Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray

ANK1 inhibits malignant progression of osteosarcoma by promoting ferroptosis

PURPOSE

Osteosarcoma (OS) is a primary bone tumor with high malignancy and poor prognosis. Ferroptosis plays a crucial role in OS. This study aimed to evaluate the effects of Ankyrin 1 (ANK1) on OS and to investigate its specific mechanisms.

METHODS

Microarray datasets related to "osteosarcoma" were selected for this study. Relevant hub genes in OS were identified through bioinformatics analysis. Transfected U-2OS and MG-63 cells were used for in vitro experiments. The effects of ANK1 overexpression on cell viability, migration, and invasion were determined through CCK-8, wound healing, and transwell assays. An OS mouse model was established for the in vivo experiments. Hematoxylin-eosin staining and immunohistochemistry were conducted to observe the histological effects of ANK1 overexpression on mouse tumors. TUNEL staining was performed to evaluate apoptosis in mouse.

RESULTS

There were 159 common differentially expressed genes in the GSE16088 and GSE19276 datasets. The hub genes ANK1, AHSP, GYPB, GYPA, KEL, and ALAS2 were identified. Pan-cancer analysis verified that ANK1 was closely associated with cancer prognosis and immune infiltration. Furthermore, ANK1 overexpression inhibited the proliferation, migration, and invasion of OS cells and promoted ferroptosis, while ferroptosis inhibitor (fer-1) weakened these effects. Moreover, ANK1 overexpression suppressed tumor growth, promoted apoptosis, reduced the number of Ki67 positive cells, and elevated the number of caspase-3 positive cells in vivo.

CONCLUSIONS

ANK1 is a prognosis biomarker of OS that can alleviate the progression of OS by promoting ferroptosis.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.

A chronic stress-induced microbiome perturbation, highly enriched in Ruminococcaceae_UCG-014, promotes colorectal cancer growth and metastasis

Purpose: Mounting evidence indicates that psychological stress adversely affects cancer progression including tumor growth and metastasis. The aim of this study was to investigate the role of chronic stress-induced microbiome perturbation in colorectal cancer (CRC) progression. Methods: Chronic restraint stress (CRS) was used to establish the chronic stress mouse model, behavioral tests were used for the CRS model evaluation. Subcutaneous xenograft model and lung metastasis model were established to investigate the growth and metastasis of CRC promoted by CRS exposure. 16S rRNA gene sequencing and liquid chromatograph-mass spectrometer (LC-MS) were applied to observe the effects of CRS exposure on the alteration of the gut microbiome and microbial metabolites. Bioinformatics analysis and correlation analyses were applied to analyse the changes in the frequency of body mass, tumor volume, inflammatory factors, neuroendocrine hormones and metabolites of the gut microbiota. Results: In this study, we identifed that CRS exposure model was appropriately constructed by achieving expected increases in disease activity index and enhanced depressive-like behaviors. CRS exposure can promote growth and metastasis of CRC. Besides, the data indicated that CRS exposure not only increased the neuro- and immune-inflammation, but also weakened the gut mucosal immunological function. The 16s rRNA gene sequencing data showed that CRS exposure increased the abundance of g_Ruminococcaceae_UCG_014. Furthermore, the LC-MS data indicated that with only 2 exceptions of carpaine and DG (15:0/20:4(5Z,8Z,11Z,14Z)/0:0), the majority of these 24 metabolites were less abundant in CRS-exposed mice. Bioinformatics analysis and correlation analyses indicated that only Ruminoscoccaceae-UCG-014 was significantly associated with inflammation (IL-6), neurotransmission (5-HT), and microbial metabolism (PS). Conclusion: CRS exposure altered diversity, composition and metabolites of the gut microbiome, with Ruminococcaceae_UCG-014 perturbation consistently correlated to inflammatory responses, suggesting a particular role of this bacterial genus in CRC growth and metastasis.

