Identification of Potential Hub Genes and miRNA-mRNA Pairs Related to the Progression and Prognosis of Cervical Cancer Through Integrated Bioinformatics Analysis

In recent years, the incidence and mortality of cervical cancer have increased worldwide. At the same time, increasing data have confirmed that miRNA-mRNA plays a positive or negative regulatory role in many cancers. This study attempted to screen effective miRNA-mRNA in the progression of cervical cancer, and to study the mechanism of miRNA-mRNA in the progression of cervical cancer. The expression profile data of GSE7410, GSE 63514, GSE 86100 and TCGA-CESC were downloaded, and 34 overlapping differentially expressed genes (22 up-regulated and 12 down-regulated) and 166 miRNAs (74 down-regulated and 92 up-regulated) were screened through limma package. Then, miR-197-3p/TYMS pairs were obtained by PPI, functional enrichment, Kaplan-Meier plotter analysis, Cox univariate and multivariate analysis, risk modeling, WGCNA, qPCR and dual-luciferase experiments. The results showed that TYMS was an independent prognostic factor of cervical cancer, and its expression level was negatively correlated with cervical cancer tissue grade (TMN), tumor grade, age, microsatellite stability and tumor mutation load, and positively correlated with methyl expression in DNMT1, DNMT2, DNMT3A and DNMT3B. Functional experiments showed that TYMS knockout could promote the proliferation, migration and invasion of HeLa cells and reduce apoptosis. Overexpression of TYMS showed the opposite trend, miR-197-3p was negatively correlated with the expression of TYMS. MiR-197-3p inhibitor reversed the effect of si-TYMS on the proliferation of HeLa cells. In conclusion, these results reveal that TYMS plays a very important role in the prognosis and progression of cervical cancer, and has the potential to be thought of as cervical cancer biomarkers. At the same time, miR-197-3p/TYMS axis can regulate the deterioration of cervical cancer cells, which lays a foundation for the molecular diagnosis and treatment of cervical cancer.

Systematic Analysis of an Invasion-Related 3-Gene Signature and Its Validation as a Prognostic Model for Pancreatic Cancer

BackgroundPancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system that is associated with a poor prognosis in patients owing to its rapid progression and high invasiveness.MethodsNinety-seven invasive-related genes obtained from the CancerSEA database were clustered to obtain the molecular subtype of pancreatic cancer based on the RNA-sequencing (RNA-seq) data of The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) between subtypes were obtained using the limma package in R, and the multi-gene risk model based on DEGs was constructed by Lasso regression analysis. Independent datasets GSE57495 and GSE62452 were used to validate the prognostic value of the risk model. To further explore the expression of the hub genes, immunohistochemistry was performed on PAAD tissues obtained from a large cohort.ResultsThe TCGA-PAAD samples were divided into two subtypes based on the expression of the invasion-related genes: C1 and C2. Most genes were overexpressed in the C1 subtype. The C1 subtype was mainly enriched in tumor-related signaling pathways, and the prognosis of patients with the C1 subtype was significantly worse than those with the C2 subtype. A 3-gene signature consisting of LY6D, BCAT1, and ITGB6 based on 538 DEGs between both subtypes serves as a stable prognostic marker in patients with pancreatic cancer across multiple cohorts. LY6D, BCAT1, and ITGB6 were over-expressed in 120 PAAD samples compared to normal samples.ConclusionsThe constructed 3-gene signature can be used as a molecular marker to assess the prognostic risk in patients with PAAD.

CASP5 and CR1 as potential biomarkers for Kawasaki disease: an Integrated Bioinformatics-Experimental Study

Background Kawasaki disease (KD) is a pediatric inflammatory disorder causes coronary artery complications. The disease overlapping manifestations with a set of symptomatically like diseases such as bacterial and viral infections, juvenile idiopathic arthritis, Henoch-Schönlein purpura, infection of unknown etiology, group-A streptococcal and adenoviral infections, and incomplete KD could lead to misdiagnosis of the disease. Methods In the present study, we applied weighted gene co-expression network analysis (WGCNA) to identify network modules of co-expressed genes in GSE73464 and also, limma package was used to identify the differentially expressed genes (DEGs) in KD expression arrays composed of GSE73464, GSE18606, GSE109351, and GSE68004. By merging the results of WGCNA and limma, we detected hub genes. Then, analyzed the peripheral blood mononuclear cells (PBMCs) of 16 patients and 8 control subjects using Real-Time Polymerase Chain Reaction (RT-PCR) to evaluate the previous results. Results We assessed the diagnostic potency of the screened genes by plotting the area under curve (AUC). We finally identified 2 genes CASP5(Caspase 5) and CR1(Complement C3b/C4b Receptor 1) which were shown to potentially discriminate KD from other similar diseases and also from healthy people. Conclusions The results of RT-PCR and AUC confirmed the diagnostic potentials of two suggested biomarkers for KD.

Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

Background. The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods. We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣ log 2 fold change FC ∣ > 2 . Results. A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. Conclusions. We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

Lipocalin 2 Is a Regulator During Macrophage Polarization Induced by Soluble Worm Antigens

Caused by schistosomes, the human schistosomiasis is a tropical zoonotic parasitic disease. Pathologically, it occurs most often in the intestines and the liver, the sites of Schistosoma japonicum egg accumulation. The parasites’ produced eggs cause the main pathology in patients. Deposited parasite eggs in the liver induce the production of multiple cytokines that mediate the immune response, which in turn leads to granulomatous responses and liver fibrosis. These impact the hosts’ quality of life and health status, resulting in severe morbidity and even mortality. In this study, differentially expressed genes (DEGs) between ordinary samples and three 6- week infected mice were mined from microarray analysis based on the limma package. In total, we excavated the differential expression LCN2 was exhibited high expressions profile in GSE59276, GSE61376 demonstrated the result. Furthermore, CIBERSORT suggested detailed analysis of the immune subtype distribution pattern. In vivo experiments like real-time quantitative PCR, immunohistochemical (IHC) staining, and immunofluorescence (IF) demonstrated the expressions of LCN2 was significantly upregulated in S. japonicum–infected mice liver tissues and located in macrophages. Previous studies have shown that macrophages act as the first line of defense during schistosome infection and are an important part of liver granuloma. We used S. japonicum soluble worm antigens (SWA) to induce RAW264.7 cells to construct an in vitro inflammatory model. The current study aimed to investigate whether the NF-κB signaling network is involved in LCN2 upregulation induced by SWA and whether LCN2 can promote M1 polarization of macrophages under SWA treatment. Our research work suggests that LCN2 is significant in the development of early infection caused by S. japonicum and is of great value for further exploration. Collectively, the findings indicated that SWA promoted the expression of LCN2 and promoted M1 polarization of macrophages via the upregulation of NF-κB signaling pathway. Our findings demonstrate that NF-κB/LCN2 is necessary for migration and phagocytosis of M1 macrophages in response to SWA infection. Our study highlights the essential role of NF-κB/LCN2 in early innate immune response to infection.

MiR-574-5P, miR-1827, And miR-4429 As Potential Biomarkers For Schizophrenia

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Lack of a reliable mollecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, two mRNA expression arrays including GSE93987 and GSE38485, and one miRNA array, GSE54914, were downloaded from GEO, and meta-analysis was performed for mRNA expression arrays by employment of metaDE package. By WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we made protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array and dysregulated miRNAs (DEMs) were identified. Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulation of genes, expression values were evaluated by available datasets including GEO series GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate Schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, by performing Real-Time PCR, the expression level of genes and miRNAs were evaluated in 40 Schizophrenia patients compared with healthy controls. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients.

Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia

Background. Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis. Methods. Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes. Results. 360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10 , included in the models. Conclusion. CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

Identification of Prognostic Immune-Related lncRNAs Pairs in Immune Infiltration of Breast Cancer

Background: Accumulating evidence has supported that long non-coding ribonucleic acids (lncRNAs) could act as essential regulators in cancer immunity. An immune-related lncRNAs (irlncRNAs) risk signature without specific expression value was established to accurately predict prognosis in patients with breast cancer (BRCA).Methods: First, irlncRNAs were identified using co-expression analysis and differential expressed irlncRNAs (DEirlncRNAs) were recognized by “Limma” package. Then, DEirlncRNA pairs were determined using an iteration loop and a 0-or-1 screening matrix. Next, single factor test and Lasso algorithm followed by multivariate Cox regression were employed to establish risk signature. Besides, the Akaike information criterion (AIC) values were calculated to recognize the optimal cut-off point of low- or high-risk groups. Additionally, the potential role of risk score was explored in terms of overall survival, clinical variables, tumor immune microenvironment features, immunotherapeutic targets, and TMB (tumor mutation board) statues.Results: A total of 946 irlncRNAs were determined and 188 DEirlncRNAs were identified. 15 DEirlncRNA pairs were introduced into establishment of prognostic signature, which harbored powerful and independent predictive prognostic ability. Taking advantage of AIC values, risk model was demonstrated to accurately distinguish samples from the viewpoint of clinical outcome, clinicopathological features, infiltrating immune cells, and immunosuppressive biomarkers. Besides, comprehensive prognostic nomogram was constructed to quantitatively estimate risk. Finally, synergistic effect of risk score with TMB value was corroborated.Conclusions: The DEirlncRNA pairs risk signature without specific expression value possessed excellent prognostic performance and may provide direction for immunotherapy in BRCA.

Identification of a Gene Set Correlated With Immune Status in Ovarian Cancer by Transcriptome-Wide Data Mining

Immune checkpoint blocking (ICB) immunotherapy has achieved great success in the treatment of various malignancies. Although not have been approved for the treatment of ovarian cancer (OC), it has been actively tested for the treatment of OC. However, biomarkers that could indicate the immune status of OC and predict the response to ICB are rare. We downloaded RNAseq and clinical data of OC from The Cancer Genome Atlas (TCGA). Data analysis revealed both TMBhigh and immunityhigh were significantly related to better survival of OC. Up-regulated differentially expressed genes (Up-DEGs) were identified by analyzing the gene expression levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the “GSVA” and “limma” package in R software. The correlation of genes with overall survival was also analyzed by conducted Kaplan-Meier survival analysis. Four genes, CXCL13, FCRLA, MS4A1, and PLA2G2D were found positively correlated with better prognosis of OC and mainly involved in immune response-related pathways. Finally, TIMER and TIDE were used to predict gene immune function and its association with immunotherapy. We found that these four genes were positively correlated with better response to immune checkpoint blockade-based immunotherapy. Altogether, CXCL13, FCRLA, MS4A1, and PLA2G2D may be used as potential therapeutic genes for reflecting OC immune status and predicting response to immunotherapy.