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<p>Colorectal cancer (CRC) is a most deadly cancer, and effective prognostic biomarkers are urgently needed. Although anoikis has diverse regulatory roles in tumor progression, the impact of anoikis-related genes (ANRG) by single-cell and bulk transcriptome analyses on the prognostic value for CRC have not been studied. Differentially expressed genes (DEGs) associated with anoikis were obtained by performing single-cell RNA-sequencing (scRNA-seq) analysis in cells with high and low ANRG expression and weighted correlation network analysis (WGCNA) in a bulk RNA sequencing dataset. Key prognostic genes were selected from anoikis associated DEGs by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was established based on the risk score calculated from the expression levels of the identified key prognostic genes. A 10 anoikis-related-gene prognostic model (MGP, TPM2, CRIP2, TUBB6, C1orf54, NOTCH3, LTBP1, CSRP2, FSTL3, and VIM) was developed and the area under the curve (AUC) values of the model in predicting 1-, 3- and 5-year survival probabilities reached 0.744, 0.797, and 0.755, respectively. In conclusion, anoikis related genes could be promising prognostic factors for risk stratification of CRC patients.</p>

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A novel anoikis related gene prognostic model for colorectal cancer based on single cell sequencing and bulk transcriptome analyses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Lei Yang,
  • Yuan Zhang

摘要

Colorectal cancer (CRC) is a most deadly cancer, and effective prognostic biomarkers are urgently needed. Although anoikis has diverse regulatory roles in tumor progression, the impact of anoikis-related genes (ANRG) by single-cell and bulk transcriptome analyses on the prognostic value for CRC have not been studied. Differentially expressed genes (DEGs) associated with anoikis were obtained by performing single-cell RNA-sequencing (scRNA-seq) analysis in cells with high and low ANRG expression and weighted correlation network analysis (WGCNA) in a bulk RNA sequencing dataset. Key prognostic genes were selected from anoikis associated DEGs by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was established based on the risk score calculated from the expression levels of the identified key prognostic genes. A 10 anoikis-related-gene prognostic model (MGP, TPM2, CRIP2, TUBB6, C1orf54, NOTCH3, LTBP1, CSRP2, FSTL3, and VIM) was developed and the area under the curve (AUC) values of the model in predicting 1-, 3- and 5-year survival probabilities reached 0.744, 0.797, and 0.755, respectively. In conclusion, anoikis related genes could be promising prognostic factors for risk stratification of CRC patients.