Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer

Shen, Kaiyu; Chen, Binyu; Gao, Wencang

doi:10.1007/s00432-023-05058-6

Cited by 3 publications

(4 citation statements)

References 74 publications

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“…This effect can likely be attributed to its involvement in modulating the EGFR/ERK1/2‐MMP7 signalling pathway 39,40 . Furthermore, it has been established that the genes ANXA5 and GJA1, deemed as risk genes, hold significant implications in multiple prognostic models pertaining to GC 41–44 . Similarly, the genes PAPSS2 and ITPRIP have been recognized as unfavourable prognostic factors across a spectrum of cancers, including thyroid, colorectal and lung cancers 45–47 .…”

Section: Discussionsupporting

confidence: 67%

“… 39 , 40 Furthermore, it has been established that the genes ANXA5 and GJA1, deemed as risk genes, hold significant implications in multiple prognostic models pertaining to GC. 41 , 42 , 43 , 44 Similarly, the genes PAPSS2 and ITPRIP have been recognized as unfavourable prognostic factors across a spectrum of cancers, including thyroid, colorectal and lung cancers. 45 , 46 , 47 Nevertheless, it is noteworthy to highlight that within the context of GC, the gene RUNX1T1 exhibits a suppressive role, wherein it actively hinders the tumour‐promoting activity of C/EBPβ and restrains tumour proliferation.…”

Section: Discussionmentioning

confidence: 99%

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Single‐cell analysis uncovers high‐proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer

Zhang,

Wang,

Dong

et al. 2024

J Cellular Molecular Medi

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Gastric cancer (GC) remains a prominent malignancy that poses a significant threat to human well‐being worldwide. Despite advancements in chemotherapy and immunotherapy, which have effectively augmented patient survival rates, the mortality rate associated with GC remains distressingly high. This can be attributed to the elevated proliferation and invasive nature exhibited by GC. Our current understanding of the drivers behind GC cell proliferation remains limited. Hence, in order to reveal the molecular biological mechanism behind the swift advancement of GC, we employed single‐cell RNA‐sequencing (scRNA‐seq) to characterize the tumour microenvironment in this study. The scRNA‐seq data of 27 patients were acquired from the Gene Expression Omnibus database. Differential gene analysis, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis were employed to investigate 38 samples. The copy number variation level exhibited by GC cells was determined using InferCNV. The CytoTRACE, Monocle and Slingshot analysis were used to discern the cellular stemness and developmental trajectory of GC cells. The CellChat package was utilized for the analysis of intercellular communication crosstalk. Moreover, the findings of the data analysis were validated through cellular functional tests conducted on the AGS cell line and SGC‐7901 cell line. Finally, this study constructed a risk scoring model to evaluate the differences of different risk scores in clinical characteristics, immune infiltration, immune checkpoints, functional enrichment, tumour mutation burden and drug sensitivity. Within the microenvironment of GC, we identified the presence of 8 cell subsets, encompassing NK_T cells, B_Plasma cells, epithelial cells, myeloid cells, endothelial cells, mast cells, fibroblasts, pericytes. By delving deeper into the characterization of GC cells, we identified 6 specific tumour cell subtypes: C0 PSCA+ tumour cells, C1 CLDN7+ tumour cells, C2 UBE2C+ tumour cells, C3 MUC6+ tumour cells, C4 CHGA+ tumour cells and C5 MUC2+ tumour cells. Notably, the C2 UBE2C+ tumour cells demonstrated a close association with cell mitosis and the cell cycle, exhibiting robust proliferative capabilities. Our findings were fortified through enrichment analysis, pseudotime analysis and cell communication analysis. Meanwhile, knockdown of the transcription factor CREB3, which is highly active in UBE2C+ tumour cells, significantly impedes the proliferation, migration and invasion of GC cells. And the prognostic score model constructed with CREB3‐related genes showcased commendable clinical predictive capacity, thus providing valuable guidance for patients' prognosis and clinical treatment decisions. We have identified a highly proliferative cellular subgroup C2 UBE2C+ tumour cells in GC for the first time. The employment of a risk score model, which is based on genes associated with UBE2C expression, exhibits remarkable proficiency in predicting the prognosis of GC patients. In our investigation, we observed that the knockdown of the transcription factor CREB3 led to a marked reduction in cellular proliferation, migration and invasion in GC cell line models. Implementing a stratified treatment approach guided by this model represents a judicious and promising methodology.

