An integrated approach for the identification of USF1-centered transcriptional regulatory networks during liver regeneration.

Liver regeneration after partial hepatectomy (PH) is a synchronized process that is precisely controlled by system-wide transcriptional regulatory networks. To clarify the transcriptional changes and regulatory networks that involve transcription factors (TFs) and their target genes during the priming phase, an advanced mouse oligonucleotide array-based transcription factor assay (MOUSE OATFA), mRNA microarray analysis, bioinformatic analysis and ChIP-on-chip experiments were used. A total of 774 genes were upregulated or downregulated in PH liver samples compared with the sham operation (SH) group. Seventeen TFs showed significant changes in activity in the regenerating livers, some of which have not been extensively studied in previous reports, including upstream stimulatory transcription factor 1 (USF1). The TF signatures from MOUSE OATFA were combined with mRNA expression profiles and ChIP-on-chip analyses to construct experimental transcriptional regulatory networks in regenerating livers. USF1-centered regulatory networks were further confirmed by ChIP assays, revealing some of its target genes and novel coregulatory networks. The combination of MOUSE OATFA with transcriptome profiling and bioinformatic analysis represents a novel paradigm for the comprehensive prediction of transcriptional coregulatory networks during the early phase of liver regeneration.

Comprehensive evaluation of breast cancer immunotherapy and tumor microenvironment characterization based on interleukin genes-related risk model.

Breast cancer (BRCA) is the most prevalent malignancy and the leading cause of death in women. Interleukin (IL) genes are critical in tumor initiation and control. Nevertheless, the prognosis value of the IL in BRCA remains unclear. We collected data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and 94 IL genes were identified from GeneCard. Based on the random forest (RF), least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression analysis, we constructed an IL signature. GSE22219, GSE25065, and GSE21653 were derived as validation sets. The expression differences in the tumor microenvironment (TME), immunotherapy, and chemosensitivity of BRCA between the high- and low-risk groups were evaluated. Overall, 21 IL genes were selected to construct an IL risk model, of which IL18BP, IL17D, and IL23A were the first time identified as prognostic genes in BRCA. IL score could distinguish BRCA patients with inferior outcomes, and AUC of it was 0.70, 0.76, and 0.72 for 1-,3- and 5- years, respectively, which was also verified in GSE22219, GSE25065, and GSE21653 cohorts. Meanwhile, compared to luminal A and luminal B, HER2-positive and TNBC had significantly higher IL score. Besides, the high-risk group had a significantly higher prevalence of TP53 and TTN but a lower prevalence of PIK3CA, as well as higher tumor mutation burden (TMB) and neoantigen level. High- and low-risk groups exhibited notable differences in immunomodulators and tumor infiltrates immune cells (TIICs), and the high-risk group had significantly lower Tumor Immune Dysfunction and Exclusion (TIDE) score. Additionally, the high-risk group has more responders to immune or anti-HER2 combination therapy, whereas the low-risk group has higher sensitivity to docetaxel and paclitaxel. Consequently, we constructed a reliable risk model based on the IL genes, which can provide more information on both the risk stratification and personalizing management strategies for BRCA.