RESUMEN
Background: Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication. Methods: In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens. Results: 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders. Conclusion: This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.
RESUMEN
PURPOSE: Multiple myeloma (MM) is an incurable hematological malignancy characterized by clonal proliferation of malignant plasma B cells in bone marrow, and its pathogenesis remains unknown. The aim of this study was to determine the role of kinesin family member 22 (KIF22) in MM and elucidate its molecular mechanism. METHODS: The expression of KIF22 was detected in MM patients based upon the public datasets and clinical samples. Then, in vitro assays were performed to investigate the biological function of KIF22 in MM cell lines, and subcutaneous xenograft models in nude mice were conducted in vivo. Chromatin immunoprecipitation (ChIP) and luciferase reporter assay were used to determine the mechanism of KIF22-mediated regulation. RESULTS: The results demonstrated that the expression of KIF22 in MM patients was associated with several clinical features, including gender (P = 0.016), LDH (P < 0.001), ß2-MG (P = 0.003), percentage of tumor cells (BM) (P = 0.002) and poor prognosis (P < 0.0001). Furthermore, changing the expression of KIF22 mainly influenced the cell proliferation in vitro and tumor growth in vivo, and caused G2/M phase cell cycle dysfunction. Mechanically, KIF22 directly transcriptionally regulated cell division cycle 25C (CDC25C) by binding its promoter and indirectly influenced CDC25C expression by regulating the ERK pathway. KIF22 also regulated CDC25C/CDK1/cyclinB1 pathway. CONCLUSION: KIF22 could promote cell proliferation and cell cycle progression by transcriptionally regulating CDC25C and its downstream CDC25C/CDK1/cyclinB1 pathway to facilitate MM progression, which might be a potential therapeutic target in MM.
