Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma

Exploration of Potential Anti-Inflammatory Cum Anti-Rheumatoid-Arthritis Phyto-Molecule Through Integrated Green Approach: Network Pharmacology, Molecular Docking, Molecular Dynamics, In-Vitro and Ex-Vivo Study

Rheumatoid arthritis (RA) and associated inflammatory complications are the most prevalent illnesses and can turn into fatal conditions if left untreated. Allopathic medicine is not satisfactory for curing RA. Scientific literature reports reveal that several phyto-compounds viz. flavonoids, saponins, and terpenoids, can heal joints and organs from auto-inflammatory rheumatoid arthritis and pain. Gene ontology, gene network analysis, molecular clustering, and literature review were used to optimise RA-specific highly expressed genes. In-silico molecular docking was performed to short-out potential phytomolecules (Neohesperidin dihydrochalcone (NHDC)) from 1000 datasets-library against RA and validate using MD simulation running at 100 ns. In-vitro anti-inflammatory assays of NHDC inhibited egg-albumin denaturation, IC50 of 47.739±0.51 μg/ml. The ex-vivo MTT assay with NHDC rendered 67.209 % inhibition at 100 μM against fd-FLS-cells. NHDC downregulated pro-inflammatory cytokine IL-17 A production by 61.11 % and 50 % at 300 and 200 μM, respectively. Thus, this Studies recommend that NHDC may be highlighted as a novel multi-target PADI4 and JAK3 inhibitor with better efficacy and minimal toxicity in RA warranted to In-Vivo and clinical investigation. The current findings have uncovered remarkable genes and signalling pathways linked to RA, which could enhance our existing comprehension of the molecular mechanisms that drive its development and progression.

Exosome‑delivered miR‑486‑3p inhibits the progression of osteosarcoma via sponging CircKEAP1/MARCH1 axis components

Accumulating evidence shows that the disruption of competing endogenous RNA (ceRNA) networks plays a significant role in osteosarcoma (OS) initiation and progression. However, the specific roles and functions of the ceRNAs in OS remain unclear. First, differentially expressed microRNAs (DEMs) were identified by mining the E-MTAB-1136 and GSE28423 datasets. MiRWalk website was used to predict the target gene of miRNA. OS-associated circular RNA (circRNA) expression profiles were downloaded from the published microarray databases. Gene expression levels were assessed through reverse transcription-quantitative PCR and western blotting. The biological effects of circKEAP1, microRNA (miR)-486-3p and membrane-associated RINGCH finger protein 1 (MARCH1) in OS cells were investigated using Cell Counting Kit-8, Transwell, colony formation and wound healing assays. miR-486-3p was aberrantly downregulated in OS tissues and cell lines and was packed with exosomes. miR-486-3p overexpression was shown to inhibit OS cell progression and promoted cell cycle arrest in vitro. In addition, MARCH1 was identified as a direct downstream molecule of miR-486-3p in OS cells. circKEAP1 was found to be upregulated in OS tissues and cells. circKEAP1 was found to have binding sites with miR-486-3p. Mechanistically, circKEAP1 positively regulated MARCH1 expression by sponging miR-486-3p. Exosomal miR-486-3p inhibited the progression of OS by sponging the circKEAP1/MARCH1 axis. These findings may provide a promising treatment approach for OS.

Polygenic risk modeling of tumor stage and survival in bladder cancer

Introduction

Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns.

Methods

Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions.

Results

Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction).

Conclusions

The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.

Diagnostic Significance of 18F-FDG PET/CT Imaging Coupled with Magnetic Resonance Imaging of the Entire Body for Bone Metastases

Objective

To see if 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging paired with MR diffusion imaging can help doctors diagnose bone metastases.

