Background: Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies’ development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.Methods: In this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the “limma” R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.Results: With the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.Conclusions: 17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis play a key role in cancer metastasis, facilitating the detachment and survival of cancer cells from the primary tumor site. However, few studies have focused on the role of anoikis in HNSC, especially on the prognosis.Methods: Anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals were used to identify HNSCC subtypes and to construct a prognostic model for HNSCC patients. Also, we explored the immune microenvironment and enrichment pathways between different subtypes. Finally, we provide clinical experts with a novel nomogram based on ANRGs, with DCA curves indicating the potential clinical benefit of the model for clinical strategies.Results: We identified 69 survival-related HNSCC anoikis-related DEGs, from which 7 genes were selected to construct prognostic models. The prognostic risk score was identified as an independent prognostic factor. Functional analysis showed that these high and low risk groups had different immune status and drug sensitivity. Next risk scores were combined with HNSCC clinicopathological features together to construct a nomogram, and DCA analysis showed that the model could benefit patients from clinical treatment strategies.Conclusion: The predictive seven-gene signature and nomogram established in this study can assist clinicians in selecting personalized treatment for patients with HNSCC.

Alzheimer’s disease (AD) is a serious brain disorder characterized by the presence of beta-amyloid plaques, tau pathology, inflammation, neurodegeneration, and cerebrovascular dysfunction. The presence of chronic neuroinflammation, breaches in the blood-brain barrier (BBB), and increased levels of inflammatory mediators are central to the pathogenesis of AD. These factors promote the penetration of immune cells into the brain, potentially exacerbating clinical symptoms and neuronal death in AD patients. While microglia, the resident immune cells of the central nervous system (CNS), play a crucial role in AD, recent evidence suggests the infiltration of cerebral vessels and parenchyma by peripheral immune cells, including neutrophils, T lymphocytes, B lymphocytes, NK cells, and monocytes in AD. These cells participate in the regulation of immunity and inflammation, which is expected to play a huge role in future immunotherapy. Given the crucial role of peripheral immune cells in AD, this article seeks to offer a comprehensive overview of their contributions to neuroinflammation in the disease. Understanding the role of these cells in the neuroinflammatory response is vital for developing new diagnostic markers and therapeutic targets to enhance the diagnosis and treatment of AD patients.

Background: Head and neck squamous cell carcinoma (HNSCC) is the most common head and neck cancer and is highly aggressive and heterogeneous, leading to variable prognosis and immunotherapy outcomes. Circadian rhythm alterations in tumourigenesis are of equal importance to genetic factors and several biologic clock genes are considered to be prognostic biomarkers for various cancers. The aim of this study was to establish reliable markers based on biologic clock genes, thus providing a new perspective for assessing immunotherapy response and prognosis in patients with HNSCC.Methods: We used 502 HNSCC samples and 44 normal samples from the TCGA-HNSCC dataset as the training set. 97 samples from GSE41613 were used as an external validation set. Prognostic characteristics of circadian rhythm-related genes (CRRGs) were established by Lasso, random forest and stepwise multifactorial Cox. Multivariate analysis revealed that CRRGs characteristics were independent predictors of HNSCC, with patients in the high-risk group having a worse prognosis than those in the low-risk group. The relevance of CRRGs to the immune microenvironment and immunotherapy was assessed by an integrated algorithm.Results: 6-CRRGs were considered to be strongly associated with HNSCC prognosis and a good predictor of HNSCC. The riskscore established by the 6-CRRG was found to be an independent prognostic factor for HNSCC in multifactorial analysis, with patients in the low-risk group having a higher overall survival (OS) than the high-risk group. Nomogram prediction maps constructed from clinical characteristics and riskscore had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and immune checkpoint expression and were more likely to benefit from immunotherapy.Conclusion: 6-CRRGs play a key predictive role for the prognosis of HNSCC patients and can guide physicians in selecting potential responders to prioritise immunotherapy, which could facilitate further research in precision immuno-oncology.

B cells occupy a vital role in the functioning of the immune system, working in tandem with T cells to either suppress or promote tumor growth within the tumor microenvironment(TME). In addition to direct cell-to-cell communication, B cells and other cells release exosomes, small membrane vesicles ranging in size from 30-150 nm, that facilitate intercellular signaling. Exosome research is an important development in cancer research, as they have been shown to carry various molecules such as major histocompatibility complex(MHC) molecules and integrins, which regulate the TME. Given the close association between TME and cancer development, targeting substances within the TME has emerged as a promising strategy for cancer therapy. This review aims to present a comprehensive overview of the contributions made by B cells and exosomes to the tumor microenvironment (TME). Additionally, we delve into the potential role of B cell-derived exosomes in the progression of cancer.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.