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  • Review Article
  • OPEN ACCESS

Single-cell RNA Sequencing and Spatial Transcriptomic Technologies and Applications in Exploring Gastric Cancer: A Review

  • Chun-Xiao Zhu1,2 and
  • Jiang-Jiang Qin1,3,* 
 Author information  Cite
Oncology Advances   2023;1(1):6-16

doi: 10.14218/OnA.2023.00039

Abstract

Global deaths attributed to gastric cancer (GC) are increasing, yet our understanding of it remains limited. Recently, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have provided important insights into the dissection of metastasis-related biological processes in subpopulations of cells in the GC tumor microenvironment, especially intratumoral cellular heterogeneity and cell-cell interactions. In this review, we discuss the mechanisms underlying GC metastasis and potential strategies for developing upcoming immunotherapies by combining advances in scRNA-seq and spatial transcriptomics.

Keywords

Gastric cancer, Tumor microenvironment, Metastasis, scRNA-seq, Spatial transcriptomics

Introduction

Gastric cancer (GC) is a major component of global cancer incidence and mortality, with a particularly high incidence in Eastern Europe, Asia, and Central America. Metastasis is a leading cause hindering the development of anticancer therapies,1 leading to the spread of cancer cells across different tissues through a multistep biological cascade.2 The occurrence of cancer cell metastasis depends on their own molecular alterations and the reprogramming of the tumor microenvironment (TME). GC is characterized by high epithelial-mesenchymal transition (EMT), high stromal cell and immune cell infiltration, and high T-cell exhaustion, indicating that tumor metastasis and an immunosuppressive TME may be responsible for the poor prognosis.3 Explaining the heterogeneity and cell-cell interactions is critical to reveal the TME in GC and improve cancer immunotherapy.

While emerging bioinformatic programs have been developed to analyze sequencing data, bulk RNA sequencing (RNA-seq) has limitations in discerning complex cell populations and cell-cell interactions.4–6 In recent years, single-cell RNA sequencing (scRNA-seq) has become an important method for analyzing gene expression at a single-cell resolution. There are several obvious advantages for scRNA-seq over bulk RNA-seq data, including analyzing cell populations and their interactions in the TME, thereby providing a comprehensive understanding of the mechanism underlying tumor cell metastasis.7,8 However, the necessity for tissue dissociation leads to loss of spatial information, which is a limitation of scRNA-seq. To address this limitation, spatial transcriptomic platforms retaining spatial architecture have emerged, such as digital spatial profiling (DSP) and 10× Genomics Visium, allowing analysis of tumor-TME interactions at an unprecedented depth.9–11

GC metastasis cascade

GC is characterized by a poor prognosis and a low long-term survival rate, contributing significantly to the increased cancer mortality worldwide. Metastasis is the main cause of cancer mortality, presenting significant challenges in treatment.7,12 As described in “The Seed Theory”, metastatic tumor cells utilize nutrients from the new environment to grow in the new soil. GC commonly metastasizes to the peritoneum, bone, liver, and lymph nodes,13,14 as shown in Figure 1. Peritoneal metastasis (PM) accounts for 53–66% of distant metastatic GCs,15 with a median survival time of only 4 months following the diagnosis.13,14 GC patients with pleural metastasis also often have PMs (32%). Patients with neurological metastases also usually have lung metastases (21% of all cases) but very few PMs (9%). More than half of the patients with lung metastases also have liver metastases. Women with ovarian metastases often develop PMs (56%).16

Summary of the metastatic routes and sites in gastric cancer.
Fig. 1  Summary of the metastatic routes and sites in gastric cancer.

The primary routes of gastric cancer metastasis involve intraperitoneal, lymphatic, and hematogenous dissemination as well as invasion into adjacent organs. Common metastatic sites comprise the spleen, pancreas, colon, liver, peritoneum, ovaries, lymph nodes, and bones. Figure adapted from BioRender.com.

Major routes of metastasis in GC include lymphatic, intraperitoneal, hematogenous spread, and direct invasion into adjacent organs.17 Among them, the peritoneum is the most prevalent metastatic site for GC.13,14 GC cells tend to seed in the abdominal and pelvic organs, such as the intestine, ovary, diaphragm, bile, and rectum surface, often forming localized tumors. Routine cytology has been used to assess the risk of PM in GC with serosal invasion. However, its sensitivity remains limited, as PM still occurs in many patients with negative cytologic results.18 The growth of a primary tumor requires sufficient blood supply, resulting in angiogenesis that can support nutrition for tumor metastasis. These newly formed blood vessels can also serve as passages for cells to exit the tumor and enter the circulatory blood system. In addition, tumor cells may disseminate to other tissues via the lymphatic system. Once the cells are arrested in a new organ through the circulation, they may initiate the formation of a macroscopic tumor by the development of new blood vessels.19 Ultimately, successful dissemination depends on both the molecular alterations in the cancer cells themselves and the microenvironment they encounter.

Classification and characteristics of scRNA-seq and spatial transcriptomics

The scRNA-seq technique

The TME consists of various cell types and extracellular components that surround human tumor cells and are nourished by a vascular network. The cellular populations within the TME are extremely complex, and recent advances in scRNA-seq have enabled identification and analysis among various malignant cell types.20 For example, Wang et al. performed scRNA-seq on malignant peritoneal cells obtained from ascitic fluids of GC patients to find prognostic signatures.21 The scRNA-seq program involves single-cell isolation, library construction, sequencing, and data analysis, as shown in Figure 2. Currently, scRNA-seq is mainly in two forms: plate-based and droplet-based.22–24

Schematics of scRNA-seq and spatial transcription techniques.
Fig. 2  Schematics of scRNA-seq and spatial transcription techniques.

(a) The scRNA-seq workflow. Single cells are mixed with single gel microbeads, forming small droplets of oil-enclosed water. Next, the cell membrane is disrupted, releasing mRNA from the cells. mRNA binds to the DNA barcode on the gel microbeads, producing cDNA via reverse transcriptase. The final steps involve amplification and sequencing. (b) Structure of 10× Genomics Visium chips. All oligos in the same spot contain the same spatial barcode (16 nt), and the spatial barcode sequence information is obtained through sequencing analysis to determine the spatial location of the mRNA. The UMI (12 nt) of each oligo in the same spot is unique and is used for transcript quantification via sequencing analysis. Finally, a 30-nt poly (dT) is used to capture mRNA from the FFPE sample. Figure adapted from BioRender.com.

SMART-seq2

SMART-seq2 utilizes fluorescence-activated cell sorting to place individual cells, allowing for a flexible experimental set-up with optional pause points, particularly when time is limited. Once cells are lysed, reverse transcription and PCR are carried out. SMART-seq2 exhibits a low dropout rate compared to bulk RNA-seq, enabling comprehensive characterization of broader gene expression profiles. Additionally, SMART-seq2 provides full-length mRNA coverage, facilitating the quantification of high-abundance expression at the transcript level.25,26 These platforms offer a fast and efficient means to analyze 50 to 500 single cells in a single experiment. However, plate-based scRNA-seq is more expensive and has lower cellular throughput compared to droplet-based scRNA-seq.27

10× Genomics

Advances in microfluidic technology have allowed individual cells to be separated into droplets containing lysis buffer and cell barcodes. Droplet-based scRNA-seq, such as 10× Genomics, can generate scRNA-seq data for thousands of cells in a single experiment, enabling a more comprehensive exploration of cell types in a tissue compared to plate-based techniques, which are typically limited to hundreds of cells.28 However, droplet-based scRNA-seq has technical limitations. These methods sequence either the 3 or 5 ends of mRNAs, which poses challenges in assembling full-length mRNA transcripts.29

The above methods fail to capture the epigenetic heterogeneity that may drive cellular behavior, thus scRNA-seq results describe only a subset of the molecular phenotype of a cell. Moreover, these sequencing methods cannot recover splicing patterns or sequence variants. Despite their advantages and disadvantages, both techniques have been successfully applied in the analysis of the TME. Furthermore, combining complementary strengths has provided a more comprehensive understanding of the TME.30

Spatial transcriptional profiling

Because the tissues must be resolved before sequencing, both bulk RNA-seq and scRNA-seq fail to retain anatomical information. Fortunately, spatial transcriptional profiling provides gene expression information while conserving tissue architecture information. Employing high-resolution spatial transcriptomics to understand the heterogeneity and cell interactions within an intact tissue section marks the next major milestone. In particular, this approach effectively characterizes solid tumors composed of malignant cells, stromal cells, and immune cells.31 Newer “spatial transcriptomic” platforms, such as DSP and 10× Genomics Visium, promise to yield a more comprehensive understanding of cell-cell variations within and between tumors.

10× Genomics Visium

The foundation of Visium technology lies in a slide containing four capture regions where tissue can be visualized and processed for gene expression analysis. Each capture region contains 5,000 spots with spatial barcodes unique to individual features on the slide. In particular, a unique molecular identifier (UMI) and a poly (dT) sequence within a probe enable transcript quantification and the capture of poly(A)-tailed mRNA diffusing toward the slide surface, followed by reverse-transcription as cDNA and library generation.10 The structure of 10× Genomics Visium chips is shown in Figure 2. Fresh-frozen tissues are required for the Visium technique to ensure high-quality RNA. In addition, the spot diameter of 55 mm does not yet allow single-cell resolution.9

NanoString GeoMx DSP

NanoString GeoMx DSP technology is designed to analyze the spatial expression of RNA within user-defined regions of interest (ROIs) in a tissue section. Tissue sections are prepared to expose the in situ hybridizing RNA to specific gene probes. Specifically, DSP relies on fluorescent markers to visualize the selected ROI. Each ROI is exposed to UV light, facilitating the release of photocleavable barcodes from gene-specific probes. Finally, the cleaved barcodes are identified using next-generation sequencing or the NanoString nCounter system. Curated panels of probes enable high-confidence analysis of tissue sections from formalin-fixed paraffin-embedded (FFPE) samples, allowing high-throughput detection of 18,000 genes.9

Stereo-seq

Chen et al. combined DNA nanoball (DNB)-patterned arrays with in situ RNA capture to create high-resolution spatial omics sequencing, named Stereo-seq. Currently, Stereo-seq can be used to depict spatial cell heterogeneity, cell fate, and cell-cell interactions in developing tissues with single-cell resolution. As reported, the use of random barcode-labeled DNB achieves a large barcode pool with 425 distinct spots. Similar to 10× Genomics Visium described above, UMI and polyT sequences are ligated onto each spot through hybridization with an oligonucleotide sequence. Frozen tissue sections loaded onto the chip surface are fixed and permeabilized to gain the tissue polyA-tailed RNA, followed by reverse transcription and amplification. Notably, the Stereo-seq chip has an effective area of up to 13.2 cm × 13.2 cm and characterizes the single cell-type composition of complex tissues with high sensitivity.32–34

The detailed comparison of the two transcriptomic platforms is presented in Table 1.9,10,20,26,28,29,32,35

Table 1

Comparison of transcriptomic platforms

NameTissue sizeType of RNA capturedCellular resolutionAmount of data generatedRef.
The scRNA-seq technique
SMART-seq2No requirementPoly-adenylated RNASingle cell levelFull-length mRNA; tens to hundreds of cells20,26
10× GenomicsNo requirementPoly-adenylated RNASingle cell levelFull-length mRNA; thousands of cells28,29,35
Spatial transcriptional profiling
10× Genomics VisiumMaximum of 6.5 × 6.5 mm per capture area (fresh-frozen tissue)Poly-adenylated RNA onlyApprox. 10 cells/feature; a center-to-center distance of 100 µm; spot size 55 µmWhole coding transcriptome9,10
NanoString GeoMx DSPMaximum of 14.6 × 36.2 mm (whole tissue sections, fresh-frozen tissue and FFPE)Any RNA to which a probe can be designedApprox. 20–200 cells/ROIPanel-based detection; User-defined ROIs9
Stereo-seqAn effective area of 13.2 cm × 13.2 cm (frozen tissue)Poly-adenylated RNASubcellular resolution; a center-to-center distance of 500 or 715 nm; spot size 220 nm400 spots for tissue RNA capture per 100 mm232

ROI, Region of interest.

Application of scRNA-seq and spatial transcriptomics in metastatic GC

Finding new breakthroughs is urgent as existing traditional treatments of GC have reached the therapeutic plateau. Therefore, a more precise understanding of metastatic GC is needed to identify new targets and enhance the clinical management of the disease. Utilizing scRNA-seq and spatial transcriptomics could clearly identify the signatures of both primary and metastatic tumors, as well as cell-cell interactions within the TME .36

Malignant cells in GC exhibiting heterogeneity

Understanding intratumoral heterogeneity, i.e. the molecular variation among cells within a tumor, promises to improve the diagnosis and treatment of malignant cancer.37 Analysis of scRNA-seq data often reveals that each normal cluster comprises cells from multiple patients, whereas each tumor cluster consists of cells from a single patient, indicating a high level of intertumoral heterogeneity. Furthermore, Sun et al. identified differentially expressed genes (DEGs) between the tumor cell clusters and normal cell clusters, finding that tumor cells from one patient formed two distinct tumor clusters. Interestingly, one cluster appeared less advanced than the other, highlighting intratumoral heterogeneity.38 GC with peritoneal carcinoma (PC) underpins tumor cell survival, leading to treatment resistance, a major obstacle to improving patient outcomes.39,40 Wang et al. performed scRNA-seq on PC cells collected from 20 advanced-stage GC patients.21 Further studies divided the PC samples into two main subtypes: gastric phenotype (mainly gastric cell lines) and GI-mixed type (mixed gastric and colorectal cells), revealing high inter-patient heterogeneity in PC tumor cells. In addition, gastric-dominant had shorter survival compared to GI-mixed. Moreover, a 12-gene fundamental signature derived from PC cells showed prognostic significance when applied to independent cells.21,41,42 In summary, scRNA-seq is a robust and unbiased tool to assess intratumoral heterogeneity.43 Single-cell analysis of heterogeneity shows a way to overcome drug resistance and develop new drugs.

Exploring cell populations and interactions in GC

The unique ecosystem resulting from the interaction between the tumor and the associated microenvironment promotes tumor growth and invasion.44 The TME of GC consists of stromal cells, macrophages, dendritic cells (DCs), Tregs, etc. As reported, myeloid populations or the stromal cells can regulate the status of lymphocytes through complex ligand-receptor interactions.38,45 Tumor-recruited immune cells, such as M2 macrophages, are known to promote immune escape, subsequently contributing to metastasis, as shown in Figures 3 and 4.46–48 Therefore, the analysis of cell populations and cell-cell interactions is critical for early diagnosis and the development of cancer immunotherapies.

The architecture of the tumor microenvironment.
Fig. 3  The architecture of the tumor microenvironment.

(a) The tumor microenvironment comprises cancer cells, fibroblasts, endothelial cells, and various immune cells. The complicated and dynamic interactions result in great heterogeneity of the tumor microenvironment. (b) Immune cell actions include tumor killing and immunosuppressive effects; therefore, a full understanding of various immune cell effects is beneficial to the development of immune therapies. Figure adapted from BioRender.com.

Overview of metastasis and TME.
Fig. 4  Overview of metastasis and TME.

The interaction between cancer cells and the immune microenvironment leads to immune reprogramming and metastasis. Cancer immunoediting facilitates tumor escape from immune destruction, consequently contributing to a poor prognosis. Figure adapted from BioRender.com.

Epithelium

GC is a malignant tumor originating from the gastric epithelium.49 Epithelial cells consist of a complex cell lineage comprising mucinous, secretory, and endocrine cells, working together to maintain tissue homeostasis.50,51 Malignant epithelial cells exhibit enrichment in various protein-related processes, including negative regulation of protein modification and positive regulation of protein localization. These cells also demonstrate copy number variants (CNVs) in patients with distal gastric adenocarcinoma and liver metastases.48 Wang et al. discovered that ERBB2, CLDN11, and CDK12 were related to GC lymph node metastasis, while FOS and JUN were considered potential driving genes in GC.36 High-resolution scRNA-seq revealed high-level expression of NOTCH2, NOTCH2NL, KIF5B, and ERBB4 in primary cancers, while metastatic cancer displayed overexpression of CDK12, ERBB2, and CLDN11, playing an associated role in metastasis.24,52–54 CLDN11, a member of the tight junction protein family, is reported to be shared within lymph node metastasis-prone subclones.36,55 Fan et al performed scRNA-seq analysis of PC cells from 20 gastric adenocarcinoma patients and found that SOX9 was expressed in epithelial cells of both primary and metastatic gastric adenocarcinoma and was associated with poor prognosis. Further study revealed that SOX9 is highly associated with cancer stem cell traits, tumorigenicity, and metastases in GC.21,56

Using spatial DSP, Vikrant et al. confirmed the loss of LIPF in tumor epithelial cells compared to normal samples and identified LIPF as a lineage-specific target in GC. In addition, there are higher transcript levels of KLF2 in diffuse-type epithelial cells compared to intestinal-type epithelial cells.5,57 Furthermore, Bianca et al. defined stroma AReactive invasion front areas (SARIFAs) as a spatial structure of tumor glands comprising at least five tumor cells directly contacting adipocytes in the invasion front. The most upregulated genes in SARIFA-positive cases included COL15A1, FABP2, FABP4, and FGB. Because SARIFA has a high prognostic relevance, it has the potential to be a biomarker of malignant GC, thus providing a basis for GC treatment.58

Stromal cells

Cancer-associated fibroblasts (CAFs), pericytes, and endothelial cells express core components of the extracellular matrix (ECM). Gene expression profiling in patients has revealed that genes related to inflammation, cytokines, and growth factor-associated proteins are highly enriched in the surrounding stroma but not in the cancer cells themselves. Thus, stromal cells play an important role in promoting GC cell migration and metastasis.59 As reported, transcriptional reprogramming of stromal cells in the TME promotes tumor growth.60 Tumor-associated endothelial cells exhibit greater activation of SOX18 and SOX7, which regulate endothelial cell growth,61 and they secrete large numbers of cytokines that interact with SDC1, SDC4, and ITGB1 in cancer cells.62 In addition, Li et al. detected 1,873 endothelial cells using scRNA-seq and found that multiple vascular endothelial growth factor receptors play an essential role in angiogenesis and ACKR1 specifically expressed in tumor endothelial cells associated with poor prognosis.20

CAFs represent one of the most important components in the TME and play a critical role in tumor development and progression. CAF subsets express genes at high levels involved in ECM remodeling, including genes encoding collagen and collagen metabolism enzymes.59 Intrinsic fibroblasts and mesenchymal stem cells (MSCs) originating from bone marrow constitute the primary precursor cells of CAFs. Interactions between MSCs and neutrophils result in MSC differentiation into CAFs via an interleukin (IL)-6–STAT3 axis, providing a proinflammatory environment and consequently increasing metastasis.63,64 The CAF subpopulation is characterized by high expression of periostin, which promotes the adhesion and migration of epithelial cells and drives the maintenance and metastasis of cancer stem cells. CAFs enhance IL-17B expression in GC tissues, leading to the activation of MSCs and further accelerating the migration of GC cells.65 Using spatial DSP, Vikrant et al. analyzed FAP and INHBA in fibroblast regions identified by α-smooth muscle actin, revealing increased expression of these proteins in tumor fibroblasts compared to normal fibroblasts. Consequently, there is a strong correlation between FAP and INHBA coexpression levels.5

Moreover, CAFs do not exist as individual cells around tumors but interact with tumor cells to promote tumor growth and survival, thus maintaining their tendency toward malignancy.60 Tumor cells can affect the recruitment of CAF precursors and induce normal fibroblasts into CAFs. Conversely, CAFs can secrete multiple cytokines, growth factors, and extracellular matrix proteins, promoting cell proliferation, drug resistance, invasion, and metastasis. The connection between GC cells and fibroblasts through the cadherin 11-mediated juxtacrine signal activates the YAP/Tenascin-C axis, facilitating gastric cancer metastasis.66 In addition, fibroblasts serve as the major source of Wnt ligands, and the corresponding receptors, such as LGR4–RSPO3, are expressed on tumor epithelial cells, endothelial cells, and pericytes.

The association between CAFs and epithelial cells is mediated through various ligand-integrin receptor interactions, including collagen and fibronectin. COL1A1, COL1A2, and COL3A1 are highly expressed in fibroblasts and interact with cancer cells through ITGA2, DDR1, and ITGB1, which are strongly correlated with GC genesis, development, and metastasis.67–70 Furthermore, collagen alterations within the TME are associated with PM in GC through serosal invasion.71 By using scRNA-seq, Li et al. demonstrated that inflammatory CAFs (iCAFs) and extracellular matrix CAFs (eCAFs) exhibit strong pro-invasive activity. They also recruit surrounding immune cells to build a favorable tumor microenvironment. iCAFs interact with T cells through C-X-C motif chemokine ligand 12 (CXCL12) and interleukin (IL)-6, while eCAFs promote M2 macrophage polarization by expressing periostin. eCAFs, as a pre-invasive CAF subset, reduce the overall survival time of GC patients.3,72 In conclusion, CAFs are involved in angiogenesis, extracellular matrix remodeling, immune suppression, and EMT, providing a favorable microenvironment for tumor cells.73 A TME-specific intercellular communication network has the potential to influence cellular behavior.

