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

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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.

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Most cited references 125

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Ahmedin Jemal, Lindsey A Torre, Rebecca Siegel … (2018)

This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.

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Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease

Suzy Torti, Donna D. Zhang, K.A. Woerpel … (2019)

Ferroptosis is a form of regulated cell death characterized by the iron-dependent accumulation of lipid hydroperoxides to lethal levels. Emerging evidence suggests that ferroptosis represents an ancient vulnerability caused by the incorporation of polyunsaturated fatty acids into cellular membranes, and cells have developed complex systems that exploit and defend against this vulnerability in different contexts. The sensitivity to ferroptosis is tightly linked to numerous biological processes, including amino acid, iron, and polyunsaturated fatty acid metabolism, and the biosynthesis of glutathione, phospholipids, NADPH, and coenzyme Q10. Ferroptosis has been implicated in the pathological cell death associated with degenerative diseases (i.e., Alzheimer's, Huntington's, and Parkinson's diseases), carcinogenesis, stroke, intracerebral hemorrhage, traumatic brain injury, ischemia-reperfusion injury, and kidney degeneration in mammals and is also implicated in heat stress in plants. Ferroptosis may also have a tumor-suppressor function that could be harnessed for cancer therapy. This Primer reviews the mechanisms underlying ferroptosis, highlights connections to other areas of biology and medicine, and recommends tools and guidelines for studying this emerging form of regulated cell death.

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Author and article information

Journal

Journal ID (nlm-ta): Ann Med

Journal ID (iso-abbrev): Ann Med

Title: Annals of Medicine

Publisher: Taylor & Francis

ISSN (Print): 0785-3890

ISSN (Electronic): 1365-2060

Publication date (Electronic, pub): 20 November 2023

Publication date (Electronic, collection): 2023

Publication date PMC-release: 20 November 2023

Volume: 55

Issue: 2

Electronic Location Identifier: 2279748

Affiliations

[a ]Department of Physiology, Basic medical school, Xuzhou Medical University , Xuzhou, China

[b ]The Collaborative Innovation Center, Jining Medical University , Jining, Shandong, China

[c ]Department of Pathology, UT Southwestern Medical Center , Dallas, UT, USA

[d ]Department of Pathology, College of Basic Medical Sciences, China Medical University , Shenyang, China

[e ]Department of Pathology, The First Hospital of China Medical University , Shenyang, China

Author notes

[*]

These authors contributed equally towards this work.

CONTACT Rubin Tan tanrubin11@ 123456126.com Department of Physiology, Basic medical school, Xuzhou Medical University , 209 Tongshan Road, Xuzhou, 221004, China

Jinxiang Yuan yuanjinxiang18@ 123456163.com The Collaborative Innovation Center, Jining Medical University , 133 Hehua Road, Jining, Shandong China

Author information

Joseph Adu-Amankwaah https://orcid.org/0000-0003-2155-0864

Rubin Tan https://orcid.org/0000-0002-1513-5616

Article

Publisher ID: 2279748

DOI: 10.1080/07853890.2023.2279748

PMC ID: 11571739

PubMed ID: 37983519

SO-VID: 80601d33-05b0-478e-aca5-c5f2e84b3f31

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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Figures: 0, Tables: 3, Pages: 16, Words: 11252

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

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Most cited references 125

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease

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