ArticleFrontiers in immunology2022
mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Characterization of pancreatic cancer stem cells and construction of a stem cell-based prognostic signature.Scientific reports · 2025Article
- Article
- A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer.Open medicine (Warsaw, Poland) · 2025Article
- A mitochondrial lipid metabolism-related gene signature predicts prognosis and immune landscape in colorectal cancer.Frontiers in immunology · 2025Article
- Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism.Heliyon · 2024Article
- A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy.Frontiers in immunology · 2024Article
- An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes.Cancer medicine · 2024Article
- Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
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14 authors at 3 institutions in 2 countries.
Funding
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Abstract
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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