Prognostic implication of adjuvant/neoadjuvant chemotherapy consisting of doxorubicin and ifosfamide in patients with extraskeletal osteosarcoma

An analysis of extraskeletal osteosarcoma based on the literature

Extraskeletal osteosarcoma (ESOS) is a very rare malignant tumor. This study aimed to provide more evidence about the natural history and clinical features of ESOS, and clarify the impact of chemotherapy (CT) and radiotherapy (RT) on patient survival and postoperative recurrence for the sake of gaining a better understanding about the disease. Patient/tumor characteristics, recurrence, treatment, and follow-up durations were collected by searching studies in PubMed, Web of Science, Ovid, Elsevier, SpringerLink, Chinese National Knowledge Infrastructure (CNKI), and WAN FANG Data before June 30, 2023. Data were analyzed by SPSS-26. Altogether 1287 patients diagnosed with ESOS were retrieved from the literature. Of them, follow-up data were available in 981 patients, in whom 78 patients (7.6%) had metastases and 730 patients (74.5%) had localized disease at the time of diagnosis. Of the 730 patients with localized disease, 682 (93.4%) received surgical resection. The 5-year OS in all 981 patients, 78 metastatic patients, and 682 patients with localized disease was 33.40%, 4.9% and 41.1%, respectively. Of the 682 surgical patients, 367 patients underwent surgical resection alone, 170 received surgical resection + CT, 82 patients received surgical resection + RT, and 58 patients received both adjuvants. In addition, 348 (51%) of the 682 patients developed recurrence, including local recurrence (n = 102), metastasis (n = 130), and both (n = 116). Univariate analysis of 5-year PFS and 5-year OS showed that age, tumor size, CT, metastasis, and local recurrence were significant prognostic factors in the 682 patients with localized disease. There was no significant difference in 5-year OS between osteosarcoma-type and soft tissue-type regimens (P = 0.273). Multivariate analysis of 431 patients showed that postoperative recurrence and metastasis were significant prognostic factors for survival, and CT was not a significant prognostic factor, though CT decreased the incidence of local recurrence in ≤ 45-year age group (P = 0.047). RT reduced the incidence of local recurrence in patients ≤ 45years (P = 0.035) and patients with tumors > 5 cm (P = 0.044). So, we recommend that CT should be used for patients aged ≤ 45years, and RT can be used for patients ≤ 45years or those with tumors > 5 cm for the sake of decreasing the incidence of local recurrence, which we believe would indirectly benefit the survival of ESOS patients.

Shikonin suppresses proliferation of osteosarcoma cells by inducing ferroptosis through promoting Nrf2 ubiquitination and inhibiting the xCT/GPX4 regulatory axis

Osteosarcoma (OS) is a prevalent primary malignant bone tumor which lacks effective therapeutic interventions. Ferroptosis is a new form of programmed cell death characterized by iron-dependent accumulation of lethal lipid oxidation, which provides a potential alternative intervene for the OS treatment. Shikonin is the major bioactive component extracted from the roots of lithospermum erythrorhizon which is also known as "Zicao" in traditional Chinese medicine, has been proved to have exhibits remarkable anti-tumor properties in several cancers. However, whether ferroptosis participated in the shikonin mediated anti-OS activity still remains to be clarified. Herein, we provide evidence that shikonin possesses the capability to induce the ferroptosis, and elucidate the underlying mechanisms in the treatment of OS. In the present study, it was found that shikonin significantly suppressed OS cells proliferation and blocked the cell cycle progression in vitro. Subsequent results revealed that shikonin could trigger ferroptosis in OS cells by promoting the Fe2+ accumulation, reactive oxygen species and lipid peroxidation formation, malondialdehyde production and mitochondrial damage. Further study showed that the effects of OS cell proliferation and death caused by shikonin could be successfully reversed by ferroptosis inhibitor ferrostatin-1, indicating that ferroptosis participated in the shikonin mediated anti-OS activity. Mechanistically, shikonin physically interacted with Nrf2, a critical regulator of ferroptosis, and influenced Nrf2 stability via inducing ubiquitin degradation, which suppressed the expression of Nrf2 downstream targets xCT and GPX4, and led to stimulating ferroptosis. Collectively, our findings indicated that shikonin induced OS cells ferroptosis through Nrf2/xCT/GPX4 regulatory axis, which might shed light on exploiting shikonin as a promising candidate for the future OS therapy.

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.

