A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Identification of TNFRSF21 as an inhibitory factor of osteosarcoma based on a necroptosis-related prognostic gene signature and molecular experiments

BACKGROUND

Osteosarcoma is one of the most common malignant bone tumors with bad prognosis. Necroptosis is a form of programmed cell death. Recent studies showed that targeting necroptosis was a new promising approach for tumor therapy. This study aimed to establish a necroptosis-related gene signature to evaluated prognosis and explore the relationship between necroptosis and osteosarcoma.

METHODS

Data from The Cancer Genome Atlas was used for developing the signature and the derived necroptosis score (NS). Data from Gene Expression Omnibus served as validation. Principal component analysis (PCA), Cox regression, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis were used to assess the performance of signature. The association between the NS and osteosarcoma was analyzed via gene set enrichment analysis, gene set variation analysis and Pearson test. Single-cell data was used for further exploration. Among the genes that constituted the signature, the role of TNFRSF21 in osteosarcoma was unclear. Molecular experiments were used to explore TNFRSF21 function.

RESULTS

Our data revealed that lower NS indicated more active necroptosis in osteosarcoma. Patients with lower NS had a better prognosis. PCA and ROC curves demonstrated NS was effective to predict prognosis. NS was negatively associated with immune infiltration levels and tumor microenvironment scores and positively associated with tumor purity and stemness index. Single-cell data showed necroptosis heterogeneity in osteosarcoma. The cell communication pattern of malignant cells with high NS was positively correlated with tumor progression. The expression of TNFRSF21 was down-regulated in osteosarcoma cell lines. Overexpression of TNFRSF21 inhibited proliferation and motility of osteosarcoma cells. Mechanically, TNFRSF21 upregulated the phosphorylation levels of RIPK1, RIPK3 and MLKL to promote necroptosis in osteosarcoma.

CONCLUSIONS

The necroptosis prognostic signature and NS established in this study could be used as an independent prognostic factor, TNFRSF21 may be a necroptosis target in osteosarcoma therapy.

Overexpression of HSPB6 inhibits osteosarcoma progress through the ERK signaling pathway

Heat shock protein B6 (HSPB6) plays a certain role in the formation of several cancers, whereas its effect on osteosarcoma remains unclear. In this study, the effect of HSPB6 on osteosarcoma was validated through numerous experiments. HSPB6 was down-regulated in osteosarcoma. As indicated by the result of CCK-8 and colony formation assays, HSPB6 overexpression was likely to inhibit the osteosarcoma cells proliferation, whereas the flow cytometry analysis suggested that apoptosis of osteosarcoma cells was increased after HSPB6 overexpression. Furthermore, transwell and wound healing assays suggested that when HSPB6 was overexpressed, osteosarcoma cells migration and invasion were declined. Moreover, the western blotting assay suggested that the protein level of p-ERK1/2 was down-regulated in osteosarcoma when HSPB6 was overexpressed. Besides, the effect of HSPB6 on osteosarcoma in vivo was examined. As indicated by the result, HSPB6 overexpression was likely to prevent osteosarcoma growth and lung metastasis in vivo. As revealed by the findings of this study, HSPB6 overexpression exerted anticancer effects in osteosarcoma through the ERK signaling pathway and HSPB6 may be suitable target for osteosarcoma molecular therapies.

The clinical significance of CD44v6 in malignant and benign primary bone tumors

BACKGROUND

The objective of this study was to assess the expression profile of CD44v6, a potential cancer stem cell marker, and its diagnostic and predictive significance in three distinct types of primary bone tumors.

METHODS

In this study, we utilized real-time qRT-PCR and immunohistochemistry to examine the gene and protein levels of CD44v6 in a total of 138 fresh bone tissues. This included 69 tumor tissues comprising osteosarcoma (N = 23), chondrosarcoma (N = 23), and GCT (N = 23), as well as 69 corresponding non-cancerous tumor margins. Furthermore, we investigated the circulating level of CD44v6 by isolating peripheral blood mononuclear cells from 92 blood samples. Among these, 69 samples were obtained from patients diagnosed with primary bone tumors, while the remaining 23 samples were from healthy donors. The primary objectives of our investigation were to assess the correlation between CD44v6 expression levels and clinic-pathological features of the patients, as well as to evaluate the diagnostic and predictive values of CD44v6 in this context.

RESULTS

In patients with osteosarcoma and chondrosarcoma tumors, both the gene and protein expression of CD44v6 were found to be significantly higher compared to the GCT group. Furthermore, the circulating level of CD44v6 was notably elevated in patients diagnosed with osteosarcoma and chondrosarcoma in comparison to the GCT group and patients with malignant tumor characteristics. Additionally, we observed a strong correlation between the gene and protein levels of CD44v6 and important tumor indicators such as tumor grade, metastasis, recurrence, and size at the tumor site. CD44v6 shows potential in differentiating patients with bone tumors from both control groups and tumor groups with severe and invasive characteristics from those with non-severe features. Importantly, the expression level of CD44v6 also demonstrated predictive value for determining tumor grade and the likelihood of recurrence.

CONCLUSION

CD44v6 is likely to play a role in the development of primary bone tumors and has the potential to serve as a diagnostic biomarker for bone cancer. However, to obtain more accurate and conclusive findings, further mechanistic investigations involving larger population samples are necessary.

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

Osteosarcoma is the most prevalent malignant bone tumor. In this study, we identified metastasis-related genes (MRG) that are differentially expressed between primary and metastatic osteosarcoma and employed them to create metastasis-related risk tags (MRSs) for the overall survival of osteosarcoma patients. Using consistent cluster analysis, patients with osteosarcoma in the TARGET database were divided into subgroups with different metastatic scoring patterns. The clinicopathological traits, survival rates, tumor microenvironment traits, immune-related scores, and therapeutic responses of these patients varied. Additionally, we constructed MRS-based risk characteristics and nomographs and developed an MRG Score to improve patient characteristics. Thorough evaluations demonstrated that prognostic models and metastasis scores can distinguish high-risk patients from low-risk individuals, offering excellent predictive value. Finally, western blotting was used to confirm the expression of five identified MRG markers, which are crucial for osteosarcoma invasion and migration in terms of mechanism. Our findings represent a novel and practical predictive biomarker for osteosarcoma.

Development and validation of a N6 methylation regulator-related gene signature for prognostic and immune response prediction in non-small cell lung cancer

N6 methylation (m6A) has been reported to play an important role in tumor progression. Non-small cell lung cancer (NSCLC) is the predominant pathological type of lung cancer with a high mortality rate. The purpose of this study was to develop and validate a N6 methylation regulator-related gene signature for assessing prognosis and response to immunotherapy in NSCLC. Data from The Cancer Genome Atlas was used as the training cohort. Data from Gene Expression Omnibus and Xena served as the two validation cohorts. We performed Cox regression, last absolute shrinkage and selection operator, receiver operating characteristic curves and Kaplan-Meier survival analysis to generate and validate a prognostic signature based on m6A regulator-related genes. We explored the association between the signature and tumor microenvironment including genomic mutation, immune cell infiltration and tumor mutation burden. We also analyzed the association between the signature and immunotherapy. Finally, among the genes that constituted the signature, GGA2 was the only favorable factor for NSCLC prognosis. Molecular experiments were used to explore GGA2 function in NSCLC. We generated a prognostic signature based on seven m6A regulator-related genes (GGA2, CD70, BMP2, GPX8, YWHAZ, NOG and TEAD4). And the data from three cohorts showed that the signature could effectively assess prognosis in NSCLC. Patients with high risk scores had the higher mutational load and lower immune infiltration levels and were more likely to not respond to immunotherapy. The experiments revealed overexpression of GGA2 inhibited proliferation and motility of NSCLC cells. Mechanically, GGA2 downregulated METTL3 expression and thus reduced m6A abundance in NSCLC. This study developed and validated a prognostic signature based on m6A regulator-related genes, providing useful insights for the management of NSCLC. And GGA2 may be a target of m6A regulation.

