Prognostic Biomarker In Osteosarcoma Research Articles

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

Inflammasome-related gene signatures as prognostic biomarkers in osteosarcoma.

Osteosarcoma, the primary bone cancer in adolescents and young adults, is notorious for its aggressive growth and metastatic potential. Our study delved into the prognostic impact of inflammasome-related gene signatures in osteosarcoma patients, employing comprehensive genetic profiling to uncover signatures linked with patient outcomes. We identified three patient subgroups through consensus clustering, with one showing worse survival rates correlated with high FGFR3 and RARB expressions. Immune profiling revealed significant immune cell infiltration differences among these subgroups, affecting survival. Utilising advanced machine learning, including StepCox and gradient boosting machine algorithms, we developed a prognostic model with a notable c-index of 0.706, highlighting CD36 and MYD88 as key genes. Higher inflammasome risk scores from our model were associated with poorer survival, corroborated across datasets. Invitro experiments validated CD36 and MYD88's roles in promoting osteosarcoma cell proliferation, invasion and migration, emphasising their therapeutic potential. This research offers new insights into inflammasomes' role in osteosarcoma, introducing novel biomarkers for risk assessment and potential therapeutic targets. Our findings suggest a pathway towards personalised treatment strategies, potentially improving patient outcomes in osteosarcoma.

Identifying immune-related prognostic biomarkers in osteosarcoma: Development and validation of the tumor immune microenvironment risk model based on analysis of TCGA.

Osteosarcoma is a rare and aggressive malignancy with limited effective therapeutic options. This study aimed to identify immune-related prognostic biomarkers and develop a prognostic model for osteosarcoma. We performed integrated analysis of transcriptomic data and immune cell infiltration profiles of 84 osteosarcoma samples from the Cancer Genome Atlas (TCGA) database. Time-dependent receiver operating characteristic (ROC) curve analysis was used to assess the prognostic value of the TIMErisk model. We also performed functional annotation and pathway enrichment analyses to explore the potential mechanisms underlying the TIMErisk model. We identified a seven-gene TIMErisk model (C2, APBB1IP, BST2, TRPV2, CCL5, GBP1, and F13A1) that was independently associated with overall survival of osteosarcoma patients. The TIMErisk model showed significant associations with immune cell infiltration and immunosuppressive gene expression. In addition, the TIMErisk model was associated with drug sensitivity, and we found that several immune checkpoint genes were significantly differentially expressed between high- and low-TIMErisk groups. Functional annotation and pathway enrichment analyses revealed that the TIMErisk model was associated with multiple immune-related pathways, including antigen processing and presentation, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway. Our study identified a novel TIME-based prognostic model for osteosarcoma that incorporates immune-related genes and can be used to predict patient prognosis and response to immunotherapy. Our findings highlight the importance of the TIME microenvironment in osteosarcoma progression and suggest that immune-related biomarkers may have clinical significance in the management of osteosarcoma.

LncRNA SNHG25 Predicts Poor Prognosis and Promotes Progression in Osteosarcoma via the miR-497-5p/SOX4 Axis.

Osteosarcoma is a disease that primarily affects adolescents with skeletal immaturity. LncRNAs are abnormally expressed and correlated with osteosarcoma patients' prognosis. We identified aberrant expression of LncRNA SNHG25 (small nucleolar RNA host gene 25) in osteosarcoma and analyzed the molecular mechanisms by which it regulates osteosarcoma progression. The expression levels of SNHG25 in tumour specimens and cells were measured by RTqPCR. Loss-of-function assays were conducted to investigate the functional role of SNHG25 in vitro and in vivo. Bioinformatic predictions, dual-luciferase reporter assays, and western blotting were performed to explore the possible underlying mechanisms. SNHG25 was highly expressed in osteosarcoma cells and tissues. The Kaplan-Meier curve showed that the survival rate of patients with high SNHG25 expression was significantly lower than those with low SNHG25 expression. Functional studies have indicated that inhibition of SNHG25 suppresses cell proliferation, migration, and invasion, while promoting apoptosis. SNHG25 knockdown suppresses osteosarcoma tumour growth in vivo. SNHG25 functions as a sponge for miR-497-5p in osteosarcoma cells. The level of SNHG25 was negatively correlated with that of miR-497-5p. The proliferation, invasion, and migration of osteosarcoma cells were restored by transfection of the miR-497-5p inhibitor in the SNHG25 knockdown group. SNHG25 was determined to function as an oncogene by promoting osteosarcoma cell proliferation, invasion, and migration through the miR-497-5p/SOX4 axis. Upregulation of SNHG25 expression indicated poor prognosis in patients with osteosarcoma, which showed that SNHG25 may serve as a potential therapeutic target and prognostic biomarker in osteosarcoma.

Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers.

The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.

Retraction: SMARCB1 expression is a novel diagnostic and prognostic biomarker for osteosarcoma.

Retraction: SMARCB1 expression is a novel diagnostic and prognostic biomarker for osteosarcoma.

Long intergenic non-coding RNA DIO3OS promotes osteosarcoma metastasis via activation of the TGF-β signaling pathway: a potential diagnostic and immunotherapeutic target for osteosarcoma

BackgroundThe aim of this study was to determine the underlying potential mechanisms and function of DIO3OS, a lincRNA in osteosarcoma and clarify that DIO3OS can be used as a potential diagnostic biomarker and immunotherapeutic target.MethodsThe expression matrix data and clinical information were obtained from XENA platform of UCSC and GEO database as the test cohorts. The external validation cohort was collected from our hospital. Bioinformatics analysis was used to annotate the biological function of DIO3OS. Immune infiltration and immune checkpoint analysis were applied to evaluate whether DIO3OS can be used as an immunotherapeutic target. ROC curves and AUC were established to assess the diagnostic value of DIO3OS for differentiating patients from other subtypes sarcoma. The expression analysis was detected by qRT-PCR, western blot, and immunohistochemical. Wound healing assay and Transwell assay were applied to determine the migration and invasion function of DIO3OS in osteosarcoma cell lines. The tail vein injection osteosarcoma cells metastases model was used in this research.ResultsHigh expression of DIO3OS was identified as a risk lincRNA for predicting overall survival of osteosarcoma in test cohort. The outcomes of experiments in vitro and in vivo showed that low expression of DIO3OS limited osteosarcoma tumor metastasis with inhibiting TGF-β signaling pathway. Immune checkpoint genes (CD200 and TNFRSF25) expressions were inhibited in the low DIO3OS expression group. The DIO3OS expression can be applied to reliably distinguish osteosarcoma from lipomatous neoplasms, myomatous neoplasms, nerve sheath tumors, and synovial-like neoplasms. This result was further validated in the validation cohort.ConclusionsIn conclusion, our outcomes indicated that DIO3OS is a potential diagnostic and prognostic biomarker of osteosarcoma, emphasizing its potential as a target of immunotherapy to improve the treatment of osteosarcoma through TGF-β signaling pathway.Trial registration number: The present retrospectively study was approved by the Ethics Committee of The Second Affiliated Hospital of Nanchang University [Review (2020) No. (115)].

SLC17A9 expression levels in a pan‑cancer panel and validation of the role of SLC17A9 as a novel prognostic biomarker for osteosarcoma.

Previous studies have demonstrated the involvement of the solute carrier family 17 member 9 (SLC17A9) in certain types of cancer; however, the precise role of SLC17A9 is not well defined. In the present study, a comprehensive analysis was performed to determine the involvement of SLC17A9 in a pan-cancer panel. First, data on SLC17A9 expression levels from publicly available databases were obtained to determine SLC17A9 expression profiles in various types of cancer. Next, the involvement of SLC17A9 in the prognosis of patients, stemness indices and the immune microenvironment was examined in 34 types of cancer. Furthermore, CCK-8 and colony-formation assays were performed to determine the effect of SLC17A9 on osteosarcoma (OSS) cells. In a pan-cancer panel, a difference in SLC17A9 expression levels was observed in the tumor tissues as compared with healthy tissues. Furthermore, survival analysis revealed a significant association between SLC17A9 expression levels and the prognosis of patients with various cancer types, including adrenocortical carcinoma, kidney renal clear cell carcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, liver hepatocellular carcinoma, mesothelioma, lung adenocarcinoma, skin cutaneous melanoma, uveal melanoma, stomach adenocarcinoma and OSS. The results of the present study revealed correlations between stemness indices, tumor immunity and SLC17A9 expression levels. Furthermore, univariate and multivariate Cox regression analyses indicated that SLC17A9 may be utilized as an independent risk factor for overall survival of patients with OSS. In vitro experiments demonstrated that SLC17A9 promotes the proliferation and viability of OSS cells. Taken together, the results of the present study suggest an association between SLC17A9 and the prognosis of patients as well as tumor immunity in various cancer types. SLC17A9 may serve as a novel prognostic biomarker and target for improving the prognosis of patients with OSS.

