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

Novel ferroptosis signature for improving prediction of prognosis and indicating gene targets from single-cell level in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most prevalent kinds of cancers. Therefore, there is a pressing need to create a new risk scoring model to personalize the prognosis of OSCC patients and screen for patient-specific therapeutic agents and molecular targets.

Methods

Firstly, A series of bioinformatics was performed to construct a novel ferroptosis-related prognostic model; Further, drug sensitivity analysis was used to screen for specific therapeutic agents for OSCC; Single-cell analysis was employed to investigate the enrichment of FRDEGs (ferroptosis-related differentially expressed genes) in the OSCC microenvironment; Finally, various experiments were conducted to screen and validate molecular therapeutic targets for OSCC.

Results

In this study, we constructed a novel 10-FRDEGs risk scoring model. Base on the risk scoring model, we founded three potential chemotherapeutic agents for OSCC: 5Z)-7-Oxozeaenol, AT-7519, KIN001-266; In addition, FRDEGs were enriched in the epithelial cells of OSCC. Finally, we found that CA9 and CAV1 could regulate OSCC proliferation, migration and ferroptosis in vitro.

Conclusion

A novel 10-FRDEGs risk scoring model can predict the prognosis of patients with OSCC.Further,5Z)-7-Oxozeaenol, AT-7519, KIN001-266 are potential chemotherapeutic agents for OSCC.Moreover, we identified CA9、CAV1 as potential molecular target for the treatment of OSCC.Our findings provide new directions for prognostic assessment and precise treatment of oral cell squamous carcinoma.

A prognostic risk model based on lactate metabolism and transport-related lncRNAs for gastric adenocarcinoma

BACKGROUND

Increased lactate levels and metastasis in tumours are strongly associated with dismal outcomes. But prognostic value of lactate metabolism and transport-related lncRNAs in gastric adenocarcinoma (GA) patients remains unaddressed.

METHODS

Gene expression data of GA were provided by The Cancer Genome Atlas. Lactate metabolism and transport-related gene data were accessed from GSEA. LncRNAs related to lactate metabolism and transport were identified by correlation analysis. A prognostic model was built by regression analysis. Validity of prognostic model was confirmed through survival analysis and receiver operating characteristic (ROC) curve. Immunity of each risk group was evaluated by immune correlation analysis .LncRNA-mRNA network was built by correlation analysis using Cytoscape software.

RESULTS

A 12-gene prognostic model based on lactate metabolism and transport-related lncRNAs was built in GA. Median riskscore was utilized to classify GA samples into high- and low-risk groups. Survival analysis and ROC curves demonstrated validity of prognostic model. Most immune checkpoint molecules and TIDE scores were lower in the low-risk group. LINC01303 and LINC01545 may be the key prognostic factors in patients with GA.

CONCLUSION

This study successfully built a prognostic model of lactate metabolism and transport-related lncRNAs in GA. The findings guide prognostic management of GA patients.

Colorectal cancer subtyping and prognostic model construction based on interleukin-related genes

Members of the interleukin (IL) family are closely linked to cancer development and progression. However, research on the prognosis of colorectal cancer (CRC) related to IL is still lacking. This study investigated new CRC prognostic markers and offered new insights for CRC prognosis and treatment. CRC-related data and IL gene data were collected from public databases. Sample clustering was done with the NMF package to divide samples into different subtypes. Differential, enrichment, survival, and immune analyses were conducted on subtypes. A prognostic model was constructed using regression analysis. Drug sensitivity analysis was performed using GDSC database. Western blot analysis was performed to assess the effect of IL-7 on the JAK/STAT signaling pathway. Flow cytometry was used to examine the impact of IL-7 on CD8+ T cell apoptosis. Two CRC subtypes based on IL-associated genes were obtained. Cluster 1 had a higher survival rate than cluster 2, and they showed differences in some immune levels. The two clusters were mainly enriched in the JAK-STAT signaling pathway, T helper 17 cell differentiation, and the IL-17 signaling pathway. An 11-gene signature was built, and risk score was an independent prognosticator for CRC. The low-risk group showed a higher sensitivity to nine common targeted anticancer drugs. Western blot and flow cytometry results demonstrated that IL-7 could phosphorylate STAT5 and promote survival of CD8+ T cells. In conclusion, this study divided CRC samples into two IL-associated subtypes and obtained an 11-gene signature. In addition, targeted drugs that may improve the prognosis of patients with CRC were identified. These findings are of paramount importance for patient prognosis and CRC treatment.NEW & NOTEWORTHY We identified two clusters with significant survival differences in colorectal cancer (CRC) based on interleukin-related genes, constructed an 11-gene risk score model that can independently predict the prognosis of CRC, and explored some targeted drugs that may improve the prognosis of patients with CRC. The results of this study have important implications for the prognosis and treatment of CRC.

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.

Single-Cell RNA Sequencing Pro-angiogenic Macrophage Profiles Reveal Novel Prognostic Biomarkers and Therapeutic Targets for Osteosarcoma

Osteosarcoma (OS) is a malignant bone tumor that most commonly occurs in children and adolescents. OS patients have a poor prognosis, and 5-year survival rates have rarely improved significantly over the past few decades. OS prognosis may be related to the infiltration of tumor-associated macrophages (TAMs). However, the role of proangiogenic macrophages, a subtype of TAMs, in OS prognosis has not been reported. In this study, seven subtypes of TAMs were identified from single-cell RNA sequencing (scRNA-seq) data that we propose defining as proangiogenic TAMs (Angio-TAMs), interferon-primed TAMs (IFN-TAMs), inflammatory cytokine-enriched TAMs (Inflam-TAMs), immune regulatory TAMs (Reg-TAMs), lipid-associated TAMs (LA-TAMs), and resident-tissue macrophages like TAMs (RTM-TAMs) (containing two subcellular types). In the survival analysis of each macrophage subtype, it was found that patients with Angio-TAMs had the most significant difference in survival. Eight genes associated with Angio-TAMs were obtained by differential expression analysis, and these genes were built into a prognostic model using the LASSO algorithm. Clinical OS case samples were categorized into high-risk and low-risk subgroups using median risk scores. In comparison to the low-risk subgroup, the survival time of the high-risk subgroup was much shorter. Additional studies on immune cell infiltration and immune checkpoint molecule expression in the two risk subgroups were carried out. In immunotherapy response prediction, the Angio-TAM-associated gene risk signature was found to be negatively correlated with immune checkpoint responses. In addition, the associated enriched GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were mainly involved in the malignant progression of tumors. As suggested by these findings, the Angio-TAM gene risk signature may be an underlying prognostic biomarker and novel therapeutic target for OS patients.Kindly check and confirm whether the ESM file is correctly identifiedWe have checked this file and confirmed that it can be correctly identified.

