Identification of Hub Genes and Key Pathways Associated with Two Subtypes of Diffuse Large B-Cell Lymphoma Based on Gene Expression Profiling via Integrated Bioinformatics

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.

A Comprehensive Review on the Role of Interleukin-40 as a Biomarker for Diagnosing Inflammatory Diseases

Interleukins are a group of proteins that have a wide range of complex functions and are believed to be involved in several diseases and conditions. In particular, interleukin-40 (IL-40) is a recently identified cytokine associated with B cells that was first introduced by Catalan et al. in 2017. This cytokine has several roles in the body, including functioning in the formation of B cells in the bone marrow, IgA production, and expression in the intestinal microbiome. Moreover, IL-40 appears to be involved in numerous autoimmune and inflammatory conditions, such as rheumatoid arthritis, systemic lupus erythematosus, primary Sjogren's syndrome, ankylosing spondylitis, type 2 diabetes, Graves' disease, and hepatic cell carcinoma. Our understanding of this molecule is quite restricted due to its novelty. However, because of its inflammatory characteristics, there is a high probability that it contributes to a variety of inflammatory disease complications. The aim of the present review is to highlight all available data on the importance of assessing IL-40 levels in human diseases up to now, which could be used as a diagnostic biomarker for the onset or progression of numerous inflammatory diseases.

Identification of hub genes distinguishing subtypes in endometrial stromal sarcoma through comprehensive bioinformatics analysis

Diagnosing low-grade and high-grade endometrial stromal sarcoma (LG-ESS and HG-ESS) is a challenge. This study aimed to identify biomarkers. 22 ESS cases were analyzed using Illumina microarrays. Differentially expressed genes (DEGs) were identified via Limma. DEGs were analyzed with String and Cytoscape. Core genes were enriched with GO and KEGG, their pan-cancer implications and immune aspects were studied. 413 DEGs were found by exome sequencing, 2174 by GSE85383 microarray. 36 common genes were identified by Venn analysis, and 10 core genes including RBFOX1, PCDH7, FAT1 were selected. Core gene GO enrichment included cell adhesion, T cell proliferation, and KEGG focused on related pathways. Expression was evaluated across 34 cancers, identifying immune DEGs IGF1 and AVPR1A. Identifying the DEGs not only helps improve our understanding of LG-ESS, HG-ESS but also promises to be potential biomarkers for differential diagnosis between LG-ESS and HG-ESS and new therapeutic targets.

Construction and Analysis of ceRNA Networks Reveal the Key Genes Associated with Bovine Herpesvirus Type 1 Infection

Background

Virus infection can cause the changes of lncRNA expression levels to regulate the interaction between virus and host, but the relationship between BHV-1 infection and lncRNA has not been reported.

Methods

In this study, in order to reveal the molecular mechanism of RNA in BoHV-1 infection, the Madin-Darby bovine kidney (MDBK) cells were infected with BoHV-1, transcriptome sequencing were performed by next-generation sequencing at 18 h or 24 h or 33 h of viral infection and then based on the competitive endogenous RNA (ceRNA) theory, lncRNA-miRNA-mRNA networks were constructed using these high-throughput sequencing data. The network analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation and exploration of ncRNA ceRNAs in BoHV-1 infection.

Results

The results showed that 48 lncRNAs, 123 mRNAs and 20 miRNAs as differentially expressed genes, and the mitogen activated protein kinase (MAPK) pathway and calcium signaling pathway were significantly enriched in the ceRNA network. Some differentially expressed lncRNA genes were randomly selected for verification by RT-qPCR, and the results showed that their expression trend was consistent with the results of transcriptome sequencing data.

Conclusion

This study revealed that BoHV-1 infection can affect the expression of RNAs in MDBK cells and the regulation of ceRNA network to carry out corresponding biological functions in the host, but further experimental studies are still necessary to prove the hub genes function in ceRNA network and the molecular mechanism in BoHV-1 infection.

