams were generated from the analysis corresponding to Biological Processes (BP), Cellular Components (CC), Molecular Functions (MF), and pathways (BPath). These diagrams, along with their accompanying tables detailing the enriched terms and associated p-values, are presented in the manuscript to illustrate the functional significance of shared DEGs in the context of MM.

3.3. Protein-Protein Interaction (PPI) Network Establish-ment and Hub Genes Identification

To investigate the interactions between the identified DEGs, we constructed a PPI network using the Cytoscape software. This network was derived from the genes analyzed in the pathway enrichment section, facilitating the visualization of potential interactions among the proteins encoded by these genes. Using the CytoHubba ( 13 ) plugin in Cytoscape, we identified the top 10 hub genes based on their connectivity within the network. These hub genes, which are critical for understanding the regulatory mechanisms of MM, will be included in the article along with the visual representation of the PPI network.

3.4. Hub Genes - miRNAs Interaction Prediction

We used the MiRSystem database ( 14 ) or interaction prediction to explore the regulatory relationships between the identified hub genes and miRNAs. For the top 10 hub genes, we identified miRNAs with the highest predictive scores, indicating a strong potential for regulatory interactions.

The resulting interaction network, which illustrates the connections between the selected miRNAs and hub genes, is visually represented in this article. This network provides insight into the post-transcriptional regulation of hub genes and their potential implications in MM pathogenesis.

3.5.Hub Genes Survival Analysis

Survival and expression analyses for the top 10 hub genes were conducted using the Kaplan-Meier method via the UALCAN database ( 5 ), with significance assessed by the log-rank test (Mantel-Cox). Hazard ratios (HR) and 95% confidence intervals were calculated to evaluate prognostic impact. Genes with p < 0.05 (log-rank) were deemed statistically significant, and provides a comprehensive platform for cancer-related expression data. By extracting the survival curves and expression graphs, we aimed to assess the prognostic significance of these genes in the context of patient outcomes. The survival analysis results, along with the expression graphs for each of the top 10 hub genes, are included in the manuscript.

4. Results

4.1. DEGs Identification

We analyzed three microarray datasets, GSE16558, GSE141260, and GSE146649, comprising tumor and corresponding normal samples, all sourced from untreated bone marrow cells. Using stringent filtering criteria of |logFC| ≥ 1 and p < 0.05, we identified DEGs that were visually represented in volcano plots for each dataset (Fig. 1A-1C). A Venn diagram generated from this analysis revealed 13 common DEGs among the three studies (Fig. 1D). Further analysis of 264 genes located at the center of the Venn diagram provided insights into the biological significance of these genes in MM, highlighting their potential roles in disease pathogenesis.

Figure 1.

Volcano plots and Venn diagram of gene expression datasets. Volcano plots in A-C) represent differential gene expression analysis for datasets GSE141260, GSE146649, and GSE16558. The x-axis shows the log2 fold change (log2FC), and the y-axis represents the negative logarithm of the p-value (-log10P). The Venn diagram in D) illustrates the overlap of differentially expressed genes among the three datasets

4.2. GO and Pathway Enrichment Analysis

Functional annotation of shared DEGs was performed using FunRich for GO and pathway enrichment, focusing on biological processes, cellular components, and molecular functions. GO analysis identified MM-relevant terms like lymphocyte activation (immune evasion), cell cycle regulation (proliferation), and endoplasmic reticulum function (protein homeostasis). Pathway analysis emphasized TGF-β signaling (drug resistance) and post-translational modifications (proteasome targeting), key to MM pathogenesis and therapy. These results align with MM hallmarks, highlighting potential therapeutic targets, including TGF-β inhibitors and ubiquitin-proteasome pathway modulators, to address critical aspects of MM progression and treatment resistance. The top five BPs identified were embryonic development, immune cell migration, lymphocyte activation, lymphocyte proliferation, and regulation of the cell cycle (Fig. 2A). Key CC highlighted were the endoplasmic reticulum, COPII vesicle coat, intracellular membrane-bounded organelles, cyclin-dependent protein kinase holoenzyme complex, and Golgi apparatus (Fig. 2B). In terms of MF, the top activities included chromatin binding, steroid hormone receptor activity, extracellular matrix structural constituents, peroxidase activity, and transaminase activity (Fig. 2C). Finally, pathway enrichment analy-sis revealed significant pathways, including TGFBR, asparagine N-linked glycosylation, validated targets of C-MYC transcriptional repression, transport to the Golgi apparatus and subsequent modification, and post-translational protein modification (Fig. 2D). These find-ings illustrate the functional significance of the shared DEGs in the context of MM.

Figure 2.

