Excessive skin scars due to elective operations or trauma represent a challenging clinical problem. Pathophysiology of hypertrophic scars entails a prolonged inflammatory and proliferative phase of wound healing. Over expression of TGF-β1 and COX-2 play key regulatory roles of the aberrant fibrogenic responses and proinflammatory mediators. When we silenced TGF-β1 and COX-2 expression simultaneously in primary human fibroblasts, a marked increase in the apoptotic cell population occurred in contrast to those only treated with either TGF-β1 or COX-2 siRNA alone. Furthermore, using human hypertrophic scar and skin graft implant models in mice, we observed significant size reductions of the implanted tissues following intra-scar administration of TGF-β1/COX-2 specific siRNA combination packaged with Histidine Lysine Polymer (HKP). Gene expression analyses of those treated tissues revealed silencing of the target gene along with down regulations of pro-fibrotic factors such as α-SMA, hydroxyproline acid, Collagen 1 and Collagen 3. Using TUNEL assay detection, we found that the human fibroblasts in the implanted tissues treated with the TGF-β1/COX-2siRNAs combination exhibited significant apoptotic activity. Therefore we conclude that a synergistic effect of the TGF-β1/COX-2siRNAs combination contributed to the size reductions of the hypertrophic scar implants, through activation of fibroblast apoptosis and re-balancing between scar tissue deposition and degradation.

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.