Role and mechanism of actin-related protein 2/3 complex signaling in cancer invasion and metastasis: A review.

The actin 2/3 complex (Arp2/3) regulates actin polymerization and nucleation of actin filaments, is associated with cell motility, and has been shown to play a key role in the invasion and migration of cancer cells. nucleation-promoting factor (NPF) such as N-WASP (neural-WASP famly verprolin-homologous protein family), WAVE (WASP famly verprolin-homologous protein family), and WASH (WASP and Scar homologue) undergo conformational changes upon receipt of multiple upstream signals including Rho family GTPases, cdc42 (Cell division control protein 42 homolog), and phosphatidylinositol 4,5-bisphosphate (PtdIns 4,5 P2) to bind and activate the Arp2/3 complex. Once activated, the Arp2/3 complex forms actin-based membrane protrusions necessary for cancer cells to acquire an invasive phenotype. Therefore, how to influence the invasion and migration of cancer cells by regulating the activity of the Arp2/3 complex has attracted great research interest in recent years. Several studies have explored the effects of phosphorylation modifications of cortactin and several NPFs (Nucleation Promoting Factor) including N-WASP and WAVE on the activity of the Arp2/3 complex and ultimately on cancer cell invasiveness, and have attempted to suggest new strategies for antiinvasive therapy as a result. Other studies have highlighted the potential of targeting genes encoding partial or complete proteins of the Arp2/3 complex as a therapeutic strategy to prevent cancer cell invasion and metastasis. This article reviews the role of the Arp2/3 complex in the development, invasion, and metastasis of different types of cancer and the mechanisms regulating the activity of the Arp2/3 complex.

m6A-related lncRNA signature for predicting prognosis and immune response in head and neck squamous cell carcinoma.

OBJECTIVES: N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) significantly impact the prognosis and the response to immunotherapy in head and neck squamous cell carcinoma (HNSCC). Therefore, this study aimed to develop an m6A-related lncRNA (m6AlncRNA) model for predicting the prognosis and the immunotherapeutic response in HNSCC. METHODS: We identified the m6AlncRNAs and constructed a risk assessment signature by using univariable Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The Kaplan-Meier analysis, receiver-operating characteristic (ROC) curves, principal component analysis (PCA), decision curve analysis (DCA), consistency index (C-index), and nomogram were applied to assess the risk model. Finally, we investigated the predictability of this model in prognosis and response to immunotherapy and evaluated various novel compounds for the clinical treatment of HNSCC. RESULTS: HNSCC patients were assigned to high- and low-risk groups based on the median risk scores, and the high- and low-risk groups had different clinical features, tumor immune infiltration status, tumor immune dysfunction and exclusion (TIDE), tumor mutational burden (TMB), sensitivity to novel potential compounds, and immunotherapeutic response. CONCLUSIONS: The model we developed was accurate and efficient in predicting the prognosis of patients with HNSCC. It was also sensitive in stratifying HNSCC patients with good response to immunotherapy. Therefore, our study provided insight into elucidating the processes and mechanisms of m6AlncRNAs.

Identification of molecular classification and gene signature for predicting prognosis and immunotherapy response in HNSCC using cell differentiation trajectories.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis. This article aims to explore the clinical significance of cell differentiation trajectory in HNSCC, identify different molecular subtypes by consensus clustering analysis, and develop a prognostic risk model on the basis of differentiation-related genes (DRGs) for predicting the prognosis of HNSCC patients. Firstly, cell trajectory analysis was performed on single-cell RNA sequencing (scRNA-seq) data, four molecular subtypes were identified from bulk RNA-seq data, and the molecular subtypes were predictive of patient survival, clinical features, immune infiltration status, and expression of immune checkpoint genes (ICGs)s. Secondly, we developed a 10-DRG signature for predicting the prognosis of HNSCC patients by using weighted correlation network analysis (WGCNA), differential expression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. Then, a nomogram integrating the risk assessment model and clinical features can successfully predict prognosis with favorable predictive performance and superior accuracy. We projected the response to immunotherapy and the sensitivity of commonly used antitumor drugs between the different groups. Finally, we used the quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) analysis and western blot to verify the signature. In conclusion, we identified distinct molecular subtypes by cell differentiation trajectory and constructed a novel signature based on differentially expressed prognostic DRGs, which could predict the prognosis and response to immunotherapy for patients and may provide valuable clinical applications in the treatment of HNSCC.

The mechanisms of biofilm antibiotic resistance in chronic rhinosinusitis: A review.

Chronic rhinosinusitis (CRS) is a common but burdensome ailment that is still poorly understood in terms of its pathogenesis. The existence of biofilms on the sinonasal mucosa of individuals with CRS has been proven by current biofilm identification methods. Current treatments for CRS generally include functional endoscopic sinus surgery, biofilm-removing strategies, and limited therapies that target quorum sensing (QS), patients with CRS are often resistant to antimicrobial therapy at degrees achievable by oral or intravenous administration, and even a subset of patients fail to react to either medical or surgical intervention. Multidrug-resistant Pseudomonas aeruginosa, Staphylococcus aureus, especially methicillin-resistant S. aureus, Streptococcus pneumoniae, and Haemophilus influenzae are the most commonly implicated bacteria in CRS patients, which may lead to the persistence and severity of CRS and antibiotic treatment failure via the formation of biofilms. Resistance to antibiotics is attributed to the 3-dimensional structure and QS of biofilms, and the latter describes the communication of bacteria within biofilms. A better understanding of biofilms in CRS and their contribution to the antibiotic resistance of CRS is critical for novel treatment strategies. This review mainly discusses the special structure of biofilms, QS, and their mechanisms of antibiotic resistance in order to investigate prospective anti-biofilm therapies, suggest future directions for study, and potentially refine the CRS prevention paradigm.