Genomics and the Immune Landscape of Osteosarcoma

Importance of CD8 Tex cell-associated gene signatures in the prognosis and immunology of osteosarcoma

As a highly aggressive bone malignancy, osteosarcoma poses a significant therapeutic challenge, especially in the setting of metastasis or recurrence. This study aimed to investigate the potential of CD8-Tex cell-associated genes as prognostic biomarkers to reveal the immunogenomic profile of osteosarcoma and guide therapeutic decisions. mRNA expression data and clinical details of osteosarcoma patients were obtained from the TCGA database (TARGET-OS dataset). The GSE21257 dataset (from the GEO database) was used as an external validation set to provide additional information on osteosarcoma specimens. 84 samples from the TARGET-OS dataset were used as the training set, and 53 samples from the GSE21257 dataset served as the external validation cohort. Univariate Cox regression analysis was utilized to identify CD8 Tex cell genes associated with prognosis. The LASSO algorithm was performed for 1000 iterations to select the best subset to form the CD8 Tex cell gene signature (TRS). Final genes were identified using the multivariate Cox regression model of the LASSO algorithm. Risk scores were calculated to categorize patients into high- and low-risk groups, and clinical differences were explored by Kaplan-Meier survival analysis to assess model performance. Prediction maps were constructed to estimate 1-, 3-, and 5 year survival rates for osteosarcoma patients, including risk scores for CD8 Texcell gene markers and clinicopathologic factors. The ssGSEA algorithm was used to assess the differences in immune function between TRS-defined high- and low-risk groups. TME and immune cell infiltration were further assessed using the ESTIMATE and CIBERSORT algorithms. To explore the relationship between immune checkpoint gene expression levels and the two risk-defined groups. A CD8 Tex cell-associated gene signature was extracted from the TISCH database and prognostic markers including two genes were developed. The high-risk group showed lower survival, and model performance was validated by ROC curves and C-index. Predictive plots were constructed to demonstrate survival estimates, combining CD8 Tex cell gene markers and clinical factors. This study provides valuable insights into the molecular and immune characteristics of osteosarcoma and offers potential avenues for advances in therapeutic approaches.

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.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction

Background

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.

Methods

Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.

Results

Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.

Conclusion

The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

Comprehensive analysis of copper-metabolism-related genes about prognosis and immune microenvironment in osteosarcoma

Despite being significant in various diseases, including cancers, the impact of copper metabolism on osteosarcoma (OS) remains largely unexplored. This study aimed to use bioinformatics analyses to identify a reliable copper metabolism signature that could improve OS patient prognosis prediction, immune landscape understanding, and drug sensitivity. Through nonnegative matrix factorization (NMF) clustering, we revealed distinct prognosis-associated clusters of OS patients based on copper metabolism-related genes (CMRGs), showing differential gene expression linked to immune processes. The risk model, comprising 13 prognostic CMRGs, was established using least absolute shrinkage and selection operator (LASSO) Cox regression, closely associated with the OS microenvironment's immune situation and drug sensitivity. Furthermore, we developed an integrated nomogram, combining the risk score and clinical traits to quantitatively predict OS patient prognosis. The calibration plot, timeROC, and timeROC analyses demonstrated its predictable accuracy and clinical usefulness. Finally, we identified three independent prognostic signatures for OS patients: COX11, AP1B1, and ABCB6. This study confirmed the involvement of CMRGs in OS patient prognosis, immune processes, and drug sensitivity, suggesting their potential as promising prognostic signatures and therapeutic targets for OS.

Black phosphorus quantum dots camouflaged with platelet-osteosarcoma hybrid membrane and doxorubicin for combined therapy of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most prevalent primary malignant bone tumor. However, single-agent chemotherapy exhibits limited efficacy against OS and often encounters tumor resistance. Therefore, we designed and constructed an integrated treatment strategy of photothermal therapy (PTT) combined with chemotherapy and used a surface-encapsulated platelet-osteosarcoma hybrid membrane (OPM) that enhances circulation time and enables OS-specific targeting.

