Innovative insights and future research directions in gastric cancer through single-cell RNA sequencing

Abstract

Gastric cancer (GC) remains one of the leading causes of cancer-related morbidity and mortality globally. Although significant progress has been made in treatment options, the survival rates for GC patients continue to be low. This is primarily attributed to the intricate and insufficiently understood mechanisms of disease progression, as well as the considerable challenges associated with tumor heterogeneity. The recent study by Tang et al provides a detailed single-cell RNA sequencing analysis of GC across different stages, revealing dynamic changes in the tumor microenvironment and key immune responses. We aim to offer a comprehensive interpretation of the study’s findings and propose several innovative directions for future academic research in gastric cancer. These include exploring advanced multi-omics approaches, leveraging spatial transcriptomics, integrating artificial intelligence for clinical applications, and developing novel immunotherapy strategies. We further emphasize the importance of personalized medicine, early detection, and novel drug discovery techniques in improving GC treatment outcomes.

Key Words: Gastric cancer; Single-cell RNA sequencing; Tumor microenvironment; Immune cells; Paracancerous tissues; Multi-omics approaches; Spatial transcriptomics; Artificial intelligence; Immune modulation; Early detection

Core Tip: Gastric cancer (GC) remains a major global health challenge, with low survival rates due to tumor heterogeneity and complex progression mechanisms. The recent study by Tang et al provides valuable insights into the dynamic changes in the tumor microenvironment across different GC stages using single-cell RNA sequencing. We summarize the study's key findings and propose future research directions, such as multi-omics approaches, spatial transcriptomics, integration of artificial intelligence into clinical practice, and innovative immunotherapies. By emphasizing personalized medicine and early detection, these advancements demonstrate significant potential for enhancing treatment outcomes in GC and refining therapeutic strategies.

TO THE EDITOR

Gastric cancer (GC) is one of the most important malignancies in the world due to the high burden of disease and lethality[1]. Despite its observed global declines in incidence and mortality over the past several decades, GC remains responsible for over 968000 new cases and close to 660000 deaths in 2022 worldwide, accounting for 4.9% and 6.8% of all malignant tumor cases and deaths, respectively, and ranking the disease as fifth in terms of both incidence and mortality worldwide, according to Global Cancer Statistics 2022[2,3]. In 2024, the estimated new cases and deaths of GC in China were 232310 and 171388 respectively, while in the United States, these numbers are estimated at 26950 and 11568, respectively[4].

Despite remarkable progress in surgical techniques, chemotherapy, and targeted therapies, the survival rates for patients diagnosed with advanced GC remain disappointingly low[5]. The prognosis for these patients is particularly poor when the cancer is detected at late stages, often due to the lack of early symptoms and effective screening methods[6]. Advances in early cancer detection may facilitate the treatment of a greater number of patients prior to the onset of metastatic disease and, ideally, with a reduction in both the number and heterogeneity of oncogenic alterations[7].

The complexity and heterogeneity of the tumor microenvironment (TME) are key contributors to poor survival outcomes in GC[5,8]. The TME encompasses not only cancer cells but also stromal cells, immune cells, endothelial cells, and extracellular matrix components, all of which interact dynamically and contribute to tumor progression[9,10]. This intricate network of cellular and molecular interactions within the TME influences not only the tumor’s growth but also its ability to resist treatment, leading to therapeutic failure[11,12]. Furthermore, the TME is often involved in processes like immune evasion, angiogenesis, and metastasis, which complicates the development of effective treatment strategies[13-15]. Consequently, elucidating the role of the TME in GC progression and therapeutic resistance represents a critical step toward developing novel targeted therapeutics.

In this context, the groundbreaking study by Tang et al[16] offers novel insights into the progression of GC by leveraging advanced single-cell RNA sequencing (scRNA-seq) technology. The emergence of scRNA-seq technology has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level[17]. This cutting-edge approach enables an in-depth analysis of the cellular composition and gene expression profiles of GC across four distinct stages, thereby offering unprecedented resolution of the TME. The study highlights dynamic changes within the TME, specifically focusing on immune responses and the complex interactions between tumor cells and stromal components. These interactions play a pivotal role in tumor progression, immune evasion, and therapeutic resistance.

By mapping the cellular landscape of GC with such precision, Tang et al[16] offer valuable new perspectives on the heterogeneous nature of the tumor, identifying specific cell populations and gene expression patterns associated with each stage of cancer progression. These findings substantially deepen our understanding of the mechanisms contributing to the invasiveness and treatment resistance in GC. Furthermore, this research has profound implications for the development of personalized and targeted therapeutic strategies. By incorporating scRNA-seq into clinical research, future treatments could be tailored to address the unique molecular signatures of individual tumors, potentially leading to more effective therapies and improved patient outcomes. This study underscores the promise of single-cell technologies in revolutionizing cancer diagnostics and treatment.