Immune cells

The infiltrating state of immune cells in GC metastases may provide specific diagnostic and therapeutic strategies for organ-specific metastases. ScRNA-seq and spatial transcription are used to analyze different immune cell subtypes and their heterogeneous transcription factors in GC patients at single-cell resolution.48,74 The migratory properties of immune cells can play an important role in elucidating the biology of tumor-infiltrating immune cells. In addition, when analyzing tumor-infiltrating immune cells, it is essential to consider sample collection and selection of relevant control tissues within each study, as shown in Table 2.5,20,23,24,36,58,61,67,68,72,75–82

Table 2

Exploring cell populations and interactions by scRNA-seq and spatial transcriptomics

Cell subsetsSignature genesEnriched tissuesPlatformsRef.
Epithelium
Mucous and secretory lineagesNOTCH2, NOTCH2NL, KIF5B, ERBB4, CDK12, ERBB2, CLDN11, KLF2, COL15A1, FABP2, FABP4, FGBTumor tissue10× Chromium system, SMART-seq2, DSP24,36,58
Stromal cells
Cancer-associated fibroblastsCOL1A1, COL1A2, COL3A1, FAP, INHBATumor tissue10× Genomics, SMART-seq2, DSP5,67,68
Endothelial cellsPECAM, ENG, VWF, SELETumor tissue10× Genomics20,23
Immune cells
CD8[+] T cellsNaïve markers: CCR7, SELLNormal gastric tissue, peripheral blood10× Genomics, SMART-seq223,61
Tissue effector memory markers: CD69, ITGAE, ITGA1Tumor, peripheral blood
Cytotoxic genes: GZMB, GZMA, PRF1, IFNG, NKG7Tumor, peripheral blood
Exhausted T cells: LAG3, CTLA4, VSIRTumor, peripheral blood
CD4+ T cellsNaïve markers: CCR7, SELLPeripheral blood, normal gastric tissue10× Genomics20,23
Effector CD4 T cells: GZMA, GZMB, CXCL13, BATF, HLANormal and tumor tissue
B-cellETS1, FHITTumor, peripheral blood10× Genomics75
NK cellsGZMA, XCL2, CCL5, PRF1, CCL3, CCL4, GITR, CD96, KIR2DL4Tumor, peripheral blood10× Genomics23,61
DCsIL3RA, CLEC4CPeripheral blood10× Genomics20,23,76
MacrophagesCD68, CD163, MRC1, INHBA, PTGS2, C1QC, CX3CR1, MARCO, CCL5, IL2RG, CD14, FCGR3A, CD68Tumor, peripheral bloodSMART-seq2, 10× Genomics77–79,81,82
MDSCARG1, CD66b, VISTA, IDO1Tumor tissueDSP72,80
T cell

ScRNA-seq assays were conducted on immune cells isolated from peripheral blood, GC tissues, and corresponding adjacent non-tumor tissues to analyze transcription factor levels. The expression of IRF8 transcription factor was observed to be downregulated in both CD8+ tumor-infiltrating lymphocytes (TILs) from GC tissues and blood, indicating a more advanced stage in these GC patients.61 In addition, the exhaustion levels of cytotoxic CD8+ T lymphocytes (CTLs) were found to be relatively low in primary gastric tumors. Further studies have found that exhausted T cells in the TME exhibit high expression of inhibitory receptors, such as PD-1, CTLA-4, TIM-3, TIGIT, and LAG3, and elevated expression levels of MKi67, a marker of active proliferation.8 Sun et al. uncovered that Tc17 (CD8+IL17+ T) cells expressed the highest level of ITGAE (CD103), a member of the integrin family, among all detected T cell subtypes in the single-cell analysis. This suggests that Tc17 cells may be involved in cell-to-cell contacts through ITGAE-CEACAM5 interactions. Further studies indicated that tissue-resident CD8+ T cells could differentiate into Tc17 cells in the TME, which further shift to exhausted phenotype. In addition, the CD40LG-CD40 and CCL20-CCR6 interactions from CD4_C4/C6 and DC_LAMP3/cDC1_XCR1 suggest that CD4+ T cells promoted the recruitment and activation of DCs.38 The proportion of Tregs in tumor samples was significantly higher than that in adjacent normal samples, suggesting the expansion or recruitment of Tregs in gastric tumors ScRNA-seq also revealed increased expression of multiple genes associated with immune suppression in Tregs, including DUSP4, IL2RA, TNFRSF4, LAYN, and LGALS1.20

B-cell subsets

ScRNA-seq analysis of B-cell subsets between cancerous and paracancerous tissues revealed DEGs, including EIF1AY, KRT19, LCN2, and RPS4Y1.61 Pathway analysis revealed that the upregulated genes in the B-cell cluster were enriched in the TNF, nod-like, and CXCR chemokine receptor-binding pathways. In addition, a special group of B cells, named T-cell-like B cells, express both the marker CD3D of T lymphocytes and the typical surface markers CD79A, MS4A1 (CD20), CD40, and CD19 of B lymphocytes.75 In particular, T-cell-like B cells with the marker genes CD19 and CD3D were further validated in GC with lymph node metastasis and ovarian metastasis.48

Natural killer cells

As reported, natural killer (NK) cells actively participate in immunosurveillance to prevent GC. Differential analysis of gene expression in cancerous and adjacent tissues at the single-cell level showed that IL8, G0S2, HSPA6, and CXCL1 are upregulated, while IGJ, TFF1, and NCR2 are downregulated.61 These genes might be involved in cytokine-cytokine receptor interactions, MAPK signaling, T-cell receptor signaling, and chemokine signaling.61

Dendritic cell subtypes

DCs present cancer antigens and secrete inflammatory cytokines and chemokines, initiating and regulating both innate and adaptive immune responses against tumors. In GC patient blood, DCs were found to express multiple inhibitory receptors, such as FTL and IL8, and secrete cytokines, including CCL4 and CCL5.23 Li et al. characterized clusters as DCs based on low expression of CD14 and high expression of the DRα gene. Multiple studies have demonstrated that LAMP3+ DCs tend to express more types of chemokines, cytokines, and inhibitory ligands. LAMP3+ DCs can regulate the functions of tumor-infiltrating T cells, such as driving naïve T cells toward Tregs and activated CD8+ states.8,20,76,83 Sun et al. found that LAMP3+DCs, characterized by the specific expression of LAMP3 and CCR7, are involved in mediating T cell activity and forming intercellular interaction hubs with tumor-associated stromal cells. Conversely, these DCs inhibit the activity of anti-tumor T cells by expressing CD274 at high levels.38,84

Macrophages

Macrophages, a major component of the tumor microenvironment, either promote or inhibit tumorigenesis and metastasis according to their status.77 Roca et al. found that macrophage-secreted CHI3L1 promoted GC metastasis in vitro and in vivo.78 Leukemia inhibitory factor (LIF) as the top secreted molecule is regulated by SOX9 in GC with PC and mediates SOX9-induced M2 macrophage repolarization. Fan et al. uncovered that targeting SOX9/LIF axis in GC increased infiltration and cytotoxicity of CD8+ T cells and decreased M2 macrophage infiltration.56 GSVA analysis of hallmark pathways revealed increased activities of WNT signaling, hedgehog signaling, angiogenesis, EMT, and IL10 signaling in tumor-associated macrophages (TAMs), while C1QC+ macrophages were upregulated in MHC class II antigen presentation.20

Macrophages secrete cytokines interacting with SDC1, SDC4, and ITGB1 in cancer cells, leading to EMT activation and GC metastasis. Inhibiting these interactions could suppress GC metastasis.62 Studies have demonstrated that the CXCL 5-CXCR 2 interaction between cancer cells and macrophages can promote GC metastasis.79,85 Further understanding of the molecular mechanisms underlying macrophage plasticity holds promising prospects for immunopathology for GC.