RESULTS

The OPM functions as a shell structure, encapsulating multiple drug-loaded nanocores (BPQDs-DOX) and controlling the release rate of doxorubicin (DOX). Moreover, near-infrared light irradiation accelerates the release of DOX, thereby extending circulation time and enabling photostimulation-responsive release. The OPM encapsulation system improves the stability of BPQDs, enhances their photothermal conversion efficiency, and augments PTT efficacy. In vitro and ex vivo experiments demonstrate that BPQDs-DOX@OPM effectively delivers drugs to tumor sites with prolonged circulation time and specific targeting, resulting in superior anti-tumor activity compared to single-agent chemotherapy. Furthermore, these experiments confirm the favorable biosafety profile of BPQDs-DOX@OPM.

CONCLUSIONS

Compared to single-agent chemotherapy, the combined therapy using BPQDs-DOX@OPM offers prolonged circulation time, targeted drug delivery, enhanced anti-tumor activity, and high biosafety, thereby introducing a novel approach for the clinical treatment of OS.

MFNG is an independent prognostic marker for osteosarcoma

BACKGROUND

Osteosarcoma (OS) has been the most common malignancy of the bone in children and adolescents, and the unsatisfactory prognosis of OS sufferers has long been a hard nut. Here, we delved into the markers with a prognostic value for predicting the prognosis of OS patients.

METHODS

The messenger RNA (mRNA) sequencing data and clinical data of OS were retrieved from a Gene Expression Omnibus (GEO) dataset (GSE39058). Next, prognosis-related genes (PRGs) were filtered with the aid of Kaplan-Meier (K-M) curves and Cox regression analysis (CRA). Later, Gene Ontology (GO) biological process analysis was used in verifying the function of different genes. CCK-8 and cell apoptosis assay were performed to evaluate the function of MFNG in U2OS cells.

RESULTS

Among the obtained genes, Manic Fringe (MFNG) had the closest relevance to prognosis and clinical traits, thus becoming the research object herein. In light of the expression level of MFNG, patients fell into high- and low-MFNG groups. Patients with elevated MFNG expression had a worse prognosis, according to the survival analysis. It was unveiled by the univariate and multivariate analyses that MFNG expression was an independent adverse prognostic factor for disease-free survival in OS patients (p = 0.006). Meanwhile, MFNG expression was linked to gender and tumor recurrence, and it was higher in patients with OS recurrence. Moreover, overexpression of MFNG promoted the cell proliferation and inhibited the cell apoptosis of U2OS cells.

CONCLUSIONS

The expression level of MFNG negatively correlated with OS progression, and as an independent adverse prognostic factor for disease-free survival in OS patients. Moreover, MFNG regulated the cell proliferation and apoptosis of OS cells.

Immunosurveillance shapes the emergence of neo-epitope landscapes of sarcomas, revealing prime targets for immunotherapy

T cells recognize tumor-derived mutated peptides presented on MHC by tumors. The recognition of these neo-epitopes leads to rejection of tumors, an event that is critical for successful cancer immunosurveillance. Determination of tumor-rejecting neo-epitopes in human tumors has proved difficult, though recently developed systems approaches are becoming increasingly useful at evaluating their immunogenicity. We have used the differential aggretope index to determine the neo-epitope burden of sarcomas and observed a conspicuously titrated antigenic landscape, ranging from the highly antigenic osteosarcomas to the low antigenic leiomyosarcomas and liposarcomas. We showed that the antigenic landscape of the tumors inversely reflected the historical T cell responses in the tumor-bearing patients. We predicted that highly antigenic tumors with poor antitumor T cell responses, such as osteosarcomas, would be responsive to T cell-based immunotherapy regimens and demonstrated this in a murine osteosarcoma model. Our study presents a potentially novel pipeline for determining antigenicity of human tumors, provides an accurate predictor of potential neo-epitopes, and will be an important indicator of which cancers to target with T cell-enhancing immunotherapy.

Prognostic and immunological significance of an M1 macrophage-related gene signature in osteosarcoma

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies.

