Search

Intervertebral disc degeneration (IDD), which is distinguished by a variety of pathologic alterations, is the major cause of low back pain (LBP). Nonetheless, preventative measures or therapies that may delay IDD are scarcely available. In this study, we sought to identify new diagnostic biological markers for IDD. In this first-of-a-kind study combining machine learning, stem cell treatment samples and single-cell sequencing data were collected. Differentially expressed genes (DEGs) were detected from the treatment group and clusters. To filter potential markers, support vector machine analysis and LASSO were performed. LAPTM5 was found to be the hub gene for IDD. In addition, the results of single-cell sequencing demonstrated the critical function of stem cells in IDD. Finally, we found that aging is significantly associated with the rate of stem cells. In general, our results may offer fresh insights that may be used in the investigation of innovative markers for diagnosing IDD. The critical genes identified by the machine learning algorithm could provide new perspectives on IDD. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF–IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs. Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells. Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC. [ABSTRACT FROM AUTHOR]

Background: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC. Methods: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC. Results: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH,and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression. Conclusions: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH,and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment. [ABSTRACT FROM AUTHOR]

Abstract Background and objective The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC. Methods We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GC and normal samples were analyzed with limma package. Gene ontology (GO) and KEGG enrichment analysis were performed using DAVID. Then we established the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING). The prognostic analysis of hub genes were performed through Gene Expression Profiling Interactive Analysis (GEPIA). Additionally, we used real-time quantitative PCR to validate the expression of hub genes in 5 pairs of tumor tissues and corresponding adjacent tissues. Finally, the candidate small molecules as potential drugs to treat GC were predicted in CMap database. Results Through integrating five microarray datasets, a total of 172 overlap DEGs were detected including 79 up-regulated and 93 down-regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in digestion, collagen fibril organization and cell adhesion. Signaling pathway analysis indicated that these DEGs played an vital in ECM-receptor interaction, focal adhesion and metabolism of xenobiotics by cytochrome P450. Protein-protein interaction network among the overlap DEGs was established with 124 nodes and 365 interactions. Three DEGs with high degree of connectivity (NID2, COL4A1 and COL4A2) were selected as hub genes. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with worse survival of patients. Finally, the 20 most significant small molecules were obtained based on CMap database and spiradoline was the most promising small molecule to reverse the GC gene expression. Conclusions Our results indicated that NID2, COL4A1 and COL4A2 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of GC initiation and progression. [ABSTRACT FROM AUTHOR]