Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. METHODS: In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis. RESULTS: The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment. CONCLUSIONS: The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.

Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer.

In this study, we comprehensively charted the cellular landscape of colorectal cancer (CRC) and well-matched liver metastatic CRC using single-cell and spatial transcriptome RNA sequencing. We generated 41,892 CD45- nonimmune cells and 196,473 CD45+ immune cells from 27 samples of six CRC patients, and found that CD8_CXCL13 and CD4_CXCL13 subsets increased significantly in liver metastatic samples that exhibited high proliferation ability and tumor-activating characterization, contributing to better prognosis of patients. Distinct fibroblast profiles were observed in primary and liver metastatic tumors. F3+ fibroblasts enriched in primary tumors contributed to worse overall survival by expressing protumor factors. However, MCAM+ fibroblasts enriched in liver metastatic tumors might promote generation of CD8_CXCL13 cells through Notch signaling. In summary, we extensively analyzed the transcriptional differences of cell atlas between primary and liver metastatic tumors of CRC by single-cell and spatial transcriptome RNA sequencing, providing different dimensions of the development of liver metastasis in CRC.

Lactate score predicts survival, immune cell infiltration and response to immunotherapy in breast cancer.

Background: Tumor-derived lactate can modulate the function of infiltrating immune cells to establish an immunosuppressive microenvironment that favors tumor progression. However, possible effects of lactate-related genes (LRGs) on the tumor microenvironment (TME) of breast cancer (BRCA) are still unknown. Methods: LRGs were comprehensively screened from lactate metabolism-related pathways. We correlated the expression of these LRGs with immune cell infiltrating characteristics in the TME and clinicopathological features of patients. We also established a lactate score for quantifying lactate metabolism patterns of cancers and to predict of recurrence-free survival (RFS). Results: We successfully constructed a lactate score that was an independent prognostic factor in BRCA. A low lactate score, which was associated with immune activation with increased CD8+ T cells infiltration levels, indicated an inflamed TME. Consistently, higher expression levels of inhibitory immune checkpoints, including PD-L1, LAG3, CTLA4, and TIM3, as observed from high lactate score subgroup, suggested an immune-desert phenotype as well as poor prognosis. Moreover, a low lactate score predicted the increased chemotherapeutic drug sensitivity and enhanced anti-PD-1 immunotherapy responses. Conclusion: The present study analyzed the potential roles of LRGs in the TME diversity and prognosis. These results will help to improve our understanding of the characteristics of TME immune cell infiltration and guide the development of more effective immunotherapy strategies.

Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis.

BACKGROUND: Finding correlation patterns is an important goal of analyzing biological data. Currently available methods for correlation analysis mainly use non-direct associations, such as the Pearson correlation coefficient, and focus on the interpretation of networks at the level of modules. For biological objects such as genes, their collective function depends on pairwise gene-to-gene interactions. However, a large amount of redundant results from module level methods often necessitate further detailed analysis of gene interactions. New approaches of measuring direct associations among variables, such as the part mutual information (PMI), may help us better interpret the correlation pattern of biological data at the level of variable pairs. RESULTS: We use PMI to calculate gene co-expression networks of cancer mRNA transcriptome data. Our results show that the PMI-based networks with fewer edges could represent the correlation pattern and are robust across biological conditions. The PMI-based networks recall significantly more important parts of omics defined gene-pair relationships than the Pearson Correlation Coefficient (PCC)-based networks. Based on the scores derived from PMI-recalled copy number variation or DNA methylation gene-pairs, the patients with cancer can be divided into groups with significant differences on disease specific survival. CONCLUSIONS: PMI, measuring direct associations between variables, extracts more important biological relationships at the level of gene pairs than conventional indirect association measures do. It can be used to refine module level results from other correlation methods. Particularly, PMI is beneficial to analysis of biological data of the complicated systems, for example, cancer transcriptome data.