Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma.

Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Development of nomogram and discussion of radiotherapy effect for osteosarcoma survival.

This study aimed to develop a predictive system for prognostic evaluation of osteosarcoma patients. We obtained osteosarcoma sample data from 1998 to 2016 using SEER*Stat software version 8.3.8, and established a multivariable Cox regression model using R-4.0.3 software. Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The diagnosis of the model was completed through influential cases, proportionality, and multicollinearity. The predictive ability of the model was tested using area under the curve (AUC), calibration curves, and Brier scores. Finally, the bootstrap method was used to internally verify the model. In total, data from 3566 patients with osteosarcoma were included in this study. The multivariate Cox regression model was used to determine the independent prognostic variables. A nomogram and Kaplan-Meier survival curve were established. The AUC and Brier scores indicated that the model had a good predictive calibration. In addition, we found that the radiotherapy appears to be a risk factor of patients with osteosarcoma and made a discussion. We developed a prognostic evaluation system for patients with osteosarcoma for 1-, 3-, and 5-year overall survival with good predictive ability using sample data extracted from the SEER database. This has important clinical significance for the early identification and treatment of high-risk groups of osteosarcoma patients.

Pleckstrin homology-like domain family A, member 3, a miR-19a-3p-regulated gene, suppresses tumor growth in osteosarcoma by downregulating the Akt pathway.

Pleckstrin homology-like domain family A, member 3 (PHLDA3), is emerging as a critical regulator for multiple cancers. Nevertheless, the expression and role of PHLDA3 in osteosarcoma remain unknown. Herein, we purposed to elucidate the role of PHLDA3 in the progression and chemoresistance of osteosarcoma. According to the bioinformatics analysis, PHLDA3 expression was low in osteosarcoma patients, and low content was linked to poor prognosis. Additionally, activation of PHLDA3 suppressed osteosarcoma cell proliferation, migration, and chemoresistance, whereas PHLDA3 inhibition caused the opposite effects. Mechanistically, our data revealed that PHLDA3 negatively regulates the Akt/GSK3ß signaling cascade in osteosarcoma. Furthermore, we found that miR-19a-3p might exert its oncogenic function by inhibiting PHLDA3 expression in osteosarcoma. These results demonstrated miR-19a-3p/ PHLDA3/ Akt/GSK3ß axis has a pivotal role in osteosarcoma, and PHLDA3 is a prospective therapeutic target for treating osteosarcoma.

Carbon-ion Radiotherapy for Inoperable Head and Neck Bone and Soft-tissue Sarcoma: Prospective Observational Study.

BACKGROUND/AIM: Bone and soft-tissue sarcomas of the head and neck have very poor prognoses. This prospective study aimed to investigate the efficacy and safety of carbon-ion radiotherapy (C-ion RT) for bone and soft-tissue sarcoma of the head and neck. PATIENTS AND METHODS: The present study was a prospective clinical study that included 10 consecutive patients diagnosed with bone and soft-tissue sarcoma of the head and neck who were treated with C-ion RT between 2012 and 2018 at our institution. C-Ion RT consisted of 70.4 Gy (relative biological effectiveness) in 16 fractions. RESULTS: The 3-year local control, overall survival, and progression-free survival rates for patients overall were 72.9%, 77.8%, and 36%, respectively. CONCLUSION: The present study demonstrated the efficacy of C-ion RT for bone and soft-tissue sarcoma of the head and neck; adverse events were within the expected range.

Predictors and Treatment Outcomes of Pediatric Osteosarcoma in Diverse Socioeconomic Backgrounds in Southeast Asia: A Retrospective Multicenter Study.