Functional enrichment analysis of common differentially expressed genes. Bar plots in A-D) show the enrichment analysis for common genes across the MM datasets, categorized by A) biological processes, B) cellular components, C) molecular functions, and D) biological pathways. Each bar represents the percentage of genes within a specific category, along with its corresponding -log10​ p-value.

4.3. PPI Network and miRNA Regulatory Analysis

We employed Cytoscape to build a protein-protein interaction (PPI) network, mapping potential relationships between proteins encoded by our identified differentially expressed genes (DEGs). Within this network, we prioritized the ten most highly connected hub genes, as these central nodes typically exert the greatest biological influence in disease pathways. This focused approach, common in network biology studies, balances comprehensive analysis with practical validation efforts while maintaining the network’s essential structure. The selection of ten genes was supported by a noticeable decrease in connectivity scores beyond this point.

Using CytoHubba, we quantified these connections, identifying CCND1 (score=20), ITGB1 (score=17), and CREB1 (score=14) as the most interconnected nodes (Fig. 3A), highlighting their probable importance in MM pathology. To extend our investigation into gene regulation, we then utilized the MiRSystem database to predict miRNA interactions with these hub genes. This analysis uncovered multiple miRNAs with high prediction scores, suggesting potentially meaningful regulatory relationships with our key hub genes. For instance, DDX17 is associated with miRNAs such as miR-34c-5p and miR-155-5p, while CREB1 is linked to miR-181-5p and miR-727-3p. Additionally, ITGB1 interacted with miR-124-3p and miR-20a-3p, and CCND1 was associated with miR-15a-5p and miR-195-5p (Fig. 3B). Hub Genes Survival Analysis Survival and expression analyses of the top 10 hub genes were conducted using the UALCAN database to evaluate their prognostic significance in patient outcomes related to diffuse large B-cell lymphoma (DLBC). The analysis revealed that the higher expression levels of several hub genes significantly affected patient survival. Notably, CCND1 expression levels were associated with survival (p = 0.026), whereas CDKN1A (p = 0.00086), DDX17 (p = 0.00024), and ITGB1 (p < 0.0001) showed stronger correlations. Additionally, the expression levels of KAT2B were significant (p = 0.0011). Figure 4A. presents a survival analysis of these hub genes, demonstrating the correlation between their expression levels and patient survival outcomes. Figure 4B. shows the expression levels of hub genes across different stages of the disease, highlighting their potential role in tumor progression.

Figure 3.

Gene regulatory networks in common differentially expressed genes. A) Protein-protein interaction (PPI) network showing common genes (green rectangles) and identified hub genes (other colors) within the network. B) MicroRNA (miRNA) regulatory network illustrating interactions between miRNAs (pink rectangles) and their target hub genes (blue ovals).

Figure 4.

Prognostic analysis and expression profiles of selected hub genes in DLBCL. A) Kaplan-Meier survival curves showing the effect of hub gene expression levels on patient survival in DLBCL. B) Box plots illustrating the expression levels (transcripts per million) of selected hub genes across different stages of DLBCL (TCGA samples).

5. Discussion

MM is a prevalent hematological malignancy resulting from the complex interplay between tumor formation and development, which is influenced by hereditary factors ( 16 ). Despite advances in research and novel therapies for MM  ( 17 , 18 ), the pathophysiology of this disease remains incompletely understood and is still incurable for the majority of patients. Consequently, molecular research on potential etiologies of MM is essential. Recently, due to the rapid advancement of microarray technology, it has been frequently utilized to examine the overall genetic alterations in MM in an attempt to identify potential indicators of MM disease ( 10 , 19 ).

We conducted a comprehensive analysis of three microarray datasets of bone marrow cells from untreated patients to identify DEGs using stringent criteria (|logFC| ≥ 1 and p < 0.05), which are depicted in the volcano plots. Venn diagram analysis identified 13 shared DEGs that may play a critical role in MM pathogenesis. The 264 genes at the intersection of the Venn diagram were further functionally analyzed to elucidate their biological roles in MM. This approach emphasizes the interdependence of changes in gene expression and provides implications for disease progression and further investigation of therapeutic targets. Following the identification of DEGs, we performed GO and pathway enrichment analyses. The identification of critical BPs includes embryonic development, immune cell migration, lymphocyte activation, lymphocyte proliferation, and regulation of the cell cycle, underscoring the importance of immune modulation in MM. MM is characterized by malignant proliferation of plasma cells in the bone marrow and complex interactions between tumor cells and the immune microenvironment ( 20 - 22 ). It has also been demonstrated that mesenchymal stem cells from patients with MM have downregulated genes in cellular pathways, such as cell cycle progression, immune response, and bone metabolism ( 23 - 25 ). MM employs various mechanisms of immune evasion, including abnormal interaction with T-cells, dendritic cells, and natural killer cells, and production of immunosuppressive cytokines ( 26 , 27 ). The bone marrow microenvironment is also crucial during disease development; the interaction between plasma cells, stromal cells, and immune cells enhances immune evasion and tumor growth ( 28 , 29 ). Notably, lymphocyte activation and proliferation are common events in tumorigenesis and immune responses, suggesting a strong interrelationship between malignant plasma cells and the immune system ( 30 , 31 ).

