A novel hypoxia-driven gene signature that can predict the prognosis and drug resistance of gliomas

Ren, Peng; Wang, Jingya; Zeng, Zhirui; Li, Nan-Xi; Chen, Honglei; Peng, Xin-Ge; Bhawal, Ujjal K.; Guo, Wenzhi

doi:10.3389/fgene.2022.976356

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…A cuproptosis-related prognostic signature consisting of MAGEA6 and EPO promotes HCC development (44). A correlation was observed between CFHR3 and hypoxia (45). The level of FTCD served as a powerful diagnostic predictor for distinguishing early-stage HCC from benign tumors (36).…”

Section: Discussionmentioning

confidence: 99%

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Chen,

Zou,

Wang

et al. 2024

Front. Immunol.

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ObjectiveSignificant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.MethodsWe retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the “one-class logistic regression (OCLR)” algorithm to obtain the mRNAsi of patients with HCC. We performed “unsupervised consensus clustering” to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient’s prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.ResultsWe screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient’s prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.ConclusionThe novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

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Section: Discussionmentioning

confidence: 99%

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Chen,

Zou,

Wang

et al. 2024

Front. Immunol.

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“…Hypoxia-response genes, such as hypoxia-inducible factor HIF-2α and VEGF, are highly expressed in GSCs. Intriguingly, two reports have demonstrated that hypoxia-associated transcriptional signatures can be used as prognostic markers for glioblastoma patients [ 43 , 44 ] .…”

Section: Common Drug Resistance Mechanisms In Brain Cancersmentioning

confidence: 99%

Drug resistance in glioblastoma: from chemo- to immunotherapy

Sharma,

Chepurna,

Sun

2023

Cancer Drug Resist

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As the most common and aggressive type of primary brain tumor in adults, glioblastoma is estimated to end over 10,000 lives each year in the United States alone. Stand treatment for glioblastoma, including surgery followed by radiotherapy and chemotherapy (i.e., Temozolomide), has been largely unchanged since early 2000. Cancer immunotherapy has significantly shifted the paradigm of cancer management in the past decade with various degrees of success in treating many hematopoietic cancers and some solid tumors, such as melanoma and non-small cell lung cancer (NSCLC). However, little progress has been made in the field of neuro-oncology, especially in the application of immunotherapy to glioblastoma treatment. In this review, we attempted to summarize the common drug resistance mechanisms in glioblastoma from Temozolomide to immunotherapy. Our intent is not to repeat the well-known difficulty in the area of neuro-oncology, such as the blood-brain barrier, but to provide some fresh insights into the molecular mechanisms responsible for resistance by summarizing some of the most recent literature. Through this review, we also hope to share some new ideas for improving the immunotherapy outcome of glioblastoma treatment.

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Nano-imaging agents for brain diseases: Environmentally responsive imaging and therapy

Liang,

You,

et al. 2023

Nano Res.

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A novel hypoxia-driven gene signature that can predict the prognosis and drug resistance of gliomas

Cited by 5 publications

References 44 publications

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Drug resistance in glioblastoma: from chemo- to immunotherapy

Nano-imaging agents for brain diseases: Environmentally responsive imaging and therapy

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