Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome

BACKGROUND

The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS.

METHODS

The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice.

RESULTS

FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2.

CONCLUSIONS

Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

Identification of microtubule-associated biomarker using machine learning methods in osteonecrosis of the femoral head and osteosarcoma

Background

This study aims to explore the microtubule-associated gene signatures and molecular processes shared by osteonecrosis of the femoral head (ONFH) and osteosarcoma (OS).

Methods

Datasets from the TARGET and GEO databases were subjected to bioinformatics analysis, including the functional enrichment analysis of genes shared by ONFH and OS. Prognostic genes were identified using univariate and multivariate Cox regression analyses to develop a risk score model for predicting overall survival and immune characteristics. Furthermore, LASSO and SVM-RFE algorithms identified biomarkers for ONFH, which were validated in OS. Function prediction, ceRNA network analysis, and gene-drug interaction network construction were subsequently conducted. Biomarker expression was then validated on clinical samples by using qPCR.

Results

A total of 14 microtubule-associated disease genes were detected in ONFH and OS. Subsequently, risk score model based on four genes was then created, revealing that patients with low-risk exhibited superior survival outcomes compared with those with high-risk. Notably, ONFH with low-risk profiles may manifest an antitumor immune microenvironment. Moreover, by utilizing LASSO and SVM-RFE algorithms, four diagnostic biomarkers were pinpointed, enabling effective discrimination between patients with ONFH and healthy individuals as well as between OS and normal tissues. Additionally, 21 drugs targeting these biomarkers were predicted, and a comprehensive ceRNA network comprising four mRNAs, 71 miRNAs, and 98 lncRNAs was established. The validation of biomarker expression in clinical samples through qPCR affirmed consistency with the results of bioinformatics analysis.

Conclusion

Microtubule-associated genes may play pivotal roles in OS and ONFH. Additionally, a prognostic model was constructed, and four genes were identified as potential biomarkers and therapeutic targets for both diseases.

PDE1B, a potential biomarker associated with tumor microenvironment and clinical prognostic significance in osteosarcoma

PDE1B had been found to be involved in various diseases, including tumors and non-tumors. However, little was known about the definite role of PDE1B in osteosarcoma. Therefore, we mined public data on osteosarcoma to reveal the prognostic values and immunological roles of the PDE1B gene. Three osteosarcoma-related datasets from online websites were utilized for further data analysis. R 4.3.2 software was utilized to conduct difference analysis, prognostic analysis, gene set enrichment analysis (GSEA), nomogram construction, and immunological evaluations, respectively. Experimental verification of the PDE1B gene in osteosarcoma was conducted by qRT-PCR and western blot, based on the manufacturer's instructions. The PDE1B gene was discovered to be lowly expressed in osteosarcoma, and its low expression was associated with poor OS (all P < 0.05). Experimental verifications by qRT-PCR and western blot results remained consistent (all P < 0.05). Univariate and multivariate Cox regression analyses indicated that the PDE1B gene had independent abilities in predicting OS in the TARGET osteosarcoma dataset (both P < 0.05). GSEA revealed that PDE1B was markedly linked to the calcium, cell cycle, chemokine, JAK STAT, and VEGF pathways. Moreover, PDE1B was found to be markedly associated with immunity (all P < 0.05), and the TIDE algorithm further shed light on that patients with high-PDE1B expression would have a better immune response to immunotherapies than those with low-PDE1B expression, suggesting that the PDE1B gene could prevent immune escape from osteosarcoma. The PDE1B gene was found to be a tumor suppressor gene in osteosarcoma, and its high expression was related to a better OS prognosis, suppressing immune escape from osteosarcoma.

Osteosarcoma-Associated Immune Genes as Potential Immunotherapy and Prognosis Biomarkers

Immune-modulating therapies exhibit abundant promise compared to traditional treatment for osteosarcoma. We aim to establish an immune-related prognostic signatures in osteosarcoma. We identified the differentially expressed genes in osteosarcoma compared with normal controls using the GEO dataset. The intersection with immune-related genes was considered as differentially expressed immune genes. Potential prognosis-related differentially expressed genes were first analyzed with the multifactor Cox regression and then the step function performed the iteration. The best model was finally chosen as the immunological risk score signature model. And finally, we evaluated the correlation of genes in the prognostic model with immune cells, common immune checkpoints, and immune checkpoint blockade responses. We identified 1527 significantly upregulated and 2407 significantly downregulated genes in osteosarcoma compared to normal samples. In the 258 differentially expressed immune genes, 20 genes with independent prognostic significance were included in the step function. Finally, we constructed a prognostic signature for overall survival based on five immune genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) in osteosarcoma. These five genes are closely related to immune infiltration. Osteosarcoma with high JAG2 expression or low CMKLR1 expression may be associated with better immune checkpoint blockade response. JAG2 overexpression or CMKLR1 inhibition induced sensitivity to PD-1 antibody in osteosarcoma cells. We constructed a prognosis prediction signature containing five immune-related genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) with a significant prognostic value in osteosarcoma. Significant differences in immune infiltration and immunotherapy responses were identified between groups with different levels of these genes.

The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients

Host immune dysregulation involves in the initiation and development of osteosarcoma (OS). However, the exact role of immune cells in OS remains unknown. We aimed to distinguish the molecular subtypes and establish a prognostic model in OS patients based on immunocyte infiltration. The gene expression profile and corresponding clinical feature of OS patients were obtained from TARGET and GSE21257 datasets. MCP-counter and univariate Cox regression analyses were applied to identify immune cell infiltration-related molecular subgroups. Functional enrichment analysis and immunocyte infiltration analysis were performed between two subgroups. Furthermore, Cox regression and LASSO analyses were performed to establish the prognostic model for the prediction of prognosis and metastasis in OS patients. The subgroup with low infiltration of monocytic lineage (ML) was related to bad prognosis in OS patients. 435 DEGs were screened between the two subgroups. Functional enrichment analysis revealed these DEGs were involved in immune- and inflammation-related pathways. Three important genes (including TERT, CCDC26, and IL2RA) were identified to establish the prognostic model. The risk model had good prognostic performance for the prediction of metastasis and overall survival in OS patients. A novel stratification system was established based on ML-related signature. The risk model could predict the metastasis and prognosis in OS patients. Our findings offered a novel sight for the prognosis and development of OS.

Prognostic related gene index for predicting survival and immunotherapeutic effect of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common malignant liver tumor. It is an aggressive disease with high mortality rate. In this study, we investigated a new prognosis-related gene index (PRGI) that can predict the survival and efficacy of immunotherapy in patients with HCC. RNA-seq data and clinical data of HCC samples were obtained from the cancer genome atlas and ICGC databases. Prognosis-related genes were obtained using log-rank tests and univariate Cox proportional hazards regression. Univariate and multivariate analyses were performed on the overall survival rate of patients with prognosis-related genes and multiple clinicopathological factors, and a nomogram was constructed. A PRGI was then constructed based on least absolute shrinkage and selection operator or multivariate Cox Iterative Regression. The possible correlation between PRGI and immune cell infiltration or immunotherapy efficacy was discussed. Eight genes were identified to construct the PRGI. PRGI can predict the infiltration of immune cells into the tumor microenvironment of HCC and the response to immunotherapy. PRGI can accurately predict the survival rate of patients with HCC, reflect the immune microenvironment, and predict the efficacy of immunotherapy.

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 Disulfidptosis-Related Gene Signature Associated with Prognosis and Immune Cell Infiltration in Osteosarcoma

Osteosarcoma (OS) stands as a leading aggressive bone malignancy that primarily affects children and adolescents worldwide. A recently identified form of programmed cell death, termed Disulfidptosis, may have implications for cancer progression. Yet, its role in OS remains elusive. To elucidate this, we undertook a thorough examination of Disulfidptosis-related genes (DRGs) within OS. This involved parsing expression data, clinical attributes, and survival metrics from the TARGET and GEO databases. Our analysis unveiled a pronounced association between the expression of specific DRGs, particularly MYH9 and LRPPRC, and OS outcome. Subsequent to this, we crafted a risk model and a nomogram, both honed for precise prognostication of OS prognosis. Intriguingly, risks associated with DRGs strongly resonated with immune cell infiltration levels, myriad immune checkpoints, genes tethered to immunotherapy, and sensitivities to systematic treatments. To conclude, our study posits that DRGs, especially MYH9 and LRPPRC, hold potential as pivotal architects of the tumor immune milieu in OS. Moreover, they may offer predictive insights into treatment responses and serve as reliable prognostic markers for those diagnosed with OS.

PSKH1 affects proliferation and invasion of osteosarcoma cells via the p38/MAPK signaling pathway

Malignant osteosarcoma (OS) is a tumor of bone and soft tissue that metastasizes early and has a high mortality rate. Protein serine kinase H1 (PSKH1), an autophosphorylating human protein serine kinase, controls the trafficking of serine/arginine-rich domain, with downstream effects on mRNA processing. It is also associated with tumor progression. However, how this protein contributes to OS progression and metastasis is unknown. The present study evaluated the potential effect of PSKH1 on proliferation of human OS cells. OS cell lines were used in Cell Counting Kit-8, colony formation, wound-healing and Transwell assays, to investigate cellular processes such as proliferation, migration and invasion and underlying molecular mechanisms. Expression of PSKH1 in OS tissue was significantly greater than in adjacent non-malignant tissue. PSKH1 knockdown inhibited the proliferation, migration and invasion of OS cells. Conversely, PSKH1 overexpression promoted proliferation of OS cells. PSKH1 upregulated phosphorylated-p38 in OS cells. Moreover, the p38 MAPK inhibitor SB203580 effectively blocked the tumor-promoting action of PSKH1. Furthermore, PSKH1 knockdown inhibited tumor growth and metastasis in vivo. In conclusion, these findings suggested that PSKH1 promoted OS proliferation, migration and invasion. Thus, PSKH1 may serve an oncogenic role in the development of human OS.

Clinical Targeted Next-Generation Panel Sequencing Reveals MYC Amplification Is a Poor Prognostic Factor in Osteosarcoma

PURPOSE

Osteosarcoma risk stratification, on the basis of the presence of metastatic disease at diagnosis and histologic response to chemotherapy, has remained unchanged for four decades, does not include genomic features, and has not facilitated treatment advances. We report on the genomic features of advanced osteosarcoma and provide evidence that genomic alterations can be used for risk stratification.

MATERIALS AND METHODS

In a primary analytic patient cohort, 113 tumor and 69 normal samples from 92 patients with high-grade osteosarcoma were sequenced with OncoPanel, a targeted next-generation sequencing assay. In this primary cohort, we assessed the genomic landscape of advanced disease and evaluated the correlation between recurrent genomic events and outcome. We assessed whether prognostic associations identified in the primary cohort were maintained in a validation cohort of 86 patients with localized osteosarcoma tested with MSK-IMPACT.

RESULTS

In the primary cohort, 3-year overall survival (OS) was 65%. Metastatic disease, present in 33% of patients at diagnosis, was associated with poor OS (P = .04). The most frequently altered genes in the primary cohort were TP53, RB1, MYC, CCNE1, CCND3, CDKN2A/B, and ATRX. Mutational signature 3 was present in 28% of samples. MYC amplification was associated with a worse 3-year OS in both the primary cohort (P = .015) and the validation cohort (P = .012).

CONCLUSION

The most frequently occurring genomic events in advanced osteosarcoma were similar to those described in prior reports. MYC amplification, detected with clinical targeted next-generation sequencing panel tests, is associated with poorer outcomes in two independent cohorts.

Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network

Osteosarcoma accounts for 28% of primary bone malignancies in adults and up to 56% in children and adolescents (<20 years). However, early diagnosis and treatment are still inadequate, and new improvements are still needed. Missed diagnoses exist due to fewer traditional diagnostic methods, and clinical symptoms are often already present before diagnosis. This study aimed to develop novel and efficient predictive models for the diagnosis of osteosarcoma and to identify potential targets for exploring osteosarcoma markers. First, osteosarcoma and normal tissue expression microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Then we screened the differentially expressed genes (DEGs) in the osteosarcoma and normal groups in the training group. Next, in order to explore the biologically relevant role of DEGs, Metascape and enrichment analyses were also performed on DEGs. The “randomForest” and “neuralnet” packages in R software were used to select representative genes and construct diagnostic models for osteosarcoma. The next step is to validate the model of the artificial neural network. Then, we performed an immune infiltration analysis by using the training set data. Finally, we constructed a prognostic model using representative genes for prognostic analysis. The copy number of osteosarcoma was also analyzed. A random forest classifier identified nine representative genes (ANK1, TGFBR3, RSF21, HSPB8, ITGA7, RHD, AASS, GREM2, NFASC). HSPB8, RHD, AASS, and NFASC were genes we identified that have not been previously reported to be associated with osteosarcoma. The osteosarcoma diagnostic model we constructed has good performance with areas under the curves (AUCs) of 1 and 0.987 in the training and validation groups, respectively. This study opens new horizons for the early diagnosis of osteosarcoma and provides representative markers for the future treatment of osteosarcoma. This is the first study to pioneer the establishment of a genetic diagnosis model for osteosarcoma and advance the development of osteosarcoma diagnosis and treatment.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data

BACKGROUND

We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.

METHODS

Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.

RESULTS

OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.

CONCLUSIONS

Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Characterizing the tumor microenvironment at the single-cell level reveals a novel immune evasion mechanism in osteosarcoma

The immune microenvironment extensively participates in tumorigenesis as well as progression in osteosarcoma (OS). However, the landscape and dynamics of immune cells in OS are poorly characterized. By analyzing single-cell RNA sequencing (scRNA-seq) data, which characterize the transcription state at single-cell resolution, we produced an atlas of the immune microenvironment in OS. The results suggested that a cluster of regulatory dendritic cells (DCs) might shape the immunosuppressive microenvironment in OS by recruiting regulatory T cells. We also found that major histocompatibility complex class I (MHC-I) molecules were downregulated in cancer cells. The findings indicated a reduction in tumor immunogenicity in OS, which can be a potential mechanism of tumor immune escape. Of note, CD24 was identified as a novel "don't eat me" signal that contributed to the immune evasion of OS cells. Altogether, our findings provide insights into the immune landscape of OS, suggesting that myeloid-targeted immunotherapy could be a promising approach to treat OS.

The interaction between osteosarcoma and other cells in the bone microenvironment: From mechanism to clinical applications

Osteosarcoma is a primary bone tumor with a high mortality rate. The event-free survival rate has not improved significantly in the past 30 years, which brings a heavy burden to patients and society. The high heterogeneity of osteosarcoma leads to the lack of specific targets and poor therapeutic effect. Tumor microenvironment is the focus of current research, and osteosarcoma is closely related to bone microenvironment. Many soluble factors and extracellular matrix secreted by many cells in the bone microenvironment have been shown to affect the occurrence, proliferation, invasion and metastasis of osteosarcoma through a variety of signaling pathways. Therefore, targeting other cells in the bone microenvironment may improve the prognosis of osteosarcoma. The mechanism by which osteosarcoma interacts with other cells in the bone microenvironment has been extensively investigated, but currently developed drugs targeting the bone microenvironment have poor efficacy. Therefore, we review the regulatory effects of major cells and physical and chemical properties in the bone microenvironment on osteosarcoma, focusing on their complex interactions, potential therapeutic strategies and clinical applications, to deepen our understanding of osteosarcoma and the bone microenvironment and provide reference for future treatment. Targeting other cells in the bone microenvironment may provide potential targets for the development of clinical drugs for osteosarcoma and may improve the prognosis of osteosarcoma.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS.

METHODS

Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores.

RESULTS

Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression.

CONCLUSION

Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Metabolic control of CD47 expression through LAT2-mediated amino acid uptake promotes tumor immune evasion

Chemotherapy elicits tumor immune evasion with poorly characterized mechanisms. Here, we demonstrate that chemotherapy markedly enhances the expression levels of CD47 in osteosarcoma tissues, which are positively associated with patient mortality. We reveal that macrophages in response to chemotherapy secrete interleukin-18, which in turn upregulates expression of L-amino acid transporter 2 (LAT2) in tumor cells for substantially enhanced uptakes of leucine and glutamine, two potent stimulators of mTORC1. The increased levels of leucine and enhanced glutaminolysis activate mTORC1 and subsequent c-Myc-mediated transcription of CD47. Depletion of LAT2 or treatment of tumor cells with a LAT inhibitor downregulates CD47 with enhanced macrophage infiltration and phagocytosis of tumor cells, and sensitizes osteosarcoma to doxorubicin treatment in mice. These findings unveil a mutual regulation between macrophage and tumor cells that plays a critical role in tumor immune evasion and underscore the potential to intervene with the LAT2-mediated amino acid uptake for improving cancer therapies.

A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.

TYRO protein tyrosine kinase-binding protein predicts favorable overall survival in osteosarcoma and correlates with antitumor immunity

To explore the prognostic significance and underlying mechanism of TYRO protein tyrosine kinase-binding protein (TYROBP) in osteosarcoma. Firstly, the expression of TYROBP was analyzed using the t test. The Kaplan-Meier plotter analysis and a receiver operating characteristic curve were performed to evaluate the influence of TYROBP on overall survival (OS). Further, Cox regression analysis was conducted to predict the independent prognostic factors for OS of osteosarcoma patients, and a nomogram was constructed. Then, the relationship between TYROBP and clinicopathological characteristics was determined using statistical methods. Enrichment analyses were conducted to evaluate the biological functions of TYROBP. Finally, the ESTIMATE algorithm was used to assess the association of TYROBP with immune cell infiltration. TYROBP was significantly increased in osteosarcoma (all P < .001). However, the high expression of TYROBP was related to better OS in osteosarcoma patients. Cox regression analysis showed that TYROBP was an independent prognostic factor for predicting OS (P = .005), especially in patients of the male sex, age <18 years, metastasis, and tumor site leg/foot (all P < .05). Besides, TYROBP mRNA expression was significantly associated with the tumor site (P < .01) but had no remarkable relationship with age, gender, and metastasis status (all P > .05). Functional annotation and gene set enrichment analysis (GSEA) revealed that TYROBP was mainly involved in immune-related pathways. Importantly, TYROBP positively correlated with immune scores (P < .001, R = .87). TYROBP served as an independent prognostic biomarker for OS in osteosarcoma. High TYROBP expression might prolong the survival of osteosarcoma patients mainly through promoting antitumor immunity.

A novel necroptosis-related lncRNAs signature effectively predicts the prognosis for osteosarcoma and is associated with immunity

Background: Necroptosis is closely related to tumorigenesis and development. Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) are also central players in osteosarcoma (OS). However, the role of necroptosis-related lncRNAs in OS remains unclear. In the present study, we aim to craft a prognostic signature based on necroptosis-related lncRNAs to improve the OS prognosis prediction.

Methods: The signature based on necroptosis-related lncRNAs was discovered using univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The prognosis efficiency of the signature was then estimated by employing various bioinformatics methods. Subsequently, immunological analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the association between necroptosis-related lncRNAs with clinical outcomes and immune status. More importantly, several necroptosis-related lncRNAs were validated with RT-qPCR.

Results: Consequently, a novel prognosis signature was successfully constructed based on eight necroptosis-related lncRNAs. Meanwhile, the novel necroptosis-related lncRNAs model could distribute OS patients into two risk groups with a stable and accurate predictive ability. Additionally, the GSEA and immune analysis revealed that the necroptosis-related lncRNAs signature affects the development and prognosis of OS by regulating the immune status. The necroptosis-related lncRNA signature was closely correlated with multiple anticancer agent susceptibility. Moreover, the RT-qPCR results indicated several necroptosis-related lncRNAs were significantly differently expressed in osteosarcoma and osteoblast cell lines.

Conclusion: In this summary, a novel prognostic signature integrating necroptosis-related lncRNAs was firstly constructed and could accurately predict the prognosis of OS. This study may increase the predicted value and guide the personalized chemotherapy treatment for OS.

Integrated analysis of anti-tumor roles of BAP1 in osteosarcoma

Background

This study aims to screen out differentially expressed genes (DEGs) regulated by BRCA1-associated protein 1 (BAP1) in osteosarcoma cells, and to analyze their biological functions.

Methods

The microarray dataset GSE23035 of BAP1-knockdown osteosarcoma cells was obtained from Gene Expression Omnibus (GEO) database, consisting of shControl, shBAP1#1 and shBAP1#2 samples. The DEGs between the BAP1-knockdown osteosarcoma cells and the untreated osteosarcoma cells were screened with limma package, and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) was also performed for the three groups of samples. Hub genes in a protein-protein interaction (PPI) network of DEGs was filtered, and then subjected to prognostic analysis and correlation analysis with BAP1 in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Besides, the correlation between BAP1 and biological processes/pathways was analyzed by Gene Set Variation Analysis (GSVA) method and the correlation between BAP1 and immune infiltration by CIBERSORT and ESTIMATE methods. The roles of BAP1 in regulating proliferation and epithelial-mesenchymal transition (EMT) were validated by CCK-8 and western blot.

Results

58 upregulated DEGs and 81 downregulated DEGs were obtained with |logFC| ≥ 1 and adj.p < 0.05. Cell cycle, DNA repair, and focal adhesion were associated with BAP1 in datasets. Further, BAP1 was negatively correlated with naïve CD4 T cells infiltration. In vitro, BAP1 inhibited proliferation and EMT.

Conclusion

BAP1 might be a tumor suppressor in osteosarcoma and a promising therapeutic target.

Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis

This study was aimed at exploring common oncogenic genes and pathways both in osteosarcoma and Ewing's sarcoma. Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing's sarcoma tissues by RT-qPCR and western blot compared to normal tissues. 201 genes were differentially expressed. There were 121 nodes and 232 edges of the PPI network. Among 12 hub genes, hub genes FN1, COL1A1, and COL1A2 may involve in the development of osteosarcoma and Ewing's sarcoma. And they were reduced to expression both in osteosarcoma and Ewing's sarcoma tissues at mRNA and protein levels compared to normal tissues. Knockdown of FN1, COL1A1, and COL1A2 enhanced the cell proliferation and migration of U2OS under the restriction of cisplatin. Our findings revealed the common oncogenic genes such as FN1, COL1A1, and COL1A2, which may act as antioncogene by enhancing cisplatin sensitivity in osteosarcoma cells, and pathways were both in osteosarcoma and Ewing's sarcoma.

Apoptosis-related genes-based prognostic signature for osteosarcoma

Osteosarcoma (OS) is a common malignant primary tumor of skeleton, especially in children and adolescents, characterized by high lung metastasis rate. Apoptosis has been studied in various tumors, while the prognostic role of apoptosis-related genes in OS has been seldom studied. Three OS related datasets were downloaded from Gene Expression Omnibus (GEO) database. Univariate Cox and LASSO Cox regression analysis identified optimal genes, which were used for building prognostic Risk score. Subsequent multivariate Cox regression analysis and Kaplan-Meier survival analysis determined the independent prognostic factors for OS. The immune cell infiltration was analyzed in CIBERSORT. Basing on 680 apoptosis-related genes, the OS patients could be divided into 2 clusters with significantly different overall survival. Among which, 6 optimal genes were identified to construct Risk score. In both training set (GSE21257) and validation set (meta-GEO dataset), high risk OS patients had significantly worse overall survival compared with the low risk patients. Besides, high Risk score was an independent poor prognostic factor for OS with various ages or genders. Three immune cells were differentially infiltrated between high and low risk OS patients. In conclusion, a six-gene (TERT, TRAP1, DNM1L, BAG5, PLEKHF1 and PPP3CB) based prognostic Risk score signature is probably conducive to distinguish different prognosis of OS patients.

Integrated Bioinformatics Analysis and Verification of Gene Targets for Myocardial Ischemia-Reperfusion Injury

Background

Myocardial ischemia-reperfusion injury (MIRI) has become a thorny and unsolved clinical problem. The pathological mechanisms of MIRI are intricate and unclear, so it is of great significance to explore potential hub genes and search for some natural products that exhibit potential therapeutic efficacy on MIRI via targeting the hub genes.

Methods

First, the differential expression genes (DEGs) from GSE58486, GSE108940, and GSE115568 were screened and integrated via a robust rank aggregation algorithm. Then, the hub genes were identified and verified by the functional experiment of the MIRI mice. Finally, natural products with protective effects against MIRI were retrieved, and molecular docking simulations between hub genes and natural products were performed.

Results

230 integrated DEGs and 9 hub genes were identified. After verification, Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be the most significant genes during MIRI. A total of 75 natural products were discovered. Most of them (especially araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) showed good ability to bind the hub genes.

Conclusions

Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be critical in the pathological process of MIRI, and the natural products (araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) targeting these hub genes exhibited potential therapeutic efficacy on MIRI. Our findings provided new insights to explore the mechanism and treatments for MIRI and revealed new therapeutic targets for natural products with protective properties against MIRI.

Development and Validation of Novel Prognostic Models for Immune-Related Genes in Osteosarcoma

Immunotherapy has shown excellent therapeutic effects on various malignant tumors; however, to date, immunotherapy for osteosarcoma is still suboptimal. In this study, we performed comprehensive bioinformatic analysis of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs). Datasets of differentially expressed IRGs were extracted from the GEO database (GSE16088). The functions and prognostic values of these differentially expressed IRGs were systematically investigated using a series of bioinformatics methods. In addition, CCK8 and plate clone formation assays were used to explore the effect of PGF on osteosarcoma cells, and twenty-nine differentially expressed IRGs were identified, of which 95 were upregulated and 34 were downregulated. Next, PPI was established for Identifying Hub genes and biology networks by Cytoscape. Six IRGs (APLNR, TPM2, PGF, CD86, PROCR, and SEMA4D) were used to develop an overall survival (OS) prediction model, and two IRGs (HLA-B and PGF) were used to develop a relapse-free survival (RFS) prediction model. Compared with the low-risk patients in the training cohort (GSE39058) and TARGET validation cohorts, high-risk patients had poorer OS and RFS. Using these identified IRGs, we used OS and RFS prediction nomograms to generate a clinical utility model. The risk scores of the two prediction models were associated with the infiltration proportions of some TIICs, and the activation of memory CD4 T-cells was associated with OS and RFS. CD86 was associated with CTLA4 and CD28 and influenced the infiltration of different TIICs. In vitro experiments showed that the knockdown of PGF inhibited the proliferation and viability of osteosarcoma cells. In conclusion, these findings help us better understand the prognostic roles of IRGs and TIICs in osteosarcoma, and CD86 and PGF may serve as specific immune targets.

