Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma

BACKGROUNDDiffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin lymphoma. The development of immunotherapy greatly improves the patient prognosis but there are some exceptions. Thus, screening for better biomarkers for prognostic evaluation could contribute to the treatment of DLBCL patients.AIMTo screen the novel mediators involved in the development of DLBCL.METHODSThe {"type":"entrez-geo","attrs":{"text":"GSE60","term_id":"60"}}GSE60 dataset was applied to identify the differentially expressed genes (DEGs) in DLBCL, and the principal components analysis plot was used to determine the quality of the included samples. The protein-protein interactions were analyzed by the STRING tool. The key hub genes were entered into to the GEPIA database to determine their expressions in DLBCL. Furthermore, these hub gene alterations were analyzed in cBioportal. The UALCAN portal was employed to analyze the expression of the hub genes in different stages of DLBCL. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Score was conducted to evaluate the correlation between the gene expression and tumor purity. The gene-gene correlation analysis was conducted in the GEPIA. The stromal score analysis was conducted in TIMER to confirm the correlation between the gene expression and infiltrated stromal cells. The correlation between the indicated genes and infiltration level of cancer-associated fibroblasts (CAFs) was also completed in TIMER with two methods, MCP-Counter and Tumor immune dysfunction and exclusion. The correlation between fibronectin (FN1) protein level and secreted protein acidic and cysteine-rich (SPARC) messenger ribonucleic acid expression was confirmed in the cBioportal.RESULTSThe top 20 DEGs in DLBCL were identified, and the principal components analysis plot confirmed the quality of the significant DEGs. The pairwise correlation coefficient analysis among all samples showed that these DEGs have a certain co-expression pattern. The DEGs were subjected to STRING to identify the hub genes, alpha-2-macroglobulin (A2M), cathepsin B (CTSB), FN1, matrix metallopeptidase 9 (MMP9), and SPARC. The five hub genes were confirmed to be overexpressed in DLBCL. The cBioportal portal detected these five hub genes that had gene alteration, including messenger ribonucleic acid high amplification and missense mutation, and the gene alteration percentages of A2M, FN1, CTSB, MMP9, and SPARC were 5%, 8%, 5%, 2.7%, and 5%, respectively. Furthermore, the five hub genes had a potential positive correlation with tumor stage. The correlation analysis between the five genes and tumor purity confirmed that the five genes were overexpressed in DLBCL and had a positive correlation with the development of DLBCL. More interestingly, the five genes had a significant correlation with the stromal infiltration scores. The correlation analysis between the fives genes and CAFs also showed a significant value, among which the top two genes, FN1 and SPARC, had a remarkable co-expression pattern.CONCLUSIONThe top DEGs were identified, and the five hub genes were overexpressed in DLBCL. Furthermore, the gene alterations were confirmed and the positive correlation with tumor purity revealed the overexpression of the five genes and close association with the development of DLBCL. More interestingly, the five genes were positively correlated with stromal infiltration, especially in CAFs. The top two genes, FN1 and SPARC, showed a co-expression pattern, which indicates their potential as novel therapeutic targets for DLBCL.     

ITGA5 is an independent prognostic biomarker and potential therapeutic target for laryngeal squamous cell carcinoma

BackgroundIntegrin α5 (ITGA5) was involved in a variety of cancers. However, the role of ITGA5 in laryngeal squamous cell carcinoma (LSCC) remains unknown.MethodsThe expression of ITGA5 and the corresponding clinicopathological parameters of LSCC patients the TCGA database. Five datasets ({"type":"entrez-geo","attrs":{"text":"GSE51985","term_id":"51985"}}GSE51985, {"type":"entrez-geo","attrs":{"text":"GSE59102","term_id":"59102"}}GSE59102, {"type":"entrez-geo","attrs":{"text":"GSE84957","term_id":"84957"}}GSE84957, {"type":"entrez-geo","attrs":{"text":"GSE27020","term_id":"27020"}}GSE27020, and {"type":"entrez-geo","attrs":{"text":"GSE65858","term_id":"65858"}}GSE65858) were downloaded from the GEO database as validation sets. Kaplan–Meier plotter, Cox regression analysis, and nomogram were performed to determine the prognostic value of ITGA5 in LSCC. GO, KEGG, and GSEA were used to explore the underlying biological functions of ITGA5 in LSCC. The algorithms ESTIMATE and CIBERSORT were adopted to evaluate the association between ITGA5 and the infiltration of the immune cells. The algorithm pRRophetic was used to estimate the response to chemotherapeutic drugs.ResultsThe expression of ITGA5 was higher in the LSCC samples and linked to poor overall survival and recurrence‐free survival. Further, the Cox regression analysis confirmed that high expression of ITGA5 was an independent unfavorable prognostic factor. The predictive performance of nomogram based on the expression of ITGA5 was accurate and practical. The functional enrichment analysis confirmed that ITGA5 was related to the construction of the components and structures of the extracellular matrix. Finally, patients with high ITGA5 expression were more likely to benefit from docetaxel and gemcitabine.ConclusionThe expression of ITGA5 was elevated in the LSCC and was a predictor for prognosis and chemotherapeutic response in LSCC patients.     

