Comprehensive Analysis of m6A RNA Methylation Regulators in the Prognosis and Immune Microenvironment of Multiple Myeloma

Regulatory roles of N6-methyladenosine (m6A) methylation in RNA processing and non-communicable diseases

Post-transcriptional RNA modification is an emerging epigenetic control mechanism in cells that is important in many different cellular and organismal processes. N6-methyladenosine (m6A) is one of the most prevalent, prolific, and ubiquitous internal transcriptional alterations in eukaryotic mRNAs, making it an important topic in the field of Epigenetics. m6A methylation acts as a dynamical regulatory process that regulates the activity of genes and participates in multiple physiological processes, by supporting multiple aspects of essential mRNA metabolic processes, including pre-mRNA splicing, nuclear export, translation, miRNA synthesis, and stability. Extensive research has linked aberrations in m6A modification and m6A-associated proteins to a wide range of human diseases. However, the impact of m6A on mRNA metabolism and its pathological connection between m6A and other non-communicable diseases, including cardiovascular disease, neurodegenerative disorders, liver diseases, and cancer remains in fragmentation. Here, we review the existing understanding of the overall role of mechanisms by which m6A exerts its activities and address new discoveries that highlight m6A's diverse involvement in gene expression regulation. We discuss m6A deposition on mRNA and its consequences on degradation, translation, and transcription, as well as m6A methylation of non-coding chromosomal-associated RNA species. This study could give new information about the molecular process, early detection, tailored treatment, and predictive evaluation of human non-communicable diseases like cancer. We also explore more about new data that suggests targeting m6A regulators in diseases may have therapeutic advantages.

Enhancing multiple myeloma staging: a novel cell death risk model approach

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Cuproptosis-Related Gene Signature Contributes to Prognostic Prediction and Immunosuppression in Multiple Myeloma

Cuproptosis is a type of programmed cell death triggered by accumulation of intracellular copper which was considered closely related to tumor progression. The study of cuproptosis in multiple myeloma (MM) is however limited. To determine the prognostic significance of cuproptosis-related gene signature in MM, we interrogated gene expression and overall survival with other available clinical variables from public datasets. Four cuproptosis-related genes were included to establish a prognostic survival model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which showed a good performance on prognosis prediction in both training and validation cohorts. Patients with higher cuproptosis-related risk score (CRRS) exhibited worse prognosis compared with lower risk score. Survival prediction capacity and clinical benefit were elevated after integrating CRRS to existing prognostic stratification system (International Staging System, ISS or Revised International Staging System, RISS) both on 3-year and 5-year survival. Based on CRRS groups, functional enrichment analysis and immune infiltration in bone marrow microenvironment revealed correlation between CRRS and immunosuppression. In conclusion, our study found that cuproptosis-related gene signature is an independent poor prognostic factor and functions negatively on immune microenvironment, which provides another perspective on prognosis assessment and immunotherapy strategy in MM.

Exosome miRNAs profiling in serum and prognostic evaluation in patients with multiple myeloma

MicroRNAs (MiRNAs) carried by exosomes play pivotal roles in the crosstalk between cell components in the tumor microenvironment. Our study aimed at identifying the expression profile of exosomal miRNAs (exo-miRNAs) in the serum of multiple myeloma (MM) patients and investigating the regulation networks and their potential functions by integrated bioinformatics analysis. Exosomes in serum from 19 newly diagnosed MM patients and 9 healthy donors were isolated and the miRNA profile was investigated by small RNA sequencing. Differential expression of exo-miRNAs was calculated and target genes of miRNAs were predicted. CytoHubba was applied to identify the hub miRNAs and core target genes. The LASSO Cox regression model was used to develop the prognostic model, and the ESTIMATE immune score was calculated to investigate the correlation between the model and immune status in MM patients. The top six hub differentially expressed serum exo-miRNAs were identified. 513 target genes of the six hub exo-miRNAs were confirmed to be differentially expressed in MM cells in the Zhan Myeloma microarray dataset. Functional enrichment analysis indicated that these target genes were mainly involved in mRNA splicing, cellular response to stress, and deubiquitination. 13 core exo-miRNA target genes were applied to create a novel prognostic signature to provide risk stratification for MM patients, which is associated with the immune microenvironment of MM patients. Our study comprehensively investigated the exo-miRNA profiles in MM patients. A novel prognostic signature was constructed to facilitate the risk stratification of MM patients with distinct outcomes.

N6-methyladenosine regulators are potential prognostic biomarkers for multiple myeloma

N6-methyladenosine (m6A) regulators play an important role in tumorigenesis; however, their role in multiple myeloma (MM) remains unknown. This study aimed to create an m6A RNA regulators prognostic signature for MM patients. We integrated data from the Multiple Myeloma Research Foundation CoMMpass Study and the Genotype-Tissue Expression database to analyze gene expression profiles of 21 m6A regulators. Consistent clustering analysis was used to identify the clusters of patients with MM having different clinical outcomes. Gene distribution was analyzed using principal component analysis. Next, we generated an mRNA gene signature of m6A regulators using a multivariate logistic regression model with least absolute shrinkage and selection operator. The expressions of m6A regulators, except FMR1, were significantly different in MM samples compared with those in normal samples. The KIAA1429, HNRNPC, FTO, and WTAP expression levels were dramatically downregulated in tumor samples, whereas those of other signatures were remarkably upregulated. Three clusters of patients with MM were identified, and significant differences were found in terms of overall survival (p = .024). A prognostic two-gene signature (KIAA1429 and HNRNPA2B1) was constructed, which had a good prognostic significance using the ROC method (AUC = 0.792). Moreover, the risk score correlated with the infiltration immune cells. In addition, KEGG pathway analysis showed that 16 pathways were dramatically enriched. The m6A signature might be a novel biomarker for predicting the prognosis of patients with MM (p = .002). Our study is the first to explore the potential application value of m6A in MM. These findings may enhance the understanding of the functional organization of m6A in MM and provide new insights into the treatment of MM patients.

Essential m6A Methylation Regulator HNRNPC Serves as a Targetable Biomarker for Papillary Renal Cell Carcinoma

m6A RNA modification is a common abundant posttranscriptional modification of mRNAs occurring in cancer growth and progression. Accumulated evidence has proved that HNRNPC, which acts as a m6A reader, plays an essential role in the promotion of cancer occurrence and development; nevertheless, the role of HNRNPC in papillary renal cell carcinoma remained to be discovered. In this study, we comprehensively identified HNRNPC as a hub gene involved in m6A modification in pRCC. Then, the expression level, survival outcomes, PPI network, function enrichment, immune cell infiltration, and single-cell analysis were performed. Finally, we found that HNRNPC significantly promoted renal cell carcinoma proliferation and migration in vitro. In conclusion, our work proved that HNRNPC may act as a momentous m6A regulator, as well as a potential targetable biomarker for pRCC.