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Simple Summary: Multiple myeloma is a blood cancer that progresses through distinct stages, and identifying these stages accurately is crucial for selecting effective treatments. Additionally, understanding which individuals with an asymptomatic precursor condition, known as monoclonal gammopathy of undetermined significance, are at risk of developing full-blown multiple myeloma remains a significant challenge. This study used machine learning methods to analyze gene expression data from multiple datasets, aiming to improve the accuracy of disease staging and identify individuals at higher risk of progression. By finding key patterns and pathways involved in the disease, this research offers new tools for earlier intervention and personalized care. These findings could significantly benefit the research and medical communities by improving diagnosis, enhancing patient monitoring, and opening avenues for targeted therapies. Background: The accurate staging of multiple myeloma (MM) is essential for optimizing treatment strategies, while predicting the progression of asymptomatic patients, also referred to as monoclonal gammopathy of undetermined significance (MGUS), to symptomatic MM remains a significant challenge due to limited data. This study aimed to develop machine learning models to enhance MM staging accuracy and stratify asymptomatic patients by their risk of progression. Methods: We utilized gene expression microarray datasets to develop machine learning models, combined with various data transformations. For multiple myeloma staging, models were trained on a single dataset and validated across five independent datasets, with performance evaluated using multiclass area under the curve (AUC) metrics. To predict progression in asymptomatic patients, we employed two approaches: (1) training models on a dataset comprising asymptomatic patients who either progressed or remained stable without progressing to multiple myeloma, and (2) training models on multiple datasets combining asymptomatic and multiple myeloma samples and then testing their ability to distinguish between asymptomatic and asymptomatic that progressed. We performed feature selection and enrichment analyses to identify key signaling pathways underlying disease stages and progression. Results: Multiple myeloma staging models demonstrated high efficacy, with ElasticNet achieving consistent multiclass AUC values of 0.9 across datasets and transformations, demonstrating robust generalizability. For asymptomatic progression, both modeling approaches yielded similar results, with AUC values exceeding 0.8 across datasets and algorithms (ElasticNet, Boosting, and Support Vector Machines), underscoring their potential in identifying progression risk. Enrichment analyses revealed key pathways, including PI3K-Akt, MAPK, Wnt, and mTOR, as central to MM pathogenesis. Conclusions: To the best of our knowledge, this is the first study to utilize gene expression datasets for classifying patients across different stages of multiple myeloma and to integrate multiple myeloma with asymptomatic cases to predict disease progression, offering a novel methodology with potential clinical applications in patient monitoring and early intervention. [ABSTRACT FROM AUTHOR]

Background and Aims: Puberty is a crucial developmental stage marked by the transition from childhood to adulthood, organized by complex hormonal signaling within the neuroendocrine system. The hypothalamus, a central region in this system, regulates pubertal functions through the hypothalamic-pituitary-gonadal (HPG) axis. Gonadotropin-releasing hormone (GnRH) neurons, essential in puberty control, release GnRH in a pulsatile manner, initiating the production of sex hormones. Major influence in pubertal timing has been attributed to genetic predisposition, environmental factors, and nutritional status. MicroRNAs (miRNAs), small non-coding RNA molecules, have emerged as key regulators in various cellular processes by either repressing genes or activating them by inhibiting their repressors. The present study aims to investigate the involvement of miRNAs in the control of puberty., Methods: Small RNA sequencing was used to identify and compare the total population of miRNAs in the hypothalamus of female mice before, during and after puberty. Bioinformatic analysis was applied to analyse the expression profile of miRNAs with altered levels followed by pathway enrichment analysis., Results: Expression levels of several miRNAs were found up- or down-regulated from pre-pubertal to pubertal stage. Furthermore, monitoring the levels of these miRNAs at the post-pubertal stage revealed four expression patterns, in which pathway analysis displayed the associations of these miRNAs with developmental processes, cell cycle regulation, metabolic biosynthesis and epigenetic regulation., Conclusion: The findings of the present study improve our understanding of the molecular pathways underlying puberty and stress the significance of miRNAs in fine-tuning gene expression within the hypothalamus during this critical developmental stage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.)