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Mammary epithelial cells are the only cells of mammary glands with lactation capacity. They are closely related to mammary development and milk yield. Our earlier studies showed that the transformation of goat fibroblasts into induced mammary epithelial cells (iMECs) was closely correlated with SMAD3 overexpression. Therefore, we further explored the role of SMAD3 on iMECs reprogramming in this study. The SMAD3 gene was overexpressed in goat ear fibroblasts using the tetracycline-induced expression method. The outcomes demonstrated that goat ear fibroblasts can be converted into iMECs by overexpressing the SMAD3 gene. In contrast, it was discovered that SMAD3 downregulation by RNA interference significantly decrease the reprogramming efficiency of iMECs. These results show that SMAD3 plays a key regulatory role in the reprogramming of iMECs. Surprisingly, we also found a parabolic relationship between SMAD3 expression level and iMECs reprogramming efficiency, and that the reprogramming efficiency was maximum when the addition of doxycycline concentration was 5 μg/ml. In light of this, our findings may offer new perspectives on the regulatory mechanism governing mammary epithelial cell fate in goats as well as a fresh approach to studying mammary development and differentiation in vitro.

BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.