BACKGROUND: Pediatric osteosarcoma outcomes among developed and developing countries have not been previously compared. Countries in Southeast Asia (SEA) have a wide variety of socioeconomic statuses. A multi-institutional retrospective study was conducted to determine the prognostic factors and outcomes for pediatric osteosarcoma in SEA. METHODS: Pediatric patients with osteosarcoma treated between 1998 and 2017 in 4 SEA pediatric oncology centers were studied. Countries were classified using the World Bank Atlas method. Kaplan-Meier method and Cox's Proportion Hazard Model were applied to estimate survival outcomes and identify prognostic factors. RESULTS: In all, 149 patients with osteosarcoma with a mean age of 12.48±3.66 years were enrolled. The localized to metastatic disease ratio was 1.5:1. The 5-year overall survival (OS) and event-free survival (EFS) were 53.8% and 42%, respectively. Prognostic factors associated with outcomes were country, stage of disease, MTX-containing regimens, and surgery type (p-value <0.05). In patients with localized disease, EFS was superior with limb-salvage surgery (62%) than amputation or rotationplasty (40%) (p-value 0.009). MTX-containing chemotherapies provided higher OS (45.3%) and EFS (37.9%) than non-MTX regimens (12.3% and 10.7%, respectively) among metastatic patients (p-value 0.004 and 0.005, respectively). Metastatic disease was an independent prognostic factor for death but not relapse outcome.  Conclusion: The disease outcomes in SEA were acceptable compared to developed countries. The stage of disease was the only independent prognostic factor. MTX-containing regimens and limb-salvage surgery should be considered where possible.

Significance of the neutrophil-to-lymphocyte ratio in predicting the response to neoadjuvant chemotherapy in extremity osteosarcoma: a multicentre retrospective study.

BACKGROUND: At present, no predictive factor has been validated for the early efficacy of neoadjuvant chemotherapy (NACT) in osteosarcoma. The purpose of this study was to investigate the significance of the neutrophil-to-lymphocyte ratio (NLR) in predicting the response to NACT in extremity osteosarcoma. METHODS: Pathological complete response (pCR) was used to assess the efficacy of NACT. Receiver operating characteristic (ROC) curves and the Youden index (sensitivity + specificity-1) were used to determine the optimal cut-off values of the NLR. Univariate and multivariate analyses using logistic regression models were conducted to confirm the independent factors affecting the efficacy of NACT. RESULTS: The optimal NLR cut-off value was 2.36 (sensitivity, 80.0%; specificity, 71.3%). Univariate analysis revealed that patients with a smaller tumour volume, lower stage, lower NLR and lower PLR were more likely to achieve pCR. Multivariate analyses confirmed that the NLR before treatment was an independent risk factor for pCR. Compared to patients with a high NLR, those with a low NLR showed a more than 2-fold higher likelihood of achieving pCR (OR 2.82, 95% CI 1.36-5.17, p = 0.02). CONCLUSION: The NLR is a novel and effective predictive factor for the response to NACT in extremity osteosarcoma patients. Patients with a higher NLR showed a lower percentage of pCR after NACT.

The insertion and dysregulation of transposable elements in osteosarcoma and their association with patient event-free survival.

The dysregulation of transposable elements (TEs) has been explored in a variety of cancers. However, TE activities in osteosarcoma (OS) have not been extensively studied yet. By integrative analysis of RNA-seq, whole-genome sequencing (WGS), and methylation data, we showed aberrant TE activities associated with dysregulations of TEs in OS tumors. Specifically, expression levels of LINE-1 and Alu of different evolutionary ages, as well as subfamilies of SVA and HERV-K, were significantly up-regulated in OS tumors, accompanied by enhanced DNA repair responses. We verified the characteristics of LINE-1 mediated TE insertions, including target site duplication (TSD) length (centered around 15 bp) and preferential insertions into intergenic and AT-rich regions as well as intronic regions of longer genes. By filtering polymorphic TE insertions reported in 1000 genome project (1KGP), besides 148 tumor-specific somatic TE insertions, we found most OS patient-specific TE insertions (3175 out of 3326) are germline insertions, which are associated with genes involved in neuronal processes or with transcription factors important for cancer development. In addition to 68 TE-affected cancer genes, we found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. We also found that +/- 500 bps flanking regions of transcription start sites (TSS) of LINE-1 (young) and Alu showed lower methylation levels in OS tumor samples than controls. Interestingly, by incorporating patient clinical data and focusing on TE activities in OS tumors, our data analysis suggested that higher TE insertions in OS tumors are associated with a longer event-free survival time.

The activity level of follicular helper T cells in the peripheral blood of osteosarcoma patients is associated with poor prognosis.

Osteosarcoma (OS) is solid tumors with high malignancy and incidence starting in the bones. OS pathogenesis has been proved to be closely associated with immune imbalance, and follicular helper T cells (Tfh) significantly affect host humoral immune homeostasis. However, there are few reports on the effect of Tfh cell activation on the prognosis of OS patients. Hence, this investigation on the changes in the proportion of peripheral blood Tfh cells in OS patients, and the relationship between their activity level and OS prognosis. We collected peripheral blood from patients with OS, benign bone tumor (BT group) and healthy subjects (Healthy group), respectively. The number of CD4+CXCR5+ Tfh cell in peripheral blood was measured by flow cytometry and correlation analysis between its activity and OS clinicopathological characteristics was carried out. The data showed that in comparison with the BT and Healthy groups, higher proportion and activation level of peripheral blood CD4+CXCR5+ Tfh cells in CD4+ T cells were found in the OS group. In OS patients, increases of the proportion and activity level of Tfh cells were associated with poorly differentiated OS and tumor metastasis. Additionally, Kaplan-Meier and Cox regression analysis showed a longer overall survival in patients with low proportion of peripheral blood CD4+CXCR5+ Tfh cells in CD4+ T cells, and their activation level may be a prognostic factor in OS patients. In conclusion, peripheral blood CD4+CXCR5+ Tfh cell activation in OS patients was associated with a poor prognosis. This study provided ideas for improving the clinical treatment of OS patients.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma.

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan-Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells' roles in the gene signature's prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature's prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

Prospero homeobox 1 promotes proliferation, migration, and invasion of osteosarcoma cells and its clinical significance.

Osteosarcoma (OS) is the most common primary malignant bone tumor. Prospero homeobox 1 (PROX1) is a key transcription factor involved in some cancers, but the role of PROX1 in OS is unclear. This study aimed to explore the clinical and biology significance of PROX1 in OS. Fifty-four OS tissues and matched nontumor tissues were collected to explore the relationship between PROX1 expression and clinical characteristics and prognosis. qRT-PCR and immunohistochemistry were used to investigate the expression patterns of PROX1 in OS tissues and cells. CCK-8, wound healing, and transwell assays were used to detect the effects of PROX1 on the proliferation, migration, and invasion of OS cells. Transcriptome sequencing, bioinformatics analysis and qRT-PCR were used to explore the regulatory network of PROX1. PROX1 was significantly higher in OS tissues and cells compared to normal tissues and cell lines. In OS patients, high expression of PROX1 was associated with Enneking stage (P < 0.001) and M classification (P < 0.001). High PROX1 expression predicted a poorer overall survival (P = 0.0047). Compared with untreated cells, OS cells overexpressing PROX1 showed higher proliferation, migration, and invasion abilities, while knockdown of PROX1 suppressed these abilities. The results of Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the down regulated genes were mainly enriched in TNF signaling pathway, MAPK signaling pathway, and neuroactive ligand-receptor interaction. High PROX1 expression was significantly associated with poor overall survival in OS patients. PROX1 may be a promising prognostic marker and therapeutic target for OS patients.

Preoperative prognostic nutritional index and systemic immune-inflammation index predict survival outcomes in osteosarcoma: A comparison between young and elderly patients.

OBJECTIVE: This retrospective study of patients with osteosarcoma investigated the following biomarkers of inflammation and nutritional status: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, prognostic nutritional index (PNI), and systemic immune-inflammation index (SII). The efficacies of these indicators to predict overall survival (OS) of young and elderly patients were compared. METHODS: The data of 125 patients with osteosarcoma, comprising the young (≤20 years) and elderly (60-80 years), were reviewed. Receiver operating characteristic (ROC) curves were calculated to determine the optimal cut-off value and area under the ROC curve of each potential biomarker. Kaplan-Meier curves and a Cox proportional hazards model were used to perform survival analyses. RESULTS: The cut-off values for low and high PNI ( ≤48.5, >48.5) and low and high SII (≤607.3, >607.3) were determined. Osteosarcoma patients in low PNI group or high SII group exhibited poorer OS relative to those in high PNI or low SII groups. The univariate and multivariate analyses indicated that preoperative PNI and SII were independent prognostic factors for OS in both the young and elderly subjects. CONCLUSION: Preoperative PNI and SII can be viable biomarkers of prognosis for both young and elderly patients with osteosarcoma. Awareness of these valuable indexes will enable clinicians to evaluate the inflammatory and nutritional status of these patients and establish a framework for individualized therapy.