In general, the above statements suggest that the observed plasticity at the transcriptional level in GC may result from oncogenic and exogenous mediators, such as ligand-receptor interactions in the TME. Alterations in the immune environment can be observed in the early stages of multistep progression, providing an opportunity for immunotherapy.

Drug response

For patients with advanced GC, effective treatment is particularly crucial due to the poor survival rate. By examining cellular changes before and after drug administration, it is possible to discover potential mechanisms affecting drug response by scRNA-seq.

Studies of advanced GC indicate the presence of immune remodeling during chemotherapy. Research indicates a decrease in both proinflammatory genes and MHC class I antigen-presenting genes after chemotherapy. The expression of M2 type macrophage-related genes decreases, suggesting that macrophages transform from M1 cells to M2 cells after chemotherapy.86 One study demonstrated that treated samples exhibited damaged immune cells but increased endothelial cells and fibroblasts. T cells exhibited lower cytotoxic and proliferative properties, along with the downregulation of immune pathways. Using paired pretreatment and on-treatment samples during 5-FU treatment, Ryul et al. identified chemotherapy-induced NK-cell infiltration, macrophage repolarization, and increased antigen presentation among responders. Nonresponders exhibited increased LAG3 expression and reduced numbers of DCs, highlighting the remodeling of the TME during chemotherapy response and resistance.87 Single-cell transcriptome was used to detect differentially expressed proteins among normal gastric mucosa, primary GC and PM tissues. Ye et al. found that MYH9-induced expression of CTNNB1 was found to promote GC metastasis, which may be inhibited by staurosporine, indicating a novel approach for the treatment of GC peritoneal metastasis.88

In conclusion, scRNA-seq may offer an opportunity to expand the portion of patients benefiting from chemotherapy alone or in combination with immunotherapy.

Conclusion and perspective

Advances in understanding the molecular alterations in GC have provided valuable knowledge to reveal the complex biological phenomenon underlying metastasis. However, due to the complexity and systematization of GC metastasis, many questions about the mechanisms of GC metastasis remain unanswered. Within a relatively short period, scRNA-seq and spatial transcriptomics have illuminated and reinforced many complex facets of cancer cells and associated TME. The precise roles and plasticity of cancer cells and the TME continue to be investigated, with single-cell and spatial studies identifying even greater levels of subtype diversity within this already complex cellular compartment.

In recent years, the targeted therapy of CAF has garnered significant interest, with numerous related clinical trials under way. FAP is a major cell surface marker of immunosuppressive CAFs. Elimination of FAP + CAFs are associated with increased CD8 + T cell infiltration. Depleting FAP+ CAFs via genetic deletion or chimeric antigen receptor T cells has shown promising anti-tumor activities in preclinical animal models.89 Targeting activation signaling and downstream effectors of CAFs, such as IL-6, IL-6 receptor or JAKs, kinase inhibitor imatinib have been well verified.90 In a word, it is urgent to find specific markers and categorize them into different subpopulations using scRNA-seq.

A growing number of studies have confirmed that the composition and functional status of different cell types influences different responses to cancer immunotherapy. By utilizing scRNA-seq and spatial transcriptomics, further understanding of the TME has great potential not only in identifying reliable biomarkers but also in discovering novel therapeutic strategies to complement existing therapeutic drugs.8 For example, scRNA-seq can analyze the contribution of the rejuvenation vitality in pre-existing T cells and the recruitment of new T cells in response to anti-PD-1 treatment in GC patients. The findings revealed that the recruitment of new T cells may be more significant for anti-PD-1 therapies in basal and squamous cell carcinomas. T cells derived from peripheral tissues are essential for generating effective immunotherapy.91 Moreover, TCF1+CD8+ T cells, known for their stem-like phenotype, express low levels of PD-1 and TIM-3 inhibitory receptors, thereby enhancing the immune response to tumor suppressors. In addition to effector memory T cells, tissue-resident memory and peripheral T cells can also be important sources supporting the rejuvenation and recruitment of tumor-infiltrating T cells.92,93 In addition, a scRNA-seq study demonstrated the critical role of cytotoxic CD4+ T cells in mediating the anti-tumor effects of anti-PD-L1 therapy in an MHC class II-dependent manner.94 The development of immune checkpoint inhibitors has enhanced cancer therapy by providing clinical treatment for some previously incurable patients. A comprehensive understanding of the complex immune cell composition and molecular pathways could help identify the mechanisms underlying immunotherapy response and indicate the potential of new targets to overcome resistance.95–97

By consolidating the reported studies, we can gain deep insights into the mechanisms of GC metastasis. Considering the persistent increase in the number of patients with metastatic GC, we urgently hope that the understanding and findings regarding the mechanism of cancer metastasis can be continuously applied to clinical practice. The road ahead will involve the integration of single-cell and spatial analytics in the comprehensive monitoring of patients during clinical trials, providing effective solutions to address the various mechanisms behind resistance and ineffective treatment, and opportunities for progress in the treatment of devastating diseases.

Abbreviations

CXCL12: 

C-X-C motif chemokine ligand 12

CAFs: 

cancer-associated fibroblasts

CNVs: 

copy number variants

CTLs: 

cytotoxic CD8+ T lymphocytes

DCs: 

dendritic cells

DEGs: 

differentially expressed genes

DSP: 

digital spatial profiling

EMT: 

epithelial–mesenchymal transition

ECM: 

extracellular matrix

FFPE: 

formalin-fixed paraffin-embedded

GC: 

gastric cancer

IL: 

interleukinLIF, leukemia inhibitory factor

MSCs: 

mesenchymal stem cells

MDSCs: 

myeloid-derived suppressor cells

NK: 

natural killer

PC: 

peritoneal carcinoma

PM: 

peritoneal metastasis

ROIs: 

regions of interest

scRNA-seq: 

single-cell RNA sequencing

SARIFAs: 

stroma AReactive invasion front areas

TME: 

tumor microenvironment

TAMs: 

tumor-associated macrophages

TILs: 

tumor-infiltrating lymphocytes

UMI: 

unique molecular identifier

Declarations

Acknowledgement

None.

Funding

This research was supported by grants from the National Key R and D Program of China (2021YFA0910101), the Natural Science Foundation of Zhejiang Province (LR21H280001), and the Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province (2022E10021).