Methods

From September 2020 to December 2021, a total of 30 individuals with probable bone metastases were recruited for the trial. With an average interval of four days, MAGNETIC resonance whole-body diffusion imaging (MR whole-body diffusion imaging) was performed on each of the 30 patients who had 18F-FDG PET/CT. The SUVmax values of the group with bone metastases were compared to those of the group without bone metastases. In this study, 18F-FDG PET/CT imaging, MR whole-body diffusion imaging, and their combination were examined. The researchers compared the results when 18F-FDG PET/CT imaging, whole-body MRI diffusion scans, and their combination indicated abnormal bone lesions. By comparing the diagnostic efficacy of 18F-FDG PET/CT imaging, MR whole-body diffusion imaging, and their combination, as well as accuracy, sensitivity, and specificity, the three techniques for diagnosing bone metastases will be evaluated for diagnostic usefulness. Results: the SUV max values of patients with bone metastases were significantly different from those of patients without bone metastases, as determined by 18F-FDG PET/CT imaging (P < 0.05). Using 18F-FDG PET/CT imaging, MR whole-body diffusion imaging, and their combined detection of aberrant bone lesions in various areas, we found statistically significant differences.

Conclusion

The use of 18F-FDG PET/CT imaging in conjunction with MR whole-body diffusion imaging in the diagnosis of bone metastases can be very helpful.

CXCR4 and TYROBP mediate the development of atrial fibrillation via inflammation

Atrial fibrillation (AF) is a rapid supraventricular arrhythmia. However, the pathogenesis of atrial fibrillation remains controversial. We obtained transcriptome expression profiles GSE41177, GSE115574 and GSE79768 from GEO database. WGCNA was performed, DEGs were screened, PPI network was constructed using STRING database. CTD database was used to identify the reference score of hub genes associated with cardiovascular diseases. Prediction of miRNAs of hub genes was performed by TargetScan. DIANA‐miRPath v3.0 was applied to make functional annotation of miRNA. The animal model of atrial fibrillation was constructed, RT‐PCR was used to verify the expression of hub genes. Immunofluorescence assay for THBS2 and VCAN was made to identify molecular. Design of BP neural network was made to explore the prediction relationship of CXCR4 and TYROBP on AF. The merged datasets contained 104 up‐regulated and 34 down‐regulated genes. GO and KEGG enrichment analysis results of DEGs showed they were mainly enriched in ‘regulation of release of sequestered calcium ion into cytosol’, ‘actin cytoskeleton organization’ and ‘focal adhesion’. The hub genes were CXCR4, SNAI2, S100A4, IGFBP3, CSNK2A1, CHGB, VCAN, APOE, C1QC and TYROBP, which were up‐regulated expression in the AF compared with control tissues. There was strong correlation among the CXCR4, TYROBP and AF based on the BP neural network. Through training, best training performance is 9.6474e‐05 at epoch 14, and the relativity was 0.99998. CXCR4 and TYROBP might be involved in the development of atrial fibrillation by affecting inflammation‐related signalling pathways and may serve as targets for early diagnosis and preventive treatment.

Clinicopathological value of the upregulation of cyclin-dependent kinases regulatory subunit 2 in osteosarcoma

Background

Cyclin-dependent kinase subunit 2 (CKS2) is a member of cyclin dependent kinase subfamily and the relationship between CKS2 and osteosarcoma (OS) remains to be further analyzed.

Methods

80 OS and 41 non-tumor tissue samples were arranged to perform immunohistochemistry (IHC) to evaluate CKS2 expression between OS and non-tumor samples. The standard mean deviation (SMD) was calculated based on in-house IHC and tissue microarrays, and exterior high-throughput datasets for further verification of CKS2 expression trend in OS. The effect of CKS2 expression on clinicopathological parameters of OS patients, and single-cell in OS tissues was analyzed through public high-throughput datasets and functional enrichment analysis was conducted for co-expression genes of CKS2 in accordance with weighted correlation network analysis.

Results

A total of 217 OS samples and 87 non-tumor samples (including tissue and cell line) were obtained from in-house IHC, microarrays and exterior high-throughput datasets. The analysis of integrated expression status demonstrated up-regulation of CKS2 in OS (SMD = 1.57, 95%CI [0.27–2.86]) and the significant power of CKS2 expression in distinguishing OS samples from non-tumor samples (AUC = 0.97 95%CI [0.95–0.98]). Clinicopathological analysis of GSE21257 indicated that OS patients with higher CKS2 expression was more likely to suffer OS metastasis. Although Kaplan–Meier curves showed no remarkable difference of overall survival rate between OS patients with high and low-CKS2, CKS2 was found up-regulated in proliferating osteosarcoma cells. Co-expression genes of CKS2 were mainly assembled in function and pathways such as cell cycle, cell adhesion, and intercellular material transport.

