Prognosis Of Osteosarcoma Research Articles

Abstract B003: Dogs with cancer have higher plasma exosome concentrations compared with healthy dogs in the COED (canine osteosarcoma early detection) study

Abstract Background: Osteosarcoma a common cancer in dogs that can serve as a translational model for certain aspects of human osteosarcoma. We previously developed a serum exosome gene signature in dogs with osteosarcoma capable of detecting minimal residual disease and predicting prognosis after therapy. Our goal is to continue expanding our understanding of exosomes for liquid biopsy applications in dogs with cancer, and to leverage this information in the design of liquid biopsy tests to address risk, early detection, and management of human cancer patients. For this study, we hypothesized that dogs with cancer would have higher plasma exosome concentrations compared with healthy control dogs. Methods: Plasma exosomes were enriched from 29 dogs with cancer, including 8 dogs with osteosarcoma, and 26 healthy control dogs. Exosome size and concentration in the plasma samples were characterized with nanoparticle tracking analysis. Wilcoxon rank sum tests were performed to assess differences between groups. Results: Dogs with cancer had a higher mean exosome concentration at 1.4 x 1012 (+/- 1.1 x 1012) particles/ml, compared with 5.0 x 1011 (+/- 2.7 x 1011) particles/ml in healthy control dogs (p = 0.0046). There was no difference in exosome size between dogs with cancer (mean diameter = 99.9 nm) and healthy control dogs (107.8 nm; p = 0.16) when all cancer types were included. However, exosomes enriched from healthy control samples were found to be significantly larger when compared to the exosomes enriched from the subset of cancer dogs that had osteosarcoma (mean exosome diameter = 83.9 nm; p = 0.0065). Similarly, when comparing exosome size between dogs with osteosarcoma and dogs with other cancers, plasma exosomes were larger in the dogs with other cancers (107.4 nm; p = 0.023). Conclusions: In this population, dogs with cancer have higher plasma exosome concentrations compared with healthy control dogs. Interestingly, we also found that dogs with osteosarcoma had significantly smaller exosome size than dogs with other cancers and healthy control dogs. Though the size of exosomes from dogs with osteosarcoma was found to be smaller, it is possible that the larger exosome concentration in dogs with osteosarcoma results in a similar total exosomal mass. Ongoing work includes analysis of exosomal cargo to develop a liquid biopsy test capable of assessing risk of osteosarcoma development in dogs, and applying similar methodology to determine exosomal genes associated with prognosis in pediatric osteosarcoma. The higher exosome concentrations in dogs with cancer suggests that this feature, combined with analysis of exosomal cargo, may have utility in development of a liquid biopsy cancer test in dogs. Citation Format: Kelly M. Makielski, Jaron M. Magstadt, Courtney H. Labe, Ali Khammanivong, Meagan Wojtysiak, Kyle Duval, Mitzi Lewellen, Amber Winter, Kelly Reid, Andrea Chehadeh, Michelle Buettner, Caitlin Feiock, Aaron K. Rendahl, Gary R. Cutter, Logan G. Spector, Brenda J. Weigel, Jaime F. Modiano. Dogs with cancer have higher plasma exosome concentrations compared with healthy dogs in the COED (canine osteosarcoma early detection) study [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B003.

The Interplay Between the MYC Oncogene and Ribosomal Proteins in Osteosarcoma Onset and Progression: Potential Mechanisms and Indication of Candidate Therapeutic Targets

High-grade osteosarcoma (OS) is the most common primary bone tumor mainly affecting children and young adults. First-line treatment consists of neo-adjuvant chemotherapy with doxorubicin, cisplatin, and methotrexate and surgery. The mean long-term survival rate for localized disease at diagnosis is 65–70%, dropping down to 20% when metastases are present at diagnosis. Therefore, curing OS is a clinical challenge, particularly for patients that do not respond to standard treatments. MYC has frequently been reported to be involved in the pathogenesis of OS and its high expression may be associated with drug resistance and patients’ worse prognosis. Moreover, MYC is a master regulator of ribosomal proteins (RPs) synthesis and ribosome biogenesis (RiBi), which is often up-regulated in human tumors. In recent years, RPs have been recognized not only for their traditional role in ribosome assembly but also for their extra-ribosomal functions, many of which are linked to the onset and progression of cancer. In this review we focus on the role and possible interplay of MYC and RPs expression in association with drug resistance and worse prognosis in OS and discuss therapeutic options that target de-regulated MYC, RiBi, or RPs, which are already clinically available or under evaluation in clinical trials.

