Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

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.

Ferroptosis resistance in cancer: recent advances and future perspectives

Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death and has been implicated in the occurrence and development of various diseases, including heart disease, nervous system diseases and cancer. Ferroptosis induction recently emerged as an attractive strategy for cancer therapy. Ferroptosis has become a potential target for intervention in these diseases or injuries in relevant preclinical models. This review summarizes recent progress on the mechanisms of ferroptosis resistance in cancer, highlights redox status and metabolism's role in it. Combination therapy for ferroptosis has great potential in cancer treatment, especially malignant tumors that are resistant to conventional therapies. This review will lead us to have a comprehensive understanding of the future exploration of ferroptosis and cancer therapy. A deeper understanding of the relationship between ferroptosis resistance and metabolism reprogramming may provide new strategies for tumor treatment and drug development based on ferroptosis.

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.