The Potential of Single Cell RNA-Sequencing Data for the Prediction of Gastric Cancer Serum Biomarkers

2020 ◽  
pp. 79-84
Author(s):  
Kirill E. Medvedev ◽  
Anna V. Savelyeva ◽  
Aditya Bagrodia ◽  
Nick V. Grishin
Keyword(s):  
Gastric Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Serum Biomarkers ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

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

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.

scholarly journals IMMU-27. SINGLE CELL RNA-SEQUENCING IDENTIFIES NOVEL BONE MARROW DERIVED MYELOID CELLS IN GLIOBLASTOMA ASSOCIATED WITH TUMOR AGGRESSION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii110-ii110
Author(s):  
Christina Jackson ◽  
Christopher Cherry ◽  
Sadhana Bom ◽  
Hao Zhang ◽  
John Choi ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Metabolic Pathways ◽  
Myeloid Cells ◽  
Tumor Grade ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Two Populations

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

scholarly journals Correction to: The effect of methanol fixation on single-cell RNA sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinlei Wang ◽  
Lei Yu ◽  
Angela Ruohao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data

2017 ◽  
Vol 45 (19) ◽  
pp. 10978-10988 ◽  
Author(s):  
Cheng Jia ◽  
Yu Hu ◽  
Derek Kelly ◽  
Junhyong Kim ◽  
Mingyao Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Technical Noise ◽  
Single Cell Rna Sequencing

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

Sign in / Sign up

Export Citation Format

Share Document