Construction of an oxidative phosphorylation-related gene signature for predicting prognosis and identifying immune infiltration in osteosarcoma

BACKGROUND

Osteosarcoma is a prevalent malignant tumor that originates from mesenchymal tissue. It typically affects children and adolescents. Although it is known that the growth of osteosarcoma relies on oxidative phosphorylation for energy production, limited attention has been paid to exploring the potential of oxidative phosphorylation-related genes in predicting the prognosis of individuals suffering from osteosarcoma.

METHODS

All the data were retrieved from the UCSC Xena and GEO (GENE EXPRESSION OMNIBUS). Identification of the oxidative phosphorylation genes linked to the prognosis of individuals with osteosarcoma was done by means of univariate COX and LASSO regression analyses. Following that, patients were categorized into a high-risk group and a low-risk group as per the risk score determined by the identified oxidative phosphorylation genes. Furthermore, a comparison was made in terms of the survival and immune infiltration between both groups, and the prognostic model was established.

RESULTS

Five oxidative phosphorylation genes (ATP6V0D1, LHPP, COX6A2, MTHFD2, NDUFB9) associated with the prognosis of individuals with osteosarcoma were identified and the risk prognostic models were constructed. In the current research, the analysis of the ROC curves indicated a superior predictive accuracy exhibited by the risk model. The prognosis was adversely affected by immune infiltration in the high-risk group in comparison with the low-risk group. The function of the oxidative phosphorylation-related prognostic gene set was verified by GO and KEGG analysis. Furthermore, the link between oxidative phosphorylation-related genes and osteosarcoma immune infiltration was examined by GSEA analysis.

CONCLUSIONS

In this study, a prognostic model that demonstrated good predictive performance was constructed. Additionally, this study highlighted a correlation between oxidative phosphorylation-related genes and immune infiltration.

Survival Differences of Patients with Resected Extraskeletal Osteosarcoma Receiving Two Different (Neo)Adjuvant Chemotherapy Regimens: A Systematic Review and Meta-analysis

AIMS

Extraskeletal osteosarcoma (ESOS) is a malignant tumour developing in soft tissues, characterised by the production of osteoid or bone matrix by tumour cells. The standard treatment for localised ESOS is wide resection. Radiotherapy and chemotherapy are usually incorporated into the management of patients. Two types of chemotherapy regimen are mostly used: an osteosarcoma-type chemotherapy, based on cisplatin, and a soft-tissue sarcoma (STS)-type chemotherapy, using the combination of doxorubicin and ifosfamide. To investigate the difference in survival between these two chemotherapy regimens, a systematic review of studies reporting the 5-year disease-free survival (DFS) rates among patients with ESOS submitted to surgery and who received (neo)adjuvant chemotherapy with osteosarcoma-type or STS-type chemotherapy was carried out.

MATERIALS AND METHODS

Of the 401 articles identified by systematically searching the PubMed, Embase and Cochrane Central Register of Controlled Trials databases, six retrospective studies were included in the final analysis. In total, 319 patients with localised/resected ESOS were included in the study.

RESULTS

Our meta-analysis showed a benefit in 5-year DFS favouring the use of osteosarcoma-type chemotherapy (relative risk = 1.32, 95% confidence interval 1.03-1.69; P = 0.54); I2 heterogeneity was 0%. The 5-year DFS rate was 56.3% (95% confidence interval 48.3-64.3) with osteosarcoma-type chemotherapy and 45.2% (95% confidence interval 34.5-55.9) with STS-type chemotherapy, with I2 heterogeneity of 27% and 0%, respectively.

CONCLUSIONS

Our analysis suggests that there may be a difference regarding the type of (neo)adjuvant chemotherapy regimen used in the treatment of patients with resected ESOS in favour of osteosarcoma-type chemotherapy. Future studies evaluating the role of this treatment modality in this scenario need to consider the type of chemotherapy regimen when comparing with an arm of surgery with/without radiotherapy alone.

Multidisciplinary management for primary uterine osteosarcoma, including gene panel testing: case report and literature review

Primary osteosarcoma of the uterus (uOS) is rare, and its standard treatment has not yet been established. Herein, we present the case of a 50-year-old woman with uOS who demonstrated an improved prognosis after multiple surgeries to the metastatic sites. After the initial diagnosis of uOS, the patient showed recurrence and distant metastasis and hence expected to exhibit a poor prognosis. The patient underwent multiple surgical resections of the metastatic as well as primary tumors, which enabled the patient to survive for 24 months after the initial surgery. Considering that the median survival time of patients with uOS is approximately 6 months, the survival rate of our patient is noteworthy. Based on our observations, it is suggested that the resection of the primary and metastatic tumors might contribute to the extension of the survival period of the patient with chemo-resistant uOS.