Survival after pulmonary metastasectomy for relapsed osteosarcoma.

OBJECTIVES: The purpose of this study was to evaluate the postrelapse survival of relapsed osteosarcoma with pulmonary metastases in patients who received pulmonary metastasectomy using intent to treat and propensity score analysis. METHODS: Patients with osteosarcoma who relapsed with pulmonary metastases between 2004 and 2018 who were treated in a hospital affiliated with a medical school were included. All the enrolled patients were evaluated as operable with assessment algorithm at the time of diagnosis of pulmonary relapse and intent to treat analysis was done. Multiple propensity score methods (eg, matching, stratification, covariate adjustment, and inverse probability of treatment weighting) were performed to balance confounding bias. Cox proportional hazards regression and the Kaplan-Meier method were used to evaluate patient survival. RESULTS: A total of 125 patients met the study criteria. Of these, 59 (47.2%) patients received pulmonary metastasectomy combined with chemotherapy and 66 (52.8%) received chemotherapy alone. The 2-year and 5-year postrelapse survival rate of metastasectomy group and nonmetastasectomy group were 68.4% versus 25.0% and 41.0% versus 0%, respectively. The median postrelapse survival was 24.9 versus 13.5 months, respectively. Pulmonary metastasectomy was independently associated with improved survival (hazard ratio, 0.185; 95% confidence interval, 0.103-0.330; P < .001). These results were confirmed by multiple propensity score analyses. Further stratified analysis revealed that the survival advantage associated with metastasectomy was not significant in patients with metastases involving ≥3 lung lobes and patients with very high pretreatment serum alkaline phosphatase (more than twice the upper limit). CONCLUSIONS: Pulmonary metastasectomy is associated with improved survival in patients with recurrent osteosarcoma.

Treatment of 120 adult osteosarcoma patients with metachronous and synchronous metastases: A retrospective series of the French Sarcoma Group.

Treatment options for metastatic osteosarcomas are scarce. Following failure of standard first line therapy, patients who relapse present a challenging treatment dilemma, and have a poor prognosis. Surgical removal of all metastases is essential. A retrospective analysis of patients with metastatic osteosarcomas was conducted in 15 French Sarcoma Group centers. From January 2009 to December 2018, we identified 120 adult patients; 36 with synchronous and 84 with metachronous metastases with 74 males and 46 females. Mean age was 30 years (18-53). Metastatic sites were lung, bone and other in 91, 11 and 24 patients, respectively. Mean time to first metachronous metastases was 22 months (4-97). All patients except 13 (10.8%) with metachronous metastases received a first line systemic treatment for relapse, and 39 patients (32.5%) were included in a clinical trial. Eighty-one patients (67.5%) had local treatment of distant metastases. Median progression free survival (PFS) and overall survival (OS) were 5.5 (95% CI 4.6-6.4) and 20.5 months (95% CI 13.2-27.7) respectively for the overall group. In multivariate analysis, more than five metastases, time to first metastases <24 months, were statistically significant negative prognostic factors for OS and PFS (P = .002, ≤.001 and P = .006, ≤.001, respectively). Surgery of metastases was associated with better prognosis on OS and PFS (P = .001 and .037, respectively). The presence of bone metastases was a negative prognostic factor on OS but not on PFS (P = .021). In reference sarcoma centers, relapsed osteosarcoma patients with more than one metastasis commonly receive more than one line of systemic therapy, and are included in clinical trial if available.

A Simulation Study to Compare the Predictive Performance of Survival Neural Networks with Cox Models for Clinical Trial Data.

