Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

The prognostic value of ubiquitin/ubiquitin-like-related genes along with immune cell infiltration and clinicopathological features in osteosarcoma

BACKGROUND

Ubiquitin/ubiquitin-like (Ub/UBL)-related genes have been reported to be associated with the survival of osteosarcoma patients but have not yet been systematically explored.

METHODS

The prognostic value of Ub/UBL-related genes, immune cell infiltration and clinicopathological features of patients were explored by Cox and LASSO regression analyses. A prognostic model was established and then validated in the GSE21257 dataset. The differential expression of hub genes in osteosarcoma was confirmed by qRT-PCR, western blotting and immunohistochemistry.

RESULTS

Tripartite Motif Containing 8 (TRIM8) and Ubiquitin Like With PHD And Ring Finger Domains 2 (UHRF2) were screened as genes with prognostic value in osteosarcoma. Kaplan-Meier analysis and scatter plots indicated that patients in the high gene significance score group tended to have a worse prognosis. The concordance index, calibration analysis and receiver operating characteristic analysis suggested that the model had good prediction accuracy and high sensitivity and specificity. Decision curve analysis revealed that patients could obtain greater net benefit from this model. Functional analyses of the differentially expressed genes indicated that they were involved in important functions and pathways. TRIM8 and UHRF2 were confirmed to be highly expressed in osteosarcoma cell lines and tissues.

CONCLUSIONS

TRIM8 and UHRF2 are potential prognostic genes in osteosarcoma, and these results provide insights into the roles of these genes and their implications for patient outcomes.

New gene signature from the dominant infiltration immune cell type in osteosarcoma predicts overall survival

The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.

Decoding the Impact of Tumor Microenvironment in Osteosarcoma Progression and Metastasis

Osteosarcoma (OS) is a heterogeneous, highly metastatic bone malignancy in children and adolescents. Despite advancements in multimodal treatment strategies, the prognosis for patients with metastatic or recurrent disease has not improved significantly in the last four decades. OS is a highly heterogeneous tumor; its genetic background and the mechanism of oncogenesis are not well defined. Unfortunately, no effective molecular targeted therapy is currently available for this disease. Understanding osteosarcoma's tumor microenvironment (TME) has recently gained much interest among scientists hoping to provide valuable insights into tumor heterogeneity, progression, metastasis, and the identification of novel therapeutic avenues. Here, we review the current understanding of the TME of OS, including different cellular and noncellular components, their crosstalk with OS tumor cells, and their involvement in tumor progression and metastasis. We also highlight past/current clinical trials targeting the TME of OS for effective therapies and potential future therapeutic strategies with negligible adverse effects.

Metabolism and senescence in the immune microenvironment of osteosarcoma: focus on new therapeutic strategies

Osteosarcoma is a highly aggressive and metastatic malignant tumor. It has the highest incidence of all malignant bone tumors and is one of the most common solid tumors in children and adolescents. Osteosarcoma tissues are often richly infiltrated with inflammatory cells, including tumor-associated macrophages, lymphocytes, and dendritic cells, forming a complex immune microenvironment. The expression of immune checkpoint molecules is also high in osteosarcoma tissues, which may be involved in the mechanism of anti-tumor immune escape. Metabolism and senescence are closely related to the immune microenvironment, and disturbances in metabolism and senescence may have important effects on the immune microenvironment, thereby affecting immune cell function and immune responses. Metabolic modulation and anti-senescence therapy are gaining the attention of researchers as emerging immunotherapeutic strategies for tumors. Through an in-depth study of the interconnection of metabolism and anti- senescence in the tumor immune microenvironment and its regulatory mechanism on immune cell function and immune response, more precise therapeutic strategies can be developed. Combined with the screening and application of biomarkers, personalized treatment can be achieved to improve therapeutic efficacy and provide a scientific basis for clinical decision-making. Metabolic modulation and anti- senescence therapy can also be combined with other immunotherapy approaches, such as immune checkpoint inhibitors and tumor vaccines, to form a multi-level and multi-dimensional immunotherapy strategy, thus further enhancing the effect of immunotherapy. Multidisciplinary cooperation and integrated treatment can optimize the treatment plan and maximize the survival rate and quality of life of patients. Future research and clinical practice will further advance this field, promising more effective treatment options for patients with osteosarcoma. In this review, we reviewed metabolic and senescence characteristics in the immune microenvironment of osteosarcoma and related immunotherapies, and provide a reference for development of more personalized and effective therapeutic strategies.

