ArticleJournal of cancer research and clinical oncology2023
Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Tumor-associated mesenchymal stem/stromal cells in tumor microenvironment and carcinogenesis.Experimental hematology & oncology · 2025Review
- Identification and validation of T cell senescence-related prognostic genes in gastric carcinoma and investigation of their potential regulatory mechanisms.Discover oncology · 2025Article
- A machine learning-based prognostic signature utilizing MSC proteomics for predicting bladder cancer prognosis and treatment response.Translational oncology · 2025Article
- Early Detection of Malignant Cells in Gastric Lavage via Hexokinase 2 and Single-Cell Sequencing for Gastric Cancer Diagnosis.Risk management and healthcare policy · 2025Article
- Single-cell analysis uncovers high-proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer.Journal of cellular and molecular medicine · 2024Article
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3 authors at 1 institution in 1 country.
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Abstract
backgroundMesenchymal stem cells (MSCs) play an important role in regulating all stages of the immune response, angiogenesis, and transformation of matrix components in the tumor microenvironment. The aim of this study was to identify the prognostic value of MSC-related signatures in patients with gastric cancer (GC).
methodsMSC marker genes were identified by analyzing single-cell RNA sequencing (scRNA-seq) data for GC from the Gene Expression Omnibus (GEO) database. Using bulk sequencing data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD), as a training cohort, and data from GEO, as a validation cohort, we developed a risk model consisting of MSC prognostic signature genes, and classified GC patients into high- and low-MSC risk subgroups. Multifactorial Cox regression was used to evaluate whether MSC prognostic signature was an independent prognostic factor. An MSC nomogram was constructed combining clinical information and risk grouping. Subsequently, we evaluated the effect of MSC prognostic signature on immune cell infiltration, antitumor drugs and immune checkpoints and verified the expression of MSC prognostic signature by in vitro cellular assays.
resultsIn this study, 174 MSC marker genes were identified by analyzing scRNA-seq data. We identified seven genes (POSTN, PLOD2, ITGAV, MMP11, SDC2, MARCKS, ANXA5) to construct MSC prognostic signature. MSC prognostic signature was an independent risk factor in the TCGA and GEO cohorts. GC patients in the high-MSC risk group had worse prognoses. In addition, the MSC nomogram has a high clinical application value. Notably, the MSC signature can induce the development of a poor immune microenvironment. GC patients in the high MSC-risk group were more sensitive to anticancer drugs and tended to have higher levels of immune checkpoint markers. In qRT-PCR assays, the MSC signature was more highly expressed in GC cell lines.
conclusionsThe MSC marker gene-based risk signature developed in this study can not only be used to predict the prognosis of GC patients, but also has the potential to reflect the efficacy of antitumor therapies.
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