ArticleOncology letters2025
Profile and clinical significance of SPARCL1 and its prognostic significance in breast cancer.
Article in Oncology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
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8 authors.
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Abstract
Secreted protein acidic and cysteine-rich like 1 (SPARCL1; also known as MAST9) exhibits low expression levels in several malignant tumors and is positively associated with tumor growth and poor prognosis. Furthermore, there may be an association between SPARCL1 and breast cancer; however, further comprehensive research is necessary. The present study assessed the relationship between SPARCL1 expression and breast cancer using RNA sequencing and clinical data from The Cancer Genome Atlas database (including tumor mutations, immune infiltration and prognosis data), cell experiments and clinical samples. The findings indicated that SPARCL1 expression was significantly lower in breast cancer tissues compared with other malignant tumors, with its downregulation negatively associated with the overall survival rate of patients. Moreover, analysis of receiver operator characteristic curve analysis, and univariate and multivariate Cox regression models suggested that SPARCL1 has potential as a diagnostic biomarker for breast cancer detection. Additionally, low expression of SPARCL1 was demonstrated to be associated with the degree of immune infiltration, whilst functional enrichment analysis revealed its involvement in key areas such as cell cycle regulation, protein/ATP binding processes, cellular aging mechanisms, oocyte meiosis pathways and DNA replication processes. Overall, the results of the present study highlight how big data mining combined with experimental verification can provide insights into the role of SPARCL1 in breast cancer pathogenesis and its potential as a biomarker for the disease. However, further investigations are warranted to validate these findings and provide a more comprehensive understanding of the implications of SPARCL1 therapy in patients with breast cancer.
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