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<p>Recent studies have identified a biological process called adherent-to-suspension transition (AST) as a key factor in promoting metastasis. The involvement of AST in the progression of clear cell renal cell carcinoma (ccRCC) remains largely unexplored. We comprehensively investigated the overall landscape of all 20 AST factors at the pan-cancer level. The risk stratification method and the prognostic model based on AST factors were established with consensus clustering analysis and LASSO regression algorithm. The potential molecular mechanism of AST was further investigated by multi-omics analysis and cell experiments. We found that AST factors play distinct roles in different cancer types. Consequently, we utilized AST factors to develop a risk stratification method and a prognostic model, which can effectively guide the treatment of ccRCC patients. We proposed that the promotion of AST is facilitated by SPIB through the SAA1-AKT pathway, enhancing the likelihood of ccRCC metastasis, and conducted rigorous analyses using multi-omics data and cellular experiments. Our study presents a novel risk stratification method and prognostic model for ccRCC. And we identified the SPIB-SAA1-AKT pathway as one of the potential mechanisms by which AST factors promote ccRCC metastasis.</p>

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The role of adherent-to-suspension transition factors in clear cell renal cell carcinoma progression: a comprehensive analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月19日

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  • Chenglong Xu,
  • Haotian Wei,
  • Yue Wang,
  • Xiaocheng Ma,
  • Qi Li,
  • Yingjie Bi,
  • Zifei Li,
  • Zhaochen Li,
  • Shimiao Zhu,
  • Jing Tian,
  • Changyi Quan,
  • Shiyong Qi

摘要

Recent studies have identified a biological process called adherent-to-suspension transition (AST) as a key factor in promoting metastasis. The involvement of AST in the progression of clear cell renal cell carcinoma (ccRCC) remains largely unexplored. We comprehensively investigated the overall landscape of all 20 AST factors at the pan-cancer level. The risk stratification method and the prognostic model based on AST factors were established with consensus clustering analysis and LASSO regression algorithm. The potential molecular mechanism of AST was further investigated by multi-omics analysis and cell experiments. We found that AST factors play distinct roles in different cancer types. Consequently, we utilized AST factors to develop a risk stratification method and a prognostic model, which can effectively guide the treatment of ccRCC patients. We proposed that the promotion of AST is facilitated by SPIB through the SAA1-AKT pathway, enhancing the likelihood of ccRCC metastasis, and conducted rigorous analyses using multi-omics data and cellular experiments. Our study presents a novel risk stratification method and prognostic model for ccRCC. And we identified the SPIB-SAA1-AKT pathway as one of the potential mechanisms by which AST factors promote ccRCC metastasis.