A Five-gene Signature based on MicroRNA for Predicting Prognosis and Immunotherapy in Stomach Adenocarcinoma


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Abstract

Aims:We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD).

Background:STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD.

Objective:The objective of this study is to investigate the molecular subtypes and prognostic model for STAD.

Methods:A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature.

Results:We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets.

Conclusion:This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

About the authors

Tianwei Wang

Department of Radiology, China-Japan Union Hospital of Jilin University

Email: info@benthamscience.net

Piji Chen

Department of Clinical Laboratory,, Yantian People’s Hospital of Southern University of Science and Technology,

Email: info@benthamscience.net

Tingting Li

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Jianong Li

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Dong Zhao

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Fanfei Meng

Department of Translational Medicine, YuceBio Technology Co., Ltd

Email: info@benthamscience.net

Yujie Zhao

Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis,, YuceBio Technology Co., Ltd

Email: info@benthamscience.net

Zhendong Zheng

Department of Oncology, Northern Theater Command General Hospital

Author for correspondence.
Email: info@benthamscience.net

Xuefei Liu

Department of Oncology, Northern Theater Command General Hospital

Author for correspondence.
Email: info@benthamscience.net

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