Literature DB >> 31533939

A Novel Scoring System for Pivotal Autophagy-Related Genes Predicts Outcomes after Chemotherapy in Advanced Ovarian Cancer Patients.

Yuequn Niu1, Wenjie Sun2, Kelie Chen1, Zhiqin Fu3, Yaqing Chen3, Jianqing Zhu3, Hanwen Chen1,4, Yu Shi5, Honghe Zhang2, Liming Wang6, Han-Ming Shen6, Dajing Xia7, Yihua Wu7.   

Abstract

BACKGROUND: In the clinical practice of ovarian cancer, the application of autophagy, an important regulator of carcinogenesis and chemoresistance, is still limited. This study aimed to establish a scoring system based on expression profiles of pivotal autophagy-related (ATG) genes in patients with stage III/IV ovarian cancer who received chemotherapy.
METHODS: Data of ovarian serous cystadenocarcinoma in The Cancer Genome Atlas (TCGA-OV) were used as training dataset. Two validation datasets comprised patients in a Chinese local database and a dataset from the Gene Expression Omnibus (GEO). ATG genes significantly (P < 0.1) associated with overall survival (OS) were selected and aggregated into an ATG scoring scale, of which the abilities to predict OS and recurrence-free survival (RFS) were examined.
RESULTS: Forty-three ATG genes were selected to develop the ATG score. In TCGA-OV, patients with lower ATG scores had better OS [HR = 0.41; 95% confidence interval (CI), 0.26-0.65; P < 0.001] and RFS [HR = 0.47; 95% CI, 0.27-0.82; P = 0.007]. After complete or partial remission to primary therapy, the rate of recurrence was 47.2% in the low-score group and 68.3% in the high-score group (odds ratio = 0.42; 95% CI, 0.18-0.92; P = 0.03). Such findings were verified in the two validation datasets.
CONCLUSIONS: We established a novel scoring system based on pivotal ATG genes, which accurately predicts the outcomes of patients with advanced ovarian cancer after chemotherapy. IMPACT: The present ATG scoring system may provide a novel perspective and a promising tool for the development of personalized therapy in the future. ©2019 American Association for Cancer Research.

Entities:  

Year:  2019        PMID: 31533939     DOI: 10.1158/1055-9965.EPI-19-0359

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  4 in total

1.  Autophagy and Tumor Database: ATdb, a novel database connecting autophagy and tumor.

Authors:  Kelie Chen; Dexin Yang; Fan Zhao; Shengchao Wang; Yao Ye; Wenjie Sun; Haohua Lu; Zhi Ruan; Jinming Xu; Tianru Wang; Guang Lu; Liming Wang; Yu Shi; Honghe Zhang; Han Wu; Weiguo Lu; Han-Ming Shen; Dajing Xia; Yihua Wu
Journal:  Database (Oxford)       Date:  2020-01-01       Impact factor: 3.451

2.  Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma.

Authors:  Masakazu Sato; Sho Sato; Daisuke Shintani; Mieko Hanaoka; Aiko Ogasawara; Maiko Miwa; Akira Yabuno; Akira Kurosaki; Hiroyuki Yoshida; Keiichi Fujiwara; Kosei Hasegawa
Journal:  BMC Cancer       Date:  2022-01-13       Impact factor: 4.430

3.  Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO.

Authors:  Shaodi Wen; Weiwei Peng; Yuzhong Chen; Xiaoyue Du; Jingwei Xia; Bo Shen; Guoren Zhou
Journal:  BMC Cancer       Date:  2022-02-21       Impact factor: 4.430

4.  Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma.

Authors:  Zibo Meng; Dianyun Ren; Kun Zhang; Jingyuan Zhao; Xin Jin; Heshui Wu
Journal:  Aging (Albany NY)       Date:  2020-03-17       Impact factor: 5.682

  4 in total

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