Molecular characterization of ferroptosis in soft tissue sarcoma constructs a prognostic and immunotherapeutic signature through experimental and bioinformatics analyses.

Ferroptosis regulators have been found to affect tumor progression. However, studies focusing on ferroptosis and soft tissue sarcoma (STS) are rare. Somatic mutation, copy number variation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, consensus clustering, differentially expressed genes analysis (DEGs), principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used to identify and explore different ferroptosis modifications in STS. A nomogram was constructed to predict the prognosis of STS. Moreover, three immunotherapy datasets were used to assess the Fescore. Western blotting, siRNA transfection, EdU assay and reactive oxygen species (ROS) measurement were performed. 16 prognostic ferroptosis regulators were screened and significant differences were observed in somatic mutation, copy number variation (CNV) and RT-qPCR among these ferroptosis regulators. 2 different ferroptosis modification patterns were found (Fe cluster A and B). Fe cluster A with higher Fescore was correlated with p53 pathway and had better prognosis of STS (p = 0.002) while Fe cluster B with lower Fescore was correlated with angiogenesis and MYC pathway and showed a poorer outcome. Besides, the nomogram effectively predicted the outcome of STS and the Fescore could also well predict the prognosis of other 16 tumors and immunotherapy response. Downregulation of LOX also inhibited growth and increased ROS production in sarcoma cells. The molecular characterization of ferroptosis regulators in STS was explored and an Fescore was constructed. The Fescore quantified ferroptosis modification in STS patients and effectively predicted the prognosis of a variety of tumors, providing novel insights for precision medicine.

Molecular Characteristics of m6A Regulators and Tumor Microenvironment Infiltration in Soft Tissue Sarcoma: A Gene-Based Study.

Background: N6-methyladenosine (m6A) methylation played a key role in tumor growth. However, the relationship between m6A and soft tissue sarcoma (STS) was still unclear. Methods: The characterization and patterns of m6A modification in STS (TCGA-SARC and GSE17674) were analyzed comprehensively through bioinformatics and real-time polymerase chain reaction (RT-PCR). The effects of different m6A modification patterns on prognosis and immune infiltration of STS were further explored. Differentially expressed gene (DEG) analysis was performed. Moreover, an m6Ascore was constructed by principal component analysis (PCA). In addition, two immunotherapy datasets (IMvigor210 and GSE78220) and a sarcoma dataset (GSE17618) were used to evaluate the m6Ascore. Results: Huge differences were found in somatic mutation, CNV, and expression of 25 m6A regulators in STS. Two modification patterns (A and B) in STS were further identified and the m6A cluster A showed a better clinical outcome with a lower immune/stromal score compared with the m6A cluster B (p < 0.050).In addition to , most STS samples from m6A cluster A showed a high m6Ascore, which was related to mismatch repair and a better prognosis of STS (p < 0.001). In contrast, the m6A cluster B, characterized by a low m6Ascore, was related to the MYC signaling pathway, which led to a poor prognosis of STS. A high m6Ascore also contributed to a better outcome of PD-1/PD-L1 blockade immunotherapy. Conclusion: The modification patterns of 25 m6A regulators in the STS microenvironment were explored comprehensively. The novel m6Ascore effectively predicted the characteristics of the tumor microenvironment (TME) and outcome in STS and provided novel insights for future immunotherapy.

Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma.

BACKGROUND: Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. METHODS: Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan-Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. RESULTS: 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma (p < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. CONCLUSIONS: A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

Monocytes affect bone mineral density in pre- and postmenopausal women through ribonucleoprotein complex biogenesis by integrative bioinformatics analysis.

Osteoporosis is one of the most common metabolic bone disease among pre- and postmenopausal women. As the precursors of osteoclast cells, circulating monocytes play important role in bone destruction and remodeling. The aim of study is to identify potential key genes and pathways correlated with the pathogenesis of osteoporosis. Then we construct novel estimation model closely linked to the bone mineral density (BMD) with key genes. Weighted gene co-expression network analysis (WGCNA) were conducted by collecting gene data set with 80 samples from gene expression omnibus (GEO) database. Besides, hub genes were identified by series of bioinformatics and machine learning algorithms containing protein-protein interaction (PPI) network, receiver operating characteristic curve and Pearson correlation. The direction of correlation coefficient were performed to screen for gene signatures with high BMD and low BMD. A novel BMD score system was put forward based on gene set variation analysis and logistic regression, which was validated by independent data sets. We identified six modules correlated with BMD. Finally 100 genes were identified as the high bone mineral density signatures while 130 genes were identified as low BMD signatures. Besides, we identified the significant pathway in monocytes: ribonucleoprotein complex biogenesis. What's more, our score validated it successfully.