A Novel Defined Necroptosis-Related Genes Prognostic Signature for Predicting Prognosis and Treatment of Osteosarcoma

Osteosarcoma (OS) is a frequent primary malignant bone tumor, with a poor prognosis. Necroptosis is strongly correlated with OS and may be an influential target for treating OS. This study's objective was to establish a necroptosis-related gene (NRG) prognostic signature that could predict OS prognosis and guide OS treatment. First, we identified 20 NRGs associated with OS survival based on the TARGET database. We then derived a 7 NRG prognostic signature. Our findings revealed that the 7 NRG prognostic signature performed well in predicting the survival of OS patients. We next analyzed differences in immunological status and immune cell infiltration. In addition, we examined the relationship between chemo/immunotherapeutic response and the 7-NRG prognostic signature. In addition, to probe the mechanisms underlying the NRG prognostic signature, we performed functional enrichment assays including GO and KEGG. Finally, CHMP4C was selected for functional experiments. Silencing CHMP4C prevented OS cells from proliferating, migrating, and invading. This 7-NRG prognostic signature seems to be an excellent predictor that can provide a fresh direction for OS treatment.

Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma

BACKGROUND

Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient.

METHODS

HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis.

RESULTS

PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs.

CONCLUSIONS

We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Harnessing ferroptosis for enhanced sarcoma treatment: mechanisms, progress and prospects

Sarcoma is a malignant tumor that originates from mesenchymal tissue. The common treatment for sarcoma is surgery supplemented with radiotherapy and chemotherapy. However, patients have a 5-year survival rate of only approximately 60%, and sarcoma cells are highly resistant to chemotherapy. Ferroptosis is an iron-dependent nonapoptotic type of regulated programmed cell death that is closely related to the pathophysiological processes underlying tumorigenesis, neurological diseases and other conditions. Moreover, ferroptosis is mediated via multiple regulatory pathways that may be targets for disease therapy. Recent studies have shown that the induction of ferroptosis is an effective way to kill sarcoma cells and reduce their resistance to chemotherapeutic drugs. Moreover, ferroptosis-related genes are related to the immune system, and their expression can be used to predict sarcoma prognosis. In this review, we describe the molecular mechanism underlying ferroptosis in detail, systematically summarize recent research progress with respect to ferroptosis application as a sarcoma treatment in various contexts, and point out gaps in the theoretical research on ferroptosis, challenges to its clinical application, potential resolutions of these challenges to promote ferroptosis as an efficient, reliable and novel method of clinical sarcoma treatment.

Multiomics analysis for ferroptosisrelated genes as prognostic factors in cutaneous melanoma

OBJECTIVES

Melanoma is highly malignant and heterogeneous. It is essential to develop a specific prognostic model for improving the patients' survival and treatment strategies. Recent studies have shown that ferroptosis results from the overproduction of lipid peroxidation and is an iron-dependent form of programmed cell death. Despite this, ferroptosis-related genes (FRGs) and their clinical significances remain unknown in malignant melanoma. This study aims to assess the role of FRGs in melanoma, with the goal of developing a novel prognostic model that provides new insights into personalized treatment and improvement of therapeutic outcomes for melanoma.

METHODS

We systematically characterized the genetic alterations and mRNA expression of 73 FRGs in The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) dataset in this study. The results were validated with real-time RT-PCR and Western blotting. Subsequently, a multi-gene feature model was constructed using the TCGA-SKCM cohort. Melanoma patients were classified into a high-risk group and a low-risk group based on the feature model. As a final step, correlations between ferroptosis-related signatures and immune features, immunotherapy efficacy, or drug response were analyzed.

RESULTS

By analyzing melanoma samples from TCGA-SKCM dataset, FRGs exhibited a high frequency of genetic mutations and copy number variations (CNVs), significantly impacting gene expression. Additionally, compared with normal skin tissue, 30 genes with significantly differential expression were identified in melanoma tissues. A prognostic model related to FRGs, constructed using the LASSO Cox regression method, identified 13 FRGs associated with overall survival prognosis in patients and was validated with external datasets. Finally, functional enrichment and immune response analysis further indicated significant differences in immune cell infiltration, mutation burden, and hypoxia status between the high-risk group and the low-risk group, and the model was effective in predicting responses to immunotherapy and drug sensitivity.

CONCLUSIONS

This study develops a strong ferroptosis-related prognostic signature model which could put forward new insights into target therapy and immunotherapy for patients with melanoma.

Development and validation of neutrophil extracellular traps-derived signature to predict the prognosis for osteosarcoma patients

Neutrophil extracellular traps (NETs) have been reported to be crucial in tumorigenesis and malignant progression. However, their prognostic significance, association with tumor immune microenvironment (TIME), and therapeutic response in osteosarcoma (OS) stills remain unclear. Hence, TARGET and GSE21257 cohorts were included for analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were conducted to extract NETs-derived genes. Subsequently, the NETs score (NETScore) model, consisting of 4 signature genes, was established and validated with the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Our results indicated that NETScore has satisfactory predictability of the patient's overall survival, with AUC values at 1-, 3- and 5-year in the training cohort of 0.798, 0.792 and 0.804, respectively; similar prominent prediction performance was obtained in three validation cohorts. Further, real-time quantitative PCR (RT-qPCR) assay was conducted to determine the expression of signature genes in human osteoblasts and OS cells. Besides, NETScore and clinical factors (age, gender, metastatic status) were integrated to construct a nomogram. C-index and AUC values at 1-, 3-, and 5-year were above 0.800, displaying robust predictive performance. Patients with high and low NETScore had different immune statuses and drug sensitivity. Meanwhile, several positive regulatory immune function pathways, including T cell proliferation, activation and migration, were significantly suppressed among patients with high NETScore. Summarily, we established a novel NETScore that can accurately predict OS patients' prognosis, which correlated closely with the immune landscape and therapeutic response and might help to guide clinical decision-making.

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma

BACKGROUND

Risk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment.

METHODS

High-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated.

RESULTS

High-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB, CEP55, SRSF3, TCF7L2, and FOXP1) and the SE-derived LMFS-signature (comprising SRSF3, TCF7L2, FOXP1, and APOLD1). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors.

CONCLUSIONS

Oncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

Palladium (II) compounds containing oximes as promising antitumor agents for the treatment of osteosarcoma: An in vitro and in vivo comparative study with cisplatin

Drug resistance, evasion of cell death and metastasis are factors that contribute to the low cure rate and disease-free survival in osteosarcomas (OS). In this study, we demonstrated that a new class of oxime-containing organometallic complexes called Pd-BPO (O3) and Pd-BMO (O4) are more cytotoxic than cisplatin (CDDP) for SaOS-2 and U2OS cells using the MTT assay. Annexin-FITC/7-AAD staining demonstrated a greater potential for palladium-oxime complexes to induce death in SaOS-2 cells than CDDP, an event confirmed using the pan-caspase inhibitor Z-VAD-FMK. Compared to CDDP, only palladium-oxime complexes eradicated the clonogenicity of SaOS-2 cells after 7 days of treatment. The involvement of the lysosome-mitochondria axis in the cell death-inducing properties of the complexes was also evaluated. Using LysoTracker Red to label the acidic organelles of SaOS-2 cells treated with the O3 and O4 complexes, a decrease in the fluorescence intensity of this probe was observed in relation to CDDP and the control. Lysosomal membrane permeabilization (LMP) was also induced by the O3 and O4 complexes in an assay using acridine orange (A/O). The greater efficiency of the complexes in depolarizing the mitochondrial membrane compared to SaOS-2 cells treated with CDDP was also observed using TMRE (tetramethyl rhodamine, ethyl ester). For in vivo studies, C. elegans was used and demonstrated that both complexes reduce body bends and pharyngeal pumping after 24 h of treatment to the same extent as CDDP. We conclude that both palladium-oxime complexes are more effective than CDDP in inducing tumor cell death. The toxicity of these complexes to C. elegans was like that induced by CDDP. These results encourage preclinical studies aimed at developing more effective drugs for the treatment of osteosarcoma (OS). Furthermore, we propose palladium-oxime complexes as a new class of antineoplastic agents.