Development and validation of a novel endoplasmic reticulum stress-related lncRNAs signature in osteosarcoma

Osteosarcoma (OS) is a cancerous tumor, and its development is greatly influenced by long non-coding RNA (lncRNA). Endoplasmic reticulum stress (ERS) is an essential biological defense process in cells and contributes to the progression of tumors. However, the exact mechanisms remain elusive. This study aims to develop a signature of lncRNAs associated with ERS in OS. This signature will guide the prognosis prediction and the determination of appropriate treatment strategies. The UCSC Xena database collected transcriptional and clinical data of OS and muscle, after identifying ERS differentially expressed genes, we utilized correlation analysis to determine the endoplasmic reticulum stress lncRNAs (ERLs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analysis were utilized to develop an ERLs signature. To clarify the fundamental mechanisms controlling gene expression in low and high-risk groups, Gene Set Variation Analysis (GSVA) were conducted. In addition, the distinction between the two groups regarding drug sensitivity and immune-related activity was investigated to determine the immunotherapy effects. Utilizing RT-qPCR, the expression of model lncRNAs in OS cell lines was ascertained. The functional analysis of LINC02298 was carried out through in vitro experiments and pan-cancer analysis. This study successfully constructed an ERLs prognostic signature for OS, which comprised 5 lncRNAs (AC023157.3, AL031673.1, LINC02298, LINC02328, SNHG26). The risk signature predicted overall survival in patients with OS and was confirmed by assessing the validation and whole cohorts. Further, it was discovered that individuals classified as high-risk displayed suppressed immune activation, decreased infiltration of immune cells, and decreased responsiveness to immunotherapy. The RT-qPCR showed that the constructed risk prognosis model is reliable. Experimental validation has demonstrated that LINC02298 can promote OS cells’ invasion, migration, and proliferation. In addition, LINC02298 exhibited significant differential expression in many types of cancer. Moreover, LINC02298 is an important biomarker in a variety of tumors. This study established a novel ERLs signature, which successfully predicted the prognosis of OS. The function of LINC02298 in OS was elucidated via in vitro experiments. Therefore, it offers new opportunities for predicting the clinical prognosis of OS and establishes the basis for targeted therapy in OS.

Integrated immunogenomic analyses of single-cell and bulk profiling construct a T cell-related signature for predicting prognosis and treatment response in osteosarcoma

PurposesT cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear.MethodsFirst, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups.ResultsFrom the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis.ConclusionIn conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.

Tumor necrosis drives prognosis in osteosarcoma: No difference in chemotherapy response and survival between chondroblastic and osteoblastic osteosarcoma

