Identifying osteosarcoma metastasis associated genes by weighted gene co-expression network analysis (WGCNA)

Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis

Background

The present study aims to explore the metastasis-related signatures in connection with tumor microenvironment (TME), revealing new molecular targets promising in improving osteosarcoma (OS) patients' outcomes.

Methods

The high-throughput sequencing data was downloaded from the TARGET database and performed the ESTIMATE algorithm. Metastasis-related information was obtained from the GSE21257 dataset. Differentially expressed genes (DEGs) associated with the stromal and immune cell infiltration patterns were identified. DEGs with similar biological functions were grouped into the same module by Gene Ontology (GO) analysis and MCODE analysis. Prognostic DEGs were selected in two datasets through survival analysis. Weighted gene co-expression network analysis (WGCNA) was performed to find metastasis-related modules and genes. RT-PCR was utilized to evaluate the expression of the key prognostic DEGs associated with metastasis in OS patients.

Results

The median scores of the stromal and immune groups of OS samples were 58 and -416, and a total of 200 overlapping DEGs were identified. These DEGs basically played fundamental roles in immune response relevant GO terms and were clustered into 9 different modules. Among them, 24 metastasis-related DEGs were selected from the GSE21257 dataset which contains the stromal and immune cell infiltration patterns. Finally, IRF8, HLA-DMA, and HLA-DMB were proved to exhibit significant higher expression levels in cancerous tissues than in para-cancerous tissues for OS patients.

Conclusion

We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. Exploration of metastasis-related signatures in TME may be valuable for enhancing treatment strategies for OS.

Prioritizing Context-Dependent Cancer Gene Signatures in Networks

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.

Prognostic Model and Immune Response of Clear Cell Renal Cell Carcinoma Based on Co-Expression Genes Signature

BACKGROUND

The identification of reliable prognostic markers is crucial for optimizing patient management and improving clinical outcomes in clear cell renal cell carcinoma (ccRCC).

METHODS

We used the GSE89563 dataset from the GEO database and the Kidney Clear Cell Carcinoma (KIRC) dataset from the TCGA database to develop a prognostic model based on weighted gene co-expression network analysis (WGCNA) and non-negative matrix factorization (NMF) to predict disease progression and prognosis in ccRCC.

RESULT

We utilized WGCNA to identify risk genes and applied NMF to stratify high-risk populations in ccRCC. We characterized the immune gene features of these high-risk groups and ultimately developed a risk prediction model for ccRCC patients using a Lasso regression approach. The risk score was calculated as follows: Risk score = SUM (-0.136394797 ANK3 + 0.004238138 BIVM_ERCC5 - 0.046248451 C4orf19 - 0.036013206 F2RL3 - 0.125531316 GNG7 - 0.012698109 METTL7A + 0.078462369 MSTO1 - 0.050450656 PINK1 - 0.059446590 SLC16A12 - 0.039883686 SLC2A9 + 0.083310722 TLCD1 - 0.059801739 WDR72 + 0.071430088 ZNF117).

CONCLUSION

We develop a prognostic model for clear cell renal cell carcinoma and analyzed immune response in subgroups and confirmed protein-level expression concordance.

mir-744-5p inhibits cell growth and angiogenesis in osteosarcoma by targeting NFIX

BACKGROUND

Osteosarcoma (OS) is a malignant bone tumor that commonly occurs in children and adolescents under the age of 20. Dysregulation of microRNAs (miRNAs) is an important factor in the occurrence and progression of OS. MicroRNA miR-744-5p is aberrantly expressed in various tumors. However, its roles and molecular targets in OS remain unclear.

METHODS

Differentially expressed miRNAs in OS were analyzed using the Gene Expression Omnibus dataset GSE65071, and the potential hub miRNA was identified through weighted gene co-expression network analysis. Quantitative real-time PCR (qRT-PCR) was used to detect the expression of miR-744-5p in OS cell lines. In vitro experiments, including CCK-8 assays, colony formation assays, flow cytometry apoptosis assays, and tube formation assays, were performed to explore the effects of miR-744-5p on OS cell biological behaviors. The downstream target genes of miR-744-5p were predicted through bioinformatics, and the binding sites were validated by a dual-luciferase reporter assay.

