Expression Profiling Datasets Research Articles

Unveiling Novel Therapeutic Targets for CAR Therapy in Multiple Myeloma through Single-Cell RNA Sequencing

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells. Among the different therapeutic options in this setting, chimeric antigen receptor (CAR) T cells recently showed unprecedent achievements in term of progression free survival. However, despite advancements in current therapies, the disease's heterogeneity remains a major challenge and all patients unavoidably relapse, rendering innovative approaches to identify precise therapeutic targets eagerly awaited. Along this line, the revolution of single-cell (sc) technologies brings new opportunities to identify precise therapeutic targets, including abundant and unique surface proteins for CAR therapy. In this study, by analyzing individual cancer cells at sc level, we aim to identify novel surface targets, facilitating the development of novel personalized cellular therapies as well as treatment sequences. To this end, we used samples collected from 9 publicly available single-cell RNA sequencing (scRNA-seq) datasets (GEO accession numbers: GSE176131, GSE189460, GSE223060, GSE210079, GSE145977, GSE124310, GSE161801, GSE163278, GSE161722) obtained from 156 patients affected by monoclonal gammopathies (19 monoclonal gammopathy of undetermined significance (MGUS), 10 smoldering multiple myeloma (SMM), 78 MM and 25 relapsed/refractory multiple myeloma (RRMM)) and 24 normal bone marrows (NBM), to conduct an unbiased search for genes showing specific expression in plasma cells (PCs), regardless of disease status. We found 15 genes showing differential expression in PCs (adj.pval<0.01 and logFC>1) and coding for surface proteins: TNFRSF17(BCMA), SDC1 (CD138), FCRL5 (GPRC5D), TNFRSF13B (TACI), CD38, SLAMF7 (CS1), CD59, FCGR2B, FGFR3, SLC44A1 (CTL1), CD320, FCRL2, IL15RA, INSR and SLAMF1. As expected, most of these molecules already represent critical therapeutic targets. After completing our initial analysis, we compared PCs transcriptomes from MM/RRMM patients with NBM, MGUS, and SMM samples. Among previously identified genes, CD320 only was significantly overexpressed in MM PCs. Next, we investigated associations between cytogenetic-related cell subclusters and previously identified surface markers. Interestingly, we found a significant association between the expression levels of TACI/CD59 and the overexpression of CCND2 and MAF, both associated with the presence of t(14:16). Next, we decided to focus on the first five genes (according to expression ranking) that do not currently have an active clinical development program in advanced phases, namely: TNFRSF13B, CD59, FCGR2B, SLC44A1, and CD320 (Fig. 1 A, values dichotomized based on their median) to uncover their (potential) impact/association with patient's outcome. In our analysis of different available expression profiling datasets (GSE4204, GSE2658, GSE57317, GSE4581, GSE4452, GSE9782, the CoMMpass study NCT01454297) involving around 2000 MM patients, we observed TNFRSF13B, CD59, and FCGR2B expression correlated with improved outcome while CD320 and SLC44A1 expression associated with worse outcome (Fig. 1 B). In conclusion, we integrated results from both scRNAseq and bulk-RNAseq/microarray data to identify new targets genes to be used in the context of personalized medicine. Interestingly, CD320 emerges as a potential candidate biomarker useful for monitoring disease evolution and predicting worse outcomes. Additionally, this approach led us to identify a series of potential new targets for MM patients, depicting a new scenario where each patient could be “screened” to identify the best molecule to be targeted. While further investigation is necessary to assess off-target toxicity and confirm clinical relevance, our analyses significantly streamline the search for tumor markers with a method potentially applicable to different malignancies, bringing us closer to identifying the best candidates for effective CAR therapy.