Conclusions

In summary, up-regulation of CKS2 expression in OS tissue was found through multiple technical approaches. In addition, scRNA-seq and co-expression analysis showed that CKS2 may have an impact on important biological process linked with cell cycle, cell adhesion, and intercellular material transport. Present study on CKS2 in OS indicated a promising prospect for CKS2 as a biomarker for OS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01234-8.

Network analysis of atherosclerotic genes elucidates druggable targets

Background

Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis.

Result

From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool.

Conclusion

Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01195-y.

Growth arrest-specific 5 lncRNA as a valuable biomarker of chemoresistance in osteosarcoma

Osteosarcoma is the most common primary malignant bone tumour in children and teenagers, and it is characterised by drug resistance and high metastatic potential. Increasing studies have highlighted the critical roles of long noncoding RNAs (lncRNAs) as oncogenes or tumour suppressors as well as new biomarkers and therapeutic targets in osteosarcoma. The growth arrestspecific 5 (GAS5) lncRNA can function as a tumour suppressor in several cancers. The present study aimed to validate GAS5 and other chemoresistanceassociated lncRNAs as biomarkers in a cohort of primary osteosarcoma samples, to obtain predictive information on resistance or sensitivity to treatment. The GAS5 and a panel of lncRNAs related to chemoresistance [SNGH1, FOXD2-AS1, deleted in lymphocytic leukemia (DLEU2) and LINC00963] were evaluated in a cohort of osteosarcoma patients enrolled at the Careggi University Hospital. Total RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue sections and the expression levels of the lncRNAs were quantified by qPCR. A bioinformatic analysis on deposited RNA-seq data was performed to validate the qPCR results. Clustering analysis shows that GAS5 could be linked to the expression of isoforms 02 and 04 of the lncRNA DLEU2, whereas the DLEU2 isoform 08 is linked to the lncRNA LINC00963. We found that GAS5 is significantly increased in patients with a good prognosis and is expressed differently between chemosensitive and chemoresistant osteosarcoma patients. However, the results obtained are not concordant with the in-silico analysis performed on the TARGET osteosarcoma dataset. In the future, we would enlarge the case series, including different disease settings.

Resolvin E1-ChemR23 Axis Regulates the Hepatic Metabolic and Inflammatory Transcriptional Landscape in Obesity at the Whole Genome and Exon Level

Resolvin E1 (RvE1) is an immunoresolvent that is synthesized from eicosapentaenoic acid and can bind the receptor ERV1/ChemR23. We previously showed activation of the RvE1-ChemR23 axis improves hyperglycemia and hyperinsulinemia of obese mice; however, it remains unclear how RvE1 controls glucose homeostasis. Here we investigated hepatic metabolic and inflammatory transcriptional targets of the RvE1-ChemR23 axis using lean and obese wild type (WT) and ChemR23 knockout (KO) mice. We conducted an in-depth transcriptional study by preforming whole gene-level and exon-level analyses, which provide insight into alternative splicing variants and miRNA regulation. Compared to controls, WT and KO obese mice in the absence of RvE1 displayed similar gene-level profiles, which entailed dysregulated pathways related to glucose homeostasis. Notably, obese WT mice relative to lean controls showed a robust decrease in pathways related to the biosynthesis of unsaturated fatty acids. At the exon-level, obese ChemR23 KOs compared to obese WT mice displayed changes in pathways related to hepatic lipid transport, cholesterol metabolism, and immunological functions such as complement cascades and platelet activation. Importantly, upon RvE1 administration to WT obese mice, we discovered upregulated genes in pathways relating to insulin sensitivity and downregulated genes related to regulators of TGF-β signaling. This transcriptional profile was generally not recapitulated with obese ChemR23 KO mice administered RvE1. Collectively, gene and exon-level analyses suggest RvE1 controls the hepatic transcriptional profile related to glucose homeostasis, insulin sensitivity, and inflammation in a manner that is largely dependent on ChemR23. These studies will drive future mechanistic experiments on the RvE1-ChemR23 axis.

TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma

We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. “Estimate” was used for immune infiltrating estimation and “xCell” was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BACKGROUND

It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.

METHODS

Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.

RESULTS

1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.

CONCLUSIONS

In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.