RESULTS

The lowly expressed miRNA, miR-744-5p, was identified as a hub miRNA involved in OS progression through bioinformatic analysis. Nuclear factor I X (NFIX) was confirmed as a direct target for miR-744-5p in OS. In vitro studies revealed that overexpression of miR-744-5p could restrain the growth of OS cells, whereas miR-744-5p inhibition showed the opposite effect. It was also observed that treatment with the conditioned medium from miR-744-5p-overexpressed OS cells led to poorer proliferation and angiogenesis in human umbilical vein endothelial cells (HUVECs). Furthermore, NFIX overexpression restored the suppression effects of miR-744-5p overexpression on OS cell growth and HUVECs angiogenesis.

CONCLUSION

Our results indicated that miR-744-5p is a potential tumor-suppressive miRNA in OS progression by targeting NFIX to restrain the growth of OS cells and angiogenesis in HUVECs.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH

INTRODUCTION

Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood.

OBJECTIVES

We studied the mechanism of action of Wen-Dan decoction in treating steroidinduced necrosis of the femoral head (SINFH) via network pharmacology and in vivo experiments.

METHODS

The active compounds of Wen-Dan decoction and SINFH-related target genes were identified through public databases. Then, network pharmacological analysis was conducted to explore the potential key active compounds, core targets and biological processes of Wen-Dan decoction in SINFH. The potential mechanisms of Wen-Dan decoction in SINFH obtained by network pharmacology were validated through in vivo experiments.

RESULTS

We identified 608 DEGs (differentially expressed genes) (230 upregulated, 378 downregulated) in SINFH. GO analysis revealed that the SINFH-related genes were mainly involved in neutrophil activation and the immune response. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SINFH-related genes were mainly associated with cytokine receptor interactions, lipids, atherosclerosis, and tuberculosis. We identified 147 active ingredients of Wen-Dan decoction; the core ingredient was quercetin, and licorice was an active ingredient. Moreover, 277 target genes in the treatment of SINFH with Wen-Dan decoction were identified, and NCF1, PTGS2, and RUNX2 were selected as core target genes. QRT-PCR of peripheral blood from SINFH patients showed higher levels of PGTS2 and NCF1 and showed lower levels of RUNX2 compared to controls. QRT-PCR analysis of peripheral blood and femoral bone tissue from a mouse model of SINFH showed higher levels of PGTS2 and NCF1 and lower levels of RUNX2 in the experimental animals than the controls, which was consistent with the bioinformatics results. HE, immunohistochemistry, and TUNEL staining confirmed a significant reduction in hormone-induced femoral head necrosis in the quercetintreated mice. HE, immunohistochemistry, and TUNEL staining confirmed significant improvement in hormone-induced femoral head necrosis in the quercetin-treated mice.

CONCLUSION

We provide new insights into the genes and related pathways involved in SINFH and report that PTGS2, RUNX2, and NCF1 are potential drug targets. Quercetin improved SINFH by promoting osteogenesis and inhibiting apoptosis.

Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients

Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.

A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution

Spatial transcriptome (ST) technology provides both the spatial location and transcriptional profile of spots, as well as tissue images. ST data can be utilized to construct gene regulatory networks, which can help identify gene modules that facilitate the understanding of biological processes such as cell communication. Correlation measurement is the core basis for constructing a gene regulatory network. However, due to the high noise and sparsity in ST data, common correlation measurement methods such as the Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SPCC) are not suitable. In this work, a new gene correlation measurement method called STgcor is proposed. STgcor defines vertexes as spots in a two-dimensional coordinate plane consisting of axes X and Y from the gene pair (X and Y). The joint probability density of Gaussian distribution of the gene pair (X and Y) is calculated to identify and eliminate outliers. To overcome sparsity, the degree, trend, and location of the distribution of vertexes are used to measure the correlation between gene pairs (X, Y). To validate the performance of the STgcor method, it is compared with the PCC and SPCC in a weighted coexpression network analysis method using two ST datasets of breast cancer and prostate cancer. The gene modules identified by these methods are then compared and analyzed. The results show that the STgcor method detects some special gene modules and cancer-related pathways that cannot be detected by the other two methods.

