Overall Survival Of Osteosarcoma Patients Research Articles

MTA2 knockdown suppresses human osteosarcoma metastasis by inhibiting uPA expression.

The relationship between metastasis-associated protein 2 (MTA2) overexpression and tumor growth and metastasis has been extensively studied in a variety of tumor cells but not in human osteosarcoma cells. This study aims to elucidate the clinical significance, underlying molecular mechanisms, and biological functions of MTA2 in human osteosarcoma in vitro and in vivo. Our results show that MTA2 was elevated in osteosarcoma cell lines and osteosarcoma tissues and was associated with tumor stage and overall survival of osteosarcoma patients. Knockdown of MTA2 inhibited osteosarcoma cell migration and invasion by reducing the expression of urokinase-type plasminogen activator (uPA). Bioinformatic analysis demonstrated that high levels of uPA in human osteosarcoma tissues correlated positively with MTA2 expression. Furthermore, treatment with recombinant human uPA (Rh-uPA) caused significant restoration of OS cell migration and invasion in MTA2 knockdown osteosarcoma cells. We found that ERK1/2 depletion increased the expression of uPA, facilitating osteosarcoma cell migration and invasion. Finally, MTA2 depletion significantly reduced tumor metastasis and the formation of lung nodules in vivo. Overall, our study suggests that MTA2 knockdown suppresses osteosarcoma cell metastasis by decreasing uPA expression via ERK signaling. This finding provides new insight into potential treatment strategies against osteosarcoma metastasis by targeting MTA2.

A diagnostic signature developed based on the necroptosis-related genes and its association with immune infiltration in osteosarcoma

IntroductionOsteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death. Material and methodsThe RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs. ResultsThree clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model. ConclusionsOur prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.

KNTC1 knockdown inhibits the proliferation and migration of osteosarcoma cells by MCM2.

Osteosarcoma (OS) is a common primary malignant bone tumor, and it is necessary to further investigate the molecular mechanism of OS progression. The expression of kinetochore associated protein 1 (KNTC1) and minichromosome maintenance 2 (MCM2) was detected by immunohistochemistry, quantitative PCR (qPCR) and Western blot. Gene knockdown or overexpression cell models were constructed and the proliferation, apoptosis, cell cycle and migration were detected in vitro, besides, xenograft models were established to explore the effects of KNTC1 downregulation in vivo. Public databased and bioinformatics analysis were performed to screen the downstream molecules and determine the expression of MCM2 in cancers. KNTC1 was overexpressed in OS tissues and positively correlated with overall survival of OS patients. KNTC1 knockdown inhibited the proliferation and migration, and arrested G2 phase, and induced apoptosis. Besides, KNTC1 downregulation restricted the xenograft tumor formation. MCM2, one of the coexpressed genes, was highly expressed in sarcoma and downregulated after KNTC1 knockdown. MCM2 overexpression heightened the proliferation and migration ability of OS cells, which was reversed the inhibiting effects of KNTC1 knockdown. KNTC1 was overexpressed in OS and promoted the progression of OS by upregulating MCM2.

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

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

The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients

Host immune dysregulation involves in the initiation and development of osteosarcoma (OS). However, the exact role of immune cells in OS remains unknown. We aimed to distinguish the molecular subtypes and establish a prognostic model in OS patients based on immunocyte infiltration. The gene expression profile and corresponding clinical feature of OS patients were obtained from TARGET and GSE21257 datasets. MCP-counter and univariate Cox regression analyses were applied to identify immune cell infiltration-related molecular subgroups. Functional enrichment analysis and immunocyte infiltration analysis were performed between two subgroups. Furthermore, Cox regression and LASSO analyses were performed to establish the prognostic model for the prediction of prognosis and metastasis in OS patients. The subgroup with low infiltration of monocytic lineage (ML) was related to bad prognosis in OS patients. 435 DEGs were screened between the two subgroups. Functional enrichment analysis revealed these DEGs were involved in immune- and inflammation-related pathways. Three important genes (including TERT, CCDC26, and IL2RA) were identified to establish the prognostic model. The risk model had good prognostic performance for the prediction of metastasis and overall survival in OS patients. A novel stratification system was established based on ML-related signature. The risk model could predict the metastasis and prognosis in OS patients. Our findings offered a novel sight for the prognosis and development of OS.

