A Multi-Center Validated Subtyping Model of Esophageal Cancer Based on Three Metabolism-Related Genes

Mechanisms of tumor immunosuppressive microenvironment formation in esophageal cancer

As a highly invasive malignancy, esophageal cancer (EC) is a global health issue, and was the eighth most prevalent cancer and the sixth leading cause of cancer-related death worldwide in 2020. Due to its highly immunogenic nature, emer-ging immunotherapy approaches, such as immune checkpoint blockade, have demonstrated promising efficacy in treating EC; however, certain limitations and challenges still exist. In addition, tumors may exhibit primary or acquired resistance to immunotherapy in the tumor immune microenvironment (TIME); thus, understanding the TIME is urgent and crucial, especially given the im-portance of an immunosuppressive microenvironment in tumor progression. The aim of this review was to better elucidate the mechanisms of the suppressive TIME, including cell infiltration, immune cell subsets, cytokines and signaling pathways in the tumor microenvironment of EC patients, as well as the downregulated expression of major histocompatibility complex molecules in tumor cells, to obtain a better understanding of the differences in EC patient responses to immunotherapeutic strategies and accurately predict the efficacy of immunotherapies. Therefore, personalized treatments could be developed to maximize the advantages of immunotherapy.

Combining bulk RNA-sequencing and single-cell RNA-sequencing data to reveal the immune microenvironment and metabolic pattern of osteosarcoma

Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level.

Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis.

Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis.

Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.