IntroductionThe percentage of tumor necrosis is a crucial prognostic factor in osteosarcoma. Many studies adopt a 90 % cutoff based on osteoblastic osteosarcoma, but these findings are generalized to all conventional subtypes, including chondroblastic osteosarcoma. We sought to answer these questions: (1) Is tumor necrosis ≥90 % associated with better overall survival (OS) and disease-free survival (DFS) in osteoblastic and chondroblastic osteosarcoma? (2) Does the osteosarcoma subtype impact tumor necrosis? (3) Does the osteosarcoma subtype in “good” responders (tumor necrosis ≥90 %) affect OS and DFS?. Materials and methodsWe conducted a retrospective study of 156 patients with osteoblastic and chondroblastic osteosarcoma treated at our institution. All patients received a standardized chemotherapy protocol and underwent surgery with the goal of achieving negative margins (R0 resection). Propensity-score matching was performed to adjust for potential confounders. Kaplan-Meier survival analysis and Cox proportional hazards modeling were performed. ResultsPatients with osteoblastic osteosarcoma and tumor necrosis ≥90 % had higher 5- and 10-year OS and DFS compared to those with necrosis <90 %. In chondroblastic osteosarcoma, a trend towards higher OS and DFS was seen in patients with tumor necrosis ≥90 %; this, however, was not significant. Chondroblastic osteosarcoma was not a risk factor for either tumor necrosis <90 % (p = 0.89) or tumor necrosis <70 % (p = 0.57). Patients with osteoblastic or chondroblastic osteosarcoma that were deemed “good” responders (tumor necrosis ≥90 %) had similar OS and DFS at the 5- and 10-year marks. ConclusionConventional osteosarcoma subtype was not a risk factor for “poor” response. Survival outcomes (OS and DFS) were similar for osteoblastic and chondroblastic osteosarcoma with good response to chemotherapy.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type’s preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs’ risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Disulfiram Upgrades the Radiosensitivity of Osteosarcoma by Enhancing Apoptosis and P53-Induced Cell Cycle Arrest.

The prognosis of osteosarcoma has not been improved for decades. As radioresistance is one of the major reasons, effective radiotherapy sensitization drugs need to be discovered. HOS and K7M2 osteosarcoma cell lines were treated with disulfiram (DSF) and radiation to assess cell viability, proliferation, migration ability, apoptosis level, ROS and Ca2+ level, and cell cycle in vitro. A HOS-derived subcutaneous tumor mouse model was constructed to evaluate tumor growth after DSF combined with radiation, and the Tunel assay and immunohistochemistry of Ki67 were conducted. Western blot was used to evaluate the protein expression level. The IC50 and working concentration of DSF in osteosarcoma cell lines were ascertained. When combined with radiation, DSF effectively suppressed cell viability, proliferation, and migration, while enhancing apoptosis in osteosarcoma cells. The cell cycle postirradiation exhibited a downward shift in the G1 phase, but the addition of DSF counteracted this trend. The combination of DSF and radiation exhibited inhibitory effects on tumor growth invivo, which was corroborated by Ki67 staining and Tunel assay. Western blot analysis revealed that DSF upregulated the expression of P53, P21, CDKN2C, BAX, and cleaved Caspase-3 while downregulating BCL2, CDK4/6, and CyclinD1 after irradiation. Our results document that DSF exerts its radiosensitization effects invivo and in vitro, and is a valuable radiosensitizing drug option for osteosarcoma. The radiosensitization effect is mainly achieved by activating the apoptotic pathway and promoting cell cycle arrest induced by P53/P21 and CDKN2C after irradiation.

MiRNA encoded PTEN’s impact on clinical-pathological features and prognosis in osteosarcoma: A systematic review and meta-analysis

Background Osteosarcoma (OSC) is considered one of the most common malignant bone tumours in adolescents. Due to OSC’s poor prognosis, a comprehensive approach to exploring these aspects is highly needed to improve the survival probability of OSC. In this study, we tried to explore the significance of miRNA-encoded PTEN for clinical-pathological features and prognostic value in OSC. Method We performed this systematic review and meta-analysis using articles and sources published between 2013 and 2023 from six databases (Scopus, PubMed, ProQuest, Science Direct, Sage Pub, and Cochrane). Included studies were clinical cross-sectional studies. Other study designs, articles not written in English, without full text, and not relevant—were excluded. Then, ROBINS-I is used to evaluate the distance. The results are constructed according to the PICOS criteria in a table. The expression of miRNA related to OSC is assessed in the meta-analysis as the main outcome to determine its ability as a diagnostic and prognostic agent for OSC. This systematic review followed the PRISMA guidelines. Results A total of 17 studies were included in the final screening. The meta-analysis showed significantly increased (p &lt; 0.00001) miRNA expression in patients with OSC compared to healthy controlled with pooled md (2.85) (95% CI: 2.69, 3.02; I2 = 22%, p = 0.20), the high inverse correlation (p &lt; 0.001) between miRNA and PTEN expression was shown as mean effect size (-0.681) (95% CI: -0.787, -0.536; I2 = 75%, p &lt; 0.0001), and the prognostic evaluation of OS was significantly increased in low expression miRNA (p &lt; 0.00001) with pooled OR. Conclusion Fifteen miRNAs from 17 studies were found, and together with PTEN expression, they may serve as potential prognostic biomarkers for OSC. High-level levels of miRNA expression are correlated with low PTEN expression, leading to a bad prognosis for OSC.

GNAS, not a Highly Mutated Gene, Has Prognostic Significance and Carcinogenic Effects in Osteosarcoma.

Osteosarcoma is a prevalent and aggressive primary malignant bone tumor affecting children and adolescents. Despite advancements in sequencing technologies, there remains a lack of reliable prognostic biomarkers and effective targeted therapies for osteosarcoma. This study focuses on identifying key prognostic genes, particularly the role of GNAS, in osteosarcoma progression. Bioinformatics analyses were performed on osteosarcoma datasets from the Gene Expression Omnibus (GEO). Differential gene expression analysis, weighted correlation network analysis (WGCNA), and survival analysis identified potential prognostic hub genes. The expression and function of these genes were validated through immunohistochemistry and animal experiments. Specifically, the role of GNAS was investigated through siRNA-mediated knockdown in osteosarcoma cell lines and nude mice models. Five hub genes (PROP1, GNAS, CYP4F2, LHX3, CNGB1) were identified as significantly related to osteosarcoma prognosis. Among these, GNAS was found to be highly expressed in osteosarcoma tissues compared to normal tissues. Immunohistochemical analysis confirmed the elevated expression of GNAS in osteosarcoma samples. GNAS mutation analysis revealed a low mutation rate in osteosarcoma, suggesting its oncogenic role is independent of mutational status. Animal experiments demonstrated that knocking down GNAS significantly inhibited tumor growth and induced apoptosis in osteosarcoma cells. GNAS is highly expressed in osteosarcoma and associated with poor prognosis, acting as an oncogene in osteosarcoma progression. Targeting GNAS could be a potential therapeutic strategy for osteosarcoma. Further studies on GNAS-related signaling pathways may provide deeper insights into the molecular mechanisms driving osteosarcoma malignancy.

Multi-omics and Single Cell Sequencing Analyses Reveal Associations of Mitophagy-Related Genes Predicting Clinical Prognosis and Immune Infiltration Characteristics in Osteosarcoma.

Despite recent advances in clinical treatments, identifying high-risk osteosarcoma (OS) patients remains an unresolved clinical challenge. Mitophagy, a specialized form of cellular autophagy, selectively reduces the number of mitochondria or repairs their abnormal functions in response to external stress, thereby ensuring mitochondrial quality and maintaining mitochondrial function. Mitophagy plays a crucial role in cancer development, including processes such as mitochondrial repair, homeostasis maintenance, and tumor metabolism. However, its impact on OS has not yet been reported. In this study, we collected 58 mitophagy-related genes (MPRGs) from the TARGET and GEO databases and bioinformatically screened for those associated with OS prognosis. By LASSO-multivariable Cox regression algorithm, we subsequently developed a novel scoring system, the MPRG score, and validated its significance in predicting OS prognosis. Immune landscape analysis showed patients in the low MPRG group had a higher immune infiltration level than those in the high MPRG group. Drug sensitivity differences highlighted the potential need for alternative therapeutic strategies based on MPRG scoring system. The distribution characteristics of the MPRG signature in different cell subtypes of OS were explored by single-cell sequencing analyses. In vitro experiments further confirmed the abnormal expression of screened targets in OS. Our findings highlight the role of mitophagy in OS and its potential as a therapeutic target.