Bazedoxifene Suppresses the Growth of Osteosarcoma Cells by Inhibiting IL-6 and IL-11/GP130 Signaling Pathway

Osteosarcoma is the most common primary bone tumor. Using the multiple ligands simultaneous docking method, we found that bazedoxifene could bind to the GP130 D1 domain. We then demonstrated that bazedoxifene can decrease cell viability and cell migration of osteosarcoma cells by inhibiting interleukin 6 (IL-6) and IL-11/GP130 signaling. Consistently, treatment with IL-6 or IL-11 antibody or knockdown of GP130 by siRNA silenced the activation of STAT3, ERK, and AKT. Similarly, recombinant IL-6 and IL-11 proteins antagonized the inhibitory effect of bazedoxifene on osteosarcoma cells. Finally, the combinational treatment of temsirolimus and bazedoxifene synergistically suppressed osteosarcoma development in vitro and in vivo. Our findings suggest that bazedoxifene directly prompts the deactivation of GP130 and inhibits the osteosarcoma progression in vitro and in vivo. Therefore, bazedoxifene could be effectively applied as a therapeutic drug for human osteosarcoma in the future.

Expression and Prognostic Value of m6A RNA Methylation-Related Genes in Thyroid Cancer

Background

N6-methyladenosine (m6A) methylation modification is involved in tumorigenesis and progression and can affect various stages of RNA processing. We aimed to determine m6A methylation modifications on a transcriptome-wide scale in thyroid cancer.

Methods

RNA samples from cancerous tissues and adjacent tissues extracted from patients with papillary thyroid carcinoma (PTC) from Hangzhou First People's Hospital, Zhejiang, China from January 2019 to January 2020 were used for m6A-sequencing. The biological function of differentially expressed genes (DEGs) was analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Correlation analysis between the results of transcriptome sequencing and m6A-sequencing was also performed. The key m6A immune-related genes were downloaded from Immport. LASSO regression was performed on the resulting genes to establish a prognostic risk model, which was verified by multivariate Cox proportional hazards regression analyses, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis.

Results

An increase in m6A content in the total RNA of PTC was observed. A total of 123 genes with significant differential expression and differential methylation sites in thyroid cancer were selected, related to protein digestion and absorption, linoleic acid metabolism, legionellosis and alpha-linolenic acid metabolism. Seven genes (GDNF, EBI3, CCL2, BMP5, TGFB2, CGB3 and RLN2) were found to be predictive of PTC.

Conclusion

We analyzed the expression, enrichment pathways and functions of m6A methylation-related genes in the whole transcriptome of thyroid cancer and provided a prognostic risk model for thyroid cancer patients.

Construction and validation of an oxidative-stress-related risk model for predicting the prognosis of osteosarcoma

BACKGROUND

Osteosarcoma is the most common bone malignancy in teenagers, and warrants effective measures for diagnosis and prognosis. Oxidative stress (OS) is the key driver of several cancers and other diseases.

METHODS

The TARGET-osteosarcoma database was employed as the training cohort and GSE21257 and GSE39055 was applied for external validation. The patients were classified into the high- and low-risk groups based on the median risk score of each sample. ESTIMATE and CIBERSORT were applied for the evaluation of tumor microenvironment immune infiltration. GSE162454 of single-cell sequencing was employed for analyzing OS-related genes.

RESULTS

Based on the gene expression and clinical data of 86 osteosarcoma patients in the TARGET database, we identified eight OS-related genes, including MAP3K5, G6PD, HMOX1, ATF4, ACADVL, MAPK1, MAPK10, and INS. In both the training and validation sets, the overall survival of patients in the high-risk group was significantly worse than that in the low-risk group. The ESTIMATE algorithm revealed that patients in the high-risk group had higher tumor purity but lower immune score and stromal score. In addition, the CIBERSORT algorithm showed that the M0 and M2 macrophages were the predominant infiltrating cells in osteosarcoma. Based on the expression analysis of immune checkpoint, CD274(PDL1), CXCL12, BTN3A1, LAG3, and IL10 were identified as potential immune therapy targets. Analysis of the single cell sequencing data also revealed the expression patterns of OS-related genes in different cell types.

CONCLUSIONS

An OS-related prognostic model can accurately provide the prognosis of osteosarcoma patients, and may help identify suitable candidates for immunotherapy.

