Nuclear receptor modulators inhibit osteosarcoma cell proliferation and tumour growth by regulating the mTOR signaling pathway

Yuan, Baoshi; Kai-quan, Shi; Zha, Juanmin; Cai, Yujia; Gu, Yong; Huang, Kai; Yue, Wenchang; Zhai, Qiaocheng; Ding, Ning; Ren, Wenyan; He, Wei–Qi; Xu, Ying; Wang, Tao

doi:10.1038/s41419-022-05545-7

Cited by 3 publications

(1 citation statement)

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“…Osteosarcoma is characterized by aberrant gene expression, which exerts significant influence on the initiation and progression of tumor cells ( 4 , 5 ). Key molecular pathways implicated in osteosarcoma include the STING/IRF3/IFN-β, PI3K/AKT, and mTOR signaling pathways ( 6 – 8 ). Considering the complexity of osteosarcoma pathogenesis and the heterogeneity of its molecular landscape, identifying critical genes affecting prognosis and constructing a simple yet effective prognostic model is paramount.…”

Section: Introductionmentioning

confidence: 99%

A novel prognostic signature related to programmed cell death in osteosarcoma

Jiang,

Xu,

Wang

et al. 2024

Front. Immunol.

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BackgroundOsteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.MethodWe retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.ResultIn our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes—CLTCL1, EDIL3, and SQLE—resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.ConclusionThe five genes constituting the OS-PCDS model—CLTCL1, MTM1, MLH1, EDIL3, and SQLE—were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

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Section: Introductionmentioning

confidence: 99%

A novel prognostic signature related to programmed cell death in osteosarcoma

Jiang,

Xu,

Wang

et al. 2024

Front. Immunol.

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Pyroptosis and chemical classification of pyroptotic agents

Hara,

Ramadan,

Abdelhameid

et al. 2024

Mol Divers

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Pyroptosis, as a lytic-inflammatory type of programmed cell death, has garnered considerable attention due to its role in cancer chemotherapy and many inflammatory diseases. This review will discuss the biochemical classification of pyroptotic inducers according to their chemical structure, pyroptotic mechanism, and cancer type of these targets. A structure-activity relationship study on pyroptotic inducers is revealed based on the surveyed pyroptotic inducer chemotherapeutics. The shared features in the chemical structures of current pyroptotic inducer agents were displayed, including an essential cyclic head, a vital linker, and a hydrophilic tail that is significant for π-π interactions and hydrogen bonding. The presented structural features will open the way to design new hybridized classes or scaffolds as potent pyroptotic inducers in the future, which may represent a solution to the apoptotic-resistance dilemma along with synergistic chemotherapeutic advantage. Graphical Abstract

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