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Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD. To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan–Meier plotter online tool was used to perform survival analysis of the hub gene. Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues. This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Purpose Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment. Materials and methods We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs. Results A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues. Conclusion The multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Hepatocellular carcinoma (HCC) accounts for approximately 85–90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 normal liver tissues to identify the candidate genes and pathways in the progression of HCC. A total of 89 consistent differentially expression genes (DEGs) were identified. Gene-set enrichment analysis revealed that these genes were significantly enriched for cellular response to zinc ion in biological process group, collagen trimer in the cellular component group, extracellular matrix (ECM) structural constituent conferring tensile strength in the molecular function group, protein digestion and absorption, mineral absorption and ECM-receptor interaction. Network system biology based on the protein–protein interaction (PPI) network was also performed to identify the most connected and important genes based on our DEGs. The top five hub genes including osteopontin (SPP1), Collagen alpha-2(I) chain (COL1A2), Insulin-like growth factor I (IGF1), lipoprotein A (LPA), and Galectin-3 (LGALS3) were identified. Western blot and immunohistochemistry analysis were employed to verify the differential protein expression of hub genes in HCC patients. More importantly, we identified that these five hub genes were significantly associated with poor disease-free survival and overall survival. In summary, we have identified a potential clinical significance of these genes as prognostic biomarkers for HCC patients who would benefit from experimental approaches to obtain optimal outcome.

Purpose Erectile dysfunction (ED) is one of the most common male-disease globally. Despite efforts to explain its pathogenesis, the molecular mechanisms of ED are still not well understood. Methods The microarray dataset GSE10804 was downloaded from the Gene Expression Omnibus (GEO) to find candidate genes in ED progression. After differentially expressed genes (DEGs) were identified, functional enrichment analysis was performed. In addition, a protein-protein interaction network (PPI) was established and module analysis was performed through the STRING and Cytoscape. Results and Conclusions A total of 618 DEGs were identified in all, containing 430 downregulated genes and 188 upregulated genes. The enriched functions and pathways of the DEGs include transcription from RNA polymerase II promoter, cell adhesion, calcium ion binding, receptor binding, Akt signaling pathway, receptor interaction, protein digestion, and absorption. We picked out twenty-five hub genes, with biological process (BP) analyses revealing that the genes were principally associated with cellular responses to amino acid stimuli, extracellular matrix structural constituent, collagen trimer, protein digestion and absorption, ECM-receptor interaction and PI3K-Akt signaling pathway. To sum up, DEGs and hub genes distinguished in this study not only help us understand the molecular mechanisms behind the carcinogenesis and progression of ED, but also play a part in the diagnosis and treatment of ED by providing candidate targets.

BackgroundRecent studies have found that probenecid has neuroprotective and reparative effects on central nervous system injuries. However, its effect on genome-wide transcription in acute spinal cord injury (SCI) remains unknown. In the present study, RNA sequencing (RNA-Seq) is used to analyze the effect of probenecid on the local expression of gene transcription 8 h after spinal injury.MethodsAn Infinite Horizon impactor was used to perform contusive SCI in mice. The SCI model was made by using a rod (1.3 mm diameter) with a force of 50 Kdynes. Sham-operated mice only received a laminectomy without contusive injury. The injured mice were randomly assigned into either the control (SCI_C) or probenecid injection (SCI_P) group. In the latter group, the probenecid drug was intraperitoneally injected (0.5 mg/kg) immediately following injury. Eight hours after the injury or laminectomy, the spinal cords were removed from the mice in both groups. The total RNAs were extracted and purified for library preparation and transcriptome sequencing. Differential gene expressions (DEGs) of the three groups—sham, SCI_C and SCI_P—were analyzed using a DESeq software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs were performed using a GOseq R package and KOBAS software. Real-time quantitative reverse-transcriptase polymerase chain reaction was used to validate RNA-Seq results.ResultsRNA-Seq showed that, compared to the SCI_C group, the number of DEGs was 641 in the SCI_P group (286 upregulated and 355 downregulated). According to GO analysis, DEGs were most enriched in extracellular matrix (ECM), collagen trimer, protein bounding and sequence specific DNA binding. KEGG analysis showed that the most enriched pathways included: cell adhesion molecules, Leukocyte transendothelial migration, ECM-receptor interactions, PI3K-Akt signaling pathways, hematopoietic cell lineages, focal adhesions, the Rap1 signaling pathway, etc. The sequence data have been deposited into the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA554464).

Patients taking glucocorticoid or glucocorticoid-like drugs for an extended period of time may develop osteoporosis, termed glucocorticoid-induced osteoporosis (GIOP). GIOP is the most common form of secondary osteoporosis, but the mechanism underlying its development is unclear. In the present study, we used prednisolone to treat zebrafish larvae to investigate GIOP. Our RNA deep-sequencing (RNA-seq) results show that prednisolone affects genes known to act in the extracellular region. Therefore the extracellular region, extracellular matrix, and collagen trimer might be involved in glucocorticoid-induced osteoporosis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the focal adhesion signaling pathway is the most enriched signaling pathway in terms of differentially expressed genes (DEGs). In this pathway, integrin subunit alpha 10 (itga10) and integrin subunit beta like 1 (itgbl1), genes encoding two adapter proteins, were down-regulated in the prednisolone-treated larvae. Further experiments showed that prednisolone contributes to GIOP by down-regulating itga10 and itgbl1., Summary: Glucocorticoids may contribute to osteoporosis through effects on the extracellular region, the extracellular matrix and collagen trimer, and focal adhesion signaling pathway genes (itga10 and itgbl1).