Construction and validation of a prognostic model for osteosarcoma patients based on autophagy-related genes

BACKGROUND

Osteosarcoma is the most frequent primary bone malignancy with a poor prognosis because of pulmonary metastasis. Autophagy is strongly associated with tumor metastasis, and it is valuable to construct an autophagy-related gene risk model for predicting the prognosis of osteosarcoma.

METHODS

We obtained ARGs from the Human Autophagy Database and RNA-sequencing data of osteosarcoma patients from the Gene Expression Omnibus (GEO) database. Subsequently, univariate and multivariate cox regression analyses were performed to construct a three-gene prognostic model and its accuracy was further confirmed in the Therapeutic Applications Research to Generate Effective Treatments (TARGET) database. Afterward, we detected the expression levels and effects on osteosarcoma cells metastasis of MYC and MBTPS2, which were involved in the model.

RESULTS

In both training and verification cohorts, patients with lower risk scores had longer OS, and the model was identified as an independent prognostic factor in osteosarcoma. Besides, the ROC curve demonstrated the reliability of the model. Furthermore, RT-qPCR, Western Blotting and IHC results indicated that MYC and MBTPS2 were differently expressed in osteosarcoma tissues and cell lines. MYC knockdown or MBTPS2 overexpression prevented the capacity of migration and invasion in osteosarcoma cell lines through inhibiting cellular autophagy.

CONCLUSION

The risk model based on three ARGs had a strong ability to predict the prognosis of osteosarcoma patients. Our findings also suggested that MYC and MBTPS2 were two major factors regulating autophagy in osteosarcoma, and could serve as potential therapeutic targets for osteosarcoma.

A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients

This study aims to construct a novel risk score model based on glycolysis-related genes in osteosarcoma and to build and validate a prognostic model for predicting overall survival of patients with osteosarcoma. The transcriptome data and corresponding clinical data of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) as the training set, and from Gene Expression Omnibus (GEO) database as the validation set. Univariate Cox regression analysis was used to screen the prognostic glycolysis-related genes. The risk coefficient of each glycolysis-related gene was calculated using LASSO regression analysis. Using the median risk score as the cut-off point, patients were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to determine whether there was a significant difference in the overall survival between the two groups. The nomogram was constructed according to the results of multivariate Cox regression. The C-index was calculated, the calibration chart, clinical decision curve and receiver operating characteristic curve were drawn to evaluate the predictive performance of the nomogram. We performed Gene Ontology and Kyoto encyclopedia of genes and genomics enrichment analysis to explore the potential mechanism of prognostic-related glycolysis genes in osteosarcoma. A total of 88 and 53 cases were obtained from the TCGA and GEO database, respectively. A total of 10 key glycolytic genes related to prognosis were screened out. The Kaplan-Meier survival curve revealed that the overall survival of the high-risk group was significantly shorter than that of the low-risk group. The C indices of the training set and the verification set were 0.882 and 0.828, respectively. Our findings will provide further understanding of clinical prognostic outcomes of osteosarcoma patients.

Unfolded Protein Response-Related Signature Associates With the Immune Microenvironment and Prognostic Prediction in Osteosarcoma

Background: Osteosarcoma is a highly malignant bone tumor commonly occurring in adolescents with a poor 5-year survival rate. The unfolded protein response (UPR) can alleviate the accumulation of misfolded proteins to maintain homeostasis under endoplasmic reticulum stress. The UPR is linked to the occurrence, progression, and drug resistance of tumors. However, the function of UPR-related genes (UPRRGs) in disease progression and prognosis of osteosarcoma remains unclear.

Methods: The mRNA expression profiling and corresponding clinical features of osteosarcoma were acquired from TARGET and GEO databases. Consensus clustering was conducted to confirm different UPRRG subtypes. Subsequently, we evaluated the prognosis and immune status of the different subtypes. Functional analysis of GO, GSEA, and GSVA was used to reveal the molecular mechanism between the subtypes. Finally, four genes (STC2, PREB, TSPYL2, and ATP6V0D1) were screened to construct and validate a risk signature to predict the prognosis of patients with osteosarcoma.

Result: We identified two subtypes according to the UPRRG expression patterns. The subgroup with higher immune scores, lower tumor purity, and active immune status was linked to a better prognosis. Meanwhile, functional enrichment revealed that immune-related signaling pathways varied markedly in the two subtypes, suggesting that the UPR might influence the prognosis of osteosarcoma via influencing the immune microenvironment. Moreover, prognostic signature and nomogram models were developed based on UPRRGs, and the results showed that our model has an excellent performance in predicting the prognosis of osteosarcoma. qPCR analysis was also conducted to verify the expression levels of the four genes.

