Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the "limma" package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein-protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Identification of SV2C and DENR as Key Biomarkers for Parkinson's Disease Based on Bioinformatics, Machine Learning, and Experimental Verification

The objective of this study is to investigate the potential biomarkers and therapeutic target genes for Parkinson's disease (PD). We analyzed four datasets (GSE8397, GSE20292, GSE20163, GSE20164) from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and differential expression analysis to select genes and perform functional analysis. We applied three algorithms, namely, random forest, support vector machine recursive feature elimination, and least absolute shrinkage and selection operator, to identify hub genes, perform functional analysis, and assess their clinical diagnostic potential using receiver operating characteristic (ROC) curve analysis. We employed the xCell website to evaluate differences in the composition patterns of immune cells in the GEO datasets. We also collected serum samples from PD patients and established PD cell model to validate the expression of hub genes using enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction. Our findings identified SV2C and DENR as two hub genes for PD and decreased in PD brain tissue compared with controls. ROC analysis showed effectively value of SV2C and DENR to diagnose PD, and they were downregulated in the serum of PD patients and cell model. Functional analysis revealed that dopamine vesicle transport and synaptic vesicle recycling are crucial pathways in PD. Besides, the differences in the composition of immune cells, especially basophils and T cells, were discovered between PD and controls. In summary, our study identifies SV2C and DENR as potential biomarkers for diagnosing PD and provides a new perspective for exploring the molecular mechanisms of PD.

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

Parkinson's Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein-protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy.