Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization.

Lin Cui,Jing Shao,Xiaoling Zhong,Wanqi Mi,Shizhu Jin,Ping Wang,Chunlong Zhang,Dajian Li,Guiyuan Tan,Dandan Ning

doi:10.3389/fcell.2021.631534

Abstract

Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and non-coding RNA (ncRNA) levels have been identified by high-throughput expression profiling for GC. To date, there have been no reports on integrated optimization analysis based on the GC global lncRNA-miRNA-mRNA network and the prognostic mechanism has not been studied. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was constructed based on the ceRNA hypothesis by combining miRNA-target interactions and data on the expression of GC. To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. We further obtained candidate prognostic signatures by calculating the integrated score and analyzed the robustness of these signatures by combination strategy. The biological roles of key candidate signatures were also explored. Finally, we targeted the PHF10 gene and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guidelines.

Highlights

Gastric Cancer (GC) is the second leading cause of cancer death globally according to the latest WHO statistics in 2018 (Bray et al, 2018)
The mRNA/Long non-coding RNAs (lncRNAs) expression was generated by HTseq-FPKM and miRNA expression was generated by miRNAseq-BCGSC-miRNA Profiling analysis
Step 2, based on the expression matrix and clinical information of GC, we respectively identified GC prognostic related mRNAs, miRNAs, and lncRNAs by using the univariate cox method

Summary

Introduction

Gastric Cancer (GC) is the second leading cause of cancer death globally according to the latest WHO statistics in 2018 (Bray et al, 2018). There is growing evidence that non-coding RNAs (ncRNAs), which make up the majority of human RNAs, play key roles in regulating gene expression, though they are not translated into proteins (Kaikkonen et al, 2011; Zhang et al, 2019). Some miRNAs may serve as an indicator of poor survival for cancer patients (Lan et al, 2015). Long non-coding RNAs (lncRNAs) are defined as ncRNAs over 200 nucleotides in length (Ransohoff et al, 2018). More evidence has revealed that lncRNAs can regulate the expression of protein-coding genes at the epigenetic, transcriptional, and posttranscriptional levels, with prototypes including scaffolds, signals, guides, and decoys (Chew et al, 2018; Li et al, 2019; Liu et al, 2019). Dysregulation of lncRNA expression has been documented in a variety of diseases, especially in cancers (Prensner and Chinnaiyan, 2011)

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