Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data.
Kun XuDongshuo YuSiwen ZhangLanming ChenZhenhao LiuLu XiePublished in: Genes (2024)
The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets.
Keyphrases
- single cell
- rna seq
- poor prognosis
- induced apoptosis
- high throughput
- long non coding rna
- transcription factor
- electronic health record
- cell cycle arrest
- estrogen receptor
- oxidative stress
- papillary thyroid
- stem cells
- emergency department
- big data
- gene expression
- deep learning
- machine learning
- dna methylation
- bone marrow
- endoplasmic reticulum
- squamous cell
- breast cancer cells
- cell migration
- adverse drug
- artificial intelligence
- signaling pathway
- childhood cancer