Clinical and Translational Research
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Jan 24, 2024; 15(1): 89-114
Published online Jan 24, 2024. doi: 10.5306/wjco.v15.i1.89
Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes
Kui-Ling Wang, Kai-Di Chen, Wen-Wen Tang, Ze-Peng Chen, Yu-Ji Wang, Guo-Ping Shi, Yu-Gen Chen
Kui-Ling Wang, Kai-Di Chen, Wen-Wen Tang, Ze-Peng Chen, Yu-Ji Wang, Guo-Ping Shi, Yu-Gen Chen, Department of Colorectal Surgery, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
Author contributions: Chen YG provided the acquisition of funding and formulated research goals; Wang KL wrote the original manuscript; Chen KD, and Chen ZP were involved in the software analysis; Tang WW, Wang YJ, and Shi GP performed the data collation; All authors have read and agreed to the published version of the manuscript.
Supported by Jiangsu Province Science and Technology Plan Project-Youth Fund Project, No. BK2020040973.
Institutional review board statement: The ethical approval is not applicable to this article.
Informed consent statement: There were no human subjects included in this article, and therefore informed consent is not applicable.
Conflict-of-interest statement: All the authors declare that the study was carried out without any commercial or financial relationships which could be considered a potential conflict of interest.
Data sharing statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Yu-Gen Chen, Doctor, MD, PhD, Chief Doctor, Professor, Surgeon, Department of Colorectal Surgery, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Qinhuai District, Nanjing 210029, Jiangsu Province, China.
Received: October 25, 2023
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
Research background

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.

Research motivation

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.

Research objectives

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.

Research methods

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.

Research results

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.

Research conclusions

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.

Research perspectives

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.