A Novel Approach to Reducing Lung Metastasis in Osteosarcoma: Increasing Cell Stiffness with Carbenoxolone

AIM

Primary malignant bone tumor osteosarcoma can metastasize to the lung. Diminishing lung metastasis would positively affect the prognosis of patients. Our previous studies demonstrated that highly metastatic osteosarcoma cell lines are significantly softer than low-metastasis cell lines. We therefore hypothesized that increasing cell stiffness would suppress metastasis by reducing cell motility. In this study, we tested whether carbenoxolone (CBX) increases the stiffness of LM8 osteosarcoma cells and prevents lung metastasis in vivo.

METHODS

We evaluated the actin cytoskeletal structure and polymerization of CBX-treated LM8 cells using actin staining. Cell stiffness was measured using atomic force microscopy. Metastasis-related cell functions were analyzed using cell proliferation, wound healing, invasion, and cell adhesion assays. Furthermore, lung metastasis was examined in LM8-bearing mice administered with CBX.

RESULTS

Treatment with CBX significantly increased actin staining intensity and stiffness of LM8 cells compared with vehicle-treated LM8 cells (p < 0.01). In Young's modulus images, compared with the control group, rigid fibrillate structures were observed in the CBX treatment group. CBX suppressed cell migration, invasion, and adhesion but not cell proliferation. The number of LM8 lung metastases were significantly reduced in the CBX administration group compared with the control group (p < 0.01).

CONCLUSION

In this study, we demonstrated that CBX increases tumor cell stiffness and significantly reduces lung metastasis. Our study is the first to provide evidence that reducing cell motility by increasing cell stiffness might be effective as a novel anti-metastasis approach in vivo.

A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common bone cancer in adolescents, and has a high propensity to metastasize. Ferroptosis is a unique modality of cell death, driving the metastasis of cancer cells. Identifying ferroptosis-related genes (FRGs) as prognostic factors will be critical to predict the outcomes of OS. This study aimed to explore the prognostic value of FRGs in OS and build a prognostic model to indirectly improve OS patients' outcomes.

METHODS

OS data were downloaded from the TARGET database and 2 Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess FRGs. A risk score model basing on 5 FRGs was constructed via LASSO-Cox regression. Multivariate Cox regression analysis was used to determine the independent prognostic factors. The Nomogram model was built using independent prognostic factors. The relationship between the risk score and the immune cell infiltration was estimated by CIBERSORT, and the correlation between the risk score and immune checkpoints was also analyzed.

RESULTS

Based on the prognosis-related FRGs, we built a regression model: Risk score = (-0.01382853 × ACSL4) - (0.05371778 × HMOX1) - (0.02434655 × GPX4) - (0.16432810 × PRNP) - (0.15567120 × ATG7). OS patients with high risk score tended to suffer from poor prognosis, validated in 2 Gene Expression Omnibus datasets. The Nomogram model showed the combination of the risk score and the tumour-node-metastasis stage improved predictive effectiveness. The risk score was also related to immune cell infiltration and immune checkpoint expression.

CONCLUSION

The risk score model based on 5 FRGs was a reliable prognostic predictive indicator for OS patients.

PKIB involved in the metastasis and survival of osteosarcoma

Osteosarcoma is frequently metastasized at the time of diagnosis in patients. However, the underlying mechanism of osteosarcoma metastasis remains poorly understood. In this study, we evaluated DNA methylation profiles combined with gene expression profiles of 21 patients with metastatic osteosarcoma and 64 patients with non-metastatic osteosarcoma from TARGET database and identified PKIB and AIM2 as hub genes related to the metastasis of osteosarcoma. To verify the effects of PKIB on migration and invasion of osteosarcoma, we performed wound-healing assay and transwell assay. The results showed that PKIB significantly inhibited the migration and invasion of osteosarcoma cells, and the Western blot experiments showed that the protein level of E-cad was upregulated and of VIM was downregulated in 143-B cell recombinant expression PKIB. These results indicate that PKIB inhibit the metastasis of osteosarcoma. CCK-8 assay results showed that PKIB promote the proliferation of osteosarcoma. In addition, the Western blot results showed that the phosphorylation level of Akt was upregulated in 143-B cells overexpressing PKIB, indicating that PKIB promotes the proliferation of osteosarcoma probably through signaling pathway that Akt involved in. These results give us clues that PKIB was a potential target for osteosarcoma therapy. Furthermore, combined clinical profiles analysis showed that the expression of AIM2- and PKIB- related risk scores was significantly related to the overall survival of patients with osteosarcoma. Thus, we constructed a nomogram based on AIM2 and PKIB expression–related risk scores for osteosarcoma prognostic assessment to predict the 1-, 2-, 3-, and 5-year overall survival rate of patients with metastatic osteosarcoma, assisting clinicians in the diagnosis and treatment of metastatic osteosarcoma.

m6A-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Osteosarcoma

Background

m6A-related lncRNAs have demonstrated great potential tumor diagnostic and therapeutic targets. The goal of this work was to find m6A-regulated lncRNAs in osteosarcoma patients.

