Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment.

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

Multidrug resistance (MDR) is a complicated ever-changing problem in cancer treatment, and P-glycoprotein (P-gp), a drug efflux pump, is regarded as the major cause. In the way of developing P-gp inhibitors, natural products such as phenolic acids have gotten a lot of attention recently. The aim of the present study was to investigate the modulating effects and mechanisms of caffeic acid on human P-gp, as well as the attenuating ability on cancer MDR. Calcein-AM, rhodamine123, and doxorubicin were used to analyze the interaction between caffeic acid and P-gp, and the ATPase activity of P-gp was evaluated as well. Resistance reversing effects were revealed by SRB and cell cycle assay. The results indicated that caffeic acid uncompetitively inhibited rhodamine123 efflux and competitively inhibited doxorubicin efflux. In terms of P-gp ATPase activity, caffeic acid exhibited stimulation in both basal and verapamil-stimulated activity. The combination of chemo drugs and caffeic acid resulted in decreased IC₅₀ in ABCB1/Flp-In^TM-293 and KB/VIN, indicating that the resistance was reversed. Results of molecular docking suggested that caffeic acid bound to P-gp through GLU74 and TRY117 residues. The present study demonstrated that caffeic acid is a promising candidate for P-gp inhibition and cancer MDR attenuation.

P-glycoprotein (P-gp) effluxes lots of chemotherapeutic agents and leads to multidrug resistance (MDR) in cancer treatments. The development of P-gp inhibitors from natural products provide a potential strategy for the beneficial clinical outcomes. This study aimed to evaluate the effects of the natural flavonoid taxifolin, luteolin, (−)-gallocatechin, and (−)-catechin on human P-gp activity. The kinetic interactions and underlying mechanisms of taxifolin-mediated transporter inhibition were further investigated. The transporter inhibition ability was evaluated in human P-gp stable expression cells (ABCB1/Flp-In^TM-293) by calcein-AM uptake assays. The kinetics study for P-gp inhibition was evaluated by doxorubicin and rhodamine123 efflux assays. The MDR reversal ability of taxifolin were performed by SRB assays to detect the cell viability in sensitive cancer cell line (HeLaS3), and resistant cancer cell line (KB-vin). Cell cycle analysis and ABCB1 real-time RT-PCR were used for mechanical exploration. The results demonstrated that taxifolin decreased ABCB1 expression in a concentration-dependent manner. The function of P-gp was inhibited by taxifolin through uncompetitive inhibition of rhodamine 123 and doxorubicin efflux. The combination of taxifolin significantly resensitized MDR cancer cells to chemotherapeutic agents. These results suggested that taxifolin may be considered as a potential P-gp modulator for synergistic treatment of MDR cancers.

Accurate triage is required for efficient allocation of resources and to decrease patients’ length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study developed machine learning models that incorporated natural language processing (NLP) to predict patient disposition. The models were assessed by comparing their performance with the judgements of emergency physicians (EPs). This retrospective study obtained data from patients visiting EDs between January 2018 and December 2019. Internal validation data came from China Medical University Hospital (CMUH), while external validation data were obtained from Asia University Hospital (AUH). Nontrauma patients aged ≥ 20 years were included. The models were trained using structured data and unstructured data (free-text notes) processed by NLP. The primary outcome was death in the ED or admission to the intensive care unit, and the secondary outcome was either admission to a general ward or transferal to another hospital. Six machine learning models (CatBoost, Light Gradient Boosting Machine, Logistic Regression, Random Forest, Extremely Randomized Trees, and Gradient Boosting) and one Logistic Regression derived from triage level were developed and evaluated using EPs’ predictions as reference. A total of 17,2101 and 41,883 patients were enrolled from CMUH and AUH, respectively. EPs achieved F1 core of 0.361 and 0.498 for the primary and secondary outcomes, respectively. All machine learning models achieved higher F1 scores compared to EPs and Logistic Regression derived from triage level. Random Forest was selected for further evaluation and fine-tuning, because of its robust calibration and predictive performance. In internal validation, it achieved Brier scores of 0.072 and 0.089 for the primary and secondary outcomes, respectively, and 0.076 and 0.095 in external validation. Further analysis revealed that incorporating unstructured data significantly enhanced the model’s performance. Threshold adjustments were applied to improve clinical applicability, aiming to balance the trade-off between sensitivity and positive predictive value. This study developed and validated machine learning models that integrate structured and unstructured triage data to predict patient dispositions, distinguishing between general ward and critical conditions like ICU admissions and ED deaths. Integrating both structured and unstructured data significantly improved model performance.