Transcriptome profiling analysis of uterus during chicken laying periods

The avian eggshell is formed in the uterus. Changes in uterine function may have a significant effect on eggshell quality. To identify the vital genes impacting uterine functional maintenance in the chicken, uteri in three different periods (22W, 31W, 51W) were selected for RNA sequencing and bioinformatics analysis. In our study, 520, 706 and 736 differentially expressed genes (DEGs) were respectively detected in the W31 vs W22 group, W51 vs W31 group and W51 vs W22 group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated DEGs were enriched in the extracellular matrix, extracellular region part, extracellular region, extracellular matrix structural constituent, ECM receptor interaction, collagen-containing extracellular matrix and collagen trimer in the uterus (P < 0.05). Protein-protein interaction analysis revealed that FN1, LOX, THBS2, COL1A1, COL1A2, COL5A1, COL5A2, POSTN, MMP13, VANGL2, RAD54B, SPP1, SDC1, BTC, ANGPTL3 might be key candidate genes for uterine functional maintenance in chicken. This study discovered dominant genes and pathways which enhanced our knowledge of chicken uterine functional maintenance.

Clinical Prognostic Implications of Wnt Hub Genes Expression in Medulloblastoma

Medulloblastoma is the most common type of pediatric malignant primary brain tumor, and about one-third of patients die due to disease recurrence and most survivors suffer from long-term side effects. MB is clinically, genetically, and epigenetically heterogeneous and subdivided into at least four molecular subgroups: WNT, SHH, Group 3, and Group 4. We evaluated common differentially expressed genes between a Brazilian RNA-seq GSE181293 dataset and microarray GSE85217 dataset cohort of pediatric MB samples using bioinformatics methodology in order to identify hub genes of the molecular subgroups based on PPI network construction, survival and functional analysis. The main finding was the identification of five hub genes from the WNT subgroup that are tumor suppressors, and whose lower expression is related to a worse prognosis for MB patients. Furthermore, the common genes correlated with the five tumor suppressors participate in important pathways and processes for tumor initiation and progression, as well as development and differentiation, and some of them control cell stemness and pluripotency. These genes have not yet been studied within the context of MB, representing new important elements for investigation in the search for therapeutic targets, prognostic markers or for understanding of MB biology.

Intracellular osteopontin protects from autoimmunity-driven lymphoma development inhibiting TLR9-MYD88-STAT3 signaling

BACKGROUND

Autoimmune disorders, including Systemic Lupus Erythematosus (SLE), are associated with increased incidence of hematological malignancies. The matricellular protein osteopontin (OPN) has been linked to SLE pathogenesis, as SLE patients show increased serum levels of OPN and often polymorphisms in its gene. Although widely studied for its pro-tumorigenic role in different solid tumours, the role of OPN in autoimmunity-driven lymphomagenesis has not been investigated yet.

METHODS

To test the role of OPN in the SLE-associated lymphomagenesis, the SLE-like prone Fas^lpr/lpr mutation was transferred onto an OPN-deficient background. Spleen from Fas^lpr/lpr and OPN-/-Fas^lpr/lpr mice, as well as purified B cells, were analysed by histopathology, flow cytometry, Western Blot, immunohistochemistry, immunofluorescence and gene expression profile to define lymphoma characteristics and investigate the molecular mechanisms behind the observed phenotype. OPN cellular localization in primary splenic B cells and mouse and human DLBCL cell lines was assessed by confocal microscopy. Finally, gain of function experiments, by stable over-expression of the secreted (sOPN) and intracellular OPN (iOPN) in OPN-/-Fas^lpr/lpr -derived DLBCL cell lines, were performed for further validation experiments.

RESULTS

Despite reduced autoimmunity signs, OPN-/-Fas^lpr/lpr mice developed splenic lymphomas with higher incidence than Fas^lpr/lpr counterparts. In situ and ex vivo analysis featured such tumours as activated type of diffuse large B cell lymphoma (ABC-DLBCL), expressing BCL2 and c-MYC, but not BCL6, with activated STAT3 signaling. OPN-/-Fas^lpr/lpr B lymphocytes showed an enhanced TLR9-MYD88 signaling pathway, either at baseline or after stimulation with CpG oligonucleotides, which mimic dsDNA circulating in autoimmune conditions. B cells from Fas^lpr/lpr mice were found to express the intracellular form of OPN. Accordingly, gene transfer-mediated re-expression of iOPN, but not of its secreted isoform, into ABC-DLBCL cell lines established from OPN-/-Fas^lpr/lpr mice, prevented CpG-mediated activation of STAT3, suggesting that the intracellular form of OPN may represent a brake to TLR9 signaling pathway activation.