Network Pharmacology Analysis of the Therapeutic Mechanisms Underlying Beimu-Gualou Formula Activity against Bronchiectasis with In Silico Molecular Docking Validation

BACKGROUND

The classical Chinese herbal prescription Beimu-Gualou formula (BMGLF) has been diffusely applied to the treatment of respiratory diseases, including bronchiectasis. Although concerning bronchiectasis the effects and mechanisms of action of the BMGLF constituents have been partially elucidated, it remains to be determined how the formula in its entirety exerts therapeutic effects.

METHODS

In this study, the multitarget mechanisms of BMGLF against bronchiectasis were predicted with network pharmacology analysis. Using prepared data, a drug-target interaction network was established and subsequently the core therapeutic targets of BMGLF were identified. Furthermore, the biological function and pathway enrichment of potential targets were analyzed to evaluate the therapeutic effects and pivotal signaling pathways of BMGLF. Finally, virtual molecular docking was performed to assess the affinities of compounds for the candidate targets.

RESULTS

The therapeutic action of BMGLF against bronchiectasis involves 18 core target proteins, including the aforementioned candidates (i.e., ALB, ICAM1, IL10, and MAPK1), which are assumed to be related to biological processes such as drug response, cellular response to lipopolysaccharide, immune response, and positive regulation of NF-κB activity in bronchiectasis. Among the top 20 signaling pathways identified, mechanisms of action appear to be primarily related to Chagas disease, allograft rejection, hepatitis B, and inflammatory bowel disease.

CONCLUSION

In summary, using a network pharmacology approach, we initially predicted the complex regulatory profile of BMGLF against bronchiectasis in which multilink suppression of immune/inflammatory responses plays an essential role. These results may provide a basis for novel pharmacotherapeutic approaches for bronchiectasis.

Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

MiR-1224-5p Activates Autophagy, Cell Invasion and Inhibits Epithelial-to-Mesenchymal Transition in Osteosarcoma Cells by Directly Targeting PLK1 Through PI3K/AKT/mTOR Signaling Pathway

Background

Osteosarcoma (OS) is one of the most common malignant bone tumors with a poor overall prognosis. MiR-1224-5p plays an important role in cancer, but its function and mechanism in OS have not been studied.

Materials and Methods

The expression of miR-1224-5p and PLK1 was detected by qRT-PCR in OS cells, adjacent tissues, and cell lines. Dual-luciferase reporter gene assay was used to verify the interaction between miR-1224-5p and PLK1. The expression of miR-1224-5p and PLK1 was intervened by transfection with miR-1224-5p mimic, NC mimic, pc-NC and PLK1, respectively. MTT, colony formation assay, Transwell and flow cytometry were used to observe the cell proliferation, invasion and apoptosis. Western blot was used to detect the expression levels of PLK1, PI3K/AKT/mTOR signaling pathway-related proteins, autophagy-related proteins, and epithelial-mesenchymal transition (EMT)-related proteins in the cells.

Results

We found that miR-1224-5p was down-regulated and PLK1 expression was up-regulated in OS tissues and cells. On the other hand, it is further confirmed that PLK1 was a target gene of miR-1224-5p. Overexpression of miR-1224-5p inhibited the proliferation, invasion while promoted the apoptosis of OS cells, whereas overexpression of PLK1 promoted the proliferation, invasion and inhibited the apoptosis of OS cells. In the miR-1224-5p group (overexpression of miR-1224-5p), PI3K, AKT, and mTOR protein phosphorylation levels were significantly reduced, while autophagic activity was significantly activated, and the degree of EMT was significantly reduced. But the results in the PLK1 group (overexpression of PLK1) were the opposite. In addition, overexpression of miR-1224-5p reversed the effect of PLK1 upregulation on OS cells.

Conclusion

MiR-1224-5p targets PLK1 to inhibit PI3K/AKT/mTOR signaling pathway, thus mediating the proliferation, invasion, apoptosis, autophagy and EMT in OS cells.

