Expression profile analysis identifies key genes as prognostic markers for metastasis of osteosarcoma

Integrated bioinformatics analysis and experimental validation reveal the relationship between ALOX5AP and the prognosis and immune microenvironment in glioma

BACKGROUND

Treatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood.

METHODS

The differential expression of ALOX5AP was evaluated based on public Databases. Kaplan-Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray.

RESULT

ALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma.

CONCLUSION

ALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis

Background

The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies.

Methods

This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes.

Results

In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells.

Conclusions

The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.

ALOX5AP is an Indicator for High CD8 Lymphocyte Infiltration and "Hot" Tumor Microenvironment in Osteosarcoma: A Bioinformatic Study

Little progress has been made in the treatment and prognosis of osteosarcoma in the past 40 years. Tumor microenvironment (TME) plays a critical role in the progression of osteosarcoma. This study aims to determine immune-associated prognostic biomarkers for osteosarcoma patients. With the help of analytical tools including ESTIMATE, differential gene expression, LASSO, and univariate cox and multivariate cox regression analysis, osteosarcoma gene expression data from Gene Expression Omnibus (GEO) databases were investigated. Following the establishment of a prognostic risk score model, internal and external validations using the GEO and TARGET databases were carried out. A total of 44 and 55 samples respectively in the GSE21257 and the TARGET databases were included. Our analysis found 93 differentially expressed genes (DEGs) between the high and low-ImmuneScore groups. Through univariate cox and LASSO analysis, ALOX5AP was identified as an indicator of TME in osteosarcomas. ALOX5AP was then used to construct a prognostic risk model. Internal and external verification revealed that higher expression of ALOX5AP was correlated with lower risk. Through the CIBERSORT algorithm, the level of CD8 T cells was found to negatively correlate with the risk score. This study revealed that ALOX5AP is an indicator for predicting high CD8 lymphocyte infiltration and "hot" tumor microenvironment in osteosarcomas. Thus, ALOX5AP has the potential to act as a biomarker for effective immunotherapies in osteosarcoma patients.

Comprehensive Analyses Revealed Eight Immune Related Signatures Correlated With Aberrant Methylations as Prognosis and Diagnosis Biomarkers for Kidney Renal Papillary Cell Carcinoma

BACKGROUND

Kidney renal papillary cell carcinoma (KIRP) is a common type of renal cell carcinoma. DNA methylation plays an important role in the development of several cancers. The aim of our study was to identify differentially expressed genes associated with abnormal DNA methylation as biomarkers for predicting the outcome of KIRP.

METHOD

We downloaded KIRP methylation data, RNA sequencing (RNAseq) data, and their corresponding clinical information from the Cancer Genome Atlas (TCGA) database. ChAMP and DEGseq2 packages in R software were used to screen differentially methylated probes (DMPs) and differentially expressed genes (DEGs). Univariate and multivariate Cox regression analyses were used to identify suitable immune related genes correlated with aberrant methylations as prognosis biomarkers.

RESULTS

We identified 8 DEGs (Cysteine And Glycine Rich Protein 1 [CSRP1], major histocompatibility complex, Class II, DM Beta [HLA-DMB], LIF Receptor Subunit Alpha [LIFR], Leukotriene B4 receptor 2 [LTB4R2], Mitogen-Activated Protein Kinase Kinase Kinase 14 [MAP3K14], Nuclear Receptor Subfamily 2 Group F Member 1 [NR2F1], Secreted And Transmembrane 1 [SECTM1], and Vimentin [VIM]) that were independently associated with the overall survival (months) (OS) of KIRP. The time dependent area under the curve (AUC) for each receiver operating characteristic (ROC) of the risk assessment model at 1, 3, 5, and 10-years reached 0.8415, 0.8131, 0.7873, and 0.7667. The risk assessment model was correlated with several immune cells and factors. The AUC value of the diagnosis model using those 8 DEGs reached 0.99.

CONCLUSIONS

The risk assessment model constructed by those 8 DEGs was well able to predict the prognosis and diagnose of KIRP. However, whether the prognosis and diagnosis model could be applied in clinical practice requires further study.

Construction of the prognostic enhancer RNA regulatory network in osteosarcoma

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Our enhancer RNAs-based prognostic model showed good predictive ability in osteosarcoma.

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CCAAT enhancer binding protein alpha (CEBPA) may regulate CD8A molecule (CD8A).

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CD8A activation may promote CD3E molecule (CD3E) expression and activate allograft rejection in CD8+ T cells.

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Above signal axis provided new insights in the mechanism of osteosarcoma tumorigenesis.

Background

Osteosarcoma (OS) is a common malignant tumor in osteoarticular system, the 5-year overall survival of which is poor. Enhancer RNAs (eRNAs) have been implicated in the tumorigenesis of various cancer types, whereas their roles in OS tumorigenesis remains largely unclear.