Association between CDK4 expression and overall survival of osteosarcoma patients

Osteosarcoma is the most common primary bone tumor malignancy, accounting for 30 - 80% of all primary bone tumors. It is presented in a bimodal distribution manner with the age of onset divided into two groups, 10-20 and >60 y.o. Various factors have significance in the patient's prognosis, including the expression of cyclin-dependent kinase 4 (CDK4). This CDK4 has an essential role in the pathogenesis of osteosarcoma through inactivation of the Rb gene, which is associated with the patient's survival. This study was conducted to evaluate the correlation between CDK4 expression and the survival of osteosarcoma patients. It was a cross-sectional study involving 50 patients diagnosed with osteosarcoma based on clinical, radiological, and histopathological examination. Available formalin-fixed paraffin-embedded (FFPE) samples were retrieved for immunohistochemical (IHC) staining of CDK4. The survival data was collected from medical records. CDK4 expression and survival data were analyzed statistically using the Kaplan-Meier curve. Out of 50 subjects, CDK4 was found to be expressed in 38 samples (76%). The group with negative CDK4 showed a slightly longer overall survival (by 0.2 mo) than the positive CDK4 group. However, these results were not statistically significant (p = 0.821). In conclusion, the overexpression of CDK4 may not directly affect the survival rate in osteosarcoma. Other factors need to be considered to understand the complexity of the disease.

Effects of combined sintilimab and chemotherapy on progression-free survival and overall survival in osteosarcoma patients with metastasis.

To explore the safety and efficacy of metastatic osteosarcoma treatment with combined sintilimab injection and chemotherapy. We performed a retrospective analysis of 32 patients with metastatic osteosarcoma admitted to the Affiliated Hospital of Beihua University between January 2019 and June 2020. The sample was divided into an observation group, treated with sintilimab injection combined with chemotherapy (n= 16) and a control group, treated with chemotherapy (n = 16). Clinical efficacy and adverse reactions were compared between the two groups. The overall response rates were 68.75% in the observation group and 31.25% in the control group (p < 0.05). The incidences of adverse reactions were 56.25% in the observation group and 81.25% in the control group. This was not a significant difference. In the observation group, the progression-free survival time was 8.13 ± 2.50 months, and the overall survival time was 22.75 ± 4.95 months. These were both significantly longer than the respective 6.44 ± 1.93 months and 19.69 ± 2.68 months in the control group (p < 0.05). The treatment of metastatic osteosarcoma with combined sintilimab injection and chemotherapy was found to prolong progression-free survival and overall survival time without increasing the incidence of adverse reactions.

Targeting PERK-ATF4-P21 axis enhances the sensitivity of osteosarcoma HOS cells to Mppα-PDT.

Osteosarcoma (OS) is the most prevalent type of malignant bone tumor in adolescents. The overall survival of OS patients has reached a plateau recently. Thus, there is an urgent need to develop approaches to improve the sensitivity of OS to therapies. Pyropheophorbide-α methyl ester-mediated photodynamic therapy (MPPα-PDT) is a new type of tumor therapy, and elucidating its mechanism is helpful to improve its anti-tumor efficacy. Here, we investigated how PERK signaling promotes the human OS (HOS) cell survival induced by MPPα-PDT, as overcoming this may enhance sensitivity to MPPα-PDT. We found that MPPα-PDT combined with PERK inhibitor GSK2656157 enhanced HOS cell apoptosis by suppressing autophagy and p21. Autophagy inhibition and p21 depletion enhanced cell death, indicating pro-survival effects in MPPα-PDT. Notably, p21 was found to be an effector of the PERK-Atf4 pathway, which could positively regulate autophagy mediated by MPPα-PDT. In conclusion, we found that the combination of MPPα-PDT and GSK2656157 enhanced apoptosis in HOS cells by inhibiting autophagy. Mechanistically, this autophagy is p21-dependent and can be suppressed by GSK2656157, thereby enhancing sensitivity to MPPα-PDT.