MicroRNA-652 promotes cell proliferation and osteosarcoma invasion by directly targeting KLF9

Previous studies have demonstrated that various microRNAs (miRNAs or miRs) are abnormally expressed in osteosarcoma (OS) and serve roles in its malignant development. An in-depth understanding of the specific roles of dysregulated miRNAs in OS may be important for cancer research and the identification of novel therapeutic targets. In the current study, reverse transcription-quantitative PCR was performed to determine miR-652 expression in OS tissues and cell lines. Cell Counting Kit-8 and Transwell invasion assays were used for assessing the effect of miR-652 on the proliferation and invasion of OS cells. Herein, miR-652 expression was assessed in OS and the effects and molecular mechanisms of miR-652 in OS cells were examined. The results revealed that miR-652 expression was significantly upregulated in OS tissues and cell lines compared with adjacent normal tissues and a normal human osteoblast cell line. Furthermore, miR-652 downregulation inhibited the proliferation and invasion of OS cells. miR-652 was also demonstrated to directly interact with the 3'-untranslated region of kruppel-like factor 9 (KLF9) and miR-652 negatively regulated KLF9 expression in OS cells. miR-652 and KLF9 mRNA levels were also revealed to be inversely correlated in OS tissues. Treatment with KLF9 small interfering RNA abolished the suppression of OS proliferation and invasion induced by miR-652 downregulation. miR-652 may serve an oncogenic role in OS cells by targeting KLF9 directly. The results also indicated that miR-652 may be an effective novel therapeutic target for the treatment of patients with OS.

Unravelling host-pathogen interactions: ceRNA network in SARS-CoV-2 infection (COVID-19)

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Bioinformatics analysis of mouse mRNA expression dataset for presumptive SARS-CoV-2 targets.

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Induction of ISGs-Isg15, Oasl1, Usp18 and Ddx58 with no marked changes in the expression of IFNs.

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No induction of ACE2 and TMPRSS2, raising implications for host factor limitations.

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Identification of ceRNA network including miR-124-3p, Ddx58, lncRNA (Gm26917) and circRNAs (Ppp1r10, C330019G07RiK).

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Virus regulates the expression of lnc and circRNAs, acting as sponges for miR-124-3p targeting Ddx58.

COVID-19 is a lurking calamitous disease caused by an unusual virus, SARS-CoV-2, causing massive deaths worldwide. Nonetheless, explicit therapeutic drugs or clinically approved vaccines are not available for COVID-19. Thus, a comprehensive research is crucially needed to decode the pathogenic tools, plausible drug targets, committed to the development of efficient therapy. Host-pathogen interactions via host cellular components is an emerging field of research in this respect. miRNAs have been established as vital players in host-virus interactions. Moreover, viruses have the capability to manoeuvre the host miRNA networks according to their own obligations. Besides protein coding mRNAs, noncoding RNAs might also be targeted in infected cells and viruses can exploit the host miRNA network via ceRNA effect. We have predicted a ceRNA network involving one miRNA (miR-124-3p), one mRNA (Ddx58), one lncRNA (Gm26917) and two circRNAs (Ppp1r10, C330019G07RiK) in SARS-CoV infected cells. We have identified 4 DEGs-Isg15, Ddx58, Oasl1, Usp18 by analyzing a mRNA GEO dataset. There is no notable induction of IFNs and IFN-induced ACE2, significant receptor responsible for S-protein binding mediated viral entry. Pathway enrichment and GO analysis conceded the enrichment of pathways associated with interferon signalling and antiviral-mechanism by IFN-stimulated genes. Further, we have identified 3 noncoding RNAs, playing as potential ceRNAs to the genes associated with immune mechanisms. This integrative analysis has identified noncoding RNAs and their plausible targets, which could effectively enhance the understanding of molecular mechanisms associated with viral infection. However, validation of these targets is further corroborated to determine their therapeutic efficacy.

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

Objective

The main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA‐sequencing (RNA‐seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.

Methods

The sarcoma RNA‐seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.

Results

We had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co‐expression genes suggests that some of them were closely related to cell cycle‐related biological processes and tumor‐related signaling pathways, such as Wnt, mitogen‐activated protein kinase, target of rapamycin, and nuclear factor‐kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle‐related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.

Conclusion

Our study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.

Identification of potential crucial genes and key pathways in osteosarcoma

Background

The aim of this study is to identify the potential pathogenic and metastasis-related differentially expressed genes (DEGs) in osteosarcoma through bioinformatic analysis of Gene Expression Omnibus (GEO) database.