We will critically analyze the key findings of the study, discuss their implications for the future of GC research, and propose several innovative research directions to further advance our understanding of GC and improve its clinical management.

KEY FINDINGS OF THE STUDY

The study by Tang et al[16] provides a comprehensive and groundbreaking analysis of the cellular landscape in GC through the use of scRNA-seq. By analyzing a total of 73645 individual cells extracted from eight tissues four patients at various stages of GC (stages I, II, III, and IV), the authors offer a detailed single-cell atlas that sheds new light on the dynamic changes within the TME. This detailed study categorizes these cells into 25 distinct clusters, representing 10 major cell types. These include various immune cells, such as T cells, NK cells, B cells, myeloid cells, and neutrophils, as well as non-immune cells, including fibroblasts, epithelial cells, and endothelial cells. This extensive analysis represents a major step forward in our understanding of the complex cellular interactions that underlie the progression of GC.

One of the key findings from this study is the substantial shift in the cellular composition of the TME across different stages of GC. This shift highlights the dynamic and evolving nature of the TME and provides new insights into the interplay between cancer cells and the surrounding stromal components. Specifically, the authors found that the number of immune cells, particularly CD4⁺ and CD8⁺ T cells, was significantly elevated in cancerous tissues compared to paracancerous tissues. This suggests that there is an active immune response against the tumor, which is a typical feature of the body's attempt to control tumor growth. However, despite this apparent immune activation, the study also observed signs of immune exhaustion, particularly in the CD4⁺ and CD8⁺ T cells. These exhausted T cells displayed upregulated expressions of immune checkpoint molecules such as programmed cell death protein 1 and CTLA-4, both of which are associated with the suppression of T cell activity. Immune exhaustion plays a pivotal role in enabling tumors to evade immune surveillance. This mechanism of immune evasion represents one of the major challenges in the treatment of GC, particularly in the context of immunotherapy.

The observation of immune exhaustion in GC is particularly notable because it provides a potential explanation for the limited success of many immune-based therapies in this disease. Although immune checkpoint inhibitors have shown promise in several cancers, including melanoma and non-small cell lung cancer, their efficacy in GC has been less impressive. The findings by Tang et al[16] suggest that a more nuanced approach may be required to overcome immune exhaustion and reinvigorate the immune system’s ability to target and destroy cancer cells in GC. Elucidating the specific molecular pathways involved in immune exhaustion, as well as the contributing factors within the GC TME, is crucial for enhancing the efficacy of immunotherapies.

In addition to immune cells, the study also provides valuable insights into the changes occurring in non-immune cells within the TME. The authors observed significant alterations in the populations of epithelial cells, which are crucial to the development and progression of malignant tumors. Epithelial cells in cancerous tissues were found to be significantly reduced in number compared to paracancerous tissues[18]. This decrease in epithelial cell populations may reflect the processes of tumor cell transformation and progression, including epithelial-to-mesenchymal transition (EMT), a hallmark of cancer metastasis[18]. The EMT phenomenon was both in a few cells of primary tumors and abundantly in circulating tumor cells from the blood of GC patients, which might be used to monitor therapy response[19]. Interestingly, the study also identified differences in the characteristics of epithelial cells at different stages of the disease. In earlier stages (I and II), epithelial cells exhibited characteristics associated with malignancy, such as increased proliferation and alterations in gene expression patterns. However, in later stages (III and IV), epithelial cells showed signs of degeneration, with reduced differentiation and increased heterogeneity. This observation suggests that, as the disease progresses, tumor cells may undergo dedifferentiation, losing their epithelial characteristics and becoming more plastic. This plasticity may augment the tumor's capacity for tissue invasion and distant metastasis, thereby contributing to the poor clinical prognosis associated with GC.

The study by Tang et al[16] also highlights the importance of understanding the interactions between different cell types within the TME. The authors observed significant changes in cell-cell interactions as GC progressed to later stages (II, III, IV). One notable finding was the increased interaction between B cells, mast cells, and myeloid cells, particularly in advanced stages of GC. These interactions seem to play a key role in facilitating tumor growth and promoting immune suppression. For instance, myeloid-derived suppressor cells, which are well-documented for their ability to suppress anti-tumor immunity, were observed to engage in close interactions with other immune cells and stromal components, thereby potentially facilitating the immune evasion mechanisms of the tumor[20,21]. The interaction between B cells and mast cells was also particularly pronounced in later stages, suggesting that these cells might support tumor progression by promoting inflammation and angiogenesis, both of which are critical for sustaining tumor growth.

