Apoptosis-related genes-based prognostic signature for osteosarcoma

Anti-cancer targets and molecular mechanisms of formononetin in treating osteosarcoma based on network pharmacology

Osteosarcoma (OS) is a multifactorial bone malignancy that accounts for most cancers in children and adolescents. Formononetin has been proven to exhibit various pharmacological effects including anti-tumor, anti-obesity, anti-inflammation, and neuroprotective effects. Few studies have examined the pharmacological activities of formononetin in OS treatment, but the mechanism has not yet been completely elucidated. Network pharmacology is a new method based on the theory of system biology for analyzing the network of biological systems and selecting specific signal nodes for multi-target drug molecular design. Here, we used network pharmacology to explore the possible mechanism of formononetin in OS treatment. Human OS cell line MG63 was processed with four concentrations (0, 2, 5, 8 μg/mL) of formononetin. Subsequently, an MTT assay was performed to test cell proliferation and a scratch test was used to evaluate the migration ability of cancer cells. Caspase-3, p53, p21, and bcl-2 expression levels incubated with different concentrations of formononetin in MG63 cells were determined using Western blotting. After treated with formononetin for 48 h, MG63 cells exhibited marked apoptosis. The results revealed that certain concentrations of formononetin significantly exerted inhibitory effects on MG63 cell proliferation. Furthermore, formononetin decreased the bcl-2 level in MG63 cells but increased caspase-3, p21, and p53 levels in a concentration-dependent manner. Additionally, formononetin suppressed the expression of SATB2. Therefore, formononetin could dose-dependently inhibit MG63 cell proliferation and induce apparent cell apoptosis, providing a candidate treatment for OS, whereas SATB2 could be a potential prognostic biomarker for screening OS and therapeutic target of formononetin.

Pan-Cancer analysis of TERT and Validation in Osteosarcoma Cell Lines

PURPOSE

To investigate the possible functions of TERT in pan-cancer and OS.

METHODS

First, differential TERT gene expression analysis was conducted using multi-omics data integrative analyses, including differential expression, prognosis, the correlation between infiltrating inflammatory immune cells, and mutation in pan-cancer. Furthermore, differential TERT gene expression analysis was conducted using mRNA expression profiles related to OS based on the GEO Datasets. Various differentially expressed genes were chosen based on a fitness threshold for further investigations. Finally, the function of the TERT gene was assessed in OS cells, including cellular proliferation, migration, and metastasis.

RESULTS

Pan-cancer research demonstrated that variable expression of TERT was not only associated with numerous types of human cancer but was also intimately linked to DNA methylation. Bioinformatic investigation revealed a link between the differential expression of TERT with immune cell infiltration in the tumor microenvironment (TME). In vitro studies indicated that inhibition of TERT decreased OS cell proliferation, motility, and metastasis.

CONCLUSION

TERT may serve as a useful genomic biomarker for the diagnosis and prediction of pan-cancer and as a prospective therapeutic target for the treatment of OS.

Construction of Molecular Subtype and Prognosis Prediction Model of Osteosarcoma Based on Aging-Related Genes

Background

Osteosarcoma (OS) is a rare form of malignant bone cancer that is usually detected in young adults and adolescents. This disease shows a poor prognosis owing to its metastatic status and resistance to chemotherapy. Hence, it is necessary to design a risk model that can successfully forecast the OS prognosis in patients.

Methods

The researchers retrieved the RNA sequencing data and follow-up clinical data related to OS patients from the TARGET and GEO databases, respectively. The coxph function in R software was used for carrying out the Univariate Cox regression analysis for deriving the aging-based genes related sto the OS prognosis. The researchers conducted consistency clustering using the ConcensusClusterPlus R package. The R software package ESTIMATE, MCPcounter, and GSVA packages were used for assessing the immune scores of various subtypes using the ssGSEA technique, respectively. The Univariate Cox and Lasso regression analyses were used for screening and developing a risk model. The ROC curves were constructed, using the pROC package. The performance of their developed risk model and designed survival curve was conducted, with the help of the Survminer package.

Results

The OS patients were classified into 2 categories, as per the aging-related genes. The results revealed that the Cluster 1 patients showed a better prognosis than the Cluster 2 patients. Both clusters showed different immune microenvironments. Additional screening of the prognosis-associated genes revealed the presence of 5 genes, i.e., ERCC4, GPX4, EPS8, TERT, and STAT5A, and these data were used for developing the risk model. This risk model categorized the training set samples into the high- and low-risk groups. The patients classified into the high-risk group showed a poor OS prognosis compared to the low-risk patients. The researchers verified the reliability and robustness of the designed 5-gene signature using the internal and external datasets. This risk model was able to effectively predict the prognosis even in the samples having differing clinical features. Compared with other models, the 5- gene model performs better in predicting the risk of osteosarcoma.

Conclusion

The 5-gene signature developed by the researchers in this study could be effectively used for forecasting the OS prognosis in patients.

Glycosylation modification patterns reveal distinct tumor metabolism and immune microenvironment landscape in lower-grade gliomas

Glycosylation alterations, a key driver throughout tumorigenesis and tumor progression, could regulate the microenvironment and immune response as well as lead to harmful metabolism and cell signaling. In this study, we first comprehensively evaluated the glycosylation modification patterns of LGGs based on glycosyltransferase family genes and systematically integrated these modification patterns with tumor metabolism and immune microenvironment characteristics. Glycosylation score was also developed to quantify glycosylation modification patterns of individuals. As a result, two glycosylation modification patterns were identified, with distinct prognosis, metabolism, and immune microenvironment features. The glycosylation subtype A and cluster A were characterized by higher carbohydrates and amino acid metabolism activity, higher levels of infiltrating cells, and poor prognosis, whereas an opposite modification pattern was observed in glycosylation subtype B and cluster B. In addition, a high glycosylation score is closer to a microenvironment characterized by chronic inflammation, immunosuppression, and tumor promotion. Following analysis and validation, the glycosylation score was a reliable and independent prognostic index. More importantly, the glycosylation score influenced the response to immunotherapy, chemotherapy, or targeted therapy, which provided a novel insight into promoting personalized therapy in the future and may contribute to developing novel therapeutic drugs or exploring promising drug combination therapy strategies.