Single-cell RNA sequencing reveals differential expression of EGFL7 and VEGF in giant-cell tumor of bone and osteosarcoma

Emerging roles of EGFL family members in neoplastic diseases: Molecular mechanisms and targeted therapies

Epidermal growth factor-like proteins (EGFLs) contain more than a single EGF/EGF-like domain within their protein structure. To date, ten EGFL family members (EGFL1-10) have been characterized across diverse tissues and developmental stages under different conditions. In this review, we conclude that EGFLs are instrumental in regulating biological activities and pathological processes. Under physiological conditions, EGFLs participate in angiogenesis, neurogenesis, osteogenesis, and other processes. Under pathological conditions, EGFLs are linked with different diseases, particularly cancers. Furthermore, we highlight recent advancements in the study of EGFLs in biological conditions and cancers. In addition, the regulatory role and key underlying mechanism of EGFLs in mediating tumorigenesis are discussed. This paper also examines potential antagonists that target EGFL family members in cancer therapeutics. In summary, this comprehensive review elucidates the critical role of EGFLs in neoplastic diseases and highlights their potential as therapeutic targets.

Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine (Review)

Osteosarcoma, the most prevalent primary bone malignancy in children and adolescents, poses significant challenges due to its aggressive nature and propensity for metastasis. Despite advances in treatment, survival rates for high‑risk patients remain unsatisfactory, underscoring the urgent need for innovative approaches. This review explores the vital role of multi‑omics‑integrating genomics, transcriptomics, proteomics and metabolomics‑in unraveling the complex biological landscapes of osteosarcoma. By providing comprehensive insights into tumor heterogeneity, signaling pathways and metabolic reprogramming, multi‑omics facilitates the identification of novel biomarkers and therapeutic targets. The objective of the present study was to highlight the transformative potential of multi‑omics in enhancing the understanding and management of osteosarcoma, ultimately paving the way for personalized treatment strategies and improved patient outcomes. Through this synthesis, the study calls for a concerted effort to integrate multi‑omics into clinical practice, fostering a more precise approach to osteosarcoma care.

Denosumab Enhances Antitumor Immunity by Suppressing SPP1 and Boosting Cytotoxic T Cells

Denosumab, a RANK ligand inhibitor, is primarily used to prevent osteoclastogenesis in the treatment of conditions such as osteoporosis, bone metastasis, and giant cell tumor of bone (GCTB). RANK ligand also plays an important role in immunity by activating NF-κB and its target genes, including the osteopontin-coding gene SPP1 (also known as OPN), which is linked to CXCL9:SPP1 macrophage polarization and prognosis. In this study, we explored an additional role of denosumab in enhancing antitumor immunity in patients. Single-cell RNA sequencing was performed on nine human GCTB samples, including six untreated and three treated only with denosumab, to exclude confounding treatment factors linked with bone metastasis samples. We further analyzed paired samples collected before and after denosumab treatment from a cohort of nine patients with GCTB and conducted a pan-cancer analysis of 34 distinct types of cancers. Our single-cell analysis of GCTB resulted in a comprehensive cell atlas revealing an antitumor role of denosumab in inhibiting SPP1 expression and augmenting active cytotoxic T-cell abundance. Furthermore, we validated this immunomodulatory role of denosumab using the paired GCTB samples. Finally, the pan-cancer analysis supported a negative correlation between SPP1 and CD8A levels, with the CD8A:SPP1 ratio correlating with overall survival in 14 cancer types, which was superior to either CD8A or SPP1 alone. Our research provides clinical evidence that denosumab improves antitumor immunity by decreasing SPP1 expression and enhancing cytotoxic T-cell activity, serving as a milestone in the development of innovative use of denosumab and offering potential benefits to patients with elevated levels of SPP1.

Single-cell RNA sequencing reveals key signaling pathways and biological functions in giant cell tumors of bone

BACKGROUND

Giant cell tumor of bone (GCTB) represents a group of tumors that characterized by their heterogeneity and the presence of multiple cell types. It is essential to understand their molecular mechanisms for better designing therapeutic approaches.

METHODS

We applied single cell RNA-sequencing technology to investigate differentiation process during GCTBs formation. DAVID was used to perform a pathway enrichment and Gene Ontology (GO) analyses on signaling pathways and biological functions. Cell-cell interactions and signaling networks are depicted with network diagrams and usages of heatmaps.

RESULTS

The analysis identified numerous significant pathways, including Pathways in Parkinson's disease, Prion diseases and COVID-19. ViBe identified a wide range of biological process, cellular component and molecular function associated with tumorigenesis. Furthermore, we validate that the SPP1 signaling pathway is essential for cell-cell crosstalk, specifically functioning as a positive feedback loop between cancer-associated fibroblasts and macrophages.