Method

The Cancer Genome Atlas (TCGA) database was used to retrieve RNA sequencing and medical information from osteosarcoma sufferers. The Pearson's correlation test was used to identify the m6A-related lncRNAs. A risk model was built using univariate and multivariable Cox regression analysis. Kaplan–Meier survival analysis and receiver functional requirements were used to assess the risk model's performance (ROC). By using the CIBERSORT method, the associations between the relative risks and different immune cell infiltration were investigated. Lastly, the bioactivities of high-risk and low-risk subgroups were investigated using Gene Set Enrichment Analysis (GSEA).

Result

A total of 531 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs have demonstrated prognostic values. A total of 88 OS patients were separated into cluster 1, cluster 2, and cluster 3. The overall survival rate of OS patients in cluster 3 was more favorable than that of those in cluster 1 and cluster 2. The average Stromal score was much higher in cluster 1 than in cluster 2 and cluster 3 (P < 0.05). The expression levels of lncRNAs used in the construction of the risk prediction model in the high-risk group were generally lower than those in the low-risk group. Analysis of patient survival indicated that the survival of the low-risk group was higher than that of the high-risk group (P < 0.0001) and the area under the curve (AUC) of the ROC curve was 0.719. Using the CIBERSORT algorithm, the results revealed that Macrophages M0, Macrophages M2, and T cells CD4 memory resting accounted for a large proportion of immune cell infiltration. By GSEA analysis, our results implied that the high-risk group was mainly involved in unfolded protein response, DNA repair signaling, and epithelial-mesenchymal transition signaling pathway and glycolysis pathway; meanwhile, the low-risk group was mainly involved in estrogen response early and KRAS signaling pathway.

Conclusion

Our investigation showed that m6A-related lncRNAs remained tightly connected to the immunological microenvironment of osteosarcoma tumors, potentially influencing carcinogenesis and development. The immune microenvironment and immune-related biochemical pathways can be changed by regulating the transcription of M6A modulators or lncRNAs. In addition, we looked for risk-related signaling of m6A-related lncRNAs in osteosarcomas and built and validated the risk prediction system. The findings of our current analysis will facilitate the assessment of outcomes and the development of immunotherapies for sufferers of osteosarcomas.

Gold Nanostars Combined with the Searched Antibody for Targeted Oral Squamous Cell Carcinoma Therapy

Oral squamous cell carcinoma (OSCC) is the most common cancer in the oral and maxillofacial region. Due to the special physiological and anatomical position of the oral cavity, the disease often has a significant impact on the chewing, swallowing, language, and breathing functions of patients. In recent years, with the development of medical molecular biology, molecular targeted therapy has received increasing clinical attention and has gradually become a new method for the treatment of malignant tumors. In this research, gold nanostars with a high photothermal effect combined with the searched targeted antibody were used for OSCC therapy. We use the data set in the public database and construct a gene co-expression module by weighted gene co-expression network analysis (WGCNA). It was found that the turquoise module and the midnight blue module had the greatest connection to tumorigenesis. Cytoscape software was used to analyze the important modules, and the top 10 genes of each module were selected; the survival analysis of the top 10 genes was carried out by gene expression profiling interactive analysis (GEPIA), which indicated that these genes (SERPINH1, MMP11, ADAM12, FADS3, SLC36A2, C1QTNF7, SCRG1, and APOBEC2) have statistical significance as key genes that are related to the tumorigenesis of OSCC. Then, the anti-SERPINH1 antibody targeted to SERPINH1 was chosen as the inhibitor and combined with gold nanostars for photothermal assisted targeted therapy. Thus, the searched key genes can be regarded as biomarkers and therapeutic targets for further precise diagnosis.

