Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

Abstract

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells’ proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

KEY MESSAGES

• lncRNAs play crucial roles in cancer biology, regulating proliferation, migration, and drug resistance.

• Emerging evidence suggests that machine learning can predict cancer prognosis using specific lncRNA combinations.

• Comprehensive information on the predictive abilities of lncRNA combinations in oncology concerning machine learning is lacking.

• This review offers up-to-date vital references on diverse lncRNA combinations pertinent to future research and clinical trials.

Keywords:

• lncRNA
• cancer
• machine learning
• prognosis prediction

Authors’ contributions

RT, JY, JZ, and LJ: conceptualisation and resources, original draft preparation. YM, JA, and WQ: original draft preparation, review, and editing. TG, XH, MF, and XL: review and editing. All authors have read and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Additional information

Funding

The National Natural Science Foundation of China (81700055, RT), the Outstanding Talent Research Funding of Xuzhou Medical University (D2016021, RT), and the Natural Science Foundation of Jiangsu Province (BK20160229, RT), Jining Medical University (600791001, JY).