Development and experimental verification of novel angiogenesis related prognostic model and immune infiltration characterization in osteosarcoma

BackgroundAs the most common primary bone cancer, osteosarcoma (OS) still lacks satisfactory therapeutic outcomes. Therefore, it is crucial to further evaluate OS at different risk levels and identify new intervention targets. Many evidences suggest the important role of angiogenesis in OS, but further exploration is needed.MethodsWe utilized public databases TARGET and GEO and employed bioinformatics algorithms such as LASSO, univariate and multivariate Cox regression analyses, and unsupervised consensus clustering to explore the role of angiogenesis-related genes (AGRGs) in OS. By calculating AGRG scores, we further analyzed OS molecular subtypes based on AGRGs. The correlation between AGRG scores and immune infiltration was subsequently examined. In vitro experiments, including WB, PCR, siRNA, migration, and invasion assays, were used to determine the value of the selected targets for OS.ResultsUltimately, we established an OS prognosis model based on five AGRGs (COL5A2, CXCL6, FSTL1, NRP1, and TNFRSF21) that can independently validate prognosis levels. In vitro experiments confirmed the aberrant expression of CXCL6 in OS and its potential role in migration and invasion.ConclusionOur study reveals the impact of angiogenesis on OS from a novel perspective and provides potential intervention targets.

Comprehensive analysis of splicing factor SRs-related gene characteristics: predicting osteosarcoma prognosis and immune regulation status.

To investigate the impact of SRs-related genes on the overall survival and prognosis of osteosarcoma patients through bulk and single-cell RNA-seq transcriptome analysis. In this study, we constructed a prognosis model based on serine/arginine-rich splicing factors (SRs) and predicted the survival of osteosarcoma patients. By analyzing single-cell RNA sequencing data and applying AUCell enrichment analysis, we revealed oncogenic pathways of SRs in osteosarcoma immune cells. Additionally, we described the regulatory role of SRSF7 in pan-cancer. Lasso regression analysis identified 6 key SRs-related genes, and a prognosis prediction model was established. The upregulation of these pathways revealed that SRs promote tumor cell proliferation and survival by regulating related signaling pathways and help tumor cells evade host immune surveillance. Additionally, by grouping single-cell data using AUCell, we found significant differences in T cell expression between high and low-risk groups. The analysis results indicated that the regulatory activity of SRs is closely related to T cell function, particularly in regulating immune responses and promoting immune evasion. Furthermore, SRSF7 regulates cell proliferation and apoptosis. SRs-related genes play a critical regulatory role in osteosarcoma. T cells are key in regulating immune responses and promoting immune evasion through SRs genes. SRSF7 is a significant gene influencing the occurrence and development of osteosarcoma.

Constructing a Nomogram for Predicting Pelvic Osteosarcoma: A Retrospective Study Based on the SEER Database and a Chinese Cohort.