Methods

Differentially expressed eRNAs (DEEs), transcription factors (DETFs), target genes (DETGs) were identified using limma (Linear Models for Microarray Analysis) package. Prognosis-related DEEs were accessed by univariate Cox regression analysis. A multivariate model was constructed to evaluate the prognosis of OS samples. Prognosis-related DEEs, DETFs, DETGs, immune cells, and hallmark gene sets were co-analyzed to construct an regulatory network. Specific inhibitors were also filtered by connectivity Map analysis. External validation and scRNA-seq analysis were performed to verify our key findings.

Results

3,981 DETGs, 468 DEEs, 51 DETFs, and 27 differentially expressed hallmark gene sets were identified. A total of Multivariate risk predicting model based on 18 prognosis-related DEEs showed a high accuracy (area under curve (AUC) = 0.896). GW-8510 was the candidate inhibitor targeting prognosis-related DEEs (mean = 0.670, p < 0.001). Based on the OS tumorigenesis-related regulation network, we identified that CCAAT enhancer binding protein alpha (CEBPA, DETF) may regulate CD8A molecule (CD8A, DEE), thereby promoting the transcription of CD3E molecule (CD3E, DETG), which may affect allograft rejection based on CD8+ T cells.

Conclusion

We constructed an eRNA-based prognostic model for predicting the OS patients’ prognosis and explored the potential regulation network for OS tumorigenesis by an integrated bioinformatics analysis, providing promising therapeutic targets for OS patients.

Secreted Protein Acidic and Rich in Cysteine Mediates the Development and Progression of Diabetic Retinopathy

BACKGROUNDS

Diabetic retinopathy (DR) is one of the most severe microvascular complications of diabetes mellitus (DM). Secreted protein acidic and rich in cysteine (SPARC) has been found to play an important role in many diseases, but its role and mechanism in DR remain unknown.

METHODS

We studied the role of SPARC and integrin β1 in vascular pathophysiology and identified potential therapeutic translation. The SPARC levels were tested in human serum and vitreous by ELISA assay, and then the Gene Expression Omnibus (GEO) dataset was used to understand the key role of the target gene in DR. In human retinal capillary endothelial cells (HRCECs), we analyzed the mRNA and protein level by RT-PCR, immunohistochemistry, and Western blotting. The cell apoptosis, cell viability, and angiogenesis were analyzed by flow cytometry, CCK-8, and tube formation.

RESULTS

In this study, we investigated the role of SPARC in the development and progression of human DR and high glucose-induced HRCEC cells and found that the SPARC-ITGB1 signaling pathway mimics early molecular and advanced neurovascular pathophysiology complications of DR. The result revealed that DR patients have a high-level SPARC expression in serum and vitreous. Knockdown of SPARC could decrease the expressions of inflammatory factors and VEGFR, inhibit cell apoptosis and angiogenesis, and increase cell viability by regulating integrin β1 in HRCECs.

CONCLUSION

SPARC promotes diabetic retinopathy via the regulation of integrin β1. The results of this study can provide a potential therapeutic application for the treatment of DR.

Development and Validation of Ten-RNA Binding Protein Signature Predicts Overall Survival in Osteosarcoma

Osteosarcoma is a malignant tumor that originates in the bones with the characteristics of high malignancy, predisposition to metastasis, and poor prognosis. RNA binding proteins (RBPs) are closely related to various tumors, but their relationship with osteosarcoma remains unclear. Based on GTEx and TARGET RNA sequencing data, we applied differential analysis to obtain RBP genes that are differentially expressed in osteosarcoma, and analyzed the functions of these RBPs. After applying univariate and LASSO Cox regression analysis, 10 key prognostic RBPs (TDRD6, TLR8, NXT2, EIF4E3, RPS27L, CPEB3, RBM34, TERT, RPS29, and ZC3HAV1) were screened, and an RBP prognostic risk assessment model for patients with osteosarcoma was established. The independent cohort GSE21257 was used for external verification, and the results showed that the signature has an excellent ability to predict prognosis. In addition, a nomogram that can be used for clinical evaluation was constructed. Finally, the expression levels of 10 prognostic RBPs in osteosarcoma cells and tissues were confirmed through experiments. Our study identified a ten-gene prognostic marker related to RBP, which is of great significance for adjusting the treatment strategy of patients with osteosarcoma and exploring prognostic markers.

ALOX5AP Predicts Poor Prognosis by Enhancing M2 Macrophages Polarization and Immunosuppression in Serous Ovarian Cancer Microenvironment

Background

Serous ovarian cancer (SOC) is a highly lethal gynecological malignancy with poor prognosis. Given the importance of the immune-related tumor microenvironment (TME) in ovarian cancer, investigating tumor-immune interactions and identifying novel prognostic and therapeutic targets in SOC is a promising avenue of research. ALOX5AP (Arachidonate 5-Lipoxygenase Activating Protein) is a key enzyme in converting arachidonic acid to leukotriene: a crucial immune-modulating lipid mediator. However, the role of ALOX5AP in SOC has yet to be studied.