SLC9A2, suppressing by the transcription suppressor ETS1, restrains growth and invasion of osteosarcoma via inhibition of aerobic glycolysis

Recent studies have shown that Solute Carrier Family 9 Member A2 (SLC9A2) could serve as a biomarker for cancer. However, its mechanism of action in osteosarcoma (OS) was still unclear. In this study, the data sets GSE154530 and GSE99671 were downloaded from the Gene Expression Omnibus (GEO) database, and 31 differentially expressed genes (DEGs) related to methylation were screened by bioinformatics analysis tools. Subsequently, SLC9A2 was screened as a candidate gene from DEGs, which was significantly downregulated in OS. CCK-8, transwell, western blotting and Seahorse XFe24 Cell Metabolic Analyzer assays demonstrated that overexpression of SLC9A2 could constrain OS cell proliferation, invasion, and aerobic glycolysis. Dual-luciferase reporter gene assay and chromatin immunoprecipitation (ChIP) assays indicated ETS proto-oncogene 1 (ETS1) was a transcription suppressor of SLC9A2, and overexpression of ETS1 could promote methylation levels in specific regions of the SLC9A2 promoter. ETS1 could promote the proliferation, invasion, and aerobic glycolysis ability of OS cells, as well as tumor growth in vivo by inhibiting the expression of SLC9A2. In addition, SLC9A2, suppressing by ETS1, restrains growth and invasion of OS via inhibition of aerobic glycolysis. Thus, SLC9A2 can function as a key inhibitory factor in the aerobic glycolysis to inhibit proliferation and invasion of OS. This indicated that SLC9A2 has a potential targeted therapeutic effect on OS.

Pyroptosis-related gene signature elicits immune response in rosacea

Rosacea is a complex chronic inflammatory skin disorder with high morbidity. Pyroptosis is known as a regulated inflammatory cell death. While its association with immune response to various inflammatory disorders is well established, little is known about its functional relevance of rosacea. So, we aimed to explore and enrich the pathogenesis involved in pyroptosis-related rosacea aggravations. In this study, we evaluated the pyroptosis-related patterns of rosacea by consensus clustering analysis of 45 ferroptosis-related genes (FRGs), with multiple immune cell infiltration analysis to identify the pyroptosis-mediated immune response in rosacea using GSE65914 dataset. The co-co-work between PRGs and WGCNA-revealed hub genes has established using PPI network. FRG signature was highlighted in rosacea using multi-transcriptomic and experiment analysis. Based on this, three distinct pyroptosis-related rosacea patterns (non/moderate/high) were identified, and the notably enriched pathways have revealed through GO, KEGG and GSEA analysis, especially immune-related pathways. Also, the XCell/MCPcount/ssGSEA/Cibersort underlined the immune-related signalling (NK cells, Monocyte, Neutrophil, Th2 cells, Macrophage), whose hub genes were identified through WGCNA (NOD2, MYD88, STAT1, HSPA4, CXCL8). Finally, we established a pyroptosis-immune co-work during the rosacea aggravations. FRGs may affect the progression of rosacea by regulating the immune cell infiltrations. In all, pyroptosis with its mediated immune cell infiltration is a critical factor during the development of rosacea.

A novel ferroptosis-related microRNA signature with prognostic value in osteosarcoma

The induction of ferroptosis is suggested to be a potential therapeutic strategy for cancers. MicroRNAs (miRNAs) are reported to play an important role in cell death processes. This study aims to construct and validate a risk model based on ferroptosis-related miRNAs (FR_miRNAs) to predict prognosis and identify novel therapeutic targets for osteosarcoma. Data from the Therapeutically Applicable Research to Generate Effective Treatments database are used as the training cohort. A prognostic signature based on two FR_miRNAs (miR-635 and miR-593) is developed using univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The area under the curve values of the prognostic signature to predict the 1-year, 2-year, 3-year, and 5-year overall survival rates in patients with osteosarcoma are 0.782, 0.781, 0.722, and 0.777, respectively, indicating a good predictive ability. Based on the risk score, patients are divided into low-risk and high-risk groups. Patients with high-risk scores are associated with poor survival. The risk level is determined to be an independent prognostic factor. A nomogram is established for predicting prognosis. The expression levels of PRNP (miR-635-related ferroptosis-related gene (FRG); P=0.024) and HILPDA (miR-593-related FRG; P=0.025) are significantly different between the low-risk and high-risk groups. All results are validated in an external cohort (GSE39040). The results of the functional assay reveal that miR-635 mimics inhibit osteosarcoma (OS) cell proliferation and migration, whereas miR-593 overexpression exerts the opposite effect. In conclusion, miR-635 and miR-593 exert contrasting regulatory effects on OS cell proliferation and migration.

High expression of integrin-binding sialoprotein (IBSP) is associated with poor prognosis of osteosarcoma

INTRODUCTION

Osteosarcoma is a malignant tumor, accounting for 20% of primary malignant bone tumors worldwide. However, the role of IBSP as a biomarker in osteosarcoma progression has not been studied yet.

METHODS

85 cases of IBSP expression and clinical characteristics were obtained from TARGET database. Through the Kaplan-Meier curve, subgroup analysis, and univariate and multivariate Cox analysis, we further assessed the independent predictive capacity of IBSP expression for overall survival (OS) and relapse-free survival (RFS).

RESULTS

The mRNA expression of IBSP was higher in osteosarcoma than normal tissue (P < 0.0001). IBSP expression grouped by vital status showed statistical differences (P = 0.042). The race (P = 0.0183), vital status (P = 0.0034), and sample type (P = 0.0020) showed significant differences. IBSP expression exhibited satisfied diagnostic ability for osteosarcoma. The univariate and multivariate analysis confirmed that IBSP expression was an independent risk factor for OS (HR = 3.425, 95% CI: 1.604-7.313, P = 0.002) and RFS (HR = 3.377, 95% CI: 1.775-6.424, P < 0.001) in osteosarcoma patients. High IBSP expression was significantly associated with poor OS and RFS (P < 0.0001). The higher IBSP expression was observed in osteosarcoma (P < 0.001), confirmed by the IHC staining. The CCK-8 and colony formation assay showed that IBSP knockdown inhibits cell proliferation while overexpression promotes cell proliferation (P < 0.05).

CONCLUSION

High expression of IBSP was associated with poor OS and RFS. IBSP could serve as a potential biomarker for osteosarcoma, which could aid in early detection and disease monitoring.

CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma

BACKGROUND

Most sarcomatoid differentiated renal cell carcinoma was differentiated from Chromophobe renal cell carcinoma (KICH) and related to a bad prognosis. Thus, finding biomarkers is important for the therapy of KICH.

METHODS

The UCSC was used for determining the expression of mRNA and miRNA and clinical data in KICH and normal samples. KEGG and GO were used for predicting potential function of differently expressed genes (DEGs). Optimal prognostic markers were determined by Lasso regression. Kaplan-Meier survival, ROC, and cox regression were used for assessing prognosis value. GSEA was used for predicting potential function of markers. The relations between markers and immune cell infiltration were determined by Pearson method. The upstream miRNA of markers was predicted in TargetScan and DIANA.

RESULTS

The 6162 upregulated and 13,903 downregulated DEGs were identified in KICH. Further CENPE and LDHA were screened out as optimal prognostic risk signatures. CENPE was highly expressed while LDHA was lowly expressed in KICH samples, and the high expressions of 2 genes contributed to bad prognosis. The functions of CENPE and LDHA were mainly enriched in proliferation related pathways such as cell cycle and DNA replication. In addition, the correlation of 2 genes with immune infiltrates in KICH was also observed. Finally, we found that has-miR-577 was the common upstream of 2 genes and the binding sites can be predicted.

CONCLUSION

CENPE and LDHA were identified as the important prognostic biomarkers in KICH, and they might be involved in the proliferation of cancer cell.

Circular RNA circBLNK promotes osteosarcoma progression and inhibits ferroptosis in osteosarcoma cells by sponging miR‑188‑3p and regulating GPX4 expression

As a newly identified circular RNA (circRNA), the role of circBLNK in cancer progression has not been probed. The objective of the present study was to functionally dissect the role of circBLNK in osteosarcoma (OS) tumorigenesis and progression. With regards of the experimental procedure, the levels of mRNAs and proteins were assessed using reverse transcription‑quantitative PCR and western blot analysis, respectively. The subcellular location of circBLNK in OS cells was determined by cell cytosolic/nuclear fractionation assay. Cell ferroptosis ability was assessed through MTT assay. Cell proliferative abilities were assessed by clonogenic and Cell Counting Kit‑8 assays, and cell apoptosis was measured using flow cytometry. The relationships among circBLNK, miR‑188‑3p, and glutathione peroxidase 4 (GPX4) were validated by luciferase reporter and RNA pull‑down assays, as well as RNA immunoprecipitation. The stability of circBLNK and linear BLNK was confirmed using RNase R treatment assay. The association between circBLNK expression and overall survival rate was assessed by Kaplan‑Meier plot. The correlation between the expression levels of circBLNK, miR‑188‑3p, and GPX4 in OS tissues was assessed by Pearson's χ2 test. The results revealed that CircBLNK and GPX4 were significantly upregulated in OS tissues, which predicted the poor prognosis. CircBLNK knockdown led to suppressed cell proliferation and enhanced cell apoptosis, an effect that could be reversed by the inhibition of miR‑188‑3p. In an in vivo circBLNK deficiency model, tumor growth was observed to be markedly suppressed. Moreover, circBLNK deficiency elevated levels of intracellular free iron (Fe2+), malondialdehyde, lipid reactive oxygen species and mitochondrial superoxide, while diminishing mitochondrial membrane potential in Erastin‑treated OS cells, which were eliminated by overexpressing GPX4. Furthermore, mechanistic investigations revealed that circBLNK sponged miR‑188‑3p to regulate the expression of GPX4, thereby affecting OS progression. In conclusion, the present study delineated a new regulatory axis involving circBLNK/miR‑188‑3p/GPX4 in OS progression, adding to the growing evidence that circRNAs are critical gene regulators in cancer progression.

New gene signature from the dominant infiltration immune cell type in osteosarcoma predicts overall survival

The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.

Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.

Decoding the Impact of Tumor Microenvironment in Osteosarcoma Progression and Metastasis

Osteosarcoma (OS) is a heterogeneous, highly metastatic bone malignancy in children and adolescents. Despite advancements in multimodal treatment strategies, the prognosis for patients with metastatic or recurrent disease has not improved significantly in the last four decades. OS is a highly heterogeneous tumor; its genetic background and the mechanism of oncogenesis are not well defined. Unfortunately, no effective molecular targeted therapy is currently available for this disease. Understanding osteosarcoma's tumor microenvironment (TME) has recently gained much interest among scientists hoping to provide valuable insights into tumor heterogeneity, progression, metastasis, and the identification of novel therapeutic avenues. Here, we review the current understanding of the TME of OS, including different cellular and noncellular components, their crosstalk with OS tumor cells, and their involvement in tumor progression and metastasis. We also highlight past/current clinical trials targeting the TME of OS for effective therapies and potential future therapeutic strategies with negligible adverse effects.

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.

Chemokine Ligand 2 Promotes Migration in Osteosarcoma by Regulating the miR-3659/MMP-3 Axis

Osteosarcoma is a common malignant tumor in children and adolescents, known for its aggressive invasion and distant metastasis, leading to a poor prognosis. Matrix metalloproteinases (MMPs) can degrade the extracellular matrix and basement membranes through their proteolytic activity, thereby promoting osteosarcoma metastasis. Chemokine ligand 2 (CCL2) is a well-studied chemokine that plays a significant role in the cell motility of many cancers. However, its specific involvement in osteosarcoma metastasis is not fully understood. The aim of this study is to examine the role of miRNAs in CCL2-mediated MMP expression and cell motility in human osteosarcoma. The analysis of immunohistochemistry data and databases associated a positive correlation between CCL2 or MMP-3 levels with the metastasis of osteosarcoma patients. The in vivo lung metastatic osteosarcoma model also demonstrated similar effects, showing higher levels of CCL2 and MMP-3 in lung metastatic osteosarcoma tissues. The stimulation of osteosarcoma cells with CCL2 enhanced migration and invasion abilities through the upregulation of MMP-3 synthesis. Our results also indicate that CCL2 enhances MMP-3-dependent cell motility by inhibiting miR-3659 synthesis. Therefore, CCL2 represents a promising therapeutic target for treating metastasis in osteosarcoma.

A novel clinical tool and risk stratification system for predicting the event-free survival of neuroblastoma patients: A TARGET-based study

Neuroblastoma (NB), considered the most common non-intracranial solid tumor in children, accounts for nearly 8% of pediatric malignancies. This study aimed to develop a simple and practical nomogram to predict event-free survival (EFS) in NB patients and establish a new risk stratification system. In this study, 763 patients primarily diagnosed with NB in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were included and randomly assigned to a training set (70%) and a validation set (30%) in a 7:3 ratio. First, the independent prognostic factors of EFS for NB patients were identified through univariate and multivariate Cox regression analyses. Second, a nomogram was created based on these factors and was validated for calibration capability, discriminative, and clinical significance by C-curves, receiver operating characteristic (ROC) curves, and decision curve analysis. Finally, a new risk stratification system was established for NB patients based on the nomogram. The univariate Cox analysis demonstrated that NB patients with age at diagnosis >318 days, International Neuroblastoma Staging System (INSS) stage 4, DNA diploidy, MYCN amplification status, and children oncology group (COG) high-risk group had a relatively poor prognosis. However, according to the multivariate Cox regression analysis, only age, INSS stage, and DNA ploidy were independent predictive factors in NB patients regarding EFS, and a nomogram was created based on these factors. The area under the curve (AUC) values of the ROC curves for the 3-, 5-, and 10-year EFS of this nomogram were 0.681, 0.706, and 0.720, respectively. Additionally, the AUC values of individual independent prognostic factors of EFS were lower than those of the nomogram, suggesting that the developed nomogram had a higher predictive reliability for prognosis. In addition, a new risk stratification system was developed to better stratify NB patients and provide clinical practitioners with a better reference for clinical decision-making. NB patients' EFS could be predicted more accurately and easily through the constructed nomogram and event-occurrence risk stratification system, allowing clinicians to better differentiate NB patients and establish individualized treatment plans to maximize patient benefits.

