miR-138 Reduces the Dysfunction of T Follicular Helper Cells in Osteosarcoma via the PI3K/Akt/mTOR Pathway by Targeting PDK1

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.

Retracted: miR-138 Reduces the Dysfunction of T Follicular Helper Cells in Osteosarcoma via the PI3K/Akt/mTOR Pathway by Targeting PDK1

[This retracts the article DOI: 10.1155/2021/2895893.].

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Background

Gene play an important role in malignant tumors. However, there is still insufficient research on genetic variations in osteosarcoma (OS) patients. Therefore, we aimed to analyze the gene expression profile of OS using bioinformatics and to explore the pathogenesis of OS at the molecular level.

Methods

The gene chip dataset of OS samples was downloaded from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The R language clusterProfiler software package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs. The central node proteins of the protein interaction network were analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, Cytoscape, and its plug-ins cytoHubba and NetworkAnalyzer to find the key genes.

Results

A total of 631 DEGs were obtained, including 362 upregulated genes and 269 downregulated genes. DEGs were mainly involved in the regulation of leukocyte chemotaxis and migration, vascular development, and other biological processes (BPs); mediation of receptor ligand activity, growth factor binding, growth factor activity, integrin binding, and other molecular functions (MFs); and were enriched in the extracellular matrix (ECM).

Conclusions

DEGs in the ECM and growth factors play a key role in the development of OS. The leukocyte transendothelial migration pathway and the PI3K-AKT pathway are closely related to OS, and the related molecular mechanism is worthy of further study.

Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology

Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell dataset GSE162454 was analyzed using the Seurat package. The bulk-RNA transcriptome dataset GSE36001 was downloaded and analyzed using the CIBERSORT algorithm. The key targets for OS therapy were determined using Pearson's correlation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on key targets. The DeepDR algorithm was used to predict potential drugs for Osteosarcoma treatment. Molecular docking analysis was performed to verify the binding abilities of the predicted drugs and key targets. qRT-PCR assay was used to detect the expression of key targets in osteoblasts and OS cells. Results: A total of 21 cell clusters were obtained based on the GSE162454 dataset, which were labeled as eight cell types by marker gene tagging. Four cell types (B cells, cancer-associated fibroblasts (CAFs), endothelial cells, and plasmocytes) were identified in Osteosarcoma and normal tissues, based on differences in cell abundance. In total, 17 key targets were identified by Pearson's correlation analysis. GO and KEGG analysis showed that these 17 genes were associated with immune regulation pathways. Molecular docking analysis showed that RUNX2, OMD, and CD4 all bound well to vincristine, dexamethasone, and vinblastine. The expression of CD4, OMD, and JUN was decreased in Osteosarcoma cells compared with osteoblasts, whereas RUNX2 and COL9A3 expression was increased. Conclusion: We identified five key targets (CD4, RUNX2, OMD, COL9A3, and JUN) that are associated with Osteosarcoma progression. Vincristine, dexamethasone, and vinblastine may form a promising drug-target pair with RUNX2, OMD, and CD4 for Osteosarcoma treatment.

Tumor Microenvironment and Metabolism: Role of the Mitochondrial Melatonergic Pathway in Determining Intercellular Interactions in a New Dynamic Homeostasis

There is a growing interest in the role of alterations in mitochondrial metabolism in the pathoetiology and pathophysiology of cancers, including within the array of diverse cells that can form a given tumor microenvironment. The 'exhaustion' in natural killer cells and CD8+ t cells as well as the tolerogenic nature of dendritic cells in the tumor microenvironment seems determined by variations in mitochondrial function. Recent work has highlighted the important role played by the melatonergic pathway in optimizing mitochondrial function, limiting ROS production, endogenous antioxidants upregulation and consequent impacts of mitochondrial ROS on ROS-dependent microRNAs, thereby impacting on patterned gene expression. Within the tumor microenvironment, the tumor, in a quest for survival, seeks to 'dominate' the dynamic intercellular interactions by limiting the capacity of cells to optimally function, via the regulation of their mitochondrial melatonergic pathway. One aspect of this is the tumor's upregulation of kynurenine and the activation of the aryl hydrocarbon receptor, which acts to metabolize melatonin and increase the N-acetylserotonin/melatonin ratio, with effluxed N-acetylserotonin acting as a brain-derived neurotrophic factor (BDNF) mimic via its activation of the BDNF receptor, TrkB, thereby increasing the survival and proliferation of tumors and cancer stem-like cells. This article highlights how many of the known regulators of cells in the tumor microenvironment can be downstream of the mitochondrial melatonergic pathway regulation. Future research and treatment implications are indicated.

The activity level of follicular helper T cells in the peripheral blood of osteosarcoma patients is associated with poor prognosis

Osteosarcoma (OS) is solid tumors with high malignancy and incidence starting in the bones. OS pathogenesis has been proved to be closely associated with immune imbalance, and follicular helper T cells (Tfh) significantly affect host humoral immune homeostasis. However, there are few reports on the effect of Tfh cell activation on the prognosis of OS patients. Hence, this investigation on the changes in the proportion of peripheral blood Tfh cells in OS patients, and the relationship between their activity level and OS prognosis. We collected peripheral blood from patients with OS, benign bone tumor (BT group) and healthy subjects (Healthy group), respectively. The number of CD4⁺CXCR5⁺ Tfh cell in peripheral blood was measured by flow cytometry and correlation analysis between its activity and OS clinicopathological characteristics was carried out. The data showed that in comparison with the BT and Healthy groups, higher proportion and activation level of peripheral blood CD4⁺CXCR5⁺ Tfh cells in CD4⁺ T cells were found in the OS group. In OS patients, increases of the proportion and activity level of Tfh cells were associated with poorly differentiated OS and tumor metastasis. Additionally, Kaplan-Meier and Cox regression analysis showed a longer overall survival in patients with low proportion of peripheral blood CD4⁺CXCR5⁺ Tfh cells in CD4⁺ T cells, and their activation level may be a prognostic factor in OS patients. In conclusion, peripheral blood CD4⁺CXCR5⁺ Tfh cell activation in OS patients was associated with a poor prognosis. This study provided ideas for improving the clinical treatment of OS patients.