The role of SPI1-TYROBP-FCER1G network in oncogenesis and prognosis of osteosarcoma, and its association with immune infiltration

Osteosarcoma is an aggressive malignant bone sarcoma worldwide. A causal gene network with specific functions underlying both the development and progression of OS was still unclear. Here we firstly identified the differentially expressed genes (DEGs) between control and OS samples, and then defined the hub genes and top clusters in the protein–protein interaction (PPI) network of these DEGs. By focusing on the hub gene TYROBP in the top 1 cluster, a conserved TYROBP co-expression network was identified. Then the effect of the network on OS overall survival was analyzed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and Gene Set Enrichment Analysis (GSEA) were used to explore the functions of the network. XCell platform and ssGSEA algorithm were conducted to estimate the status of immune infiltration. ChEA3 platform, GSEA enrichment analysis, and Drug Pair Seeker (DPS) were used to predict the key transcription factor and its upstream signal. We identified the downregulated SPI1-TYROBP-FCER1G network in OS, which were significantly enriched in immune-related functions. We also defined a two-gene signature (SPI1/FCER1G) that can predict poorer OS overall survival and the attenuated immune infiltration when downregulated. The SPI1-TYROBP-FCER1G network were potentially initiated by transcription factor SPI1 and would lead to the upregulated CD86, MHC-II, CCL4/CXCL10/CX3CL1 and hence increased immune infiltrations. With this study, we could better explore the mechanism of OS oncogenesis and metastasis for developing new therapies.

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Simple Summary

Osteosarcomas are heterogeneous bone tumors with complex genetic and chromosomic alterations. The numerous patients with metastatic osteosarcoma have a very poor prognosis, and only those who can have full surgical resection of the primary tumor and of all the macro metastasis can survive. Despite the recent improvements in prediction and early detection of metastasis, big efforts are still required to understand the specific mechanisms of osteosarcoma metastatic progression, in order to reveal novel therapeutic targets.

Abstract

Metastases of osteosarcomas are heterogeneous. They may grow simultaneously with the primary tumor, during treatment or shortly after, or a long time after the end of the treatment. They occur mainly in lungs but also in bone and various soft tissues. They can have the same histology as the primary tumor or show a shift towards a different differentiation path. However, the metastatic capacities of osteosarcoma cells can be predicted by gene and microRNA signatures. Despite the identification of numerous metastasis-promoting/predicting factors, there is no efficient therapeutic strategy to reduce the number of patients developing a metastatic disease or to cure these metastatic patients, except surgery. Indeed, these patients are generally resistant to the classical chemo- and to immuno-therapy. Hence, the knowledge of specific mechanisms should be extended to reveal novel therapeutic approaches. Recent studies that used DNA and RNA sequencing technologies highlighted complex relations between primary and secondary tumors. The reported results also supported a hierarchical organization of the tumor cell clones, suggesting that cancer stem cells are involved. Because of their chemoresistance, their plasticity, and their ability to modulate the immune environment, the osteosarcoma stem cells could be important players in the metastatic process.

Identification and preliminary validation of a four-gene signature to predict metastasis and survival in osteosarcoma

Osteosarcoma is a primary malignant bone tumor that occurs frequently in children and adolescents and has a propensity for drug resistance, recurrence, and metastasis. The purpose of this study was to identify potential target genes to predict metastasis and survival in patients with osteosarcoma. We analyzed gene expression profiles and corresponding clinical data of patients with osteosarcoma in the Gene Expression Omnibus database and identified 202 genes that were differentially expressed between osteosarcoma cells and normal osteoblasts. Univariate and multivariable Cox regression analyses identified four risk genes that affected osteosarcoma prognosis: MCAM, ENPEP, LRRC1, and CPE. Independent prognostic analyses and clinical correlation studies showed that the four risk genes constituted an independent prognostic signature that correlated with survival and clinical parameters including age and distant metastasis. In a single-sample Gene Set Enrichment Analysis, risk scores based on the prognostic signature correlated with tumor infiltration by immune cells and immune functions in osteosarcoma. A subsequent analysis showed that the expression levels of the four genes in the prognostic signature were predictive of overall survival and metastasis-free survival of patients with osteosarcoma. Furthermore, Human Cancer Metastasis Database and qRT-PCR analyses demonstrated that the four risk genes are overexpressed in osteosarcoma tissues and cell lines. In summary, we developed and validated a four-gene prognostic signature that may be useful in osteosarcoma diagnosis and metastasis prediction.

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

Background

Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.

Methods

Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.

Results

A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.

Conclusion

TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

circUSP34 accelerates osteosarcoma malignant progression by sponging miR-16-5p

Osteosarcoma (OS) is a primary and highly malignant mesenchymal tissue tumor. The specific pathological mechanism underlying disease initiation or progression remains unclear. Circular RNAs (circRNAs) are a type of covalently circular RNA with a head-to-tail junction site. In this study, we aimed to investigate the sponging mechanism between circRNAs and microRNAs (miRNAs) in OS. Based on the inhibited effect of miR-16-5p reported on OS, circUSP34 was analyzed as a sponge of miR-16-5p via Starbase. We found that circUSP34 promoted the proliferation, migration, and invasion of OS in vitro and in vivo. circUSP34 increased but miR-16-5p decreased in OS by qRT-PCR. Function assays showed that the malignancy of OS cells, including proliferation, migration, and invasion, was inhibited after knocking out circUSP34. Western blotting results showed that the expression level of vimentin and Ki-67 decreased. Similarly, miR-16-5p mimic compromised the proliferation, migration, and invasion of OS cells. FISH assay results indicated that circUSP34 and miR-16-5p were colocalized in the cytoplasm. The sponging mechanism of circUSP34 and miR-16-5p was verified by dual-luciferase reporter assay, RNA immunoprecipitation (RIP), and RNA pull down assays. Interestingly, the miR-16-5p inhibitor partly reversed the inhibitory effect of sh-circUSP34 on the malignancy of OS cells. Further, mice tumors for IHC indicated that vimentin, N-cadherin, and Ki-67 protein expression decreased, but E-cadherin protein expression increased. Collectively, circUSP34 promoted OS malignancy, including proliferation, migration, and invasion, by sponging miR-16-5p. It can serve as a potential therapeutic target and biomarker.

