MIF is a critical regulator of mononuclear phagocytic infiltration in hepatocellular carcinoma

Tumoral EIF4EBP1 regulates the crosstalk between tumor-associated macrophages and tumor cells in MRTK

Malignant renal rhabdoid tumor (MRTK) is an aggressive and rare malignancy primarily affecting infants and young children. The intricate interactions within the Tumor Microenvironment (TME) are crucial in shaping MRTK's progression. This study elucidates the significance of tumor-associated macrophages(TAMs) within this milieu and their interplay with eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1) in tumor cells, collectively contributing to MRTK's malignant advancement. Through comprehensive analysis of clinical samples and the TARGET database, EIF4EBP1 emerges as a central macrophage-associated gene with robust prognostic implications. Elevated EIF4EBP1 expression correlates with poor prognosis and heightened infiltration of TAMs. Functional validation demonstrates that EIF4EBP1 knockdown in G401 cells significantly attenuates self-proliferation, migration, and invasion. Moreover, EIF4EBP1 regulates macrophage recruitment and M2 polarization through the ERK/P38 MAPK-MIF axis. Notably, M2 macrophages reciprocally foster the malignant behavior of MRTK tumor cells. This study unveils the pivotal role of EIF4EBP1 in propelling MRTK's malignant progression, unraveling a complex regulatory network involving EIF4EBP1 and TAMs. These findings underscore EIF4EBP1 as a promising biomarker and highlight its therapeutic potential in MRTK management.

Unveiling Atherosclerotic Plaque Heterogeneity and SPP1+/VCAN+ Macrophage Subtype Prognostic Significance Through Integrative Single-Cell and Bulk-Seq Analysis

Background

Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.

Methods

This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.

Results

Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1+/VCAN+ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.

Conclusion

Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion

Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not-rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non-rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection-associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data.