Victims or deceased names from the Patient cohort: – No victims or deceased names were mentioned in the given information. : Hierarchical clustering using ORC reveals subtypes with low PFS rates in MM

Death – Obituary – Accident and Crime News : A new study analyzing Patient cohortCoMMpass IA19 RNA-Seq and CNA data has revealed important insights into multiple myeloma. The study, which included data from 659 patients, found that hierarchical clustering using Ollivier-Ricci curvature (ORC) differentiated subtypes with low progression-free survival (PFS) rates. The clustering was able to identify biological differences not captured by the ISS prognostic score.

The analysis produced 8 clusters based on CNA data and 6 clusters based on RNA-Seq data, both of which were significant for PFS. Interestingly, the clustering showed a relatively even distribution of ISS stages in each cluster, indicating that the clustering method was able to identify distinct subtypes within each stage.

Further analysis of the ORC-based risk groups revealed differential gene expression related to DNA damage and immune system signaling. The high-risk group showed enrichment in inflammatory response, IL-6/JAK/STAT3 signaling, and DNA damage response pathways. Importantly, there was no significant difference in p53 function by traditional methods, suggesting that the ORC analysis captured more global dysregulation in DNA damage signaling.

Pathway analysis of the differentially expressed genes identified 118 genes for further analysis. Of these genes, 8 were found to be predictors of PFS, including BUB1, MCM1, NOSTRIN, PAM, RNF115, SNCAIP, SPRR2A, and WEE1. These genes were also significant when analyzing based on CNA, indicating their potential importance in multiple myeloma.

In addition, network analysis of the gene interactions revealed interesting patterns in DNA damage and immune system signaling. The TP53 and ATM signaling pathways were found to become more robust in the high-risk group, suggesting increased effects. This finding challenges the traditional association of loss of p53 function with poor prognosis in cancer.

Overall, this study highlights the power of ORC-based clustering and gene network analysis in identifying distinct subtypes and uncovering novel biological features in multiple myeloma. The findings provide valuable insights into the disease and may have implications for prognosis and treatment strategies. Further research is needed to validate these findings and explore their clinical implications.

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