ADAM19 and TUBB1 correlates with tumor infiltrating immune cells and predicts prognosis in osteosarcoma.

Osteosarcoma is the most common type of primary malignant bone tumor. This study aimed to explore potential key prognostic genes and their roles in osteosarcoma. Three microarray datasets for osteosarcoma were downloaded from GEO database. Differentially expressed genes (DEGs) were screened by Limma package. Functional enrichment analysis was performed based on DAVID, GeneMANIA, and Metascape databases. Prognostic value of DEGs was elevated by survival analysis. CIBERSORT was used to assess the infiltrating abundance of 22 immune cells, followed by the Pearson correlation analysis between immune cells and prognosis-related genes. Gene set enrichment analysis and drug-gene interactions prediction were performed for prognosis-related genes. A total of 8 common up-regulated DEGs and 13 common down-regulated DEGs were screened in GSE36001 and GSE56001 datasets. Enrichment analysis showed these DEGs were implicated in platelet activation, SMAD protein phosphorylation, lymphocyte/leukocyte/T cells activation, and cell migration. Survival analysis indicated that elevated expression of ADAM19 and TUBB1 were associated with favorable prognosis. CIBERSORT algorithm revealed higher infiltrating level of CD8 T cells, macrophages M0, and M2 in osteosarcoma. ADAM19 expression positively correlated with naïve B cells and negatively correlated with activated dendritic cells infiltrating abundance. TUBB1 expression positively correlated with gamma delta T cells while negatively correlated with helper follicular T cells infiltrating abundance. Total of 56 drugs were found to target TUBB1. ADAM19 and TUBB1 could be prognostic biomarkers in osteosarcoma. Both their expression correlates with tumor infiltrating immune cells. TUBB1 was a multi-drug target that might be a therapeutic target in osteosarcoma.

Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set

Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis. A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as "collagen catabolic process", "positive regulation of pathway-restricted SMAD protein phosphorylation", "osteoblast differentiation", "regulation of release of cytochrome c from mitochondria" and "interleukin-1 production". The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells. Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3.

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach. Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry. Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues. HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

Commentary on: SMARCB1 as a novel diagnostic and prognostic biomarker for osteosarcoma.

In the last couple of decades, biomarkers have been on the rise for diagnostic and predictive value. There has been a rush to identify new markers using new technologies and drug repurposing approaches. SMARCB1 acronym arises from the SWI/SNF (SWItch/Sucrose Non-Fermentable)-related Matrix-associated Actin-dependent Regulator of Chromatin subfamily B member 1 (SMARCB1). It is a molecule, whose role is associated with the sucrose metabolism. SMARCB1 is also called INI1 (Integrase Interactor 1). The molecule was discovered in the mid-1990s. Its role as a loss-of-function marker for malignant rhabdoid tumors (MRT) of renal and extrarenal origin has enormously expanded the spectrum of involved neoplasms since that time. Several tumors have been characterized by genetic aberrations in the SMARCB1 gene. They include reduction in expression, loss of expression, and mosaic expression. Most of the tumors are sarcomas, but a variegated group of tumors with mixed phenotypes has also been delineated. It is well known that the outcome of patients harboring genetic aberrations in the SMARCB1 gene has been poor. Guo et al. reported that reduced SMARCB1 expression occurred in 70% of osteosarcomas. Their data significantly correlated with poor neoadjuvant response. These authors emphasize a shorter progression-free and overall survival of the patients demonstrating an altered expression of this gene. Interestingly, mRNA in silico analysis established that SMARCB1 expression correlates with the response to chemotherapy of osteosarcoma patients, but there was no reliable correlation between SMARCB1 expression level and metastasis, response to neoadjuvant therapy, overall survival, and progression-free survival. The study involved a tissue microarray (TMA) on bone tumors that may limit the full evaluation of the gene expression. Nevertheless, Guo et al.’s study is remarkable. It expands the list of the tumors harboring an altered SMARCB1 gene expression and suggests that this marker should be investigated in every pathology workup for potential predictive value. On the other side, much work needs to be done if we hope that we strive to provide additional therapeutic strategies for osteosarcoma patients with altered SMARCB1 gene expression.

Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets

Osteosarcoma (OS) is one of the most common primary bone malignant tumors. Osteoclasts have been shown to have a valuable role in OS. In the present study, we analyzed the differentiation states of osteoclasts in OS and their prognostic significance based on integrated scRNA-seq and bulk RNA-seq data. Osteoclasts in distinct differentiation states were characterized, and 661 osteoclasts differentiation-related genes (ODRGs) were obtained. ORDGs in distinct differentiation states were enriched in distinct functions and pathways. TPM1, S100A13, LOXL1, PSMD10, ST3GAL4, PEF1, SERPINE2, TUBB, FAM207A, TUBA1A, and DCN were identified as the significant survival-predicting ODRGs. We successfully developed a risk score model based on these survival-predicting ODRGs. In addition, we generated a nomogram applicable for clinical with both ODRGs signatures and clinicopathological parameters, and validated in OS cohorts to predict OS patient outcome. This study proposed and verified the important roles of osteoclasts differentiation in the prognosis of patients with OS, suggesting promising therapeutic targets for OS.

Identification of LTF as a Prognostic Biomarker for Osteosarcoma.

Osteosarcoma remains a major health problem in teenagers. However, its pathogenesis mechanism remains not fully elucidated. This study aims to identify the prognostic biomarkers for osteosarcoma. In this study, we selected genes with a median absolute deviation (MAD) value of the top 5000 in the GSE32981 dataset for subsequent analysis. Weighted correlation network analysis (WGCNA) was used to construct a coexpression network. WGCNA showed that the tan module and midnight blue module were highly correlated with origin and metastases of osteosarcoma, respectively. Enrichment analysis was conducted using genes in the tan module and midnight blue module. A gene coexpression network was constructed by calculating the Spearman correlation coefficients. Four key genes (LTF, C10orf107, HIST1H2AK, and NEXN) were identified to be correlated with the prognosis of osteosarcoma patients. LTF has the highest AUC value, and its effect on osteosarcoma cells was then evaluated. The effect of LTF overexpression on proliferation, migration, and invasion of MG63 and 143B cells was detected by the CCK-8 assay, transwell cell migration assay, and transwell invasion assay, respectively. The overexpression of LTF promoted the proliferation, migration, and invasion of MG63 and 143B cells. In conclusion, LTF may serve as a prognostic biomarker for osteosarcoma.

N6-Methyladenosine-Related LncRNAs Are Potential Remodeling Indicators in the Tumor Microenvironment and Prognostic Markers in Osteosarcoma.

N6-Adenosine methylation, yielding N6-methyladenosine (m6A), is a reversible epigenetic modification found in messenger RNAs and long non-coding RNAs (lncRNAs), which affects the fate of modified RNA molecules and is essential for the development and differentiation of immune cells in the tumor microenvironment (TME). Osteosarcoma (OS) is the most common primary bone tumor in children and adolescents, and is characterized by high mortality. Currently, the possible role of m6A modifications in the prognosis of OS is unclear. In the present study, we investigated the correlation between m6A-related lncRNA expression and the clinical outcomes of OS patients via a comprehensive analysis. Clinical and workflow-type data were obtained from the Genotype-Tissue Expression Program and The Cancer Genome Atlas. We examined the relationship between m6A modifications and lncRNA expression, conducted Kyoto Encyclopedia of Genes analysis and also gene set enrichment analysis (GSEA), implemented survival analysis to investigate the association of clinical survival data with the expression of m6A-related lncRNAs, and utilized Lasso regression to model the prognosis of OS. Furthermore, we performed immune correlation analysis and TME differential analysis to investigate the infiltration levels of immune cells and their relationship with clinical prognosis. LncRNA expression and m6A levels were closely associated in co-expression analysis. The expression of m6A-related lncRNAs was quite low in tumor tissues; this appeared to be a predicting factor of OS in a prognostic model, independent of other clinical features. The NOD-like receptor signaling pathway was the most significantly enriched pathway in GSEA. In tumor tissues, SPAG4 was overexpressed while ZBTB32 and DEPTOR were downregulated. Tissues in cluster 2 were highly infiltrated by plasma cells. Cluster 2 presented higher ESTIMATE scores and stromal scores, showing a lower tumor cell purity in the TME. In conclusion, m6A-related lncRNA expression is strongly associated with the occurrence and development of OS, and can be used to as a prognostic factor of OS. Moreover, m6A-related lncRNAs and infiltrating immune cells in the TME could serve as new therapeutic targets and prognostic biomarkers for OS.