Comprehensive analysis of metabolism-related lncRNAs related to the progression and prognosis in osteosarcoma from TCGA

Background

Osteosarcoma is one of the most common malignant neoplasms in children and adolescents. Studies have shown that metabolism-related pathways are vital for the development and metastasis of osteosarcoma. Long non-coding RNA (lncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways. However, the detailed molecular mechanisms of metabolism-related lncRNA in osteosarcoma remain to be deeply elucidated.

Methods

In this study, all metabolism-related mRNAs and lncRNAs in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. Usingsurvival analysis, univariate and multivariate independent prognostic analysis, gene set enrichment analysis, and nomogram, a prognostic signature with metabolic lncRNAs as prognostic factors was constructed.

Results

Nine prognostic factors included lncRNA AC009779.2, lncRNA AL591895.1, lncRNA AC026271.3, lncRNA LPP-AS2, lncRNA LINC01857, lncRNA AP005264.1, lncRNA LINC02454, lncRNA AL133338.1, and lncRNA AC135178.5, respectively. Survival analysis indicated that alterations of specific lncRNA expression were strongly correlated with poor prognosis in osteosarcoma. Univariate and multivariate independent prognostic analysis showed that the prognostic signature had a good independent predictive ability for patient survival. The results of GSEA suggested that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram was further drawn for clinical guidance and assistance in clinical decision-making.

Conclusions

This study identified multiple metabolism-related lncRNAs, which may be novel therapeutic targets for osteosarcoma, and contributed to better explore the specific metabolic regulatory mechanisms of lncRNA in osteosarcoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02647-4.

Aberrant expression of PROS1 correlates with human papillary thyroid cancer progression

Background

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). Considering the important association between cellular immunity and PTC progression, it is worth exploring the biological significance of immune-related signaling in PTC.

Methods

Several bioinformatics tools, such as R software, WEB-based Gene SeT AnaLysis Toolkit (WebGestalt), Database for Annotation, Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to identify the immune-related hub genes in PTC. Furthermore, in vitro experiments were adopted to identify the proliferation and migration ability of PROS1 knockdown groups and control groups in PTC cells.

Results

The differentially expressed genes (DEGs) of five datasets from Gene Expression Omnibus (GEO) contained 154 upregulated genes and 193 downregulated genes, with Protein S (PROS1) being the only immune-related hub gene. Quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) have been conducted to prove the high expression of PROS1 in PTC. Moreover, PROS1 expression was significantly correlated with lymph nodes classification. Furthermore, knockdown of PROS1 by shRNAs inhibited the cell proliferation and cell migration in PTC cells.

Conclusions

The findings unveiled the clinical relevance and significance of PROS1 in PTC and provided potential immune-related biomarkers for PTC development and prognosis.

The Immune Landscape of Osteosarcoma: Implications for Prognosis and Treatment Response

Osteosarcoma (OS) is a high-grade malignant stromal tumor composed of mesenchymal cells producing osteoid and immature bone, with a peak of incidence in the second decade of life. Hence, although relatively rare, the social impact of this neoplasm is particularly relevant. Differently from carcinomas, molecular genetics and the role of the tumor microenvironment in the development and progression of OS are mainly unknown. Indeed, while the tumor microenvironment has been widely studied in other solid tumor types and its contribution to tumor progression has been definitely established, tumor-stroma interaction in OS has been quite neglected for years. Only recently have new insights been gained, also thanks to the availability of new technologies and bioinformatics tools. A better understanding of the cross-talk between the bone microenvironment, including immune and stromal cells, and OS will be key not only for a deeper knowledge of osteosarcoma pathophysiology, but also for the development of novel therapeutic strategies. In this review, we summarize the current knowledge about the tumor microenvironment in OS, mainly focusing on immune cells, discussing their role and implication for disease prognosis and treatment response.

Advancing therapy for osteosarcoma

Improving the survival of patients with osteosarcoma has long proved challenging, although the treatment of this disease is on the precipice of advancement. The increasing feasibility of molecular profiling together with the creation of both robust model systems and large, well-annotated tissue banks has led to an increased understanding of osteosarcoma biology. The historical invariability of survival outcomes and the limited number of agents known to be active in the treatment of this disease facilitate clinical trials designed to identify efficacious novel therapies using small cohorts of patients. In addition, trial designs will increasingly consider the genetic background of the tumour through biomarker-based patient selection, thereby enriching for clinical activity. Indeed, osteosarcoma cells are known to express a number of surface proteins that might be of therapeutic relevance, including B7-H3, GD2 and HER2, which can be targeted using antibody-drug conjugates and/or adoptive cell therapies. In addition, immune-checkpoint inhibition might augment the latter approach by helping to overcome the immunosuppressive tumour microenvironment. In this Review, we provide a brief overview of current osteosarcoma therapy before focusing on the biological insights from the molecular profiling and preclinical modelling studies that have opened new therapeutic opportunities in this disease.

Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer

Background

Compared with colon cancer, the increase of morbidity is more significant for rectal cancer. The current study set out to identify novel and critical biomarkers or features that may be used as promising targets for early diagnosis and treatment monitoring of rectal cancer.