Results

Gene expression profiles of GSE14359, GSE16088, and GSE33383, in total 112 osteosarcoma tissue samples and 7 osteoblasts, were analyzed. Seventy-four normal-primary DEGs (NPDEGs) and 764 primary-metastatic DEGs (PMDEGs) were screened. VAMP8, A2M, HLA-DRA, SPARCL1, HLA-DQA1, APOC1 and AQP1 were identified continuously upregulating during the oncogenesis and metastasis of osteosarcoma. The enriched functions and pathways of NPDEGs include procession and presentation of antigens, activation of MHC class II receptors and phagocytosis. The enriched functions and pathways of PMDEGs include mitotic nuclear division, cell adhesion molecules (CAMs) and focal adhesion. With protein-protein interaction (PPI) network analyzed by Molecular Complex Detection (MCODE) plug-in of Cytoscape software, one hub NPDEG (HLA-DRA) and 7 hub PMDEGs (CDK1, CDK20, CCNB1, MTIF2, MRPS7, VEGFA and EGF) were eventually selected, and the most significant pathways in NPDEGs module and PMDEGs module were enriched in the procession and presentation of exogenous peptide antigen via MHC class II and the nuclear division, respectively.

Conclusions

By integrated bioinformatic analysis, numerous DEGs related to osteosarcoma were screened, and the hub DEGs identified in this study are possibly part of the potential biomarkers for osteosarcoma. However, further experimental studies are still necessary to elucidate the biological function and mechanism of these genes.

Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.

HIF-1α Stabilization Increases miR-210 Eliciting First Trimester Extravillous Trophoblast Mitochondrial Dysfunction

Preeclampsia is associated with first trimester placental dysfunction. miR-210, a small non-coding RNA, is increased in the preeclamptic placenta. The effects of elevated miR-210 on placental function remain unclear. The objectives of this study were to identify targets of miR-210 in first trimester primary extravillous trophoblasts (EVTs) and to investigate functional pathways altered by elevated placental miR-210 during early pregnancy. EVTs isolated from first trimester placentas were exposed to cobalt chloride (CoCl₂), a HIF-1α stabilizer and hypoxia mimetic, and miR-210 expression by qPCR, HIF1α protein levels by western blot and cell invasion were assessed. A custom TruSeq RNA array, including all known/predicted miR-210 targets, was run using miR-210 and miR-negative control transfected EVTs. Mitochondrial function was assessed by high resolution respirometry in transfected EVTs. EVTs exposed to CoCl₂ showed a dose and time-dependent increase in miR-210 and HIF1α and reductions in cell invasion. The TruSeq array identified 49 altered genes in miR-210 transfected EVTs with 27 genes repressed and 22 enhanced. Three of the top six significantly repressed genes, NDUFA4, SDHD, and ISCU, are associated with mitochondrial function. miR-210 transfected EVTs had decreased maximal, complex II and complex I+II mitochondrial respiration. This study suggests that miR-210 alters first trimester trophoblast function. miR-210 overexpression alters EVT mitochondrial function in early pregnancy. Mitochondrial dysfunction may lead to increased reactive oxygen species, trophoblast cell damage and likely contributes to the pathogenesis of preeclampsia.

NK cell and macrophages confer prognosis and reflect immune status in osteosarcoma

Osteosarcoma (OS) is a common primary malignant bone tumor in young adolescents. About 30% of patients with OS have a recurrence, and the overall survival after OS recurrence is not good. In this study, we aimed to analyze and identify factors that influence prognosis after OS relapse. We retrieved the Gene Expression Omnibus data set and collected a series of transcriptome data with clinical information, including microRNA (miRNA) and messenger RNA (mRNA) expression profiles in recurrent OS. Upon comparison of the dysregulated genes of survival status in the recurrent OS samples, it was found that there were 268 differential expressed (DE) mRNAs and six DE miRNAs. These genes are related to pathways in cancer. We also predicted the interaction networks of these DE mRNAs and miRNAs. Further, we applied cibersort to estimate the proportion of immune cell types and we discovered that natural killer cells and macrophages have different abundance between good prognosis and poor prognosis. Our study indicates that for recurrent OS samples, there are several differences between these two groups, including gene expression and the status of immune activation. The immunity status is a candidate signature for disease prediction, prevention, and therapy choices.

