Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer-Reference-Cited by-同舟云学术

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Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer

Published:2024-04-12 Issue: Volume: Page:
ISSN:1945-4589
Container-title:Aging
language:en
Short-container-title:Aging

Author:

Li Guo-Xing¹,Chen Yun-Peng²,Hu You-Yang²,Zhao Wen-Jing¹,Lu Yun-Yan¹,Wan Fu-Jian³,Wu Zhi-Jun⁴,Wang Xiang-Qian¹,Yu Qi-Ying¹

Affiliation:

1. Department of Oncology and Central Laboratory, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu 226361, P.R. China

2. Department of Oncology, The Affiliated Hospital of Nantong University, Nantong, Jiangsu 226361, P.R. China

3. Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China

4. Department of Oncology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu 226361, P.R. China

Publisher

Impact Journals, LLC

Reference35 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.;Bray;CA Cancer J Clin,2021

2. Gastric cancer.;Lordick;Lancet,2020

3. Gastric cancer treatment: recent progress and future perspectives.;Xu;J Hematol Oncol,2023

4. Neoadjuvant therapy with immune checkpoint blockade, antiangiogenesis, and chemotherapy for locally advanced gastric cancer.;Wang;Nat Commun,2023

5. Early results of a randomized two-by-two factorial phase II trial comparing neoadjuvant chemotherapy with two and four courses of cisplatin/S-1 and docetaxel/cisplatin/S-1 as neoadjuvant chemotherapy for locally advanced gastric cancer.;Yoshikawa;Ann Oncol,2017

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