MCAM is associated with metastasis and poor prognosis in osteosarcoma by modulating tumor cell migration

BackgroundAlthough there are standard treatment options for osteosarcoma (OS), the prognoses of patients with OS remain varied. Therefore, it is important to profile OS patients at a high risk of mortality to develop focused interventions. Although tumor biomarkers are closely associated with clinical outcomes, data on prognostic biomarkers for OS remain scarce.MethodsWe collected RNA expression profiles and clinical data of 90 OS patients from the GEO database (dataset {"type":"entrez-geo","attrs":{"text":"GSE21257","term_id":"21257"}}GSE21257 and {"type":"entrez-geo","attrs":{"text":"GSE39055","term_id":"39055"}}GSE39055) and 96 patients in the TARGET program. The data were analyzed using univariate Kaplan‐Meier survival analysis to screen candidate gene sets that might be associated with OS survival.ResultsOur analysis demonstrated that melanoma cell adhesion molecule (MCAM) was associated with overall survival of patients with OS in the three cohorts. The data showed that MCAM was upregulated in OS patients who had metastases within 5 years compared to those without metastases. GO analysis revealed that genes correlated with MCAM were mainly involved in cell migration and wound healing processes. In addition, wound healing assays and gene set enrichment analysis results from RNA sequencing data of small interfering (si)‐MCAM‐transfected OS cells demonstrated that MCAM modulated tumor cell migration.ConclusionsOur data demonstrate that MCAM may be a novel prognostic biomarker for OS. MCAM is associated with increased cell migration ability and risk of metastasis, thus leading to poor prognoses in OS patients.     

Identification of key genes in allergic rhinitis by bioinformatics analysis

ObjectiveThis study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.MethodsThe dataset {"type":"entrez-geo","attrs":{"text":"GSE19187","term_id":"19187"}}GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. {"type":"entrez-geo","attrs":{"text":"GSE44037","term_id":"44037"}}GSE44037 and {"type":"entrez-geo","attrs":{"text":"GSE43523","term_id":"43523"}}GSE43523 datasets were screened to validate critical genes.ResultsA total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six (CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset.ConclusionsOur findings suggest that hub genes play important roles in the development of AR.     

Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer

BackgroundPancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5‐year survival rate. Poor outcomes associated with PDA are attributable to late detection and inoperability. Most patients with PDA are diagnosed with locally advanced and metastatic disease. Such cases are primarily treated with chemotherapy and radiotherapy. Because of the lack of effective molecular targets, early diagnosis and successful therapies are limited. The purpose of this study was to screen key candidate genes for PDA using a bioinformatic approach and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.Materials and methodsTo identify novel genes associated with carcinogenesis and progression of PDA, we analyzed the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE62165","term_id":"62165"}}GSE62165, {"type":"entrez-geo","attrs":{"text":"GSE125158","term_id":"125158"}}GSE125158, and {"type":"entrez-geo","attrs":{"text":"GSE71989","term_id":"71989"}}GSE71989 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein‐protein interaction (PPI) network was constructed using STRING, and module analysis was performed using Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the differential expression of hub genes in patients with PDA. In addition, we verified the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.ResultsA total of 202 DEGs were identified and these were found to be enriched for various functions and pathways, including cell adhesion, leukocyte migration, extracellular matrix organization, extracellular region, collagen trimer, membrane raft, fibronectin‐binding, integrin binding, protein digestion, and absorption, and focal adhesion. Among these DEGs, 12 hub genes with high degrees of connectivity were selected. Survival analysis showed that the hub genes (HMMR, CEP55, CDK1, UHRF1, ASPM, RAD51AP1, DLGAP5, KIF11, SHCBP1, PBK, and HMGB2) may be involved in the tumorigenesis and development of PDA, highlighting their potential as diagnostic and therapeutic factors in PDA.ConclusionsIn summary, the DEGs and hub genes identified in the present study not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.     

