Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer

Dynamic single-cell sequencing unveils the tumor microenvironment evolution of gastric cancer abdominal wall metastases during radiotherapy

Although the combination of immunotherapy and radiotherapy (RT) for the treatment of malignant tumors has shown rapid development, the insight of how RT remodels the tumor microenvironment to prime antitumor immunity involves a complex interplay of cell types and signaling pathways, much of which remains to be elucidated. Four tumor samples were collected from the same abdominal wall metastasis site of the patient with gastric cancer at baseline and during fractionated RT for single-cell RNA and T-cell receptor sequencing. The Seurat analysis pipeline and immune receptor analysis were used to characterize the gastric cancer metastasis ecosystem and investigated its dynamic changes of cell proportion, cell functional profiles and cell-to-cell communication during RT. Immunohistochemical and immunofluorescent staining and bulk RNA sequencing were applied to validate the key results. We found tumor cells upregulated immune checkpoint genes in response to RT. The infiltration and clonal expansion of T lymphocytes declined within tumors undergoing irradiation. Moreover, RT led to the accumulation of proinflammatory macrophages and natural killer T cells with enhanced cytotoxic gene expression signature. In addition, subclusters of dendritic cells and endothelial cells showed decrease in the expression of antigen present features in post-RT samples. More ECM component secreted by myofibroblasts during RT. These findings indicate that RT induced the dynamics of the immune response that should be taken into consideration when designing and clinically implementing innovative multimodal cancer treatment regimens of different RT and immunotherapy approaches.

Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm

The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.

Cancer molecular subtyping using limited multi-omics data with missingness

Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data fusion-based diagnostic solutions build on the requisite of sufficient samples with complete multi-omics data, which is challenging to obtain in clinical applications. To address the bottleneck of collecting sufficient samples with complete data in clinical applications, we proposed a flexible integrative model (CancerSD) to diagnose cancer subtype using limited samples with incomplete multi-omics data. CancerSD designs contrastive learning tasks and masking-and-reconstruction tasks to reliably impute missing omics, and fuses available omics data with the imputed ones to accurately diagnose cancer subtypes. To address the issue of limited clinical samples, it introduces a category-level contrastive loss to extend the meta-learning framework, effectively transferring knowledge from external datasets to pretrain the diagnostic model. Experiments on benchmark datasets show that CancerSD not only gives accurate diagnosis, but also maintains a high authenticity and good interpretability. In addition, CancerSD identifies important molecular characteristics associated with cancer subtypes, and it defines the Integrated CancerSD Score that can serve as an independent predictive factor for patient prognosis.

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains incompletely understood. By analyzing single-cell RNA sequencing data from over 600,000 cells in gastric cancer (GSE163558 and GSE183904), colorectal cancer (GSE205506), and lung cancer (GSE207422), we identified neutrophil subsets in primary gastric cancer that are associated with the treatment response to ICIs. Specifically, we focused on neutrophils with high expression of CD44 (CD44_NEU), which are abundant during tumor progression and exert significant influence on the gastric cancer immune microenvironment. Machine learning analysis revealed 22 core genes associated with CD44_NEU, impacting inflammation, proliferation, migration, and oxidative stress. In addition, multiple immunofluorescence staining and gastric cancer spatial transcriptome data (GSE203612) showed a correlation between CD44_NEU and T-cell infiltration in gastric cancer tissues. A risk score model derived from seven essential genes (AQP9, BASP1, BCL2A1, PLEK, PDE4B, PROK2, and ACSL1) showed better predictive capability for patient survival compared to clinical features alone, and integrating these scores with clinical variables resulted in a prognostic nomogram. Overall, this study highlights the heterogeneity of TANs, particularly the CD44_NEU critical influence on immunotherapy outcomes, paving the way for personalized treatment strategies.

Advancing gastric cancer treatment: nanotechnology innovations and future prospects

Gastric cancer (GC) is the fifth most common cancer worldwide, particularly prevalent in Asia, especially in China, where both its incidence and mortality rates are significantly high. Meanwhile, nanotechnology has demonstrated great potential in the treatment of GC. In particular, nanodrug delivery systems have improved therapeutic efficacy and targeting through various functional modifications, such as targeting peptides, tumor microenvironment responsiveness, and instrument-based methods. For instance, silica (SiO2) has excellent biocompatibility and can be used as a drug carrier, with its porous structure enhancing drug loading capacity. Polymer nanoparticles regulate drug release rates and mechanisms by altering material composition and preparation methods. Lipid nanoparticles efficiently encapsulate hydrophilic drugs and promote cellular uptake, while carbon-based nanoparticles can be used in biosensors and drug delivery. Targets such as integrins, HER2 receptors, and the tumor microenvironment have been used to improve drug efficacy in GC treatment. Nanodrug delivery techniques not only enhance drug efficacy and delivery capabilities but also selectively target tumor cells. Currently, there is a lack of systematic summarization and synthesis regarding the relationship between nanodrug delivery systems and GC treatment, which to some extent hinders researchers and clinicians from efficiently searching for and referencing related studies, thereby reducing work efficiency. This study aims to systematically summarize the existing research on the relationship between nanodrug delivery systems and GC treatment, making it easier for professionals to search and reference, and thereby promoting further research on the role of nanodrug delivery systems and their clinical applications in GC. This review discusses the applications of functionalized nanocarriers in the treatment of GC in recent years, including surface modifications with targeted markers, the combination of phototherapy, chemotherapy, and immunotherapy, along with their advantages and challenges. It also examines the future prospects of targeted nanomaterials in GC treatment. The review particularly focuses on the combined application of nanocarriers in multiple treatment modalities, such as phototherapy, chemotherapy, and immunotherapy, demonstrating their potential in multimodal treatments. Furthermore, it thoroughly explores the specific challenges that nanocarriers face in GC treatment, such as biocompatibility, drug release control, and clinical translation issues, while providing a systematic outlook on future developments. Additionally, this study emphasizes the potential value and feasibility of nanocarriers in clinical applications, contrasting with most reviews that focus on basic research. Through these innovations, we offer new perspectives and directions for the development of nanotechnology in the treatment of GC.

Integrated single-cell analysis reveals heterogeneity and therapeutic insights in osteosarcoma

Osteosarcoma (OSA) is a primary bone malignancy characterized by its aggressive nature and high propensity for metastasis. Despite advancements in multimodal therapies, the clinical outcomes for OSA patients remain suboptimal, necessitating deeper molecular insights for improved therapeutic strategies. Here, we employed single-cell RNA sequencing (scRNA-seq) to elucidate the cellular heterogeneity and transcriptional dynamics of OSA tumors. Our study identified eleven distinct tumor cell subpopulations, including osteoblastic, chondroblastic, and myeloid lineages, each exhibiting unique transcriptional profiles associated with disease progression and metastasis. Epithelial-mesenchymal transition (EMT) emerged as a critical process driving aggressive phenotypes, supported by gene set enrichment analyses (GSVA) and transcription factor regulatory network analyses. Integration of copy number variation (CNV) data highlighted genomic alterations in osteoblastic and chondroblastic cells, implicating potential therapeutic targets. Furthermore, immune cell infiltration analyses revealed distinct immune profiles across OSA subtypes, correlating with tumor mutational burden (TMB) and clinical outcomes. Our findings underscore the complexity of OSA biology and provide a foundation for developing personalized treatment strategies targeting tumor heterogeneity and immune interactions.