Identification of Hub Genes Associated with Nonspecific Orbital Inflammation by Weighted Gene Coexpression Network Analysis

Background

Nonspecific orbital inflammation is a common ophthalmopathy with a high prevalence among adult females. Yet, its molecular mechanisms behind are poorly understood. Regulation of gene expression probably plays an important role in this disease. Thus, we utilized gene coexpression networks to identify key modules and hub genes involved in nonspecific orbital inflammation.

Methods

Data of gene expression in nonspecific orbital inflammation samples (n = 61) and healthy samples (n = 28) were obtained from the public Gene Expression Omnibus database. Afterward, differentially expressed genes were performed. Then, weighted correlation network analysis was done to define the key modules. Next, functional enrichment analysis was conducted by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway in key modules. Finally, a protein-protein interaction network and Cytohubba plugin were used to screen hub genes.

Results

Differential expression of 716 genes was identified, among which 169 genes were upregulated and 547 genes were downregulated in the nonspecific orbital inflammation group. In weighted correlation network analysis, we clarified 2 key modules (MEturquoise and MEblue) that are likely to play key roles in nonspecific orbital inflammation. Functional enrichment analysis indicated that these genes are predominately involved in immune response and matrix homeostasis. In addition, among 2 key modules, there are 20 hub genes identified.

Conclusion

With this new approach, we identified several genes that could be critical to pathologies of nonspecific orbital inflammation. These findings may contribute to further therapeutic target development.

A new finding in the key prognosis-related proto-oncogene FYN in hepatocellular carcinoma based on the WGCNA hub-gene screening trategy

Background

Hepatocellular carcinoma (HCC) is the third-most deadly cancer worldwide. More breakthroughs are needed in the clinical practice for liver cancer are needed, and new treatment strategies are required. This study aims to determine the significant differences in genes associated with LIHC and further analyze its prognostic value further.

Methods

Here, we used the TCGA-LIHC database and the profiles of GSE25097 from GEO to explore the differentially co-expressed genes in HCC tissues compared with paratumor (or healthy) tissues. Then, we utilized WGCNA to screen differentially co-expressed genes. Finally, we explored the function of FYN in HCC cells and xenograft tumor models.

Results

We identified ten hub genes in the protein–protein interaction (PPI) network, but only three (COLEC10, TGFBR3, and FYN) appeared closely related to the prognosis. The expression of FYN was positively correlated with the prognosis of HCC patients. The xenograft model showed that overexpression of FYN could significantly inhibit malignant tumor behaviors and promote tumor cell apoptosis.

Conclusion

Thus, FYN may be central to the development of LIHC and maybe a novel biomarker for clinical diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09388-5.

Downregulation of FXYD2 Is Associated with Poor Prognosis and Increased Regulatory T Cell Infiltration in Clear Cell Renal Cell Carcinoma

BACKGROUND

FXYD2, a gene coding for the γ subunit of Na⁺/K⁺-ATPase, was demonstrated to involve in carcinogenesis recently. However, the specific role of FXYD2 in clear cell renal cell carcinoma (ccRCC) remains unknown. The current study was conducted to investigate the expression, biological function, and potentially immune-related mechanisms of FXYD2 in ccRCC. Materials and methods. The data from TCGA-KIRC, ICGC, GEO, Oncomine, ArrayExpress, TIMER, HPA datasets, and our clinical samples were used to determine and validate the expression level, prognostic roles, and potentially immune-related mechanisms in ccRCC. Cell function assays were performed to investigate the biological role of FXYD2 in vitro.

RESULTS

FXYD2 was identified to be downregulated in ccRCC tissue compared to normal tissue, which was confirmed by our RT-PCR, WB, and IHC analyses. Kaplan-Meier survival analysis and Cox regression analysis suggested that downregulated FXYD2 could independently predict poor survival of ccRCC patients. Through the ESTIMATE algorithm, ssGSEA algorithm, CIBERSORT algorithm, TIMER database, and our laboratory experiment, FXYD2 was found to correlate with the immune landscape, especially regulatory T cells (Treg), in ccRCC. Gain-of-function experiment revealed that FXYD2 could restrain cell proliferation, migration, and invasion in vitro. Functional enrichment analysis illustrated that TGF-β-SMAD2/3, Notch, and PI3K-Akt-mTOR signaling pathways may be potential signaling pathways of FXYD2 in ccRCC.