Identification of autophagy‐related biomarker and analysis of immune infiltrates in oral carcinoma

BackgroundAutophagy plays a vital role in the progression of the tumor. We aimed to investigate the expression, prognostic value, and immune infiltration of autophagy‐related genes in oral carcinoma via bioinformatics analysis.MethodsThe microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE146483","term_id":"146483"}}GSE146483 and {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558) of oral carcinoma were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal and diseased groups were identified by the Limma package. The screened autophagy‐related gene was further validated by the human protein atlas (HPA) database, TCGA database, and {"type":"entrez-geo","attrs":{"text":"GSE78060","term_id":"78060"}}GSE78060 dataset.ResultsA total of 18 upregulated (top 10: EGFR, TNF, FADD, AURKA, E2F1, CHEK1, BRCA1, BIRC5, EIF2AK2, and CSF2) and 31 downregulated (top 10: MAP1LC3A, PARK2, AGT, IGF1, TP53INP1, CXCL12, IKBKB, SESN1, ULK2, and RRAGD) autophagy‐related (DEGs) were identified, and FADD was found to be related to the prognosis of oral cancer patients. Gene set enrichment analysis indicated that FADD‐associated genes were significantly enriched in immune‐related pathways. Moreover, correlation analysis revealed that FADD expression was associated with immune infiltrates. Upregulation of FADD is associated with poor survival and immune infiltrates in oral cancer.ConclusionWe speculated that FADD is involved in the immune regulation of oral cancer, as well as autophagy.     

Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BackgroundThe tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.MethodsThe TME mRNAs were screened following the immune and stromal scores with the data from {"type":"entrez-geo","attrs":{"text":"GSE15641","term_id":"15641"}}GSE15641, {"type":"entrez-geo","attrs":{"text":"GSE29609","term_id":"29609"}}GSE29609, {"type":"entrez-geo","attrs":{"text":"GSE36895","term_id":"36895"}}GSE36895, {"type":"entrez-geo","attrs":{"text":"GSE46699","term_id":"46699"}}GSE46699, {"type":"entrez-geo","attrs":{"text":"GSE53757","term_id":"53757"}}GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.ResultsIn the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group.ConclusionThe TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients'' therapy.     

Identification of ITGAX and CCR1 as potential biomarkers of atherosclerosis via Gene Set Enrichment Analysis

ObjectiveAtherosclerosis (AS) is a life-threatening disease in aging populations worldwide. However, the molecular and gene regulation mechanisms of AS are still unclear. This study aimed to identify gene expression differences between atheroma plaques and normal tissues in humans.MethodsThe expression profiling dataset {"type":"entrez-geo","attrs":{"text":"GSE43292","term_id":"43292"}}GSE43292 was obtained from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were identified between the atheroma plaques and normal tissues via GEO2R, and functional annotation of the DEGs was performed by GSEA. STRING and MCODE plug-in of Cytoscape were used to construct a protein–protein interaction (PPI) network and analyze hub genes. Finally, quantitative polymerase chain reaction (qPCR) was performed to verify the hub genes.ResultsOverall, 134 DEGs were screened. Functional annotation demonstrated that these DEGs were mainly enriched in sphingolipid metabolism, apoptosis, lysosome, and more. Six hub genes were identified from the PPI network: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. qPCR analysis suggested that the relative expression levels of the six hub genes were significantly higher in AS samples.ConclusionsWe used bioinformatics to identify six hub genes: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. These hub genes are potential promising diagnostic and therapeutic targets for AS.     

Gonadotropin-releasing hormone receptor inhibits triple-negative breast cancer proliferation and metastasis

BackgroundGonadotropin-releasing hormone receptor (GnRHR) is expressed in several malignant tumors and inhibits the proliferation and metastasis of cancer cells, but its role in triple-negative breast cancers (TNBCs) is unclear. This study investigated the biological effects of GnRHR and their influence on TNBC prognosis.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE21653","term_id":"21653"}}GSE21653 database was used to obtain information about GnRHR expression and clinicopathological factors in patients with TNBC. GnRHR was activated in cultured MDA-MB-231 and MDA-MB-468 cells by leuprolide acetate and antagonized by elagolix sodium. Cell proliferation was assessed by the cell counting kit-8 and colony formation assays. Cell metastasis was detected by the wound healing assay and Transwell assay. Apoptosis and the cell cycle were investigated by flow cytometry. GnRHR protein expression was determined by western blotting.ResultsGnRHR mRNA expression was significantly higher in patients with TNBC than in hormone receptor+/human epidermal growth factor receptor (HER)2– and HER2+ patients with breast cancer. Patients with high GnRHR expression had significantly better disease-free survival than those with lower expression. Activated GnRHR significantly inhibited cell proliferation and metastasis, increased apoptosis, and enhanced GnRHR protein expression levels.ConclusionGnRHR inhibits TNBC proliferation and metastasis, suggesting it could be targeted for TNBC treatment.     

