2026-10-13T12:09:18Z https://www.scidb.cn/oai

10.57760/sciencedb.012gg 2026-09-20T10:10:58Z

2026-09-20 Single-cell and bulk transcriptomics reveal immune-telomere biomarkers in colorectal cancer doi:10.57760/sciencedb.012gg en This dataset was generated from a study on immune-telomere-related biomarkers in colorectal cancer (CRC), aiming to identify candidate biomarkers associated with the immune microenvironment and telomere regulation through integrated analysis of bulk and single-cell transcriptomic data, and to construct a risk model for prognostic assessment. The data generation process was as follows: transcriptomic data were collected from publicly accessible databases and subjected to quality control, normalization, and batch correction. Through differential expression analysis and regression analysis, seven candidate biomarkers were identified: CCNA2, CHEK1, BRCA1, SNAI1, TERT, PLCB4, and PLCG2. A risk model was constructed based on these genes, categorizing CRC patients into a high-risk group (HRG) and a low-risk group (LRG). Functional enrichment analysis, immune microenvironment analysis, and drug sensitivity analysis were then conducted. Immune analysis revealed differences in 22 immune cell types between the two groups, with activated B cells showing significant differences (adj. p < 0.05). Drug sensitivity analysis covered 168 drugs, among which pyrimethamine exhibited significantly different IC50 values between the HRG and LRG (p < 0.05). Single-cell analysis annotated nine cell types, with PLCG2, CCNA2, and CHEK1 differentially expressed in T cells, which were identified as key cells. Finally, RT-qPCR validation using clinical samples confirmed that the expression patterns of all seven biomarkers matched the bioinformatic predictions.The dataset consists of three parts: raw data, processed data, and analysis code. The raw data include transcriptomic expression matrices and sample metadata downloaded from public databases. The processed data include quality-controlled expression matrices, differentially expressed gene lists, risk score tables, immune infiltration results, drug sensitivity results, and single-cell annotation results. The analysis code covers data preprocessing, differential analysis, risk model construction, functional enrichment, immune analysis, drug sensitivity analysis, and single-cell analysis workflows. In the tabular data, rows represent genes or cells and columns represent samples or cell types, with units depending on the specific file (e.g., TPM, log2 fold change, IC50 values). The number of records is detailed in each file's description. Batch effects may exist due to the different public database sources, which have been corrected during processing, though residual errors should be interpreted with caution. All files are in common formats (CSV, TXT, R scripts) and can be viewed without specialized software; the analysis code requires an R environment to run. Colorectal cancer; Immune cell-related telomere-associated genes; Biomarkers; Risk model; Transcriptomics Ning Liang Jie Ding Yankang Feng Junlong Wei Zhongjun Wang Feiyang Zheng Yongsheng Du Guanli Gao Xiaolong Wei Zhufu Shen PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank