Literature DB >> 27986415

Esophageal cancer: Risk factors, genetic association, and treatment.

Fang-Liang Huang1, Sheng-Jie Yu2.   

Abstract

The poor prognosis and rising incidence of esophageal cancer highlight the need for improved detection and prediction methods that are essential prior to treatment. Esophageal cancer is one of the most fatal malignancies worldwide, with a dramatic increase in incidence in the Western world occurring over the past few decades. Despite improvements in the management and treatment of esophageal cancer patients, the general outcome remains very poor for overall 5-year survival rates (∼10%) and 5-year postesophagectomy survival rates (∼15-40%). Esophageal cancer is often diagnosed during its advanced stages, the main reason being the lack of early clinical symptoms. In an attempt to improve the outcome of patients after surgery, such patients are often treated with neoadjuvent concurrent chemoradiotherapy (CCRT) in order to decrease tumor size. However, CCRT may enhance toxicity levels and possibly cause a delay in surgery for patients who respond poorly to CCRT. Thus, precise biomarkers that could predict or identify patients who may or may not respond well to CCRT can assist physicians in choosing the appropriate therapy for patients. Identifying susceptible gene and biomarkers can help in predicting the treatment response of patients while improving their survival rates.
Copyright © 2016. Published by Elsevier Taiwan.

Entities:  

Keywords:  esophageal cancer; genetic factors; risk factors

Mesh:

Substances:

Year:  2016        PMID: 27986415     DOI: 10.1016/j.asjsur.2016.10.005

Source DB:  PubMed          Journal:  Asian J Surg        ISSN: 1015-9584            Impact factor:   2.767


  201 in total

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Journal:  Mol Clin Oncol       Date:  2020-06-11

2.  Identification of NCCRP1 as an epigenetically regulated tumor suppressor and biomarker for malignant phenotypes of squamous cell carcinoma of the esophagus.

Authors:  Takashi Miwa; Mitsuro Kanda; Masahiko Koike; Naoki Iwata; Haruyoshi Tanaka; Shinichi Umeda; Chie Tanaka; Daisuke Kobayashi; Masamichi Hayashi; Suguru Yamada; Tsutomu Fujii; Michitaka Fujiwara; Yasuhiro Kodera
Journal:  Oncol Lett       Date:  2017-08-14       Impact factor: 2.967

Review 3.  Esophageal carcinoma: Towards targeted therapies.

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Journal:  Cell Oncol (Dordr)       Date:  2019-12-17       Impact factor: 6.730

4.  Carcinostatic effects of alkanoyl ascorbate plus platinum nano-colloid and stabilization of the esterolytically resultant ascorbate by hydrogen.

Authors:  Shinya Kato; Yasukazu Saitoh; Nobuhiko Miwa
Journal:  Hum Cell       Date:  2021-01-02       Impact factor: 4.174

5.  LncRNA-NBAT-1 modulates esophageal cancer proliferation via PKM2.

Authors:  Bo Zhao; Peng Cao; Shan Hu; Fan Li; Kangle Kong; Yukun Zu
Journal:  Am J Transl Res       Date:  2019-09-15       Impact factor: 4.060

6.  A positive feedback loop between AlkB homolog 5 and miR-193a-3p promotes growth and metastasis in esophageal squamous cell carcinoma.

Authors:  Jinliang Xue; Peng Xiao; Xiangdong Yu; Xiao Zhang
Journal:  Hum Cell       Date:  2020-11-24       Impact factor: 4.174

7.  Whole-exome sequencing of familial esophageal squamous cell carcinoma identified rare pathogenic variants in new predisposition genes.

Authors:  F F Golyan; T E Druley; M R Abbaszadegan
Journal:  Clin Transl Oncol       Date:  2019-07-18       Impact factor: 3.405

8.  Direct Downregulation of B-Cell Translocation Gene 3 by microRNA-93 Is Required for Desensitizing Esophageal Cancer to Radiotherapy.

Authors:  Hujun Cui; Shengqiang Zhang; Hongbo Zhou; Ling Guo
Journal:  Dig Dis Sci       Date:  2017-04-22       Impact factor: 3.199

9.  The effects of radiotherapy after thoracic and laparoscopic surgery on patients with esophageal cancer and on their prognoses.

Authors:  Yu Gao; Mei Kang; Li Niu; Lei Xu; Xiaoque Xie; Dongbo Chen; Liyang Zhu; Fan Wang
Journal:  Am J Transl Res       Date:  2021-06-15       Impact factor: 4.060

10.  Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network.

Authors:  Wenju Du; Nini Rao; Changlong Dong; Yingchun Wang; Dingcan Hu; Linlin Zhu; Bing Zeng; Tao Gan
Journal:  Biomed Opt Express       Date:  2021-05-03       Impact factor: 3.732

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