ABSTRACT
Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC.
Subject(s)
Colorectal Neoplasms , Early Detection of Cancer , Humans , Colorectal Neoplasms/genetics , Colorectal Neoplasms/blood , Colorectal Neoplasms/diagnosis , Male , Middle Aged , Female , Early Detection of Cancer/methods , Aged , Cell-Free Nucleic Acids/genetics , Cell-Free Nucleic Acids/blood , Biomarkers, Tumor/genetics , Biomarkers, Tumor/blood , Machine Learning , Adult , Whole Genome Sequencing/methods , DNA FragmentationABSTRACT
Colorectal cancer, the third most prevalent malignant cancer, is associated with poor prognosis. Recent studies have investigated the mechanisms underlying cuproptosis and disulfidptosis in colorectal cancer. However, whether genes linked to these processes impact the prognosis of colorectal cancer patients through analogous mechanisms remains unclear. In this study, we developed a model of cuproptosis and disulfidptosis in colorectal cancer and concurrently explored the role of the pivotal model gene HSPA8 in colorectal cancer cell lines. Our results revealed a positive correlation between cuproptosis and disulfidptosis, both of which are emerging as protective factors for the prognosis of CRC patients. Consequently, a prognostic model encompassing HSPA8, PDCL3, CBX3, ATP6V1G1, TAF1D, RPL4, and RPL14 was constructed. Notably, the key gene in our model, HSPA8, exhibited heightened expression and was validated as a protective prognostic factor in colorectal cancer, exerting inhibitory effects on colorectal cancer cell proliferation. This study offers novel insights into the interplay between cuproptosis and disulfidptosis. The application of the prognostic model holds promise for more effectively predicting the overall survival of colorectal cancer patients.
