biostudies-literatureMa LNatural Science Foundation of Guangdong ProvinceGuangzhou Planned Project of Science and Technology32https://www.ebi.ac.uk/biostudies/studies/S-EPMC12644115biostudies-literatureUnknown26(1)The high heterogeneity of colorectal cancer (CRC) complicates accurate prognosis prediction. Hypoxia can affect cell death mechanisms, leading to resistance to many antitumor therapies and potentially causing relapse. Programmed cell death (PCD) forms are also crucial in regulating cancer progression. Given this, the prognostic value of the cross-talk between hypoxic conditions and PCD mechanisms in CRC merits deeper exploration. Our methodology involved a large-scale analysis of four multicenter cohorts (n = 1294) utilizing a reliable and efficient data processing pipeline to explore the associations between hypoxia and 18 PCD signatures. Thirteen widely used machine learning algorithms were applied to identify cell death risk features associated with hypoxia, and NOL3 was selected as a kClinical and experimental medicineDecoding colorectal cancer chemotherapy and immunotherapy resistance: revealing the role of NOL3 using single-cell RNA sequencing and machine learning.PMC126441152021A15150111132023A03J0966Li WLuan YLu LMa LWei YfalseDecoding colorectal cancer chemotherapy and immunotherapy resistance: revealing the role of NOL3 using single-cell RNA sequencing and machine learning.The high heterogeneity of colorectal cancer (CRC) complicates accurate prognosis prediction. Hypoxia can affect cell death mechanisms, leading to resistance to many antitumor therapies and potentially causing relapse. Programmed cell death (PCD) forms are also crucial in regulating cancer progression. Given this, the prognostic value of the cross-talk between hypoxic conditions and PCD mechanisms in CRC merits deeper exploration. Our methodology involved a large-scale analysis of four multicenter cohorts (n = 1294) utilizing a reliable and efficient data processing pipeline to explore the associations between hypoxia and 18 PCD signatures. Thirteen widely used machine learning algorithms were applied to identify cell death risk features associated with hypoxia, and NOL3 was selected as a k2025-01-01T00:00:00Z2025 Nov2026-05-20T03:23:19.017Z2026-05-20T03:14:51.1ZS-EPMC126441154128647010.1007/s10238-025-01913-7