Conclusion: We revealed the crucial contribution of UPRRGs in the immune microenvironment and prognostic prediction of osteosarcoma patients and provided new insights for targeted therapy and prognostic assessment of the disease.

The Effect of Adjuvant Chemotherapy on Localized Extraskeletal Osteosarcoma: A Systematic Review

(1) Background: Extraskeletal osteosarcoma (ESOS) is a malignant tumor characterized by the production of bone or bone matrix by tumor cells without any continuity into the skeletal bones. The standard treatment for localized ESOS is wide resection; however, the effect of (neo)adjuvant chemotherapy remains unclear. To investigate the effect of (neo)adjuvant chemotherapy for localized ESOS, we conducted a systematic review of studies comparing the 5-year disease-free survival rate between patients who underwent surgery combined with (neo)adjuvant chemotherapy and those who underwent surgery alone. (2) Methods: Of the 210 studies identified by systematically searching the PubMed, Embase, and Cochrane Central Register of Controlled Trials databases, 12 were included in the final analysis. These 12 articles were not randomized controlled trials, but retrospective studies. In total, 761 patients with localized ESOS were included in this study. (3) Results: The 5-year disease-free survival rate was 47.9% (187 of 390 patients) in the surgery and (neo)adjuvant chemotherapy group and 40.4% (150 of 371 patients) in the surgery alone group. The overall pooled odds ratio was 1.23 (95% confidence interval, 0.69-2.19; p = 0.479) and the heterogeneity I² was 37%. (4) Conclusions: The effect of adjuvant chemotherapy on localized ESOS seems to be limited. Therefore, routine use of adjuvant chemotherapy for localized ESOS should be avoided. However, further randomized controlled trials are required to confirm these results.

A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients.

Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset.

Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan–Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent.

Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a common malignant bone tumor in children and adolescents. DNA methylation plays a crucial role in the prognosis prediction of cancer. Identification of novel DNA methylation sites biomarkers could be beneficial for the prognosis of OS patients. In this study, we aim to find an efficient methylated site model for predicting survival in OS.

METHODS

DNA methylation data were downloaded from the Cancer Genome Atlas database (TCGA) and the GEO database. Cox proportional hazard regression and random survival forest algorithm (RSFVH) were applied to identify DNA methylated site signature in the samples randomly assigned to the training subset and the other samples as the test subset. By randomizing 71 clinical samples into two individual groups and a series of statistical analyses between the two groups, a DNA methylation signature is verified.

RESULTS

This signature comprises four methylation sites (cg04533248, cg12401425, cg13997435, and cg15075357) associated with the patient training group from the univariate Cox proportional hazards regression analysis, RSFVH, and multivariate Cox regression analysis. Kaplan-Meier survival curves showed the OS patients in the high-risk group have a poor 5-year overall survival compared with the low-risk group, and this finding was identified in the test data set. A ROC analysis was performed in the current research. The results revealed that this signature was an independent predictor of patient survival by investigating the AUC of the four methylation sites signature in the training data set (AUC =0.861) and test data set, respectively (AUC =0.920). The nomogram described in the current study placed a great guiding value for predicting 1-, 2-, 3-year survival of the OS by combining age, gender, grade, and TNM stage as covariates with the RS of patients' methylation related signatures.

CONCLUSIONS

Our study proved that this signature might be a powerful prognostic tool for survival rate evaluation and guide tailored therapy for OS patients.

Intraperitoneal extraosseous osteosarcoma: a case report and literatures review

Background

To investigate the clinical imaging manifestations, diagnosis and treatment of intraperitoneal extraosseous osteosarcoma.

Case presentation

A 52-year-old male patient with intraperitoneal extraosseous osteosarcoma was retrospectively analyzed. He suffered from left lower abdominal pain accompanied by mass for 6 months. On abdominal CT scan, multiple patchy and banded calcification were found. The largest is about six centimeters in diameter and underwent mass resection. Postoperative pathology revealed retroperitoneal osteosarcoma. The reported intraperitoneal extraosseous osteosarcoma was analyzed and the related literature was reviewed. Two years after operation, the patients had recurrence of the tumors and invaded sigmoid colon, peritoneum and bladder. Palliative operation was performed to remove the tumors in the bladder and transverse colostomy was performed. The patients were followed up by telephone and died 2 months after the second operation.

Conclusions

Intraperitoneal extraosseous osteosarcoma has a low incidence and has no specific imaging features. Surgical resection is the main treatment and the prognosis is poor.

A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma

BACKGROUND

This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients.

METHODS

A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted.

RESULT

Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway.

CONCLUSION

An eight-gene predictive model and nomogram were developed to predict OS prognosis.