Investigating the potential molecular players and therapeutic drug molecules in carfilzomib resistant multiple myeloma by comprehensive bioinformatics analysis

Ghosal, Somnath; Banerjee, Subrata

doi:10.1080/10428194.2022.2087064

Cited by 3 publications

(4 citation statements)

References 46 publications

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“…Notably, several carfilzomib-resistant cell lines such as KMS-11 and KMS-34 can have distinct DEGs; this may be owing to the different protocols used to generate different resistant cell lines [ 36 ]. Therefore, future studies should use a standardized protocol to investigate the common determinants of carfilzomib resistance in different MM cell lines and profile primary patient tumor samples with carfilzomib resistance so as to provide more conclusive information to guide therapeutic strategies.…”

Section: Discussionmentioning

confidence: 99%

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

ZHAO,

YANG,

ZHANG

et al. 2024

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Multiple myeloma (MM) is a hematological tumor with high mortality and recurrence rate. Carfilzomib is a new-generation proteasome inhibitor that is used as the first-line therapy for MM. However, the development of drug resistance is a pervasive obstacle to treating MM. Therefore, elucidating the drug resistance mechanisms is conducive to the formulation of novel therapeutic therapies. To elucidate the mechanisms of carfilzomib resistance, we retrieved the GSE78069 microarray dataset containing carfilzomib-resistant LP-1 MM cells and parental MM cells. Differential gene expression analyses revealed major alterations in the major histocompatibility complex (MHC) and cell adhesion molecules. The upregulation of the tumor necrosis factor (TNF) receptor superfamily member 1A ( TNFRSF1A ) gene was accompanied by the downregulation of MHC genes and cell adhesion molecules. Furthermore, to investigate the roles of these genes, we established a carfilzomib-resistant cell model and observed that carfilzomib resistance induced TNFRSF1A overexpression and TNFRSF1A silencing reversed carfilzomib resistance and reactivated the expression of cell adhesion molecules. Furthermore, TNFRSF1A silencing suppressed the tumorigenesis of MM cells in immunocompetent mice, indicating that TNFRSF1A may lead to carfilzomib resistance by dampening antitumor immunity. Furthermore, our results indicated that TNFRSF1A overexpression conferred carfilzomib resistance in MM cells and suppressed the expression of MHC genes and cell adhesion molecules. The suppression of MHC genes and cell adhesion molecules may impair the interaction between immune cells and cancer cells to impair antitumor immunity. Future studies are warranted to further investigate the signaling pathway underlying the regulatory role of TNFRSF1A in MM cells.

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Section: Discussionmentioning

confidence: 99%

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

ZHAO,

YANG,

ZHANG

et al. 2024

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“…Li's research showed that miR-22-3p had the ability to raise p-EGFR levels and lengthen neurites through the STAT3/GAP43 pathway (52). Additionally, miR-548t-5p and other non-coding RNAs could regulate cell proliferation and metastasis in multiple human cancers (53)(54)(55). As shown in Table 2, we revealed that miRNAs involved in hsa-miR-22-3p, hsa-miR-548t-5p, and hsa-miR-6134 are related to ALL central nervous system metastasis and neuronal development.…”

Section: Genes Predicted Mirnas Category P-valuementioning

confidence: 99%

Simulating neuronal development: exploring potential mechanisms for central nervous system metastasis in acute lymphoblastic leukemia

Li,

Guo,

Xiao

et al. 2024

Front. Oncol.

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BackgroundAcute lymphoblastic leukemia (ALL) is prone to metastasize to the central nervous system (CNS), which is an important cause of poor treatment outcomes and unfavorable prognosis. However, the pathogenesis of CNS metastasis of ALL cells has not been fully illuminated. Recent reports have shed some light on the correlation between neural mechanisms and ALL CNS metastasis. These progressions prompt us to study the relationship between ALL central nervous system metastasis and neuronal development, exploring potential biomarkers and therapeutic targets of CNS metastasis.Materials and methodsALL central nervous system metastasis- and neuronal development-related differentially expressed genes (DEGs) were identified by analyzing gene expression datasets GSE60926 and GSE13715. Target prediction and network analysis methods were applied to assess protein–protein interaction networks. Gene Ontology (GO) terms and pathway enrichment for DEGs were assessed. Co-expressed differentially expressed genes (co-DEGs) coupled with corresponding predicted microRNAs (miRNAs) were studied as well. Reverse transcription–polymerase chain reaction (RT–PCR) and flow cytometry were employed for the validation of key co-DEGs in primary ALL cells. Furthermore, ALL cells were treated with a vascular endothelial growth factor (VEGF) inhibitor to block neuronal development and assess changes in the co-DEGs.ResultsWe identified 216, 208, and 204 DEGs in ALL CNS metastasis specimens and neuronal development samples (GSE60926 and GSE13715). CD2, CD3G, CD3D, and LCK may be implicated in ALL CNS metastasis. LAMB1, MATN3, IGFBP3, LGALS1, and NEUROD1 may be associated with neuronal development. Specifically, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) may link ALL central nervous system metastasis and neuronal development process. The miRNAs for each co-DEG could be potential biomarkers or therapeutic targets for ALL central nervous system metastasis, especially hsa-miR-22-3p, hsa-miR-548t-5p, and hsa-miR-6134. Additionally, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) were validated in CNS-infiltrated ALL cells. The VEGF inhibitor demonstrated a suppressive effect on mRNA and protein expression of key co-DEGs.ConclusionThe bioinformatic survey and key gene validation suggest a possible correlation between ALL CNS metastasis and the neuronal development process. Simulating the neuronal development process might be a possible strategy for CNS metastasis in ALL. LGALS1, TMEM71, SHISA2, and S100A11 genes are promising and novel biomarkers and targets in ALL CNS metastasis.

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“… 18 , 19 High‐throughput microarray technology is widely used for the molecular diagnosis, classification and prognosis of MM, providing a novel avenue for the identification of potential biomarkers and pathways. 20 , 21 , 22 , 23 , 24 , 25 Despite the significant roles of certain biomarkers in the onset and progression of the disease during subsequent validation, challenges persist in establishing causality owing to issues such as reverse causation and confounding factors. Mendelian randomization (MR), an analytical approach aligned with Mendel's law of inheritance, utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to infer causality between a modifiable exposure and a clinically relevant outcome, which could address these limitations.…”

Section: Introductionmentioning

confidence: 99%

“…In the era of big biological data, the analysis of vast datasets is feasible by integrating biology, computer science and information technology 18,19 . High‐throughput microarray technology is widely used for the molecular diagnosis, classification and prognosis of MM, providing a novel avenue for the identification of potential biomarkers and pathways 20‐25 . Despite the significant roles of certain biomarkers in the onset and progression of the disease during subsequent validation, challenges persist in establishing causality owing to issues such as reverse causation and confounding factors.…”

Section: Introductionmentioning

confidence: 99%

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization

Zhu,

Liu,

Wang

2024

J Cellular Molecular Medi

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Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein–protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune‐related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune‐related pathways.

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Investigating the potential molecular players and therapeutic drug molecules in carfilzomib resistant multiple myeloma by comprehensive bioinformatics analysis

Cited by 3 publications

References 46 publications

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

Simulating neuronal development: exploring potential mechanisms for central nervous system metastasis in acute lymphoblastic leukemia

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization

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