Identification of hub genes and key pathways associated with angioimmunoblastic T-cell lymphoma using weighted gene co-expression network analysis

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Immune infiltration and drug specificity analysis of different subtypes based on functional status in angioimmunoblastic T-cell lymphoma

Angioimmunoblastic T-cell lymphoma (AITL) is a subtype of peripheral T-cell lymphoma (PTCL) strongly correlated with worse clinical outcomes. However, the role of characteristic pathway-related genes in patients with AITL (e.g., subtype typing and pathogenesis) remains unknown. In this study, we intended to understand the potential role and prognostic value of characteristic pathways in AITL and identified a model for subtype identification based on pathway-related functional status. Transcriptomic (RNA-seq) data were obtained from the Gene Expression Omnibus database for three sets of tumor tissues from AITL patients. AITL was divided into three clusters based on the pathway profile of patients and the best clustering k = 3, and differentially expressed genes (DEGs) in the three clusters were analyzed. The top 45 important variables associated with characteristic pathways, such as Huntington's disease, VEGF signaling pathway, nucleotide excision repair, ubiquitin-mediated proteolysis, purine metabolism, olfactory transduction, etc., were used to construct a subtype identification model. The model was experimentally validated and proved to possess good predictive efficacy. In addition, pathway-related subtype typing was significantly associated with different immune cell infiltration in AITL. Further analysis revealed that the drug IC50 values predicted also differed markedly among the different subtypes, thus further identifying some subtype-specific drugs. Our study indicates a potential role of characteristic pathways in AITL staging for the first time, provides novel insights for future research targeting AITL, and points to potential therapeutic options for patients with different subtypes of AITL.

IFI27 is a potential therapeutic target for HIV infection

BACKGROUND

Therapeutic studies against human immunodeficiency virus type 1 (HIV-1) infection have become one of the important works in global public health.

METHODS

Differential expression analysis was performed between HIV-positive (HIV+) and HIV-negative (HIV-) patients for GPL6947 and GPL10558 of GSE29429. Coexpression analysis of common genes with the same direction of differential expression identified modules. Module genes were subjected to enrichment analysis, Short Time-series Expression Miner (STEM) analysis, and PPI network analysis. The top 100 most connected genes in the PPI network were screened to construct the LASSO model, and AUC values were calculated to identify the key genes. Methylation modification of key genes were identified by the chAMP package. Differences in immune cell infiltration between HIV + and HIV- patients, as well as between antiretroviral therapy (ART) and HIV + patients, were calculated using ssGSEA.

RESULTS

We obtained 3610 common genes, clustered into nine coexpression modules. Module genes were significantly enriched in interferon signalling, helper T-cell immunity, and HIF-1-signalling pathways. We screened out module genes with gradual changes in expression with increasing time from HIV enrolment using STEM software. We identified 12 significant genes through LASSO regression analysis, especially proteasome 20S subunit beta 8 (PSMB8) and interferon alpha inducible protein 27 (IFI27). The expression of PSMB8 and IFI27 were then detected by quantitative real-time PCR. Interestingly, IFI27 was also a persistently dysregulated gene identified by STEM. In addition, 10 of the key genes were identified to be modified by methylation. The significantly infiltrated immune cells in HIV + patients were restored after ART, and IFI27 was significantly associated with immune cells.

CONCLUSION

The above results provided potential target genes for early diagnosis and treatment of HIV + patients. IFI27 may be associated with the progression of HIV infection and may be a powerful target for immunotherapy.

Comprehensive bioinformatics analysis reveals the hub genes and pathways associated with multiple myeloma

PURPOSE

While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention.

MATERIALS AND METHODS

Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein-protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA).

RESULTS

A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway.

CONCLUSION

The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

High Expression of BCL11A Predicts Poor Prognosis for Childhood MLL-r ALL

Background

Despite much improvement in the treatment for acute lymphoblastic leukemia (ALL), childhood ALLs with MLL-rearrangement (MLL-r) still have inferior dismal prognosis. Thus, defining mechanisms underlying MLL-r ALL maintenance is critical for developing effective therapy.

Methods

GSE13159 and GSE28497 were selected via the Oncomine website. Differentially expressed genes (DEGs) between MLL-r ALLs and normal samples were identified by R software. Next, functional enrichment analysis of these DEGs were carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Then, the key hub genes and modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA). Therapeutically Applicable Research to Generate Effective Treatments (TARGET) ALL (Phase I) of UCSC Xena analysis, qPCR, and Kaplan-Meier analysis were conducted for validating the expression of key hub genes from bone marrow cells of childhood ALL patients or ALL cell lines.

Results

A total of 1,045 DEGs were identified from GSE13159 and GSE28497. Through GO, KEGG, GSEA, and STRING analysis, we demonstrated that MLL-r ALLs were upregulating “nucleosome assembly” and “B cell receptor signal pathway” genes or proteins. WGCNA analysis found 18 gene modules using hierarchical clustering between MLL-r ALLs and normal. The Venn diagram was used to filter the 98 hub genes found in the key module with the 1,045 DEGs. We identified 18 hub genes from this process, 9 of which were found to be correlated with MLL-r status, using the UCSC Xena analysis. By using qPCR, we validated these 9 hub key genes to be upregulated in the MLL-r ALLs (RS4;11 and SEM) compared to the non-MLL-r ALL (RCH-ACV) cell lines. Three of these genes, BCL11A, GLT8D1 and NCBP2, were shown to be increased in MLL-r ALL patient bone marrows compared to the non-MLL-r ALL patient. Finally, Kaplan–Meier analysis indicated that childhood ALL patients with high BCL11A expression had significantly poor overall survival.