CONCLUSION

These data indicate that, in the setting of SLE-like syndrome in which double strand-DNA chronically circulates and activates TLRs, B cell intracellular OPN exerts a protective role in autoimmunity-driven DLBCL development, mainly acting as a brake in the TLR9-MYD88-STAT3 signaling pathway.

Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs. Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

Deciphering Key Genes and miRNAs Associated With Hepatocellular Carcinoma via Network-Based Approach

Hepatocellular carcinoma (HCC)is a common type of liver cancer and has a high mortality world-widely. The diagnosis, prognoses, and therapeutics are very poor due to the unclear molecular mechanism of progression of the disease. To unveil the molecular mechanism of progression of HCC, we extract a large sample of mRNA expression levels from the GEO database where a total of 167 samples were used for study, and out of them, 115 samples were from HCC tumor tissue. This study aims to investigate the module of differentially expressed genes (DEGs)which are co-expressed only in HCC sample data but not in normal tissue samples. Thereafter, we identified the highly significant module of significant co-expressed genes and formed a PPI network for these genes. There were only six genes (namely, MSH3, DMC1, ALPP, IL10, ZNF223, and HSD17B7)obtained after analysis of the PPI network. Out of six only MSH3, DMC1, HSD17B7, and IL10 were found enriched in GO Term & Pathway enrichment analysis and these candidate genes were mainly involved in cellular process, metabolic and catalytic activity, which promote the development & progression of HCC. Lastly, the composite 3-node FFL reveals the driver miRNAs and TFs associated with our key genes.

High ETV6 Levels Support Aggressive B Lymphoma Cell Survival and Predict Poor Outcome in Diffuse Large B-Cell Lymphoma Patients

The identification of prognostic factors for aggressive B-cell lymphomas still represents an unmet clinical need. We used forward phase protein arrays (FFPA) to identify proteins associated with overall survival (OS) from diagnostic formalin-fixed paraffin-embedded material of diffuse large B-cell lymphoma (DLBCL) patients (n = 47). Univariate Cox regression analysis identified numerous proteins, including immune check-point molecules (PDCD1, PDCD2 and PD1L2) and BCL2 to be significantly associated with OS. However, only ETV6 and PIM2 proteins persisted following multivariate Cox analysis. Independent validation studies by immunohistochemistry and analysis of public gene expression profiles of DLBCL confirmed a prognostic role for high ETV6 and ETV6/PIM2 ratios in DLBCL. ETV6 is a recurrently mutated/deleted gene in DLBCL for which its function in this disease entity is currently unknown. We find that ETV6 is upregulated during oncogenic transformation of germinal center B-cells and that it regulates DLBCL survival, as its acute loss results in marked apoptosis. Fluctuations in survivin (BIRC5) expression levels were associated with this phenomenon. Furthermore, an inverse correlation between ETV6 and BIRC5 expression levels was found and correlated with a response to the BIRC5 inhibitor, YM155. In conclusion, we present evidence for an oncogenic function of ETV6 in DLBCL.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

IL-40: A New B Cell-Associated Cytokine Up-Regulated in Rheumatoid Arthritis Decreases Following the Rituximab Therapy and Correlates With Disease Activity, Autoantibodies, and NETosis

Background

Interleukin 40 (IL-40) is a newly identified B cell-associated cytokine implicated in humoral immune responses and B cell homeostasis. As B cells play a pivotal role in autoimmunity, we investigated the function of IL-40 in rheumatoid arthritis (RA).

Methods

IL-40 expression was determined in the synovial tissue from RA and osteoarthritis (OA) patients. IL-40 was analysed in the serum/synovial fluid of patients with RA (n=50), systemic lupus erythematosus (SLE, n=69), OA (n=44), and healthy controls (HC, n=50). We assessed the changes of IL-40 levels in RA patients following the B cell depletion by rituximab (n=29) or after the TNF inhibition by adalimumab (n=25). We examined the relationship between IL-40, disease activity, autoantibodies, cytokines, and NETosis markers. Effect of IL-40 on synovial fibroblasts was determined.