Our analysis revealed key structures in the CC, including the endoplasmic reticulum and Golgi apparatus, which are essential for protein synthesis and post-translational modification. This further corroborates that these components play a significant role in MM, as the pathway enrichment analysis elucidated key pathways, such as TGFBR signaling and post-translational protein modification. TGF-β signaling plays a critical role in the progression and drug resistance of MM as a consequence of induction of myelomagenesis and suppression of anti-myeloma immunity through cytokine secretion, angiogenesis, and osteoclastogenesis ( 32 , 33 ). This is substantiated by the finding that inhibition of TGF-β signaling with the receptor I kinase inhibitor SD 208 reduces cytokine secretion and growth of MM cells. Consequently, TGF-β represents a promising target for treatment ( 34 ). Furthermore, post-translational modifications (PTMs) provide a critical impetus in MM pathogenesis and influence protein homeostasis and signaling pathways ( 35 ), where ubiquitination is central, rendering targeting the UPS an effective strategy for treatment ( 36 ). For instance, modifications of histones and the heat-shock response orchestrated by HSF1 contribute to the biology and treatment resistance observed in MM, emphasizing the utilization of histone modifier inhibitors and proteasome inhibitors in therapy ( 37 ). These pathways most probably form an integral part of the dysregulation of cellular processes in MM and give an insight into how tumor cells escape normal growth controls.

To illustrate the relationship between these differen-tially expressed genes (DEGs), a protein-protein interaction (PPI) network was constructed to visualize potential interactions among proteins encoded by those DEGs and, consequently, identify critical hub genes representing key elements of multiple myeloma (MM)-regulating mechanisms. Notably, among all the genes in the network, CCND1, ITGB1, and CREB1 emerged as the top three hub genes with high connectivity scores, suggesting their central position within the network. The results further indicated that the elevated expression of several hub genes, including CCND1, CDKN1A, DDX17, and ITGB1, was significantly associated with patient survival. This finding reiterates that one of the hallmarks of MM is cell proliferation, as evidenced by the prominence of CCND1, a well-established cell cycle regulator. CCND1 (Cyclin D1) translocation and overexpression are present in 19.6% of all cases, predominantly in IgD and non-secretory subtypes (38). The CCND1 870G>A polymorphism is associated with the risk of translocation; similarly, higher expression of cyclin D1 in bone marrow biopsies predicts shorter overall survival, indicating that CCND1 is relevant for the pathogenesis of MM and a potential target for therapies ( 39 , 40 ). In addition, enhanced expression of cyclin-dependent kinase inhibitor 1A (CDKN1A) related to sustained cell proliferation, the renewal of cancer stem cells (CSCs), epithelial–mesenchymal transition (EMT), cell migration, and resistance to chemotherapy in different cancers ( 41 ). Also,the DEAD-box (DDX) protein family, known as the largest group of RNA helicases, is composed of enzymes that unravel double-stranded RNA. Individuals exhibiting elevated levels of DDX5/DDX17 tend to have more aggressive cancer and reduced survival durations. Consequently, DDX5/DDX17 can serve as clinical indicators for diagnosing cancer and predicting patient outcomes ( 42 ). Also Our study identified CCND1, ITGB1, and CREB1 as hub genes with significant associations to survival outcomes in MM patients. These findings align with existing research highlighting the critical role of these genes in MM pathogenesis. For example, targeting similar genes to our hub genes can improve survival in newly diagnosed MM patients, especially considering that Bortezomib based regimens are still widely used ( 43 ). Cyclin D1 (CCND1), a key regulator of the cell cycle, is frequently overexpressed in MM, promoting uncontrolled proliferation of malignant plasma cells. Similarly, integrin subunit beta 1 (ITGB1) plays a crucial role in cell adhesion and migration, contributing to the dissemination of MM cells within the bone marrow microenvironment. CREB1, a transcription factor involved in various cellular processes, has also been implicated in MM progression through its regulation of cell survival and drug resistance.