Single-cell RNA-seq analysis of cancer-endothelial cell interactions in primary tumor and peritoneal metastasis from a single patient with colorectal cancer

BACKGROUND

Peritoneal metastasis, a major complication of colorectal cancer (CRC), often leads to poor quality of life and unfavorable outcomes. Despite numerous studies characterizing its biological features in CRC, intratumor heterogeneity and interactions between cancer cells and tumor microenvironment cells remain poorly understood.

METHODS

To explore these aspects, we performed single-cell transcriptome analysis of matched primary tumor and peritoneal metastasis samples from a treatment-naïve patient.

RESULTS

Our analysis revealed enrichment of "tip" endothelial cells in the primary tumor, driving angiogenic sprouting, whereas these cells were absent in peritoneal metastases. Moreover, cancer cells in peritoneal metastasis displayed a distinct expression signature associated with epithelial-mesenchymal transition and tumor invasiveness. Analysis of cell-cell communication between endothelial and tumor cells revealed decreased VEGF signaling and increased CXCL-ACKR1 interactions in peritoneal metastasis.

CONCLUSIONS

Although limited by its N-of-1 design and requiring further validation, our study provides preliminary observations suggesting that alterations in cancer-endothelial cell interactions could reduce dependence on VEGF signaling and influence immune cell infiltration in CRC peritoneal metastasis.

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

Background

Gastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms.

Methods

Stem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network.

Results

By analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy.

Conclusion

We identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.

Neutrophil-centric analysis of gastric cancer: prognostic modeling and molecular insights

Gastric cancer remains a significant global health concern with poor prognosis. This study investigates the role of neutrophils in gastric cancer progression and their potential as prognostic indicators. Using multi-omics approaches, including Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-cell analysis, we identified neutrophil-associated gene signatures and developed a robust prognostic model. Our findings reveal distinct gastric cancer subtypes based on neutrophil-associated genes, with one subtype showing increased neutrophil infiltration and poorer prognosis. Single-cell analysis uncovered neutrophil-associated alterations in cell composition, gene expression profiles, and intercellular communication within the tumor microenvironment. Additionally, we explored the relationship between neutrophil-associated genes, microbiota composition, and alternative splicing events in gastric cancer. Furthermore, we identified QKI as a key regulator of alternative splicing and demonstrated its role in promoting malignant phenotypes and enhancing TGF-beta signaling and epithelial-mesenchymal transition in gastric cancer cells by wet experiment. Lastly, the role of QKI in the association with drug resistance and the identification of specific agents for treating QKI-associated drug resistance were also explored. This comprehensive study provides novel insights into the complex interplay between neutrophils, the tumor microenvironment, microbiota, alternative splicing and gastric cancer progression, offering potential new targets for therapeutic intervention.

Tumor microenvironment RNA test to predict immunotherapy outcomes in advanced gastric cancer: The TIMES001 trial

BACKGROUND

Clinical trials support the efficacy of immune checkpoint blockades (ICBs) plus chemotherapy in a subset of patients with metastatic gastric cancer (mGC). To identify the determinants of response, we developed a TMEscore model to assess tumor microenvironment (TME), which was previously proven to be a biomarker for ICBs.

METHODS

A reference database of TMEscore assays was established using PCR assay kits containing 30 TME genes. This multi-center prospective clinical trial (NCT#04850716) included patients with mGC who were administered ICB combined with chemotherapy as a first-line regimen. Eighty-six tumor samples extracted from five medical centers before treatment were used to estimate the TMEscore, PD-L1 (CPS), and mismatch repair deficiency.

FINDINGS

The objective response rate (ORR) and median PFS of the cohort were 31.4% and six months. Enhanced ORR was observed in TMEscore-high mGC patients (ORR = 59%). The survival analysis demonstrated that high TMEscore was significantly associated with a more favorable PFS and OS. Moreover, TMEscore was found to be a predictive biomarker that surpassed MSI and CPS (AUC = 0.873, 0.511, and 0.524, respectively). By integrating the TMEscore and clinical variables, the fused model further enhances the predictive efficiency and translational application in a clinical setting.

CONCLUSIONS

This prospective clinical study indicates that the TMEscore assay is a robust biomarker for screening patients with mGC who may derive survival benefits from ICB plus chemotherapy.

FUNDING

Guangdong Basic and Applied Basic Research Foundation (2023A1515011214), Science and Technology Program of Guangzhou (202206080011), and Guangzhou Science and Technology Project (2023A03J0722 and 2023A04J2357).

The immunological landscape and silico analysis of key paraptosis regulator LPAR1 in gastric cancer patients

This study aims to identify key regulators of paraptosis in gastric cancer (GC) and explore their potential in guiding therapeutic strategies, especially in stomach adenocarcinoma (STAD). Genes associated with paraptosis were identified from the references and subjected to Cox regression analysis in the TCGA-STAD cohort. Using machine learning models, LPAR1 consistently ranked highest in feature importance. Multiple sequencing data showed that LPAR1 was significantly overexpressed in cancer-associated fibroblasts (CAFs). LPAR1 expression was significantly higher in normal tissues, and ROC analysis demonstrated its discriminative ability. Copy number alterations and microsatellite instability were significantly associated with LPAR1 expression. High LPAR1 expression correlated with advanced tumor grades and specific cancer immune subtypes, and multivariate analysis confirmed LPAR1 as an independent predictor of poor prognosis. LPAR1 expression was associated with different immune response metrics, including immune effector activation and upregulated chemokine secretion. High LPAR1 expression also correlated with increased sensitivity to compounds, such as BET bromodomain inhibitors I-BET151 and RITA, suggesting LPAR1 as a biomarker for predicting drug activity. FOXP2 showed a strong positive correlation with LPAR1 transcriptional regulation, while increased methylation of LPAR1 promoter regions was negatively correlated with gene expression. Knockdown of LPAR1 affected cell growth in most tumor cell lines, and in vitro experiments demonstrated that LPAR1 influenced extracellular matrix (ECM) contraction and cell viability in the paraptosis of CAFs. These findings suggest that LPAR1 is a critical regulator of paraptosis in GC and a potential biomarker for drug sensitivity and immunotherapy response. This underscores the role of CAFs in mediating tumorigenic effects and suggests that targeting LPAR1 could be a promising strategy for precision medicine in GC.

Molecular mechanisms and clinicopathological characteristics of inhibin βA in thyroid cancer metastasis

The present study aimed to investigate the role and mechanism of inhibin βA (INHBA) in thyroid cancer (TC), and to determine its potential impact on the aggressive behavior of TC cells. The present study employed a comprehensive approach, using public databases, such as the Gene Expression Omnibus and The Cancer Genome Atlas, to identify and analyze the expression of INHBA in TC. Cell transfection, reverse transcription‑quantitative PCR, western blot analysis, immunohistochemistry and in vivo assays were conducted to investigate the functional effects of INHBA on TC. In addition, the present study explored the molecular mechanisms underlying the effects of INHBA, focusing on the potential impact on the RhoA signaling pathway and associated molecular cascades. Bioinformatics analysis revealed a significant association between INHBA expression and TC, and INHBA expression was markedly upregulated in TC tissues compared with in healthy control tissues. The results of functional studies demonstrated that INHBA overexpression increased the migration and invasion of TC cells, and the opposite result was observed following INHBA knockdown. Mechanistic investigations indicated that INHBA modulated the RhoA pathway, leading to alterations in the phosphorylation status of LIM kinase 1 (LIMK) and cofilin, key regulators of cytoskeletal dynamics and cell motility. Following the introduction of transfected TC cells into zebrafish and nude mouse models, the results of the present study demonstrated that INHBA knockdown attenuated the metastatic potential of TC cells. In conclusion, INHBA may serve a pivotal role in promoting the aggressive phenotype of TC cells through modulating the RhoA/LIMK/cofilin signaling axis. These findings highlight INHBA as a potential biomarker and therapeutic target for the management of aggressive TC.