MAT2A inhibits the ferroptosis in osteosarcoma progression regulated by miR-26b-5p

Osteosarcoma (OS) is the most frequent primary malignant bone tumor. Ferroptosis, a form of regulated cell death, is a key tumor suppression mechanism. Although methionine adenosyltransferase II alpha (MAT2A) has been reported to inhibit several tumor cells, it is unclear whether inhibition of MAT2A in OS cells can reduce ferroptosis. CCK-8, flow cytometry, and Transwell assays were performed to evaluate cell viability, cell apoptosis/cycle, and cell migration, respectively. The levels of ferrous iron and glutathione (GSH) levels in cells were measured to evaluate the degree of cell ferroptosis. Western blot analysis was performed to detect protein levels of MAT2A, p-STAT3 (Ser727)/STAT3, and solute carrier family 7 member 11 (SLC7A11) in OS cells. MAT2A was significantly upregulated in OS specimens and high MAT2A expression was associated with a poorer prognosis in OS patients. shRNA targeting MAT2A significantly increased OS cell apoptosis, triggered cell cycle arrest in the G2 phase, and attenuated migration ability in vitro. MAT2A depletion dramatically inhibited tumor progression of OS in vivo. Overexpression of MAT2A rescued the tumor inhibition caused by miR-26b-5p. MAT2A knockdown promoted OS cell ferroptosis. miR-26b-5p/MAT2A regulates tumor malignant progression and OS cell ferroptosis by controlling p-STAT3 and SLC7A11 expressions. Taken together, our study displayed that miR-26b-5p/MAT2A triggers ferroptosis in OS cells by increasing intracellular ferrous iron levels and inhibiting the STAT3/SLC7A11 axis. Our results reveal a MAT2A-mediated ferroptosis defense mechanism used by OS cells and propose a potential ferroptosis-inducing strategy for the treatment of OS patients.

Mining of clinical and prognosis related genes in the tumor microenvironment of endometrial cancer: A field synopsis of observational study

Endometrial cancer (EC) is the sixth most common malignant tumor in women worldwide, and its morbidity and mortality are on the rise. The purpose of this study was to explore potential tumor microenvironment (TME)-related biomarkers associated with the clinical features and prognosis of EC. The Estimating Stromal and Immune Cells in Malignancy Using Expression Data (ESTIMATE) algorithm was used to calculate TME immune and stromal scores of EC samples and to analyze the relationship between immune/stromal scores, clinical features, and prognosis. Heat maps and Venn maps were used to screen for differentially expressed genes (DEGs). The ESTIMATE algorithm revealed immune score was significantly correlated with overall survival and tumor grade in patients with EC. A total of 1448 DEGs were screened, of which 387 were intersecting genes. Gene Ontology (GO) analysis revealed that the biological processes (BP) related to intersecting genes mainly included T cell activation and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the intersecting genes were closely related to immune-related signaling pathways. Thirty core genes with more than 7 nodes were identified using protein-protein interaction (PPI) analysis. Six independent prognostic genes of EC were identified using Kaplan-Meier survival analysis and multivariate Cox analysis, namely CD5, BATF, CACNA2D2, LTA, CD52, and NOL4, which are all immune-infiltrating genes that are closely related to clinical features. The current study identified 6 key genes closely related to immune infiltration in the TME of EC that predict clinical outcomes, which may provide new insights into novel prognostic biomarkers and immunotherapy for patients with EC.

Identification of Novel Targets for Treatment of Dilated Cardiomyopathy Based on the Ferroptosis and Immune Heterogeneity

Purpose

This study aimed to investigate the role of ferroptosis in dilated cardiomyopathy (DCM) and to identify new targets for treatment and diagnosis of DCM.

Methods

GSE116250 and GSE145154 were downloaded from the Gene Expression Omnibus database. Unsupervised consensus clustering of DCM patients was used to confirm the impact of ferroptosis. Ferroptosis-related hub genes were identified by WGCNA and single cell sequencing analyses. Finally, we established a DCM mouse model via injection of Doxorubicin to verify the expression level of OTUD1 and colocalization between cell markers and OTUD1 in DCM mouse heart.

Results

A total of 13 ferroptosis-related differentially expressed genes (DEGs) were identified. The DCM patients were divided into two clusters according to the expression of 13 DEGs. The DCM patients in different clusters showed discrepancies in immune infiltration. Four hub genes were further identified by WGCNA analysis. Single cell data analysis revealed that OTUD1 may regulate B cells and DC cells and then participate in immune infiltration discrepancy. The upregulation of OTUD1 and the colocalization of OTUD1 with CD19 (B cell maker) and CD11c (DCs markers) markers were confirmed in DCM mouse hearts.

Conclusion

Ferroptosis and the immune microenvironment are closely associated with DCM, and OTUD1 may play an important role through B cells and DCs.

Construction and validation of an oxidative-stress-related risk model for predicting the prognosis of osteosarcoma

BACKGROUND

Osteosarcoma is the most common bone malignancy in teenagers, and warrants effective measures for diagnosis and prognosis. Oxidative stress (OS) is the key driver of several cancers and other diseases.

METHODS

The TARGET-osteosarcoma database was employed as the training cohort and GSE21257 and GSE39055 was applied for external validation. The patients were classified into the high- and low-risk groups based on the median risk score of each sample. ESTIMATE and CIBERSORT were applied for the evaluation of tumor microenvironment immune infiltration. GSE162454 of single-cell sequencing was employed for analyzing OS-related genes.

RESULTS

Based on the gene expression and clinical data of 86 osteosarcoma patients in the TARGET database, we identified eight OS-related genes, including MAP3K5, G6PD, HMOX1, ATF4, ACADVL, MAPK1, MAPK10, and INS. In both the training and validation sets, the overall survival of patients in the high-risk group was significantly worse than that in the low-risk group. The ESTIMATE algorithm revealed that patients in the high-risk group had higher tumor purity but lower immune score and stromal score. In addition, the CIBERSORT algorithm showed that the M0 and M2 macrophages were the predominant infiltrating cells in osteosarcoma. Based on the expression analysis of immune checkpoint, CD274(PDL1), CXCL12, BTN3A1, LAG3, and IL10 were identified as potential immune therapy targets. Analysis of the single cell sequencing data also revealed the expression patterns of OS-related genes in different cell types.