CONCLUSIONS

Downregulation of FXYD2 is associated with ccRCC tumorigenesis, poor prognosis, and increased Treg infiltration in ccRCC, which may be related to TGF-β-SMAD2/3, Notch, and PI3K-Akt-mTOR signaling pathways. This will probably provide a novel prognostic marker and potential therapeutic target for ccRCC.

Identifying stress responsive genes using overlapping communities in co-expression networks

Background

This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems’ regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment.

Results

The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice.

Conclusions

A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.

Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

Background

Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated.

Methods

In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis.

Results

Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA.

Conclusion

Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.

Weighted Gene Co-Expression Network Analysis Identifies Five Hub Genes Associated with Metastasis in Synovial Sarcoma

BACKGROUND

Synovial sarcoma (SS) refers to a malignant soft tissue sarcoma (STS) which often occurs in children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only.

METHOD

We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and hub genes were screened in Oncomine analysis.

RESULT

We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was the turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis.

CONCLUSION

In this study, we screened five hub genes that may help in clinical diagnosis and serve as the latent purpose of SS treatment.

Construction of a Five-Super-Enhancer-Associated-Genes Prognostic Model for Osteosarcoma Patients

Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.

Identifying 8-mRNAsi Based Signature for Predicting Survival in Patients With Head and Neck Squamous Cell Carcinoma via Machine Learning

Cancer stem cells (CSCs) have been characterized by several exclusive features that include differentiation, self-renew, and homeostatic control, which allows tumor maintenance and spread. Recurrence and therapeutic resistance of head and neck squamous cell carcinomas (HNSCC) have been identified to be attributed to CSCs. However, the biomarkers led to the development of HNSCC stem cells remain less defined. In this study, we quantified cancer stemness by mRNA expression-based stemness index (mRNAsi), and found that mRNAsi indices were higher in HNSCC tissues than that in normal tissue. A significantly higher mRNAsi was observed in HPV positive patients than HPV negative patients, as well as in male patients than in female patients. The 8-mRNAsi signature was identified from the genes in two modules which were mostly related to mRNAsi screened by weighted gene co-expression network analysis. In this prognostic signatures, high expression of RGS16, LYVE1, hnRNPC, ANP32A, and AIMP1 focus in promoting cell proliferation and tumor progression. While ZNF66, PIK3R3, and MAP2K7 are associated with a low risk of death. The riskscore of eight signatures have a powerful capacity for 1-, 3-, 5-year of overall survival prediction (5-year AUC 0.77, 95% CI 0.69-0.85). These findings based on stemness indices may provide a novel understanding of target therapy for suppressing HNSCC stem cells.

Application of weighted gene co-expression network analysis to reveal key modules and hub genes in generalized aggressive periodontitis

OBJECTIVE

The aim of this study was to construct a gene co-expression network to identify key modules and genes in people with generalized aggressive periodontitis.

METHODS

We used database GSE79705 to construct a co-expression network by weighted gene co-expression network analysis (WGCNA). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted.

RESULTS

A total of 51 co-expression modules were conducted, darkseagreen1 and blue1 modules were the most significantly related to generalized aggressive periodontitis. Genes in the darkseagreen1 module enriched in affecting cellular response to tumor necrosis factor and vascular endothelial growth factor production, and the blue1 module enriched in the regulation of ion transport, proteinaceous extracellular matrix and neuropeptide binding. Besides, we found that 4 hub genes (SNRPG, MRPL22, MRPS18C and CEP290) played an important role in the occurrence of generalized aggressive periodontitis.

CONCLUSION

Through this study, we identified two modules and four hub genes associated with generalized aggressive periodontitis. Besides, 4 hub genes (SNRPG, MRPL22, MRPS18C and CEP290) can be expected to trigger new therapeutic drug development for generalized aggressive periodontitis.