CXADR promote epithelial–mesenchymal transition in endometriosis by modulating AKT/GSK-3β signaling

ABSTRACT Endometriosis is a benign high prevalent disease exhibiting malignant features. However, the underlying pathogenesis and key molecules of endometriosis remain unclear. By integrating and analysis of existing expression profile datasets, we identified coxsackie and adenovirus receptor (CXADR), as a novel key gene in endometriosis. Based on the results of immunohistochemistry (IHC), we confirmed significant down-regulation of CXADR in ectopic endometrial tissues obtained from women with endometriosis compared with healthy controls. Further in vitro investigation indicated that CXADR regulated the stability and function of the phosphatases and AKT inhibitors PHLPP2 (pleckstrin homology domain and leucine-rich repeat protein phosphatase 2) and PTEN (phosphatase and tensin homolog). Loss of CXADR led to phosphorylation of AKT and glycogen synthase kinase-3β (GSK-3β), which resulted in stabilization of an epithelial–mesenchymal transition (EMT) factor, SNAIL1 (snail family transcriptional repressor 1). Therefore, EMT processs was induced, and the proliferation, migration and invasion of Ishikawa cells were enhanced. Over-expression of CXADR showed opposite effects. These findings suggest a previously undefined role of AKT/GSK-3β signaling axis in regulating EMT and reveal the involvement of a CXADR-induced EMT, in pathogenic progression of endometriosis.

Bioinformatics analysis of immune cell infiltration patterns and potential diagnostic markers in atherosclerosis

This study aimed to investigate efficient diagnostic markers and molecular mechanisms of atherosclerosis and to analyze the role of immune infiltration through bioinformatics analysis. Expression profile datasets (GSE28829 and GSE43292) of patients with atherosclerosis and healthy controls were downloaded from the GEO database. Glutamine (GLN) metabolism-associated genes were obtained from the Molecular Signatures Database (MSigDB). The limma package in R was used to identify differentially expressed genes (DEGs). Significant modules were filtered using Weighted Gene Co-expression Network Analysis (WGCNA). MSigDB sets were subjected to Gene Set Enrichment Analysis and Gene Set Variation Analysis. The biological functions of DEGs were examined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. STRING and Cytoscape software were used to identify hub genes and functional modules through protein–protein interaction (PPI) network analysis. The xCell software was adopted to assess the composition patterns of immune and stromal cells. Correlation analyses were performed for key genes and immune cell subtypes. We identified 308 DEGs and GLN-associated genes. Functional enrichment analysis showed that these genes were strongly enriched in muscle contract, muscle tissue development, cutile fiber, mycobacterial, and actin binding. Enriched KEGG pathways comprised dilated cardiomyopathy, hypertrophic cardiomyopathy, and the cAMP signaling pathway. In the PPI network analysis, 27 genes were identified as hub genes. The area under the curve (AUC) values of 27 biomarkers were relatively high, indicating high diagnostic values. The atherosclerosis group exhibited a markedly higher degree of infiltration than the control group. This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis. It provides a new perspective on immune responses that facilitates exploration of the molecular mechanisms of atherosclerosis.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species.

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thalianaand Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sitesand chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysisand Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

Background: Invasive ductal carcinoma (IDC) is the most common type of breast cancer (BC) worldwide. Nowadays, due to its heterogeneity and high capacity for metastasis, it is necessary to discover novel diagnostic and prognostic biomarkers. Therefore, this study aimed to identify novel candidate prognostic genes for IDC using an integrated bioinformatics approach. Methods: Three expression profile data sets were obtained from GEO (GSE29044, GSE3229, and GSE21422), from which differentially expressed genes (DEGs) were extracted for comparative transcriptome analysis of experimental groups (IDC versus control). Next, STRING was utilized to construct a protein interaction network with the shared DEGs, and MCODE and cytoHubba were used to identify the hub genes, which were then characterized using functional enrichment analysis in DAVID and KEGG. Finally, using the Kaplan-Meier tracer database, we determined the correlation between the expression of hub genes and overall survival in BC. Results: We identified seven hub genes (Kinesin-like protein KIF23 [KIF23], abnormal spindle-like microcephaly [ASPM]-associated protein [ASPMAP], Aurora kinase A [AURKA], Rac GTPase-activating protein 1 [RACGAP1], centromere protein F [CENPF], hyaluronan-mediated motility receptor [HMMR], and protein regulator of cytokinesis 1 [PRC1]), which were abundant in microtubule binding and tubulin binding, pathways linked to fundamental cellular structures including the mitotic spindle, spindle, microtubule, and spindle pole. The role of these genes in the pathophysiology of IDC is not yet well characterized; however, they have been associated with other common types of BC, modulating pathways such as Wnt/β-catenin, the epithelial-to-mesenchymal transition (EMT) process, chromosomal instability (CIN), PI3K/AKT/mTOR, and BRCA1 and BRCA2, playing an important role in its progression and being associated with a poor prognosis, thus representing a way to improve our understanding of the process of tumorigenesis and the underlying molecular events of IDC. Conclusions: Genes identified may lead to the discovery of new prognostic targets for IDC.

