Frontiers in Immunology (Aug 2022)

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning

  • Meilin Weng,
  • Meilin Weng,
  • Ting Li,
  • Ting Li,
  • Jing Zhao,
  • Miaomiao Guo,
  • Miaomiao Guo,
  • Wenling Zhao,
  • Wenling Zhao,
  • Wenchao Gu,
  • Wenchao Gu,
  • Caihong Sun,
  • Caihong Sun,
  • Ying Yue,
  • Ying Yue,
  • Ziwen Zhong,
  • Ziwen Zhong,
  • Ke Nan,
  • Ke Nan,
  • Qingwu Liao,
  • Qingwu Liao,
  • Minli Sun,
  • Minli Sun,
  • Di Zhou,
  • Di Zhou,
  • Changhong Miao,
  • Changhong Miao

DOI
https://doi.org/10.3389/fimmu.2022.950782
Journal volume & issue
Vol. 13

Abstract

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Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.

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