Methods

ALOX5AP expression patterns across ovarian cancer and their normal tissue counterparts were cross-checked using public microarray and RNA-seq analyses and then validated in clinical samples by qRT-PCR. Kaplan-Meier survival analysis was performed in multiple independent SOC patient cohorts. Univariate and multivariate Cox regression analysis were then employed to identify clinical risk parameters associated with survival, and a genomic-clinicopathologic nomogram was built. Gene enrichment, immune infiltration, and immunosuppressor correlation analyses were then evaluated.

Results

ALOX5AP mRNA levels in SOC tissues were significantly upregulated compared to normal tissues. Elevated ALOX5AP was markedly associated with poor overall survival and progression-free survival in multiple SOC patient cohorts as well as with adverse clinicopathological features, including lymphatic invasion, unsatisfactory cytoreductive surgery, rapid relapse after primary treatment, and platinum non-responsiveness. A predictive nomogram, which integrated ALOX5AP expression and two independent prognosis factors (primary therapy outcome and tumor residual), was conducted to predict the 3-year and 5-year survival rate of SOC patients. Mechanistically, functional and pathway enrichment analyses revealed that ALOX5AP was primarily involved in immune response and regulation. Further exploration demonstrated that ALOX5AP was highly expressed in the immunoreactive subtype of ovarian cancer and closely related to immunocyte infiltration, especially M2 macrophage polarization. Additionally, ALOX5AP was enriched in the C4 (lymphocyte depleted) immune subtype of SOC and associated with crucial immune-repressive receptors in the tumor microenvironment at the genomic level.

Conclusions

ALOX5AP expression indicates a worse survival outcome and has the potential to be utilized as a prognostic predictor for SOC patients. Given the availability of well-studied ALOX5AP inhibitors, this study has immediate clinical implications for the exploitation of ALOX5AP as an immunotherapeutic target in SOC.

Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients

Glycolysis ensures energy supply to cancer cells, thereby facilitating tumor progression. Here, we identified glycolysis-related genes that could predict the prognosis of patients with osteosarcoma. We examined 198 glycolysis-related genes that showed differential expression in metastatic and non-metastatic osteosarcoma samples in the TARGET database, and identified three genes (P4HA1, ABCB6, and STC2) for the establishment of a risk signature. Based on the signature, patients in the high-risk group had poor outcomes. An independent Gene Expression Omnibus database GSE21257 was selected as the validation cohort. Receiver operating characteristic curve analysis was performed and the accuracy of predicting the 1- and 3-year survival rates was shown by the areas under the curve. The results were 0.884 and 0.790 in the TARGET database, and 0.740 and 0.759 in the GSE21257, respectively. Furthermore, we applied ESTIMATE algorithm and performed single sample gene set enrichment analysis to compare tumor immunity between high- and low-risk groups. We found that the low-risk group had higher immune scores and immune infiltration levels than the high-risk group. Finally, we chose P4HA1 as a representative gene to verify the function of risk genes in vitro and in vivo and found that P4HA1 could promote the metastasis of osteosarcoma cells. Our study established a novel glycolysis-related risk signature that could predict the prognosis of patients with osteosarcoma.

Gene signatures with predictive and prognostic survival values in human osteosarcoma

Osteosarcoma is a common malignancy seen mainly in children and adolescents. The disease is characterized by poor overall prognosis and lower survival due to a lack of predictive markers. Many gene signatures with diagnostic, prognostic, and predictive values were evaluated to achieve better clinical outcomes. Two public data series, GSE21257 and UCSC Xena, were used to identify the minimum number of robust genes needed for a predictive signature to guide prognosis of patients with osteosarcoma. The lasso regression algorithm was used to analyze sequencing data from TCGA-TARGET, and methods such as Cox regression analysis, risk factor scoring, receiving operating curve, KMplot prognosis analysis, and nomogram were used to characterize the prognostic predictive power of the identified genes. Their utility was assessed using the GEO osteosarcoma dataset. Finally, the functional enrichment analysis of the identified genes was performed. A total of twenty-gene signatures were found to have a good prognostic value for predicting patient survival. Gene ontology analysis showed that the key genes related to osteosarcoma were categorized as peptide–antigen binding, clathrin-coated endocytic vesicle membrane, peptide binding, and MHC class II protein complex. The osteosarcoma related genes in these modules were significantly enriched in the processes of antigen processing and presentation, phagocytosis, cell adhesion molecules, Staphylococcus aureus infection. Twenty gene signatures were identified related to osteosarcoma, which would be helpful for predicting prognosis of patients with OS. Further, these signatures can be used to determine the subtypes of osteosarcoma.

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

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

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.

Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma

Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B.

Identification of potential crucial genes and key pathways in osteosarcoma

Background

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

Results

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

Conclusions

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