BACKGROUND: Studies focusing on prediction models are widespread in medicine. There is a trend in applying machine learning (ML) by medical researchers and clinicians. Over the years, multiple ML algorithms have been adapted to censored data. However, the choice of methodology should be motivated by the real-life data and their complexity. Here, the predictive performance of ML techniques is compared with statistical models in a simple clinical setting (small/moderate sample size and small number of predictors) with Monte-Carlo simulations. METHODS: Synthetic data (250 or 1000 patients) were generated that closely resembled 5 prognostic factors preselected based on a European Osteosarcoma Intergroup study (MRC BO06/EORTC 80931). Comparison was performed between 2 partial logistic artificial neural networks (PLANNs) and Cox models for 20, 40, 61, and 80% censoring. Survival times were generated from a log-normal distribution. Models were contrasted in terms of the C-index, Brier score at 0-5 years, integrated Brier score (IBS) at 5 years, and miscalibration at 2 and 5 years (usually neglected). The endpoint of interest was overall survival. RESULTS: PLANNs original/extended were tuned based on the IBS at 5 years and the C-index, achieving a slightly better performance with the IBS. Comparison with Cox models showed that PLANNs can reach similar predictive performance on simulated data for most scenarios with respect to the C-index, Brier score, or IBS. However, Cox models were frequently less miscalibrated. Performance was robust in scenario data where censored patients were removed before 2 years or curtailing at 5 years was performed (on training data). CONCLUSION: Survival neural networks reached a comparable predictive performance with Cox models but were generally less well calibrated. All in all, researchers should be aware of burdensome aspects of ML techniques such as data preprocessing, tuning of hyperparameters, and computational intensity that render them disadvantageous against conventional regression models in a simple clinical setting.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma cancer.

BACKGROUNDS: Osteosarcomas are one of the most common primary malignant tumors of bone. It primarily occurs in children and adolescents, with the second highest incidence among people over 50 years old. Although there were immense improvements in the survival of patients with osteosarcoma in the past 30 years, targetable mutations and agents of osteosarcomas still have been generally not satisfactory. Therefore, it is of great importance to further explore the highly specialized immune environment of bone, genes related to macrophage infiltration and potential therapeutic biomarkers and targets. METHODS: The 11 expression data sets of OS tissues and the 11 data sets of adjacent non-tumorous tissues available in the GEO database GSE126209 were used to conduct immune infiltration analysis. Then, through WGCNA analysis, we acquired the co-expression modules related to Mast cells activated and performed the GO and KEGG enrichment analysis. Next, we did the survival prognosis analysis and plotted a survival curve. Finally, we analyzed the COX multivariate regression of gene expression on clinical parameters and drew forest maps for visualization by the forest plot package. RESULTS: OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group. Then, we identified co-expression modules related to Mast cells activated. In sum, a total of 822 genes from the top three strongest positive correlation module MEbrown4, MEdarkslateblue and MEnavajowhite2 and the strongest negative correlation module MEdarkturquoise. From that, we identified nine genes with different levels in immune cell infiltration related to osteosarcoma, eight of which including SORBS2, BAIAP2L2, ATAD2, CYGB, PAMR1, PSIP1, SNAPC3 and ZDHHC21 in their low abundance have higher disease-free survival probability than the group in their high abundances. CONCLUSION: These results could assist clinicians to select targets for immunotherapies and individualize treatment strategies for patients with OS.

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

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

The Role of Chemotherapy in the Survival Benefits of Patients Aged Older Than 40 Years With Osteosarcoma.

Objectives: The value of chemotherapy in the survival benefits of patients aged > 40 years with osteosarcoma is controversial. We aimed to explore the impact of chemotherapy on the survival benefits of patients aged >40 years with osteosarcoma. Methods: The Surveillance, Epidemiology, and End Results database was used to select eligible patients. The selected patients were divided into the chemotherapy and non-chemotherapy groups. Logistic regression analysis was performed to investigate the potential factors contributing to the selection of chemotherapy. The overall survival (OS) and cancer-specific survival (CSS) of the two groups were compared using the Kaplan-Meier method with a log-rank test. Cox proportional risk models were used to determine the prognostic factors for OS and CSS. Stratified analysis was performed according to tumour grade and stage. Results: A total of 1032 eligible patients were included in our analysis. Of these, 586 and 446 patients were in the chemotherapy and nonchemotherapy groups, respectively. Multivariate logistic analysis indicated that grade III/IV and distant stage were associated with chemotherapy. Kaplan-Meier plots showed that patients did not achieve an improved OS or CSS after receiving chemotherapy. Cox regression analysis indicated that age > 60 years, axial, grade III/IV, and regional and distant stage were independent risk factors for poor prognosis in both OS and CSS. Stratified analysis revealed a survival benefit from chemotherapy in patients with grade III/IV and distant stage. Conclusions: Chemotherapy did not significantly improve OS and CSS in patients aged > 40 years with osteosarcoma. In this age group, survival benefit from chemotherapy was observed in patients with high-grade tumours (grade III/IV) and metastasis (distant stage).