CONCLUSIONS

An OS-related prognostic model can accurately provide the prognosis of osteosarcoma patients, and may help identify suitable candidates for immunotherapy.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Identification and validation of novel signature associated with hepatocellular carcinoma prognosis using Single-cell and WGCNA analysis

Background: Hepatocellular carcinoma is a rapidly advancing malignancy with a poor prognosis. Therefore, further research is needed on its potential pathogenesis and therapeutic targets. Methods: In this study, the relevant datasets were downloaded from the TCGA database and the key modules were identified using WGCNA in the necroptosis-related gene set, while single-cell datasets were scored using the necroptosis gene set. Differential genes in the high- and low-expression groups were determined using the WGCNA module genes as intersection sets to identify key genes involved in necroptosis in liver cancer. Then, prognostic models were constructed using LASSO COX regression followed by multi-faceted validation. Finally, model genes were found to be correlated with key proteins of the necroptosis pathway and used to identify the most relevant genes, followed by their experimental validation. Subsequently, on the basis of the analysis results, the most relevant SFPQ was selected for cell-level verification. Results: We constructed a prognosis model that included five necroptosis-related genes (EHD1, RAC1, SFPQ, DAB2 and PABPC4) to predict the prognosis and survival of HCC patients. The results showed that the prognosis was more unfavorable in the high-risk group compared to the low-risk group, which was corroborated using ROC curves and risk factor plots. In addition, we further checked the differential genes using GO and KEGG analyses and found that they were predominantly enriched in the neuroactive ligand-receptor interaction pathway. The results of the GSVA analysis demonstrated that the high-risk group was mainly enriched in DNA replication, regulation of the mitotic cycle, and regulation of various cancer pathways, while the low-risk group was predominantly enriched in the metabolism of drugs and xenobiotics using cytochrome P450. SFPQ was found to be the main gene that affects the prognosis and SFPQ expression was positively correlated with the expression of RIPK1, RIPK3 and MLKL. Furthermore, the suppression of SFPQ could inhibit hyper-malignant phenotype HCC cells, while the WB results showed that inhibition of SFPQ expression also resulted in lower expression of necroptosis proteins, compared to the sh-NC group. Conclusions: Our prognostic model could accurately predict the prognosis of patients with HCC to further identify novel molecular candidates and interventions that can be used as alternative methods of treatment for HCC.

Integrative analyses of ferroptosis and immune related biomarkers and the osteosarcoma associated mechanisms

Osteosarcoma (OS) is the most common primary malignant bone tumor with high metastatic potential and relapse risk. To study the regulatory mechanism of the OS microenvironment, a complex regulatory network involving the ferroptosis- and immune response-related genes remains to be established. In the present study, we determined the effect of a comprehensive evaluation system established on the basis of ferroptosis- and immune-related genes on the immune status, related biomarkers, prognosis, and the potential regulatory networks underlying OS based on the TARGET and Gene Expression Omnibus databases that contain information on OS patients by bioinformatics analyses. We first characterized individual ferroptosis scores and immune scores through gene set variation analysis (GSVA) against TARGET-OS datasets. We then identified differentially expressed genes by score groups. Weighted gene co-expression network analysis was performed to identify the most relevant ferroptosis-related and immune-related gene modules, which facilitated the identification of 327 ferroptosis gene and 306 immune gene candidates. A 4-gene (WAS, CORT, WNT16, and GLB1L2) signature was constructed and valuation using the least absolute shrinkage and selection operator-Cox regression models to effectively predict OS prognosis. The prediction efficiency was further validated by GSE39055. We stratified patients based on the prognostic scoring systems. Eight hub genes (namely CD3D, CD8A, CD3E, IL2, CD2, MYH6, MYH7, and MYL2) were identified, and TF-miRNA target regulatory networks were constructed. Furthermore, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and GSVA were used to determine the signature's potential pathways and biological functions, which showed that the hub genes were enriched in ferroptosis-associated biological functions and immune-associated molecular mechanisms. Thereafter, we investigated the proportion and infiltration extent of 22 infiltrating immune cells by using CIBERSORT, which revealed significant subgroup differences in CD8 + T cells, M0 macrophages, and M2 macrophages. In conclusion, we determined a new ferroptosis-related and immune-related gene signature for predicting OS patients' prognosis and further explored the ferroptosis and immunity interactions during OS development, which provides insights into the exploration of molecular mechanisms and targeted therapies in patients with OS.

A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes

Background

Tumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma.

Methods

ScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro.

Results

Eleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes.

Conclusions

This study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.

The Potential Roles of Ferroptosis in Pathophysiology and Treatment of Musculoskeletal Diseases—Opportunities, Challenges, and Perspectives

Ferroptosis is different from other forms of cell death, such as apoptosis, autophagy, pyroptosis, and cuproptosis, mainly involving iron metabolism and lipid peroxidation. Ferroptosis plays an important role in various disease, such as malignant tumors, neuron-degenerative diseases, and cardiovascular diseases, and has become the focus of current research. Both iron overload and lipid peroxide accumulation contribute to the occurrence, development, and treatment of musculoskeletal diseases, such as osteoporosis, osteoarthritis, osteosarcoma, intervertebral disc degeneration, and spinal cord injury. For a better understanding of the potential roles ferroptosis may play in pathophysiology and treatment of common musculoskeletal disorders, this article briefly reviewed the relationship and possible mechanisms. Through an investigation of ferroptosis’ role in musculoskeletal diseases’ occurrence, development, and treatment, ferroptosis could offer new opportunities for clinical diagnosis and treatment.

Expression pattern and prognostic value of key regulators for N7-methylguanosine RNA modification in prostate cancer

Alterations in the regulators of RNA methylation modifications, such as N7-methylguanosine (m7G), have been implicated in a variety of diseases. Therefore, the analysis and identification of disease-related m7G modification regulators will accelerate advances in understanding disease pathogenesis. However, the implications of alterations in the regulators of m7G modifications remain poorly understood in prostate adenocarcinoma. In the present study, we analyze the expression patterns of 29 m7G RNA modification regulators in prostate adenocarcinoma using The Cancer Genome Atlas (TCGA) and perform consistent clustering analysis of differentially expressed genes (DEGs). We find that 18 m7G-related genes are differentially expressed in tumor and normal tissues. In different cluster subgroups, DEGs are mainly enriched in tumorigenesis and tumor development. Furthermore, immune analyses demonstrate that patients in cluster 1 have significantly higher scores for stromal and immune cells, such as B cells, T cells, and macrophages. Then, a TCGA-related risk model is developed and successfully validated using a Gene Expression Omnibus external dataset. Two genes ( EIF4A1 and NCBP2) are determined to be prognostically significant. Most importantly, we construct tissue microarrays from 26 tumor specimens and 20 normal specimens, and further confirm that EIF4A1 and NCBP2 are associated with tumor progression and Gleason score. Therefore, we conclude that the m7G RNA methylation regulators may be involved in the poor prognosis of patients with prostate adenocarcinoma. The results of this study may provide support for exploring the underlying molecular mechanisms of m7G regulators, especially EIF4A1 and NCBP2.

Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction

BACKGROUND

This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients.

METHODS

Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model's accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes' mRNA and protein expressions were verified.

RESULTS

A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB).

CONCLUSION

The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.

Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma

OBJECTIVES

Osteosarcoma is highly aggressive and prone to metastasis, with a poor prognosis. Increasing evidence identified anoikis has a critical effect in tumor metastasis and invasion. However, the prognostic value of anoikis-related genes (ANRGs) in osteosarcoma and their role in the immune landscape of osteosarcoma remain unclear.