Conclusion

These findings suggest that upregulated BCL11A gene expression in childhood ALLs may lead to MLL-r ALL development and BCL11A represents a new potential therapeutic target for childhood MLL-r ALL.

Fetal malnutrition is associated with impairment of endogenous melatonin synthesis in pineal via hypermethylation of promoters of protein kinase C alpha and cAMP response element-binding

This study investigated whether and how fetal malnutrition would influence endogenous melatonin synthesis, and whether such effect of fetal malnutrition would transmit to the next generation. We enrolled 2466 participants and 1313 of their offspring. The urine 6-hydroxymelatonin sulfate and serum melatonin rhythm were measured. Methylation microarray detection and bioinformatics analysis were performed to identify hub methylated sites. Additionally, rat experiment was performed to elucidate mechanisms. The participants with fetal malnutrition had lower 6-hydroxymelatonin sulfate (16.59 ± 10.12 μg/24 hours vs 24.29 ± 11.99 μg/24 hours, P < .001) and arear under curve of melatonin rhythm (67.11 ± 8.16 pg/mL vs 77.11 ± 8.04 pg/mL, P < .001). We identified 961 differentially methylated sites, in which the hub methylated sites were locating on protein kinase C alpha (PRKCA) and cAMP response element-binding protein (CREB1) promoters, mediating the association of fetal malnutrition with impaired melatonin secretion. However, such effects were not observed in the offspring (all P > .05). Impaired histomorphology of pineal, decreased melatonin in serum, pineal, and pinealocyte were also found in the in vivo and in vitro experiments (P < .05 for the differences of the indicators). Hypermethylation of 10 CpG sites on the PRKCA promoter and 8 CpG sites on the CREB1 promoter were identified (all P < .05), which down-regulated PRKCA and CREB1 expressions, leading to decreased expression of AANAT, and then resulting in the impaired melatonin synthesis. Collectively, fetal malnutrition can impair melatonin synthesis through hypermethylation of PRKCA and CREB1 promoters, and such effects cannot be transmitted to the next generation.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

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.

Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

Interleukin-1 receptor-associated kinase 4 as a potential biomarker: Overexpression predicts poor prognosis in patients with glioma

The undetectable onset of glioma and the difficulty of surgery lead to a poor prognosis. Appropriate biomarkers for diagnosis and treatment need to be identified. Interleukin-1 receptor-associated kinase 4 (IRAK4) is involved in the initiation and progression of cancer. However, up until now, no report has revealed the relationship between IRAK4 and glioma. The present study aimed to examine the expression of IRAK4 in glioma, and to determine if there was a relationship between IRAK4 expression and clinical outcomes or survival prognosis. Thousands of glioma tissue samples and corresponding clinical information were obtained from various databases. Then a series of bioinformatics methods were used to reveal the role of IRAK4 in glioma. Finally, reverse transcription-quantitative PCR technology was used to verify the bioinformatics results. The study found that the expression of IRAK4 was significantly increased in glioma compared with the control brain tissue samples, and IRAK4, as an independent prognostic factor, shortened the overall survival time of patients with glioma. Gene Set Enrichment Analysis showed that IRAK4 promoted the activation of cell signalling pathways, such as NOD-like and Toll-like receptor signalling pathways. Co-expression analysis showed that the expression of IRAK4 was correlated with CMTM6, MOB1A and other genes. The present study demonstrated the role of IRAK4 as an oncogene in the pathological process of glioma for the first time, and highlights the potential of IRAK4 as a biomarker for prognostic evaluation and treatment of glioma.

Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis

Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method—Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.

Application of NanoString technologies in angioimmunoblastic T cell lymphoma

BACKGROUND

Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive disease. Most cancer diagnoses are determined by anatomical histology. Therefore, many samples are stored in FFPE blocks for H&E staining. However, RNAs extracted from the FFPE block have a high level of fragmentation, making it difficult to perform accurate DEG analysis using RNA sequencing.

OBJECTIVE

To overcome fragmented RNA's drawback in NGS application, we applied the NanoString nCounter^® technique of hybridization method that can be used for DEG analysis without PCR amplification.

METHODS

We characterized the gene expression profiling of AITLs though transcriptome analysis based on the nCounter^® PanCancer IO 360™ Panel and NanoString platform. To perform the analysis of differential expression gene (DEG) profiles in AITLs, we compared the NanoString data from eight AITL patients with a healthy control donor.

RESULTS

Ninety-one genes were up-regulated and six genes were down-regulated in AITLs compared to control. The Gene Ontology (GO) analysis of 97-DEGs revealed that they were closely related to cytokine, MAPK cascade, leukocyte differentiation, and immune response, suggesting that this affect the immune system. In addition, KEGG analysis revealed that AITL DEGs were found to be highly involved in cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway.

CONCLUSION

We believe that comprehensive multiplex studies, along with NanoString analysis, may be helpful to understand the molecular mechanisms of AITL, including mutations, gene expression, and protein expression studies.