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

Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes

Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment strategies. However, identifying robust gene signatures to predict osteosarcoma outcomes remains a significant challenge. In this study, we analyzed gene expression data from 138 osteosarcoma samples across two multicenter cohorts and identified 14 consensus prognosis-associated genes via univariate Cox regression analysis. Using 66 combinations of 10 machine learning (ML) algorithms, we developed a machine learning-derived prognostic signature (MLDPS) optimized by the average C-index across TARGET, GSE21257, and merged cohorts. The MLDPS effectively stratified osteosarcoma patients into high- and low-risk score groups, achieving strong predictive performance for 1-, 3-, and 5-year overall survival (AUC range: 0.852 - 0.963). The MLDPS, comprising seven genes (CTNNBIP1, CORT, DLX2, TERT, BBS4, SLC7A1, NKX2-3), exhibited superior predictive accuracy compared to 10 established gene signatures. The findings of the MLDPS carry significant clinical implications for osteosarcoma treatment. Patients with a high-risk score demonstrated worse prognosis, increased metastasis risk, reduced immune infiltrations, and greater sensitivity to immunotherapy. Conversely, low-risk patients exhibited prolonged survival and distinct drug sensitivities. These findings underscore the potential of MLDPS to guide risk stratification, inform personalized therapeutic strategies, and improve clinical management in osteosarcoma.

Based on single-cell and transcriptome data, ferroptosis and the immunological landscape in osteosarcoma were discovered

Ferroptosis has been demonstrated to have a significant role in osteosarcoma (OS), a highly aggressive and invasive malignant bone tumor. Nevertheless, the precise molecular mechanism underlying OS remains unknown. Understanding the makeup of the immune microenvironment in OS is crucial for its therapy, as the disease grows in the highly specialized, complex, and dynamic bone microenvironment. Resveratrol (Res) possesses anti-inflammatory, immunomodulatory, chemopreventive, antioxidant, and anticancer properties, it is unknown if it can modify ferroptosis to prevent OS. This time, using single-cell analysis and other bioinformatic studies, we will clarify the targets and composition of the immunological microenvironment of the ferroptosis process in OS, as well as the role of certain transcription factors in it. Ultimately, network pharmacology and vitro experiment have led to the initial identification of the molecular processes governing ferroptosis in OS, which are regulated by Res. The findings suggested the potential use of ALB, EGFR, GPX4, IL6, STAT3, and PTEN as OS prognostic and diagnostic biomarkers. Chondroblastic, myeloid cells, osteoblastic OS, CD4 + T, NK, CD8 + T, B cells, M1 macrophages, Chondro_Proli, etc. made up the majority of the immunological microenvironment of OS. The entire cellular trajectory demonstrates that immune cells infiltrating during the early stages of OS are mostly CD4 + T, NK, CD8 + T, B_cell, and M1 macrophages. This affects the development of myeloid cells and chondroblastic cells, which ultimately leads to the progression of highly malignant chondro cells to OS. Numerous pathways allow transcription factors including BCLAF1, MAF, SP1, TCF12, KLF11, and KMT2D to contribute to the development of tumors. Finally, by interacting with the aforementioned targets, cells, Res is thought to impede the evolution of OS. In conclusion, ferroptosis and alterations in the immunological milieu are significant factors in the development of OS, and Res may one day be employed as a therapeutic drug to treat OS.

Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics

BACKGROUND

Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown.

MATERIALS AND METHODS

The NET-related gene signature was constructed by LASSO Cox regression analysis using the TCGA-SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high- and low-NET score (high-score, low-score) groups were explored. The scRNA-seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution.

RESULTS

A five NET genes-based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET-related prognostic model for SKCM. The OS of SKCM patients with low-score was better than that in patients with high-score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8-T cells, CD4-T cells). Moreover, patients with high-score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high-score.

CONCLUSION

We established a NET-related five gene signature in SKCM and found that the NET-related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

Ferroptosis defense mechanisms: The future and hope for treating osteosarcoma

Currently, challenges such as chemotherapy resistance, resulting from preoperative and postoperative chemotherapy, postoperative recurrence, and poor bone regeneration quality, are becoming increasingly prominent in osteosarcoma (OS) treatment. There is an urgent need to find more effective ways to address these issues. Ferroptosis is a novel form of iron-dependent programmed cell death, distinct from other forms of cell death. In this paper, we summarize how, through the three major defense systems of ferroptosis, not only can substances from traditional Chinese medicine, antitumor drugs, and nano-drug carriers induce ferroptosis in OS cells, but they can also be combined with immunotherapy, differentiation therapy, and other treatment modalities to significantly enhance chemotherapy sensitivity and inhibit tumor growth. Thus, ferroptosis holds great potential in treating OS, offering more choices and possibilities for future clinical interventions.

Development and validation of an immune-related gene prognostic index for lung adenocarcinoma

BACKGROUND

Lung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD.

METHODS

Based on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups.

RESULTS

The IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score.

CONCLUSIONS

IRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

Targeting Ferroptosis in Bone-Related Diseases: Facts and Perspectives

Ferroptosis is a new cell fate decision discovered in recent years. Unlike apoptosis, autophagy or pyroptosis, ferroptosis is characterized by iron-dependent lipid peroxidation and mitochondrial morphological changes. Ferroptosis is involved in a variety of physiological and pathological processes. Since its discovery, ferroptosis has been increasingly studied concerning bone-related diseases. In this review, we focus on the latest research progress and prospects, summarize the regulatory mechanisms of ferroptosis, and discuss the role of ferroptosis in the pathogenesis of bone-related diseases, such as osteoporosis (OP), osteoarthritis (OA), rheumatoid arthritis (RA), and osteosarcoma (OS), as well as its therapeutic potential.

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma. Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates. Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils. Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.

Identification and Validation of Ferroptosis-Related DNA Methylation Signature for Predicting the Prognosis and Guiding the Treatment in Cutaneous Melanoma

Cutaneous melanoma (CM) is one of the most aggressive skin tumors with a poor prognosis. Ferroptosis is a newly discovered form of regulated cell death that is closely associated with cancer development and immunotherapy. The aim of this study was to establish and validate a ferroptosis-related gene (FRG) DNA methylation signature to predict the prognosis of CM patients using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. A reliable four-FRG DNA methylation prognostic signature was constructed via Cox regression analysis based on TCGA database. Kaplan-Meier analysis showed that patients in the high-risk group tended to have a shorter overall survival (OS) than the low-risk group in both training TCGA and validation GEO cohorts. Time-dependent receiver operating characteristic (ROC) analysis showed the areas under the curve (AUC) at 1, 3, and 5 years were 0.738, 0.730, and 0.770 in TCGA cohort and 0.773, 0.775, and 0.905 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the signature was an independent prognostic indicator of OS in patients with CM. Immunogenomic profiling showed the low-risk group of patients had a higher immunophenoscore, and most immune checkpoints were negatively associated with the risk signature. Functional enrichment analysis revealed that immune response and immune-related pathways were enriched in the low-risk group. In conclusion, we established and validated a four-FRG DNA methylation signature that independently predicts prognosis in CM patients. This signature was strongly correlated with the immune landscape, and may serve as a biomarker to guide clinicians in making more precise and personalized treatment decisions for CM patients.