WTAP contributes to the tumorigenesis of osteosarcoma via modulating ALB in an m6A-dependent manner

PURPOSE

Osteosarcoma (OS) is a prevalent bone malignancy mainly occurred in adolescents. WTAP/N6-methyladenosine (m6A) modification is confirmed to be involved in OS progression. This study is conducted to bring some novel insights to the action mechanism of WTAP/m6A under the hidden pathogenesis of OS.

METHODS

qRT-PCR was executed to evaluate the expression levels of WTAP and ALB. ALB protein level in OS cells was measured by western blotting. The content of m6A in total RNA was assessed by m6A quantification assay. Me-RIP, dual luciferase reporter, and mRNA stability assays confirmed the target relationship of WTAP with ALB. With the use of the wound healing, CCK-8, and transwell invasion assays, the functional relationship between WTAP and ALB in OS cells was confirmed. The influences of WTAP on tumor growth in vivo were performed in the xenograft model of mouse.

RESULTS

WTAP was increased but ALB was diminished in OS tissues and/or cell lines. WTAP modulated ALB expression in an m6A-dependent manner. Silencing of WTAP retarded the development of OS via inhibiting cell viability, migration, invasion, and tumor growth. Knockdown of ALB exerted the opposite effects on OS progression. Additionally, ALB deficiency partially eliminated the inhibiting effects of WTAP silencing on cellular processes in OS.

CONCLUSIONS

This is the first report to clarify the interaction of WTAP/m6A with ALB in OS progression. These experimental data to some extent broadened the horizons of WTAP/m6A in the development of OS.

Bioinformatics analysis of the key genes in osteosarcoma metastasis and immune invasion

BACKGROUND

This study sought to identify potential key genes for osteosarcoma metastasis and analyze their immune infiltration patterns using bioinformatic methods.

METHODS

We obtained transcriptomic data related to osteosarcoma and osteosarcoma with metastasis from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) and The Gene Expression Omnibus (GEO) databases and identified the differentially expressed genes (DEGs). We also identified potential key genes for osteosarcoma metastasis by a protein-protein interaction network analysis, and we conducted a Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the core genes for prognosis, immune cell infiltration, and drug sensitivity, and the risk prediction and prognosis models of metastasis were constructed.

RESULTS

By comparing the transcriptome data of osteosarcomas without metastasis and those with metastasis, a total of 19 core DEGs were identified, and the GO and KEGG analyses revealed an association between these DEGs and the regulation of cell division, secretory granule lumen, the Ras-associated protein 1 (Rap1) signaling pathway, and the mitogen-activated protein kinase (MAPK) signaling pathway. Compared with other immune cells, macrophage infiltration was predominant in osteosarcoma samples with metastatic osteosarcoma, and insulin-like growth factors-1 (IGF1) and myelocytomatosis protein 2 (MYC2) genes were predicted to more than 50 targeted therapeutic agents. A metastasis prediction model with 5 genes [i.e., ecotropic viral integration site 2B (EVI2B), CCAAT/enhancer binding protein (CEBPA), lymphocyte cytosolic protein 2 (LCP2), selectin L (SELL), and Niemann-Pick disease, type C2A (NPC2A)], and a prognostic model with 4 genes [i.e., insulin-like growth factors-2 (IGF2), cathepsin O (CTSO), Niemann-Pick disease, type C2 (NPC2), and amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein (APBB1IP)] were developed.

CONCLUSIONS

We constructed a metastasis prediction model with 5 genes (i.e., EVI2B, CEBPA, LCP2, SELL, and NPC2A), and a prognostic model with 4 genes (i.e., IGF2, CTSO, NPC2, and APBB1IP) that may be potential biomarkers for osteosarcoma metastasis. Macrophages are the predominant immune infiltrating cells in osteosarcoma metastasis and may provide a new direction for the treatment of osteosarcoma.

Development of a Risk Score Model for Osteosarcoma Based on DNA Methylation-Driven Differentially Expressed Genes

Osteosarcoma (OS) is the commonest malignant bone tumor in adolescent patients, and patients face amputation, tumor metastasis, chemotherapy resistance, and even death. We investigated the potential connection between abnormal methylation differentially expressed genes and the survival rate of osteosarcoma patients. GSE36002 and GSE12865 datasets of GEO database were utilized for abnormal methylation differentially expressed genes, followed by function and pathway enrichment analyses, the protein-protein interaction network in the STRING database, and cluster analysis in the MCODE app of Cytoscape. The RNA-seq and clinical data from the TARGET-OS project of TCGA were used for univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to predict the risk genes of osteosarcoma. 1191 hypermethylation-downregulated genes might function through plasma membrane, negative regulation of transcription from the RNA polymerase II promoter, and pathways, including transcriptional misregulation in cancer. 127 hypomethylation-upregulated genes were enriched in proteolysis, negative regulation of the canonical Wnt signaling pathway, and metabolic signaling pathways. The univariate Cox analysis revealed 638 genes ( $P<0.01$ ), including 50 hypermethylation-downregulated genes and 4 hypomethylation-upregulated genes, subsequently based on LASSO Cox regression analysis for 54 aberrant methylation-driven genes, and three genes (COL13A1, MXI1, and TBRG1) were selected to construct the risk score model. The three genes (COL13A1, MXI1, and TBRG1) regulated by DNA methylation were identified to relate with the outcomes of OS patients, which might provide a new insight to the pathological mechanism of osteosarcoma.

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.

The Release of Inflammatory Mediators from Acid-Stimulated Mesenchymal Stromal Cells Favours Tumour Invasiveness and Metastasis in Osteosarcoma

Simple Summary

We aimed to validate the correlation between tumour glycolysis/acidosis and inflammation in osteosarcoma-associated mesenchymal stromal cells and investigate the role of acidity-induced inflammation in the development of metastasis in this very aggressive cancer. We confirmed the presence of an acidic microenvironment in osteosarcoma xenografts, both subcutaneous and orthotopic, using state-of-the-art imaging technologies; corroborated the correlation between tumour glycolysis, acidosis, and inflammatory markers in human patients; and finally, explored the use of anti-IL6 antibody to target these pathogenic pathways, using advanced 3D microfluidic models. In the future, advanced imaging systems for the measurement of tumour glycolysis and/or pH may help identify osteosarcoma patients who would benefit from anti-IL6 therapies to complement conventional therapy.

Abstract

Osteosarcoma is the most frequent primary malignant bone tumour with an impressive tendency to metastasise. Highly proliferative tumour cells release a remarkable amount of protons into the extracellular space that activates the NF-kB inflammatory pathway in adjacent stromal cells. In this study, we further validated the correlation between tumour glycolysis/acidosis and its role in metastases. In patients, at diagnosis, we found high circulating levels of inflammatory mediators (IL6, IL8 and miR-136-5p-containing extracellular vesicles). IL6 serum levels significantly correlated with disease-free survival and ¹⁸F-FDG PET/CT uptake, an indirect measurement of tumour glycolysis and, hence, of acidosis. In vivo subcutaneous and orthotopic models, co-injected with mesenchymal stromal (MSC) and osteosarcoma cells, formed an acidic tumour microenvironment (mean pH 6.86, as assessed by in vivo MRI-CEST pH imaging). In these xenografts, we enlightened the expression of both IL6 and the NF-kB complex subunit in stromal cells infiltrating the tumour acidic area. The co-injection with MSC also significantly increased lung metastases. Finally, by using 3D microfluidic models, we directly showed the promotion of osteosarcoma invasiveness by acidosis via IL6 and MSC. In conclusion, osteosarcoma-associated MSC react to intratumoural acidosis by triggering an inflammatory response that, in turn, promotes tumour invasiveness at the primary site toward metastasis development.

Identification of hub genes specific to pulmonary metastasis in osteosarcoma through integrated bioinformatics analysis

BACKGROUND

Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis.

OBJECTIVE

The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected.

METHODS

Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database.

RESULTS

In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient.

CONCLUSION

Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.