Results

IL-40 was overexpressed in RA synovial tissue, particularly by synovial lining and infiltrating immune cells. The levels of IL-40 were up-regulated in the synovial fluid of RA versus OA patients (p<0.0001). Similarly, IL-40 was increased in the serum of RA patients compared to HC, OA, or SLE (p<0.0001 for all) and decreased after 16 and 24 weeks (p<0.01 and p<0.01) following rituximab treatment. No significant effect of adalimumab on IL-40 was observed. IL-40 levels in RA patients correlated with rheumatoid factor-IgM and anti-cyclic citrullinated peptides (anti-CCP) in the serum (p<0.0001 and p<0.01), as well as in the synovial fluid (p<0.0001 and p<0.001). Synovial fluid IL-40 was also associated with disease activity score DAS28 (p<0.05), synovial fluid leukocyte count (p<0.01), neutrophil attractants IL-8 (p<0.01), MIP-1α (p<0.01), and markers of neutrophil extracellular traps externalization (NETosis) such as proteinase 3 (p<0.0001) and neutrophil elastase (p<0.0001). Synovial fibroblasts exposed to IL-40 increased the secretion of IL-8 (p<0.01), MCP-1 (p<0.05), and MMP-13 (p<0.01) compared to the unstimulated cells.

Conclusions

We show the up-regulation of IL-40 in RA and its decrease following B cell depleting therapy. The association of IL-40 with autoantibodies, chemokines, and markers of NETosis may imply its potential involvement in RA development. Moreover, IL-40 up-regulates the secretion of chemokines and MMP-13 in synovial fibroblasts, indicating its role in the regulation of inflammation and tissue destruction in RA.

Identifying Hub Genes, Key Pathways and Immune Cell Infiltration Characteristics in Pediatric and Adult Ulcerative Colitis by Integrated Bioinformatic Analysis

BACKGROUND AND AIMS

In the present study, we investigated the differentially expressed genes (DEGs), pathways and immune cell infiltration characteristics of pediatric and adult ulcerative colitis (UC).

METHODS

We conducted DEG analysis using the microarray dataset GSE87473 containing 19 pediatric and 87 adult UC samples downloaded from the Gene Expression Omnibus. Gene ontology and pathway enrichment analyses were conducted using Metascape. We constructed the protein-protein interaction (PPI) network and the drug-target interaction network of DEGs and identified hub modules and genes using Cytoscape and analyzed immune cell infiltration in pediatric and adult UC using CIBERSORT.

RESULTS

In total, 1700 DEGs were screened from the dataset. These genes were enriched mainly in inter-cellular items relating to cell junctions, cell adhesion, actin cytoskeleton and transmembrane receptor signaling pathways and intra-cellular items relating to the splicing, metabolism and localization of RNA. CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC were identified as hub DEGs. Immune cell infiltration analysis revealed higher proportions of naive B cells, resting memory T helper cells, regulatory T cells, monocytes, M0 macrophages and activated mast cells in pediatric UC, along with lower proportions of memory B cells, follicular helper T cells, γδ T cells, M2 macrophages, and activated dendritic cells.

CONCLUSIONS

Our study suggested that hub genes CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC and immune cells including B cells, T cells, monocytes, macrophages and mast cells play vital roles in the pathological differences between pediatric and adult UC and may serve as potential biomarkers in the diagnosis and treatment of UC.

Identification of GNA12-driven gene signatures and key signaling networks in ovarian cancer