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.

Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma

Background

Glycolysis and cholesterol synthesis are crucial in cancer metabolic reprogramming. The aim of this study was to identify a glycolysis and cholesterol synthesis-related genes (GCSRGs) signature for effective prognostic assessments of osteosarcoma patients.

Methods

Gene expression data and clinical information were obtained from GSE21257 and TARGET-OS datasets. Consistent clustering method was used to identify the GCSRGs-related subtypes. Univariate Cox regression and LASSO Cox regression analyses were used to construct the GCSRGs signature. The ssGSEA method was used to analyze the differences in immune cells infiltration. The pRRophetic R package was utilized to assess the drug sensitivity of different groups. Western blotting, cell viability assay, scratch assay and Transwell assay were used to perform cytological validation.

Results

Through bioinformatics analysis, patients diagnosed with osteosarcoma were classified into one of 4 subtypes (quiescent, glycolysis, cholesterol, and mixed subtypes), which differed significantly in terms of prognosis and tumor microenvironment. Weighted gene co-expression network analysis revealed that the modules strongly correlated with glycolysis and cholesterol synthesis were the midnight blue and the yellow modules, respectively. Both univariate and LASSO Cox regression analyses were conducted on screened module genes to identify 5 GCSRGs (RPS28, MCAM, EN1, TRAM2, and VEGFA) constituting a prognostic signature for osteosarcoma patients. The signature was an effective prognostic predictor, independent of clinical characteristics, as verified further via Kaplan-Meier analysis, ROC curve analysis, univariate and multivariate Cox regression analysis. Additionally, GCSRGs signature had strong correlation with drug sensitivity, immune checkpoints and immune cells infiltration. In cytological experiments, we selected TRAM2 as a representative gene to validate the validity of GCSRGs signature, which found that TRAM2 promoted the progression of osteosarcoma cells. Finally, at the pan-cancer level, TRAM2 had been correlated with overall survival, progression free survival, disease specific survival, tumor mutational burden, microsatellite instability, immune checkpoints and immune cells infiltration.

Conclusion

Therefore, we constructed a GCSRGs signature that efficiently predicted osteosarcoma patient prognosis and guided therapy.

Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients

Background

Osteosarcoma is a common malignant bone tumor with a poor prognosis. The progression and metastasis of osteosarcoma are significantly influenced by the tumor microenvironment (TME). This study aimed to develop a personalized classifier based on metastasis and immune cells in the TME to achieve better prognostic prediction in osteosarcoma.

Methods

Firstly, osteosarcoma metastasis-related differentially expressed genes (DEGs) and infiltrating immune cells in the TME were analyzed using a series of bioinformatics methods. The metastasis-related gene signature (MRS) and TME score of osteosarcoma patients were then developed. On this basis, a personalized MRS-TME classifier was constructed and validated in other clinical cohorts and different subgroups. In addition, the relationship between the MRS-related genes and the immune microenvironment was also clarified. Finally, the signaling pathways and immune response genes in osteosarcoma patients among different MRS-TME subgroups were analyzed to explore the underlying molecular mechanism.

Results

We first identified the metastasis-related DEGs in osteosarcoma, which were primarily involved in the muscle system process, calcium ion homeostasis, cell chemotaxis, and leukocyte migration. A personalized MRS-TME classifier was then constructed by integrating the MRS (10 genes) and TME (six immune cells) scores. The MRS-TME classifier demonstrated a potent capacity of predicting the survival prognosis in diverse osteosarcoma cohorts as well as in the clinical feature subgroups. The MRS score was negatively associated with the TME score, and patients in the MRS^low/TME^high subgroup exhibited a better prognosis compared to all other subgroups. Significant differences existed between the cellular signaling pathways and immune response profiles among the different MRS-TME subgroups, especially in relation to the metabolism-related biological processes and the inflammatory response.

