Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma-Reference-Cited by-同舟云学术

Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma

Published:2024-06-24 Issue: Volume: Page:
ISSN:0937-4477
Container-title:European Archives of Oto-Rhino-Laryngology
language:en
Short-container-title:Eur Arch Otorhinolaryngol

Author:

Wang Zhenzhen,Cheng Lixin,Huang Juntao^ORCID,Shen Yi

Funder

National Natural Science Foundation of China

Ningbo Public Science Research Foundation

Ningbo Natural Science Foundation

Zhejiang Provincial Natural Science Foundation

Zhejiang Provincial Medical and Health Science Research Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00405-024-08765-z.pdf

Reference49 articles.

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2. Shi Y, Huang J, Hu Y et al (2023) Multiomics data analyses to identify SLC25A17 as a novel biomarker to predict the prognosis and immune microenvironment in head and neck squamous cell carcinoma. BMC Bioinformatics 24:269. https://doi.org/10.1186/s12859-023-05399-6

3. Huang J, Xu Z, Zhou C et al (2023) 5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma. World J Surg Oncol 21:180. https://doi.org/10.1186/s12957-023-03067-w

4. Huang J, Xu Z, Yuan Z et al (2022) Identification of a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape of head and neck squamous cell carcinoma. Front Oncol 12:983956. https://doi.org/10.3389/fonc.2022.983956

5. Kaidar-Person O, Gil Z, Billan S (2018) Precision medicine in head and neck cancer. Drug Resist Updat 40:13–16. https://doi.org/10.1016/j.drup.2018.09.001