Gene Expression Analysis in Cancer Genomics

Gene expression analysis is central to decoding the molecular heterogeneity of cancer and guiding precision oncology. Bulk transcriptomic profiling by microarrays and RNA sequencing has enabled classification of tumours into subtypes distinguished by characteristic expression signatures, underpinning prognostic stratification and the identification of potential therapeutic targets. Advances in single-cell RNA‐sequencing and spatial transcriptomics have further resolved intratumoural diversity and the complex interplay between malignant cells and their microenvironment. Computational and statistical innovations, including differential expression analysis, gene set enrichment and network‐based approaches, support the robust identification of dysregulated pathways and key regulatory genes. Integration of multi-omic layers, such as chromatin accessibility and protein–RNA associations, deepens insights into regulatory mechanisms driving tumour initiation, progression and resistance to therapy. Collectively, gene expression analysis in cancer genomics informs biomarker discovery, guides personalised treatment strategies and fosters a more nuanced understanding of tumour biology on a global scale.

Research from Nature Portfolio

Recent studies have deployed high-throughput single-cell transcriptomic platforms that encapsulate cells in microdroplets for unbiased 3′ mRNA counting, achieving scalable profiling of tens of thousands of individual cells per sample. This approach has been adapted to dissect tumour samples, revealing rare malignant subpopulations and defining the transcriptional programmes of infiltrating immune cells with high sensitivity and throughput. In parallel, integrative web-based portals have been developed to streamline the analysis of large gene lists derived from cancer expression datasets. These resources combine functional enrichment, interactome mapping and gene annotation across multiple knowledgebases, facilitating comparative pathway analysis and rapid interpretation of omics results. By unifying diverse datasets and automating enrichment workflows, they provide cancer researchers with accessible, comprehensive tools to translate complex expression data into actionable biological insights.

Gene Expression Analysis in Cancer Genomics publication trend

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

Technical terms

Transcriptome: The complete set of RNA transcripts produced by a cell under specific conditions.

Single-cell RNA-sequencing (scRNA-seq): A method for quantifying gene expression at the resolution of individual cells.

Differential expression analysis: Statistical comparison of gene expression levels between experimental conditions or sample groups.

Pathway enrichment analysis: A computational approach to identify biological pathways overrepresented among differentially expressed genes.

Tumour microenvironment: The non-malignant cells, molecules and blood vessels surrounding and interacting with a tumour.

References

1. Single-cell RNA-sequencing reveals radiochemotherapy-induced innate immune activation and MHC-II upregulation in cervical cancer. Signal Transduction and Targeted Therapy (2023).
2. Massively parallel digital transcriptional profiling of single cells. Nature Communications (2017).
3. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications (2019).
4. Integrated analysis of multimodal single-cell data. Cell (2021).
5. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology (2014).