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<p>Urinary pseudouridine levels have been proposed as diagnostic biomarkers for various malignancies; however, their association with colorectal cancer (CRC) remains unclear. This study investigates the molecular mechanisms underlying pseudouridine-related genes (PRGs) in CRC. The study incorporated a training cohort (TCGA-CRC), a validation cohort (GSE87211), a single-cell dataset (GSE200997), and PRGs retrieved from public databases. Quality control was performed on the single-cell dataset, followed by cell type annotation. Differentially expressed genes (DEGs) across distinct cell populations were identified. Weighted gene co-expression network analysis (WGCNA) was employed to screen module genes strongly correlated with PRG scores. DEGs between tumor and normal samples in the training cohort were also determined. Candidate genes were selected by intersecting DEGs from key cell types, tumor-normal comparisons, and WGCNA-derived module genes. A prognostic risk model was constructed using Cox regression analyses. Independent prognostic factors were identified through univariate and multivariate Cox analyses, integrating clinical parameters and risk scores, to establish a prognostic nomogram. Comparative analyses of mutation profiles, immune infiltration, and functional pathways were conducted between high- and low-risk groups, and molecular mechanisms of prognostic genes were explored. Additionally, pseudo-temporal trajectory analysis was applied to assess prognostic gene expression dynamics in key cell types. Seven cell types were annotated in the single-cell dataset, with T cells and epithelial cells representing predominant and functionally significant populations. A total of 116 candidate genes were identified by overlapping 4,762 DEGs from T cells, 4,525 DEGs from epithelial cells, 9,772 tumor-normal DEGs, and 2,990 module genes. A prognostic risk model incorporating three PRGs—BCL10, TAF1B, and WWTR1—was developed and validated across training and validation cohorts. Risk score, age, T stage, N stage, and tumor stage were recognized as independent prognostic factors for constructing the nomogram. Pseudo-temporal trajectory analysis revealed that TAF1B expression was relatively elevated at the terminal differentiation phase in epithelial cells. A pseudouridine-related prognostic model based on three PRGs was established and validated, offering a potential reference for CRC treatment and risk stratification.</p>

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A pseudouridine-related prognostic model of colorectal cancer based on single-cell sequencing analysis and transcriptome analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月4日

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  • Zijing Wang,
  • Liyuan Ma,
  • Jinzhong Cao,
  • Shengtao Lin,
  • Ruxue Ma,
  • Jiang Wang,
  • Hengyi Lv,
  • Zixin Zhang,
  • Tao Jiang

摘要

Urinary pseudouridine levels have been proposed as diagnostic biomarkers for various malignancies; however, their association with colorectal cancer (CRC) remains unclear. This study investigates the molecular mechanisms underlying pseudouridine-related genes (PRGs) in CRC. The study incorporated a training cohort (TCGA-CRC), a validation cohort (GSE87211), a single-cell dataset (GSE200997), and PRGs retrieved from public databases. Quality control was performed on the single-cell dataset, followed by cell type annotation. Differentially expressed genes (DEGs) across distinct cell populations were identified. Weighted gene co-expression network analysis (WGCNA) was employed to screen module genes strongly correlated with PRG scores. DEGs between tumor and normal samples in the training cohort were also determined. Candidate genes were selected by intersecting DEGs from key cell types, tumor-normal comparisons, and WGCNA-derived module genes. A prognostic risk model was constructed using Cox regression analyses. Independent prognostic factors were identified through univariate and multivariate Cox analyses, integrating clinical parameters and risk scores, to establish a prognostic nomogram. Comparative analyses of mutation profiles, immune infiltration, and functional pathways were conducted between high- and low-risk groups, and molecular mechanisms of prognostic genes were explored. Additionally, pseudo-temporal trajectory analysis was applied to assess prognostic gene expression dynamics in key cell types. Seven cell types were annotated in the single-cell dataset, with T cells and epithelial cells representing predominant and functionally significant populations. A total of 116 candidate genes were identified by overlapping 4,762 DEGs from T cells, 4,525 DEGs from epithelial cells, 9,772 tumor-normal DEGs, and 2,990 module genes. A prognostic risk model incorporating three PRGs—BCL10, TAF1B, and WWTR1—was developed and validated across training and validation cohorts. Risk score, age, T stage, N stage, and tumor stage were recognized as independent prognostic factors for constructing the nomogram. Pseudo-temporal trajectory analysis revealed that TAF1B expression was relatively elevated at the terminal differentiation phase in epithelial cells. A pseudouridine-related prognostic model based on three PRGs was established and validated, offering a potential reference for CRC treatment and risk stratification.