Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Abstract

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Keywords: bioinformatics; biomarker; gastric cancer; machine learning; multi-omics; prediction; prognosis.

Figure 1

Schematic diagram of validation of biomarker by the integration of bioinformatic analysis with multi-omics platforms. Different omics technologies and their features, such as omics targeted, sequencing techniques, data processing, data analysis, and output, were displayed. The first step is the identification of high-quality samples obtained by blood, tissue samples from GC patients, or high-throughput datasets. Multi-platform omics technologies are analyzed individually to identify the DNA, RNA, proteins, and metabolites responsible for the GC progression. Omics data are extracted by the search for various omics databases, mining of the data, and obtaining the pre-processed omics data. Thereby, data are transformed and processed through normalization, quality control, and feature selection to mine interpretable information. These multidimensional data can be integrated based on statistical analysis, machine learning, and functional analysis, which lead to the discovery of putative biomarkers.