A novel aging-associated lncRNA signature for predicting prognosis in osteosarcoma

Osteosarcoma (OS) is one of the most prevalent bone tumors in adolescents, and the correlation between aging and OS remains unclear. Currently, few accurate and reliable biomarkers have been determined for OS prognosis. To address this issue, we carried out a detailed bioinformatics analysis based on OS with data from the Cancer Genome Atlas data portal and Human Aging Genomic Resources database, as well as in vitro experiments. A total of 88 OS samples with gene expression profiles and corresponding clinical characteristics were obtained. Through univariate Cox regression analysis and survival analysis, 10 aging-associated survival lncRNAs (AASRs) were identified to be associated with the overall survival of OS patients. Based on the expression levels of the 10 AASRs, the OS patients were classified into two clusters (Cluster A and Cluster B). Cluster A had a worse prognosis, while Cluster B had a better prognosis. Then, 5 AASRs were ultimately included in the signature through least absolute shrinkage and selection operator-Cox regression analysis. Kaplan‒Meier survival analysis verified that the high-risk group exhibited a worse prognosis than the low-risk group. Furthermore, univariate and multivariate Cox regression analyses confirmed that the riskScore was an independent prognostic factor for OS patients. Subsequently, we discovered that the risk signature was correlated with the properties of the tumor microenvironment and immune cell infiltration. Specifically, there was a positive association between the risk model and naïve B cells, resting dendritic cells and gamma delta T cells, while it was negatively related to CD8+ T cells. Finally, in vitro experiments, we found that UNC5B-AS1 inhibited OS cells from undergoing cellular senescence and apoptosis, thereby promoting OS cells proliferation. In conclusion, we constructed and verified a 5 AASR-based signature, that exhibited excellent performance in evaluating the overall survival of OS patients. In addition, we found that UNC5B-AS1 might inhibit the senescence process, thus leading to the development and progression of OS. Our findings may provide novel insights into the treatment of OS patients.

Single-cell and bulk RNA sequencing reveals Anoikis related genes to guide prognosis and immunotherapy in osteosarcoma

Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.

Metastasis-related gene signature associates with immunity and predicts prognosis accurately in patients with osteosarcoma.

Osteosarcoma is the most prevalent malignant bone tumor. In this study, we identified metastasis-related genes (MRG) that are differentially expressed between primary and metastatic osteosarcoma and employed them to create metastasis-related risk tags (MRSs) for the overall survival of osteosarcoma patients. Using consistent cluster analysis, patients with osteosarcoma in the TARGET database were divided into subgroups with different metastatic scoring patterns. The clinicopathological traits, survival rates, tumor microenvironment traits, immune-related scores, and therapeutic responses of these patients varied. Additionally, we constructed MRS-based risk characteristics and nomographs and developed an MRG Score to improve patient characteristics. Thorough evaluations demonstrated that prognostic models and metastasis scores can distinguish high-risk patients from low-risk individuals, offering excellent predictive value. Finally, western blotting was used to confirm the expression of five identified MRG markers, which are crucial for osteosarcoma invasion and migration in terms of mechanism. Our findings represent a novel and practical predictive biomarker for osteosarcoma.