SMARCB1 expression is a novel diagnostic and prognostic biomarker for osteosarcoma.

Background: Although weak SWI/SNF related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 (SMARCB1) expression is a known diagnostic and prognostic biomarker in several malignancies, its expression and clinical significance in osteosarcoma remain unknown. The aim of the present study was to investigate SMARCB1 expression in osteosarcoma and its clinical significance with respect to chemosensitivity and prognosis.Methods: We obtained 114 specimens from 70 osteosarcoma patients to construct a tissue microarray (TMA) and assess SMARCB1 protein expression via immunohistochemistry (IHC). The mRNA expression of SMARCB1 was in-silico analyzed using open-access RNA sequencing (RNA-Seq) and clinicopathological data provided by the Therapeutically Applicable Research to Generate Effective Treatments on Osteosarcoma (TARGET-OS) project. The correlations between SMARCB1 expression and clinical features were statistically analyzed.Results: Weak SMARCB1 expression occurred in 70% of the osteosarcoma patient specimens in the TMA, and significantly correlated with poor neoadjuvant response as well as shorter overall and progression-free survival (PFS). In addition, mRNA in-silico analysis confirmed that SMARCB1 expression correlates with chemotherapeutic response and prognosis in osteosarcoma patients.Conclusion: To our knowledge, the present study is the first to analyze SMARCB1 expression in osteosarcoma. SMARCB1 may serve as a novel diagnostic and prognostic biomarker in osteosarcoma.

N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma.

Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct the risk model and assess whether the risk score was an independent prognostic factor for patients with OS. Gene Set Enrichment Analysis (GSEA) was performed to analyze the functions of genes in high-risk and low-risk groups. StarBase and Cytoscape were used to construct a competing endogenous RNA (ceRNA) network based on m6A-related prognostic lncRNA signature. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of genes involved in the ceRNA network. Results: We extracted 122 common lncRNAs from TCGA and Gene Expression Omnibus (GEO) databases. Pearson correlation results revealed 59 significant m6A-related lncRNAs in The Cancer Genome Atlas (TCGA) database, from which 2 were screened to construct a risk signature in TCGA dataset, which was then validated in the GEO dataset. A corresponding risk score was calculated and shown to be an independent prognostic factor for patients with OS. Enrichment analysis indicated that cell proliferation-related biological processes were more common in the high-risk group, while immune-related biological processes were more common in the low-risk group. Moreover, we established a nomogram that had a good ability to predict the overall survival of patients with OS. Additionally, a ceRNA network based on small nucleolar RNA host gene 7 (SNHG7) and small nucleolar RNA host gene 12 (SNHG12) was constructed, with genes that were enriched in hepatocellular carcinoma, gastric cancer, and non-small-cell lung cancer pathways. Conclusion: Our study revealed the prognostic role of m6A-related lncRNAs in OS and identified SNHG7 and SNHG12 as potential biomarkers for predicting the prognosis of patients with OS. These findings have enriched our understanding of the role of m6A modification in the dysregulation of lncRNAs in OS.

Abstract 672: Prognostic significance of urokinase plasminogen activator system in a naturally occurring model of osteosarcoma