Conflict of interest

The manuscript was submitted during Dr. Jiang-Jiang Qin’s term as an editorial board member of Oncology Advances. The authors have no other conflict of interests to declare.

Authors’ contributions

Study design, manuscript writing (CXZ); Critical revision, critical funding, administration (JJQ). All authors have made a significant contribution to this study and have approved the final manuscript.

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68(6):394-424 View Article PubMed/NCBI
  2. GBD 2017 Stomach Cancer Collaborators. The global, regional, and national burden of stomach cancer in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease study 2017. Lancet Gastroenterol Hepatol 2020;5(1):42-54 View Article PubMed/NCBI
  3. Li X, Sun Z, Peng G, Xiao Y, Guo J, Wu B, et al. Single-cell RNA sequencing reveals a pro-invasive cancer-associated fibroblast subgroup associated with poor clinical outcomes in patients with gastric cancer. Theranostics 2022;12(2):620-638 View Article PubMed/NCBI
  4. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 2019;37(7):773-782 View Article PubMed/NCBI
  5. Kumar V, Ramnarayanan K, Sundar R, Padmanabhan N, Srivastava S, Koiwa M, et al. Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer. Cancer Discov 2022;12(3):670-691 View Article PubMed/NCBI
  6. Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet 2019;20(5):257-272 View Article PubMed/NCBI
  7. Valastyan S, Weinberg RA. Tumor metastasis: molecular insights and evolving paradigms. Cell 2011;147(2):275-292 View Article PubMed/NCBI
  8. Ren X, Zhang L, Zhang Y, Li Z, Siemers N, Zhang Z. Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment. Annu Rev Immunol 2021;39:583-609 View Article PubMed/NCBI
  9. Bassiouni R, Gibbs LD, Craig DW, Carpten JD, McEachron TA. Applicability of spatial transcriptional profiling to cancer research. Mol Cell 2021;81(8):1631-1639 View Article PubMed/NCBI
  10. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016;353(6294):78-82 View Article PubMed/NCBI
  11. Goh JJL, Chou N, Seow WY, Ha N, Cheng CPP, Chang Y-C, et al. Highly specific multiplexed RNA imaging in tissues with split-FISH. Nat Methods 2020;17(7):689-693 View Article PubMed/NCBI
  12. Gupta GP, Massagué J. Cancer metastasis: building a framework. Cell 2006;127(4):679-695 PubMed/NCBI
  13. Yoo CH, Noh SH, Shin DW, Choi SH, Min JS. Recurrence following curative resection for gastric carcinoma. Br J Surg 2000;87(2):236-242 PubMed/NCBI
  14. Sasako M, Sano T, Yamamoto S, Kurokawa Y, Nashimoto A, Kurita A, et al. D2 lymphadenectomy alone or with para-aortic nodal dissection for gastric cancer. N Engl J Med 2008;359(5):453-462 View Article PubMed/NCBI
  15. Fujitani K, Yang H-K, Mizusawa J, Kim Y-W, Terashima M, Han S-U, et al. Gastrectomy plus chemotherapy versus chemotherapy alone for advanced gastric cancer with a single non-curable factor (REGATTA): a phase 3, randomised controlled trial. Lancet Oncol 2016;17(3):309-318 View Article PubMed/NCBI
  16. Riihimäki M, Hemminki A, Sundquist K, Sundquist J, Hemminki K. Metastatic spread in patients with gastric cancer. Oncotarget 2016;7(32):52307-52316 View Article PubMed/NCBI
  17. Li W, Ng JM-K, Wong CC, Ng EKW, Yu J. Molecular alterations of cancer cell and tumour microenvironment in metastatic gastric cancer. Oncogene 2018;37(36):4903-4920 View Article PubMed/NCBI
  18. Abe S, Yoshimura H, Tabara H, Tachibana M, Monden N, Nakamura T, et al. Curative resection of gastric cancer: limitation of peritoneal lavage cytology in predicting the outcome. J Surg Oncol 1995;59(4):226-229 PubMed/NCBI
  19. Chambers AF, Groom AC, MacDonald IC. Dissemination and growth of cancer cells in metastatic sites. Nat Rev Cancer 2002;2(8):563-572 PubMed/NCBI
  20. Li Y, Hu X, Lin R, Zhou G, Zhao L, Zhao D, et al. Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer. Theranostics 2022;12(8):3818-3833 View Article PubMed/NCBI
  21. Wang R, Dang M, Harada K, Han G, Wang F, Pool Pizzi M, et al. Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma. Nat Med 2021;27(1):141-151 View Article PubMed/NCBI
  22. Zhang M, Hu S, Min M, Ni Y, Lu Z, Sun X, et al. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut 2021;70(3):464-475 View Article PubMed/NCBI
  23. Sathe A, Grimes SM, Lau BT, Chen J, Suarez C, Huang RJ, et al. Single-Cell Genomic Characterization Reveals the Cellular Reprogramming of the Gastric Tumor Microenvironment. Clin Cancer Res 2020;26(11):2640-2653 View Article PubMed/NCBI
  24. Zhang P, Yang M, Zhang Y, Xiao S, Lai X, Tan A, et al. Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer. Cell Rep 2019;27(6):1934-1947.e5 View Article PubMed/NCBI
  25. Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014;9(1):171-181 View Article PubMed/NCBI
  26. Stubbington MJT, Lönnberg T, Proserpio V, Clare S, Speak AO, Dougan G, et al. T cell fate and clonality inference from single-cell transcriptomes. Nat Methods 2016;13(4):329-332 View Article PubMed/NCBI
  27. Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 2018;360(6385):176-182 View Article PubMed/NCBI
  28. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017;8:14049 View Article PubMed/NCBI
  29. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 2018;174(5):1293-1308.e36 View Article PubMed/NCBI
  30. Zheng C, Zheng L, Yoo J-K, Guo H, Zhang Y, Guo X, et al. Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell 2017;169(7):1342-1356.e16 View Article PubMed/NCBI
  31. Burgess DJ. Spatial transcriptomics coming of age. Nat Rev Genet 2019;20(6):317 View Article PubMed/NCBI
  32. Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 2022;185(10):1777-1792.e21 View Article PubMed/NCBI
  33. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14(11):1083-1086 View Article PubMed/NCBI
  34. Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature 2021;596(7871):211-220 View Article PubMed/NCBI
  35. Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W, O’Brien SA, et al. Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer. Cell 2020;181(2):442-459.e29 View Article PubMed/NCBI
  36. Wang B, Zhang Y, Qing T, Xing K, Li J, Zhen T, et al. Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq. Sci Rep 2021;11(1):1141 View Article PubMed/NCBI