Targeting extracellular matrix interaction in gastrointestinal cancer: Immune modulation, metabolic reprogramming, and therapeutic strategies

The extracellular matrix (ECM) is a major constituent of the tumor microenvironment, acting as a mediator that supports the progression of gastrointestinal (GI) cancers, particularly in mesenchymal subtypes. Beyond providing structural support, the ECM actively shapes the tumor microenvironment (TME) through complex biochemical and biomechanical remodeling. Dysregulation of ECM composition and signaling is closely linked to increased cancer aggressiveness, poor prognosis, and resistance to therapy. ECM components, such as collagen, fibronectin, laminin, and periostin, influence tumor growth, metastasis, immune modulation, and metabolic reprogramming by interacting with tumor cells, immune cells, and cancer-associated fibroblasts. In this review, we highlight the heterogeneous nature of the ECM and the dualistic roles of its components across GI cancers, with a focus on their contributions to immune evasion and metabolic remodeling via intercellular interactions. Additionally, we explore therapeutic strategies targeting ECM remodeling and ECM-centered interactions, emphasizing their potential in enhancing existing anti-tumor therapies.

SPP1+ macrophages and FAP+ fibroblasts promote the progression of pMMR gastric cancer

Immunotherapy has become a primary and secondary treatment for gastric cancer (GC) patients with mismatch repair deficiency (dMMR), and is used in both perioperative and advanced stages. The tumor immune microenvironment (TiME) is crucial for immunotherapy efficacy, yet the impact of MMR status on TiME remains understudied. We employed single-cell RNA sequencing (scRNA-seq) to analyze 33 fresh tissue samples from 25 patients, which included 10 normal tissues, 6 dMMR tumor tissues, and 17 pMMR tumor tissues, aiming to characterize the cellular and molecular components of the TiME. The proficient mismatch repair (pMMR) group displayed a significantly higher prevalence of a specific GC cell type, termed GC2, characterized by increased hypoxia, epithelial-mesenchymal transition (EMT), and angiogenic activities compared to the dMMR group. GC2 cells overexpressed BEX3 and GPC3, and they significantly correlated with poorer survival. The pMMR group also showed increased infiltration of SPP1 + macrophages and FAP + fibroblasts, exhibiting strong hypoxic and pro-angiogenic features. Furthermore, a higher proportion of E2 endothelial cells, involved in extracellular matrix (ECM) remodeling and showing heightened VEGF pathway, HIF pathway, and angiogenesis activity, were identified in pMMR patients. Intercellular communication analyses revealed that GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells interact through VEGF, SPP1, and MIF signals, forming a TiME characterized by hypoxia, pro-angiogenesis, and ECM remodeling. This study uncovered TiME heterogeneity among GC patients with different MMR states, highlighting that the pMMR TiME is distinguished by hypoxia, pro-angiogenesis, and ECM remodeling, driven by the presence of GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells. These findings are pivotal for developing targeted immunotherapies for GC patients with pMMR.

Fructose-bisphosphate Aldolase C Expression is Associated with Poor Prognosis and Stemness in Gastric Cancer

Gastric cancer (GC) is the third leading cause of cancer-related deaths in Japan, underscoring the urgent need for deeper insights into its pathogenesis. Spheroids provide a more realistic and versatile model for studying cancers and cancer stem cells (CSCs). While fructose-bisphosphate aldolase C (ALDOC) has been identified in colorectal cancer spheroids, its role in GC has remained largely unexplored. This study aimed to elucidate the role of ALDOC in GC by performing single-cell and functional analyses of GC spheroids and cell lines, along with immunohistochemistry of 127 GC samples to assess its correlation with CSC markers. Our single-cell analysis revealed upregulation of ALDOC in spheroids, with pseudotime analysis indicating that ALDOC-expressing cells were predominantly undifferentiated and co-expressed LGR5 and CD44. Further investigation into cell-cell interactions suggested that the stem cell state may be maintained by WNT, BMP, and EGF signaling. Functional assays demonstrated that ALDOC knockdown led to a marked reduction in the growth, invasiveness, and spheroid colony formation capacity of GC cell lines. Clinically, ALDOC was detected in the cytoplasm of 56.7% (72/127) of GC cases, and high ALDOC expression was significantly associated with poor overall survival (p < 0.01), and was an independent prognostic factor. Moreover, a significant association between ALDOC and CD44 expression in GC (p = 0.031). Conclusively, our findings identify ALDOC as a crucial prognostic marker and provide new insights into GC pathogenesis.

Immunological Strategies in Gastric Cancer: How Toll-like Receptors 2, -3, -4, and -9 on Monocytes and Dendritic Cells Depend on Patient Factors?

(1) Introduction: Toll-like receptors (TLRs) are key in immune response by recognizing pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). In gastric cancer (GC), TLR2, TLR3, TLR4, and TLR9 are crucial for modulating immune response and tumor progression. (2) Objective: This study aimed to assess the percentage of dendritic cells and monocytes expressing TLR2, TLR3, TLR4, and TLR9, along with the concentration of their soluble forms in the serum of GC patients compared to healthy volunteers. Factors such as disease stage, tumor type, age, and gender were also analyzed. (3) Materials and Methods: Blood samples from newly diagnosed GC patients and healthy controls were immunophenotyped using flow cytometry to assess TLR expression on dendritic cell subpopulations and monocytes. Serum-soluble TLRs were measured by ELISA. Statistical analysis considered clinical variables such as tumor type, stage, age, and gender. (4) Results: TLR expression was significantly higher in GC patients, except for TLR3 on classical monocytes. Soluble forms of all TLRs were elevated in GC patients, with significant differences based on disease stage but not tumor type, except for serum TLR2, TLR4, and TLR9. (5) Conclusions: Elevated TLR expression and soluble TLR levels in GC patients suggest a role in tumor pathogenesis and progression, offering potential biomarkers and therapeutic targets.

Single-cell transcriptomic profiling uncovers cellular complexity and microenvironment in gastric tumorigenesis associated with Helicobacter pylori

INTRODUCTION

Helicobacter pylori (H. pylori) infection is the main risk for gastric cancer (GC). However, the cellular heterogeneity and underlying molecular mechanisms in H. pylori-driven gastric tumorigenesis are poorly understood.

OBJECTIVE

Here, we generated a single-cell atlas of gastric tumorigenesis comprising 18 specimens of gastritis, gastric intestinal metaplasia (IM) and GC with or without H. pylori infection.

METHODS

Single-cell RNA sequencing (scRNA-seq) was performed. Immunofluorescence, immunohistochemistry and qRT-PCR analysis were applied in a second human gastric tissues cohort for validation. Bioinformatics analyses of public TCGA and GEO datasets were applied.

RESULTS

Single-cell RNA profile highlights cellular heterogeneity and alterations in tissue ecology throughout the progression of gastric carcinoma. Various cell lineages exhibited unique cancer-associated expression profiles, such as tumor-like epithelial cell subset (EPC), inflammatory cancer-associated fibroblasts (iCAFs) and Tumor-associated macrophage (TAM). Notably, we revealed that the specific epithelial subset enterocytes from the precancerous lesion GIM, exhibited elevated expression of genes related to lipid metabolism, and HNF4G was predicted as its specific transcription factor. Furthermore, we identified differentially expressed genes in H. pylori-positive and negative epithelial cells, fibroblasts and myeloid cells were identified. Futhermore, H. pylori-positive specimens exhibited enriched cell-cell communication, characterized by significantly active TNF, SPP1, and THY1 signaling networks.

CONCLUSIONS

Our study provides a comprehensive landscape of the gastric carcinogenesis ecosystem and novel insights into the molecular mechanisms of different cell types in H. pylori-induced GC.

Contactin-4 suppresses antitumor T cell responses by engaging amyloid precursor protein

Immune checkpoint inhibitors have substantial advanced tumor treatment, but their limited benefits and strong responses in only a subset of patients remain challenging. In this study, we explored the immunomodulatory function of contactin-4 (CNTN4). CNTN4 was highly expressed in tumor tissues, and expression impaired the antitumor function of T cells. CNTN4 bound to amyloid precursor protein (APP) on T cells, which attenuated conjugation between cancer cells and T cells, and diminished T cell receptor signaling cascades. We developed an anti-CNTN4 antibody (GENA-104A16) and an anti-APP antibody (5A7) that blocked the binding between CNTN4 and APP. Administration of either GENA-104A16 or 5A7 promoted antitumor T cell responses in a syngeneic mouse model and increased tumor-infiltrating lymphocytes in vivo. Furthermore, elevated CNTN4 levels were associated with poor prognosis and negatively correlated with various cytotoxic immune-related markers. These results suggest that CNTN4-APP is an inhibitory checkpoint in T cells and represents a promising therapeutic strategy for cancer immunotherapy.

Single-cell RNA transcriptomic analyses of tumor microenvironment of ovarian metastasis in gastric cancer

PURPOSE

Ovarian metastasis of gastric cancer (GC), commonly referred to as Krukenberg tumors, leads to a poor prognosis. However, the cause of metastasis remains unknown. Here, we present an integrated single-cell RNA sequencing (scRNA-Seq) analysis of the immunological microenvironment of two paired clinical specimens with ovarian metastasis of GC.

METHODS

scRNA-Seq was performed to determine the immunological microenvironment in ovarian metastasis of gastric cancer. CellChat was employed to analyze cell-cell communications across different cell types. Functional enrichment analysis was done by enrichKEGG in clusterProfiler. GEPIA2 was used to assess the influence of certain genes and gene signatures on prognosis.

RESULTS

The ovarian metastasis tissues exhibit a heterogenous immunological microenvironment compared to the primary tumors. Exhaustion of T and B cells is observed in the ovarian metastasis tissues. Compared to the paired adjacent non-tumoral and primary tumors, the ratio of endothelial cells and fibroblasts is high in the ovarian metastasis tissues. Compared to primary ovarian cancers, we identify a specific group of tumor-associated fibroblasts with MFAP4 and CAPNS1 expression in the ovarian metastatic tissues of GC. We further define metastasis-related-endothelial and metastasis-related-fibroblast signatures and indicate that patients with these high signature scores have a poor prognosis. In addition, the ovarian metastasis tissue has a lower level of intercellular communications compared to the primary tumor.

CONCLUSION

Our findings reveal the immunological microenvironment of ovarian metastasis of gastric cancer and will promote the discovery of new therapeutic strategies for ovarian metastasis in gastric cancer.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Proteomic Heterogeneity of the Extracellular Matrix Identifies Histologic Subtype-Specific Fibroblast in Gastric Cancer

Gastric cancer (GC) is a highly heterogeneous disease regarding histologic features, genotypes, and molecular phenotypes. Here, we investigate extracellular matrix (ECM)-centric analysis, examining its association with histologic subtypes and patient prognosis in human GC. We performed quantitative proteomic analysis of decellularized GC tissues that characterizes tumorous ECM, highlighting proteomic heterogeneity in ECM components. We identified 20 tumor-enriched proteins including four glycoproteins, serpin family H member 1 (SERPINH1), annexin family (ANXA3/4/5/13), S100A family (S100A6/8/9), MMP14, and other matrisome-associated proteins. In addition, histopathological characteristics of GC reveals differential expression in ECM composition, with the poorly cohesive carcinoma-not otherwise specified (PCC-NOS) subtype being distinctly demarcated from other histologic subtypes. Integrating ECM proteomics with single-cell RNA sequencing, we identified crucial molecular markers in the PCC-NOS-specific stroma. PCC-NOS-enriched matrisome proteins and gene expression signatures of adipogenic cancer-associated fibroblasts (CAFadi) are closely linked, both associated with adverse outcomes in GC. Using tumor microarray analysis, we confirmed the CAFadi surface marker, ATP binding cassette subfamily A member 8 (ABCA8), predominantly present in PCC-NOS tumors. Our ECM-focused analysis paves the way for studies to determine their utility as biomarkers for patient stratification, offering valuable insights for linking molecular and histologic features in GC.

Expression, DNA methylation pattern and transcription factor EPB41L3 in gastric cancer: a study of 262 cases

PURPOSE

DNA methylation prominently inactivates tumor suppressor genes and facilitates oncogenesis. Previously, we delineated a chromosome 18 deletion encompassing the erythrocyte membrane protein band 4.1-like 3 (EPB41L3) gene, a progenitor for the tumor suppressor that is differentially expressed in adenocarcinoma of the lung-1 (DAL-1) in gastric cancer (GC).

METHODS

Our current investigation aimed to elucidate EPB41L3 expression and methylation in GC, identify regulatory transcription factors, and identify affected downstream pathways. Immunohistochemistry demonstrated that DAL-1 expression is markedly reduced in GC tissues, with its downregulation serving as an independent prognostic marker.

RESULTS

High-throughput bisulfite sequencing of 70 GC patient tissue pairs revealed that higher methylation of non-CpGs in the EPB41L3 promoter was correlated with more malignant tumor progression and higher-grade tissue classification. Such hypermethylation was shown to diminish DAL-1 expression, thus contributing to the malignancy of GC phenotypes. The DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine (5-aza-CdR) was found to partially restore DAL-1 expression. Moreover, direct binding of the transcription factor CDC5L to the upstream region of the EPB41L3 promoter was identified via chromosome immunoprecipitation (ChIP)-qPCR and luciferase reporter assays. Immunohistochemistry confirmed the positive correlation between CDC5L and DAL-1 protein levels. Subsequent RNA-seq analysis revealed that DAL-1 significantly influences the extracellular matrix and space-related pathways. GC cell RNA-seq post-5-Aza-CdR treatment and single-cell RNA-seq data of GC tissues confirmed the upregulation of AREG and COL17A1, pivotal tumor suppressors, in response to EPB41L3 demethylation or overexpression in GC epithelial cells.

CONCLUSION

In conclusion, this study elucidates the association between non-CpG methylation of EPB41L3 and GC progression and identifies the key transcription factors and downstream molecules involved. These findings enhance our understanding of the role of EPB41L3 in gastric cancer and provide a solid theoretical foundation for future research and potential clinical applications.

Advances in spatial multi-omics in tumors

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

Single-cell characterization of differentiation trajectories and drug resistance features in gastric cancer with peritoneal metastasis

BACKGROUND

Gastric cancer patients with peritoneal metastasis (GCPM) experience a rapidly deteriorating clinical trajectory characterized by therapeutic resistance and dismal survival, particularly following the development of malignant ascites. However, the intricate dynamics within the peritoneal microenvironment (PME) during the treatment process remain largely unknown.

METHODS

Matched samples from primary tumours (PT), peritoneal metastases (PM), and paired pre-treatment and post-chemo/immunotherapy (anti-PD-1/PD-L1) progression malignant ascites samples, were collected from 48 patients. These samples were subjected to single-cell RNA sequencing (n = 30), multiplex immunofluorescence (n = 30), and spatial transcriptomics (n = 3). Furthermore, post hoc analyses of a phase 1 clinical trial (n = 20, NCT03710265) and an in-house immunotherapy cohort (n = 499) were conducted to validate the findings.

RESULTS

Tracing the evolutionary trajectory of epithelial cells unveiled the terminally differentially MUC1+ cancer cells with a high epithelial-to-mesenchymal transition potential, and they demonstrated spatial proximity with fibroblasts and endothelial cells, correlating with poor prognosis. A significant expansion of macrophage infiltrates, which exhibited the highest proangiogenic activity, was observed in the ascites compared with PT and PM. Besides, higher C1Q+ macrophage infiltrates correlated with significantly lower GZMA+ T-lymphocyte infiltrates in therapeutic failure cases, potentially mediated by the LGALS9-CD45 and SPP1-CD44 ligand-receptor interactions. In the chemoresistant group, intimate interactions between C1Q+ macrophages and fibroblasts through the complement activation pathway were found. In the group demonstrating immunoresistance, heightened TGF-β production activity was detected in MUC1+ cancer cells, and they were skewed to interplay with C1Q+ macrophages through the GDF15-TGF-βR2 axis. Ultimately, post hoc analyses indicated that co-targeting TGF-β and PDL1 pathways may confer superior clinical benefits than sole anti-PD-1/PD-L1 therapy for patients presenting with GCPM at the time of diagnosis.

CONCLUSIONS

Our findings elucidated the cellular differentiation trajectories and crucial drug resistance features within PME, facilitating the exploration of effective targets for GCPM treatment.

HIGHLIGHTS

MUC1+ cancer cells with a high epithelial-to-mesenchymal transition potential and exhibiting spatial proximity to fibroblasts and endothelial cells constitute the driving force of gastric cancer peritoneal metastasis (GCPM). Higher C1Q+ macrophage infiltrates correlated with significantly lower GZMA+ T-lymphocyte infiltrates within the peritoneal microenvironment in therapeutic failure cases. Co-targeting TGF-β and PDL1 pathways may confer superior clinical benefits than sole anti-PD-1/PD-L1 therapy for patients presenting with GCPM at diagnosis.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

CCKBR+ cancer cells contribute to the intratumor heterogeneity of gastric cancer and confer sensitivity to FOXO inhibition

The existence of heterogeneity has plunged cancer treatment into a challenging dilemma. We profiled malignant epithelial cells from 5 gastric adenocarcinoma patients through single-cell sequencing (scRNA-seq) analysis, demonstrating the heterogeneity of gastric adenocarcinoma (GA), and identified the CCKBR+ stem cell-like cancer cells associated poorly differentiated and worse prognosis. We further conducted targeted analysis using single-cell transcriptome libraries, including 40 samples, to confirm these screening results. In addition, we revealed that FOXOs are involved in the progression and development of CCKBR+ gastric adenocarcinoma. Inhibited the expression of FOXOs and disrupting cancer cell stemness reduce the CCKBR+ GA organoid formation and impede tumor progression. Mechanically, CUT&Tag sequencing and Lectin pulldown revealed that FOXOs can activate ST3GAL3/4/5 as well as ST6GALNAC6, promoting elevated sialyation levels in CCKBR+ tumor cells. This FOXO-sialyltransferase axis contributes to the maintenance of homeostasis and the growth of CCKBR+ tumor cells. This insight provides novel perspectives for developing targeted therapeutic strategies aimed at the treating CCKBR associated gastric cancer.

Integrated analysis to identify biological features and molecular markers of poorly cohesive gastric carcinoma (PCC)

As one of the two main histologic subtypes of gastric cancer (GC), diffuse-type gastric cancer (DGC) containing poorly cohesive gastric carcinoma (PCC) components has a worse prognosis and does not respond well to typical therapies. Despite the large number of studies revealing the complex pathogenic network of DGC, the molecular heterogeneity of DGC is still not fully understood. We obtained single-cell RNA-seq data and bulk data from the tumor immune single cell hub, the public gene expression omnibus, and the cancer genome atlas databases. A series of bioinformatics analyses were performed using R software. Immunofluorescence staining, hematoxylin and eosin staining, western blot, and functional experiments were used for experimental validation. Caudin-3, -4 and -7 were lowly expressed in DGC and their expression levels were further reduced in PCC. The PCC components were mainly located in the deeper layers of the DGC and had a high level of hypoxic Wnt/β-catenin signaling and stemness. We further identified Insulin Like Growth Factor Binding Protein 7 (IGFBP7) as a marker for PCC components in the deep layer. IGFBP7 is stimulated by hypoxia and promotes cancer cell invasiveness and reduced claudin expression. In addition, programmed death-1 ligand (PD-L1) was specifically expressed in the deep layer, reflecting deep layer-specific immunosuppression. The PCC components are predominantly situated in the deeper layers of DGC. Initial molecular characterization of these PCC components revealed distinct features, including low expression of claudin-3, -4, and -7, high expression of IGFBP7, and the presence of PD-L1. These molecular traits may partially account for the pronounced tumor heterogeneity observed in GC.

Functional gene signature offers a powerful tool for characterizing clinicopathological features and depicting tumor immune microenvironment of colorectal cancer

BACKGROUND

Colorectal cancer, a prevalent malignancy worldwide, poses a significant challenge due to the lack of effective prognostic tools. In this study, we aimed to develop a functional gene signature to stratify colorectal cancer patients into different groups with distinct characteristics, which will greatly facilitate disease prediction.