Prognostic value and mechanism of long non-coding RNA DLEU1 in osteosarcoma

To investigate the prognostic value and mechanism of long non-coding RNA DLEU1(LncRNA DLEU1) in osteosarcoma. The tissue samples and clinical data of 86 patients with osteosarcoma treated by orthopaedic surgery in our hospital from January 2012 to December 2014 were retrospectively collected. The expression of LncRNA DLEU1 in pathological tissues was detected by qRT-PCR, then the patients were divided into high and low expression of LncRNA DLEU1 groups. Osteosarcoma cell line HOS was divided into two groups, down-regulated expression group (si-DLEU1 group) and negative control group (si-NC group). LncRNA DLEU1 siRNA and negative control sequence were transfected by Lipofectamine 3000. Chi-square test was used to analyze the relationship between the expression of LncRNA DLEU1 and the clinicopathological factors of osteosarcoma. Kaplan-Meier method was used to compare the difference of the overall survival rate of osteosarcoma patients between the high and low expression groups of LncRNA DLEU1. The risk factors affecting the overall survival rate of osteosarcoma were analyzed by single factor and multifactor analysis. The number of invasive cells in the two groups was determined and compared by Transwell assay. The expression of LncRNA DLEU1 in osteosarcoma tissue was higher than that in adjacent tissues (P<0.001). The expression of LncRNA DLEU1 in human osteosarcoma cell lines (MG-63, U-2 OS, and HOS) was significantly higher than that in human osteoblast line hFOB 1.19 (P<0.001). The expression of LncRNA DLEU1 was significantly correlated with Enneking stage (P<0.001), distant metastasis (P=0.016), and histological grade (P=0.028). The 1-year overall survival rate of the LncRNA DLEU1 high expression group was significantly higher than that of the low expression group (90.7% vs 60.5%, P<0.001). The 5-year overall survival rate of the LncRNA DLEU1 high expression group was significantly higher than that of the low expression group (32.6% vs 11.6%, P<0.001). Univariate analysis showed that Enneking stage (P<0.001), tumor size (P=0.043), distant metastasis (P<0.001), histological grade (P<0.001), and expression of LncRNA DLEU1 (P<0.001) were risk factors for overall survival of osteosarcoma patients. Multivariate analysis showed that high expression of LncRNA DLEU1 [HR=1.948, 95% CI(1.141, 3.641), P=0.012] and distant metastasis[HR=4.108, 95% CI(2.169, 7.780), P<0.001] were independent risk factors for overall survival of osteosarcoma patients. The number of invasive cells in si-DLEU1 group was significantly lesser than that in si-NC group(139±13 vs 357±31, P<0.001). High expression of LncRNA DLEU1 is a molecular marker affecting the prognosis of osteosarcoma patients. Downregulation of LncRNA DLEU1 can inhibit the invasion of osteosarcoma cells.

ALOX5AP suppresses osteosarcoma progression via Wnt/β-catenin/EMT pathway and associates with clinical prognosis and immune infiltration

Osteosarcoma (OS) is one of the most common malignant neoplasms in children and adolescents. Immune infiltration into the microenvironment of the tumor has a positive correlation with overall survival in patients with OS. The purpose of this study was to search for potential diagnostic markers that are involved in immune cell infiltration for OS. Patients with OS who acquired metastases within 5 years (n = 34) were compared to patients who did not develop metastases within 5 years (n = 19). Differentially expressed genes (DEGs) were tested for in both patient groups. To discover possible biomarkers, the LASSO regression model and the SVM–RFE analysis were both carried out. With the assistance of CIBERSORT, the compositional patterns of the 22 different types of immune cell fraction in OS were estimated. In this research, a total of 33 DEGs were obtained: 33 genes were significantly downregulated. Moreover, we identified six critical genes, including ALOX5AP, HLA-DOA, HLA-DMA, HLA-DRB4, HCLS1 and LOC647450. ROC assays confirmed their diagnostic value with AUC > 0.7. In addition, we found that the six critical genes were associated with immune infiltration. Then, we confirmed the expression of ALOX5AP was distinctly decreased in OS specimens and cell lines. High expression of ALOX5AP predicted an advanced clinical stage and overall survival of OS patients. Functionally, we found that overexpression of ALOX5AP distinctly suppressed the proliferation, migration, invasion and EMT via modulating Wnt/β‐catenin signaling. Overall, we found that ALOX5AP overexpression inhibits OS development via regulation of Wnt/β‐catenin signaling pathways, suggesting ALOX5AP as a novel molecular biomarker for enhanced therapy of OS.

Immunogenic cell death-related classifications guide prognosis and immunotherapy in osteosarcoma

Immunogenic cell death (ICD) is a form of cell death that stimulates the immune system to produce an immune response by releasing tumour-associated antigens and tumour-specific antigens and is considered to play an important role in tumour immunotherapy. In the present study, we identified two ICD-related subtypes in osteosarcoma (OS) by consensus clustering. The ICD-low subtype was associated with favourable clinical outcomes, abundant immune cell infiltration, and high activity of immune response signalling. We also established and validated an ICD-related prognostic model, which could not only be used to predict the overall survival of OS patients but was also found to be closely related to the tumour immune microenvironment of OS patients. Overall, we established a new classification system for OS based on ICD-related genes, which can be used to predict the prognosis of OS patients and to select appropriate immunotherapy drugs.

