Prediction of the Prognosis Based on Chromosomal Instability-Related DNA Methylation Patterns of ELOVL2 and UBAC2 in PTCs

Single-cell RNA sequencing reveals cellular and molecular reprograming landscape of gliomas and lung cancer brain metastases

BACKGROUND

Brain malignancies encompass gliomas and brain metastases originating from extracranial tumours including lung cancer. Approximately 50% of patients with lung adenocarcinoma (LUAD) will eventually develop brain metastases. However, the specific characteristics of gliomas and lung-to-brain metastases (LC) are largely unknown.

METHODS

We applied single-cell RNA sequencing to profile immune and nonimmune cells in 4 glioma and 10 LC samples.

RESULTS

Our analysis revealed that tumour microenvironment (TME) cells are present in heterogeneous subpopulations. LC reprogramed cells into immune suppressed state, including microglia, macrophages, endothelial cells, and CD8+ T cells, with unique cell proportions and gene signatures. Particularly, we identified that a subset of macrophages was associated with poor prognosis. ROS (reactive oxygen species)-producing neutrophils was found to participant in angiogenesis. Furthermore, endothelial cells participated in active communication with fibroblasts. Metastatic epithelial cells exhibited high heterogeneity in chromosomal instability (CIN) and cell population.

CONCLUSIONS

Our findings provide a comprehensive understanding of the heterogenicity of the tumor microenvironment and tumour cells and it will be crucial for successful immunotherapy development for brain metastasis of lung cancer.

Metabolism drives macrophage heterogeneity in the tumor microenvironment

Tumor-associated macrophages (TAMs) are a major cellular component in the tumor microenvironment (TME). However, the relationship between the phenotype and metabolic pattern of TAMs remains poorly understood. We performed single-cell transcriptome profiling on hepatic TAMs from mice bearing liver metastatic tumors. We find that TAMs manifest high heterogeneity at the levels of transcription, development, metabolism, and function. Integrative analyses and validation experiments indicate that increased purine metabolism is a feature of TAMs with pro-tumor and terminal differentiation phenotypes. Like mouse TAMs, human TAMs are highly heterogeneous. Human TAMs with increased purine metabolism exhibit a pro-tumor phenotype and correlate with poor therapeutic efficacy to immune checkpoint blockade. Altogether, our work demonstrates that TAMs are developmentally, metabolically, and functionally heterogeneous and purine metabolism may be a key metabolic feature of a pro-tumor macrophage population.

A novel methylation signature predicts inferior outcome of patients with PDAC

Pancreatic ductal adenocarcinoma (PDAC) will become the second most common cause of death in North America and Europe over the next 10 years owing to the lack of early diagnosis, poor treatment, and poor prognosis. This study evaluated the methylation array data of 184 patients with PDAC in The Cancer Genome Atlas database to explore methylation biomarkers related to patient outcome. Using Univariable Cox regression analysis and Lasso regression analysis method in the training dataset, it was found that the four DNA methylation markers (CCNT1, ITGB3, SDS, and HMOX2) were significantly correlated with the overall survival of patients with PDAC. Kaplan-Meier analysis showed that these four DNA methylation markers could significantly distinguish high-risk and low-risk patients. Receiver operating characteristic analysis further confirmed that the four DNA methylation markers had high sensitivity and specificity, which could predict the prognosis of patients. Moreover, there was a difference in the genetic mutations between high-risk and low-risk patients distinguished by the four-DNA methylation model, which can provide information for clinical treatment. Finally, compared with known biomarkers, the model was more accurate in predicting the prognosis of PDAC. This four-DNA methylation model has potential as a new independent prognostic indicator, and could be used for the diagnosis, monitoring, and precision medicine of pancreatic cancer.