Molecular Biology of Osteosarcoma

Osteosarcoma (OS) is the most frequent primary bone cancer in children and adolescents and the third most frequent in adults. Many inherited germline mutations are responsible for syndromes that predispose to osteosarcomas including Li Fraumeni syndrome, retinoblastoma syndrome, Werner syndrome, Bloom syndrome or Diamond-Blackfan anemia. TP53 is the most frequently altered gene in osteosarcoma. Among other genes mutated in more than 10% of OS cases, c-Myc plays a role in OS development and promotes cell invasion by activating MEK-ERK pathways. Several genomic studies showed frequent alterations in the RB gene in pediatric OS patients. Osteosarcoma driver mutations have been reported in NOTCH1, FOS, NF2, WIF1, BRCA2, APC, PTCH1 and PRKAR1A genes. Some miRNAs such as miR-21, -34a, -143, -148a, -195a, -199a-3p and -382 regulate the pathogenic activity of MAPK and PI3K/Akt-signaling pathways in osteosarcoma. CD133+ osteosarcoma cells have been shown to exhibit stem-like gene expression and can be tumor-initiating cells and play a role in metastasis and development of drug resistance. Although currently osteosarcoma treatment is based on adriamycin chemoregimens and surgery, there are several potential targeted therapies in development. First of all, activity and safety of cabozantinib in osteosarcoma were studied, as well as sorafenib and pazopanib. Finally, novel bifunctional molecules, of potential imaging and osteosarcoma targeting applications may be used in the future.

LncRNA-XIST promotes the oxidative stress-induced migration, invasion, and epithelial-to-mesenchymal transition of osteosarcoma cancer cells through miR-153-SNAI1 axis

Osteosarcoma (OS) is the most common type of primary bone tumor that exhibits invasive growth and long-distance organ metastasis. Thus, investigating the specifically targeted therapeutic agents against metastatic osteosarcoma depends on understanding the molecular mechanisms. The long noncoding RNAs (lncRNA) XIST (X-inactive specific transcript) has been reported to have oncogenic roles in various malignant tumors including OS. However, its molecular mechanisms in OS migration and invasion are still under investigation. In the current study, we demonstrate that XIST is significantly upregulated in 30 pairs of OS tissues compared with their matched adjacent nontumor tissues by the quantitative reverse transcription polymerase chain reaction. Overexpression of XIST significantly induced the invasion, migration, and the epithelial-to-mesenchymal transition (EMT) phenotype. The epithelial marker, E-cadherin was effectively suppressed by XIST overexpression. On the other way, the mesenchymal marker, Fibronectin, Snail, and Vimentin were significantly activated by exogenous XIST overexpression. Furthermore, we observed XIST was upregulated by the oxidative stress-induced EMT. Bioinformatical analysis indicated that miR-153 has multiple biding sites for XIST and miR-153 was inversely suppressed by oxidative stress. XIST was verified to directly downregulate miR-153 via sponging. We identified the mesenchymal marker, SNAI1 was a direct messenger RNA target of miR-153. Importantly, inhibiting XIST successfully blocked the H₂ O₂ -induced EMT of OS cells. In conclusion, this work demonstrates that lncRNA-XIST promotes the oxidative stress-induced OS cell invasion, migration, and EMT through the miR-153/SNAI1 pathway, presenting lncRNA-XIST as a promising therapeutic target for treating metastatic OS.

Metastasis-associated gene MAPK15 promotes the migration and invasion of osteosarcoma cells via the c-Jun/MMPs pathway

Osteosarcoma (OS) is the most common and destructive primary bone malignancy to affect children and adolescents. Metastases remain the primary cause of death in patients with OS. In the present study, weight gene co-expressed network analysis (WGCNA) and differentially-expressed gene analysis were used to identify key genes associated with the metastasis of OS. Reverse transcription-quantitative PCR and immunohistochemical staining were then used to detect the expression levels of these key genes in OS tissues, and to determine the hub genes of interest. Wound-healing and transwell assays, in addition to a lung metastasis model, were used to detect the effects of the hub genes on OS cell proliferation and metastasis in vitro and in vivo. Using WGCNA and differential expression analysis, deleted in lung and esophageal cancer protein 1 (DLEC1), Forkhead box J1 (FOXJ1) and mitogen-activated protein kinase 15 (MAPK15) were predicted to be key metastasis-associated genes, and highly expressed in metastatic OS tissues; among them, the protein and mRNA expression levels of MAPK15 were most significantly increased in our OS tissues from patients who exhibited metastases at diagnosis, and thus MAPK15 was determined to be a metastasis-associated hub gene to further study. Furthermore, inhibiting MAPK15 expression significantly decreased OS cell metastasis in vitro and in vivo, as well as suppressing c-Jun/matrix metalloproteinase (MMP)-associated pathways. Overexpression of MAPK15 activated the c-Jun/MMPs pathway and promoted OS cell metastasis, while inhibition of c-Jun blocked this effect. Taken together, MAPK15 was indicated to be an OS metastasis-associated gene, and was confirmed to promote the migration and invasion of OS cells via the c-Jun/MMP pathway. MAPK15 may therefore be an effective target for the treatment of OS.