Constructing a 10‐core genes panel for diagnosis of pediatric sepsis

BackgroundThe lack of sensitivity and specificity of most biomarkers or the lack of relevant studies to demonstrate their effectiveness in sepsis.MethodsDownloaded three sets of sepsis expression data ({"type":"entrez-geo","attrs":{"text":"GSE13904","term_id":"13904"}}GSE13904, {"type":"entrez-geo","attrs":{"text":"GSE25504","term_id":"25504"}}GSE25504, {"type":"entrez-geo","attrs":{"text":"GSE26440","term_id":"26440"}}GSE26440) from GEO. Then, using the R limma package and WGCNA analysis tocore genes. Finally, the value of these core genes was confirmed by clinical samples.ResultsCompared to normal samples, we obtain many abnormally expressed genes in the pediatric sepsis. WGCNA co‐expression analysis showed that genes from blue and turquoise module were close correlation with pediatric sepsis. The top 20 genes (TIMP2, FLOT1, HCK, NCF4, SERPINA1, IL17RA, PGD, PRKCD, GLT1D1, ALOX5, SIRPA, DOK3, ITGAM, S100A11, ZNF438, PLIN3, LTB4R, TSPO, MAPK14, GAS7) of the blue module of pediatric sepsis were mainly enriched in neutrophil degranulation, etc The top 20 genes (TBC1D4, NOL11, NLRC3, ZNF121, DYRK2, ABCE1, MAGEH1, TMEM263, MCUB, MALT1, DDHD2, TRAC, NOC3L, LCK, TRMT61B, ZNF260, ENOPH1, LOC93622, NAE1, TRBC1) for turquoise module were mainly enriched in rRNA‐containing ribonucleoprotein complexes exported from the nucleus, etc The selected hub gene of pediatric sepsis was combined with the markers of cell surface and found 10 core genes (HCK, PRKCD, SIRPA, DOK3, ITGAM, LTB4R, MAPK14, MALT1, NLRC3, LCK). ROC showed that AUC of the 10 core genes for diagnosis of pediatric sepsis was above 0.9.ConclusionThere were many abnormally expressed genes in patients with pediatric sepsis. The panel constructed by the 10 core genes was expected to become a biomarker panel for clinical application of pediatric sepsis.     

A novel circRNA–miRNA–mRNA network reveals hsa-circ-0040039 as a biomarker for intervertebral disc degeneration

ObjectiveAlterations in the structure and function of intervertebral discs by multifaceted chronic processes can result in intervertebral disc degeneration (IDD). The mechanisms involved in IDD are still unknown.MethodsWe investigated the possible mechanisms underlying IDD using a bioinformatics analysis of publicly available microarray expression datasets and built a circular RNA–microRNA–mRNA (circRNA–miRNA–mRNA) network based on the results. Datasets {"type":"entrez-geo","attrs":{"text":"GSE67566","term_id":"67566"}}GSE67566 and {"type":"entrez-geo","attrs":{"text":"GSE116726","term_id":"116726"}}GSE116726 were downloaded from the Gene Expression Omnibus (GEO) and analyzed using the limma package in R. The CircInteractome database was used to detect miRNAs related to circRNA, and TargetScan, miRDB, and miRTarBase were used to predict target mRNAs. Key target genes were annotated using Gene Ontology terms.ResultsThe circRNA hsa-circ-0040039 was found to have the top log fold-change score. Analysis using Metascape showed that the associated genes were enriched mainly in the cell cycle. The Cytoscape plugin MCODE predicted that two members of the RAS oncogene family—RAB1A and RAB1B—and multiple coagulation factor deficiency (MCFD2) may play key roles in IDD.ConclusionOur results suggested that hsa-circ-0040039 and the related network may be potential biomarkers for IDD.     

Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease

BackgroundCoronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD.MethodWe downloaded two mRNA profiles, {"type":"entrez-geo","attrs":{"text":"GSE66360","term_id":"66360"}}GSE66360 and {"type":"entrez-geo","attrs":{"text":"GSE60993","term_id":"60993"}}GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed.ResultsA total of 2127 differentially expressed genes (DEGs) were identified in {"type":"entrez-geo","attrs":{"text":"GSE66360","term_id":"66360"}}GSE66360, and 527 DEGs in {"type":"entrez-geo","attrs":{"text":"GSE60993","term_id":"60993"}}GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls.ConclusionWe identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.     

Long noncoding RNA LUNAR1 associates with cell proliferation and predicts a poor prognosis in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous group of B-cell lymphomas. Exploring a novel and important biomarker is indispensable for understanding the mechanism and clinical course of DLBCL. Emerging studies have shown that aberrant expression of long noncoding RNA (lncRNAs) is strongly associated with carcinogenesis. The aim of this study was to investigate the value of lncRNA LUNAR1 in DLBCL. Quantitative real-time PCR was performed to illustrate the patterns of LUNAR1 expression in tumor tissues and cell lines. The higher expression of LUNAR1 was significantly correlated with stage, rituximab and IPI. Univariate and multivariate analyses showed that LUNAR1 expression served as an independent predictor for overall survival and progression-free survival. Receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic values and the area under the ROC curve of LUNAR1 was up to 0.9420. Further experiments revealed that LUNAR1 knockdown significantly repressed cell proliferation of DLBCL by regulating E2F1, cyclin D1 and p21. In conclusion, our results indicate that LUNAR1 may serve as a candidate prognostic biomarker through growth regulation in DLBCL.     

miR‐548d‐3p inhibits the invasion and migration of gastric cancer cells by targeting GKN1

BackgroundThe aim of this study was to explore the function and mechanism of GKN1 in gastric cancer (GC) progression.MethodsFirstly, we used GEO2R to perform differential gene analysis on {"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942 and {"type":"entrez-geo","attrs":{"text":"GSE79973","term_id":"79973"}}GSE79973 and constructed the protein–protein interaction network of differential genes by STRING. Next, the cytoHubba, Mcode plugins, and GEPIA were used to obtain our follow‐up research object GKN1. Then, the function of GKN1 in GC was verified by scratch and transwell assay in GC cells. We further analyzed the genes related to GKN1 through LinkedOmics, and exported top 100 genes positively or negatively correlated with GKN1. Meanwhile, Metascape was performed on these genes. Finally, we analyzed the miRNAs that bind to GKN1 through the miRDB and verified the correlation between miR‐548d‐3p and GKN1 using dual‐fluorescence and quantitative PCR experiments.ResultsBioinformatics analysis showed that there were 52 differential genes on {"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942 and {"type":"entrez-geo","attrs":{"text":"GSE79973","term_id":"79973"}}GSE79973. In addition, the results of functional assays indicated that overexpressed GKN1 can inhibit GC cell migration and invasion, while GKN1 knockdown demonstrated the opposite effect. Additionally, Metascape analysis results showed that the 3′‐UTR region of mRNA is rich in AU sequences, based on which we infer that mRNA may be regulated by miRNA. Dual‐fluorescence and quantitative PCR assays clarified that miR‐548d‐3p may be one of the target miRNAs of GKN1, which was up‐regulated in GC tissues.ConclusionsIn summary, we clarified that miR‐548d‐3p regulates GKN1 to participate in GC cell migration and invasion, and provides a possible target for the prognostic diagnosis and treatment of GC.     

Interleukin-27 is a novel candidate diagnostic biomarker for bacterial infection in critically ill children

Introduction

Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children.

Methods

Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis.

Results

Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone.

Conclusions

Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27.The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number {"type":"entrez-geo","attrs":{"text":"GSE4607","term_id":"4607"}}GSE4607.     

IFI27 is a potential therapeutic target for HIV infection

BackgroundTherapeutic studies against human immunodeficiency virus type 1 (HIV-1) infection have become one of the important works in global public health.MethodsDifferential expression analysis was performed between HIV-positive (HIV+) and HIV-negative (HIV-) patients for {"type":"entrez-geo","attrs":{"text":"GPL6947","term_id":"6947"}}GPL6947 and {"type":"entrez-geo","attrs":{"text":"GPL10558","term_id":"10558"}}GPL10558 of {"type":"entrez-geo","attrs":{"text":"GSE29429","term_id":"29429"}}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.ResultsWe 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.ConclusionThe 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.     