Identification of lncRNA-miRNA-mRNA regulatory network associated with epithelial ovarian cancer cisplatin-resistant

To construct a long noncoding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatin-resistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein-protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA-miRNA-mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant.

Identification of B-cell translocation gene 1-controlled gene networks in diffuse large B-cell lymphoma: A study based on bioinformatics analysis

B-cell translocation gene 1 (BTG1) is a member of the BTG/transducer of Erb family. The present study evaluated the impact of BTG1 gene expression on the clinical outcome of diffuse large B-cell lymphoma (DLBCL) and investigated potential mechanisms using the Gene Expression Omnibus (GEO) database. The gene expression profile datasets GSE31312, GSE10846, GSE65420 and GSE87371 were downloaded from the GEO database. BTG1 expression and clinicopathological data were obtained from the GSE31312 dataset. In 498 cases, the expression of BTG1 in DLBCL was associated with treatment response (χ²=19.020; P<0.001) and International Prognostic Index score (χ²=5.320; P=0.025). Using the Kaplan-Meier method, it was identified that the expression of BTG1 was associated with overall survival (OS) and progression-free survival (PFS) times. Univariate and multivariate Cox regression analysis demonstrated that BTG1 was an independent predictive factor for OS and PFS. From the overlapping analysis of 407 BTG1-associated genes and 22,187 DLBCL-associated genes, 401 genes were identified as BTG1-associated DLBCL genes. Pathway analysis revealed that BTG1-associated DLBCL genes were associated with cancer progression and DLBCL signaling pathways. Subsequently, a protein-protein interaction network was constructed of the BTG1-associated genes, which consisted of 235 genes and 601 interactions. Additionally, 24 genes with high degrees in the network were identified as hub genes, which included genes associated with ‘ribosome’ [ribosomal protein (RP) L11, RPL3, RPS29, RPL19, RPL15 and RPL12], ‘cell cycle’ (ubiquitin carboxyl extension protein 52, ATM and Ras homolog family member H), ‘mitogen-activated protein kinase pathway’ (mitogen-activated protein kinase 1), ‘histone modification’ (ASH1-like protein) and ‘transcription/translation’ (eukaryotic translation initiation factor 3 subunit E, eukaryotic translation elongation factor 1 δ, transcription termination factor 1, cAMP responsive element binding protein 1 and RNA polymerase II subunit F). In conclusion, BTG1 may serve as a predictive biomarker for DLBCL prognosis. Additionally, bioinformatics analysis indicated that BTG1 may exhibit key functions in the progression and development of DLBCL.

WNT6 is an effective marker for osteosarcoma diagnosis and prognosis

Wingless-Type MMTV Integration Site Family, Member 6 (WNT6) is a member of the Wnt family and its expression is abnormal in different human cancer cell lines. The purpose of this study was to investigate the clinical significance of WNT6 in osteosarcoma.The levels of WNT6 mRNA and protein in tissue and serum were detected through quantitative real-time polymorperase chain reaction (qRT-PCR) and Enzyme Lined Immunosorbent Assay (ELISA), respectively. Chi-square test was performed to estimate the association of WNT6 expression with clinical parameters among osteosarcoma patients. Receiver operation characteristic (ROC) curve was plotted to determine diagnostic performance of serum WNT6 in osteosarcoma. Survival analysis was performed using Kaplan-Meier method. Cox regression analysis was adopted to evaluate prognostic significance of WNT6 expression among osteosarcoma patients.Compared with the controls, WNT6 mRNA and protein levels were significantly elevated in patients with osteosarcoma (P > .05 for all). Furthermore, WNT6 upregulation showed positive correlation with patients' age (P < .001), tumor grade (P < .001) and distant metastasis (P = .001). WNT6 might be a diagnostic marker for osteosarcoma with an AUC of 0.854 combining a specificity of 88.4% and a sensitivity of 77.8%. Survival analysis result indicated that high WNT6 expression predicted poor survival (log rank test, P = .001). WNT6 might be a potential prognostic biomarker for osteosarcoma (HR = 2.227, 95%CI = 1.061-10.842, P = .027).WNT6 may be a diagnostic and prognostic marker in osteosarcoma.