CONCLUSION

Our study revealed comprehensive molecular and intercellular interaction networks in GCTB, which may advance the understanding of the pathogenic mechanisms in GCTB and contribute essential information for its treatment. Our findings make confution in the complex functional dynamics in the TME.

Prioritizing Context-Dependent Cancer Gene Signatures in Networks

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.

Single-cell RNA sequencing reveals the communications between tumor microenvironment components and tumor metastasis in osteosarcoma

Introduction

Osteosarcoma is a common type of bone cancer characterized by a poor prognosis due to its metastatic nature. The tumor microenvironment (TME) plays a critical role in tumor metastasis and therapy response. Therefore, our study aims to explore the metastatic mechanism of osteosarcoma, potentially opening new avenues for cancer treatment.

Methods

In this study, we collected data from the GSE152048, GSE14359, and GSE49003 datasets. Differentially expressed genes (DEGs) were identified in osteosarcoma cases with primary and metastatic features using R software and the limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate metastasis-related genes. A protein-protein interaction (PPI) network was established using the STRING database to further analyze these metastasis-associated genes. The abundances of different cell types with a mixed cell population were estimated using the CIBERSORT approach. The scRNA-seq data were analyzed by the Seurat package in R software, and intercellular communications were elucidated using the CellChat R package.

Results

In this study, 92 DEGs related to metastasis were identified, including 41 upregulated and 51 downregulated genes in both the GSE14359 and GSE49003 datasets. Metastasis-associated pathways were identified, including those involving the cyclin-dependent protein kinase holoenzyme complex, transferase complex, transferring phosphorus-containing groups, SCF ubiquitin ligase complex, and the serine/threonine protein kinase complex. KEGG and PPI network analyses revealed 15 hub genes, including Skp2, KIF20A, CCNF, TROAP, PHB, CKS1B, MCM3, CCNA2, TRIP13, CENPM, Hsp90AB1, JUN, CKS2, TK1, and KIF4A. Skp2 has been known as an E3 ubiquitin ligase involved in osteosarcoma progression. The proportion of CD8+ T cells was found to be higher in metastatic osteosarcoma tissues, and high expression of PHB was associated with a favorable prognosis in osteosarcoma patients. Additionally, 23 cell clusters were classified into eight cell types, including chondrocytes, MSC, T cells, monocytes, tissue stem cells, neurons, endothelial cells, and macrophages. The 15 hub genes were expressed across various cell types, and interactions between different cell types were observed.

Conclusion

Our study reveals the intricate communication between tumor microenvironment components and tumor metastasis in osteosarcoma.

Identification of a pro-protein synthesis osteosarcoma subtype for predicting prognosis and treatment

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor that is aggressive and has a poor prognosis. Although combining surgery and chemotherapy has significantly improved patient outcomes, the prognosis for OS patients with metastatic or recurrent OS has remained unsatisfactory. Therefore, it is imperative to gain a fresh perspective on OS development mechanisms and treatment strategies. After studying single-cell RNA sequencing (scRNA-seq) data in public databases, we identified seven OS subclonal types based on intra-tumor heterogeneity. Subsequently, we constructed a prognostic model based on pro-protein synthesis osteosarcoma (PPS-OS)-associated genes. Correlation analysis showed that the prognostic model performs extremely well in predicting OS patient prognosis. We also demonstrated that the independent risk factors for the prognosis of OS patients were tumor primary site, metastatic status, and risk score. Based on these factors, nomograms were constructed for predicting the 3- and 5-year survival rates. Afterward, the investigation of the tumor immune microenvironment (TIME) revealed the vital roles of γδ T-cell and B-cell activation. Drug sensitivity analysis and immune checkpoint analysis identified drugs that have potential application value in OS. Finally, the jumping translocation breakpoint (JTB) gene was selected for experimental validation. JTB silencing suppressed the proliferation, migration, and invasion of OS cells. Therefore, our research suggests that PPS-OS-related genes facilitate the malignant progression of OS and may be employed as prognostic indicators and therapeutic targets in OS.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Current updates in sarcoma biomarker discovery: emphasis on next-generation sequencing-based methods

Soft tissue sarcomas comprise a heterogeneous group of neoplasms. Although soft tissue malignancies make up only 2% of adult cancers, classification based on histomorphology presents a diagnostic challenge. Characterisation of soft tissue sarcomas by molecular analysis is rapidly evolving to improve diagnostic accuracy and develop targeted therapies. This review highlights the advances in molecular techniques, including current next-generation sequencing-based assays (fusion detection by RNA sequencing, targeted/whole exome sequencing, microRNA profiling), as well as emerging methods (liquid biopsies, DNA methylation profiling, single-cell molecular profiling and next-generation immunohistochemistry) for future clinical applications.