Aims/Background Generally, pelvic osteosarcoma has a worse prognosis compared with limb osteosarcoma. This study aims to create and validate a new nomogram for predicting the prognosis of pelvic osteosarcoma. Methods Clinical data of 62 patients derived from the Surveillance, Epidemiology, and End Results (SEER) database and 31 Chinese patients diagnosed with pelvic osteosarcoma were gathered. Kaplan-Meier survival analysis was utilized to calculate the median survival time for all variables. Univariate and multivariate Cox regression models were employed to identify the prognostic factors of pelvic osteosarcoma. A nomogram was constructed using data gleaned from the SEER cohort and verified using the receiver operating characteristic (ROC) curve and calibration plot in the Chinese cohort. Results Kaplan-Meier analysis revealed that individuals of other races (Asians) (hazard ratio (HR) = 0.24, 95% confidence interval (CI): 0.1-0.57, p = 0.001), aged ≤51 years old (HR = 0.4, 95% CI: 0.22-0.73, p = 0.003), and with tumor size ≤160 mm (HR = 0.37, 95% CI: 0.2-0.71, p = 0.03) had better survival outcomes. Conversely, factors such as no primary surgery (HR = 3.6, 95% CI: 1.81-7.15, p < 0.001), lung metastasis (HR = 1.96, 95% CI: 1.17-3.28, p = 0.010), and radiotherapy (HR = 1.89, 95% CI: 1.10-3.25, p = 0.021) were associated with poorer survival. Multivariate Cox analysis indicated that lung metastasis (HR = 2.57, 95% CI: 1.29-5.13, p = 0.008), other races (Asians) (HR = 0.23, 95% CI: 0.07-0.75, p = 0.015), tumor size (HR = 0.28, 95% CI: 0.13-0.62, p = 0.001) and age (HR = 0.3, 95% CI: 0.16-0.59, p < 0.001) were independent prognostic factors for pelvic osteosarcoma. Univariate and multivariate Cox regression models identified three independent variables in the training cohort: age, lung metastasis, and tumor size. A predictive nomogram was developed based on the data from the SEER cohort and validated in the Chinese cohort. The areas under the curves (AUCs) that are used to predict 1-year, 2-year, and 3-year survival rates were 0.81 (95% CI: 0.68-0.94), 0.75 (95% CI: 0.63-0.86), and 0.80 (95% CI: 0.70-0.89) in the training cohort, and 0.67 (95% CI: 0.30-1.04), 0.66 (95% CI: 0.43-0.90) and 0.71 (95% CI: 0.50-0.93) in the validation cohort. Conclusion The predictive nomogram constructed in this study facilitates accurate and effective prediction of the overall survival of patients with pelvic osteosarcoma and helps enhance the clinical decision-making process.

Targeted anti-angiogenesis therapy for advanced osteosarcoma.

To date, despite extensive research, the prognosis of advanced osteosarcoma has not improved significantly. Thus, patients experience a reduced survival rate, suggesting that a reevaluation of current treatment strategies is required. Recently, in addition to routine surgery, chemotherapy and radiotherapy, researchers have explored more effective and safer treatments, including targeted therapy, immunotherapy, anti-angiogenesis therapy, metabolic targets therapy, and nanomedicine therapy. The tumorigenesis and development of osteosarcoma is closely related to angiogenesis. Thus, anti-angiogenesis therapy is crucial to treat osteosarcoma; however, recent clinical trials found that it has insufficient efficacy. To solve this problem, the causes of treatment failure and improve treatment strategies should be investigated. This review focuses on summarizing the pathophysiological mechanisms of angiogenesis in osteosarcoma and recent advances in anti-angiogenesis treatment of osteosarcoma. We also discuss some clinical studies, with the aim of providing new ideas to improve treatment strategies for osteosarcoma and the prognosis of patients.

Identification of UNC5B as a novel aggressive biomarker for osteosarcoma based on basement membrane genes

BackgroundThe prognosis of patients with metastatic osteosarcoma is poor, and the variation of basement membrane genes (BMGs) is associated with cancer metastasis. However, the role of BMGs in osteosarcoma has been poorly studied. MethodsBMGs were collected and differentially expressed BMGs (DE-BMGs) were found through difference analysis. DE-BMGs were further screened by univariate Cox regression and Lasso regression analyses, and six key BMGs were identified and defined as basement membrane genes signatures (BMGS). Then, BMGS was used to construct the osteosarcoma BMGS risk score system, and the osteosarcoma patients were divided into high- and low-risk groups based on the median risk score. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE scores were used to investigate the differences in immune infiltration between the two scoring groups. Additionally, we investigated whether UNC5B affects various features in tumors by bioinformatic analysis and whether UNC5B was involved in multiple biological functions of osteosarcoma cells by wound healing assay, transwell assay, and western blot. ResultsThe osteosarcoma BMGS risk score reliably predicts the risk of metastasis, patient prognosis, and immunity. UNC5B expression was elevated in osteosarcoma, and correlated with various characteristics such as immune infiltration, prognosis, and drug sensitivity. In vitro assays showed that UNC5B knockdown reduced osteosarcoma cells’ capacity for migration and invasion, and EMT process. ConclusionA novel BMGS risk score system that can effectively predict the prognosis of osteosarcoma was developed and validated. The UNC5B gene in this system is one of the key aggressive biomarkers of osteosarcoma.