Identification of signature of tumor-infiltrating CD8 T lymphocytes in prognosis and immunotherapy of colon cancer by machine learning

BackgroundTo explore the specific marker of CD8+ T cell subsets which are closely related to the prognosis and immunotherapy of patients with colon cancer. Methods18 kinds of immune cell expression profile data sets were obtained from GEO database. Compared with other immune cell types, the specific markers of CD8 (+) T cells (TI-CD8) in colorectal cancer were screened. Regression analyses were used to further screen prognostic related genes and construct a prognostic evaluation model. The patients were stratified and analyzed according to the risk scores, KRAS mutation status, stage, lymphatic infiltration and other indicators. The landscape of infiltration level, mutation and copy number variation of immune subsets in high and low TI-CD8Sig score groups were compared and analyzed. The difference of drug response between high and low TI-CD8Sig score groups was analyzed. Differential expression of the model genes was verified by the HPA database. ResultsSix prognostic-related CD8T cell-specific gene targets were further screened, and the prognostic evaluation model was constructed. The AUC value of the model is >0.75. FAT3 and UNC13C showed a high mutation state in the low-risk group, while USH2A, MUC5B et al. specifically showed a high mutation state in the high-risk group. Compared with the low-risk group, the high-risk group had lower effective rate of drug response. The expression of PD-1 gene was positively correlated with the level of TI-CD8Sig score. ConclusionThe risk assessment model based on CD8T cell-specific marker genes can effectively predict the prognosis and the drug response of patients with CRC.

Identification of novel potential genes in testicular germ cell tumors: A transcriptome analysis.

Testicular germ cell tumors (TGCTs), containing pure seminoma and non-seminoma, occupy the most majority of testicular cancers in adolescents and young men, which has increased dramatically in recent decades. Therefore, it is important to find crucial genes for improving diagnosis and prognosis in TGCTs. However, the diagnostic and prognostic markers of TGCTs are limited. In this study, our main objective is to explore novel potential genes that can be used as diagnostic and prognostic biomarkers in TGCTs. Our study detected 732 differentially expressed genes (DEGs) using three microarray expression profiling datasets from Gene Expression Omnibus (GEO). Multiple analysis was performed to identify the roles of DEGs, including pathway and functional enrichment analysis, protein-protein interaction (PPI) network analysis, module analysis, and survival analysis. In total, 322 upregulated genes and 406 downregulated genes were identified as DEGs The functional and pathway enrichment analysis shows that DEGs were highly enriched in multiple biological attributes such as T cell activation, reproduction in multicellular organism, sperm flagellum, antigen processing and presentation Then, seven potential crucial genes were identified via PPI network analysis, module analysis, and survival analysis. Furthermore, 7 potential crucial genes had shown to play a key role in regulating immune cell infiltration level in patients with TGCTs. We identified seven potential crucial genes (LAPTM5, NCF2, PECAM1, CD14, COL4A2, ANPEP and RGS1), which may be molecular markers in improving the way of diagnosis and prognosis in TGCTs.

Interspecies co-expression analysis of lateral root development using inducible systems in rice, Medicago, and Arabidopsis.

Lateral roots are crucial for plant growth and development, making them an important target for research aiming to improve crop yields and food security. However, their endogenous ontogeny and, as it were, stochastic appearance challenge their study. Lateral Root Inducible Systems (LRIS) can be used to overcome these challenges by inducing lateral roots massively and synchronously. The combination of LRISs with transcriptomic approaches significantly advanced our insights in the molecular control of lateral root formation, in particular for Arabidopsis. Despite this success, LRISs have been underutilized for other plant species or for lateral root developmental stages later than the initiation. In this study, we developed and/or adapted LRISs in rice, Medicago, and Arabidopsis to perform RNA-sequencing during time courses that cover different developmental stages of lateral root formation and primordium development. As such, our study provides three extensive datasets of gene expression profiles during lateral root development in three different plant species. The three LRISs are highly effective but timing and spatial distribution of lateral root induction vary among the species. Detailed characterization of the stages in time and space in the respective species enabled an interspecies co-expression analysis to identify conserved players involved in lateral root development, as illustrated for the AUX/IAA and LBD gene families. Overall, our results provide a valuable resource to identify potentially conserved regulatory mechanisms in lateral root development, and as such will contribute to a better understanding of the complex regulatory network underlying lateral root development.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration.

Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease. The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed. A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively. ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AimAutophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.MethodsTwo gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.ResultsAccording to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.ConclusionBioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma.

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

ENO2 as a Biomarker Regulating Energy Metabolism to Promote Tumor Progression in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and metastatic type of renal cell carcinoma. Despite significant advancements, the current diagnostic biomarkers for ccRCC lack the desired specificity and sensitivity, necessitating the identification of novel biomarkers and elucidation of their underlying mechanisms. Three gene expression profile datasets were obtained from the GEO database, and differentially expressed genes (DEGs) were screened. Gene Ontology and KEGG pathway analysis were conducted in ccRCC. To clarify the diagnosis and prognostic role of ENO2, Kaplan-Meier analysis and Cox proportional hazards regression analysis were performed. Functional experiments were also carried out to verify the significant role of ENO2 in ccRCC. Finally, tumor mutational burden analysis was utilized to investigate the potential role of ENO2 in gene mutations in ccRCC. The study showed that ENO2 is a potential biomarker for the diagnosis of ccRCC and can independently predict the clinical prognosis of ccRCC. Furthermore, we found that ENO2 can promote the occurrence and progression of ccRCC by affecting the glycolysis level of cells through the "Warburg effect". These findings provide new theories for the occurrence and development of ccRCC and can help formulate new strategies for its diagnosis and treatment.

RNA-Sequencing and Bioinformatics Analysis of Exosomal Long Noncoding RNAs Revealed a Novel ceRNA Network in Stable COPD.

Exosomes are able to exchange their bioactive RNA cargo to recipient cells. In COPD, exosomes can be controlled and engineered for its use as targeted diagnostic and therapeutic tool.Our study explored novel lncRNAs and mRNAs in plasma exosomes that could be involved in the pathogenesis of COPD. High-throughput sequencing was conducted to detect the alterations in the expression of exosomal lncRNAs and mRNAs. Gene ontology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to determine the significant functions and pathways associated with differentially expressed (DE) lncRNAs. The mRNA expression profile dataset, GSE76925, and microRNA expression profile dataset, GSE70080, were obtained from the GEO database. Venn diagrams were used to find common DE mRNAs between my mRNAs dataset and GSE76925. These common DEGs were subjected to PPI analyses to identify Hub genes. Subsequently, Venn diagrams were used to identify common genes between the target genes of DE-miRNAs and Hub genes as well as DE-miRNAs and my lncRNAs dataset. Finally, a lncRNA-miRNA-mRNA co-expression network was constructed byprediction using proprietary software. The lncRNA and mRNA expressions were then validated by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). We identified 1578 differentially regulated lncRNAs and 3071 differentially regulated mRNAs. GO and KEGG pathway analyses suggested that the DE lncRNAs are involved in the pathogenesis of COPD. A lncRNA-miRNA-mRNA meshwork was established to predict the potential interactions among these RNAs. RP3-329A5.8 and MRPS11 expression was then subjected to qRT-PCR for validation. Correlations between MRPS11 and clinic-pathological features were explored. Our study provided a set of lncRNAs and mRNAs that may be involved in the pathogenesis of COPD, thereby highlighting the need for further research on both diagnostic biomarkers and molecular mechanisms.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration.

