Single-cell RNA sequencing reveals a pro-invasive cancer-associated fibroblast subgroup associated with poor clinical outcomes in patients with gastric cancer

Autophagy has been associated with tumor progression, prognosis, and treatment response. However, an autophagy-related model and their clinical significance have not yet been fully elucidated. In the present study, through the integrative analysis of bulk RNA sequencing and single-cell RNA sequencing, an autophagy-related risk model was identified. The model was capable of distinguishing the worse prognosis of patients with gastric cancer (GC), which was validated in TCGA and two independent Gene Expression Omnibus cohorts utilizing the survival analysis, and was also independent of other clinical covariates evaluated by multivariable Cox regression. The clinical value of this model was further assessed using a receiver operating characteristic (ROC) and nomogram analysis. Investigation of single-cell RNA sequencing uncovered that this model might act as an indicator of the dysfunctional characteristics of T cells in the high-risk group. Moreover, the high-risk group exhibited the lower expression of immune checkpoint markers (PDCD1 and CTLA4) than the low-risk group, which indicated the potential predictive power to the current immunotherapy response in patients with GC. In conclusion, this autophagy-associated risk model may be a useful tool for prognostic evaluation and will facilitate the potential application of this model as an indicator of the predictive immune checkpoint biomarkers.

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Abstract 4339: Single cell RNA sequencing dissects cellular growth factor dependencies and oncogenic driver effects in an organoid model of gastric cancer

10.1158/1538-7445.am2017-4339 ◽

2017 ◽

Author(s):

Jiamin Chen ◽

Noemi Andor ◽

Susan M. Grimes ◽

Billy Lau ◽

Hanlee P. Ji

Keyword(s):

Gastric Cancer ◽

Growth Factor ◽

Single Cell ◽

Rna Sequencing ◽

Cellular Growth ◽

Single Cell Rna Sequencing ◽

Oncogenic Driver

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Single-cell RNA sequencing reveals a dynamic stromal niche within the evolving tumour microenvironment

10.1101/467225 ◽

2018 ◽

Cited By ~ 10

Author(s):

Sarah Davidson ◽

Mirjana Efremova ◽

Angela Riedel ◽

Bidesh Mahata ◽

Jhuma Pramanik ◽

...

Keyword(s):

T Cell ◽

Lymph Nodes ◽

Single Cell ◽

Rna Sequencing ◽

Stromal Cells ◽

Clonal Expansion ◽

Tumour Microenvironment ◽

Complex Interplay ◽

Cancer Associated Fibroblast ◽

Single Cell Rna Sequencing

SummaryNon-cancerous stromal cells represent a highly diverse compartment of the tumour, yet their role across tumour evolution remains unclear. We employed single-cell RNA sequencing to determine stromal adaptations in murine melanoma at different points of tumour development. Naive lymphocytes recruited from lymph nodes underwent activation and clonal expansion within the tumour, prior to PD1 and Lag3 expression, while tumour-associated myeloid cells promoted the formation of a suppressive niche through cytokine secretion and inhibitory T cell interactions. We identified three temporally distinct cancer-associated fibroblast (CAF) populations displaying unique signatures, and verified these in human datasets. In early tumours, immune CXCL12/CSF1 and complement-expressing CAFs supported recruitment of macrophages, whereas contractile CAFs became more prevalent in later tumours. This study highlights the complex interplay and increasing diversity among cells that co-evolve with the tumour, indicating that from early stages of development, stromal cells acquire the capacity to modulate the immune landscape towards suppression.

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Identification of the Differentially Expressed Genes and Prognostic Associations in Single-Cell RNA Sequencing Data of Gastric Cancer

10.21203/rs.3.rs-150872/v1 ◽

2021 ◽

Author(s):

Shuang Gao ◽

Fazhan Li ◽

Minghai Zhao ◽

Wanqing Wu ◽

Yuming Fu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Single Cell ◽

Rna Sequencing ◽

Differentially Expressed Genes ◽

Ppi Network ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Geo Database ◽

Differential Genes

Abstract Background: Due to the lack of effective drugs, gastric cancer(GC) has a high mortality rate among other cancers, with a low 5-year survival rate and an inferior prognosis. Thus, screening of meaningful tumor biomarkers or therapeutic targets could play a vital role in the diagnosis, treatment, prognosis, and follow-up of GC. Methods: Gene expression profiles and comprehensive clinical information of 407 patients with GC were downloaded from The Cancer Genome Atlas (TCGA) database. GC-related single-cell RNA sequencing data from the GSE118916 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from transcriptomic data in GC and normal samples by R language. The DAVID database was also used to analyze the functions and pathways of DEGs. After combining differential genes with patient survival information, target genes were identified. The interaction of DEGs in the protein-protein interaction (PPI) network was also studied. Results: Our study identified a total of 209 differential genes, which might be positively related to GC. Gene Ontology (GO) analysis indicated numerous enrichment of DEGs in the extracellular matrix organization, extracellular structure organization, and muscle contraction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in focal adhesion, protein digestion and absorption, AGE-RAGE signaling pathway in diabetic complications. Further analysis showed the higher expression of Carboxypeptidase vitellogenic-like gene (CPVL) was related to the better prognosis of GC patients in both TCGA and the GEO database. FAM3 metabolism regulating signaling molecule D (FAM3D) and oxidized low-density lipoprotein receptor 1 (OLR1) were significantly associated with GC patients’ prognosis only in the GEO database. Lastly, the PPI network shows the gene expression proteins that interact most closely with CPVL protein.Conclusion: Our study revealed that CPVL gene could be a promising target for the diagnosis and treatment of GC, which has a great significance for the future research on GC. In addition, we were the first to find a close relationship between FAM3D and GC.

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Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing

10.1101/2019.12.08.868828 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ashley Maynard ◽

Caroline E. McCoach ◽

Julia K. Rotow ◽

Lincoln Harris ◽

Franziska Haderk ◽

...

Keyword(s):

Lung Cancer ◽

Targeted Therapy ◽

Single Cell ◽

Cancer Cells ◽

Rna Sequencing ◽

Clinical Outcomes ◽

Residual Disease ◽

Metastatic Cancer ◽

Immunosuppressive Cell ◽

Single Cell Rna Sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

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