Alpha-(1,6)-fucosyltransferase (FUT8) affects the survival strategy of osteosarcoma by remodeling TNF/NF-κB2 signaling.

Glycosylation is an important modification of membrane proteins that results in functional changes in many cellular activities, from cell-cell recognition to regulatory signaling. Fucosyltransferase 8 (FUT8) is the sole enzyme responsible for core fucosylation, and aberrant fucosylation by dysregulated expression of fucosyltransferases is responsible for the growth of various types of carcinomas. However, the function of FUT8 in the progress of osteosarcoma (OS) has not been reported. In this study, we found that FUT8 is expressed at lower levels in patients with OS and in human OS cell lines such as MNNG/HOS, U2OS, and 143B, suggesting that attenuated expression of FUT8 is involved in the growth and progression of OS. Mechanistically, FUT8 affects the survival strategy of OS by modifying core-fucosylation levels of TNF receptors (TNFRs). Lower fucosylation of TNFRs activates the non-canonical NF-κB signaling pathway, and in turn, decreases mitochondria-dependent apoptosis in OS cells. Together, our results point to FUT8 being a negative regulator of OS that enhances OS-cell apoptosis and suggests a novel therapeutic strategy for treating OS.

Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma.

PURPOSE: Osteosarcoma (OS) is a differentiation disease caused by the genetic and epigenetic differentiation of mesenchymal stem cells into osteoblasts. OS is a common, highly malignant tumor in children and adolescents. Fifteen to 20 % of the patients find distant metastases at their first visit. The purpose of our study was to identify biomarkers for tracking the prognosis and treatment of OS to improve the survival rate of patients. MATERIALS AND METHODS: In this study, which was based on Therapeutically Applicable Research to Generate Effective Treatments (TARGET), we searched for m6A related lncRNAs in OS. We constructed a network between lncRNA and m6A, and built an OS prognostic risk model. RESULTS: We identified 14,581 lncRNAs by using the dataset from TARGET. We obtained 111 m6A-related lncRNAs through a Pearson correlation analysis. A network was built between lncRNA and m6A genes. Eight m6A-related lncRNAs associated with survival were identified through a univariate Cox analysis. A selection operator (LASSO) Cox regression was used to construct a prognostic risk model with six genes (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) obtained through least absolute shrinkage. We also discovered upregulated levels of DLGAP1-AS2 and m6A methylation in osteosarcoma tissues/cells compared with normal tissues/osteoblasts cells. CONCLUSION: We constructed a risk score prognosis model of m6A-related lncRNAs (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) using the dataset downloaded from TRAGET. We verified the value of the model by dividing all samples into test groups and training groups. However, the role of m6A-related lncRNAs in osteosarcoma needs to be further researched by cell and in vivo studies.

LAMTOR5-AS1 regulates chemotherapy-induced oxidative stress by controlling the expression level and transcriptional activity of NRF2 in osteosarcoma cells.

Long-noncoding RNAs (lncRNAs) play roles in regulating cellular functions. High-throughput sequencing analysis identified a new lncRNA, termed LAMTOR5-AS1, the expression of which was much higher in the chemosensitive osteosarcoma (OS) cell line G-292 than in the chemoresistant cell line SJSA-1. Further investigations revealed that LAMTOR5-AS1 significantly inhibits the proliferation and multidrug resistance of OS cells. In vitro assays demonstrated that LAMTOR5-AS1 mediates the interaction between nuclear factor erythroid 2-related factor 2 (NFE2L2, NRF2) and kelch-like ECH-associated protein 1 (KEAP1), which regulate the oxidative stress. Further mechanistic studies revealed that LAMTOR5-AS1 inhibited the ubiquitination degradation pathway of NRF2, resulting in a higher level of NRF2 but a loss of NRF2 transcriptional activity. High level of NRF2 in return upregulated the downstream gene heme oxygenase 1 (HO-1). Moreover, NRF2 controls its own activity by promoting LAMTOR5-AS1 expression, whereas the feedback regulation is weakened in drug-resistant cells due to high antioxidant activity. Overall, we propose that LAMTOR5-AS1 globally regulates chemotherapy-induced cellular oxidative stress by controlling the expression and activity of NRF2.