Published online Jan 24, 2024. doi: 10.5306/wjco.v15.i1.89
Peer-review started: October 26, 2023
First decision: December 12, 2023
Revised: December 17, 2023
Accepted: January 4, 2024
Article in press: January 4, 2024
Published online: January 24, 2024
Processing time: 89 Days and 18.8 Hours
Colorectal cancer (CRC) is an extremely fatal disease that is the third fastest-growing cause of cancer-related death globally. Disulfidptosis is one particular type of cell death that has been associated to the growth, escape, and regeneration of cancer cells. With disulfidptosis, colorectal cancer treatments and survival predictions could be altered.
A large number of clinical studies incorporate statistical significance to present their results. However, to be able to assess a therapy's adaptability and relevance in routine clinical practice, clinical measurements of significance are necessary.
The main goal of this work is to construct a stable biological biomarker that utilizes long non-coding RNA (LncRNA) linked to disulfidptosis-induced cell death. This may provide innovative viewpoints on the assessment of immunotherapy response and prognosis in patients suffering from CRC.
The Cancer Genome Atlas (TCGA) database offered transcriptome, clinical, and genetic mutation data relating to CRC. The minimal absolute shrinkage and selection operator approach and univariate and multivariate Cox regression models were applied to discover and assess critical LncRNA correlated with disulfidptosis. Ultimately, the critical LncRNA served as the foundation for the prognostic model.
Through multivariate analysis, we succeeded to identify eight critical long non-coding RNAs linked to disulfidptosis. These LncRNAs had significant accuracy for the consequences of CRCs. Compared to the high-risk group, patients in the low-risk group had a higher rate of overall survival. As a result, the nomogram prediction model we created exhibits good predictive validity and incorporates clinical characteristics and risk scores.
As a way to predict the prognosis of patients with colorectal cancer, we constructed a prediction model of disulfidptosis-related LncRNAs based on the TCGA-COAD and TCGA-READ cohort using bioinformatics technology and clinical patient data. The application of this model in clinical practice makes it much simpler to classify CRC patients precisely, pinpoint subgroups that are more likely to benefit from immunotherapy and radiation therapy, and provide evidence-based, targeted therapies for CRC patients.
In subsequent research, we must enhance the animal and cell experiments in order to validate the functional characteristics of disulfidaptosis-related lncRNA and the immune checkpoints' anticancer mechanisms.