A Hypoxia Gene-Based Signature to Predict the Survival and Affect the Tumor Immune Microenvironment of Osteosarcoma in Children

Discovery of core genes and intercellular communication role in osteosarcoma

Osteosarcoma is a primary malignant bone tumor that affects children and young adults. Understanding the molecular mechanisms underlying osteosarcoma is critical to develop effective treatments. This study aimed to identify core genes and explore the role of intercellular communication in osteosarcoma. We used GSE87437 and GSE152048 dataset to conduct a weighted correlation network analysis (WGCNA) and identify co-expression modules. The enriched biological processes and cellular components of the genes in the steelblue module were analyzed. Next, we explored the expression, diagnostic value, correlation, and association with immune infiltrate of CCSER1 and LOC101929154. Finally, we utilized CIBERSORT algorithm to predict the infiltrated immune cells in osteosarcoma tissues. Our results identified 44 co-expression modules, and the steelblue module was mainly associated with axon development, axonogenesis, and innervation. CCSER1 and LOC101929154 were significantly upregulated in osteosarcoma tissues with poor response to preoperative chemotherapy. Moreover, the expressions of CCSER1 and LOC101929154 were positively correlated. The area under the receiver operating characteristic curve of CCSER1 and LOC101929154 was 0.800 and 0.773, respectively. The expression of CCSER1 was negatively correlated with follicular helper T cells and positively correlated with M0 macrophages, while LOC101929154 was negatively correlated with activated mast cells. Besides, CD4 memory-activated T cells were observed at lower levels in patients who responded well to chemotherapy. Our study identified core genes CCSER1 and LOC101929154 and provided insight into the intercellular communication profile in osteosarcoma. Our results suggested that targeting CCSER1, LOC101929154, and CD4 memory-activated T cells may be a promising strategy for the treatment of osteosarcoma.

Develop a Novel Signature to Predict the Survival and Affect the Immune Microenvironment of Osteosarcoma Patients: Anoikis-Related Genes

Objective

Osteosarcoma (OS) represents a prevalent primary bone neoplasm predominantly affecting the pediatric and adolescent populations, presenting a considerable challenge to human health. The objective of this investigation is to develop a prognostic model centered on anoikis-related genes (ARGs), with the aim of accurately forecasting the survival outcomes of individuals diagnosed with OS and offering insights into modulating the immune microenvironment.

Methods

The study's training cohort comprised 86 OS patients sourced from The Cancer Genome Atlas database, while the validation cohort consisted of 53 OS patients extracted from the Gene Expression Omnibus database. Differential analysis utilized the GSE33382 dataset, encompassing three normal samples and 84 OS samples. Subsequently, the study executed gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses. Identification of differentially expressed ARGs associated with OS prognosis was carried out through univariate COX regression analysis, followed by LASSO regression analysis to mitigate overfitting risks and construct a robust prognostic model. Model accuracy was assessed via risk curves, survival curves, receiver operating characteristic curves, independent prognostic analysis, principal component analysis, and t-distributed stochastic neighbor embedding (t-SNE) analysis. Additionally, a nomogram model was devised, exhibiting promising potential in predicting OS patient prognosis. Further investigations incorporated gene set enrichment analysis to delineate active pathways in high- and low-risk groups. Furthermore, the impact of the risk prognostic model on the immune microenvironment of OS was evaluated through tumor microenvironment analysis, single-sample gene set enrichment analysis (ssGSEA), and immune infiltration cell correlation analysis. Drug sensitivity analysis was conducted to identify potentially effective drugs for OS treatment. Ultimately, the verification of the implicated ARGs in the model construction was conducted through the utilization of real-time quantitative polymerase chain reaction (RT-qPCR).

Results

The ARGs risk prognostic model was developed, comprising seven high-risk ARGs (CBS, MYC, MMP3, CD36, SCD, COL13A1, and HSP90B1) and four low-risk ARGs (VASH1, TNFRSF1A, PIP5K1C, and CTNNBIP1). This prognostic model demonstrates a robust capability in predicting overall survival among patients. Analysis of immune correlations revealed that the high-risk group exhibited lower immune scores compared to the low-risk group within our prognostic model. Specifically, CD8+ T cells, neutrophils, and tumor-infiltrating lymphocytes were notably downregulated in the high-risk group, alongside significant downregulation of checkpoint and T cell coinhibition mechanisms. Additionally, three immune checkpoint-related genes (CD200R1, HAVCR2, and LAIR1) displayed significant differences between the high- and low-risk groups. The utilization of a nomogram model demonstrated significant efficacy in prognosticating the outcomes of OS patients. Furthermore, tumor metastasis emerged as an independent prognostic factor, suggesting a potential association between ARGs and OS metastasis. Notably, our study identified eight drugs-Bortezomib, Midostaurin, CHIR.99021, JNK.Inhibitor.VIII, Lenalidomide, Sunitinib, GDC0941, and GW.441756-as exhibiting sensitivity toward OS. The RT-qPCR findings indicate diminished expression levels of CBS, MYC, MMP3, and PIP5K1C within the context of OS. Conversely, elevated expression levels were observed for CD36, SCD, COL13A1, HSP90B1, VASH1, and CTNNBIP1 in OS.

Conclusion

The outcomes of this investigation present an opportunity to predict the survival outcomes among individuals diagnosed with OS. Furthermore, these findings hold promise for progressing research endeavors focused on prognostic evaluation and therapeutic interventions pertaining to this particular ailment.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Objective

To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS.

Methods

We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes.

Results

We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores.

Conclusion

The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

OBJECTIVE

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.

METHODS

Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.

RESULTS

We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.

CONCLUSION

The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.

Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes

Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China.

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.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Hypoxia-immune-related microenvironment prognostic signature for osteosarcoma

Introduction: Increasing evidences have shown that hypoxia and the immune microenvironment play vital roles in the development of osteosarcoma. However, reliable gene signatures based on the combination of hypoxia and the immune status for prognostic prediction of osteosarcoma have so far not been identified. Methods: The individual hypoxia and immune status of osteosarcoma patients were identified with transcriptomic profiles of a training cohort from the TARGET database using ssGSEA and ESTIMATE algorithms, respectively. Lasso regression and stepwise Cox regression were performed to develop a hypoxia-immune-based gene signature. An independent cohort from the GEO database was used for external validation. Finally, a nomogram was constructed based on the gene signature and clinical features to improve the risk stratification and to quantify the risk assessment for individual patients. Results: Hypoxia and the immune status were significantly associated with the prognosis of osteosarcoma patients. Seven hypoxia- and immune-related genes (BNIP3, SLC38A5, SLC5A3, CKMT2, S100A3, CXCL11 and PGM1) were identified to be involved in our prognostic signature. In the training cohort, the prognostic signature discriminated high-risk patients with osteosarcoma. The hypoxia-immune-based gene signature proved to be a stable and predictive method as determined in different datasets and subgroups of patients. Furthermore, a nomogram based on the prognostic signature was generated to optimize the risk stratification and to quantify the risk assessment. Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature. Conclusion: The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.

Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan-Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.

A novel m7G methylation–related signature associated with chromosome homeostasis in patients with lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor of the respiratory system with poor prognosis. Recent studies have revealed that N7-methylguanosine (m7G) methylation is a widespread modification occurring in RNA. But the expression of m7G methylation–related genes in LUAD and their correlations with prognosis are still unclear. In this study, we found 12 m7G methylation–related regulators with differential expression between LUAD and normal lung tissues. According to differentially expressed genes (DEGs), all LUAD cases were separated into two subtypes. The prognostic value of each m7G methylation–related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Finally, an m7G methylation–related prognostic signature based on three genes was built to classify LUAD patients into two risk groups. Patients in the high-risk group showed significantly reduced overall survival (OS) when compared with patients in the low-risk group (p &lt; 0.05). The receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the signature. The Gene Ontology (GO) functional annotation analysis disclosed that chromosome homeostasis plays an important role in this process. The gene set enrichment analysis (ssGSEA) implied that the immune status was decreased in the high-risk group. To sum up, m7G methylation–related genes play a vital role in tumor immunity and the related signature is a reliable predictor for LUAD prognosis.

The Role of the Hypoxia-Related Unfolded Protein Response (UPR) in the Tumor Microenvironment

Despite our understanding of the unfolded protein response (UPR) pathways, the crosstalk between the UPR and the complex signaling networks that different cancers utilize for cell survival remains to be, in most cases, a difficult research barrier. A major problem is the constant variability of different cancer types and the different stages of cancer as well as the complexity of the tumor microenvironments (TME). This complexity often leads to apparently contradictory results. Furthermore, the majority of the studies that have been conducted have utilized two-dimensional in vitro cultures of cancer cells that were exposed to continuous hypoxia, and this approach may not mimic the dynamic and cyclic conditions that are found in solid tumors. Here, we discuss the role of intermittent hypoxia, one of inducers of the UPR in the cellular component of TME, and the way in which intermittent hypoxia induces high levels of reactive oxygen species, the activation of the UPR, and the way in which cancer cells modulate the UPR to aid in their survival. Although the past decade has resulted in defining the complex, novel non-coding RNA-based regulatory networks that modulate the means by which hypoxia influences the UPR, we are now just to beginning to understand some of the connections between hypoxia, the UPR, and the TME.

Engineering Novel 3D Models to Recreate High-Grade Osteosarcoma and its Immune and Extracellular Matrix Microenvironment

Osteosarcoma (OS) is the most common primary bone cancer, where the overall 5-year surviving rate is below 20% in resistant forms. Accelerating cures for those poor outcome patients remains a challenge. Nevertheless, several studies of agents targeting abnormal cancerous pathways have yielded disappointing results when translated into clinic because of the lack of accurate OS preclinical modeling. So, any effort to design preclinical drug testing may consider all inter-, intra-, and extra-tumoral heterogeneities throughout models mimicking extracellular and immune microenvironment. Therefore, the bioengineering of patient-derived models reproducing the OS heterogeneity, the interaction with tumor-associated macrophages (TAMs), and the modulation of oxygen concentrations additionally to recreation of bone scaffold is proposed here. Eight 2D preclinical models mimicking several OS clinical situations and their TAMs in hypoxic conditions are developed first and, subsequently, the paired 3D models faithfully preserving histological and biological characteristics are generated. It is possible to shape reproducibly M2-like macrophages cultured with all OS patient-derived cell lines in both dimensions. The final 3D models pooling all heterogeneity features are providing accurate proliferation and migration data to understand the mechanisms involved in OS and immune cells/biomatrix interactions and sustained such that engineered 3D preclinical systems will improve personalized medicine.

Construction and validation model of necroptosis-related gene signature associates with immunity for osteosarcoma patients

Osteosarcoma is the most common malignant tumor in children and adolescents and its diagnosis and treatment still need to be improved. Necroptosis has been associated with many malignancies, but its significance in diagnosing and treating osteosarcoma remains unclear. The objective is to establish a predictive model of necroptosis-related genes (NRGs) in osteosarcoma for evaluating the tumor microenvironment and new targets for immunotherapy. In this study, we download the osteosarcoma data from the TARGET and GEO websites and the average muscle tissue data from GTEx. NRGs were screened by Cox regression analysis. We constructed a prediction model through nonnegative matrix factorization (NMF) clustering and the least absolute shrinkage and selection operator (LASSO) algorithm and verified it with a validation cohort. Kaplan–Meier survival time, ROC curve, tumor invasion microenvironment and CIBERSORT were assessed. In addition, we establish nomograms for clinical indicators and verify them by calibration evaluation. The underlying mechanism was explored through the functional enrichment analysis. Eight NRGs were screened for predictive model modeling. NRGs prediction model through NMF clustering and LASSO algorithm was established. The survival, ROC and tumor microenvironment scores showed significant statistical differences among subgroups (P < 0.05). The validation model further verifies it. By nomogram and calibration, we found that metastasis and risk score were independent risk factors for the poor prognosis of osteosarcoma. GO and KEGG analyses demonstrate that the genes of osteosarcoma cluster in inflammatory, apoptotic and necroptosis signaling pathways. The significant role of the correlation between necroptosis and immunity in promoting osteosarcoma may provide a novel insight into detecting molecular mechanisms and targeted therapy.

A novel signature to guide osteosarcoma prognosis and immune microenvironment: Cuproptosis-related lncRNA

Objective

Osteosarcoma (OS) is a common bone malignancy with poor prognosis. We aimed to investigate the relationship between cuproptosis-related lncRNAs (CRLncs) and the survival outcomes of patients with OS.

