Bioinformatics Analysis of Gastric Cancer Biomarkers

Bioinformatics analysis has become central to the discovery and validation of biomarkers in gastric cancer. By integrating high-throughput data from transcriptomic, proteomic and metabolomic studies, researchers are able to identify differentially expressed genes and molecules that distinguish malignant tissue from normal counterparts. Techniques such as microarray profiling, RNA sequencing and mass spectrometry are combined with statistical models and network-based approaches to reveal key pathways involved in tumour initiation, progression and immune evasion. Protein–protein interaction networks and weighted gene coexpression modules uncover functional clusters of genes, while machine-learning methods predict candidate markers with diagnostic or prognostic potential. Single-cell sequencing further refines these insights by resolving intratumour heterogeneity and mapping the tumour microenvironment. Collectively, these computational pipelines are guiding the development of noninvasive assays, personalised risk stratification and novel therapeutic targets, thereby improving early detection and informing treatment decisions on a global scale.

Research from Nature Portfolio

A study published in Scientific Reports employed microarray analysis of gastric adenocarcinoma alongside adjacent normal tissue to extract hundreds of differentially expressed genes. Enrichment analyses highlighted alterations in extracellular matrix organisation and cell adhesion pathways. Construction of a protein–protein interaction network identified hub genes, notably FN1, SPARC and SERPINE1, which exhibited strong correlations with poor patient survival. This work established a robust pipeline for pinpointing tumour-associated biomarkers and underscored the prognostic value of extracellular matrix components in gastric cancer.

Bioinformatics Analysis of Gastric Cancer Biomarkers publication trend

The graph below shows the total number of articles in bioinformatics analysis of gastric cancer biomarkers across all publications each year (not limited to Nature Index journals).

Technical terms

Differentially expressed gene (DEG): A gene showing statistically significant changes in expression level between two biological conditions.

Protein–protein interaction (PPI) network: A graphical representation of physical or functional associations between proteins.

Weighted gene coexpression network analysis (WGCNA): A systems biology method for clustering genes into modules based on expression patterns and relating these modules to phenotypic traits.

Single-cell RNA sequencing (scRNA-seq): A technique for profiling transcriptomes of individual cells to reveal cellular heterogeneity within tissues.

Random forest model: A machine-learning algorithm that constructs an ensemble of decision trees to improve predictive accuracy and assess feature importance.

References

1. FN1, SPARC, and SERPINE1 are highly expressed and significantly related to a poor prognosis of gastric adenocarcinoma revealed by microarray and bioinformatics. Scientific Reports (2019).
2. Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer. Frontiers in Immunology (2023).
3. New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model. Cancer Immunology, Immunotherapy (2024).
4. Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis. International Journal of Molecular Sciences (2024).