In recent decades, with the continuous development of high‐throughput sequencing technology, data volume in medical research has increased, at the same time, almost all clinical researchers have their own independent omics data, which provided a better condition for data mining and a deeper understanding of gene functions. However, for these large amounts of data, many common and cutting‐edge effective bioinformatics research methods still cannot be widely used. This has encouraged the establishment of many analytical platforms, a portion of databases or platforms were designed to solve the special analysis needs of users, for instance, MG RAST, IMG/M, Qiita, BIGSdb, and TRAPR were developed for specific omics research, and some databases or servers provide solutions for special problems solutions. Metascape was designed to only provide functional annotations of genes as well as function enrichment analysis; BioNumerics and RidomSeqSphere+ perform multilocus sequence typing; CARD provides only antimicrobial resistance annotations. Additionally, some web services are outdated, and inefficient interaction often fails to meet the needs of researchers, such as our previous versions of the platform. Therefore, the demand to complete massive data processing tasks urgently requires a comprehensive bioinformatics analysis platform. Hence, we have developed a website platform, Sangerbox 3.0 (http://vip.sangerbox.com/), a web‐based tool platform. On a user‐friendly interface that also supports differential analysis, the platform provides interactive customizable analysis tools, including various kinds of correlation analyses, pathway enrichment analysis, weighted correlation network analysis, and other common tools and functions, users only need to upload their own corresponding data into Sangerbox 3.0, select required parameters, submit, and wait for the results after the task has been completed. We have also established a new interactive plotting system that allows users to adjust the parameters in the image; moreover, optimized plotting performance enables users to adjust large‐capacity vector maps on the web site. At the same time, we have integrated GEO, TCGA, ICGC, and other databases and processed data in batches, greatly reducing the difficulty to obtain data and improving the efficiency of bioimformatics study for users. Finally, we also provide users with rich sources of bioinformatics analysis courses, offering a platform for researchers to share and exchange knowledge.

Enhancer RNAs (eRNAs) participate in tumor growth and immune regulation through complex signaling pathways. However, the immune-related function of the eRNA-mRNA axis in lung adenocarcinoma (LUAD) is unclear. Data on the expression of eRNAs and mRNAs were downloaded from The Cancer Genome Atlas, GEO, and UCSC Xena, including LUAD, and pan-cancer clinical data and mutational information. Immune gene files were obtained from ImmLnc and ImmPort databases. Survival indices, including relapse-free and overall survival, were analyzed using the Kaplan–Meier and log-rank methods. The level of immune cell infiltration, degree of tumor hypoxia, and tumor cell stemness characteristics were quantified using the single-sample gene set enrichment analysis algorithm. The immune infiltration score and infiltration degree were evaluated using the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden and microsatellite instability were examined using the Spearman test. The LUAD-associated immune-related LINC00987/A2M axis was down-regulated in most cancer types, indicating poor survival and cancer progression. Immune cell infiltration was closely related to abnormal expression of the LINC00987/A2M axis, linking its expression to a possible evaluation of sensitivity to checkpoint inhibitors and response to chemotherapy. Abnormal expression of the LINC00987/A2M axis was characterized by heterogeneity in the degree of tumor hypoxia and stemness characteristics. The abnormal distribution of immune cells in LUAD was also verified through pan-cancer analysis. Comprehensive bioinformatic analysis showed that the LINC00987/A2M axis is a functional and effective tumor suppressor and biomarker for assessing the immune microenvironment and prognostic and therapeutic evaluations of LUAD.

Background Lung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients. Methods lncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Results A total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669. Conclusions The present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.

Background: Brucella spp. are Gram-negative bacteria that cause a zoonotic disease called brucellosis in humans as well as many animals. Brucella suis (B.suis) is one of the greatest threats to the human health and food safety. Studying macrophage and B. suis interaction is critical for understanding the chronic infection mechanism. However, the interaction mechanisms, especially for molecular events triggered by B. suis infected macrophage, such as biological pathways, are still obscure. Objectives: We will use gene set enrichment analysis (GSEA) to microarray in an attempt to find critical pathways in the interaction of macrophage and B. suis. Methods: We applied a standardized microarray preprocessing and GSEA to 2 independent macrophage and B. suis interaction studies including smooth virulent B. suis strain 1330 (S1330) data sets and rough attenuated B. suis strain VTRS1 (VTRS1) data sets. Integrative analysis was used to find critical pathways for 2 independent macrophage and B. suis interaction data sets. Results:The results demonstrated that for S1330 data sets, 8 and 13 common up-and down-regulated pathways were found in 4 interaction stages including 4h, 8h, 24h, and 48h post macrophage infected S1330 B. suis, and for VTRS1 data sets, we found 30 and 19 common up-and down-regulated pathways. Comparing the results of S1330 and VTRS1 data sets, 6 and 8 common up-and downregulated pathways were identified. Conclusions:The study of macrophage and B. suis interaction through pathway analysis highlighted genes weakly connected to the phenotype, and discovered common critical pathways in the process of macrophages and different phenotypes of B. suis interaction. The identified pathways will shed light on the understanding of the functional events within macrophage post infected B. suis.

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological pathways and key genes implicated in NSCLC, and providing insight in the molecular mechanism underlying NSCLC. Methods: In this case-control bioinformatics study, the researchers used four microarray data of NSCLC from public gene expression omnibus (GEO) database at the national center for biotechnology information (NCBI) website. The microarray data came from studies of American, Spanish, and Taiwanese NSCLC patients, and in total contained 190 NSCLC tissue and 180 normal lung tissue. A standardized microarray preprocessing and gene set enrichment analysis (GSEA) were used to analyze each microarray data and obtained significantly regulated pathways. Venn analysis was used to identify the common significantly regulated biological pathways. Protein and protein interaction (PPI) network analysis was used to identify the key genes within common significantly regulated pathways. The PPI information was retrieved from STRING database, and cytoscape software was used to construct and visualize the PPI network. Results: Through integrating GSEA results of four microarray data, finally, the researchers identified 22 common up-regulated and 85 common down-regulated pathways. Many genes within 107 common significantly regulated pathways were significantly enriched within cell cycle pathway (P value of 2.58e-79) and focal adhesion pathway (P value of 2.44e-81). The PPI network showed that up-regulated CDK1 (P value = 1.33e-18 and logFC = 1.41) and down-regulated PIK3R1 (P value = 5.09e-22 and logFC = -1.13) genes shared the most abundant edges, and were associated with NSCLC. Conclusions: This cross-sectional study showed increased concordance between gene expression profiling data. These identified pathways and genes provide some insight in the molecular mechanisms of NSCLC, and the genes may serve as candidate diagnostic and therapeutic targets of NSCLC.