METHODS

The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes.

RESULTS

Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma.

CONCLUSION

Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.

A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common bone cancer in adolescents, and has a high propensity to metastasize. Ferroptosis is a unique modality of cell death, driving the metastasis of cancer cells. Identifying ferroptosis-related genes (FRGs) as prognostic factors will be critical to predict the outcomes of OS. This study aimed to explore the prognostic value of FRGs in OS and build a prognostic model to indirectly improve OS patients' outcomes.

METHODS

OS data were downloaded from the TARGET database and 2 Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess FRGs. A risk score model basing on 5 FRGs was constructed via LASSO-Cox regression. Multivariate Cox regression analysis was used to determine the independent prognostic factors. The Nomogram model was built using independent prognostic factors. The relationship between the risk score and the immune cell infiltration was estimated by CIBERSORT, and the correlation between the risk score and immune checkpoints was also analyzed.

RESULTS

Based on the prognosis-related FRGs, we built a regression model: Risk score = (-0.01382853 × ACSL4) - (0.05371778 × HMOX1) - (0.02434655 × GPX4) - (0.16432810 × PRNP) - (0.15567120 × ATG7). OS patients with high risk score tended to suffer from poor prognosis, validated in 2 Gene Expression Omnibus datasets. The Nomogram model showed the combination of the risk score and the tumour-node-metastasis stage improved predictive effectiveness. The risk score was also related to immune cell infiltration and immune checkpoint expression.

CONCLUSION

The risk score model based on 5 FRGs was a reliable prognostic predictive indicator for OS patients.

Molecular characterization of immunogenic cell death indicates prognosis and tumor microenvironment infiltration in osteosarcoma

Introduction

Osteosarcoma (OS) is a highly aggressive bone malignancy with a poor prognosis, mainly in children and adolescents. Immunogenic cell death (ICD) is classified as a type of programmed cell death associated with the tumor immune microenvironment, prognosis, and immunotherapy. However, the feature of the ICD molecular subtype and the related tumor microenvironment (TME) and immune cell infiltration has not been carefully investigated in OS.

Methods

The ICD-related genes were extracted from previous studies, and the RNA expression profiles and corresponding data of OS were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. The ICD-related molecular subtypes were classed by the "ConsensusclusterPlus" package and the construction of ICD-related signatures through univariate regression analysis. ROC curves, independent analysis, and internal validation were used to evaluate signature performance. Moreover, a series of bioinformatic analyses were used for Immunotherapy efficacy, tumor immune microenvironments, and chemotherapeutic drug sensitivity between the high- and low-risk groups.

Results

Herein, we identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune response signaling among distinct ICD subtypes. Subsequently, a novel ICD-related prognostic signature was developed to determine its predictive performance in OS. Also, a highly accurate nomogram was then constructed to improve the clinical applicability of the novel ICD-related signature. Furthermore, we observed significant correlations between ICD risk score and TME, immunotherapy response, and chemotherapeutic drug sensitivity. Notably, the in vitro experiments further verified that high GALNT14 expression is closely associated with poor prognosis and malignant progress of OS.

Discussion

Hence, we identified and validated that the novel ICD-related signature could serve as a promising biomarker for the OS's prognosis, chemotherapy, and immunotherapy response prediction, providing guidance for personalized and accurate immunotherapy strategies for OS.

Identification of B cell marker genes based on single-cell sequencing to establish a prognostic model and identify immune infiltration in osteosarcoma

Background

Tumor-infiltrating B cells play a crucial role in the promotion or inhibition of tumor development. However, the role of B cells in osteosarcoma remains largely unknown. The aim of this study was to investigate the effect of B cells on the prognosis and immunity infiltration of osteosarcoma.

Methods

Marker genes of B cells were identified based on the single-cell sequencing results of osteosarcoma in the GEO database. The prognostic model was established by the TCGA database and verified by the GEO data. The divergence in immune infiltration between the low-risk and high-risk groups was then compared according to the established prognostic model. Finally, the differential genes in the low-risk and high-risk groups were enriched and analyzed.

Results

A total of 261 B cell marker genes was obtained by single-cell sequencing and a prognostic model of 4 B cell marker genes was established based on TCGA data. The model was found to have a good prediction performance in the TCGA and GEO data. A remarkable difference in immune infiltration between the low-risk and high-risk groups was also observed. The obtained results were verified by enrichment analysis.

Conclusion

In summary, a prognostic model with good predictive performance was established that revealed the indispensable role of B cells in the development of osteosarcoma. This model also provides a predictive index and a novel therapeutic target for immunotherapy for clinical patients.

Proteomic analysis of spinal cord tissue in a rat model of cancer-induced bone pain

Background

Cancer-induced bone pain (CIBP) is a moderate to severe pain and seriously affects patients' quality of life. Spinal cord plays critical roles in pain generation and maintenance. Identifying differentially expressed proteins (DEPs) in spinal cord is essential to elucidate the mechanisms of cancer pain.

Methods

CIBP rat model was established by the intratibial inoculation of MRMT-1 cells. Positron emission tomography (PET) scan and transmission electron microscopy (TEM) were used to measure the stats of spinal cord in rats. Label free Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) were used to analyze the whole proteins from the lumbar spinal cord. Differentially expressed proteins (DEPs) were performed using Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and verified using Western blot and immunofluorescence assay.

Results

In the current study, CIBP rats exhibited bone damage, spontaneous pain, mechanical hyperalgesia, and impaired motor ability. In spinal cord, an hypermetabolism and functional abnormality were revealed on CIBP rats. An increase of synaptic vesicles density in active zone and a disruption of mitochondrial structure in spinal cord of CIBP rats were observed. Meanwhile, 422 DEPs, consisting of 167 up-regulated and 255 down-regulated proteins, were identified among total 1539 proteins. GO enrichment analysis indicated that the DEPs were mainly involved in catabolic process, synaptic function, and enzymic activity. KEGG pathway enrichment analysis indicated a series of pathways, including nervous system disease, hormonal signaling pathways and amino acid metabolism, were involved. Expression change of synaptic and mitochondrial related protein, such as complexin 1 (CPLX1), synaptosomal-associated protein 25 (SNAP25), synaptotagmin 1 (SYT1), aldehyde dehydrogenase isoform 1B1 (ALDH1B1), Glycine amidinotransferase (GATM) and NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), were further validated using immunofluorescence and Western blot analysis.

Conclusion

This study provides valuable information for understanding the mechanisms of CIBP, and supplies potential therapeutic targets for cancer pain.

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.

Ferroptosis in osteosarcoma: A promising future

The incidence of osteosarcoma (OS) is increasing year by year, and the prognosis of patients with advanced OS is extremely poor due to the tendency of recurrence and chemotherapy resistance after surgery. Ferroptosis is a novel form of programmed cell death (PCD) that kills cells through iron-dependent lipid peroxidation. Current studies have shown that ferroptosis is closely related to OS and could reduce chemotherapy resistance to a certain extent, which has great therapeutic potential. In this paper, we review the regulatory mechanism of ferroptosis and its research progress in OS, hoping to provide new help for the clinical treatment of OS.

