Correlations between the modification patterns mediated by pyroptosis-related genes, tumor microenvironment, and immunotherapy in soft tissue sarcoma.

Soft tissue sarcoma (STS) incidence, progression, and metastasis are tightly linked to the tumor microenvironment (TME). The modification patterns mediated by pyroptosis-related genes (PRGs) in STS are unknown regarding the immune cell infiltration landscape of TME, immunotherapy effect, and prognostic value. First, we downloaded STS samples from the Cancer Genome Atlas (TCGA) and gene-expression omnibus (GEO) databases. Based on 52 PRGs, 2 pyroptosis modification patterns were analyzed, and the associations of pyroptosis modification patterns with immune cell infiltration in the TME were elucidated systematically. To quantify PRG modification patterns in STS patients, we generated a pyroptosis scoring system using principal component analysis (PCA). We identified 2 distinct pyroptosis modification patterns in STS. Compared to PRG cluster A, the prognosis of cluster B was better. These 2 pyroptosis modification patterns corresponded to different characteristics of immune cell infiltration in the TME and biological behaviors. In the pyroptosis scoring system, a high pyroptosis score was connected to higher immune cell infiltration, stronger immune surveillance, immune-killing effects on tumor cells, and better clinical benefits. The results from 3 anti-PD1/PD-L1-treated immune cohorts demonstrated that higher pyroptosis scores are also closely connected to better immunotherapy results. We demonstrated that pyroptosis modification is essential to the STS microenvironment. Moreover, the pyroptosis score is a reliable and independent prognostic factor in STS patients, enabling a richer understanding of the STS microenvironment and the screening of immunotherapy candidates, predicting the immunotherapeutic effects for individual STS patients, and guiding the use of chemotherapy drugs.

Construction and validation of a predictive nomogram for ferroptosis-related genes in osteosarcoma.

BACKGROUND: Ferroptosis is a new type of cellular regulation of necrosis that has attracted great attention in recent years, which is different from the traditional mode of autophagy, apoptosis, and necrosis. Studies suggest that ferroptosis is key to the occurrence and development of tumors. METHODS: Here, we investigated the prognostic significance of ferroptosis-related genes (FRGs) in osteosarcoma (OS) using RNA transcriptome data from 88 OS samples collected from the UCSC Xena platform. We defined the OS sample from the UCSC platform as the training cohort and the GEO dataset (GSE21257 and GSE16091) as the validation cohorts. We assessed 73 up-regulated and 63 down-regulated FRGs. We divided patients from the UCSC database into groups at high risk and low risk and built a prognostic risk model to assess prognosis using five FRGs: MT1G, G6PD, ARNTL, BNIP3, and SQLE. RESULTS: High-risk OS patients presented a lower survival rate. These results were confirmed in the validation groups. In the training group, the areas under the ROC curves (AUC) were as follows: 0.880 for 1 year, 0.833 for 3 years, and 0.818 for 5 years. In the GSE21257 validation cohort, the AUC were as follows: 0.770 for 1 year, 0.641 for 3 years, and 0.632 for 5 years survival, and in the GSE16091 were 0.729 for 1 year, 0.663 for 3 years, and 0.735 for 5 years survival. CONCLUSIONS: These findings suggest that FRGs are associated with the prognosis of osteosarcoma. Moreover, our prognostic risk model can predict overall survival in osteosarcoma. This provides new ideas for the clinical diagnosis and personalized treatment of osteosarcoma.

Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis.

Background: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro. Material and methods: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers' efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot. Results: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis. Conclusions: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways.