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<p>This study aims to develop a risk model for the prognostic prediction for colorectal cancer (CRC) patients according to the phenotype related to damage-associated molecular patterns (DAMPs). The data were sourced from the Cancer Genome Atlas (TCGA) and cBioportal databases. The DAMP score was calculated based on the TCGA cohort data using the “ssGSEA” method. Differentially expressed genes (DEGs) identified by the “limma” package were compressed by performing Lasso Cox regression analysis using the “glmnet” package. Subsequently, biomarkers obtained were used to construct a risk model and a nomogram. The CRC subjects were divided by the median RiskScore into low- and high-risk groups. Kaplan-Meier (KM) survival analysis was conducted, and the “timeROC” package was used for model validation. The “estimate” package, “MCP-COUNTER”, “ssGSEA” and “TIDE” were employed to perform immune infiltration analyses. Drug sensitivity analysis and pathway analysis were conducted using the “pRRophetic” package and “ssGSEA”, respectively. According to the results, cancer-adjacent samples showed higher DAMP score and immune cell infiltration, lower tumor purity, and a better prognosis. Nine biomarkers (<i>PAH</i>, <i>SIGLEC14</i>, <i>MMP1</i>, <i>JAKMIP1</i>, <i>FCGR3B</i>, <i>KCNT1</i>, <i>SLC2A3</i>, <i>SLC11A1</i>, and <i>HOXC4)</i> were determined to build a reliable risk model, which showed a relatively high AUC value. Notably, patients classified by the model into the high-risk group had a worse prognostic outcome. Furthermore, a nomogram was constructed, and both the nomogram and RiskScore demonstrated a strong predictive power. The results of immune infiltration and drug sensitivity analysis showed higher immune infiltration and greater immunotherapy benefit in the low-risk group. Also, the low-risk group was enriched in immune-related pathways. We developed a reliable DAMP signature for CRC, contributing to the diagnosis and treatment of CRC.</p>

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Development of a prognostic prediction model based on damage-associated molecular pattern for colorectal cancer applying bulk RNA-seq analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月16日

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  • Yang Wu,
  • Yangjing Xu,
  • Yongtong Chen,
  • Wei Xu,
  • Minwu Yao,
  • Wencong Ding

摘要

This study aims to develop a risk model for the prognostic prediction for colorectal cancer (CRC) patients according to the phenotype related to damage-associated molecular patterns (DAMPs). The data were sourced from the Cancer Genome Atlas (TCGA) and cBioportal databases. The DAMP score was calculated based on the TCGA cohort data using the “ssGSEA” method. Differentially expressed genes (DEGs) identified by the “limma” package were compressed by performing Lasso Cox regression analysis using the “glmnet” package. Subsequently, biomarkers obtained were used to construct a risk model and a nomogram. The CRC subjects were divided by the median RiskScore into low- and high-risk groups. Kaplan-Meier (KM) survival analysis was conducted, and the “timeROC” package was used for model validation. The “estimate” package, “MCP-COUNTER”, “ssGSEA” and “TIDE” were employed to perform immune infiltration analyses. Drug sensitivity analysis and pathway analysis were conducted using the “pRRophetic” package and “ssGSEA”, respectively. According to the results, cancer-adjacent samples showed higher DAMP score and immune cell infiltration, lower tumor purity, and a better prognosis. Nine biomarkers (PAH, SIGLEC14, MMP1, JAKMIP1, FCGR3B, KCNT1, SLC2A3, SLC11A1, and HOXC4) were determined to build a reliable risk model, which showed a relatively high AUC value. Notably, patients classified by the model into the high-risk group had a worse prognostic outcome. Furthermore, a nomogram was constructed, and both the nomogram and RiskScore demonstrated a strong predictive power. The results of immune infiltration and drug sensitivity analysis showed higher immune infiltration and greater immunotherapy benefit in the low-risk group. Also, the low-risk group was enriched in immune-related pathways. We developed a reliable DAMP signature for CRC, contributing to the diagnosis and treatment of CRC.