Conclusions

The MRS-TME classifier might be a beneficial tool to aid in the prognostic evaluation and risk stratification of osteosarcoma patients.

Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

Purpose

The relationship between indeterminate pulmonary nodules (IPNs) and metastasis is difficult to determine. We expect to explore a predictive model that can assist in indicating the nature of IPNs, as well as predicting the probability of metachronous metastasis in osteosarcoma patients.

Patients and methods

We conducted a retrospective study including 184 osteosarcoma patients at West China Hospital from January 2016 to January 2021. Hematological markers and clinical features of osteosarcoma patients were collected and analyzed.

Results

In this study, we constructed an osteosarcoma immune prognostic index (OIPI) based on the lung immune prognostic index (LIPI). Compared to other hematological markers and clinical features, OIPI had a better ability to predict metastasis. OIPI divided 184 patients into four groups, with the no-OIPI group (34 patients), the light-OIPI group (35 patients), the moderate-OIPI group (75 patients), and the severe-OIPI group (40 patients) (P < 0.0001). Subgroup analysis showed that the OIPI could have a stable predictive effect in both the no-nodule group and the IPN group. Spearman’s rank correlation test and Kruskal–Wallis test demonstrated that the OIPI was related to metastatic site and metastatic time, respectively. In addition, patients with IPNs in high-OIPI (moderate and severe) groups were more likely to develop metastasis than those in low-OIPI (none and light) groups. Furthermore, the combination of OIPI with IPNs can more accurately identify patients with metastasis, in which the high-OIPI group had a higher metastasis rate, and the severe-OIPI group tended to develop metastasis earlier than the no-OIPI group. Finally, we constructed an OIPI-based nomogram to predict 3- and 5-year metastasis rates. This nomogram could bring net benefits for more patients according to the decision curve analysis and clinical impact curve.

Conclusion

This study is the first to assist chest CT in diagnosing the nature of IPNs in osteosarcoma based on hematological markers. Our findings suggested that the OIPI was superior to other hematological markers and that OIPI can act as an auxiliary tool to determine the malignant transformation tendency of IPNs. The combination of OIPI with IPNs can further improve the metastatic predictive ability in osteosarcoma patients.

Integrated analyses of an RNA binding protein-based signature related to tumor immune microenvironment and candidate drugs in osteosarcoma

OBJECTIVE

Osteosarcoma is the most frequent primary bone malignancy, associated with frequent recurrence and lung metastasis. RNA-binding proteins (RBPs) are pivotal in regulating several aspects of cancer biology. Nonetheless, interaction between RBPs and the osteosarcoma immune microenvironment is poorly understood. We investigated whether RBPs can predict prognosis and immunotherapy response in osteosarcoma patients.

METHODS

We constructed an RBP-related prognostic signature (RRPS) by univariate coupled with multivariate analyses and verified the independent prognostic efficacy of the signature. Single-sample Gene Set Enrichment Analysis (ssGSEA) along with ESTIMATE analysis were carried out to investigate the variations in immune characteristics between subgroups with various RRPS-scores. Furthermore, we investigatedpossible small molecule drugs using the connectivity map database and validated the expression of hub RBPs by qRT-PCR.

RESULTS

The RRPS, consisting of seven hub RBPs, was an independent prognostic factor compared to traditional clinical features. The RRPS could distinguish immune functions, immune score, stromal score, tumor purity and tumor infiltration by immune cells in different osteosarcoma subjects. Additionally, patients with high RRPS-scores had lower expression of immune checkpoint genes than patients with low RRPS-scores. We finally identified six small molecule drugs that may improve prognosis in osteosarcoma patients and substantiated notable differences in the contents of these RBPs.

CONCLUSION

We evaluated the prognostic value and clinical application of an RBPs-based prognostic signature and identified promising biomarkers to predict immune cell infiltration and immunotherapy response in osteosarcoma.

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.