Incidence, survival, and associated factors estimation in osteosarcoma patients with lung metastasis: a single-center experience of 11 years in Tianjin, China

BackgroundOsteosarcoma is the most common primary malignant bone tumor. The current study was conducted to describe the general condition of patients with primary osteosarcoma in a single cancer center in Tianjin, China and to investigate the associated factors in osteosarcoma patients with lung metastasis.MethodsFrom February 2009 to October 2020, patients from Tianjin Medical University Cancer Institute and Hospital, China were retrospectively analyzed. The Kaplan–Meier method was used to evaluate the overall survival of osteosarcoma patients. The Cox proportional hazard regression analysis was performed to analyze the prognostic factors of all osteosarcoma patients and those patients with lung metastasis, respectively. Furthermore, risk factors for developing lung metastasis were identified in synchronous lung metastasis (SLM) and metachronous lung metastasis (MLM) patients.ResultsA total of 203 patients were involved and 150 patients were successfully followed up for survival status. The 5-year survival rate of osteosarcoma was 70.0% and the survival months for patients with SLM and MLM were 33.3 ± 12.6 and 45.8 ± 7.4 months, respectively. The presence of lung metastasis was one of the independent prognostic factors for prognosis of osteosarcoma. In patients with lung metastasis, twenty-one (10.3%) showed lung metastasis at the diagnosis of osteosarcoma and 67 (33%) were diagnosed with lung metastases during the later course. T3 stage (OR = 11.415, 95%CI 1.362–95.677, P = 0.025) and bone metastasis (OR = 6.437, 95%CI 1.69–24.51, P = 0.006) were risk factors of SLM occurrence. Bone metastasis (OR = 1.842, 95%CI 1.053–3.224, P = 0.032), good necrosis (≥ 90%, OR = 0.032, 95%CI 0.050–0.412, P < 0.001), elevated Ki-67 (OR = 2.958, 95%CI 1.098–7.969, P = 0.032) and elevated LDH (OR = 1.791, 95%CI 1.020–3.146, P = 0.043) were proved to be independent risk factors for developing MLM.ConclusionThe overall survival, prognostic factors and risk factors for lung metastasis in this single center provided insight about osteosarcoma management.

Identification and validation of an anoikis-related lncRNA signature to predict prognosis and immune landscape in osteosarcoma.

Anoikis is a specialized form of programmed apoptosis that occurs in two model epithelial cell lines and plays an important role in tumors. However, the prognostic value of anoikis-related lncRNA (ARLncs) in osteosarcoma (OS) has not been reported. Based on GTEx and TARGET RNA sequencing data, we carried out a thorough bioinformatics analysis. The 27 anoikis-related genes were obtained from the Gene Set Enrichment Analysis (GSEA). Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis were successively used to screen for prognostic-related ARLncs. To create the prognostic signature of ARLncs, we performed multivariate Cox regression analysis. We calculated the risk score based on the risk coefficient, dividing OS patients into high- and low-risk subgroups. Additionally, the relationship between the OS immune microenvironment and risk prognostic models was investigated using function enrichment, including Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample gene set enrichment analysis (ssGSEA), and GSEA analysis. Finally, the potential effective drugs in OS were found by immune checkpoint and drug sensitivity screening. A prognostic signature consisting of four ARLncs (AC079612.1, MEF2C-AS1, SNHG6, and TBX2-AS1) was constructed. To assess the regulation patterns of anoikis-related lncRNA genes, we created a risk score model. According to a survival analysis, high-risk patients have a poor prognosis as they progress. By using immune functional analysis, the lower-risk group demonstrated the opposite effects compared with the higher-risk group. GO and KEGG analysis showed that the ARLncs pathways and immune-related pathways were enriched. Immune checkpoints and drug sensitivity analysis might be used to determine the better effects of the higher group. We identified a novel prognostic model based on a four-ARLncs signature that might serve as potential prognostic indicators that can be used to predict the prognosis of OS patients, and immunotherapy and drugs that may contribute to improving the overall survival of OS patients and advance our understanding of OS.

Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients

Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.

Comprehensive analysis of hypoxia-related genes for prognosis value, immune status, and therapy in osteosarcoma patients.

Osteosarcoma is a common malignant bone tumor in children and adolescents. The overall survival of osteosarcoma patients is remarkably poor. Herein, we sought to establish a reliable risk prognostic model to predict the prognosis of osteosarcoma patients. Patients ' RNA expression and corresponding clinical data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus databases. A consensus clustering was conducted to uncover novel molecular subgroups based on 200 hypoxia-linked genes. A hypoxia-risk models were established by Cox regression analysis coupled with LASSO regression. Functional enrichment analysis, including Gene Ontology annotation and KEGG pathway analysis, were conducted to determine the associated mechanisms. Moreover, we explored relationships between the risk scores and age, gender, tumor microenvironment, and drug sensitivity by correlation analysis. We identified two molecular subgroups with significantly different survival rates and developed a risk model based on 12 genes. Survival analysis indicated that the high-risk osteosarcoma patients likely have a poor prognosis. The area under the curve (AUC) value showed the validity of our risk scoring model, and the nomogram indicates the model's reliability. High-risk patients had lower Tfh cell infiltration and a lower stromal score. We determined the abnormal expression of three prognostic genes in osteosarcoma cells. Sunitinib can promote osteosarcoma cell apoptosis with down-regulation of KCNJ3 expression. In summary, the constructed hypoxia-related risk score model can assist clinicians during clinical practice for osteosarcoma prognosis management. Immune and drug sensitivity analysis can provide essential insights into subsequent mechanisms. KCNJ3 may be a valuable prognostic marker for osteosarcoma development.

A deep belief network-based clinical decision system for patients with osteosarcoma.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

Prognostic significance of modified lung immune prognostic index in osteosarcoma patients.

Purpose: Osteosarcoma is the most common primary malignancy of bone with a dismal prognosis for patients with pulmonary metastases. Evaluation of osteosarcoma prognosis would facilitate the prognosis consultation as well as the development of personalized treatment decisions. However, there is limited effective prognostic predictor at present. Lung Immune Prognostic Index (LIPI) is a novel prognostic factor in pulmonary cancers, whereas, the prognostic significance of LIPI in osteosarcoma has not yet been well clarified. In this study, we firstly explore the prognostic role of LIPI and further modify this predictive model in osteosarcoma. Patients and methods: A retrospectively study was conducted at Musculoskeletal Tumor Center of West China Hospital between January 2016 and January 2021. Hematological factors and clinical features of osteosarcoma patients were collected and analyzed. The area under curve (AUC) and optimal cuff-off of each single hematological factor was calculated. Results: In this study, lactate dehydrogenase (LDH), derived neurtrophil to lymphocyte ratio (dNLR), and Hydroxybutyrate dehydrogenase (HBDH) have higher AUC values. LIPI was composed of LDH and dNLR and was further modified by combing the HBDH, forming the osteosarcoma immune prognostic index (OIPI). OIPI divided 223 osteosarcoma patients divided into four groups, none, light, moderate, and severe (p < 0.0001). OIPI has a higher AUC value than LIPI and other hematological indexes in t-ROC curve. According to the univariate and multivariate analysis, pathological fracture, metastasis, NLR, platelet–lymphocyte ratio (PLR), and OIPI were associated with the prognosis; and metastasis and OIPI were independent prognostic factors of osteosarcoma patients. An OIPI-based nomogram was also established and could predict the 3-year and 5-year overall survival. In addition, OIPI was also revealed correlated with metastasis and pathological fracture in osteosarcoma. Conclusion: This study first explore the prognostic significance of LIPI in osteosarcoma patients. In addition, we developed a modified LIPI, the OIPI, for osteosarcoma patients. Both the LIPI and OIPI could predict the overall survival of osteosarcoma patients well, while OIPI may be more suitable for osteosarcoma patients. In particular, OIPI may have the ability to identify some high-risk patients from clinically low-risk patients.