biostudies-literatureWang ZHMU Marshal Initiative FundingDepartment of Heilongjiang ProvincePostdoctoral project of Heilongjiang ProvinceExcellent Talents Program of Harbin Medical UniversityInnovative Scientific Research Fund of Harbin Medical UniversityPostdoctoral Project of Heilongjiang ProvinceHeilongjiang Natural Science Fund ProjectNational Science Foundation of Heilongjiang Province12460https://www.ebi.ac.uk/biostudies/studies/S-EPMC9604003biostudies-literatureUnknown23(20)Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II-III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemInternational journal of molecular sciencesIdentification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II-III Colorectal Cancer.PMC96040032021-KYYWF-027912541415LBH-Z14130Grant Nos. YQ2019C012HMUMIF-21008Grant No. LH2019C087Grant Nos. HMUMIF-21008Grant No. 12541415YQ2019C012LH2019C087Grant number 2021-KYYWF-0279Grant No. LBH-Z14130Xing KZhang YZhang XQin XXu CChai TGeng JWang ZZhang BfalseIdentification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II-III Colorectal Cancer.Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II-III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chem2022-01-01T00:00:00Z2022 Oct2026-04-08T18:26:01.164Z2025-04-19T04:21:13.803ZS-EPMC96040033629331910.3390/ijms232012460