The study further provides insight into the potential role of the stromal compartment in shaping the immune and epithelial cell populations within the TME. The stromal cells, including fibroblasts and endothelial cells, were found to undergo significant changes as GC progressed. These stromal cells are crucial for maintaining the structure of the TME and supporting tumor growth through the secretion of extracellular matrix components and growth factors. In advanced stages of GC, the TME becomes more immunosuppressive, with stromal cells actively participating in the recruitment and activation of immune cells that contribute to the immune-suppressive environment. This highlights the dual role of stromal cells in GC: While they can support tumor growth, they also contribute to the regulation of the immune response and may serve as potential targets for therapeutic intervention.

The findings of Tang et al[16] underscore the complexity of the TME in GC and highlight the importance of examining tumor progression at a single-cell level. By identifying the cellular and molecular alterations that occur across different stages of the disease, the study offers new opportunities for therapeutic intervention. The identification of immune exhaustion markers and the complex interactions between different cell types within the TME could lead to the development of more effective strategies for treating GC. For instance, therapies targeting immune checkpoint molecules such as PD-1 and CTLA-4 could be more precisely tailored to address the specific immune alterations observed in GC. Moreover, the study's findings suggest that strategies aimed at modulating the stromal compartment may also hold promise for improving patient outcomes[16].

In conclusion, the study by Tang et al[16] provides a comprehensive and detailed map of the cellular changes that occur in GC across its various stages. The use of scRNA-seq has allowed the authors to uncover new insights into the complex interactions within the TME, offering valuable clues for the development of more targeted and effective therapeutic strategies. As we move forward in understanding GC at a deeper molecular level, studies like this will be critical in guiding the development of personalized treatment approaches that can more effectively combat this deadly disease.

FUTURE RESEARCH DIRECTIONS: INNOVATIVE APPROACHES TO GC RESEARCH

The findings of Tang et al[16] offer valuable insights into the complexities of GC and its TME. However, as with any pioneering study, several challenges remain, and numerous exciting opportunities for future research exist. Below, we propose several innovative directions for advancing GC research based on the findings from this study.

Advancing multi-omics approaches for holistic understanding of GC

Although scRNA-seq has provided a wealth of information on gene expression at the single-cell level, it represents only one aspect of the biological complexity of GC. To gain a more comprehensive understanding of GC progression, it is crucial to adopt multi-omics approaches that integrate genomics, transcriptomics, proteomics, and metabolomics. Each of these molecular layers provides unique insights into the biology of the tumor, and their integration holds the promise of yielding a more nuanced understanding of the mechanisms by which various molecular pathways contribute to tumor initiation, progression, and metastasis.

For instance, genomic alterations in GC, such as mutations in the TP53 and CDH1 genes, are well-documented drivers of tumorigenesis. Integrating genomic data with transcriptomic and proteomic analyses could help identify key drivers of immune evasion, tumor-stromal interactions, and resistance to therapy. Additionally, metabolomic profiling has the potential to reveal metabolic shifts within the TME that support tumor growth and metastasis. These metabolic changes could uncover novel therapeutic targets, particularly those involved in regulating energy metabolism, which is increasingly recognized as a critical factor in cancer progression.

The development of advanced computational models capable of integrating multi-omics datasets is essential for uncovering new biomarkers and therapeutic targets in GC. By creating comprehensive maps that link genomic alterations to transcriptional and proteomic changes, these models could provide deeper insights into the molecular drivers of GC and facilitate the identification of personalized treatment strategies.

Spatial transcriptomics for understanding tumor heterogeneity

While scRNA-seq has provided a high-resolution view of gene expression at the single-cell level, it lacks spatial context, which is crucial for understanding how different cell types interact within the tissue architecture. In this regard, spatial transcriptomics represents an emerging technology that allows for the direct mapping of gene expression onto tissue sections, preserving spatial information and offering a more nuanced understanding of tumor heterogeneity.

GC, characterized by its complex and dynamic TME, offers a unique opportunity to leverage spatial transcriptomics for investigating the changes in the distribution and interactions of various cell types as the disease progresses. For example, mapping immune cells and stromal components across different stages of GC could reveal regions within the tumor that are particularly immunosuppressive or tumor-permissive. Identifying such areas could lead to more effective strategies for targeting specific regions of the TME, improving therapeutic outcomes.

In addition to understanding the spatial organization of immune cells, spatial transcriptomics could also provide insight into how the physical architecture of the TME influences cell-cell interactions and tumor evolution. This is particularly important in understanding the mechanisms by which tumors evade immune surveillance and resist therapy. By identifying spatial patterns of immune dysfunction or therapeutic resistance, researchers can design interventions that specifically target these areas, potentially enhancing the efficacy of immunotherapy and chemotherapy.