MicroRNAs as the critical regulators of protein kinases in prostate and bladder cancers

Background

Bladder cancer (BCa) and prostate cancer (PCa) are frequent urothelial and genital malignancies with a high ratio of morbidity and mortality which are more common among males. Since BCa and PCa cases are mainly diagnosed in advanced stages with clinical complications, it is required to introduce the efficient early detection markers. Protein kinases are critical factors involved in various cellular processes such as cell growth, motility, differentiation, and metabolism. Deregulation of protein kinases can be frequently observed through the neoplastic transformation and tumor progression. Therefore, kinases are required to be regulated via different genetic and epigenetic processes. MicroRNAs (miRNAs) are among the critical factors involved in epigenetic regulation of protein kinases. Since miRNAs are noninvasive and more stable factors in serum and tissues compared with mRNAs, they can be used as efficient diagnostic markers for the early detection of PCa and BCa.

Main body

In present review, we have summarized all of the reported miRNAs that have been associated with regulation of protein kinases in bladder and prostate cancers.

Conclusions

For the first time, this review highlights the miRNAs as critical factors in regulation of protein kinases during prostate and bladder cancers which paves the way of introducing a noninvasive kinase-specific panel of miRNAs for the early detection of these malignancies. It was observed that the class VIII receptors of tyrosine kinases and non-receptor tyrosine kinases were the most frequent targets for the miRNAs in bladder and prostate cancers, respectively.

Exploration of Immune-Related Gene Expression in Osteosarcoma and Association With Outcomes

IMPORTANCE

Host immune dysregulation is associated with initiation and development of osteosarcoma. In addition, immunotherapy for osteosarcomas requires some knowledge of the immune state of patients.

OBJECTIVE

To perform an immunogenomic landscape analysis based on The Cancer Genome Atlas (TCGA) project, which provides osteosarcoma samples with clinical information.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study was conducted from July 20, 2020, to September 20, 2020, as a secondary analysis of public data. Cox regression and risk score analyses were used to construct signatures of immune-related genes (IRGs) in 84 patients with osteosarcoma from TCGA with corresponding clinical information. Patients were divided into high- and low-risk groups with 42 individuals in each group according to their risk scores. Data were analyzed from July 20 to September 20, 2020.

MAIN OUTCOMES AND MEASURES

Differentially expressed genes (DEGs) were analyzed between groups, and potential molecular mechanisms, expression regulation, and immune cell infiltration were also explored using bioinformation methods. A prognostic model based on independent risk factors selected from multivariate Cox hazard ratio regression was established to estimate 1-year overall survival.

RESULTS

In this genetic association study based on 84 samples from patients with osteosarcoma from TCGA (mean [SD] age, 15.0 [4.8] years; 47 [56.0%] men; mean [SD] follow-up time, 4.1 [2.8] years), a total of 14 survival-associated IRGs were identified. Patients assigned to the high-risk group had worse survival than patients from the low-risk group (1 death [2.4%] vs 26 deaths [61.9%%]; P < .001). The protein digestion and absorption pathway was one of the associated pathways in the functional enrichment analysis (gene ratio, 2:8; P < .001). The prognostic model based on metastases at diagnosis and risk score performed well in 1-year overall survival estimations (area under the curve, 0.947; 95% CI, 0.832-0.972). The risk score was correlated with immune cell infiltration (B cells: r = 0.331; P = .002; macrophages: r = 0.410; P < .001; CD8 T cells: r = 0.230; P = .04).

CONCLUSIONS AND RELEVANCE

This genetic association study developed a prognostic modeling tool for osteosarcoma based on IRG expression profiles, which could result in improved survival rates through more individualized therapies. Further research on IRG expression profiles could provide potential targets for future studies on immune treatment for osteosarcoma.

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

BACKGROUND

In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in lung metastasis originating from osteosarcoma.

METHODS

Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 were applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.

RESULTS

A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p, has-miR-495-3p, and hsa-miR-96-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.

CONCLUSION

The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of lung metastasis originating from osteosarcoma, and provide potential diagnostic and therapeutic targets.

