Characterization the prognosis role and effects of snoRNAs in melanoma patients

Abstract

Melanoma is a melanocyte‐derived malignant cancer and is known for its early metastasis and high mortality rates. It is a highly cutaneous tumour disease that could be related to the abnormal immune microenvironment, and the identification of reliable diagnostic and prognostic markers is crucial for improving patient outcomes. In the search for biomarkers, various types of RNAs have been discovered and recognized as reliable prognostic markers. Among these, small nucleolar RNAs (snoRNAs) have emerged as a promising avenue for studying early diagnosis and prognostic markers in tumours due to their widespread presence in tissues, tumour specificity and stability. In our study, we analysed snoRNAs data from melanoma samples in the TCGA‐SKCM cohort and developed a prognostic model comprising 12 snoRNAs (SNORD9, SNORA31, SNORD14E, SNORA14A, SNORA5A, SNORD83A, SNORA75, AL096855, AC007684, SNORD14A, SNORA65 and AC004839). This model exhibited unique prognostic accuracy and demonstrated a significant correlation with the immune infiltration tumour microenvironment. Additionally, analysis of the GSE213145 dataset, which explored the sensitivity and resistance of immune checkpoint inhibitors, further supported the potential of snoRNAs as prognostic markers for immunotherapy. Overall, our study contributes reliable prognostic and immune‐related biomarkers for melanoma patients. These findings can offer valuable insights for the future discovery of novel melanoma treatment strategies and hold promise for improving clinical outcomes in melanoma patients.

Subverting the Canon: Novel Cancer-Promoting Functions and Mechanisms for snoRNAs

“…Consequently, literature reports of the underlying molecular mechanisms are highly limited. Wang et al constructed a prognostic model based on the expressional signature of 12 snoRNAs whose risk score was positively correlated with the presence of naive B-cells and CD4 + T-cells in the tumor microenvironment and had potential for predicting patient sensitivity to immune checkpoint inhibition therapies ( Table 1 ) [ 128 ]. Yi et al created a prognostic model using four other snoRNAs, which may be associated with CNS activity [ 204 ].…”