Multiomics Identification of Radioresistance-Associated Biomarkers and Prognostic Model Construction in Rectal Cancer

Area of Science:

• Oncology
• Genomics
• Radiotherapy Research

Background:

• Radiotherapy is crucial for rectal cancer (RC) management, but radioresistance limits its effectiveness and worsens patient prognosis.
• Identifying biomarkers for radioresistance and developing prognostic models are essential for improving RC treatment outcomes.

Purpose of the Study:

• To identify biomarkers associated with radioresistance in rectal cancer.
• To develop a prognostic model based on radiotherapy-related genes for rectal cancer patients.

Main Methods:

• Utilized bulk and single-cell RNA-seq data from public repositories (GEO, TCGA) for 993 rectal cancer patients.
• Developed a prognostic model using differential expression, functional enrichment, and LASSO regression analyses.
• Assessed risk score associations with prognosis, immune infiltration, and signaling pathways; validated protein expression and in vivo.

Main Results:

• A risk score model was developed, showing significant associations with immune cell infiltration, chemotherapy sensitivity, and signaling pathways.
• Five key genes were identified as potentially related to radiotherapy sensitivity in rectal cancer.
• Protein expression of key genes was verified using the Human Protein Atlas and immunohistochemistry in animal models.

Conclusions:

• Multiomics analysis clarified molecular mechanisms of radiotherapy response in rectal cancer.
• The identified prognostic genes can serve as novel biomarkers for rectal cancer diagnosis, prognosis, and clinical management.
• These genes represent potential therapeutic targets for enhancing rectal cancer treatment efficacy.