Bio-analytical Identification of Key Genes that Could Contribute to the Progression and Metastasis of Osteosarcoma

Background:

Osteosarcoma (OS) is one of the most common primary malignant bone tumors in children and adolescents. OS metastasis has been a challenge in the treatment of OS. The present study screened progression related genes in OS by analyzing a public dataset GSE42352, and identified 691 up-regulated and 945 down-regulated genes in advanced stage OS compared to early-stage OS samples.

Methods:

Protein-protein interaction (PPI) networks were further employed to reveal the interaction among these genes. Bioinformatics analysis showed that progression related differently expressed genes (DEGs) were significantly associated with the regulation of cell proliferation and metabolisms.

Results:

This study revealed that progression related DEGs were dysregulated in metastatic OS compared to non-metastatic OS samples. Further analysis showed CSF1R, CASP1, CD163, AP1B1, LAPTM5, PEX19, SLA, STAB1, YWHAH, PLCB2, and GPR84 were associated with the metastasis-free survival time in patients with OS.

Conclusions:

These findings provided novel information for us to understand the mechanisms underlying the progression and metastasis of OS.

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.

Untargeted LC-MS/MS analysis reveals metabolomics feature of osteosarcoma stem cell response to methotrexate

Background

Cancer stem cell (CSC) is identified in osteosarcoma (OS) and considered resistant to chemotherapeutic agents. However, the mechanism of osteosarcoma stem cell (OSC) resistant to chemotherapy remains debatable and vague, and the metabolomics feature of OSC is not clarified.

Materials and methods

OSC was isolated by using sphere forming assay and identified. Untargeted LC-MS/MS analysis was performed to reveal the metabolomics feature of OSC and underlying mechanisms of OSC resistant to methotrexate (MTX).

Results

OSC was efficiently isolated and identified from human OS 143B and MG63 cell lines with enhanced chemo-resistance to MTX. The untargeted LC-MS analysis revealed that OSC showed differential metabolites and perturbed signaling pathways, mainly involved in metabolisms of fatty acid, amino acid, carbohydrate metabolism and nucleic acid. After treated with MTX, metabolomics feature of OSC was mainly involved metabolisms of amino acid, fatty acid, energy and nucleic acid. Moreover, compared with their parental OS cells response to MTX, the differential metabolites and perturbed signaling pathways were mainly involved in metabolism of amino acid, fatty acid and nucleic acid. What's more, Rap1 signaling pathway and Ras signaling pathway were involved in OS cells and their SCs response to MTX.

Conclusion

Sphere-forming assay was able to efficiently isolate OSC from human OS cell lines and the untargeted LC-MS/MS analysis was suggested a sufficient methodology to investigate metabolomics features of OS cells and OSCs. Moreover, the metabolomics features of OSCs response to MTX might reveal a further understanding of chemotherapeutic resistance in OS.

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein-protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

Down-regulation of RAC2 by small interfering RNA restrains the progression of osteosarcoma by suppressing the Wnt signaling pathway

Osteosarcoma (OS) is the most common primary malignancy of bone and is characterized by a high malignant and metastatic potential. Microarray-based differentially expressed gene screening determined RAC2 as the candidate gene related to OS. Highly expressed RAC2 and activated Wnt signaling pathway were determined in OS tissues using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot analysis. The OS cells were transfected with siRNA-RAC2 or treated with BIO (activator of Wnt pathway), whereby the effects of siRNA-RAC2 on cell proliferation, invasion, cycle and apoptosis were analyzed by CCK-8, Transwell assay and flow cytometry. The mRNA and protein levels of RAC2 and the Wnt signaling pathway-, proliferation- and apoptosis-related genes in OS cells were determined by RT-qPCR and Western blot assay. Importantly, siRNA-mediated RAC2 silencing inhibited the activation of the Wnt signaling pathway in OS. siRNA-RAC2 inhibited the proliferation and invasion, while impeded OS cell cycle progression and facilitated cell apoptosis. However, activation of Wnt signaling pathway reversed the effects of siRNA-RAC2. Finally, orthotopic xenograft OS mouse model confirmed the in vivo anti-tumor effects by silencing RAC2. Taken together, RAC2 gene silencing could suppress OS progression. The mechanism was obtained by inhibiting the activation of the Wnt signaling pathway.