With the focus on defining the oncogenic network stimulated by lysophosphatidic acid (LPA) in ovarian cancer, the present study sought to interrogate the oncotranscriptome regulated by the LPA-mediated signaling pathway. LPA, LPA-receptor (LPAR) and LPAR-activated G protein 12 α-subunit, encoded by G protein subunit α 12 (GNA12), all serve an important role in ovarian cancer progression. While the general signaling mechanism regulated by LPA/LPAR/GNA12 has previously been characterized, the global transcriptomic network regulated by GNA12 in ovarian cancer pathophysiology remains largely unknown. To define the LPA/LPAR/GNA12-orchestrated oncogenic networks in ovarian cancer, transcriptomic and bioinformatical analyses were conducted using SKOV3 cells, in which the expression of GNA12 was silenced. Array analysis was performed in Agilent SurePrint G3 Human Comparative Genomic Hybridization 8×60 microarray platform. The array results were validated using Kuramochi cells. Gene and functional enrichment analyses were performed using Database for Annotation, Visualization and Integrated Discovery, Search Tool for Retrieval of Interacting Genes and Cytoscape algorithms. The results indicated a paradigm in which GNA12 drove ovarian cancer progression by upregulating a pro-tumorigenic network with AKT1, VEGFA, TGFB1, BCL2L1, STAT3, insulin-like growth factor 1 and growth hormone releasing hormone as critical hub and/or bottleneck nodes. Moreover, GNA12 downregulated a growth-suppressive network involving proteasome 20S subunit (PSM) β6, PSM α6, PSM ATPase 5, ubiquitin conjugating enzyme E2 E1, PSM non-ATPase 10, NDUFA4 mitochondrial complex-associated, NADH:ubiquinone oxidoreductase subunit B8 and anaphase promoting complex subunit 1 as hub or bottleneck nodes. In addition to providing novel insights into the LPA/LPAR/GNA12-regulated oncogenic networks in ovarian cancer, the present study identified several potential nodes in this network that could be assessed for targeted therapy.

GNAi2/gip2-Regulated Transcriptome and Its Therapeutic Significance in Ovarian Cancer

Increased expression of GNAi2, which encodes the α-subunit of G-protein i2, has been correlated with the late-stage progression of ovarian cancer. GNAi2, also referred to as the proto-oncogene gip2, transduces signals from lysophosphatidic acid (LPA)-activated LPA-receptors to oncogenic cellular responses in ovarian cancer cells. To identify the oncogenic program activated by gip2, we carried out micro-array-based transcriptomic and bioinformatic analyses using the ovarian cancer cell-line SKOV3, in which the expression of GNAi2/gip2 was silenced by specific shRNA. A cut-off value of 5-fold change in gene expression (p < 0.05) indicated that a total of 264 genes were dependent upon gip2-expression with 136 genes coding for functional proteins. Functional annotation of the transcriptome indicated the hitherto unknown role of gip2 in stimulating the expression of oncogenic/growth-promoting genes such as KDR/VEGFR2, CCL20, and VIP. The array results were further validated in a panel of High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines that included Kuramochi, OVCAR3, and OVCAR8 cells. Gene set enrichment analyses using DAVID, STRING, and Cytoscape applications indicated the potential role of the gip2-stimulated transcriptomic network involved in the upregulation of cell proliferation, adhesion, migration, cellular metabolism, and therapy resistance. The results unravel a multi-modular network in which the hub and bottleneck nodes are defined by ACKR3/CXCR7, IL6, VEGFA, CYCS, COX5B, UQCRC1, UQCRFS1, and FYN. The identification of these genes as the critical nodes in GNAi2/gip2 orchestrated onco-transcriptome establishes their role in ovarian cancer pathophysiology. In addition, these results also point to these nodes as potential targets for novel therapeutic strategies.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

Identification of collagen genes related to immune infiltration and epithelial-mesenchymal transition in glioma

Background

Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression.

Methods

We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers.

Results

We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells.

Conclusions

In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01982-0.

Transcriptome Analysis Identifies Candidate Genes and Signaling Pathways Associated With Feed Efficiency in Xiayan Chicken

Feed efficiency is an important economic factor in poultry production, and the rate of feed efficiency is generally evaluated using residual feed intake (RFI). The molecular regulatory mechanisms of RFI remain unknown. Therefore, the objective of this study was to identify candidate genes and signaling pathways related to RFI using RNA-sequencing for low RFI (LRFI) and high RFI (HRFI) in the Xiayan chicken, a native chicken of the Guangxi province. Chickens were divided into four groups based on FE and sex: LRFI and HRFI for males and females, respectively. We identified a total of 1,015 and 742 differentially expressed genes associated with RFI in males and females, respectively. The 32 and 7 Gene Ontology (GO) enrichment terms, respectively, identified in males and females chiefly involved carbohydrate, amino acid, and energy metabolism. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 11 and 5 significantly enriched signaling pathways, including those for nutrient metabolism, insulin signaling, and MAPK signaling, respectively. Protein-protein interaction (PPI) network analysis showed that the pathways involving CAT, ACSL1, ECI2, ABCD2, ACOX1, PCK1, HSPA2, and HSP90AA1 may have an effect on feed efficiency, and these genes are mainly involved in the biological processes of fat metabolism and heat stress. Gene set enrichment analysis indicated that the increased expression of genes in LRFI chickens was related to intestinal microvilli structure and function, and to the fat metabolism process in males. In females, the highly expressed set of genes in the LRFI group was primarily associated with nervous system and cell development. Our findings provide further insight into RFI regulation mechanisms in chickens.