Development and validation of an oxidative stress‑related prognostic signature in osteosarcoma: A combination of molecular experiments and bioinformatics

Osteosarcoma (OS) is one of the most prevalent malignancies with a bad prognosis. Oxidative stress is closely associated with various type of cancer. The present study aimed to establish an oxidative stress-related gene prognostic signature. Supported by The Cancer Genome Atlas and Gene Expression Omnibus, the least absolute shrinkage and selection operator regression, Cox regression, receiver operating characteristic curves and Kaplan-Meier survival analysis were used to construct and validate a prognostic signature and the derived risk score. Tumor microenvironment scores and immune infiltration levels in OS were calculated. Correlation between these parameters and risk score was analyzed. In addition, single analysis of each hub gene was performed. Finally, a series of molecular experiments was used to detect the role of MAP3K5 (one of the hub genes) in OS. A total of five genes most associated with OS prognosis were identified as independent predictors, namely catalase (CAT), mitogen-activated protein kinase 1 (MAPK1), glucose-6-phosphate dehydrogenase (G6PD), mitogen-activated protein kinase kinase kinase 5 (MAP3K5) and C-C motif chemokine ligand 2 (CCL2). Based on the signature, higher risk score indicated poorer prognosis. Nomogram performed well and reliably predicted 3- and 5-year survival rate in OS. Patients with increasing risk scores had higher tumor purity and lower immune infiltration levels. Compared with an osteoblast cell line, the expression of CAT, CCL2, MAPK1 and G6PD was upregulated and MAP3K5 was downregulated. MAP3K5 inhibited cellular proliferation and motility, promoted cellular apoptosis and induced reactive oxygen species generation. Overall, the signature could effectively predict the prognosis of patients with OS and were expected to be potential biomarkers. And it provided new ideas for understanding the interactions between oxidative stress and OS.

A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes

Background

Tumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma.

Methods

ScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro.

Results

Eleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes.

Conclusions

This study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.

Pediatric and Young Adult Image-Guided Percutaneous Bone Biopsy-A New Standard of Care?

BACKGROUND

There are no consensus guidelines regarding the use of percutaneous needle biopsy for the diagnosis of soft tissue and bone tumors. The aim of this study was to understand the efficacy of image-guided percutaneous biopsy for pediatric patients with soft tissue and bony masses, the role of intraoperative image guidance, and diagnostic accuracy.

PATIENTS AND METHODS

A retrospective institutional chart review was performed on patients who underwent percutaneous biopsy of soft tissue or bone tumors between 2007 and 2017. Data collected included preoperative imaging, type of biopsy, demographics, insurance status, number of samples taken, and pathologic results.

RESULTS

One hundred forty-one children and young adults underwent 169 biopsies. Female patients received 48.2% of biopsies. The mean age was 14.3 ± 7.0 years. Core needle biopsies made up 89.4% of procedures, while 10.6% were fine needle aspirate. The mean number of samples per patient was 3.6 ± 2.5. All patients had imaging guidance, with computed tomography used in 44.7% of patients, 9.9% using fluoroscopy, 7.1% using ultrasound for guidance, and 53 (37.6%) patients had more than one modality. Diagnostic specimens were obtained in 97.9% of biopsies. The most common overall pathology was osteoid osteoma. The most common malignant tumors were osteosarcoma and Ewing's sarcoma.

CONCLUSION

Image-guided percutaneous biopsy is a safe and effective method of obtaining accurate tissue samples in children and young adults with soft tissue or bone masses.

LEVEL OF EVIDENCE

Level 4-Study of diagnostic test.

Identification of B cell marker genes based on single-cell sequencing to establish a prognostic model and identify immune infiltration in osteosarcoma

Background

Tumor-infiltrating B cells play a crucial role in the promotion or inhibition of tumor development. However, the role of B cells in osteosarcoma remains largely unknown. The aim of this study was to investigate the effect of B cells on the prognosis and immunity infiltration of osteosarcoma.

Methods

Marker genes of B cells were identified based on the single-cell sequencing results of osteosarcoma in the GEO database. The prognostic model was established by the TCGA database and verified by the GEO data. The divergence in immune infiltration between the low-risk and high-risk groups was then compared according to the established prognostic model. Finally, the differential genes in the low-risk and high-risk groups were enriched and analyzed.

Results

A total of 261 B cell marker genes was obtained by single-cell sequencing and a prognostic model of 4 B cell marker genes was established based on TCGA data. The model was found to have a good prediction performance in the TCGA and GEO data. A remarkable difference in immune infiltration between the low-risk and high-risk groups was also observed. The obtained results were verified by enrichment analysis.

Conclusion

In summary, a prognostic model with good predictive performance was established that revealed the indispensable role of B cells in the development of osteosarcoma. This model also provides a predictive index and a novel therapeutic target for immunotherapy for clinical patients.