Retracted: MicroRNA-34a inhibit hepatocellular carcinoma progression by repressing hexokinase-1

Hepatocellular carcinoma (HCC) is known as a frequent type of primary cancer in the liver, and it is the third-most common cause of cancer-related death all over the world. However, the molecular mechanism in the progression of HCC is still unclear. The current study was designed to investigate the expression and function of microRNA-34a (miR-34a) in HCC. In HCC tissues and cells, the expression levels of miR-34a were analyzed by quantitative real-time polymerase chain reaction. The association between the level of miR-34a and hexokinase (HK)-1 was also investigated via luciferase reporter assay. Cell viability and proliferation were detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and flow cytometry. To assess whether miR-34a can limit tumor growth in vivo, animal models and terminal deoxynucleotidyl transferase dUTP nick end labeling assay were used for examining the role of miR-34a on the development of HCC and cell apoptosis. The expression level of miR-34a was reduced in HCC samples and cells. The expression of miR-34a was associated with the viability and proliferation capacity of HCC cells, and miR-34a could inhibit HCC cells proliferation by inhibiting HK1. In the mouse model of HCC, volumes and weight of the tumors were significantly decreased by transfection with miR-34a mimic compared with the control group. Furthermore, miR-34a mimics could induce apoptosis in a greater proportion of cells compared with the control group. Taken together, the data may provide some novel insights into the molecular mechanism of miR-34a and HK1 in the progression of HCC. Thus, miR-34a/HK1 axis might be a novel promising therapeutic target for treating HCC.

Potential Regulatory Effects of miR-182-3p in Osteosarcoma via Targeting EBF2

Osteosarcoma (OS) is one of the most common primary malignant bone tumors in adolescents with a high mortality rate. MicroRNA (miRNA) is a kind of noncoding RNAs and has been proved to participate in many physiological processes. Many miRNAs have been reported to act as function regulators in OS. In our study, the miRNA and gene expression profiles of OS were downloaded from GEO Datasets and the differential expression analysis was performed using GEO2R. 58 up- and 126 downregulated miRNAs were found. In the three OS gene profiles, 125 up- and 27 downregulated genes were found to be differentially expressed in at least two profiles. The miRNA-mRNA networks were constructed to predict the potential target genes of 10 most up- and downregulated miRNA. Venn analysis was used to detect the coexpressed differentially expressed genes (DEGs). EBF2, one of the upregulated DEGs, was also a potential target gene of miR-182-3p. Knockdown and overexpression of miR-182-3p resulted in overexpression and downexpression of EBF2 separately. Luciferase reporter gene experiment further verified the binding site of miR-182-3p and EBF2. CCK8 assay showed that miR-182-3p knockdown can further enhance the proliferation activity of OS cells, while overexpressing miR-182-3p can inhibit the proliferation activity of OS cells. Our research indicated that downexpression of miR-182-3p in OS cells results in overexpression of EBF2 and promotes the progression of OS.

Down-regulation of microRNA-144-3p and its clinical value in non-small cell lung cancer: a comprehensive analysis based on microarray, miRNA-sequencing, and quantitative real-time PCR data

BACKGROUND

Previous studies have shown that miR-144-3p might be a potential biomarker in non-small cell lung cancer (NSCLC). Nevertheless, the comprehensive mechanism behind the effects of miR-144-3p on the origin, differentiation, and apoptosis of NSCLC, as well as the relationship between miR-144-3p and clinical parameters, has been rarely reported.

METHODS

We investigated the correlations between miR-144-3p expression and clinical characteristics through data collected from Gene Expression Omnibus (GEO) microarrays, the relevant literature, The Cancer Genome Atlas (TCGA), and real-time quantitative real-time PCR (RT-qPCR) analyses to determine the clinical role of miR-144-3p in NSCLC. Furthermore, we investigated the biological function of miR-144-3p by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Protein-protein interaction (PPI) network was created to identify the hub genes.