RESULTS

Patients were stratified into high- and low-risk groups using a prediction model built based on the functional gene signature. This innovative approach not only predicts clinicopathological features but also reveals tumor immune microenvironment types and responses to immunotherapy. The study reveals that patients in the high-risk group exhibit poorer pathological features, including invasion depth, lymph node metastasis, and distant metastasis, as well as unfavorable survival outcomes in terms of overall survival and disease-free survival. The underlying mechanisms for these observations are attributed to upregulated tumor-related signaling pathways, increased infiltration of pro-tumor immune cells, decreased infiltration of anti-tumor immune cells, and a lower tumor mutation burden. Consequently, patients in the high-risk group exhibit a diminished response to immunotherapy. Furthermore, the high-risk group demonstrates enrichment in extracellular matrix-related functions and significant infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptional data analysis identifies CAFs as the primary cellular type expressing hub genes, namely ACTA2, TPM2, MYL9, and TAGLN. This finding is further validated through multiple approaches, including multiplex immunohistochemistry (mIHC), polymerase chain reaction (PCR), and western blot analysis. Notably, TPM2 emerges as a potential biomarker for identifying CAFs in colorectal cancer, distinguishing them from both colorectal cancer cell lines and normal colon epithelial cell lines. Co-culture of CAFs and colorectal cancer cells revealed that CAFs could enhance the tumorigenic biofunctions of cancer cells indirectly, which could be partially inhibited by knocking down CAF original TPM2 expression.

CONCLUSIONS

This study introduces a functional gene signature that effectively and reliably predicts clinicopathological features and the tumor immune microenvironment in colorectal cancer. Moreover, the identification of TPM2 as a potential biomarker for CAFs holds promising implications for future research and clinical applications in the field of colorectal cancer.

Exploring copper metabolism-induced cell death in gastric cancer: a single-cell RNA sequencing study and prognostic model development

BACKGROUND

Gastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood.

OBJECTIVE

This study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets.

METHODS

scRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted.

RESULTS

Sixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.

Dynamic changes of the immune microenvironment in the development of gastric cancer caused by inflammation

Precancerous lesions typically precede gastric cancer (GC), but the molecular mechanisms underlying the transition from these lesions to GC remain unclear. Therefore, it is urgent to understand this transition from precancerous lesions to GC, which is crucial for the early diagnosis and treatment of GC. In this study, we merged multiple single-cell RNA sequencing datasets to investigate the molecular changes in distinct cell types associated with the progression of GC. First, we observed an increasing abundance of immune cells and a decrease in non-immune cells from non-atrophic gastritis to GC. Five immune cell types were significantly enriched in GC compared to precancerous lesions. Moreover, we found that the interleukin (IL)-17 signaling pathway and Th17 cell differentiation were significantly up-regulated in immune cell subsets during GC progression. Some genes in these processes were predominantly expressed at the GC stage, highlighting their potential as diagnostic markers. Furthermore, we validated our findings using bulk RNA sequencing data from The Cancer Genome Atlas and confirmed consistent immune cell changes during GC progression. Our study provides insights into the immune infiltration and signaling pathways involved in the development of GC, contributing to the development of early diagnosis and targeted treatment strategies for this malignancy.

Single-cell RNA sequencing to map tumor heterogeneity in gastric carcinogenesis paving roads to individualized therapy

Gastric cancer (GC) is a highly heterogeneous disease with a complex tumor microenvironment (TME) that encompasses multiple cell types including cancer cells, immune cells, stromal cells, and so on. Cancer-associated cells could remodel the TME and influence the progression of GC and therapeutic response. Single-cell RNA sequencing (scRNA-seq), as an emerging technology, has provided unprecedented insights into the complicated biological composition and characteristics of TME at the molecular, cellular, and immunological resolutions, offering a new idea for GC studies. In this review, we discuss the novel findings from scRNA-seq datasets revealing the origin and evolution of GC, and scRNA-seq is a powerful tool for investigating transcriptional dynamics and intratumor heterogeneity (ITH) in GC. Meanwhile, we demonstrate that the vital immune cells within TME, including T cells, B cells, macrophages, and stromal cells, play an important role in the disease progression. Additionally, we also overview that how scRNA-seq facilitates our understanding about the effects on individualized therapy of GC patients. Spatial transcriptomes (ST) have been designed to determine spatial distribution and capture local intercellular communication networks, enabling a further understanding of the relationship between the spatial background of a particular cell and its functions. In summary, scRNA-seq and other single-cell technologies provide a valuable perspective for molecular and pathological disease characteristics and hold promise for advancing basic research and clinical practice in GC.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

System analysis based on T-cell exhaustion-related genes identifies PTPRT as a promising diagnostic and prognostic biomarker for gastric cancer

Multiple investigations have demonstrated the crucial involvement of T-cell exhaustion (TEX) in anti-tumor immune response and their strong correlation with prognosis. This study aimed at creating a strong signature using TEX for gastric cancer through bioinformatics analysis and experimental validation. We utilized data from The Cancer Genome Atlas (TCGA) databases to retrieve RNA-seq data from patients with stomach adenocarcinoma (STAD). Genes related to TEX were discovered using gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Subsequently, prognostic signature based on TEX was developed using LASSO-Cox analysis. Relationship between key genes and immune cells were examined. Finally, biological function of a key TEX-related gene PTPRT in gastric cancer was verified by in vivo experiment. A total of 29 TEX-related biomarkers were screened by WGCNA and random forest. Among them, five core signatures (PTPRT, CAV2, PPIH, PRDM2, and FGF1), further identified by LASSO-Cox, were considered as strong predictors of prognosis for gastric cancer and associated with immune infiltration. PTPRT gene had the largest number of SNPs, with the most mutation types. In vivo experiments revealed that PTPRT overexpression significantly inhibited tumor malignant progression and accelerated apoptosis through stimulating the secretion of killer cytokines such as TNF-α and IFN-γ. In addition, flow cytometry revealed that PTPRT overexpression alleviated TEX by increasing the abundance of CD8+ T cells, with inhibition of cell surface PD-1 and Tim-3. The predictive prognostic value of TEX gene expression levels was evaluated in patients with gastric cancer, providing a new perspective for precision immuno-oncology studies.

Unraveling the regulatory cell death pathways in gastric cancer: a multi-omics study

Gastric cancer (GC) is a prevalent form of cancer worldwide and has a high death rate, with less than 40% of patients surviving for 5 years. GC demonstrates a vital characteristic of evading regulatory cell death (RCD). However, the extent to which RCD patterns are clinically significant in GC has not been well investigated. The study created a regulatory cell death index (RCDI) signature by employing 101 machine-learning algorithms. These algorithms were based on the expression files of 1292 GC patients from 6 multicenter cohorts. RCDI is a reliable and robust determinant of the likelihood of surviving in general. Furthermore, the precision of RCDI surpasses that of the 20 signatures that have been previously disclosed. The presence of RCDI signature is closely linked to immunological characteristics, such as the infiltration of immune cells, the presence of immunotherapy markers, and the activation of immune-related functions. This suggests that there is a higher level of immune activity in cases with RCDI signature. Collectively, the use of RCDI has the potential to be a strong and encouraging method for enhancing the clinical results of individual individuals with GC.

Revealing the crosstalk between LOX+ fibroblast and M2 macrophage in gastric cancer by single-cell sequencing

BACKGROUND/AIMS

Gastric cancer (GC) ranks among the prevalent types of cancer, and its progression is influenced by the tumor microenvironment (TME). A comprehensive comprehension of the TME associated with GC has the potential to unveil therapeutic targets of significance.

METHODS

The complexity and heterogeneity of TME interactions were revealed through our investigation using an integrated analysis of single-cell and bulk-tissue sequencing data.