Prognostic biomarkers correlated with immune infiltration in non-small cell lung cancer

Lung cancer is the leading cause of cancer-related mortality in men and women globally. Non-small cell lung cancer (NSCLC) is the most prevalent subtype, accounting for 85-90% of all cancers. Although there have been dramatic advances in therapeutic approaches in recent decades, the recurrence and metastasis rates of NSCLC are as high as 30-40% with the 5-year overall survival rate being less than 15%. Therefore, it is necessary to explore the pathogenesis of NSCLC at the genetic level and identify prognostic biomarkers and novel therapeutic targets. Here, we aimed to identify mutated genes with high frequencies in Chinese NSCLC patients using next-generation sequencing and to investigate their relationships with the tumor mutation burden (TMB) and tumor immune microenvironment. A total of 110 NSCLC patients were enrolled to profile the genetic variations. Mutations in EGFR (62.37%), TP53 (61.29%), LRP1B (13.98%), FAT1 (12.90%), KMT2D (11.83%), CREBBP (10.75%), and RB1 (9.68%) were most prevalent. TP53, LRP1B, KMT2D, and CREBBP mutations were all significantly associated with high TMB (P < 0.05 or P < 0.01). The infiltrating levels of immune cells and immune molecules were enriched significantly in the LRP1B mutation group. LRP1B mutations significantly correlated with stimulating and inhibitory immunoregulators. Gene set enrichment analysis revealed that cell cycle, the Notch signaling pathway, the insulin signaling pathway, and the mTOR signaling pathway are related to LRP1B mutations in the immune system. LRP1B mutations may be of clinical importance in enhancing the anti-tumor immune response and may be a promising biomarker for predicting immunotherapy responsiveness.

Current Status and Prospects of Targeted Therapy for Osteosarcoma

Osteosarcoma (OS) is a highly malignant tumor occurring in bone tissue with a high propensity to metastasize, and its underlying mechanisms remain largely elusive. The OS prognosis is poor, and improving the survival of OS patients remains a challenge. Current treatment methods such as surgical approaches, chemotherapeutic drugs, and immunotherapeutic drugs remain ineffective. As research progresses, targeted therapy is gradually becoming irreplaceable. In this review, several treatment modalities for osteosarcoma, such as surgery, chemotherapy, and immunotherapy, are briefly described, followed by a discussion of targeted therapy, the important targets, and new technologies for osteosarcoma treatment.

Verification of Ferroptosis Subcluster-Associated Genes Related to Osteosarcoma and Exploration of Immune Targeted Therapy

Background

Despite tremendous advances in treating osteosarcoma (OS), the survival rates of patients have failed to improve dramatically over the past decades. Ferroptosis, a newly discovered iron-dependent type of regulated cell death, is implicated in tumors, and its features in OS remain unascertained. We designed to determine the involvement of ferroptosis subcluster-related modular genes in OS progression and prognosis.

Methods

The OS-related datasets retrieved from GEO and TARGET database were clustered for identifying molecular subclusters with different ferroptosis-related genes (FRGs) expression patterns. Weighted gene coexpression network analysis (WGCNA) was applied to identify modular genes from FRG subclusters. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariable Cox regression analysis were adopted to develop the prognostic model. Potential mechanisms of development and prognosis in OS were explored by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Then, a comprehensive analysis was conducted for immune checkpoint markers and assessment of predictive power to drug response. The protein expression levels of the three ferroptosis subcluster-related modular genes were verified by immunohistochemistry.

Results

Two independent subclusters presenting diverse expression profiles of FRGs were obtained, with significantly different survival states. Ferroptosis subcluster-related modular genes were screened with WGCNA, and the GESA results showed that ferroptosis subcluster-related modular genes could affect the cellular energy metabolism, thus influencing the development and prognosis of osteosarcoma. A prognostic model was established by incorporating three ferroptosis subcluster-related modular genes (LRRC1, ACO2, and CTNNBIP1) and a nomogram by integrating clinical features, and they were evaluated for the predictive power on OS prognosis. The 20 immune checkpoint-related genes confirmed the insensitivity to tumor immunotherapy in high-risk patients. IC50s of Axitinib and Cytarabine suggested a higher sensitivity to the targeted drug. Finally, the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemistry were consistent with bioinformatics analysis.