Methods

Microarray datasets of rectal cancer with a minimum sample size of 30 and RNA-sequencing datasets of rectal adenocarcinoma (READ) were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The method of robust rank aggregation was utilized to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was structured using the STRING platform, and hub genes were identified using the Cytoscape plugin cytoHubba and an UpSet diagram. R software was employed to perform functional enrichment analysis. Receiver operating characteristic (ROC) curves based on the GEO data and Kaplan-Meier curves based on the TCGA data were drawn to assess the diagnostic and prognostic values of the hub genes. Immune cell infiltration analysis was conducted with CIBERSORT, and the diagnostic value and correlations between prognostic genes and infiltrated immune cells were analyzed by principal component analysis (PCA), ROC curves, and correlation scatter plots.

Results

A total of 137 robust DEGs were obtained by integrating datasets in GEO. Twenty-four hub genes, including CHGA, TTR, SAA1, SPP1, MMP1, TGFBI, COL1A1, and PCK1, were identified as a diagnostic gene biomarker group for rectal cancer, and SAA1, SPP1, and SI were identified as potential novel prognostic biomarkers. Functionally, the hub genes were mainly involved in the rectal cancer related interleukin (IL)-17 and proximal tubule bicarbonate reclamation pathways. Twelve sensitive infiltrated immune cells were identified, and were correlated with prognostic genes.

Conclusions

The integrated gene biomarker group combined with immune cell infiltration can effectively indicate rectal cancer.

Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis (P < 0.0001). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Objectives

Osteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.

Methods

Clinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.

Results

Two molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.

Conclusion

Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

C3AR1 mRNA as a Potential Therapeutic Target Associates With Clinical Outcomes and Tumor Microenvironment in Osteosarcoma

Objective: Targeting cancer-specific messenger RNAs (mRNAs) may offer novel insights into therapeutic strategies in osteosarcoma. This study aimed to discover possible osteosarcoma-specific mRNA and probe its biological functions.

Methods: Based on mRNA-seq data from the TARGET database, stromal and immune scores were estimated for each osteosarcoma sample via the ESTIMATE algorithm. Stromal and immune mRNAs were obtained via integration of differentially expressed mRNAs between high and low stromal / immune score groups. Among hub and prognostic mRNAs, C3AR1 mRNA was focused and its prognostic value was assessed. The associations between C3AR1 mRNA and immune cells were analyzed via the CIBERSORT algorithm. Its expression was verified in osteosarcoma tissues and cells by RT-qPCR and western blot. The functions of C3AR1 were investigated by a series of experiments.

Results: Low stromal and immune scores were both indicative of unfavorable outcomes for osteosarcoma patients. Eighty-eight up-regulated and seven down-regulated stromal and immune mRNAs were identified. Among 30 hub mRNAs, low expression of C3AR1 mRNA indicated worse outcomes than its high expression. There was a lower mRNA expression of C3AR1 in metastatic than non-metastatic osteosarcoma. C3AR1 mRNA was closely correlated to various immune cells such as macrophages. C3AR1 was verified to be down-regulated in osteosarcoma tissues and cells. Its overexpression suppressed proliferation, migration and invasion and induced apoptosis in osteosarcoma cells.

Conclusion: C3AR1 mRNA could be a promising therapeutic target for osteosarcoma, linked with prognosis and tumor microenvironment.

Immune classification of osteosarcoma

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

Screening and identification of potential biomarkers and therapeutic drugs in melanoma via integrated bioinformatics analysis

Melanoma is a highly aggressive malignant skin tumor with a high rate of metastasis and mortality. In this study, a comprehensive bioinformatics analysis was used to clarify the hub genes and potential drugs. Download the GSE3189, GSE22301, and GSE35388 microarray datasets from the Gene Expression Omnibus (GEO), which contains a total of 33 normal samples and 67 melanoma samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) approach analyze DEGs based on the DAVID. Use STRING to construct protein-protein interaction network, and use MCODE and cytoHubba plug-ins in Cytoscape to perform module analysis and identified hub genes. Use Gene Expression Profile Interactive Analysis (GEPIA) to assess the prognosis of genes in tumors. Finally, use the Drug-Gene Interaction Database (DGIdb) to screen targeted drugs related to hub genes. A total of 140 overlapping DEGs were identified from the three microarray datasets, including 59 up-regulated DEGs and 81 down-regulated DEGs. GO enrichment analysis showed that these DEGs are mainly involved in the biological process such as positive regulation of gene expression, positive regulation of cell proliferation, positive regulation of MAP kinase activity, cell migration, and negative regulation of the apoptotic process. The cellular components are concentrated in the membrane, dendritic spine, the perinuclear region of cytoplasm, extracellular exosome, and membrane raft. Molecular functions include protein homodimerization activity, calmodulin-binding, transcription factor binding, protein binding, and cytoskeletal protein binding. KEGG pathway analysis shows that these DEGs are mainly related to protein digestion and absorption, PPAR signaling pathway, signaling pathways regulating stem cells' pluripotency, and Retinol metabolism. The 23 most closely related DEGs were identified from the PPI network and combined with the GEPIA prognostic analysis, CDH3, ESRP1, FGF2, GBP2, KCNN4, KIT, SEMA4D, and ZEB1 were selected as hub genes, which are considered to be associated with poor prognosis of melanoma closely related. Besides, ten related drugs that may have therapeutic effects on melanoma were also screened. These newly discovered genes and drugs provide new ideas for further research on melanoma.