Genomic Mutation Profile of Primary Gastrointestinal Diffuse Large B-Cell Lymphoma

Primary gastrointestinal diffuse large B-cell lymphoma (GI-DLBCL) is the most common gastrointestinal lymphoma, but its genetic features are poorly understood. We performed whole-exome sequencing of 25 primary tumor samples from patients with GI-DLBCL and 23 matched normal tissue samples. Oncogenic mutations were screened, and the correlations between genetic mutations and clinicopathological characteristics were analyzed. Twenty-five patients with GI-DLBCL were enrolled in the genetic mutation analysis with a median of 184 (range 79–382) protein-altering variants per patient. We identified recurrent oncogenic mutations in GI-DLBCL, including those in TP53, MUC16, B2M, CCND3, HIST1H1C, NEB, and ID3. Compared with nodal DLBCL, GI-DLBCL exhibited an increased mutation frequency of TP53 and reduced mutation frequencies of PIM1, CREBBP, BCL2, KMT2D, and EZH2. Moreover, GI-DLBCL exhibited fewer MYD88 and CD79B mutations than DLBCL in the testis and central nervous system. GI-DLBCLs with HLA-B, MEF2A, RHOA, and NAV3 mutations exhibited a tendency toward a high proliferation index. MUC16 and ETV6 mutations often occurred in tumors with early clinical staging. Our data provide a comprehensive understanding of the landscape of mutations in a small subset of GI-DLBCLs. The genetic mutation profiles of GI-DLBCL differ from those of nodal DLBCL and DLBCL in immune-privileged sites. The different mutated genes are related to the NF-κB and JAK-STAT pathways, and the different pathogenetic mechanisms leading to the development of DLBCL may be influenced by the tissue microenvironment. Differences in genetic alterations might influence the clinicopathological characteristics of GI-DLBCL.

Comprehensive analysis of tumour mutational burden and its clinical significance in prostate cancer

BACKGROUND

The tumorigenesis of prostate cancer involves genetic mutations. Tumour mutational burden (TMB) is an emerging biomarker for predicting the efficacy of immunotherapy.

RESULTS

Single-nucleotide polymorphisms were the most common variant type, and C>T transversion was the most commonly presented type of single-nucleotide variant. The high-TMB group had lower overall survival (OS) than the low-TMB group. TMB was associated with age, T stage and N stage. Functional enrichment analysis of differentially expressed genes (DEGs) showed that they are involved in pathways related to the terms spindle, chromosomal region, nuclear division, chromosome segregation, cell cycle, oocyte meiosis and other terms associated with DNA mutation and cell proliferation. Six hub genes, PLK1, KIF2C, MELK, EXO1, CEP55 and CDK1, were identified. All the genes were associated with disease-free survival, and CEP55 and CDK1 were associated with OS.

CONCLUSIONS

The present study provides a comprehensive analysis of the significance of TMB and DEGs and infiltrating immune cells related to TMB, which provides helpful information for exploring the significance of TMB in prostate cancer.

