The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.

BACKGROUND: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. METHODS: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. RESULTS: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. CONCLUSIONS: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning.

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.

Host Factors Modulate Virus-Induced IFN Production via Pattern Recognition Receptors.

Innate immunity is the first line of defense in the human body, and it plays an important role in defending against viral infection. Viruses are identified by different pattern-recognition receptors (PRRs) that activate the mitochondrial antiviral signaling protein (MAVS) or transmembrane protein 173 (STING), which trigger multiple signaling cascades that cause nuclear factor-κB (NF-κB) and interferon regulatory factor 3 (IRF3) to produce inflammatory factors and interferons (IFNs). PRRs play a pivotal role as the first step in pathogen induction of interferon production. Interferon elicits antiviral activity by inducing the transcription of hundreds of IFN-stimulated genes (ISGs) via the janus kinase (JAK) - signal transducer and activator of transcription (STAT) pathway. An increasing number of studies have shown that environmental, pathogen and host factors regulate the IFN signaling pathway. Here, we summarize the mechanisms of host factor modulation in IFN production via pattern recognition receptors. These regulatory mechanisms maintain interferon levels in a normal state and clear viruses without inducing autoimmune disease.

High Glycolytic Activity Signature Reveals CCNB2 as a Key Therapeutic Target in Triple-Negative Breast Cancer.

BACKGROUND: Aerobic glycolysis and the cell cycle are well-established tumor hallmarks. Understanding their relationship could help to unravel the pathogenic mechanisms of breast cancer (BC) and suggest potential new strategies for treatment. METHODS: Glycolysis-related genes (GRGs) were downloaded from the Reactome database and screened using univariate Cox analysis. The consensus clustering method was employed to identify a glycolytic activity signature (GAS) using the Gene Expression Omnibus (GEO) dataset. A nomogram risk prediction model was constructed using coefficients from univariate Cox analysis. Immune cell infiltration was evaluated using single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE algorithm. Gene co-expression modules were created using weighted correlation network analysis (WGCNA) to identify hub genes. Gene expression in three BC cell lines was quantified using Quantitative Reverse Transcriptase Polymera (qRT-PCR). Single-cell RNA sequencing (scRNA-seq) data was used to examine the relationship between GAS and hub genes. The sensitivity of different groups to cell cycle-related clinical drugs was also examined. RESULTS: BC with high GAS (HGAS) showed high tumor grade and recurrence rate. HGAS was a prognostic indicator of worse overall survival (OS) in BC patients. HGAS BC showed more abundant immune cells and significantly higher expression of immunomodulators compared to BC with low GAS (LGAS). HGAS BC also showed enhanced cell cycle pathway, with high mRNA and protein expression levels of Cyclin B2 (CCNB2), a key component of the cell cycle pathway. Importantly, scRNA-seq analysis revealed that elevated CCNB2 expression was positively correlated with HGAS in triple-negative BC (TNBC). This was validated in clinical samples from TNBC patients. High expression of CCNB2 was found in three BC cell lines, and was also an indicator of poor prognosis. HGAS BC showed high sensitivity to several cell cycle-related clinical drugs, with 9 of these also showing activity in BC with high CCNB2 expression. CONCLUSIONS: HGAS was associated with enhanced cell cycle pathway and immune activity in BC. These results suggest that CCNB2 is a potential key therapeutic target in BC patients.

Replenishment of TCA cycle intermediates and long-noncoding RNAs regulation in breast cancer.

The tricarboxylic acid (TCA) cycle is an essential interface that coordinates cellular metabolism and is as a primary route determining the fate of a variety of fuel sources, including glucose, fatty acid and glutamate. The crosstalk of nutrients replenished TCA cycle regulates breast cancer (BC) progression by changing substrate levels-induced epigenetic alterations, especially the methylation, acetylation, succinylation and lactylation. Long non-coding RNAs (lncRNA) have dual roles in inhibiting or promoting energy reprogramming, and so altering the metabolic flux of fuel sources to the TCA cycle, which may regulate epigenetic modifications at the cellular level of BC. This narrative review discussed the central role of the TCA cycle in interconnecting numerous fuels and the induced epigenetic modifications, and the underlying regulatory mechanisms of lncRNAs in BC.