Background: Shenling Baizhu Powder (SBP), a famous Traditional Chinese Medicine (TCM) formulation, has been widely used in the adjuvant treatment of cancers, including breast cancer. This study aims to identify potential new targets for breast cancer treatment based on the network pharmacology of SBP. Methods: By analyzing the relationship between herbs and target proteins, potential targets of multiple herbs in SBP were identified by network pharmacology analysis. Besides, by comparing the data of breast cancer tissue with normal tissue, upregulated genes in two breast cancer expression profiles were found. Thereafter, the expression level and prognosis of activator of heat shock protein 90 (HSP90) ATPase activity 1 (AHSA1) were further analyzed in breast cancer by bioinformatics analysis, and the network module of AHSA1 binding protein was constructed. Furthermore, the effect of knocking down AHSA1 on the proliferation, migration, and invasion of breast cancer cells was verified by MTT, clone formation assay, and transwell assay. Results: Vascular endothelial growth factor A (VEGFA), intercellular adhesion molecule 1 (ICAM1), chemokine (C-X-C motif) ligand 8 (CXCL8), AHSA1, and serpin family E member 1 (SERPINE1) were associated with multiple herbs in SBP. AHSA1 was remarkably upregulated in breast cancer tissues and positively correlated with poor overall survival and disease metastasis-free survival. Furthermore, knockdown of AHSA1 significantly inhibited the migration and invasion in MCF-7 and MDA-MB-231 breast cancer cells but had no obvious effect on proliferation. In addition, among the proteins that bind to AHSAl, the network composed of proteasome, chaperonin, and heat shock proteins is closely connected, and these proteins are associated with poor prognosis in a variety of cancers. Conclusion: AHSA1 is positively correlated with breast cancer progression and might act as a novel therapeutic target for breast cancer.
Background Telomeres are strongly associated with cancer, as their shortening over time is associated with an increased risk of tumor growth and progression. However, the prognostic value of telomere-related genes (TRGs) in breast cancer has not been systematically elucidated. Material/Methods The transcriptome and clinical data of breast cancer were downloaded from TCGA and GEO databases, and prognostic TRGs were identified by differential expression analysis and univariate and multivariate Cox regression analyses. Gene set enrichment analysis (GSEA) of different risk groups was performed. Molecular subtypes of breast cancer were constructed by consensus clustering analysis, and the differences in immune infiltration and chemotherapy sensitivity between subtypes were analyzed. Results Differential expression analysis revealed 86 significantly differentially expressed TRGs in breast cancer, of which 43 were significantly associated with breast cancer prognosis. A predictive risk signature consisting of 6 tumor-related genes (TRGs) was developed, which can accurately stratify patients with breast cancer into 2 distinct groups with significantly different prognoses. Significantly different risk scores were found among different racial groups, treatment groups, and pathological features groups. GSEA results showed that patients in the low-risk group had activated immune responses and repressed cilium-related biological processes. Consistent clustering analysis based on these 6 TRGs obtained 2 molecular models with significant prognosis differences, which revealed distinct immune infiltration and chemotherapy sensitivity. Conclusions This study conducted a systematic investigation of the expression pattern of TRGs in breast cancer and its prognostic and clustering implications, thereby offering a reference for utilizing it to predict prognosis and evaluate treatment response.
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