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Chronobiology International
The Journal of Biological and Medical Rhythm Research
Volume 40, 2023 - Issue 6
170
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Research Article

Identification and validation of a novel prognostic circadian rhythm-related gene signature for stomach adenocarcinoma

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Pages 744-758 | Received 14 Sep 2022, Accepted 18 Apr 2023, Published online: 25 Apr 2023
 

ABSTRACT

Circadian rhythm genes were reported to be strongly associated with the development and prognosis of circadian rhythm disorders related to stomach adenocarcinoma (STAD), which is one of the most prevalent cancers. This study aimed to identify a circadian rhythm-related gene signature that could help predict STAD outcome. Using bioinformatics analysis approaches, 105 genes were examined in 350 patients with STAD. Overall, six hub-type circadian rhythm-associated genes (GNA11, PER1, SOX14, EZH2, MAGED1, and NR1D1) were identified using univariate and multivariate Cox regression analyses. These genes were then used to build a genetic predictive model, which was further validated using a publicly available dataset (GSE26899). Overall, genes associated with the circadian rhythm were found to be substantially correlated with the characteristics of the STAD patients (grade, sex, and M stage). In addition, the circadian rhythm-related gene signature was significantly associated with the MAPK and Notch signaling pathways, which are known risk factors for poorer STAD outcome. Taken together, these findings suggest that the herein proposed prognostic model based on six circadian rhythm-associated genes may have predictive value and potential application for clinical decision-making and for personalized treatment of STAD.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in The Cancer Genome Atlas (TCGA) (https://xena.ucsc.edu/; STAD dataset) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/; GSE26899 dataset) databases.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07420528.2023.2205936

Additional information

Funding

The work was supported by the Key Research and Development Program of Shaanxi [2021SF-307].

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