  37. Lewis SM, Asselin-Labat M-L, Nguyen Q, Berthelet J, Tan X, Wimmer VC, et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods 2021;18(9):997-1012 View Article PubMed/NCBI
  38. Sun K, Xu R, Ma F, Yang N, Li Y, Sun X, et al. scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory. Nat Commun 2022;13(1):4943 View Article PubMed/NCBI
  39. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014;513(7517):202-209 View Article PubMed/NCBI
  40. Wang R, Song S, Harada K, Ghazanfari Amlashi F, Badgwell B, Pizzi MP, et al. Multiplex profiling of peritoneal metastases from gastric adenocarcinoma identified novel targets and molecular subtypes that predict treatment response. Gut 2020;69(1):18-31 View Article PubMed/NCBI
  41. Ikoma N, Das P, Hofstetter W, Ajani JA, Estrella JS, Chen H-C, et al. Preoperative chemoradiation therapy induces primary-tumor complete response more frequently than chemotherapy alone in gastric cancer: analyses of the National Cancer Database 2006-2014 using propensity score matching. Gastric Cancer 2018;21(6):1004-1013 View Article PubMed/NCBI
  42. Moghimi-Dehkordi B, Safaee A, Zali MR. Comparison of colorectal and gastric cancer: survival and prognostic factors. Saudi J Gastroenterol 2009;15(1):18-23 View Article PubMed/NCBI
  43. Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 2016;352(6282):189-196 View Article PubMed/NCBI
  44. Eum HH, Kwon M, Ryu D, Jo A, Chung W, Kim N, et al. Tumor-promoting macrophages prevail in malignant ascites of advanced gastric cancer. Exp Mol Med 2020;52(12):1976-1988 View Article PubMed/NCBI
  45. Adams JL, Smothers J, Srinivasan R, Hoos A. Big opportunities for small molecules in immuno-oncology. Nat Rev Drug Discov 2015;14(9):603-622 View Article PubMed/NCBI
  46. Gao Y, Bado I, Wang H, Zhang W, Rosen JM, Zhang XHF. Metastasis Organotropism: Redefining the Congenial Soil. Dev Cell 2019;49(3):375-391 View Article PubMed/NCBI
  47. Massagué J, Ganesh K. Metastasis-Initiating Cells and Ecosystems. Cancer Discov 2021;11(4):971-994 View Article PubMed/NCBI
  48. Jiang H, Yu D, Yang P, Guo R, Kong M, Gao Y, et al. Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing. Clin Transl Med 2022;12(2):e730 View Article PubMed/NCBI
  49. Birchenough GMH, Johansson MEV, Gustafsson JK, Bergström JH, Hansson GC. New developments in goblet cell mucus secretion and function. Mucosal Immunol 2015;8(4):712-719 View Article PubMed/NCBI
  50. Choi E, Roland JT, Barlow BJ, O’Neal R, Rich AE, Nam KT, et al. Cell lineage distribution atlas of the human stomach reveals heterogeneous gland populations in the gastric antrum. Gut 2014;63(11):1711-1720 View Article PubMed/NCBI
  51. Willet SG, Mills JC. Stomach Organ and Cell Lineage Differentiation: from Embryogenesis to Adult Homeostasis. Cell Mol Gastroenterol Hepatol 2016;2(5):546-559 PubMed/NCBI
  52. Quigley DA, Dang HX, Zhao SG, Lloyd P, Aggarwal R, Alumkal JJ, et al. Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer. Cell 2018;175(3):889 View Article PubMed/NCBI
  53. Takahashi N, Iwasa S, Taniguchi H, Sasaki Y, Shoji H, Honma Y, et al. Prognostic role of ERBB2, MET and VEGFA expression in metastatic colorectal cancer patients treated with anti-EGFR antibodies. Br J Cancer 2016;114(9):1003-1011 View Article PubMed/NCBI
  54. Li J, Zhou C, Ni S, Wang S, Ni C, Yang P, et al. Methylated associated with metastasis and poor survival of colorectal cancer. Oncotarget 2017;8(56):96249-96262 View Article PubMed/NCBI
  55. Liu F, Koval M, Ranganathan S, Fanayan S, Hancock WS, Lundberg EK, et al. Systems Proteomics View of the Endogenous Human Claudin Protein Family. J Proteome Res 2016;15(2):339-359 View Article PubMed/NCBI
  56. Fan Y, Li Y, Yao X, Jin J, Scott A, Liu B, et al. Epithelial SOX9 drives progression and metastases of gastric adenocarcinoma by promoting immunosuppressive tumour microenvironment. Gut 2023;72:624-637 View Article PubMed/NCBI
  57. Imielinski M, Guo G, Meyerson M. Insertions and Deletions Target Lineage-Defining Genes in Human Cancers. Cell 2017;168(3):460-472.e14 View Article PubMed/NCBI
  58. Grosser B, Glückstein M-I, Dhillon C, Schiele S, Dintner S, VanSchoiack A, et al. Stroma AReactive Invasion Front Areas (SARIFA) - a new prognostic biomarker in gastric cancer related to tumor-promoting adipocytes. J Pathol 2022;256(1):71-82 View Article PubMed/NCBI
  59. Busuttil RA, George J, Tothill RW, Ioculano K, Kowalczyk A, Mitchell C, et al. A signature predicting poor prognosis in gastric and ovarian cancer represents a coordinated macrophage and stromal response. Clin Cancer Res 2014;20(10):2761-2772 View Article PubMed/NCBI
  60. De Val S, Black BL. Transcriptional control of endothelial cell development. Dev Cell 2009;16(2):180-195 View Article PubMed/NCBI
  61. Fu K, Hui B, Wang Q, Lu C, Shi W, Zhang Z, et al. Single-cell RNA sequencing of immune cells in gastric cancer patients. Aging (Albany NY) 2020;12(3):2747-2763 View Article PubMed/NCBI
  62. Chen L-L, Gao G-X, Shen F-X, Chen X, Gong X-H, Wu W-J. SDC4 Gene Silencing Favors Human Papillary Thyroid Carcinoma Cell Apoptosis and Inhibits Epithelial Mesenchymal Transition Wnt/β-Catenin Pathway. Mol Cells 2018;41(9):853-867 View Article PubMed/NCBI
  63. Zhu Q, Zhang X, Zhang L, Li W, Wu H, Yuan X, et al. The IL-6-STAT3 axis mediates a reciprocal crosstalk between cancer-derived mesenchymal stem cells and neutrophils to synergistically prompt gastric cancer progression. Cell Death Dis 2014;5:e1295 View Article PubMed/NCBI
  64. Owen KL, Brockwell NK, Parker BS. JAK-STAT Signaling: A Double-Edged Sword of Immune Regulation and Cancer Progression. Cancers (Basel) 2019;11(12):2002 View Article PubMed/NCBI
  65. Bie Q, Zhang B, Sun C, Ji X, Barnie PA, Qi C, et al. IL-17B activated mesenchymal stem cells enhance proliferation and migration of gastric cancer cells. Oncotarget 2017;8(12):18914-18923 View Article PubMed/NCBI
  66. Zhou Q, Wu X, Wang X, Yu Z, Pan T, Li Z, et al. The reciprocal interaction between tumor cells and activated fibroblasts mediated by TNF-α/IL-33/ST2L signaling promotes gastric cancer metastasis. Oncogene 2020;39(7):1414-1428 View Article PubMed/NCBI
  67. Min J, Han T-S, Sohn Y, Shimizu T, Choi B, Bae S-W, et al. microRNA-30a arbitrates intestinal-type early gastric carcinogenesis by directly targeting ITGA2. Gastric Cancer 2020;23(4):600-613 View Article PubMed/NCBI