Identification of modules and hub genes associated with platinum-based chemotherapy resistance and treatment response in ovarian cancer by weighted gene co-expression network analysis

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and malignant ovarian tumor.To identify co-expression modules and hub genes correlated with platinum-based chemotherapy resistant and sensitive HGSOC, we performed weighted gene co-expression network analysis (WGCNA) on microarray data of HGSOC with 12 resistant samples and 16 sensitive samples of GSE51373 dataset.A total of 5122 genes were included in WGCNA, and 16 modules were identified. Module-trait analysis identified that the module salmon (cor = 0.50), magenta (cor = 0.49), and black (cor = 0.45) were discovered associated with chemotherapy resistant, and the significance for these platinum-resistant modules were validated in the GSE63885 dataset. Given that the black module was validated to be the most related one, hub genes of this module, alcohol dehydrogenase 1B, cadherin 11, and vestigial like family member 3were revealed to be expressional related with platinum resistance, and could serve as prognostic markers for ovarian cancer.Our analysis might provide insight for molecular mechanisms of platinum-based chemotherapy resistance and treatment response in ovarian cancer.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

A risk score model for the prediction of osteosarcoma metastasis

Osteosarcoma is the most common primary solid malignancy of the bone, and its high mortality usually correlates with early metastasis. In this study, we developed a risk score model to help predict metastasis at the time of diagnosis. We downloaded and mined four expression profile datasets associated with osteosarcoma metastasis from the Gene Expression Omnibus. After data normalization, we performed LASSO logistic regression analysis together with 10-fold cross validation using the GSE21257 dataset. A combination of eight genes (RAB1,CLEC3B,FCGBP,RNASE3,MDL1,ALOX5AP,VMO1 and ALPK3) were identified as being associated with osteosarcoma metastasis. These genes were put into a gene risk score model, and the prediction efficiency of the model was then validated using three independent datasets (GSE33383, GSE66673, and GSE49003) by plotting receiver operating characteristic curves. The expression levels of the eight genes in all datasets were shown as heatmaps, and gene ontology gene annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. These eight genes play a role in cancer-related biological processes, such as apoptosis and biosynthetic processes. Our results may aid in elucidating the possible mechanisms of osteosarcoma metastasis, and may help to facilitate the individual management of patients with osteosarcoma after treatment.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.

Prognostic significance of matrix metalloproteinase 9 expression in osteosarcoma: A meta-analysis of 16 studies

BACKGROUND

Matrix metalloproteinase 9 (MMP-9) is significant in the progression of osteosarcoma (OS) via increasing tumor growth, invasion and metastasis. Although previous reports indicate the prognostic value of MMP-9 in OS, there is still a great degree on inconsistency between studies. Here we report a comprehensive evaluation of the value of MMP-9 in metastasis of OS by conducting a meta-analysis of published studies.

METHODS

The quantity of the studies was evaluated using the Newcastle-Ottawa quality assessment scale (NOS). Sixteen studies with a total of 816 patients with OS were examined and we calculated the pooled odds ratio (OR) with corresponding 95% confidence interval (CI) (95% CI) to evaluate that the positive expression of MMP-9 predicts neoplasm metastasis and poor survival in OS.

RESULTS

The results of Meta-analysis indicated that patients with positive expression of MMP-9 were significantly associated with neoplasm metastasis (OR = 4.69, 95% CI: 3.05-7.21, P <.001) and poor survival in OS with the pooled OR of 7.19 (95% CI 4.32-11.98, P <.001) when compared to their counterparts with a negative expression of MMP-9. The results of sensitivity analysis showed that the pooled OR was stable. It doesn't significantly change when a single study was removed.

CONCLUSIONS

The results of meta-analysis indicated that MMP-9 may be a prognostic biomarker guiding the clinical therapy for OS.