RESULTS

From the comprehensive meta-analysis, the combined SMD of miR-144-3p was - 0.95 with 95% CI of (- 1.37, - 0.52), indicating that less miR-144-3p was expressed in the NSCLC tissue than in the normal tissue. MiR-144-3p expression was significantly correlated with stage, lymph node metastasis and vascular invasion (all P <  0.05). As for the bioinformatics analyses, a total of 37 genes were chosen as the potential targets of miR-144-3p in NSCLC. These promising target genes were highly enriched in various key pathways such as the protein digestion and absorption and the thyroid hormone signaling pathways. Additionally, PPI revealed five genes-C12orf5, CEP55, E2F8, STIL, and TOP2A-as hub genes with the threshold value of 6.

CONCLUSIONS

The current study validated that miR-144-3p was lowly expressed in NSCLC. More importantly, miR-144-3p might function as a latent tumor biomarker in the prognosis prediction for NSCLC. The results of bioinformatics analyses may present a new method for investigating the pathogenesis of NSCLC.

Signatures of altered long noncoding RNAs and messenger RNAs expression in the early acute phase of spinal cord injury

Spinal cord injury (SCI) is a highly severe disease and it can lead to the destruction of the motor and sensory function resulting in temporary or permanent disability. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nt that play a critical role in central nervous system (CNS) injury. However, the exact roles of lncRNAs and messenger RNAs (mRNAs) in the early acute phase of SCI remain to be elucidated. We examined the expression of mRNAs and lncRNAs in a rat model at 2 days after SCI and identified the differentially expressed lncRNAs (DE lncRNAs) and differentially expressed mRNAs (DE mRNAs) using microarray analysis. Subsequently, a comprehensive bioinformatics analysis was also performed to clarify the interaction between DE mRNAs. A total of 3,193 DE lncRNAs and 4,308 DE mRNAs were identified between the injured group and control group. Classification, length distribution, and chromosomal distribution of the dysregulated lncRNAs were also performed. The gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to identify the critical biological processes and pathways. A protein-protein interaction (PPI) network indicated that IL6, TOP2A, CDK1, POLE, CCNB1, TNF, CCNA2, CDC20, ITGAM, and MYC were the top 10 core genes. The subnetworks from the PPI network were identified to further elucidate the most significant functional modules of the DE mRNAs. These data may provide novel insights into the molecular mechanism of the early acute phase of SCI. The identification of lncRNAs and mRNAs may offer potential diagnostic and therapeutic targets for SCI.

MiR-216b inhibits osteosarcoma cell proliferation, migration, and invasion by targeting Forkhead Box M1

Osteosarcoma (OS) is considered the most common type of primary malignant bone tumor, which has a high rate of mortality in children and adolescents. However, the current treatment methods for OS are ineffective. Therefore, there is an urgent requirement to identify the critical targets. This study aimed to identify the roles and significance of microRNA-216b (miR-216b) in OS. To explore the cellular and molecular functions of miR-216b and Forkhead Box M1 (FoxM1) in OS, the expression of miR-216b and FoxM1 at the transcriptional level was measured using quantitative real-time PCR (qRT-PCR). Wound healing assay, 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyltetrazolium bromide assay (MTT) assay, flow cytometry, and transwell invasion assay were conducted to study the function of miR-216b and FoxM1 in OS cells. Dual luciferase reporter assay was performed to identify the relationships between miR-216b and FoxM1. qRT-PCR results revealed that miR-216b expression was significantly downregulated, and FoxM1 was observed to be significantly upregulated in human OS cell lines (MG-63) and tissues. MTT data showed that upregulation of miR-216b expression led to cell growth inhibition in MG-63 cells. The results of the invasion assay and wound healing assay illustrated that miR-216b upregulation or FoxM1 downregulation could inhibit the invasion and migration in MG-63 cells. In vivo, the tumor volume was significantly decreased by miR-194 mimic treatment compared with the control group. Furthermore, the results of the luciferase assay indicated that FoxM1 is a direct target of miR-216b. These findings may provide novel insights into the molecular mechanism of miR-216b and FoxM1 in the progression of OS, and suggested that miR-216b may serve as a potential tumor inhibitor of OS by targeting FoxM1.