Transcriptome landscapes of differentially expressed genes related to fat deposits in Nandan-Yao chicken

Nandan-Yao chicken is a Chinese native chicken with lower fat deposition and better meat quality. Fat deposition is a quite complex and important economic trait. However, its molecular mechanism is still unknown in chickens. In the current study, Nandan-Yao chicken was divided into two groups based on the rate of abdominal fat at 120 days old, namely the high-fat group and low-fat group. The total RNAs were isolated and sequenced by RNA sequencing (RNA-seq). After quality control, we gained 1222, 902, 784, 624, and 736 differentially expressed genes (DEGs) in abdominal fat, back skin, liver, pectoral muscle, and leg muscle, respectively. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that significantly enriched GO term and KEGG signaling pathway mainly involved cytosolic ribosome, growth development, PPAR signaling pathway, Wnt signaling pathway, and linoleic acid metabolism in abdominal fat, back skin, and liver. While in pectoral muscle and leg muscle, it is mainly enriched in phosphatidylinositol signaling system, adrenergic signaling in cardiomyocytes, cytosolic ribosome, and cytosolic part. Sixteen genes were differentially expressed in all five tissues. Among them, PLA2G4A and RPS4Y1 might be the key regulators for fat deposition in Nandan-Yao chicken. The protein-protein interaction (PPI) network analysis of DEGs showed that PCK1 was the most notable genes. The findings in the current study will help to understand the regulation mechanism of abdominal fat and intramuscular fat in Nandan-Yao chicken and provide a theoretical basis for Chinese local chicken breeding.

Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis

BACKGROUND

Improving feed efficiency is one of the important breeding targets for poultry industry. The aim of current study was to investigate the breast muscle transcriptome data of native chickens divergent for feed efficiency. Residual feed intake (RFI) value was calculated for 1008 closely related chickens. The 5 most efficient (LRFI) and 5 least efficient (HRFI) birds were selected for further analysis. Transcriptomic data were generated from breast muscle collected post-slaughter.

RESULTS

The differently expressed genes (DEGs) analysis showed that 24 and 325 known genes were significantly up- and down-regulated in LRFI birds. An enrichment analysis of DEGs showed that the genes and pathways related to inflammatory response and immune response were up-regulated in HRFI chickens. Moreover, Gene Set Enrichment Analysis (GSEA) was also employed, which indicated that LRFI chickens increased expression of genes related to mitochondrial function. Furthermore, protein network interaction and function analyses revealed ND2, ND4, CYTB, RAC2, VCAM1, CTSS and TLR4 were key genes for feed efficiency. And the 'phagosome', 'cell adhesion molecules (CAMs)', 'citrate cycle (TCA cycle)' and 'oxidative phosphorylation' were key pathways contributing to the difference in feed efficiency.

CONCLUSIONS

In summary, a series of key genes and pathways were identified via bioinformatics analysis. These key genes may influence feed efficiency through deep involvement in ROS production and inflammatory response. Our results suggested that LRFI chickens may synthesize ATP more efficiently and control reactive oxygen species (ROS) production more strictly by enhancing the mitochondrial function in skeletal muscle compared with HRFI chickens. These findings provide some clues for understanding the molecular mechanism of feed efficiency in birds and will be a useful reference data for native chicken breeding.

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

TNF-α Differentially Regulates Cell Cycle Genes in Promyelocytic and Granulocytic HL-60/S4 Cells

Tumor necrosis factor alpha (TNF-α) is a potent cytokine involved in systemic inflammation and immune modulation. Signaling responses that involve TNF-α are context dependent and capable of stimulating pathways promoting both cell death and survival. TNF-α treatment has been investigated as part of a combined therapy for acute myeloid leukemia due to its modifying effects on all-trans retinoic acid (ATRA) mediated differentiation into granulocytes. To investigate the interaction between cellular differentiation and TNF-α, we performed RNA-sequencing on two forms of the human HL-60/S4 promyelocytic leukemia cell line treated with TNF-α. The ATRA-differentiated granulocytic form of HL-60/S4 cells had an enhanced transcriptional response to TNF-α treatment compared to the undifferentiated promyelocytes. The observed TNF-α responses included differential expression of cell cycle gene sets, which were generally upregulated in TNF-α treated promyelocytes, and downregulated in TNF-α treated granulocytes. This is consistent with TNF-α induced cell cycle repression in granulocytes and cell cycle progression in promyelocytes. Moreover, we found evidence that TNF-α treatment of granulocytes shifts the transcriptome toward that of a macrophage. We conclude that TNF-α treatment promotes a divergent transcriptional program in promyelocytes and granulocytes. TNF-α promotes cell cycle associated gene expression in promyelocytes. In contrast, TNF-α stimulated granulocytes have reduced cell cycle gene expression, and a macrophage-like transcriptional program.