{'fraction_non_alphanumeric': 0.040022870211549454, 'fraction_numerical': 0.002858776443682104, 'mean_word_length': 5.25, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A centromere-associated gene score for rapid determination of risk in multiple myeloma.

Risk stratification in patients with multiple myeloma (MM) remains a challenge. As clinicopathological characteristics have been proven deficient for accurately defining risk stratification, molecular markers have gradually become the focus of interests. This study investigated the expressions of centromere-associated genes in MM patients, their potential as prognostic markers, and their roles in disease progression. Several cohorts of 2301 MM patients were enrolled and gene expression profiling (GEP) was used to screen for CENP-A through CENP-W. Correlations between centromere-associated genes and clinicopathological characteristics, proliferative activity and recurrence of MM patients were analyzed. Clinically, CENP-E/H/K/L/N/U/W expressions were present at high-risk MM, which were even stronger elevated in patients with high tumor burden and recurrence. Mechanistically, CENP-E/H/K/L/N/U/W and FOXM1 were positively expressed in MM patients, which play synergistic or additive effects in clinical outcome. Furthermore, CENP-E/H/K/L/N/U/W were used to construct a centromere-associated gene score (CGS) model, which proved to be strongly prognostic values in several independent cohorts compared to usual clinical prognostic parameters using multivariate Cox analysis. Patients in the CGS low-risk group were significantly related to better clinical outcome than those in high-risk group. In this study, we provided proof-of-concept that CENP-E/H/K/L/N/U/W have critical roles in MM patients' progression and prognosis. The CGS model validated in different datasets clearly indicated novel risk stratification for personalized anti-MM treatments.