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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Single‐cell analysis uncovers high‐proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer

Zhang,

Wang,

Dong

et al. 2024

J Cellular Molecular Medi

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“…ScRNA-Seq is a high-throughput technology that allows for quantitative sequencing of gene expression profiles at the single-cell level, aiding in the deciphering of hidden heterogeneity within cell populations [ 48 ]. In a recent study, Shen et al utilized scRNA-Seq to explore the expression characteristics of mesenchymal stem cells in GC and their role in treatment and prognosis [ 49 ]. To investigate the correlation between parthanatos and single cells in GC, we included a dataset from the GEO database (GSE163558) for scRNA-Seq analysis.…”

Section: Discussionmentioning

confidence: 99%

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer

Qiao,

Sun,

Ren

et al. 2024

Aging

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Background: Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain. Methods: In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes. Results: Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients’ survival times and statuses. Conclusions: In this study, the molecular characteristics of parthanatos’ unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.

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“…In LUAD patients with EGFR mutations, the heterogeneous tumors and immune cell populations in early period were also discovered as a promising diagnostic method for LUAD ( He et al, 2021 ). Shen, Chen & Gao (2023) constructed a mesenchymal stem cell-relevant model for assessing prognosis and drug sensitivity in STAD patients via scRNA-seq approach. Similarly, through integrating scRNA-seq with bulk data, CXCR4 on tumor-infiltrating B cells showed inferior survival outcomes and was considered as a therapeutic targets for STAD ( Su et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Single-cell data revealed CD14-type and FCGR3A-type macrophages and relevant prognostic factors for predicting immunotherapy and prognosis in stomach adenocarcinoma

Li,

Lu,

et al. 2024

PeerJ

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Background Stomach adenocarcinoma (STAD) exhibits profound tumor heterogeneity and represents a great therapeutic challenge. Single-cell sequencing technology is a powerful tool to identify characteristic cell types. Methods Single-cell sequencing data (scRNA-seq) GSE167297 and bulk RNA-seq data from TCGA, GTEx, GSE26901 and GSE15459 database were included in this study. By downscaling and annotating the cellular data in scRNA-seq, critical cell types in tumor progression were identified by AUCell score. Relevant gene modules were then identified by weighted gene co-expression network analysis (WGCNA). A prognostic scoring system was constructed by identifying prognostic factors in STAD by Least absolute shrinkage and selection operator (LASSO) COX model. The prognosis and model performance in the RiskScore groups were measured by Kaplan-Meier (K-M) curves and Receiver operating characteristic (ROC) curves. Nomogram was drawn based on RiskScore and prognosis-related clinical factors. In addition, we evaluated patient’s feedback on immunotherapy in the RiskScore groups by TIMER, ESTIMATE and TIDE analysis. Finally, the expression levels of prognostic factors were verified in gastric cancer cell lines (MKN7 and MKN28) and human normal gastric mucosal epithelial cells (GES-1), and the effects of prognostic factors on the viability of gastric cancer cells were examined by the CCK8 assay and cell cycle. Results scRNA-seq analysis revealed that 11 cell types were identified, and macrophages exhibited relatively higher AUCell scores and specifically expressed CD14 and FCGR3A. High macrophage scores worsened the prognosis of STAD patients. We intersected the specifically expressed genes in macrophages subgroups (670) and macrophage module genes (2,360) obtained from WGCNA analysis. Among 86 common genes, seven prognostic factors (RGS2, GNAI2, ANXA5, MARCKS, CD36, NRP1 and PDE4A) were identified and composed a RiskScore model. Patients in low Risk group showed a better survival advantage. Nomogram also provided a favorable prediction for survival at 1, 3 and 5 years in STAD patients. Besides, we found positive feedback to immunotherapy in patients with low RiskScore. The expression tendency of the seven prognostic factors in MKN7 and MKN28 was consistent with that in the RNA-seq data in addition to comparison of protein expression levels in the public HPA (The Human Protein Atlas) database. Further functional exploration disclosed that MARCKS was an important prognostic factor in regulating cell viability in STAD. Conclusion This study preliminary uncovered a single cell atlas for STAD patients, and Macrophages relevant gene signature and nomogram displayed favorable immunotherapy and prognostic prediction ability. Collectively, our work provides a new insight into the molecular mechanisms and therapeutic approach for LUAD patients.

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Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer

Cited by 3 publications

References 74 publications

Single‐cell analysis uncovers high‐proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer

Single‐cell analysis uncovers high‐proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer

Single-cell data revealed CD14-type and FCGR3A-type macrophages and relevant prognostic factors for predicting immunotherapy and prognosis in stomach adenocarcinoma

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