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signalling in OS and further constructed novel hypoxia-related gene signatures for OS prediction and prognosis. This study employed Gene Set Enrichment Analysis (GSEA), Weighted correlation network analysis (WGCNA) and Least absolute shrinkage and selection operator (LASSO) analyses to identify Stanniocalcin 2 (STC2) and Transmembrane Protein 45A (TMEM45A) as the diagnostic biomarkers, which further assessed by Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves in training and test dataset. Univariate and multivariate Cox regression analyses were used to construct the prognostic model. STC2 and metastasis were devised to forge the OS risk model. The nomogram, risk score, Kaplan Meier plot, ROC, DCA, and calibration curves results certified the excellent performance of the prognostic model. The expression level of STC2 and TMEM45A was validated in external datasets and cell lines. In immune cell infiltration analysis, cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. And the immune infiltration of CAFs was negatively associated with the expression of STC2 (P < 0.05). Pan-cancer analysis revealed that the expression level of STC2 was significantly higher in Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Lung squamous cell carcinoma (LUSC), and Stomach adenocarcinoma (STAD). Additionally, the higher expression of STC2 was associated with the poor outcome in those cancers. In summary, this study identified STC2 and TMEM45A as novel markers for the diagnosis and prognosis of osteosarcoma, and STC2 was shown to correlate with immune infiltration of CAFs negatively.

Surface Markers and Chemokines/Cytokines of Tumor-Associated Macrophages in Osteosarcoma and Other Carcinoma Microenviornments-Contradictions and Comparisons.

Osteosarcoma (OS) is the most common primary bone tumor in children and adolescents. Prognosis is improving with advances in multidisciplinary treatment strategies, but the development of new anticancer agents has not, and improvement in prognosis for patients with pulmonary metastases has stalled. In recent years, the tumor microenvironment (TME) has gained attention as a therapeutic target for cancer. The immune component of OS TME consists mainly of tumor-associated macrophages (TAMs). They exhibit remarkable plasticity, and their phenotype is influenced by the TME. In general, surface markers such as CD68 and CD80 show anti-tumor effects, while CD163 and CD204 show tumor-promoting effects. Surface markers have potential value as diagnostic and prognostic biomarkers. The cytokines and chemokines produced by TAMs promote tumor growth and metastasis. However, the role of TAMs in OS remains unclear to date. In this review, we describe the role of TAMs in OS by focusing on TAM surface markers and the TAM-produced cytokines and chemokines in the TME, and by comparing their behaviors in other carcinomas. We found contrary results from different studies. These findings highlight the urgency for further research in this field to improve the stalled OS prognosis percentages.

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma.

Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS. We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis. A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes. We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.

Interaction of ncRNAs and the PI3K/AKT/mTOR pathway: Implications for osteosarcoma.

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents, and is characterized by high heterogeneity, high malignancy, easy metastasis, and poor prognosis. Recurrence, metastasis, and multidrug resistance are the main problems that limit the therapeutic effect and prognosis of OS. PI3K/AKT/mTOR signaling pathway is often abnormally activated in OS tissues and cells, which promotes the rapid development, metastasis, and drug sensitivity of OS. Emerging evidence has revealed new insights into tumorigenesis through the interaction between the PI3K/AKT/mTOR pathway and non-coding RNAs (ncRNAs). Therefore, we reviewed the interactions between the PI3K/AKT/mTOR pathway and ncRNAs and their implication in OS. These interactions have the potential to serve as cancer biomarkers and therapeutic targets in clinical applications.