Conclusion

Ferroptosis are closely associated with the OS prognosis. The risk-scoring model incorporating three ferroptosis subcluster-related modular genes has shown outstanding advantages in predicting patient prognosis.

Osteosarcoma subtypes based on platelet-related genes and tumor microenvironment characteristics

Background

Osteosarcoma is a common metastatic tumor in children and adolescents. Because of its easy metastasis, patients often show a poor prognosis. Recently, researchers have found that platelets are closely related to metastasis of a variety of malignant tumors, but the role of platelets related characteristics in osteosarcoma is still unknown. The purpose of this study is to explore the characteristics of platelet-related subtypes and cell infiltration in tumor microenvironment.

Methods

We collected osteosarcoma cohorts from TCGA and GEO databases, and explored the molecular subtypes mediated by platelet-related genes and the related TME cell infiltration according to the expression of platelet-related genes in osteosarcoma. In addition, we also explored the differentially expressed genes (DEGs) among different molecular subtypes and established a protein-protein interaction network (PPI). Then we constructed a platelet scoring model by Univariate cox regression and least absolute shrinkage and selection operator (Lasso) cox regression model to quantify the characteristics of platelet in a single tumor. RT-PCR was used to investigate the expression of six candidate genes in osteosarcoma cell lines and normal osteoblast lines. Finally, we also predicted potential drugs with therapeutic effects on platelet-related subtypes.

Results

We found that platelet-related genes (PRGs) can distinguish osteosarcoma into two different platelet-related subtypes, C1 and C2. And the prognosis of the C2 subtype was significantly worse than that of C1 subtype. The results of ESTIMATE analysis and GO/KEGG enrichment showed that the differences between different subtypes were mainly concentrated in immune response pathways, and the immune response of C2 was inhibited relative to C1. We further studied the relationship between platelet-related subtypes and immune cell infiltration. We found that the distribution of most immune cells in C1 subtype was higher than that in C2 subtype, and there was a correlation between C1 subtype and more immune cells. Finally, we screened the PRGs related to the prognosis of osteosarcoma through Univariate Cox regression, established independent prognostic platelet characteristics consisting of six genes to predict the prognosis of patients with OS, and predicted the drugs that may be used in the treatment of osteosarcoma. RT-PCR was used to verify the expression of candidate genes in osteosarcoma cells.

Conclusion

Platelet scoring model is a significant biomarker, which is of great significance to determine the prognosis, molecular subtypes, characteristics of TME cell infiltration and therapy in patients with OS.

Immune checkpoints in osteosarcoma: Recent advances and therapeutic potential

Osteosarcoma is the most common primary malignant bone tumor and is associated with a high risk of recurrence and distant metastasis. Effective treatment for osteosarcoma, especially advanced osteosarcoma, has stagnated over the past four decades. The advent of immune checkpoint inhibitor (ICI) has transformed the treatment paradigm for multiple malignant tumor types and indicated a potential therapeutic strategy for osteosarcoma. In this review, we discuss recent advances in immune checkpoints, including programmed cell death protein-1 (PD-1), programmed cell death protein ligand-1 (PD-L1), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), and their related ICIs for osteosarcoma treatment. We present the main existing mechanisms of resistance to ICIs therapy in osteosarcoma. Moreover, we summarize the current strategies for improving the efficacy of ICIs in osteosarcoma and address the potential predictive biomarkers of ICIs treatment in osteosarcoma.

Single nucleotide mutation changes the capability of CCN3 in osteosarcoma cell invasion and differentiation

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The frequency of G-to-A mutation in CCN3 is higher than para-carcinoma tissue.

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Mutation in CCN3 change its capacity of osteosarcoma invasion and differentiation.

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Mutant CCN3 decrease nuclear ratio of glycosylated/non-glycosylated isoforms.

