Four genes predict the survival of osteosarcoma patients based on TARGET database

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

TempEasy 3D Hydrogel Coculture System Provides Mechanistic Insights into Prostate Cancer Bone Metastasis

Patients diagnosed with advanced prostate cancer (PCa) often experience incurable bone metastases; however, a lack of relevant experimental models has hampered the study of disease mechanisms and the development of therapeutic strategies. In this study, we employed the recently established Temperature-based Easy-separable (TempEasy) 3D cell coculture system to investigate PCa bone metastasis. Through coculturing PCa and bone cells for 7 days, our results showed a reduction in PCa cell proliferation, an increase in neovascularization, and an enhanced metastasis potential when cocultured with bone cells. Additionally, we observed increased cell proliferation, higher stemness, and decreased bone matrix protein expression in bone cells when cocultured with PCa cells. Furthermore, we demonstrated that the stiffness of the extracellular matrix had a negligible impact on molecular responses in both primary (PCa cells) and distant malignant (bone cells) sites. The TempEasy 3D hydrogel coculture system is an easy-to-use and versatile coculture system that provides valuable insights into the mechanisms of cell-cell communication and interaction in cancer metastasis.

Comprehensive Analysis Reveals Prognostic and Therapeutic Immunity-Related Biomarkers for Pediatric Metastatic Osteosarcoma

Background and Objectives: Osteosarcoma, the most prevalent malignant bone tumor in children and adolescents, presents a complex pathogenesis characterized by various genetic and epigenetic alterations. This study aims to identify key differentially expressed genes (DEGs) in pediatric osteosarcoma, with a focus on those influencing metastasis and patient survival. Materials and Methods: We utilized the GSE33382 dataset from the GEO database for a comprehensive bioinformatic analysis. This included a protein-protein interaction (PPI) network analysis, Cox regression, and Kaplan-Meier survival analysis to identify central DEGs associated with osteosarcoma metastasis and patient survival. Results: Our analysis identified 88 DEGs related to osteosarcoma metastasis. Among them, three survival-related central DEGs-C1QA, CD74, and HLA-DMA-were significantly linked to patient outcomes. Further correlation analysis established a strong relationship between these genes, tumor mutation burden (TMB), immune checkpoint gene expression, and overall survival. Notably, C1QA and CD74 exhibited higher expression in non-metastatic osteosarcoma cases, suggesting a potential role in disease progression. Conclusions: The identified DEGs, particularly C1QA, CD74, and HLA-DMA, may serve as critical biomarkers for pediatric osteosarcoma prognosis and potential targets for immunotherapy. These findings provide a deeper understanding of the molecular landscape of osteosarcoma and open new avenues for therapeutic intervention.

Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma

BACKGROUND

Osteosarcoma is a malignant tumor that usually occurs in adolescents aged 10-20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.

METHODS

Osteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.

RESULTS

We obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.

CONCLUSIONS

Significant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.

HIPK2 in cancer biology and therapy: Recent findings and future perspectives

Homeodomain-interacting protein kinase 2 (HIPK2) is a serine-threonine kinase that phosphorylates and regulates a plethora of transcriptional regulators and chromatin modifiers. The heterogeneity of its interactome allows HIPK2 to modulate several cellular processes and signaling pathways, ultimately regulating cell fate and proliferation. Because of its p53-dependent pro-apoptotic activity and its downregulation in many tumor types, HIPK2 is traditionally considered a bone fide tumor suppressor gene. However, recent findings revealed that the role of HIPK2 in the pathogenesis of cancer is much more complex, ranging from tumor suppressive to oncogenic, strongly depending on the cellular context. Here, we review the very recent data emerged in the last years about the involvement of HIPK2 in cancer biology and therapy, highlighting the various alterations of this kinase (downregulation, upregulation, mutations and/or delocalization) in dependence on the cancer types. In addition, we discuss the recent advancement in the understanding the tumor suppressive and oncogenic functions of HIPK2, its role in establishing the response to cancer therapies, and its regulation by cancer-associated microRNAs. All these data strengthen the idea that HIPK2 is a key player in many types of cancer; therefore, it could represent an important prognostic marker, a factor to predict therapy response, and even a therapeutic target itself.

A five metastasis-related long noncoding RNA risk signature for osteosarcoma survival prediction

BACKGROUND

Osteosarcoma is a highly malignant and common bone tumour with an aggressive disease course and a poor prognosis. Previous studies have demonstrated the relationship between long noncoding RNAs (lncRNAs) and tumorigenesis, metastasis, and progression.

METHODS

We utilized a large cohort from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database osteosarcoma project to identify potential lncRNAs related to the overall survival of patients with osteosarcoma by using univariate and multivariate Cox proportional hazards regression analyses. Kaplan-Meier curves were generated to evaluate the overall survival difference between patients in the high-risk group and the low-risk group. A time-dependent receiver operating characteristic curve (ROC) was employed, and the area under the curve (AUC) of ROC was measured to assess the sensitivity and specificity of the multi-lncRNA signature.

RESULTS

Five lncRNAs (RP11-128N14.5, RP11-231|13.2, RP5-894D12.4, LAMA5-AS1, RP11-346L1.2) were identified, and a five-lncRNA signature was constructed. The AUC for predicting 5-year survival was 0.745, which suggested good performance of the five-lncRNA signature. In addition, functional enrichment analysis of the five-lncRNA-correlated protein-coding genes (PCGs) was performed to show the biological function of the five lncRNAs. Additionally, PPI network suggested RTP1 is a potential biomarker that regulates the prognosis of osteosarcoma.

