CBX7, a Potential Prognostic Biomarker in Lung Adenocarcinoma

Chromobox proteins in cancer: Multifaceted functions and strategies for modulation (Review)

Chromobox (CBX) proteins are important epigenetic regulatory proteins and are widely involved in biological processes, such as embryonic development, the maintenance of stem cell characteristics and the regulation of cell proliferation and apoptosis. Disorder and dysfunction of CBXs in cancer usually lead to the blockade or ectoptic activation of developmental pathways, promoting the occurrence, development and progression of cancer. In the present review, the characteristics and functions of CBXs were first introduced. Subsequently, the expression of CBXs in cancers and the relationship between CBXs and clinical characteristics (mainly cancer grade, stage, metastasis and relapse) and prognosis were discussed. Finally, it was described how CBXs regulate cell proliferation and self‑renewal, apoptosis and the acquisition of malignant phenotypes, such as invasion, migration and chemoresistance, through mechanisms involving epigenetic modification, nuclear translocation, noncoding RNA interactions, transcriptional regulation, posttranslational modifications, protein‑protein interactions, signal transduction and metabolic reprogramming. The study also focused on cancer therapies targeting CBXs. The present review provides new insight and a comprehensive basis for follow‑up research on CBXs and cancer.

A novel risk score model based on fourteen chromatin regulators-based genes for predicting overall survival of patients with lower-grade gliomas

Background: Low grade gliomas(LGGs) present vexatious management issues for neurosurgeons. Chromatin regulators (CRs) are emerging as a focus of tumor research due to their pivotal role in tumorigenesis and progression. Hence, the goal of the current work was to unveil the function and value of CRs in patients with LGGs.

Methods: RNA-Sequencing and corresponding clinical data were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. A single-cell RNA-seq dataset was sourced from the Gene Expression Omnibus (GEO) database. Altogether 870 CRs were retrieved from the published articles in top academic journals. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were applied to construct the prognostic risk model. Patients were then assigned into high- and low-risk groups based on the median risk score. The Kaplan–Meier (K-M) survival curve and receiver operating characteristic curve (ROC) were performed to assess the prognostic value. Sequentially, functional enrichment, tumor immune microenvironment, tumor mutation burden, drug prediction, single cell analysis and so on were analyzed to further explore the value of CR-based signature. Finally, the expression of signature genes were validated by immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR).

Results: We successfully constructed and validated a 14 CRs-based model for predicting the prognosis of patients with LGGs. Moreover, we also found 14 CRs-based model was an independent prognostic factor. Functional analysis revealed that the differentially expressed genes were mainly enriched in tumor and immune related pathways. Subsequently, our research uncovered that LGGs patients with higher risk scores exhibited a higher TMB and were less likely to be responsive to immunotherapy. Meanwhile, the results of drug analysis offered several potential drug candidates. Furthermore, tSNE plots highlighting the magnitude of expression of the genes of interest in the cells from the scRNA-seq assay. Ultimately, transcription expression of six representative signature genes at the mRNA level was consistent with their protein expression changes.

Conclusion: Our findings provided a reliable biomarker for predicting the prognosis, which is expected to offer new insight into LGGs management and would hopefully become a promising target for future research.