  68. Dong J, Wang R, Ren G, Li X, Wang J, Sun Y, et al. HMGA2-FOXL2 Axis Regulates Metastases and Epithelial-to-Mesenchymal Transition of Chemoresistant Gastric Cancer. Clin Cancer Res 2017;23(13):3461-3473 View Article PubMed/NCBI
  69. Han T-S, Hur K, Xu G, Choi B, Okugawa Y, Toiyama Y, et al. MicroRNA-29c mediates initiation of gastric carcinogenesis by directly targeting ITGB1. Gut 2015;64(2):203-214 View Article PubMed/NCBI
  70. Yin H, Guo R, Zhang H, Liu S, Gong Y, Yuan Y. A Dynamic Transcriptome Map of Different Tissue Microenvironment Cells Identified During Gastric Cancer Development Using Single-Cell RNA Sequencing. Front Immunol 2021;12:728169 View Article PubMed/NCBI
  71. Chen D, Liu Z, Liu W, Fu M, Jiang W, Xu S, et al. Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram. Nat Commun 2021;12(1):179 View Article PubMed/NCBI
  72. Jeong HY, Ham I-H, Lee SH, Ryu D, Son S-Y, Han S-U, et al. Spatially Distinct Reprogramming of the Tumor Microenvironment Based On Tumor Invasion in Diffuse-Type Gastric Cancers. Clin Cancer Res 2021;27(23):6529-6542 View Article PubMed/NCBI
  73. Chen Y, McAndrews KM, Kalluri R. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat Rev Clin Oncol 2021;18(12):792-804 View Article PubMed/NCBI
  74. Bouquet J, Gardy JL, Brown S, Pfeil J, Miller RR, Morshed M, et al. RNA-Seq Analysis of Gene Expression, Viral Pathogen, and B-Cell/T-Cell Receptor Signatures in Complex Chronic Disease. Clin Infect Dis 2017;64(4):476-481 View Article PubMed/NCBI
  75. Guo R, Wang W, Yu L, Zhu Z, Tu P. Different regulatory effects of CD40 ligand and B-cell activating factor on the function of B cells. Int Immunopharmacol 2021;91:107337 View Article PubMed/NCBI
  76. Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, et al. Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma. Cell 2019;179(4):829-845.e20 View Article PubMed/NCBI
  77. Ruffell B, Coussens LM. Macrophages and therapeutic resistance in cancer. Cancer Cell 2015;27(4):462-472 View Article PubMed/NCBI
  78. Roca H, Jones JD, Purica MC, Weidner S, Koh AJ, Kuo R, et al. Apoptosis-induced CXCL5 accelerates inflammation and growth of prostate tumor metastases in bone. J Clin Invest 2018;128(1):248-266 View Article PubMed/NCBI
  79. Zhou Z, Xia G, Xiang Z, Liu M, Wei Z, Yan J, et al. A C-X-C Chemokine Receptor Type 2-Dominated Cross-talk between Tumor Cells and Macrophages Drives Gastric Cancer Metastasis. Clin Cancer Res 2019;25(11):3317-3328 View Article PubMed/NCBI
  80. Koh V, Chakrabarti J, Torvund M, Steele N, Hawkins JA, Ito Y, et al. Hedgehog transcriptional effector GLI mediates mTOR-Induced PD-L1 expression in gastric cancer organoids. Cancer Lett 2021;518:59-71 View Article PubMed/NCBI
  81. Hynes RO. Integrins: bidirectional, allosteric signaling machines. Cell 2002;110(6):673-687 PubMed/NCBI
  82. Martinez FO, Gordon S, Locati M, Mantovani A. Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J Immunol 2006;177(10):7303-7311 PubMed/NCBI
  83. Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D, Saatcioglu HD, et al. Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species. Immunity 2019;50(5):1317-1334.e10 View Article PubMed/NCBI
  84. Manieri NA, Chiang EY, Grogan JL. TIGIT: A Key Inhibitor of the Cancer Immunity Cycle. Trends Immunol 2017;38(1):20-28 View Article PubMed/NCBI
  85. Zhou S-L, Dai Z, Zhou Z-J, Wang X-Y, Yang G-H, Wang Z, et al. Overexpression of CXCL5 mediates neutrophil infiltration and indicates poor prognosis for hepatocellular carcinoma. Hepatology 2012;56(6):2242-2254 View Article PubMed/NCBI
  86. Chen Y, Yin J, Zhao L, Zhou G, Dong S, Zhang Y, et al. Reconstruction of the gastric cancer microenvironment after neoadjuvant chemotherapy by longitudinal single-cell sequencing. J Transl Med 2022;20(1):563 View Article PubMed/NCBI
  87. Kim R, An M, Lee H, Mehta A, Heo YJ, Kim K-M, et al. Early Tumor-Immune Microenvironmental Remodeling and Response to First-Line Fluoropyrimidine and Platinum Chemotherapy in Advanced Gastric Cancer. Cancer Discov 2022;12(4):984-1001 View Article PubMed/NCBI
  88. Ye G, Yang Q, Lei X, Zhu X, Li F, He J, et al. Nuclear MYH9-induced CTNNB1 transcription, targeted by staurosporin, promotes gastric cancer cell anoikis resistance and metastasis. Theranostics 2020;10(17):7545-7560 View Article PubMed/NCBI
  89. Desbois M, Wang Y. Cancer-associated fibroblasts: Key players in shaping the tumor immune microenvironment. Immunol Rev 2021;302(1):241-258 View Article PubMed/NCBI
  90. Zhu Y, Yu F, Tan Y, Yuan H, Hu F. Strategies of targeting pathological stroma for enhanced antitumor therapies. Pharmacol Res 2019;148:104401 View Article PubMed/NCBI
  91. Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen Y-JJ, Chitre AS, et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 2020;579(7798):274-278 View Article PubMed/NCBI
  92. Wu T, Ji Y, Moseman EA, Xu HC, Manglani M, Kirby M, et al. The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness. Sci Immunol 2016;1(6):eaai8593 View Article PubMed/NCBI
  93. Siddiqui I, Schaeuble K, Chennupati V, Fuertes Marraco SA, Calderon-Copete S, Pais Ferreira D, et al. Intratumoral Tcf1PD-1CD8 T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy. Immunity 2019;50(1):195-211.e10 View Article PubMed/NCBI
  94. Oh DY, Kwek SS, Raju SS, Li T, McCarthy E, Chow E, et al. Intratumoral CD4 T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer. Cell 2020;181(7):1612-1625.e13 View Article PubMed/NCBI
  95. Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science 2018;359(6382):1350-1355 View Article PubMed/NCBI
  96. Giladi A, Amit I. Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries. Cell 2018;172(1-2):14-21 View Article PubMed/NCBI
  97. Davis-Marcisak EF, Deshpande A, Stein-O’Brien GL, Ho WJ, Laheru D, Jaffee EM, et al. From bench to bedside: Single-cell analysis for cancer immunotherapy. Cancer Cell 2021;39(8):1062-1080 View Article PubMed/NCBI
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Single-cell RNA Sequencing and Spatial Transcriptomic Technologies and Applications in Exploring Gastric Cancer: A Review

Chun-Xiao Zhu, Jiang-Jiang Qin
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