Regulatory Crosstalk of Doxorubicin, Estradiol and TNFα Combined Treatment in Breast Cancer-derived Cell Lines

Elucidating the pharmacological mechanism by which Si-Wu-Tang induces cellular senescence in breast cancer via multilevel data integration

Traditional Chinese medicine (TCM) is a promising strategy for effectively treating cancer by inducing cellular senescence with minimal side effects. Si-Wu-Tang (SWT) is a TCM composed of four herbs that is commonly used in China for the treatment of gynecological diseases; SWT can prevent breast cancer (BC), but the molecular mechanism by which SWT induces cellular senescence and its clinical application value remain unknown. We identified 335 differentially expressed genes (DEGs) in SWT-treated MCF-7 cells through Gene Expression Omnibus (GEO) dataset analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the enrichment of biological processes and key signaling pathways including cellular senescence, the cell cycle, the MAPK signaling pathway, and the p53 signaling pathway. Additionally, SWT induced BC cell senescence by upregulating the expression of 33 aging/senescence-induced genes (ASIGs). According to LASSO regression analysis, NDRG1, ERRFI1, SOCS1, IRS2, IGFBP4, and BIRC3 levels were associated with BC prognosis and were used to develop risk scores. ERRFI1, SOCS1, IRS2, IGFBP4, and BIRC3 were identified as protective factors (P < 0.05, HR < 1), while NDRG1 was identified as a risk factor (P < 0.05, HR > 1). Notably, patients with low risk scores had increased senescence-associated secretory phenotypes (SASPs) and immune cell infiltration. Overall, we systematically integrated biological databases and biocomputational methods to reveal the mechanisms by which SWT induces senescence in breast cancer and its clinical value.

TranSNPs: A class of functional SNPs affecting mRNA translation potential revealed by fraction-based allelic imbalance

Few studies have explored the association between SNPs and alterations in mRNA translation potential. We developed an approach to identify SNPs that can mark allele-specific protein expression levels and could represent sources of inter-individual variation in disease risk. Using MCF7 cells under different treatments, we performed polysomal profiling followed by RNA sequencing of total or polysome-associated mRNA fractions and designed a computational approach to identify SNPs showing a significant change in the allelic balance between total and polysomal mRNA fractions. We identified 147 SNPs, 39 of which located in UTRs. Allele-specific differences at the translation level were confirmed in transfected MCF7 cells by reporter assays. Exploiting breast cancer data from TCGA we identified UTR SNPs demonstrating distinct prognosis features and altering binding sites of RNA-binding proteins. Our approach produced a catalog of tranSNPs, a class of functional SNPs associated with allele-specific translation and potentially endowed with prognostic value for disease risk.