Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray

Chen, Zhencong; Zhao, Mengnan; Li, Ming; Sui, Qihai; Bian, Yunyi; Liang, Jiaqi; Hu, Zhengyang; Zheng, Yuansheng; Lu, Tao; Huang, Yiwei; Zhan, Cheng; Jiang, Wei; Wang, Qun; Tan, Lijie

doi:10.1038/s41374-020-0428-1

Cited by 26 publications

(28 citation statements)

References 38 publications

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“…However, with traditional RNA-seq technology, one can primarily explore tumors on a whole sample level, but this is a limited approach to explore the cellular diversity and molecular complexity of tumor cells. In recent years, single-cell sequencing, geared on the cellular and molecular characteristics, has provided an insight into the mechanisms underlying the normal physiological or biochemical processes and diseases at a single cell level [12] , [13] , [14] . For instance, Lambrechts et al.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma

et al. 2021

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Background: Esophageal squamous cell carcinoma (ESCC) is among the most prevalent causes of cancerrelated death in adults. Tumor microenvironment (TME) has been associated with therapeutic failure and lethal outcomes for patients. However, published reports on the heterogeneity and TME in ESCC are scanty. Methods: Five tumor samples and five corresponding non-malignant samples were subjected to scRNA-seq analysis. Bulk RNA sequencing data were retrieved in publicly available databases. Findings: From the scRNA-seq data, a total of 128,688 cells were enrolled for subsequent analyses. Gene expression and CNV status exhibited high heterogeneity of tumor cells. We further identified a list of tumorspecific genes and four malignant signatures, which are potential new markers for ESCC. Metabolic analysis revealed that energy supply-related pathways are pivotal in cancer metabolic reprogramming. Moreover, significant differences were found in stromal and immune cells between the esophagus normal and tumor tissues, which promoted carcinogenesis at both cellular and molecular levels in ESCC. Immune checkpoints, regarded as potential targets for immunotherapy in ESCC were significantly highly expressed in ESCC, including LAG3 and HAVCR2. Eventually, we constructed a cell-to-cell communication atlas based on cancer cells and immune cells and performed the flow cytometry, qRT-PCR, immunofluorescence, and immunohistochemistry analyses to validate the results. Interpretation: This study demonstrates a widespread reprogramming across multiple cellular elements within the TME in ESCC, particularly in transcriptional states, cellular functions, and cell-to-cell interactions. The findings offer an insight into the exploration of TME and heterogeneity in the ESCC and provide new therapeutic targets for its clinical management in the future.

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Section: Introductionmentioning

confidence: 99%

Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma

et al. 2021

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“…We downloaded and analyzed the single-cell transcriptome data from two patients with LUAD from GSE149655. Cluster-specific genes were used to annotate cell types with classic markers described in previous studies ( Lambrechts et al, 2018 ; Chen et al, 2020b ): epithelial (CAPS, KRT8, and KRT18) and endothelial (CLDN5, FCN3, and RAMP2). The analysis identified different clusters of tumor and non-tumor cells ( Figure 8A ), epithelial and non-epithelial ( Figure 8B ), and endothelial and non-endothelial cells ( Figure 8C ).…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

Zhong

Wang

Zhu

et al. 2021

Front. Cell Dev. Biol.

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Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

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“…We also used the "FindClusters" and "FindAllMarkers" functions to conduct cell clustering analysis and detect gene expression markers. Afterwards, we used the SingleR package, CellMarker dataset, and previous studies 11,58 to annotate the cell types in our study. The "SubsetData" function was also applied to extract the sub-cluster for downstream analysis.…”

Section: X Scrna-seq Data Analysismentioning

confidence: 99%

Landscape and dynamics of single tumor and immune cells in early and advanced‐stage lung adenocarcinoma

Chen

Huang

et al. 2021

Clinical & Translational Med

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Background Lung adenocarcinoma (LUAD) patients with different American Joint Committee on Cancer stages have different overall 5‐year survival rates. The tumor microenvironment (TME) and intra‐tumor heterogeneity (ITH) have been shown to play a crucial role in the occurrence and development of tumors. However, the TME and ITH in different lesions of LUAD have not been extensively explored. Methods We present a 204,157‐cell catalog of the TME transcriptome in 29 lung samples to systematically explore the TME and ITH in the different stages of LUAD. Traditional RNA sequencing data and complete clinical information were downloaded from publicly available databases. Results Based on these high‐quality cells, we constructed a single‐cell network underlying cellular and molecular features of normal lung, early LUAD, and advanced LUAD cells. In contrast with early malignant cells, we noticed that advanced malignant cells had a remarkably more complex TME and higher ITH level. We also found that compared with other immune cells, more differences in CD8+/CTL T cells, regulatory T cells, and follicular B cells were evident between early and advanced LUAD. Additionally, cell‐cell communication analyses, revealed great diversity between different lesions of LUAD at the single‐cell level. Flow cytometry and qRT‐PCR were used to validate our results. Conclusion Our results revealed the cellular diversity and molecular complexity of cell lineages in different stages of LUAD. We believe our research, which serves as a basic framework and valuable resource, can facilitate exploration of the pathogenesis of LUAD and identify novel therapeutic targets in the future.

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Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray

Cited by 26 publications

References 38 publications

Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma

Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma

Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

Landscape and dynamics of single tumor and immune cells in early and advanced‐stage lung adenocarcinoma

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