Methods

Transcriptome and clinical data of 86 patients with OS were downloaded from The Cancer Genome Atlas (TCGA). The GSE16088 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The 10 cuproptosis-related genes (CRGs) were obtained from a recently published article on cuproptosis in Science. Combined analysis of OS transcriptome data and the GSE16088 dataset identified differentially expressed CRGs related to OS. Next, pathway enrichment analysis was performed. Co-expression analysis obtained CRLncs related to OS. Univariate COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the risk prognostic model of CRLncs. The samples were divided evenly into training and test groups to verify the accuracy of the model. Risk curve, survival, receiver operating characteristic (ROC) curve, and independent prognostic analyses were performed. Next, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the correlation between the risk prognostic models and OS immune microenvironment. Drug sensitivity analysis identified drugs with potential efficacy in OS. Real-time quantitative PCR, Western blotting, and immunohistochemistry analyses verified the expression of CRGs in OS. Real-time quantitative PCR was used to verify the expression of CRLncs in OS.

Results

Six CRLncs that can guide OS prognosis and immune microenvironment were obtained, including three high-risk CRLncs (AL645608.6, AL591767.1, and UNC5B-AS1) and three low-risk CRLncs (CARD8-AS1, AC098487.1, and AC005041.3). Immune cells such as B cells, macrophages, T-helper type 2 (Th2) cells, regulatory T cells (Treg), and immune functions such as APC co-inhibition, checkpoint, and T-cell co-inhibition were significantly downregulated in high-risk groups. In addition, we obtained four drugs with potential efficacy for OS: AUY922, bortezomib, lenalidomide, and Z.LLNle.CHO. The expression of LIPT1, DLAT, and FDX1 at both mRNA and protein levels was significantly elevated in OS cell lines compared with normal osteoblast hFOB1.19. The mRNA expression level of AL591767.1 was decreased in OS, and that of AL645608.6, CARD8-AS1, AC005041.3, AC098487.1, and UNC5B-AS1 was upregulated in OS.

Conclusion

CRLncs that can guide OS prognosis and the immune microenvironment and drugs that may have a potential curative effect on OS obtained in this study provide a theoretical basis for OS survival research and clinical decision-making.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by "pRRophetic" R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Novel Hypoxia-Associated Gene Signature Depicts Tumor Immune Microenvironment and Predicts Prognosis of Colon Cancer Patients

Hypoxia, a typical hallmark of numerous tumors, indicates poor infiltration of antitumor lymphocytes, as well as facilitates the development, progression, and drug resistance of malignant cells. Here, the present research was performed to identify novel hypoxia-related molecular markers and their correlation to the tumor immune microenvironment (TIME) in colon cancer. The expression of hypoxia-related gene signature was extracted from The Cancer Genome Atlas (TCGA) COAD cohort. Based on this signature, a risk score model was constructed using the Lasso regression model. Its discrimination ability and stability were validated in another independent cohort (GSE17536) from Gene Expression Omnibus (GEO) database. Moreover, molecular biology experiments (quantitative real-time PCR and multiple immunohistochemistry) were performed to validate the results of bioinformatics analyses. Three hub genes, including PPFIA4, SERPINE1, and STC2, were chosen to build the risk score model. All of these genes were increasingly expressed in the hypoxia subgroup (HS). Compared with the normoxia subgroup (NS), HS had worse pathological features (T, N, M, and stage) and overall survival (OS), more expression of immune checkpoint molecules, poorer infiltration of some pro-inflammation immune cells (CD4⁺ T cells and CD8⁺ T cells), and enriched infiltration of M0/M2 macrophages. After the risk model was proven to be valuable and stable, a nomogram was built based on this model and some clinicopathological factors. Moreover, it had been identified that three hub genes were all increasingly expressed in hypoxic conditions by quantitative real-time PCR (qPCR). The results of multiple immunohistochemistry (mIHC) also showed that higher expression of hub genes was associated with poorer infiltration of pro-inflammation immune cells (CD8⁺ T cells and M1 macrophages) and richer infiltration of anti-inflammation immune cells (Treg cells and M2 macrophages). In conclusion, the present study uncovered the relations among hypoxia, TIME, and clinicopathological features of colon cancer. It might provide new insight and a potential therapeutic target for immunotherapy.