This study aimed to identify significant mutations in CCN3 gene in osteosarcoma, and to explore the influence of this gene on cell invasion and differentiation and the underlying mechanism. Sanger sequencing was used to identify CCN3 gene sequence in human osteosarcoma cell lines, peripheral blood mononuclear cells (PBMC), and osteosarcoma tissues. Wild-type and mutant CCN3 (mCCN3) were ectopically expressed by lentivirus in human osteosarcoma cell lines. Tumor cell invasion was measured by trans-well assay. Osteogenic differentiation was induced by osteogenic differentiating medium and evaluated based on alkaline phosphatase activity and collagen type I alpha 1 chain and osteocalcin expression. Western blotting was used to detect protein levels of CCN3 and mCCN3 in cytoplasmic, nuclear and secreted fractions of cells. A G-to-A single nucleotide mutation in the coding region of CCN3 was found in both osteosarcoma cells and tissues. The frequency of this mutation in osteosarcoma tissue was much higher than that in para-carcinoma tissue and PBMC of healthy people. This nucleotide mutation decreased nuclear glycosylated full length protein level of CCN3 and affected osteosarcoma cell invasion and differentiation. A lower nuclear ratio of glycosylated/non-glycosylated isoforms accounted for the different behavior of mCCN3 compared with CCN3. The G-to-A mutation identified in CCN3 resulted in differential glycosylated full-length protein levels and altered the functional role of CCN3 in osteosarcoma cell invasion and differentiation.

A Comprehensive Understanding of the Genomic Bone Tumor Landscape: A Multicenter Prospective Study

Complexity and heterogeneity increases the difficulty of diagnosis and treatment of bone tumors. We aimed to identify the mutational characterization and potential biomarkers of bone tumors. In this study, a total of 357 bone tumor patients were recruited and the next generation sequencing (NGS)-based YuanSu450 panel, that includes both DNA and RNA sequencing, was performed for genomic alteration identification. The most common mutated genes in bone tumors included TP53, NCOR1, VEGFA, RB1, CCND3, CDKN2A, GID4, CCNE1, TERT, and MAP2K4. The amplification of genes such as NCOR1, VEGFA, and CCND3 mainly occurred in osteosarcoma. Germline mutation analysis reveal a high frequency of HRD related mutations (46.4%, 13/28) in this cohort. With the assistance of RNA sequencing, 16.8% (19/113) gene fusions were independently detected in 20% (16/79) of patients. Nearly 34.2% of patients harbored actionable targeted mutations, of which the most common mutation is CDKN2A deletion. The different mutational characterizations between juvenile patients and adult patients indicated the potential effect of age in bone tumor treatment. According to the genomic alterations, the diagnosis of 26 (7.28%) bone tumors were corrected. The most easily misdiagnosed bone tumor included malignant giant cell tumors of bone (2.8%, 10/357) and fibrous dysplasia of bone (1.7%, 6/357). Meanwhile, we found that the mutations of MUC16 may be a potential biomarker for the diagnosis of mesenchymal chondrosarcomas. Our results indicated that RNA sequencing effectively complements DNA sequencing and increased the detection rate of gene fusions, supporting that NGS technology can effectively assist the diagnosis of bone tumors.

Anlotinib for Recurrent or Metastatic Primary Malignant Bone Tumor: A Multicenter, Single-Arm Trial

Objective

Anlotinib, a novel multitarget kinase inhibitor of VEGFR, FGFR, PDGFR and c-Kit, has proven to be effective and safe for refractory soft tissue sarcoma patients, but has not been examined in recurrent or metastatic primary malignant bone tumors in a clinical trial setting.

Methods

This is a multicenter single-arm trial. Patients with pathologically proven recurrent or metastatic primary malignant bone tumors were eligible. Anlotinib was administered orally at 12 mg per day. Each cycle consisted of 2 weeks of treatment followed by 1-week off-treatment. The primary endpoint was progression-free survival (PFS), as assessed in the intention-to-treat (ITT) population. Secondary endpoints included objective response rate (ORR), disease control rate (DCR) and overall survival (OS). Adverse events (AEs) were assessed per NCI CTCAE version 4.03.

Results

A total of 42 patients were enrolled. Median PFS was 5.3 months (95% CI 3.5-8.4 months) in the overall analysis, 4.8 months (95%CI 3.5-7.1 months) in osteosarcoma patients and 2.8 months [95%CI 1.3 months to not reached (NR)] in chondrosarcoma patients. The median OS was 11.4 months (95% CI 10.1 months to NR) in the overall analysis, not reached (95% CI, NR, NR) in osteosarcoma patients and 11.4 months (95% CI 1.8 to 21.1 months) in chondrosarcoma patients. The ORR was 9.52% and DCR was 78.57%. Grade 3 or above AEs occurred in 54.76% of the patients, and included hypertension (19.05%), hypertriglyceridemia (9.52%) and pustulosis palmaris et plantaris (7.14%). No treatment-related death was reported.

