Bulk RNASeq Data in Colorectal Cancer
Description
Colon adenocarcinoma (CA) is a prevalent gastrointestinal malignancy, with significantly higher incidence than small intestine (SI) adenocarcinoma. However, it remains lack of comprehensive understanding about whether the distinct immune microenvironment between SI and colon is related to CA. Here, we performed bulk and single-cell analyses on immune cells of multiple matched tissues of CA patients and health donors, identifying the ileum-likeness pattern within immune microenvironment as a key feature of CA patients with favorable prognosis. The ileum-likeness pattern is primarily characterized by the less TGF-β+ long-lived plasma cells and the abundant cytotoxic and unexhausted CD160+ T cells. Tracking T cell clones confirmed that CD160+ T cells are highly cytotoxic and less prone to exhaustion. CD160 overexpression on CD8+ T cells significantly enhances anti-tumor immunity and efficiently inhibits tumor growth upon adoptive transfer to tumor-bearing mice. Our study provides comprehensive immune landscape within intestinal tissues and novel insights into intraepithelial T cells in CA, underscores potential targets for CA treatment.
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Steps to reproduce
We obtained bulk RNA sequencing data from multiple regional sites in both healthy donors and CA patients, including ileum, colon and tumor. Total RNA was extracted from relative intestine tissues using Trizol reagent. Approximately 5ug RNA was selected to remove ribosomal RNA following the instruction of Ribo-Zero Gold rRNA Removal Kit. Then the libraries were constructed through dissection, reverse transcription, amplification. And finally, 2×150bp paired-end sequencing was performed on an Illumina Novaseq 6000. To prepare data for downstream analysis, FastQC (v0.12.1) was used to assess the quality of raw sequencing data, and fastp software (v0.23.4) was applied to remove low-quality reads and any residual adapter sequences. The high-quality reads were aligned to human genome reference GRCh38 (hg38) using the STAR algorithm (v2.7.10b). ResQC (v5.0.3) is applied to assess the quality of alignment results using various alignment metrics, including coverage, mapping quality, and distribution of mapped reads. Then, FeatureCount (v2.0.6) was utilized to extract the feature count matrix from alignments. We used edgeR (v3.40.2) for normalization and multidimensional scaling.
Institutions
- Harbin Medical University