Construction and validation of a novel cuproptosis-mitochondrion prognostic model related with tumor immunity in osteosarcoma

BACKGROUND

The purpose of this study was to develop a new prognostic model for osteosarcoma based on cuproptosis-mitochondrion genes.

MATERIALS AND METHODS

The data of osteosarcoma were obtained from TARGET database. By using Cox regression and LASSO regression analysis, a novel risk score was constructed based on cuproptosis-mitochondrion genes. Kaplan-Meier, ROC curve and independent prognostic analyses were performed to validate the risk score in GSE21257 dataset. Then, a predictive nomogram was constructed and further validated by calibration plot, C-index and ROC curve. Based on the risk score, all patients were divided into high-risk and low-risk group. GO and KEGG enrichment, immune correlation and drug sensitivity analyses were performed between groups. Real-time quantitative PCR verified the expression of cuproptosis-mitochondrion prognostic model genes in osteosarcoma. And we explored the function of FDX1 in osteosarcoma by western blotting, CCK8, colony formation assay, wound healing assay and transwell assays.

RESULTS

A total of six cuproptosis-mitochondrion genes (FDX1, COX11, MFN2, TOMM20, NDUFB9 and ATP6V1E1) were identified. A novel risk score and associated prognostic nomogram were constructed with high clinical application value. Strong differences in function enrichment and tumor immune microenvironment were shown between groups. Besides, the correlation of cuproptosis-mitochondrion genes and drug sensitivity were revealed to search for potential therapeutic target. The expression of FDX1, COX11, MFN2, TOMM20 and NDUFB9 at mRNA level was elevated in osteosarcoma cells compared with normal osteoblast hFOB1.19. The mRNA expression level of ATP6V1E1 was decreased in osteosarcoma. Compared with hFOB1.19, western blotting revealed that the expression of FDX1 was significantly elevated in osteosarcoma cells. Functional experiments indicated that FDX1 mainly promoted the migration of osteosarcoma rather than proliferation.

CONCLUSIONS

We developed a novel prognostic model of osteosarcoma based on cuproptosis-mitochondrion genes, which provided great guidance in survival prediction and individualized treatment decision making for patients with osteosarcoma.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma

OBJECTIVES

Osteosarcoma is highly aggressive and prone to metastasis, with a poor prognosis. Increasing evidence identified anoikis has a critical effect in tumor metastasis and invasion. However, the prognostic value of anoikis-related genes (ANRGs) in osteosarcoma and their role in the immune landscape of osteosarcoma remain unclear.

METHODS

The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes.

RESULTS

Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma.

CONCLUSION

Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.

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.

Characterization of the immune cell infiltration landscape in myxofibrosarcoma to aid immunotherapy

Myxofibrosarcoma (MFS) is a highly malignant subtype of soft tissue sarcoma, accounting for 5% of cases. Immunotherapy guided by immune cell infiltration (ICI) is reportedly a promising treatment strategy. Here, MFS samples (n = 104) from two independent databases were classified as ICI clusters A/B/C and gene clusters A/B/C. Then, a close relationship between ICI and gene clusters was established. We found that the features of these clusters were consistent with the characteristics of immune-inflamed tumors (cluster C), immune-desert tumors (cluster B), and immune-excluded tumors (cluster A). Moreover, cluster C was sensitive to immunotherapy. Finally, an independent ICI score was established to predict the therapeutic effect, which has prospects for application in guiding immunotherapy during clinical practice.

Exonucleases: Degrading DNA to Deal with Genome Damage, Cell Death, Inflammation and Cancer

Although DNA degradation might seem an unwanted event, it is essential in many cellular processes that are key to maintaining genomic stability and cell and organism homeostasis. The capacity to cut out nucleotides one at a time from the end of a DNA chain is present in enzymes called exonucleases. Exonuclease activity might come from enzymes with multiple other functions or specialized enzymes only dedicated to this function. Exonucleases are involved in central pathways of cell biology such as DNA replication, repair, and death, as well as tuning the immune response. Of note, malfunctioning of these enzymes is associated with immune disorders and cancer. In this review, we will dissect the impact of DNA degradation on the DNA damage response and its links with inflammation and cancer.

Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications

Osteosarcoma is a primary malignant tumor that tends to threaten children and adolescents, and the 5-year event-free survival rate has not improved significantly in the past three decades, bringing grief and economic burden to patients and society. To date, the genetic background and oncogenesis mechanisms of osteosarcoma remain unclear, impeding further research. The tumor immune microenvironment has become a recent research hot spot, providing novel but valuable insight into tumor heterogeneity and multifaceted mechanisms of tumor progression and metastasis. However, the immune microenvironment in osteosarcoma has been vigorously discussed, and the landscape of immune and non-immune component infiltration has been intensively investigated. Here, we summarize the current knowledge of the classification, features, and functions of the main infiltrating cells, complement system, and exosomes in the osteosarcoma immune microenvironment. In each section, we also highlight the complex crosstalk network among them and the corresponding potential therapeutic strategies and clinical applications to deepen our understanding of osteosarcoma and provide a reference for imminent effective therapies with reduced adverse effects.

A Risk-Scoring Model Based on Evaluation of Ferroptosis-Related Genes in Osteosarcoma

Background

Osteosarcoma (OS) is a bone malignancy frequently seen in pediatrics and has high mortality and incidence. Ferroptosis is an important cell death process in regulating the apoptosis and invasion of tumor cells, so constructing the risk-scoring model based on OS ferroptosis-related genes (FRGs) will benefit the evaluation of both treatment and prognosis.

Methods

The OS dataset was screened from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and OS-related FRGs were found through the Ferroptosis Database (FerrDb) using a multivariate Cox regression model, followed by the generation of the risk scores and a risk-scoring prediction model. Further systematical exploration for immune cell infiltration and assessing the prediction of response to targeted drugs was conducted.

Results

Based on OS-related FRGs, a risk-scoring model of FRGs in OS was constructed. The six FRGs played a role in the carbon metabolism, glutathione metabolism, and pentose phosphate pathways. Results from targeted drug sensitivity analyses were concordant to pathway analyses. The response to targeted drugs statistically differed between the two groups with different risks, and the high-risk group presented a high sensitivity to targeted drugs.

Conclusions

We identified a 6-ferroptosis-gene-based prognostic signature in OS and created and verified a risk-scoring model to predict the prognosis of OS at 1, 3, and 5 years for OS patients independently.

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained.

Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples.

Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis.

Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients.

Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.

The UHRF protein family in epigenetics, development, and carcinogenesis

The UHRF protein family consists of multidomain regulatory proteins that sense modification status of DNA and/or proteins and catalyze the ubiquitylation of target proteins. Through their functional domains, they interact with other molecules and serve as a hub for regulatory networks of several important biological processes, including maintenance of DNA methylation and DNA damage repair. The UHRF family is conserved in vertebrates and plants but is missing from fungi and many nonvertebrate animals. Mammals commonly have UHRF1 and UHRF2, but, despite their high structural similarity, the two paralogues appear to have distinct functions. Furthermore, UHRF1 and UHRF2 show different expression patterns and different outcomes in gene knockout experiments. In this review, we summarize the current knowledge on the molecular function of the UHRF family in various biological pathways and discuss their roles in epigenetics, development, gametogenesis, and carcinogenesis, with a focus on the mammalian UHRF proteins.

Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes

Cancer immunotherapy is a promising therapeutic approach, but the prognostic value of immune-related genes in osteosarcoma (OS) is unknown. Here, Target-OS RNA-seq data were analyzed to detect differentially expressed genes (DEGs) between OS subgroups, followed by functional enrichment analysis. Cox proportional risk regression was performed for each immune-related gene, and a risk score model to predict the prognosis of patients with OS was constructed. The risk scores were calculated using the risk signature to divide the training set into high-risk and low-risk groups, and validation was performed with GSE21257. We identified two immune-associated clusters, C1 and C2. C1 was closely related to immunity, and the immune score was significantly higher in C1 than in C2. Furthermore, we validated 6 immune cell hub genes related to the prognosis of OS: CD8A, KIR2DL1, CD79A, APBB1IP, GAL, and PLD3. Survival analysis revealed that the prognosis of the high-risk group was significantly worse than that of the low-risk group. We also explored whether the 6-gene prognostic risk model was effective for survival prediction. In conclusion, the constructed a risk score model based on immune-related genes and the survival of patients with OS could be a potential tool for targeted therapy.