Regulatory Non-coding RNAs for Death Associated Protein Kinase Family

Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments

BACKGROUND

Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects.

RECENT FINDINGS

The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy.

CONCLUSION

The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.

Plasma Levels of mir-34a-5p Correlate with Systemic Inflammation and Low Naïve CD4 T Cells in Common Variable Immunodeficiency

PURPOSE

Common variable immunodeficiency (CVID) is a primary antibody deficiency that commonly manifests as recurrent infections. Many CVID patients also suffer from immune dysregulation, an inflammatory condition characterized by polyclonal lymphocytic tissue infiltration and associated with increased morbidity and mortality. The genetic cause is unknown in most CVID patients and epigenetic alterations may contribute to the broad range of clinical manifestations. MicroRNAs are small non-coding RNAs that are involved in epigenetic modulation and may contribute to the clinical phenotype in CVID.

METHODS

Here, we determined the circulating microRNAome and plasma inflammatory proteins of a cohort of CVID patients with various levels of immune dysregulation and compared them to healthy controls. A set of deregulated microRNAs was validated by qPCR and correlated to inflammatory proteins and clinical findings.

RESULTS

Levels of microRNA-34a correlated with 11 proteins such as CXCL9, TNF, and IL10, which were predicted to be biologically connected. Moreover, there was a negative correlation between mir-34 levels and the number of naïve CD4 T cells in CVID.

CONCLUSION

Collectively, our data show that microRNAs correlate with the inflammatory response in CVID. Further investigations are needed to elucidate the role of miRNAs in the development of CVID-related immune dysregulation.

Proteomic and phosphoproteomic characteristics of the cortex, hippocampus, thalamus, lung, and kidney in COVID-19-infected female K18-hACE2 mice

BACKGROUND

Neurological damage caused by coronavirus disease 2019 (COVID-19) has attracted increasing attention. Recently, through autopsies of patients with COVID-19, the direct identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in their central nervous system (CNS) has been reported, indicating that SARS-CoV-2 might directly attack the CNS. The need to prevent COVID-19-induced severe injuries and potential sequelae is urgent, requiring the elucidation of large-scale molecular mechanisms in vivo.

METHODS

In this study, we performed liquid chromatography-mass spectrometry-based proteomic and phosphoproteomic analyses of the cortex, hippocampus, thalamus, lungs, and kidneys of SARS-CoV-2-infected K18-hACE2 female mice. We then performed comprehensive bioinformatic analyses, including differential analyses, functional enrichment, and kinase prediction, to identify key molecules involved in COVID-19.

FINDINGS

We found that the cortex had higher viral loads than did the lungs, and the kidneys did not have SARS-COV-2. After SARS-CoV-2 infection, RIG-I-associated virus recognition, antigen processing and presentation, and complement and coagulation cascades were activated to different degrees in all five organs, especially the lungs. The infected cortex exhibited disorders of multiple organelles and biological processes, including dysregulated spliceosome, ribosome, peroxisome, proteasome, endosome, and mitochondrial oxidative respiratory chain. The hippocampus and thalamus had fewer disorders than did the cortex; however, hyperphosphorylation of Mapt/Tau, which may contribute to neurodegenerative diseases, such as Alzheimer's disease, was found in all three brain regions. Moreover, SARS-CoV-2-induced elevation of human angiotensin-converting enzyme 2 (hACE2) was observed in the lungs and kidneys, but not in the three brain regions. Although the virus was not detected, the kidneys expressed high levels of hACE2 and exhibited obvious functional dysregulation after infection. This indicates that SARS-CoV-2 can cause tissue infections or damage via complicated routes. Thus, the treatment of COVID-19 requires a multipronged approach.

INTERPRETATION

This study provides observations and in vivo datasets for COVID-19-associated proteomic and phosphoproteomic alterations in multiple organs, especially cerebral tissues, of K18-hACE2 mice. In mature drug databases, the differentially expressed proteins and predicted kinases in this study can be used as baits to identify candidate therapeutic drugs for COVID-19. This study can serve as a solid resource for the scientific community. The data in this manuscript will serve as a starting point for future research on COVID-19-associated encephalopathy.

FUNDING

This study was supported by grants from the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, the National Natural Science Foundation of China, and the Natural Science Foundation of Beijing.

High DAPK1 Expression Promotes Tumor Metastasis of Gastric Cancer

Gastric cancer (GC) is a common upper gastrointestinal tumor. Death-associated protein kinase (DAPK1) was found to participate in the development of various malignant tumors. However, there are few reports on DAPK1 in gastric cancer. In this study, the TCGA and GEO datasets were used to explore the expression and role of DAPK1 in gastric cancer. The functions of DAPK1 in gastric cancer were determined by proliferation, migration and invasion assays. In addition, genes co-expressed with DAPK1 in gastric cancer were estimated through the WGCNA and correlation analysis. A DAPK1-related gene prognostic model was constructed using the Cox regression and lasso analyses. The expression of DAPK1 was significantly up-regulated in gastric cancer tissues. Kaplan-Meier analysis showed that low expression of DAPK1 was a favorable prognostic factor of overall survival and disease-free survival for gastric cancer patients. Functional experiments demonstrated that DAPK1 can promote the migration and invasion of gastric cancer cells. WGCNA, correlation analysis, Cox regression, and lasso analyses were applied to construct the DAPK1-related prognostic model. The prognostic value of this prognostic model of DAPK1-related genes was further successfully validated in an independent database. Our results indicated that DAPK1 can promote gastric cancer cell migration and invasion and established four DAPK1-related signature genes for gastric cancer that could independently predict the survival of GC patients.