Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

ALOX5AP is an Indicator for High CD8 Lymphocyte Infiltration and "Hot" Tumor Microenvironment in Osteosarcoma: A Bioinformatic Study

Little progress has been made in the treatment and prognosis of osteosarcoma in the past 40 years. Tumor microenvironment (TME) plays a critical role in the progression of osteosarcoma. This study aims to determine immune-associated prognostic biomarkers for osteosarcoma patients. With the help of analytical tools including ESTIMATE, differential gene expression, LASSO, and univariate cox and multivariate cox regression analysis, osteosarcoma gene expression data from Gene Expression Omnibus (GEO) databases were investigated. Following the establishment of a prognostic risk score model, internal and external validations using the GEO and TARGET databases were carried out. A total of 44 and 55 samples respectively in the GSE21257 and the TARGET databases were included. Our analysis found 93 differentially expressed genes (DEGs) between the high and low-ImmuneScore groups. Through univariate cox and LASSO analysis, ALOX5AP was identified as an indicator of TME in osteosarcomas. ALOX5AP was then used to construct a prognostic risk model. Internal and external verification revealed that higher expression of ALOX5AP was correlated with lower risk. Through the CIBERSORT algorithm, the level of CD8 T cells was found to negatively correlate with the risk score. This study revealed that ALOX5AP is an indicator for predicting high CD8 lymphocyte infiltration and "hot" tumor microenvironment in osteosarcomas. Thus, ALOX5AP has the potential to act as a biomarker for effective immunotherapies in osteosarcoma patients.

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Background. In childhood and adolescence, the prevailing bone tumor is osteosarcoma associated with frequent recurrence and lung metastasis. This research focused on predicting the survival and immune landscape of osteosarcoma by developing a prognostic signature and establishing aging-related genes (ARGs) subtypes. Methods. The training group comprised of the transcriptomic and associated clinical data of 84 patients with osteosarcoma accessed at the TARGET database and the validation group consisted of 53 patients from GSE21257. The aging-related subtypes were identified using unsupervised consensus clustering analysis. The ARG signature was developed utilizing multivariate Cox analysis and LASSO regression. The prognostic value was assessed using the univariate and multivariate Cox analyses, Kaplan-Meier plotter, time-dependent ROC curve, and nomogram. The functional enrichment analyses were performed by GSEA, GO, and KEGG analysis, while the ssGSEA, ESTIMATE, and CIBERSORT analyses were conducted to reveal the immune landscape in osteosarcoma. Results. The two clusters of osteosarcoma patients formed based on 543 ARGs, depicted a considerable difference in the tumor microenvironment, and the overall survival and immune cell infiltration rate varied as well. Among these, the selected 23 ARGs were utilized for the construction of an efficient predictive prognostic signature for the overall survival prediction. The testing in the validation group of osteosarcoma patients confirmed the status of the high-risk score as an independent indicator for poor prognosis, which was already identified as such using the univariate and multivariate Cox analyses. Furthermore, the ARG signature could distinguish different immune-related functions, infiltration status of immune cells, and tumor microenvironment, as well as predict the immunotherapy response of osteosarcoma patients. Conclusion. The aging-related subtypes were identified and a prognostic signature was developed in this research, which determined different prognoses and allowed for treatment of osteosarcoma patients to be tailored. Additionally, the immunotherapeutic response of individuals with osteosarcoma could also be predicted by the ARG signature.

Development and validation of apoptosis-related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

BACKGROUND

Previous evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis-related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma.

METHODS

The GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan-Meier plotter, Cox regression, and nomogram analyses.

RESULTS

Two apoptosis-related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high-risk cohort had a lower survival rate than those in the low-risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency.

CONCLUSION

We demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis-related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.

Outstanding prognostic value of novel ferroptosis-related genes in chemoresistance osteosarcoma patients

Osteosarcoma (OS) is the most common bone-derived tumor, and chemoresistance is a pivotal factor in the poor prognosis of patients with OS. Ferroptosis, as an emerging modality of regulated cell death, has demonstrated potential value in tumor chemoresistance studies. Through the gene expression omnibus database in conjunction with the FerrDb database, we identified novel ferroptosis-related differentially expressed genes (DEGs) involving chemoresistance in OS patients. Subsequently, enrichment analysis, protein-protein interaction network analysis and survival analysis were performed sequentially to recognize the hub genes and ultimately to construct a predictive model. The model constructed from the TARGET database was exhibited in a nomogram and assessed by calibration curves. The prognostic value of the model and hub genes was validated separately by an independent cohort. Twenty-two ferroptosis-related DEGs were identified, including 16 up-regulated and 6 down-regulated. Among them, expressions of CBS, COCS1, EGFR, as hub genes, were significantly associated with the prognosis of OS patients and were evidenced as independent prognostic factors. An efficient prognostic model covering hub gene expressions and clinical variables was developed and validated. Combining the results of hub genes in differential analysis, the actions of hub genes in ferroptosis, and the prognostic relevance of hub genes in patients, we revealed that CBS, SOCS1 and EGFR might play essential roles in OS and its chemoresistance with potential research and clinical value.

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained.

Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples.

Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis.

Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients.

Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Objective: Hypoxic tumors contribute to local failure and distant metastases. Nevertheless, the molecular hallmarks of hypoxia remain ill-defined in osteosarcoma. Here, we developed a hypoxic gene signature in osteosarcoma prognoses. Methods: With the random survival forest algorithm, a prognostic hypoxia-related gene signature was constructed for osteosarcoma in the TARGET cohort. Overall survival (OS) analysis, receiver operating characteristic (ROC) curve, multivariate cox regression analysis, and subgroup analysis were utilized for assessing the predictive efficacy of this signature. Also, external validation was presented in the GSE21257 cohort. GSEA was applied for signaling pathways involved in the high- and low-risk samples. Correlation analyses between risk score and immune cells, stromal/immune score, immune checkpoints, and sensitivity of chemotherapy drugs were performed in osteosarcoma. Then, a nomogram was built by integrating risk score, age, and gender. Results: A five-hypoxic gene signature was developed for predicting survival outcomes of osteosarcoma patients. ROC curves confirmed that this signature possessed the well predictive performance on osteosarcoma prognosis. Furthermore, it could be independently predictive of prognosis. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. The low-risk samples were characterized by elevated immune cell infiltrations, stromal/immune scores, TNFRSF4 expression, and sensitivity to cisplatin. The nomogram accurately predicted 1-, 3-, and 5-years survival duration. Conclusion: These findings might offer an insight into the optimization of prognosis risk stratification and individualized therapy for osteosarcoma 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.

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.

Analysis of Immune-Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

Objective: Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune–stromal score-based gene signature and molecular subtypes in osteosarcoma.

Methods: The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs.

Results: High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal–immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment.

Conclusion: This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune–stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes.

Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan–Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature’s prognosis ability.

Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature’s capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS.

Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.