Furthermore, our analysis revealed potential interactions between these hub genes and specific microRNAs (miRNAs), such as miR-34c-5p and miR-155-5p. Dysregulation of miRNAs has been increasingly recognized as a hallmark of MM, with several miRNAs shown to act as either tumor suppressors or oncogenes ( 44 ). For instance, Zhou et al. (2023) demonstrated that miR-26 inhibits proliferation and promotes apoptosis of multiple myeloma cells by targeting BNIP3. Similarly, miR-34c-5p and miR-155-5p have been implicated in regulating apoptosis and drug resistance in various cancers, suggesting their potential as therapeutic targets in MM. Further research is warranted to elucidate the precise mechanisms by which these miRNAs regulate the expression of hub genes and contribute to MM pathogenesis. Understanding these interactions could provide new avenues for therapeutic intervention, such as developing miRNA-based therapies to restore normal gene expression patterns and enhance drug sensitivity in MM cells.

Furthermore, elevated expression of ITGB1 (Integrin beta-1) in MM cells results in significant adhesion to the bone marrow stroma, facilitating cell survival and drug resistance, particularly in bortezomib-resistant cells where ITGB1 upregulation has been associated with poor outcomes ( 45 ). Recent studies have demonstrated that CREB1 (CAMP responsive element binding protein 1) plays a significant role in MM by promoting HLA-E expression, which interferes with natural killer (NK) cell function and promotes immune evasion. Conversely, inhibition of CREB1 decreases HLA-E levels and enhances NK cell cytotoxicity against the presenting cells ( 46 ). Additionally, it is a target of the tumor suppressor miRNA ,miR-203, in MM and is epigenetically silenced ( 47 ), suggesting that re-expression would improve immune responses and reduce bone complications. Targeting these hub genes could hold significant potential for treatment due to their role in various cancer hallmarks.

These regulatory relationships between hub genes and microRNAs (miRNAs) warrant further investigation to elucidate the post-transcriptional regulation of gene expression in multiple myeloma (MM). In this study, we identified several miRNAs with robust predictive scores associated with these hub genes, indicating the presence of intricate regulatory networks in MM. For example, DDX17 interacts with miR-34c-5p and miR-155-5p, while CREB1 interacts with miR-181-5p and miR-727-3p, suggesting the potential role of these miRNAs in regulating these critical regulatory genes in MM. Inhibition of miR-34c-5p restored sensitivity to bortezomib in resistant cells through modulation of Bax/Bcl-2 expression, demonstrating that this miRNA regulates apoptosis and drug resistance, thus representing a promising therapeutic target to overcome proteasome inhibitor resistance in MM ( 48 ). Moreover, the expression of miR-155-5p was significantly lower in CD138+ plasma cells of MM patients compared to patients with smoldering multiple myeloma (sMM) ( 49 ). Conversely, overexpression of miR-181a in CD138+ was strongly associated with poor prognosis and contributed to improving early prediction of disease progression in MM patients, facilitating personalized prognosis and treatment strategies ( 50 ). These findings underscore the necessity for further investigation of the functional roles of these miRNAs in MM to fully elucidate their potential as therapeutic targets or disease biomarkers. While clinical investigations of these genes have been conducted in the context of multiple myeloma and other cancers, they remain incomplete and necessitate further exploration. Consequently, our study aims to predict the pathways and microRNAs associated with these genes, thereby identifying promising candidates for more comprehensive research and potential therapeutic strategies.

6. Conclusion

This comprehensive bioinformatics analysis provides critical insights into the molecular landscape of MM, revealing a complex interplay between genetic alterations and the immune microenvironment that collectively drive disease progression and therapeutic resistance. Through the identification of DEGs and the construction of a protein–protein interaction (PPI) network, several key hub genes — including CCND1, ITGB1, and CREB1 — were identified as central regulators of MM pathogenesis. These genes are intimately involved in essential biological processes such as cell cycle regulation, cellular adhesion, migration, and transcriptional control, and thus represent promising targets for novel therapeutic interventions. In parallel, the characterization of associated microRNAs, particularly miR-34c-5p and miR-155-5p, highlighted their crucial roles in modulating apoptosis and influencing drug sensitivity. The dysregulation of these microRNAs suggests their potential utility as prognostic biomarkers and as modulators of therapeutic response, further emphasizing their relevance in the context of personalized medicine for MM. Despite these significant findings, the study is subject to certain limitations. Notably, the analyses were conducted exclusively using in silico methodologies, without experimental validation. Consequently, functional assays, in vitro and in vivo studies, and clinical evaluations are warranted to confirm the biological and clinical relevance of the identified molecular targets. Additionally, given the heterogeneity inherent to MM, validation across diverse patient populations is necessary to ensure broader applicability and clinical translation. Overall, the results of this study contribute meaningfully to the growing understanding of MM’s molecular underpinnings, offering potential new avenues for therapeutic targeting and biomarker development. These findings lay the groundwork for future investigations aimed at advancing precision oncology approaches, ultimately striving to improve treatment efficacy, overcome resistance mechanisms, and enhance outcomes for patients with multiple myeloma.