Exploration of the Development and Cell Communication of Aneuploid Osteoblasts and Osteoclasts in Giant Cell Tumour of Bone Using Single-Cell RNA Sequencing

We aimed to explore the development and cell communication of osteoblasts and osteoclasts with aneuploidy variation in giant cell tumour of bone (GCTB). We predicted the diploid and aneuploid cells in tissue samples using the CopyKAT package. The Monocle2 package was used to analyse differentiation trajectories of aneuploid cells. We used the CellChat package to observe the signalling pathways and ligand-receptor pairs for the two interaction types, "Cell-Cell Contact" and "Secreted Signalling", respectively. A total of 9,117 cells were obtained including eight cell types. Most aneuploid cells were osteoblasts. As the cell differentiation trajectory matured, we found that aneuploid osteoblasts first increased the inflammatory response activity and then enhanced the ability to activate T cells, whereas osteoclasts gradually enhanced the cellular energy metabolism, cell adhesion, cell proliferation and immune response; the activated biological functions were gradually weakened. The analysis by CellChat indicated that CTLA4 or TIGIT might act as important immune checkpoint genes to attenuate the inhibitory effect of aneuploid osteoclasts on NK/T cells, thereby enhancing the activity of NK/T cells. Our study found that both osteoblasts and osteoclasts might be involved in the development of GCTB, which may provide a new direction for the treatment of GCTB.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Role of EGFL7 in human cancers: A review

EGFL7 is a proangiogenic factor. It has been widely described with having a vital role in tubulogenesis and regulation of angiogenesis, mainly during embryogenesis and organogenesis. It has been mainly associated with NOTCH pathway, but there are reports showing association with MAPK and integrin pathways. Given its association with angiogenesis and these other pathways, there are several studies associating EGFL7 with carcinogenesis. In fact, most of the studies have pointed to EGFL7 as an oncogene, and some of them suggest EGFL7 expression as a possible biomarker of prognosis or use for a patient's follow-up. Here, we review the molecular pathways which EGFL7 is associated and highlight several studies describing the role of EGFL7 in tumorigenesis, separated by tumor type. Besides its role on angiogenesis, EGFL7 may act in other pathways as oncogene, which makes it a possible biomarker and a candidate to targeted therapy.

Osteohematology: To be or Notch to be

Osteohematology is an emerging research field that studies the crosstalk between hematopoietic and bone stromal cells, to elucidate the mechanisms of hematological and skeletal malignancies and diseases. The Notch is an evolutionary conserved developmental signaling pathway, with critical roles in embryonic development by controlling cell proliferation and differentiation. However, the Notch pathway is also critically involved in cancer initiation and progression, such as osteosarcoma, leukemia, and multiple myeloma. The Notch-mediated malignant cells dysregulate bone and bone marrow cells in the tumour microenvironment, resulting in disorders ranging from osteoporosis to bone marrow dysfunction. To date, the complex interplay of Notch signaling molecules in hematopoietic and bone stromal cells is still poorly understood. In this mini-review, we summarize the crosstalk between cells in bone and bone marrow and their influence under the Notch signaling pathway in physiological conditions and in tumour microenvironment.

EGFL7 Secreted By Human Bone Mesenchymal Stem Cells Promotes Osteoblast Differentiation Partly Via Downregulation Of Notch1-Hes1 Signaling Pathway

BACKGROUND

Epidermal growth factor-like domain protein 7 (EGFL7) is a secreted protein that is differentially expressed in the bone microenvironment; however, the effect of EGFL7 on the osteogenesis of human bone marrow mesenchymal stem cells (hBMSCs) is largely unknown.

METHODS

EGFL7 expression in the fracture microenvironment was analyzed based on the Gene Expression Omnibus (GEO) database. Knockdown of EGFL7 by small interfering RNA (siRNA) and in vitro stimulation with recombinant human EGFL7 (rhEGFL7) protein were used to assess alterations in downstream signaling and changes in the osteogenic differentiation and proliferation of hBMSCs. A γ-secretase inhibitor was used to further explore whether inhibition of Notch signaling rescued the osteogenic-inhibitory effect of EGFL7 knockdown in hBMSCs. A femoral defect model was established to verify the effect of recombinant mouse EGFL7 on bone healing in vivo.

