Baicalin Prevents Colon Cancer by Suppressing CDKN2A Protein Expression.

OBJECTIVE: To observe the therapeutic effects and underlying mechanism of baicalin against colon cancer. METHODS: The effects of baicalin on the proliferation and growth of colon cancer cells MC38 and CT26. WT were observed and predicted potential molecular targets of baicalin for colon cancer therapy were studied by network pharmacology. Furthermore, molecular docking and drug affinity responsive target stability (DARTS) analysis were performed to confirm the interaction between potential targets and baicalin. Finally, the mechanisms predicted by in silico analyses were experimentally verified in-vitro and in-vivo. RESULTS: Baicalin significantly inhibited proliferation, invasion, migration, and induced apoptosis in MC38 and CT26 cells (all P<0.01). Additionally, baicalin caused cell cycle arrest at the S phase, while the G0/G1 phase was detected in the tiny portion of the cells. Subsequent network pharmacology analysis identified 6 therapeutic targets associated with baicalin, which potentially affect various pathways including 39 biological processes and 99 signaling pathways. In addition, molecular docking and DARTS predicted the potential binding of baicalin with cyclin dependent kinase inhibitor 2A (CDKN2A), protein kinase B (AKT), caspase 3, and mitogen-activated protein kinase (MAPK). In vitro, the expressions of CDKN2A, MAPK, and p-AKT were suppressed by baicalin in MC38 and CT26 cells. In vivo, baicalin significantly reduced the tumor size and weight (all P<0.01) in the colon cancer mouse model via inactivating p-AKT, CDKN2A, cyclin dependent kinase 4, cyclin dependent kinase 2, interleukin-1, tumor necrosis factor α, and activating caspase 3 and mouse double minute 2 homolog signaling (all P<0.05). CONCLUSION: Baicalin suppressed the CDKN2A protein level to prevent colon cancer and could be used as a therapeutic target for colon cancer.

Predictive efficacy of the preoperative neutrophil-lymphocyte ratio in lymph node metastasis of cN0 hormone receptor-positive breast cancer.

Breast cancer, as the most common cancer, has surpassed lung cancer worldwide. The neutrophil-to-lymphocyte ratio (NLR) has been linked to the onset of cancer and its prognosis in recent studies. However, quite a few studies have shown that there is a link between NLR and lymph node metastases in cN0 hormone receptor-positive (HR(+)) breast cancer. The purpose of this study was to evaluate the correlation between NLR and lymph node metastases in cN0 HR(+) breast cancer patients. From January 2012 to January 2022, 220 patients with cN0 HR(+) invasive breast cancers were enrolled in this study. The relationship between NLR and pathological data was statistically examined. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff of NLR, a chi-squared test was used for the univariate analysis, and logistic analysis was used for the multivariate analysis. The NLR had an optimal cutoff of 2.4 when the Jorden index was at a maximum. Patients with axillary lymph node metastases had a higher NLR (P < 0.05). A Univariate analysis showed that there were significant differences in cN0 HR(+) breast cancer with axillary lymph node metastasis among different clinical stages, histological grades, Ki-67 levels, tumor sizes, and NLR levels (P < 0.05). Clinical stage, tumor size, and NLR were found to be independent risk factors for lymph node metastases in multifactorial analysis. In cN0 HR(+) breast cancer, NLR is an independent risk factor for lymph node metastases. An NLR ≥ 2.4 indicates an increased probability of lymph node metastases. An elevated preoperative NLR has a high predictive value for axillary lymph node metastases.

Identification and exploration of the pyroptosis-related molecular subtypes of breast cancer by bioinformatics and machine learning.

OBJECTIVES: To classify breast cancer (BRCA) according to the expression of pyroptosis-related genes and explore their molecular characteristics. METHODS: Nonnegative matrix factorization (NMF) was used for subtype classification based on 21 pyroptosis-related genes in the TCGA database. Survival analysis and t-distributed stochastic neighbor embedding (t-SNE) analysis were conducted to assess the NMF results' performance. XGBoost, CatBoost, logistic regression, neural network, random forest, and support vector machine were utilized to perform supervised machine learning and construct prediction models. Genetic mutations, tumor mutational burden, immune infiltration, methylation, and drug sensitivity were analyzed to explore the molecular signatures of different subtypes. Lasso, RF, and Cox regression were operated to construct a prognostic model based on differentially expressed genes. RESULTS: BRCA patients were divided into two subtypes (named Cluster1 and Cluster2). Survival analysis (P = 0.02) and t-SNE analysis demonstrated that Cluster1 and Cluster2 were well classified. The XGBoost model achieved reliable predictions on both training and validation sets. Regarding molecular characteristics, Cluster1 had higher TMB, immune cell infiltration, and m6A methylation-related gene expression than Cluster2. There was also a statistically significant difference between the two subtypes concerning drug susceptibility. Finally, a 5-gene prognostic model was constructed using Lasso, RF, and Cox regression and validated in the GEO database. CONCLUSION: Our study may provide new insights from bioinformatics and machine learning for exploring pyroptosis-related subtypes and their respective molecular signatures in BRCA. In addition, our models may be helpful for the treatment and prognosis of BRCA.