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.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Five hypoxia and immunity related genes as potential biomarkers for the prognosis of osteosarcoma

Osteosarcoma accounts for a frequently occurring cancer of the primary skeletal system. In osteosarcoma cells, a hypoxic microenvironment is commonly observed that drives tumor growth, progression, and heterogeneity. Hypoxia and tumor-infiltrating immune cells might be closely related to the prognosis of osteosarcoma. In this study, we aimed to determine the biomarkers and therapeutic targets related to hypoxia and immunity through bioinformatics methods to improve the clinical prognosis of patients. We downloaded the gene expression data of osteosarcoma samples and normal samples in the UCSC Xena database and GTEx database, respectively, and downloaded the validation dataset (GSE21257) in the GEO database. Subsequently, we performed GO enrichment analysis and KEGG pathway enrichment analysis on the data of the extracted osteosarcoma hypoxia-related genes. Through univariate COX regression analysis, lasso regression analysis, multivariate COX regression analysis, etc., we established a predictive model for the prognosis of osteosarcoma. Five genes, including ST3GAL4, TRIM8, STC2, TRPS1, and FAM207A, were found by screening. In particular, we analyzed the immune cell composition of each gene based on the five genes through the CIBERSORT algorithm and verified each gene at the cell and tissue level. Our findings are valuable for the clinical diagnosis and treatment of this disease.

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Simple Summary

Osteosarcomas are heterogeneous bone tumors with complex genetic and chromosomic alterations. The numerous patients with metastatic osteosarcoma have a very poor prognosis, and only those who can have full surgical resection of the primary tumor and of all the macro metastasis can survive. Despite the recent improvements in prediction and early detection of metastasis, big efforts are still required to understand the specific mechanisms of osteosarcoma metastatic progression, in order to reveal novel therapeutic targets.

Abstract

Metastases of osteosarcomas are heterogeneous. They may grow simultaneously with the primary tumor, during treatment or shortly after, or a long time after the end of the treatment. They occur mainly in lungs but also in bone and various soft tissues. They can have the same histology as the primary tumor or show a shift towards a different differentiation path. However, the metastatic capacities of osteosarcoma cells can be predicted by gene and microRNA signatures. Despite the identification of numerous metastasis-promoting/predicting factors, there is no efficient therapeutic strategy to reduce the number of patients developing a metastatic disease or to cure these metastatic patients, except surgery. Indeed, these patients are generally resistant to the classical chemo- and to immuno-therapy. Hence, the knowledge of specific mechanisms should be extended to reveal novel therapeutic approaches. Recent studies that used DNA and RNA sequencing technologies highlighted complex relations between primary and secondary tumors. The reported results also supported a hierarchical organization of the tumor cell clones, suggesting that cancer stem cells are involved. Because of their chemoresistance, their plasticity, and their ability to modulate the immune environment, the osteosarcoma stem cells could be important players in the metastatic process.

Distinct Hypoxia-Related Gene Profiling Characterizes Clinicopathological Features and Immune Status of Mismatch Repair-Deficient Colon Cancer

Despite dramatic responses to immune checkpoint inhibitors (ICIs) in patients with colon cancer (CC) harboring deficient mismatch repair (dMMR), more than half of these patients ultimately progress and experience primary or secondary drug resistance. There is no useful biomarker that is currently validated to accurately predict this resistance or stratify patients who may benefit from ICI-based immunotherapy. As hypoxic and acidic tumor microenvironment would greatly impair tumor-suppressing functions of tumor-infiltrating lymphocytes (TILs), we sought to explore distinct immunological phenotypes by analysis of the intratumoral hypoxia state using a well-established gene signature. Based on the Gene Expression Omnibus (GEO) (n = 88) and The Cancer Genome Atlas (TCGA) (n = 49) databases of patients with CC, we found that dMMR CC patients could be separated into normoxia subgroup (NS) and hypoxia subgroup (HS) with different levels of expression of hypoxia-related genes (lower in NS group and higher in HS group) using NMF package. Tumoral parenchyma in the HS group had a relatively lower level of immune cell infiltration, particularly CD8+ T cells and M1 macrophages than the NS group, and coincided with higher expression of immune checkpoint molecules and C-X-C motif chemokines, which might be associated with ICI resistance and prognosis. Furthermore, three genes, namely, MT1E, MT2A, and MAFF, were identified to be differentially expressed between NS and HS groups in both GEO and TCGA cohorts. Based on these genes, a prognostic model with stable and valuable predicting ability has been built for clinical application. In conclusion, the varying tumor-immune microenvironment (TIME) classified by hypoxia-related genes might be closely associated with different therapeutic responses of ICIs and prognosis of dMMR CC patients.

A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.