AT-13148

Zinc finger protein 471 (ZNF471) is a member of the Krüppel-related domain zinc finger protein family, and has recently attracted attention because of its anti-cancer effects. N-glycosylation regulates expression and functions of the protein. This study aimed to investigate the effects of ZNF471 N-glycosylation on the proliferation, invasion, and docetaxel sensitivity of tongue squamous cell carcinoma (TSCC). It analyzed the expression, function, and prognostic significance of ZNF471 in TSCC using bioinformatics techniques such as gene differential expression analysis, univariate Cox regression analysis, functional enrichment analysis, and gene set enrichment analysis. Using site-specific mutagenesis, this study generated three mutant sites for ZNF471 N-glycosylation to determine the effect of N-glycosylation on ZNF471 protein levels and function. Quantitative real-time PCR, Western blot analysis, and immunohistochemistry tests confirmed the down-regulation of ZNF471 expression in TSCC. Low expression of ZNF471 is associated with poor prognosis of patients with TSCC. Overexpression of ZNF471 in vitro retarded the proliferation of TSCC cells and suppressed cell invasion and migration ability. Asparagine 358 was identified as a N-glycosylation site of ZNF471. Suppressing N-glycosylation of ZNF471 enhanced the protein stability and promoted the translocation of protein to the cell nucleus. ZNF471 binding to c-Myc gene promoter suppressed oncogene c-Myc expression, thereby playing the anti-cancer effect and enhancing TSCC sensitivity to docetaxel. In all, N-glycosylation of ZNF471 affects the proliferation, invasion, and docetaxel sensitivity of TSCC via regulation of c-Myc.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood.

We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity.

A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8⁺ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient.

Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.