A ferroptosis-related gene signature associated with immune landscape and therapeutic response in osteosarcoma

BACKGROUND

The role of ferroptosis in tumor progression and immune microenvironment is extensively investigated. However, the potential value of ferroptosis regulators in predicting prognosis and therapeutic strategies for osteosarcoma (OS) patients remains to be elucidated.

METHODS

Here, we extracted transcriptomic and survival data from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) to investigate the expression and prognostic value of ferroptosis regulators in OS patients. After comprehensive analyses, including Gene set variation analysis (GSVA), single-sample gene-set enrichment analysis (ssGSEA), Estimated Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE), single-cell RNA sequencing, and biological experiments, our constructed 8-ferroptosis-regulators prognostic signature effectively predicted the immune landscape, prognosis, and chemoradiotherapy strategies for OS patients.

RESULTS

We constructed an 8-ferroptosis-regulators signature that could predict the survival outcome of OS. The signature algorithm scored samples, and high-scoring patients were more prone to worse prognoses. The tumor immune landscape suggested the positive relevance between risk score and immunosuppression. Interfering HILPDA and MUC1 expression would inhibit tumor cell proliferation and migration, and MUC1 might improve the ferroptosis resistance of OS cells. Moreover, we predicted chemoradiotherapy strategies of cancer patients following ferroptosis-risk-score groups.

CONCLUSION

Dysregulated ferroptosis gene expression can affect OS progression by affecting the tumor immune landscape and ferroptosis resistance. Our risk model can predict OS survival outcomes, and we propose that HILPDA and MUC1 are potential targets for cancer therapy.

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.

Identification of ATG7 as a Regulator of Proferroptosis and Oxidative Stress in Osteosarcoma

Background

Ferroptosis has gained significant attention from oncologists as a vital outcome of oxidative stress. The aim of this study was to develop a prognostic signature that was based on the ferroptosis-related genes (FRGs) for osteosarcoma patients and explore their specific role in osteosarcoma.

Methods

The training cohort dataset was extracted from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Different techniques like the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, multivariate Cox regression analyses, and the Kaplan-Meier (KM) survival analyses were utilized to develop a prognostic signature. Then, the intrinsic relationship between the developed gene signature and the infiltration levels of the immune cells was further investigated. An external validation dataset from the Gene Expression Omnibus (GEO) database was employed to assess the predictive ability of the developed gene signature. Subsequently, the specific function of potential FRG in affecting the oxidative stress reaction and ferroptosis of osteosarcoma cells was identified.

Results

A prognostic signature based on 5 FRGs (CBS, MUC1, ATG7, SOCS1, and PEBP1) was developed, and the patients were classified into the low- and high-risk groups (categories). High-risk patients displayed poor overall survival outcomes. The risk level was seen to be an independent risk factor for determining the prognosis of osteosarcoma patients (p < 0.001, hazard ratio: 7.457, 95% CI: 3.302-16.837). Additionally, the risk level was associated with immune function, which might affect the survival status of osteosarcoma patients. Moreover, the findings of the study indicated that the expression of ATG7 was related to the regulation of oxidative stress in osteosarcoma. Silencing the ATG7 gene promoted the proliferation and migration in osteosarcoma cells, suppressing the oxidative stress and ferroptosis process.

Conclusions

A novel FRG signature was developed in this study to predict the prognosis of osteosarcoma patients. The results indicated that ATG7 might regulate the process of oxidative stress and ferroptosis in osteosarcoma cells and could be used as a potential target to develop therapeutic strategies for treating osteosarcoma.

Verification of Ferroptosis Subcluster-Associated Genes Related to Osteosarcoma and Exploration of Immune Targeted Therapy

Background

Despite tremendous advances in treating osteosarcoma (OS), the survival rates of patients have failed to improve dramatically over the past decades. Ferroptosis, a newly discovered iron-dependent type of regulated cell death, is implicated in tumors, and its features in OS remain unascertained. We designed to determine the involvement of ferroptosis subcluster-related modular genes in OS progression and prognosis.

Methods

The OS-related datasets retrieved from GEO and TARGET database were clustered for identifying molecular subclusters with different ferroptosis-related genes (FRGs) expression patterns. Weighted gene coexpression network analysis (WGCNA) was applied to identify modular genes from FRG subclusters. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariable Cox regression analysis were adopted to develop the prognostic model. Potential mechanisms of development and prognosis in OS were explored by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Then, a comprehensive analysis was conducted for immune checkpoint markers and assessment of predictive power to drug response. The protein expression levels of the three ferroptosis subcluster-related modular genes were verified by immunohistochemistry.

Results

Two independent subclusters presenting diverse expression profiles of FRGs were obtained, with significantly different survival states. Ferroptosis subcluster-related modular genes were screened with WGCNA, and the GESA results showed that ferroptosis subcluster-related modular genes could affect the cellular energy metabolism, thus influencing the development and prognosis of osteosarcoma. A prognostic model was established by incorporating three ferroptosis subcluster-related modular genes (LRRC1, ACO2, and CTNNBIP1) and a nomogram by integrating clinical features, and they were evaluated for the predictive power on OS prognosis. The 20 immune checkpoint-related genes confirmed the insensitivity to tumor immunotherapy in high-risk patients. IC50s of Axitinib and Cytarabine suggested a higher sensitivity to the targeted drug. Finally, the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemistry were consistent with bioinformatics analysis.

Conclusion

Ferroptosis are closely associated with the OS prognosis. The risk-scoring model incorporating three ferroptosis subcluster-related modular genes has shown outstanding advantages in predicting patient prognosis.

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

Background

Despite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.

Methods

Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.

Results

The malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.

Conclusions

Overall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.

A novel necroptosis-related lncRNAs signature effectively predicts the prognosis for osteosarcoma and is associated with immunity

Background: Necroptosis is closely related to tumorigenesis and development. Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) are also central players in osteosarcoma (OS). However, the role of necroptosis-related lncRNAs in OS remains unclear. In the present study, we aim to craft a prognostic signature based on necroptosis-related lncRNAs to improve the OS prognosis prediction.

Methods: The signature based on necroptosis-related lncRNAs was discovered using univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The prognosis efficiency of the signature was then estimated by employing various bioinformatics methods. Subsequently, immunological analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the association between necroptosis-related lncRNAs with clinical outcomes and immune status. More importantly, several necroptosis-related lncRNAs were validated with RT-qPCR.

Results: Consequently, a novel prognosis signature was successfully constructed based on eight necroptosis-related lncRNAs. Meanwhile, the novel necroptosis-related lncRNAs model could distribute OS patients into two risk groups with a stable and accurate predictive ability. Additionally, the GSEA and immune analysis revealed that the necroptosis-related lncRNAs signature affects the development and prognosis of OS by regulating the immune status. The necroptosis-related lncRNA signature was closely correlated with multiple anticancer agent susceptibility. Moreover, the RT-qPCR results indicated several necroptosis-related lncRNAs were significantly differently expressed in osteosarcoma and osteoblast cell lines.

Conclusion: In this summary, a novel prognostic signature integrating necroptosis-related lncRNAs was firstly constructed and could accurately predict the prognosis of OS. This study may increase the predicted value and guide the personalized chemotherapy treatment for OS.