CONCLUSIONS

We developed a five-lncRNA signature as a potential prognostic indicator for osteosarcoma.

An Integrative Pharmacology-Based Strategy to Uncover the Mechanism of Xiong-Pi-Fang in Treating Coronary Heart Disease with Depression

Objectives: This study aimed to explore the mechanism of Xiong-Pi-Fang (XPF) in the treatment of coronary heart disease (CHD) with depression by an integrative strategy combining serum pharmacochemistry, network pharmacology analysis, and experimental validation. Methods: An ultrahigh performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry (UPLC-Q-TOF/MS) method was constructed to identify compounds in rat serum after oral administration of XPF, and a component-target network was established using Cytoscape, between the targets of XPF ingredients and CHD with depression. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed to deduce the mechanism of XPF in treating CHD with depression. Finally, in a chronic unpredictable mild stress (CUMS)-and isoproterenol (ISO)-induced rat model, TUNEL was used to detect the apoptosis index of the myocardium and hippocampus, ELISA and western blot were used to detect the predicted hub targets, namely AngII, 5-HT, cAMP, PKA, CREB, BDNF, Bcl-2, Bax, Cyt-c, and caspase-3. Results: We identified 51 compounds in rat serum after oral administration of XPF, which mainly included phenolic acids, saponins, and flavonoids. Network pharmacology analysis revealed that XPF may regulate targets, such as ACE2, HTR1A, HTR2A, AKT1, PKIA, CREB1, BDNF, BCL2, BAX, CASP3, cAMP signaling pathway, and cell apoptosis process in the treatment of CHD with depression. ELISA analysis showed that XPF decreased Ang-II content in the circulation and central nervous system, inhibited 5-HT levels in peripheral circulation, and increased 5-HT content in the central nervous system and cAMP content in the myocardia and hippocampus. Meanwhile, western blot analysis indicated that XPF could upregulate the expression levels of PKA, CREB, and BDNF both in the myocardia and hippocampus. TUNEL staining indicated that the apoptosis index of myocardial and hippocampal cells increased in CUMS-and ISO-induced CHD in rats under depression, and XPF could increase the expression of Bcl-2, inhibit the expression of Bax, Cyt-c, and caspase-3, and rectify the injury of the hippocampus and myocardium, which exerted antidepressant and antimyocardial ischemia effects. Conclusion: Our study proposed an integrated strategy, combining serum pharmacochemistry and network pharmacology to investigate the mechanisms of XPF in treating CHD with depression. The mechanism of XPF in treating CHD with depression may be related to the activation of the cAMP signaling pathway and the inhibition of the apoptosis.

A three-gene signature based on tumour microenvironment predicts overall survival of osteosarcoma in adolescents and young adults

Evidences shows that immune and stroma related genes in the tumour microenvironment (TME) play a key regulator in the prognosis of Osteosarcomas (OSs). The purpose of this study was to develop a TME-related risk model for assessing the prognosis of OSs. 82 OSs cases aged ≤25 years from TARGET were divided into two groups according to the immune/stromal scores that were analyzed by the Estimate algorithm. The differentially expressed genes (DEGs) between the two groups were analyzed and 122 DEGs were revealed. Finally, three genes (COCH, MYOM2 and PDE1B) with the minimum AIC value were derived from 122 DEGs by multivariate cox analysis. The three-gene risk model (3-GRM) could distinguish patients with high risk from the training (TARGET) and validation (GSE21257) cohort. Furthermore, a nomogram model included 3-GRM score and clinical features were developed, with the AUC values in predicting 1, 3 and 5-year survival were 0.971, 0.853 and 0.818, respectively. In addition, in the high 3-GRM score group, the enrichment degrees of infiltrating immune cells were significantly lower and immune-related pathways were markedly suppressed. In summary, this model may be used as a marker to predict survival for OSs patients in adolescent and young adults.

Transcriptome Analysis Identifies Novel Prognostic Genes in Osteosarcoma

Osteosarcoma (OS), a malignant primary bone tumor often seen in young adults, is highly aggressive. The improvements in high-throughput technologies have accelerated the identification of various prognostic biomarkers for cancer survival prediction. However, only few studies focus on the prediction of prognosis in OS patients using gene expression data due to small sample size and the lack of public datasets. In the present study, the RNA-seq data of 82 OS samples, along with their clinical information, were collected from the TARGET database. To identify the prognostic genes for the OS survival prediction, we selected the top 50 genes of contribution as the initial candidate genes of the prognostic risk model, which were ranked by random forest model, and found that the prognostic model with five predictors including CD180, MYC, PROSER2, DNAI1, and FATE1 was the optimal multivariable Cox regression model. Moreover, based on a multivariable Cox regression model, we also developed a scoring method and stratified the OS patients into groups of different risks. The stratification for OS patients in the validation set further demonstrated that our model has a robust performance. In addition, we also investigated the biological function of differentially expressed genes between two risk groups and found that those genes were mainly involved with biological pathways and processes regarding immunity. In summary, the identification of novel prognostic biomarkers in OS would greatly assist the prediction of OS survival and development of molecularly targeted therapies, which in turn benefit patients' survival.