Human bladder cancer (BCa) is one of the most commonly diagnosed malignancies worldwide. It has high recurrence rates and low-grade malignancy, thus representing an important public health concern. An increasing number of studies suggest that long-noncoding RNAs (lncRNAs) play important roles in various biological processes and disease pathologies, including cancer. We analyzed the expression profiles of lncRNA, miRNA, and mRNA, along with the clinical information of BCa patients collected from the Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA–miRNA–mRNA global triple network (competitive endogenous RNA network) by bioinformational approach. This BCa lncRNA–miRNA–mRNA network consisted of 23 miRNA nodes, 52 mRNA nodes, 59 lncRNA nodes, and 365 edges. Subsequent gene ontology (GO) and pathway analyses were performed using BinGO for Cytoscape and Database for Annotation, Visualization, and Integration Discovery, respectively, highlighting important GO terms and pathways that were enriched in the network. Subnetworks were created using 3 key lncRNAs (MAGI2-AS3, ADAMTS9-AS2, and LINC00330), revealing associations with BCa-linked mRNAs and miRNAs. Finally, an analysis of significantly differentiating RNAs found 6 DElncRNAs (AC112721.1, ADAMTS9-AS1, ADAMTS9-AS2, HCG22, MYO16-AS1, and SACS-AS1), 1 DEmiRNA (miRNA-195), and 6 DEmRNAs (CCNB1, FAM129A, MAP1B, TMEM100, AIFM3, and HOXB5) that correlated with BCa patient survival. Our results provide a novel perspective from which to study the lncRNA-related ceRNA network in BCa, contributing to the development of future diagnostic biomarkers and therapeutic targets.

Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization.Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway.CD48, P2RY10andRASAL3were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

BACKGROUND Osteoarthritis (OA) affects the health and wellbeing of the elderly. Shaoyao Gancao decoction (SGD) is used in traditional Chinese medicine (TCM) for the treatment of OA and has two active components, shaoyao (SY) and gancao (GC). This study aimed to undertake a network pharmacology analysis of the mechanism of the effects of SGD in OA. MATERIAL AND METHODS The active compounds and candidates of SGD were obtained from the Traditional Chinese Medicine (TCM) Databases@Taiwan, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the STITCH database, the ChEMBL database, and PubChem. The network pharmacology approach involved network construction, target prediction, and module analysis. Significant signaling pathways of the cluster networks for SGD and OA were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS Twenty-three bioactive compounds were identified, corresponding to 226 targets for SGD. Also, 187 genes were closely associated with OA, of which 161 overlapped with the targets of SGD and were considered to be therapeutically relevant. Functional enrichment analysis suggested that SGD exerted its pharmacological effects in OA by modulating multiple pathways, including cell cycle, cell apoptosis, drug metabolism, inflammation, and immune modulation. CONCLUSIONS A novel approach was developed to systematically identify the mechanisms of the TCM, SGD in OA using network pharmacology analysis.

Background: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Methods: Energy metabolism-related genes were obtained from the TARGET database. We applied the “NFM” algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Results: Two subtypes associated with energy metabolism was determined using the “NFM” algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. Conclusions: The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

Rheumatoid arthritis (RA) and osteoarthritis (OA) comprise the most common forms of arthritis. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between RA and OA using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE55457 datasets, originally produced through use of the high-throughput Affymetrix Human Genome U133A Array, were downloaded from the Gene Expression Omnibus (GEO) database. The GSE55457 dataset contains information from 33 samples, including 10 normal control (NC) samples, 13 RA samples, and 10 OA samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and associated molecular and biochemical pathways, respectively, for the identified DEGs, and a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software. GO and KEGG results suggested that several biological pathways (ie, “immune response,” “inflammation,” and “osteoclast differentiation”) are commonly involved in the development of both RA and OA, whereas several other pathways (eg, “MAPK signaling pathway,” and “ECM-receptor interaction”) presented significant differences between these disorders. This study provides further insights into the underlying pathogenesis of RA and OA, which may facilitate the diagnosis and treatment of these diseases.