Artificial intelligence and machine learning for predicting GC outcomes

The integration of artificial intelligence (AI) and machine learning (ML) in cancer research is rapidly gaining traction, and GC is no exception. The ability of AI and ML to analyze complex, multidimensional datasets - such as scRNA-seq and multi-omics data - offers significant promise for advancing GC research. AI and ML can be employed to predict patient outcomes, treatment responses, and the likelihood of metastasis by recognizing complex patterns in large datasets that may not be immediately apparent to human researchers.

For instance, AI algorithms trained on gene expression data could identify patterns that correlate with GC stage, prognosis, or response to therapy[22,23]. This could enable the development of predictive models that allow clinicians to tailor treatment strategies to individual patients based on their unique molecular profiles. Moreover, AI tools could assist in identifying potential drug targets or biomarkers for early detection, facilitating more personalized and precise therapeutic approaches.

The combination of AI with other advanced technologies, such as spatial transcriptomics and imaging, could provide real-time insights into tumor evolution and immune responses. This dynamic approach could lead to the development of treatment strategies that adapt to the changing tumor landscape, offering a more personalized and effective approach to GC management. For example, AI models could monitor how the TME evolves in response to treatment and suggest adjustments to therapeutic protocols based on these changes.

Immune modulation and novel immunotherapy strategies

The findings from Tang et al[16] highlight the critical role of immune evasion in GC progression, particularly the exhaustion of key immune cells such as CD4⁺ and CD8⁺ T cells in advanced stages of the disease. This underscores the need for innovative immune modulation strategies to rejuvenate immune function and enhance the efficacy of immunotherapies.

Several novel approaches could be explored to enhance anti-tumor immunity in GC.

Combination immunotherapies: One promising strategy is the use of combination immunotherapies that target multiple immune checkpoints simultaneously. For instance, the combination of immune checkpoint inhibitors targeting PD-1/PD-L1 and CTLA-4 with other immune-modulating agents, such as cytokines or monoclonal antibodies, has the potential to overcome T cell exhaustion and augment antitumor immune responses[24].

Targeting the TME: As Tang et al[16] identified key immune interactions between B cells, mast cells, and stromal cells, targeting these specific interactions could disrupt the immunosuppressive TME. For example, agents that block the crosstalk between myeloid cells and tumor cells could restore immune surveillance and limit tumor progression[25].

Adoptive T cell therapy: Adoptive T cell therapies, such as chimeric antigen receptor T (CAR-T) or T cell receptor T (TCR-T) cell therapies, have shown promise in other cancers[26-28]. These therapies could be adapted for use in GC by engineering T cells to specifically target GC-associated antigens, offering a powerful tool for overcoming immune evasion. This approach could be further enhanced by combining CAR-T or TCR-T therapies with other immune-modulating agents to improve efficacy and reduce resistance.

By addressing the mechanisms of immune suppression within the TME, these strategies have the potential to significantly improve the efficacy of immunotherapy and provide new avenues for the treatment of advanced GC.

Early detection and liquid biopsies for personalized medicine

One of the most critical challenges in GC is its late-stage diagnosis, which significantly limits treatment options and contributes to the high mortality rate. Early detection of GC is crucial for improving patient outcomes, and the development of liquid biopsy technologies represents a promising avenue for non-invasive early detection and monitoring of the disease.

Liquid biopsies allow for the detection of tumor-derived nucleic acids, such as circulating tumor cells, circulating free DNA, circulating tumor DNA, non-coding RNAs, and exosomes, in blood or other bodily fluids[29]. These technologies could revolutionize the early detection of GC by identifying specific mutations or gene expression signatures that correlate with the early stages of the disease. By detecting GC at earlier, more treatable stages, liquid biopsies could enable earlier intervention and improve patient survival rates.

In addition to facilitating early detection, liquid biopsies can also serve as a valuable tool for monitoring treatment responses and tracking minimal residual disease, thereby offering critical insights into therapeutic efficacy and the risk of disease recurrence[30]. By identifying specific genetic alterations or expression profiles associated with therapeutic resistance, liquid biopsies could guide clinical decision-making, enabling clinicians to adjust treatment plans in real time. Furthermore, liquid biopsies could be integrated with AI-driven predictive models to provide personalized treatment recommendations based on a patient’s molecular profile.

CONCLUSION

The groundbreaking study by Tang et al[16] has provided critical insights into the complex and heterogeneous nature of GC, particularly through the use of scRNA-seq to explore the TME. However, as GC remains a highly challenging and heterogeneous disease, further research is essential to elucidate the molecular and cellular mechanisms underlying its progression and therapeutic resistance. Future research directions, including the integration of multi-omics data, the application of spatial transcriptomics, the use of AI and ML, the development of novel immune modulation strategies, and the use of liquid biopsies for early detection and personalized treatment, hold great promise for advancing GC research and improving patient outcomes. By continuing to push the boundaries of innovation in cancer research, we can expect significant strides toward more effective therapies, earlier diagnoses, and improved survival rates for patients with GC.