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers

BackgroundCellular senescence plays an essential role in the development and progression of end-stage renal disease (ESRD). However, the detailed mechanisms phenomenon remains unclear.MethodsThe mRNA expression profiling dataset GSE37171 was taken from the Gene Expression Omnibus (GEO) database. The cell senescence-associated hub genes were selected by applying protein–protein interaction (PPI), followed by correlation analysis, gene interaction analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We next explored the relationships of hub genes with miRNAs, TFs, and diseases. The absolute abundance of eight immune cells and two stromal cells were calculated by MCPcount and the correlation of hub genes with these ten cells was analyzed. Lasso was used to selecting for trait genes. ROC curves and DCA decision curves were used to assess the accuracy and predictive power of the trait genes.ResultsA total of 65 cellular senescence signature genes were identified among patients and controls. The PPI network screened out ten hub genes. GO and KEGG indicated that ten hub genes were associated with ESRD progression. Transcription factor gene interactions and common regulatory networks of miRNAs were also identified in the datasets. The hub genes were significantly correlated with immune cells and stromal cells. Then the lasso model was constructed to screen out the five most relevant signature genes (FOS, FOXO3, SIRT1, TP53, SMARCA4). The area under the ROC curve (AUC) showed that these five characteristic genes have good resolving power for the diagnostic model.ConclusionsOur findings suggested that cellular senescence-associated genes played an important role in the development of ESRD and immune regulation.

Hypoxia-related genes and PD-L1 inhibitor resistance in hepatocellular carcinoma.

158 Background: PD-L1 inhibitor in combination with anti-angiogenic drug has become the first-line treatment strategy for unresectable hepatocellular carcinoma (HCC). However, PD-L1 inhibitor resistance remains an essential issue in treating HCC. It has been proved in the tumor microenvironment that hypoxia-induced increase in PD-L1 expression makes a significant impact on drug resistance. Methods: Two public datasets of gene expression profiles (GSE 14520 and GSE 41666) from the Gene Expression Omnibus (GEO) database were analyzed using bioinformatics: (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6). HCC-signature and hypoxia-related genes were identified as the differentially expressed genes (DEGs). Gene set enrichment analyses (GSEA) were performed on the DEGs. Furthermore, multiple regression analysis on the TCGA-LIHC dataset (N=371) and construction of the protein-protein interaction (PPI) network were performed to investigate potential PD-L1 regulatory genes and hub genes. Results: A total of 52 genes overlapped in HCC-signature and hypoxia-related DEGs. Five genes were identified by GSEA as being concurrently associated with the PD-L1 expression pathways. Multiple regression analysis results showed 14 potential PD-L1 regulatory genes. Ten hub genes were identified in the PPI network. Finally, three genes (DLGAP5, KIF20A, and TPX2) were found in common and may be regulatory genes affecting PD-L1 expression. Conclusions: In conclusion, our study provides new insights into the potential hypoxia-related mechanisms of PD-L1 inhibitor resistance and contributes to exploring new therapeutic strategies for treating unresectable HCC.

Identification of Drug Targets and Agents Associated with Hepatocellular Carcinoma through Integrated Bioinformatics Analysis.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally. The mechanisms underlying the development of HCC are mostly unknown till now. The main goal of this study was to identify potential drug target proteins and agents for the treatment of HCC. The publicly available three independent mRNA expression profile datasets were downloaded from the NCBI-GEO database to explore common differentially expressed genes (cDEGs) between HCC and control samples using the Statistical LIMMA approach. Hub-cDEGs as drug targets highlighting their functions, pathways, and regulators were identified by using integrated bioinformatics tools and databases. Finally, Hub-cDEGs-guided top-ranked drug agents were identified by molecular docking study for HCC. We identified 160 common DEGs (cDEGs) from three independent mRNA expression datasets in which ten cDEGs (CDKN3, TK1, NCAPG, CDCA5, RACGAP1, AURKA, PRC1, UBE2T, MELK, and ASPM) were selected as Hub-cDEGs. The GO functional and KEGG pathway enrichment analysis of Hub-cDEGs revealed some crucial cancer-stimulating biological processes, molecular functions, cellular components, and signaling pathways. The interaction network analysis identified three TF proteins and five miRNAs as the key transcriptional and post-transcriptional regulators of HubcDEGs. Then, we detected the proposed Hub-cDEGs guided top-ranked three anti-HCC drug molecules (Dactinomycin, Vincristine, Sirolimus) that were also highly supported by the already published top-ranked HCC-causing Hub-DEGs mediated receptors. The findings of this study would be useful resources for diagnosis, prognosis, and therapies of HCC.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.