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Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer

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Abstract

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the “SingleR.” Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand–receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug–gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

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Data availability

The data are available with the corresponding author, GPDC.

Code availability

https://github.com/karthiksekaran/singlecell/

Abbreviations

scRNA-seq:

Single-cell RNA sequencing

GC:

Gastric Cancer

GEO:

Gene Expression Omnibus

PCA:

Principal Component Analysis

PC:

Principal Component

HPCA:

Human Primary Cell Atlas

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

STRING:

Search Tool for the Retrieval of Interacting Genes

DGIdb:

Drug–Gene Interaction Database

UMAP:

Uniform Manifold Approximation and Projection

DO:

Disease Ontology

PPI:

Protein–Protein Interaction Network

PDGFR:

Platelet-derived growth Factor Receptor

KM Plot:

Kaplan–Meier Plot

VEGF:

Vascular Endothelial Growth Factor

BP:

Biological Process

CC:

Cellular Component

MF:

Molecular Function

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Acknowledgements

The authors would like to thank the Vellore Institute of Technology, India authorities, for providing the necessary support in completing the manuscript. The authors acknowledge the Indian Council of Medical Research (ICMR), the Government of India agency, for the research grants (No. BMI/12(13)/2021, ID No: 2021-6359) and (No. VIR/COVID-19/31/2021/ECD-I, ID. NO: 2021-5570).

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Authors and Affiliations

  1. School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    Karthik Sekaran, Rinku Polachirakkal Varghese & C. George Priya Doss

  2. Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar

    Hatem Zayed

  3. African Genome Center, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Achraf El Allali

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  1. Karthik Sekaran
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Contributions

The study's design involved KS, RPV, HZ, AEL, and GPDC. KS and RPV were involved in the data collection and experiment. KS and RPV acquired, analyzed, and interpreted the results. HZ, AEL, and GPDC supervised the entire study. KS, RPV, HZ, and AEL drafted the manuscript. All authors edited and approved the submitted version of the article.

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Correspondence to C. George Priya Doss.

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Sekaran, K., Varghese, R.P., Zayed, H. et al. Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer. Med Oncol 40, 305 (2023). https://doi.org/10.1007/s12032-023-02174-8

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