RESULTS

EGFL7 expression increased during hBMSC osteogenesis. Knockdown of EGFL7 impaired hBMSC osteogenesis and activated Notch1/NICD/Hes1 signaling. rhEGFL7 promoted hBMSC osteogenesis and downregulated Notch1 signaling. The osteoblast-inhibitory effect of EGFL7 knockdown was rescued by Notch1 signaling inhibition. Recombinant EGFL7 led to enhanced bone healing in mice with femoral defects.

CONCLUSIONS

EGFL7 promotes osteogenesis of hBMSCs partly via downregulation of Notch1 signaling.

RNA-seq and Single-Cell Transcriptome Analyses of TRAIL Receptors Gene Expression in Human Osteosarcoma Cells and Tissues

Osteosarcoma (OS) is the most common primary cancer in the skeletal system, characterized by a high incidence of lung metastasis, local recurrence and death. Systemic treatment of this aggressive cancer has not improved significantly since the introduction of chemotherapy regimens, underscoring a critical need for new treatment strategies. TRAIL receptors have long been proposed to be therapeutic targets for cancer treatment, but their role in osteosarcoma remains unclear. In this study, we investigated the expression profile of four TRAIL receptors in human OS cells using total RNA-seq and single-cell RNA-seq (scRNA-seq). The results revealed that TNFRSF10B and TNFRSF10D but not TNFRSF10A and TNFRSF10C are differentially expressed in human OS cells as compared to normal cells. At the single cell level by scRNA-seq analyses, TNFRSF10B, TNFRSF10D, TNFRSF10A and TNFRSF10C are most abundantly expressed in endothelial cells of OS tissues among nine distinct cell clusters. Notably, in osteoblastic OS cells, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. Similarly, in an OS cell line U2-OS using RNA-seq, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. According to the TARGET online database, poor patient outcomes were associated with low expression of TNFRSF10C. These results could provide a new perspective to design novel therapeutic targets of TRAIL receptors for the diagnosis, prognosis and treatment of OS and other cancers.

Establishment of Rapid and Accurate Screening System for Molecular Target Therapy of Osteosarcoma

Introduction Comprehensive analyses using clinical sequences subcategorized osteosarcoma (OS) into several groups according to the activated signaling pathways. Mutually exclusive co-occurrences of gene amplification (PDGFRA/KIT/KDR, VEGFA/CCND3, and MDM2/CDK4) have been identified in approximately 40% of OS, representing candidate subsets for clinical evaluation of additional therapeutic options. Thus, it would be desirable to evaluate the specific gene amplification before starting therapy in patients with OS. Materials and Methods This is a retrospective study. We examined 13 cases of clinical OS samples using NanoString-based copy number variation (CNV) analysis. Decalcification and chemotherapeutic effects on this analysis were also assessed. Results First, the accuracy of this system was validated by showing that amplification/deletion data obtained from this system using various types of cancer cell lines almost perfectly matched to that from the Cancer Cell Line Encyclopedia (CCLE). We identified potentially actionable alterations in CDK4/MDM2 amplification in 10% of samples and potential additional therapeutic targets (PDGFRA/KIT/KDR and VEGFA/CCND3) in 20% of samples, which is consistent with the reported frequencies. Furthermore, this assay could identify these potential therapeutic targets regardless of the sample status (frozen vs formalin-fixed paraffin-embedded [FFPE] tissues). Conclusion We established a NanoString-based rapid and cost-effective method with a short turnaround time (TAT) to examine gene amplification status in OS. This CNV analysis using FFPE samples is recommended where the histological evaluation of viable tumor cells is possible, especially for tumors after chemotherapy with higher chemotherapeutic effects.

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.

Identification of Differentially Expressed Intronic Transcripts in Osteosarcoma

Over the past decade; the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed that they play a major role in the development of various diseases; including cancer. Intronic transcripts are one of the most fascinating lncRNAs that are located within intron regions of protein-coding genes, which have the advantage of encoding micropeptides. There have been several studies looking at intronic transcript expression profiles in cancer; but almost none in osteosarcoma. To overcome this problem; we have investigated differentially expressed intronic transcripts between osteosarcoma and normal bone tissues. The results highlighted that NRG1-IT1; FGF14-IT1; and HAO2-IT1 were downregulated; whereas ER3-IT1; SND1-IT1; ANKRD44-IT1; AGAP1-IT1; DIP2A-IT1; LMO7DN-IT1; SLIT2-IT1; RNF216-IT1; and TCF7L1-IT1 were upregulated in osteosarcoma tissues compared to normal bone tissues. Furthermore, we identified if the transcripts encode micropeptides and the transcripts' locations in a cell.