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Updated: Apr 30, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Integrative analysis of a novel snoRNA-based prognostic signature in patients with breast cancer
Yi Xun1, Xia-Hui Li2, Wen-Tao Xiao3
1Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Background:
Breast cancer (BC) is the most commonly diagnosed malignancy in women worldwide. Prognostic heterogeneity driven by molecular subtypes and tumor microenvironment underscores the need for novel biomarkers. Small nucleolar RNAs (snoRNAs) have emerged as potential regulators in cancer biology, but their prognostic value in BC remains unclear.
Methods:
We analyzed snoRNA expression profiles of 1, 025 BC patients from TCGA database. Univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to construct an 8-snoRNA prognostic signature. The model was validated in testing and entire cohorts. Gene set enrichment analysis (GSEA) and immune infiltration analyses were conducted to explore molecular mechanisms. SNORA11 was functionally validated through in vitro assays and transcriptome sequencing.
Results:
We identified an 8-snoRNA signature (SNORD114-29, SNORA5A, SNORA54, SNORA7B, SNORA9, SNORA11, SCARNA3, and SNORD64) that effectively stratified patients into high- and low-risk groups, with the high-risk group exhibiting significantly poorer survival outcomes. The prognostic model demonstrated good predictive performance, with AUC values of 0.775, 0.679, and 0.718 for 1-, 3-, and 5-year overall survival (OS) in the testing cohort, respectively, and comparable performance in the entire cohort (0.758, 0.708, and 0.715). The model correlated with aggressive clinical features such as tumor stage, subtype, and tumor mutation burden (TMB). GSEA analysis indicated that high-risk patients showed enrichment of proliferative pathways and suppression of immune signaling. Immune infiltration analysis revealed reduced anti-tumor immune cell infiltration in the high-risk group. Overexpression of SNORA11 enhanced BC cell proliferation, migration, and invasion, and transcriptomic analysis further revealed that SNORA11 overexpression is associated with enhanced proliferative signaling and suppressed immune-related pathways.
Conclusions:
We established a novel snoRNA-based prognostic model with strong predictive power and biological relevance in BC. SNORA11 was identified as a potential oncogenic snoRNA, offering new insights into BC progression and potential therapeutic targets.