RESULTS

We constructed a single-cell transcriptomic atlas of 150,913 cells isolated from GC patients. Our analysis revealed the intricate nature and heterogeneity of the GC TME and the metabolic properties of major cell types. Furthermore, two cell subtypes, LOX+ Fibroblasts and M2 Macrophages, were enriched in tumor tissue and related to the outcome of GC patients. In addition, LOX+ Fibroblasts were significantly associated with M2 macrophages. immunofluorescence double labeling indicated LOX+ Fibroblasts and M2 Macrophages were tightly localized in GC tissue. The two cell subpopulations strongly interacted in a hypoxic microenvironment, yielding an immunosuppressive phenotype. Our findings further suggest that LOX+ Fibroblasts may act as a trigger for inducing the differentiation of monocytes into M2 Macrophages via the IL6-IL6R signaling pathway.

CONCLUSIONS

Our study revealed the intricate and interdependent communication network between the fibroblast and macrophage subpopulations, which could offer valuable insights for targeted manipulation of the tumor microenvironment.

BMSC derived EVs inhibit colorectal Cancer progression by transporting MAGI2-AS3 or something similar

In this study, we investigated the molecular mechanisms underlying the impact of extracellular vesicles (EVs) derived from bone marrow stromal cells (BMSCs) on colorectal cancer (CRC) development. The focus was on the role of MAGI2-AS3, delivered by BMSC-EVs, in regulating USP6NL DNA methylation-mediated MYC protein translation modification to promote CDK2 downregulation. Utilizing bioinformatics analysis, we identified significant enrichment of MAGI2-AS3 related to copper-induced cell death in CRC. In vitro experiments demonstrated the downregulation of MAGI2-AS3 in CRC cells, and BMSC-EVs were found to deliver MAGI2-AS3 to inhibit CRC cell proliferation, migration, and invasion. Further exploration revealed that MAGI2-AS3 suppressed MYC protein translation modification by regulating USP6NL DNA methylation, leading to CDK2 downregulation and prevention of colorectal cancer. Overexpression of MYC reversed the functional effects of BMSC-EVs-MAGI2-AS3. In vivo experiments validated the inhibitory impact of BMSC-EVs-MAGI2-AS3 on CRC tumorigenicity by promoting CDK2 downregulation through USP6NL DNA methylation-mediated MYC protein translation modification. Overall, BMSC-EVs-MAGI2-AS3 may serve as a potential intervention to prevent CRC occurrence by modulating key molecular pathways.

Organoids for Cancer Research: Advances and Challenges

As 3D culture technology advances, new avenues have opened for the development of physiological human cancer models. These preclinical models provide efficient ways to translate basic cancer research into clinical tumor therapies. Recently, cancer organoids have emerged as a model to dissect the more complex tumor microenvironment. Incorporating cancer organoids into preclinical programs have the potential to increase the success rate of oncology drug development and recapitulate the most efficacious treatment regimens for cancer patients. In this review, four main types of cancer organoids are introduced, their applications, advantages, limitations, and prospects are discussed, as well as the recent application of single-cell RNA-sequencing (scRNA-seq) in exploring cancer organoids to advance this field.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma

BACKGROUND

Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival.

METHODS

We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset.

RESULTS

Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC.

CONCLUSION

The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

Immunotherapies targeting the oncogenic fusion gene CLDN18-ARHGAP in gastric cancer

The CLDN18-ARHGAP fusion gene is an oncogenic driver newly discovered in gastric cancer. It was detected in 9% (8/87) of gastric cancer patients in our center. An immunogenic peptide specifically targeting CLDN18-ARHGAP fusion gene was generated to induce neoantigen-reactive T cells, which was proved to have specific and robust anti-tumor capacity both in in vitro coculture models and in vivo xenograft gastric cancer models. Apart from the immunogenic potential, CLDN18-ARHGAP fusion gene was also found to contribute to immune suppression by inducing a regulatory T (Treg) cell-enriched microenvironment. Mechanistically, gastric cancer cells with CLDN18-ARHGAP fusion activate PI3K/AKT-mTOR-FAS signaling, which enhances free fatty acid production of gastric cancer cells to favor the survival of Treg cells. Furthermore, PI3K inhibition could effectively reverse Treg cells upregulation to enhance anti-tumor cytotoxicity of neoantigen-reactive T cells in vitro and reduce tumor growth in the xenograft gastric cancer model. Our study identified the CLDN18-ARHGAP fusion gene as a critical source of immunogenic neoepitopes, a key regulator of the tumor immune microenvironment, and immunotherapeutic applications specific to this oncogenic fusion.

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer

BACKGROUND

Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored.

METHODS

This study characterized the genetic alterations and mRNA expression of CAMs. The role of CD34, a representative molecule, was validated in 375 GC tissues. The activity of the CAM pathway was further tested using single-cell and bulk characterization. Next, data from 839 patients with GC from three cohorts was analyzed using univariate Cox and random survival forest methods to develop and validate a CAM-related prognostic model.

RESULTS

Most CAM-related genes exhibited multi-omics alterations and were associated with clinical outcomes. There was a strong correlation between increased CD34 expression and advanced clinical staging (P = 0.026), extensive vascular infiltration (P = 0.003), and unfavorable prognosis (Log-rank P = 0.022). CD34 expression was also found to be associated with postoperative chemotherapy and tumor immunotherapy response. Furthermore, the CAM pathway was significantly activated and mediated poor prognosis. Additionally, eight prognostic signature genes (PSGs) were identified in the training cohort. There was a substantial upregulation of the expression of immune checkpoints and a pronounced infiltration of immune cells in GC tissues with high PSG score, which is consistent with the prediction of increased sensitivity to immunotherapy. Moreover, 9 compounds from the CTRPv2 database and 13 from the Profiling Relative Inhibition Simultaneously in Mixture (PRISM) database were identified as potential therapeutic drugs for patients with GC with high PSG score.

CONCLUSION

Thorough understanding of CAM pathways regulation and the innovative PSG score model hold significant implications for medical diagnosis, potentially enhancing personalized treatment strategies and improving patient outcomes in GC management.

Proteome-wide Mendelian randomization and single-cell sequencing analysis identify the association between plasma proteins and gastric cancer

Background

Targeted therapy is a crucial treatment modality for advanced gastric cancer, with several targets already identified, and the exploration of new targets is important. In this study, our aim was to identify plasma proteins causally associated with gastric cancer to explore novel genetic targets for the disease.

Methods

Firstly, we utilized protein quantitative trait loci data for 4,907 plasma proteins and genome-wide association study data for gastric cancer to conduct Mendelian randomization (MR) analyses. This was followed by summary-data-based MR analysis on the identified plasma proteins. We then analyzed single-cell sequencing data from the Gene Expression Omnibus database to describe the distribution of genes corresponding to these proteins across different stages and cell types of gastric cancer.

Results

MR analysis identified 12 plasma proteins with potential causal associations with gastric cancer, among which motilin (MLN) and THSD1 passed the summary-data-based MR test. These proteins showed no evidence of pleiotropy nor heterogeneity. In single-cell sequencing analysis, EPHB4, KDR, SEMA6B, CDH1, and C1GALT1C1 were found to be enriched in specific cell types within gastric cancer. KDR and LIFR exhibited significant differential expression between gastric cancer and normal tissues. All the 12 genes displayed differential expression across different stages of gastric cancer.

