Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research.
Wireko Andrew AwuahSakshi RoyJoecelyn Kirani TanFavour Tope AdebusoyeZekai QiangTomas FerreiraArjun AhluwaliaVallabh ShetAmanda Leong Weng YeeToufik Abdul-RahmanMarios PapadakisPublished in: Journal of cellular and molecular medicine (2024)
Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.
Keyphrases
- single cell
- rna seq
- high throughput
- global health
- clinical practice
- genome wide
- gas chromatography
- stem cells
- current status
- gene expression
- cell cycle arrest
- transcription factor
- machine learning
- genome wide identification
- risk factors
- human health
- cell death
- artificial intelligence
- risk assessment
- mesenchymal stem cells
- cell proliferation
- heat shock
- climate change
- tandem mass spectrometry
- high resolution
- bioinformatics analysis