SNCA correlates with immune infiltration and serves as a prognostic biomarker in lung adenocarcinoma

SNCA is a potential therapeutic target for COVID-19 infection in diffuse large B-cell lymphoma patients

Cuprotosis related genes (CRGs) have been proved to be potential therapeutic targets for coronavirus disease 2019 (COVID-19) and cancer, but their immune and molecular mechanisms in COVID-19 infection in Diffuse Large B-cell Lymphoma (DLBC/DLBCL) patients are rarely reported. Our research goal is first to screen the key CRGs in COVID-19 through univariate analysis, machine learning and clinical samples. Secondly, we determined the expression and prognostic role of key CRGs in DLBCL through pan-cancer analysis. We validated the expression levels and prognosis using multiple datasets and independent clinical samples and validated the functional role of key CRGs in DLBCL through cell experiments. Finally, we validated the expression levels of CRGs in COVID-19 infected DLBCL patients samples and analyzed their common pathways in COVID-19 and DLBCL. The results show that synuclein-alpha (SNCA) is the common key differential gene of COVID-19 and DLBCL. DLBCL cells confirm that high expression of SNCA can significantly promote cell apoptosis and significantly inhibit the cycle progression of DLBCL. High expression of SNCA can regulate the binding of major histocompatibility complexes (MHCs) and T cell receptor (TCR) by regulating immune infiltration of Dendritic cells, effectively enhancing T cell-mediated anti-tumor immunity and clearing cancer cells. In conclusion, SNCA may be a potential therapeutic target for COVID-19 infection in DLBCL patients. Our study provides a theoretical basis for improving the clinical treatment of COVID-19 infection in DLBCL patients.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models

Background

This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease.

Methods

Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based on these findings, we constructed a diagnostic model by using the expression data of FLT1, ATP6V0E1, ATP6V0E2, and H2BC12 as inputs and training and validating the model using SVM algorithm.

Results

The prediction model demonstrated an AUC greater than 0.8 in the training, test, and validation sets, thereby validating its performance through SMOTE analysis. These findings provide strong support for early diagnosis of Parkinson's disease and offer new opportunities for personalized treatment and disease management.

Conclusion

In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson's disease.

The Hyperproliferation Mechanism of Cholesteatoma Based on Proteomics: SNCA Promotes Autophagy-Mediated Cell Proliferation Through the PI3K/AKT/CyclinD1 Signaling Pathway

Cholesteatoma is a chronic inflammatory ear disease with abnormal keratinized epithelium proliferation and tissue damage. However, the mechanism of keratinized epithelium hyperproliferation in cholesteatoma remains unknown. Hence, our study sought to shed light on mechanisms affecting the pathology and development of cholesteatoma, which could help develop adjunctive treatments. To investigate molecular changes in cholesteatoma pathogenesis, we analyzed clinical cholesteatoma specimens and paired ear canal skin with mass spectrometry-based proteomics and bioinformatics. From our screen, alpha-synuclein (SNCA) was overexpressed in middle ear cholesteatoma and might be a key hub protein associated with inflammation, proliferation, and autophagy in cholesteatoma. SNCA was more sensitive to lipopolysaccharide-induced inflammation, and autophagy marker increase was accompanied by autophagy activation in middle ear cholesteatoma tissues. Overexpression of SNCA activated autophagy and promoted cell proliferation and migration, especially under lipopolysaccharide inflammatory stimulation. Moreover, inhibiting autophagy impaired SNCA-mediated keratinocyte proliferation and corresponded with inhibition of the PI3K/AKT/CyclinD1 pathways. Also, 740Y-P, a PI3K activator reversed the suppression of autophagy and PI3K signaling by siATG5 in SNCA-overexpressing cells, which restored proliferative activity. Besides, knockdown of SNCA in RHEK-1 and HaCaT cells or knockdown of PI3K in RHEK-1 and HaCaT cells overexpressing SNCA both resulted in attenuated cell proliferation. Our studies indicated that SNCA overexpression in cholesteatoma might maintain the proliferative ability of cholesteatoma keratinocytes by promoting autophagy under inflammatory conditions. This suggests that dual inhibition of SNCA and autophagy may be a promising new target for treating cholesteatoma.

The Parkinson's disease-associated protein α-synuclein inhibits hepatoma by exosome delivery

Numerous epidemiological studies suggest a link between Parkinson's disease (PD) and cancer. However, their relevant pathogenesis is not clear. In the present study, we investigated the potential role of exosome-delivered α-synuclein (α-syn) in the regulation between PD and liver cancer. We cultured hepatocellular carcinoma (HCC) cells with exosomes derived from conditioned medium of the PD cellular model, and injected exosomes enriched with α-syn into the striatum of a liver cancer rat model. We found that α-syn-contained exosomes from the rotenone-induced cellular model of PD suppressed the growth, migration, and invasion of HCC cells. Integrin αVβ5 in exosomes from the rotenone-induced PD model was higher than that in the control, resulting in more α-syn-contained exosomes being taken up by HCC cells. Consistently, in vivo experiments with rat models also confirmed exosome-delivered α-syn inhibited liver cancer. These findings illustrate the important role of PD-associated protein α-syn inhibiting hepatoma by exosome delivery, suggesting a new mechanism underlying the link between these two diseases and therapeutics of liver cancer.

Synuclein Proteins in Cancer Development and Progression

Synucleins are a family of small, soluble proteins mainly expressed in neural tissue and in certain tumors. Since their discovery, tens of thousands of scientific reports have been published about this family of proteins as they are associated with severe human diseases. Although the physiological function of these proteins is still elusive, their relationship with neurodegeneration and cancer has been clearly described over the years. In this review, we summarize data connecting synucleins and cancer, going from the structural description of these molecules to their involvement in tumor-related processes, and discuss the putative use of these proteins as cancer molecular biomarkers.