Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer

Plac8-ERK pathway modulation of monocyte function in sepsis

Sepsis, a life-threatening condition caused by infection, is characterized by the dysregulation of immune responses and activation of monocytes. Plac8, a protein, has been implicated in various inflammatory conditions. This study aimed to investigate the effect of Plac8 upregulation on monocyte proliferation and activation in sepsis patients. Peripheral blood samples were collected from healthy individuals and sepsis patients. Monocytes were stimulated with lipopolysaccharide (LPS) to create an in vitro sepsis model, while a murine sepsis model was established using cecal ligation and puncture (CLP). The levels of monocyte markers, proliferation index (PI), and pro-inflammatory cytokines were assessed using flow cytometry and qPCR, respectively. Plac8 and phosphorylated ERK protein levels were determined by western blot, and TNF-α, IL-6, and IL-10 levels were quantified using ELISA. The CCK-8 assay was used to evaluate PBMC proliferation and activation. The results showed that Plac8 was highly expressed in sepsis models, promoting the survival, proliferation, and activation of monocytes. Plac8 upregulation activated the ERK pathway, leading to increased phosphorylation of ERK protein and elevated levels of CD14, CD16, TNF-α, IL-6, Plac8, and IL-10. In sepsis mice, Plac8 overexpression similarly activated the ERK pathway and promoted the survival, proliferation, and activation of monocytes. In conclusion, the upregulation of Plac8 enhances the activation of the ERK pathway and promotes monocyte proliferation and activation in sepsis patients.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.