Prognostic Biomarkers and Immune Microenvironment in Gastric Cancer
Summary
Gastric cancer remains a leading cause of cancer-related mortality worldwide, owing in part to late diagnosis and heterogeneous clinical behaviour. Prognostic biomarkers that accurately stratify patients by risk of recurrence and survival are essential for guiding personalised treatment. In parallel, characterisation of the immune microenvironment has revealed its dual role in tumour progression and therapeutic response. Advances in digital pathology, imaging and molecular profiling have generated a diverse array of biomarkers—from quantitative features on histological slides to noninvasive imaging signatures—that capture tumour–stroma interactions, immune cell infiltration and immunosuppressive pathways. Integrating these biomarkers with traditional staging systems has improved prognostic accuracy and offered new avenues for adjuvant therapy selection, particularly in the context of chemotherapy and immune checkpoint blockade. Comprehensive understanding of the immune contexture and its interplay with stromal components is key to identifying patients most likely to benefit from personalised interventions and to developing novel therapeutic targets.
Research from Nature Portfolio
Recent studies have demonstrated that a pathomics signature derived from routine haematoxylin and eosin-stained sections can serve as an independent predictor of both overall and disease-free survival, outperforming the conventional tumour–node–metastasis staging system. A nomogram incorporating this signature alongside clinical stage markedly improves accuracy in identifying patients who derive benefit from adjuvant chemotherapy. In parallel, a computed tomography–based radiomics score has been validated in large cohorts to noninvasively estimate the neutrophil-to-lymphocyte ratio within the tumour immune microenvironment. This imaging biomarker parallels immunohistochemical measures of immune infiltration in predicting prognosis and response to anti-PD-1 therapy, offering a scalable method for longitudinal monitoring. Together, these digital approaches illustrate how high-throughput feature extraction from pathology and radiology can refine risk stratification and guide immunotherapy decisions in gastric cancer.
Prognostic Biomarkers and Immune Microenvironment in Gastric Cancer publication trend
The graph below shows the total number of articles in prognostic biomarkers and immune microenvironment in gastric cancer across all publications each year (not limited to Nature Index journals).
Technical terms
Prognostic biomarker: A measurable indicator, molecular or imaging-based, that predicts disease outcome independently of treatment.
Pathomics: The extraction and analysis of quantitative features from digital histopathology images to assess tumour morphology and its microenvironment.
Radiomics: The process of converting medical images into high-dimensional quantitative data to reveal patterns associated with tumour biology.
Immune checkpoint: Regulatory pathways in the immune system, often exploited by tumours to evade immune destruction, targeted by therapies such as anti-PD-1 antibodies.
Tumour microenvironment: The cellular milieu surrounding cancer cells, including stromal cells, immune infiltrates, extracellular matrix and signalling molecules that influence tumour behaviour.
References
- Prognostic and predictive value of a pathomics signature in gastric cancer. Nature Communications (2022).
- Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer. Nature Communications (2022).
- Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer. Journal of Clinical Investigation (2024).
- Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Reports Medicine (2023).
- Tumour cell-derived serglycin promotes IL-8 secretion of CAFs in gastric cancer. British Journal of Cancer (2024).
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