Abstract Introduction Human pediatric and canine osteosarcoma (OSA) are both highly metastatic primary bone cancers that share molecular and clinical similarities. Urokinase plasminogen activator (uPA) and its receptor uPAR promote invasion, metastasis and angiogenesis in multiple cancer types and have been shown to play critical roles in malignant characteristics of OSA in vitro, yet their clinical significance has not been investigated in OSA patients. Methods Serum samples of dogs with naturally-occurring appendicular OSA (n=26) and clinically healthy dogs (n=6) were analyzed using commercially available canine-specific ELISA and uPA activity kits. Dogs with OSA that presented without metastatic disease at diagnosis and received treatment with amputation plus adjuvant carboplatin chemotherapy were included. Survival data and patient characteristics were obtained from medical records. Differences in serum uPA/uPAR results between the groups was tested by t test or Wilcoxan rank sum test. Kaplan Meier survival curve and log rank test were used for survival analysis in the OSA cohort after subgrouping by X-Tyle software. Well-established prognostic factors and serum uPA/uPAR results were tested for prognostic significance by cox regression analysis in both univariate and multivariate analyses. Results Dogs with OSA had significantly higher serum uPA (p=0.008; 1905.4 vs 1439.5 pg/mL) but not uPAR (p=0.866) levels compared to healthy dogs. OSA-bearing dogs with high serum uPA had significantly shorter progression-free (p=0.003; 94 vs 266 days) and overall survival (p=0.01; 123 vs 338 days) than dogs with low uPA level. Similarly, dogs with high serum uPA activity had significantly shorter progression-free survival (p=0.043; 227 vs 266 days). Serum uPAR was not prognostic in this cohort. Multivariate analysis revealed increased serum uPA level (p=0.024) as a poor prognostic indicator in addition to other established prognostic factors in dogs such as humeral location (p=0.022) and increased monocyte count (p=0.015). Conclusions Circulating uPA quantity and activity are increased in canine OSA patients with more aggressive disease, indicating importance of the uPA system in OSA disease progression. While an OSA-associated increase in circulating uPAR was not evident, this receptor likely contributes to OSA malignancy subsequent to the increased uPA ligand production. Serum uPA may be an attractive prognostic biomarker in OSA and the up-regulated uPA/uPAR axis should be further investigated as a potential therapeutic target for this disease. Citation Format: Arata Matsuyama, Anthony J. Mutsaers. Prognostic significance of urokinase plasminogen activator system in a naturally occurring model of osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 672.

A panel of eight mRNA signatures improves prognosis prediction of osteosarcoma patients.

Genetic alterations are vital to the progression of osteosarcoma carcinoma. The present study investigated a panel of gene signatures that could evaluate prognosis in osteosarcoma based on data from the Therapeutically Applicable Research To Generate Effective Treatments initiative. Osteosarcoma messenger RNA (mRNA) profiles and clinical data were downloaded from the therapeutically applicable research to generate effective treatments database. Patients with osteosarcoma were divided into two groups based on findings at diagnosis: with and without metastasis. Differentially expressed mRNAs were compared and analyzed between groups. Univariate and multivariate Cox regression analyses identified a set of eight mRNAs with the ability to classify patients into high-risk and low-risk groups with significantly different overall survival times. Further analysis indicated that the eight-mRNA signature was an independent prognostic factor after adjusting for other clinical factors. Receiver operating characteristic curve analysis demonstrated a good performance of the eight-mRNA signature. Further, the biological processes and signaling pathways of the eight-mRNA signature were reviewed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes resources. Finally, the results of the TCGA analysis were verified by other cohorts from Gene Expression Omnibus database. The identification of an eight-mRNA signature not only provides a prognostic biomarker of osteosarcoma but also offers the potential of novel therapeutic targets for its treatment.

Increased expression of microRNA-26a-5p predicted a poor survival outcome in osteosarcoma patients: An observational study.

MicroRNA (miR)-26a-5p is an oncogene significantly associated with osteosarcoma. We try to evaluate expression of circulating miR-26a-5p in osteosarcoma patients and evaluate its significance.A total of 243 consecutive osteosarcoma patients and 96 healthy participates were enrolled. Circulating miR-26a-5p levels were evaluated by using real-time quantitative reverse transcription polymerase chain reactions (RT-PCR). The association between circulating miR-26a-5p level and survival outcomes was evaluated by univariate and multivariate analysis.Circulating miR-26a-5p levels in osteosarcoma patients was significantly higher than that of healthy volunteers (P < .05). Upregulated miR-26a-5p was significantly related to advanced cancer and metastasis (both P < .05). Moreover, patients with a high serum miR-26a-5p had a poorer overall survival than those with a low serum miR-26a-5p levels (P < .05). Circulating miR-26a-5p level also been showed as independent risk factor for osteosarcoma in multivariate analysis (hazard ratio [HR], 0.38; 95% confidence interval [CI]: 0.11–0.98; P < .01).Circulating miR-26a-5p was significantly upregulated in osteosarcoma patients and remarkably associated with poor prognosis, indicating that circulating miR-26a-5p might serve as a useful diagnostic and prognostic biomarker for osteosarcoma.