Conclusions

Overall, our study identified several plasma proteins with potential causal relationships to gastric cancer. This provides potential candidate targets for gastric cancer research and advances our understanding of the disease's genetic foundations.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing

BACKGROUND

Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life.

METHOD

The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations.

RESULT

The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies.

CONCLUSIONS

We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

A novel M2-like tumor associated macrophages-related gene signature for predicting the prognosis and immunotherapy efficacy in gastric cancer

BACKGROUND

M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified.

METHODS

Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC.

RESULTS

A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes.

CONCLUSION

These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease.

Methods

This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models.

Results

Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN, SFRP4, LUM, and FRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN, SFRP4, LUM, and FRZB.

Conclusion

Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN, FRZB, LUM, and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.

Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

Atlas of Metastatic Gastric Cancer Links Ferroptosis to Disease Progression and Immunotherapy Response

BACKGROUND & AIMS

Metastases from gastric adenocarcinoma (GAC) lead to high morbidity and mortality. Developing innovative and effective therapies requires a comprehensive understanding of the tumor and immune biology of advanced GAC. Yet, collecting matched specimens from advanced, treatment-naïve patients with GAC poses a significant challenge, limiting the scope of current research, which has focused predominantly on localized tumors. This gap hinders deeper insight into the metastatic dynamics of GAC.

METHODS

We performed in-depth single-cell transcriptome and immune profiling on 68 paired, treatment-naïve, primary metastatic tumors to delineate alterations in cancer cells and their tumor microenvironment during metastatic progression. To validate our observations, we conducted comprehensive functional studies both in vitro and in vivo, using cell lines and multiple patient-derived xenograft and novel mouse models of GAC.

RESULTS

Liver and peritoneal metastases exhibited distinct properties in cancer cells and dynamics of tumor microenvironment phenotypes, supporting the notion that cancer cells and their local tumor microenvironments co-evolve at metastatic sites. Our study also revealed differential activation of cancer meta-programs across metastases. We observed evasion of cancer cell ferroptosis via GPX4 up-regulation during GAC progression. Conditional depletion of Gpx4 or pharmacologic inhibition of ferroptosis resistance significantly attenuated tumor growth and metastatic progression. In addition, ferroptosis-resensitizing treatments augmented the efficacy of chimeric antigen receptor T-cell therapy.

CONCLUSIONS

This study represents the largest single-cell dataset of metastatic GACs to date. High-resolution mapping of the molecular and cellular dynamics of GAC metastasis has revealed a rationale for targeting ferroptosis defense in combination with chimeric antigen receptor T-cell therapy as a novel therapeutic strategy with potential immense clinical implications.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Single-cell profiling reveals altered immune landscape and impaired NK cell function in gastric cancer liver metastasis

Gastric cancer (GC) is a substantial global health concern, and the development of liver metastasis (LM) in GC represents a critical stage linked to unfavorable patient prognoses. In this study, we employed single-cell RNA sequencing (scRNA-seq) to investigate the immune landscape of GC liver metastasis, revealing several immuno-suppressive components within the tumor immune microenvironment (TIM). Our findings unveiled an increased presence of cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cell (MDSC)-like macrophages, tumor-associated macrophage (TAM)-like macrophages, and naive T cells, while conventional dendritic cells (cDCs) and effector CD8 T cells declined in LM. Additionally, we identified two distinct natural killer (NK) cell clusters exhibiting differential cytotoxicity-related gene expression, with cytotoxic NK cells notably reduced in LM. Strikingly, TGFβ was identified as an inducer of NK cell dysfunction, potentially contributing to immune evasion and tumor metastasis. In preclinical LM models, the combined approach of inhibiting TGFβ and transferring NK cells exhibited a synergistic impact, resulting in a significant reduction in liver metastasis. This work highlights the importance of understanding the complex immune dynamics within GC liver metastasis and presents a promising strategy combining TGFβ inhibition and NK-based immunotherapy to improve patient outcomes.

Construction and validation of prognostic signatures related to mitochondria and macrophage polarization in gastric cancer

Background

Increasing evidence reveals the involvement of mitochondria and macrophage polarisation in tumourigenesis and progression. This study aimed to establish mitochondria and macrophage polarisation-associated molecular signatures to predict prognosis in gastric cancer (GC) by single-cell and transcriptional data.

Methods

Initially, candidate genes associated with mitochondria and macrophage polarisation were identified by differential expression analysis and weighted gene co-expression network analysis. Subsequently, candidate genes were incorporated in univariateCox analysis and LASSO to acquire prognostic genes in GC, and risk model was created. Furthermore, independent prognostic indicators were screened by combining risk score with clinical characteristics, and a nomogram was created to forecast survival in GC patients. Further, in single-cell data analysis, cell clusters and cell subpopulations were yielded, followed by the completion of pseudo-time analysis. Furthermore, a more comprehensive immunological analysis was executed to uncover the relationship between GC and immunological characteristics. Ultimately, expression level of prognostic genes was validated through public datasets and qRT-PCR.

Results

A risk model including six prognostic genes (GPX3, GJA1, VCAN, RGS2, LOX, and CTHRC1) associated with mitochondria and macrophage polarisation was developed, which was efficient in forecasting the survival of GC patients. The GC patients were categorized into high-/low-risk subgroups in accordance with median risk score, with the high-risk subgroup having lower survival rates. Afterwards, a nomogram incorporating risk score and age was generated, and it had significant predictive value for predicting GC survival with higher predictive accuracy than risk model. Immunological analyses revealed showed higher levels of M2 macrophage infiltration in high-risk subgroup and the strongest positive correlation between risk score and M2 macrophages. Besides, further analyses demonstrated a better outcome for immunotherapy in low-risk patients. In single-cell and pseudo-time analyses, stromal cells were identified as key cells, and a relatively complete developmental trajectory existed for stromal C1 in three subclasses. Ultimately, expression analysis revealed that the expression trend of RGS2, GJA1, GPX3, and VCAN was consistent with the results of the TCGA-GC dataset.

Conclusion

Our findings demonstrated that a novel prognostic model constructed in accordance with six prognostic genes might facilitate the improvement of personalised prognosis and treatment of GC patients.

Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease

Bullous pemphigoid (BP) is a type 2 inflammation- and immunity-driven skin disease, yet a comprehensive understanding of the immune landscape, particularly immune-stromal crosstalk in BP, remains elusive. Herein, using single-cell RNA sequencing (scRNA-seq) and in vitro functional analyzes, we pinpoint Th2 cells, dendritic cells (DCs), and fibroblasts as crucial cell populations. The IL13-IL13RA1 ligand-receptor pair is identified as the most significant mediator of immune-stromal crosstalk in BP. Notably, fibroblasts and DCs expressing IL13RA1 respond to IL13-secreting Th2 cells, thereby amplifying Th2 cell-mediated cascade responses, which occurs through the specific upregulation of PLA2G2A in fibroblasts and CCL17 in myeloid cells, creating a positive feedback loop integral to immune-stromal crosstalk. Furthermore, PLA2G2A and CCL17 contribute to an increased titer of pathogenic anti-BP180-NC16A autoantibodies in BP patients. Our work provides a comprehensive insight into BP pathogenesis and shows a mechanism governing immune-stromal interactions, providing potential avenues for future therapeutic research.