Bioinformatics analysis of key biomarkers for retinoblastoma

ObjectiveTo identify key genes involved in occurrence and development of retinoblastoma.MethodsThe microarray dataset, {"type":"entrez-geo","attrs":{"text":"GSE5222","term_id":"5222"}}GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape.ResultsDEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes.ConclusionOur research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.     

Differential analysis revealing APOC1 to be a diagnostic and prognostic marker for liver metastases of colorectal cancer

BACKGROUNDColorectal cancer (CRC) is one of the most malignant gastrointestinal cancers worldwide. The liver is the most important metastatic target organ, and liver metastasis is the leading cause of death in patients with CRC. Owing to the lack of sensitive biomarkers and unclear molecular mechanism, the occurrence of liver metastases cannot be predicted and the clinical outcomes are bad for liver metastases. Therefore, it is very important to identify the diagnostic or prognostic markers for liver metastases of CRC.AIMTo investigate the highly differentially expressed genes (HDEGs) and prognostic marker for liver metastases of CRC.METHODSData from three NCBI Gene Expression Omnibus (GEO) datasets were used to show HDEGs between liver metastases of CRC and tumour or normal samples. These significantly HDEGs of the three GEO datasets take the interactions. And these genes were screened through an online tool to explore the prognostic value. Then, TIMER and R package were utilized to investigate the immunity functions of the HDEGs and gene set enrichment analysis was used to explore their potential functions.RESULTSBased on the selection criteria, three CRC datasets for exploration ({"type":"entrez-geo","attrs":{"text":"GSE14297","term_id":"14297"}}GSE14297, {"type":"entrez-geo","attrs":{"text":"GSE41258","term_id":"41258"}}GSE41258, and {"type":"entrez-geo","attrs":{"text":"GSE49355","term_id":"49355"}}GSE49355) were chosen. Venn diagrams were used to show HDEGs common to the six groups and 47 HDEGs were obtained. The HDEGs were shown by using STRING and Cytoscape software. Based on the TCGA database, APOC1 showed significantly different expression between N2 and N0, and N2 and N1. And there was also a significant difference in expression between T2 and T4, and between T2 and T3. In 20 paired CRC and normal tissues, quantitative real-time polymerase chain reaction illustrated that the APOC1 mRNA was strongly upregulated in CRC tissues (P = 0.014). PrognoScan and GEPIA2 revealed the prognostic value of APOC1 for overall survival and disease-free survival in CRC (P < 0.05). TIMER showed that APOC1 has a close relationship with immune infiltration (P < 0.05).CONCLUSIONAPOC1 is a biomarker that is associated with both the diagnosis and prognosis of liver metastases of CRC.     

LAMTOR3 is a prognostic biomarker in kidney renal clear cell carcinoma

ObjectiveThe objective of the study was to investigate the expression of LAMTOR3 in kidney renal clear cell carcinoma (KIRC) and its clinical significance.MethodsThe expression of LAMTOR3 in KIRC and its relationship with clinical features were analyzed using the UALCAN online database. The results were verified using KIRC gene chip data and clinical specimens. The prognosis of KIRC patients was analyzed with the GEPIA2 database. GO, KEGG, and GSEA analyses were conducted to analyze the possible molecular mechanism of LAMTOR3 in KIRC. Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining were used for histopathological detection.ResultsUALCAN database analysis showed that LAMTOR3 expression in KIRC was significantly lower than in normal kidney tissues and correlated with the clinical stage, pathological grade, and tumor genotype (p < .05). {"type":"entrez-geo","attrs":{"text":"GSE53757","term_id":"53757"}}GSE53757 dataset analysis consistently showed that the expression of LAMTOR3 in KIRC was significantly lower than in normal kidney tissues (p < .01). GEPIA2 database analysis indicated that patients with low LAMTOR3 expression had poor overall and disease‐free survival rates. GSEA analysis suggested that LAMTOR3 positively regulated the citrate cycle and drug metabolism cytochrome P450 and negatively regulated folate biosynthesis and olfactory transduction. The expression of LAMTOR3 in KIRC was also significantly correlated with immune cell infiltration. Finally, IHC showed that LAMTOR3 expression in the KIRC tissues was lower than in the adjacent normal tissues.ConclusionLAMTOR3 expression is significantly lower in KIRC. LAMTOR3 may be a potential marker for KIRC diagnosis and therapy.