Conclusion

Anlotinib demonstrated promising antitumor activities in recurrent or metastatic primary malignant bone tumors with manageable AEs.

Development and validation of apoptosis-related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

BACKGROUND

Previous evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis-related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma.

METHODS

The GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan-Meier plotter, Cox regression, and nomogram analyses.

RESULTS

Two apoptosis-related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high-risk cohort had a lower survival rate than those in the low-risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency.

CONCLUSION

We demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis-related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.

Novel circular RNA circATRNL1 accelerates the osteosarcoma aerobic glycolysis through targeting miR-409-3p/LDHA

In recent researches, circular RNAs (circRNAs) have been shown to exert critical functions in osteosarcoma biology. Nevertheless, the contribution of circRNAs to osteosarcoma remains largely unclear. Results indicated that expression of circATRNL1 was higher in osteosarcoma tissues and cells. The high-expression of circATRNL1 was significantly correlated with aggressive features and acted as an independent risk factor for osteosarcoma patients' overall survival. Functionally, our findings demonstrate that circATRNL1 promotes the osteosarcoma aerobic glycolysis in vitro. Mechanistically, circATRNL1 up-regulated the expression level of LDHA, which was also targeted by miR-409-3p. Therefore, circATRNL1 exerted the accelerative roles of osteosarcoma aerobic glycolysis through miR-409-3p/LDHA axis. In conclusion, circATRNL1 promoted osteosarcoma progression by enhancing glycolysis via circATRNL1/miR-409-3p/LDHA axis, which may inspire a novel therapeutic target for osteosarcoma.

MicroRNA-376b-3p targets RGS1 mRNA to inhibit proliferation, metastasis, and apoptosis in osteosarcoma

Background

To investigate the role of microRNA-376b-3p (miR-376b-3p) and regulator of G protein signaling 1 (RGS1) in the proliferation, metastasis, and apoptosis of osteosarcoma.

Methods

Differentially expressed genes (DEGs) between tumor and normal tissues from GSE14359 and GSE33382 in the cancer genome atlas (TCGA) dataset were analyzed with GEO2R online. Similarly, differentially expressed miRNAs from GSE70367 were also analyzed with GEO2R. The interaction between the differentially expressed miRNAs and the shared distal metastasis-related DEGs from the two datasets were analyzed using miRWalk and Cytoscape. RGS1 and miR-376-3p were chosen to verify the prediction. RGS1 stably expressing and silencing cells were established based on the MG63 and U2OS cell lines. The targeting of RGS1 with miR-376b-3p was confirmed with Starbase prediction and luciferase reporter assay. Cell proliferation, metastasis, and apoptosis were characterized in vitro and in xenograft mice.

Results

A total of 10 up-regulated and 8 down-regulated DEGs were characterized as shared metastasis-related DEGs for GSE14359 and GSE33382. Among these DEGs, RGS1 was targeted with miR-376b-3p, a predicted down-regulated miRNA in GSE70367. High expression of RGS1 predicted proliferation, invasion, metastases, and poor prognosis in osteosarcoma. Overexpression of RGS1 promoted proliferation, invasion, mobility, and stemness in MG63 and U2OS cells, while silencing of RGS1 had the opposite effect in both cell lines. High expression of RGS1 promoted tumor growth in xenograft nude mice. RGS1 was targeted with miR-376b-3p; the addition of miR-376b-3p down-regulated RGS1, and suppressed cell proliferation, invasion, and metastasis. Meanwhile, sponging of miR-376b-3p had the opposite effect. The suppressive effects of miR-376b-3p could be abolished with RGS1, as cell proliferation, stemness, metastasis, and invasion were all promoted with RGS1 co-transfection in both cell lines.

Conclusions

Our study indicated that RGS1 is a tumor-promoting gene in osteosarcoma, which could be inhibited with miR-376b-3p.

