A Comprehensive Comparison between Primary Liver Cancer and Liver Metastases through scRNA-Seq Data Analysis.
Shuang HaoLiqun ChenWenhui DuHuiyan SunPublished in: Metabolites (2024)
Metastasis is one of the leading causes of cancer-related deaths. A comprehensive comparison of the differences between primary and metastatic cancers within the same organ can aid in understanding the growth mechanisms of cancer cells at metastatic sites, thereby helping to develop more effective targeted treatment strategies. Primary liver cancer is one of the most common types of cancer, and the liver is also one of the main metastatic sites. In this paper, we utilize single-cell RNA-Seq data to compare primary liver cancer and colorectal liver metastases from multiple perspectives, including cell types and proportions, activity of various cell types, cell-cell communication, mRNA expression differences within the same types of cells, key factors associated with cell proliferation, etc. Our analysis results show the following: (i) Compared to primary tissue, metastatic tissue contains more cytotoxic T cells and exhausted T cells, and it retains some specific characteristics of the primary site. (ii) Cells of the same type exhibit functional differences between primary and metastatic cancers, with metastatic cancer cells showing lower metabolism levels and immune cells exhibiting stronger immune activity. (iii) Interactions between monocytes and hepato-associated cells are strong in primary cancer, while depleted T cells frequently communicate with hepatocytes in metastatic cancer. (iv) Proliferation-related genes in primary and metastatic cancers are mainly involved in cell energy supply and basic metabolism activity, respectively.
Keyphrases
- single cell
- rna seq
- squamous cell carcinoma
- small cell lung cancer
- liver metastases
- cell proliferation
- induced apoptosis
- cell therapy
- papillary thyroid
- data analysis
- high throughput
- cell cycle arrest
- cell death
- signaling pathway
- squamous cell
- dendritic cells
- gene expression
- machine learning
- young adults
- oxidative stress
- dna methylation
- liver injury