Ferroptosis-related gene signature associates with immunity and predicts prognosis accurately in patients with osteosarcoma

Osteosarcoma has been the most common malignant bone tumor in children and adolescents, while the 5‐y survival of osteosarcoma patients gained no significant improvement over the past decades. This study aimed to explore the role of ferroptosis‐related genes (FRGs) in the development and prognosis of osteosarcoma. The datasets of osteosarcoma patients including RNA sequencing data and clinical information were acquired from the TRGET and Gene Expression Omnibus (GEO) databases. The identification of molecular subgroups with different FRG expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. The prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The ESTIMATE algorithm was applied for determining the stromal score, immune score, ESTIMA score, and tumor purity of osteosarcoma patients. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the underlying mechanisms in the development and prognosis of osteosarcoma. Two molecular subgroups with different FRGs expression patterns were identified. The molecular subgroups with higher immune score and more active immune status showed better prognostic survival. On the basis of FRGs, a prognostic model and a nomogram integrating clinical characteristics were constructed and their prediction efficiency for osteosarcoma prognosis were well validated. Gene functional enrichment analysis showed that these differentially expressed FRGs were mainly enriched in immunity‐related signaling pathways, indicating that FRGs may affect the development and prognosis of osteosarcoma by regulating the immune microenvironment. The expression profiles of FRGs were closely related to the immunity status and prognostic survival of osteosarcoma patients. The interaction between ferroptosis and immunity in the development of osteosarcoma could provide a new insight into the exploration of molecular mechanisms and targeted therapies of osteosarcoma patients.

Differential gene expression analysis for osteosarcoma lung metastases

PURPOSE

To explore the exact molecular mechanisms underline osteosarcoma (OS) patients with lung metastases.

METHODS

The differentially expressed gene (DEG) as well as differentially expressed miRNAs (DEMs) for OS lung metastases were deeply investigated with two independent sources of databases (GEO dataset and clinical participants); The enriched biological processes and signaling pathways were explored; the miRNAs-mRNAs network was constructed; the functions of potential DEGs and DEMs were also verified with external analysis.

RESULTS

The OS patients with lung metastases displayed 323 DEGs as C-C motif chemokine ligand 3 (CCL3), sorting nexin 10 (SNX10), alpha-2-macroglobulin (A2M), carboxypeptidase E (CPE), Rap guanine nucleotide exchange factor 4 (RAPGEF4), PDZ domain containing 2 (PDZD2), calpain 10 (CAPN10), four and a half LIM domains 2 (FHL2), alkaline phosphatase, biomineralization associated (ALPL), interleukin 6 (IL6), solute carrier family 26 member 1 (SLC26A1) as well as smoothened, frizzled class receptor (SMO) were significant differentially expressed. At the same time, 21 DEMs were potential for the progress of OS lung metastasis with hsa-miR-638, hsa-miR-451, hsa-miR-486-5p, hsa-miR-134 and hsa-miR-648 were significant distinct. It could been shown that hsa-miR-638 manipulated the largest number of target genes. The functions of hsa-miR-638 and target mRNAs for the development of lung metastasis in OS could be confirmed by quantitative Real-time PCR analysis.

CONCLUSION

This integrated study hypothesized several miRNA dependent signaling pathway for OS patients with lung metastases and initiated a potential strategy for better understanding the lung metastases in clinic.

Trophinin Is an Important Biomarker and Prognostic Factor in Osteosarcoma: Data Mining from Oncomine and the Cancer Genome Atlas Databases

Osteosarcoma (OS) is a type of bone malignancy with a high rate of treatment failure. To date, few evident biomarkers for the prognostic significance of OS have been established. Oncomine was used to integrate RNA and DNA-seq data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the published literature. The correlation of the gene Trophinin (TRO) and different types of cancers was generated using the Cancer Cell Line Encyclopedia (CCLE) online tool. Prognostic values of featured Melanoma Antigen Gene (MAGE) members were further assessed by establishing the overall survival using the Kaplan-Meier plotter. Moreover, the online tool, Database for Annotation, Visualization and Integrated Discovery version (DAVID), was used to understand the biological meaning list of the genes. MAGEB10, MAGED2, TRO, MAGEH1, MAGEB18, MAGEB6, MAGEB4, MAGEB1, MAGED4B, MAGED1, MAGEB2, and MAGEB3 were significantly overexpressed in sarcoma. TRO was further demonstrated to be distinctively upregulated in osteosarcoma cell lines and associated with shorter overall survival. TRO may play an important role in the development of OS and may be a promising potential biomarker and prognostic factor.

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.

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.