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Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer.

ABSTRACT: Lung adenocarcinoma (LUAD) and squamous carcinoma (LUSC) are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms. The heterogeneity can be attributed to genetic, transcriptional, and epigenetic parameters. Here, we established a multi-omics atlas, integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing. We investigated their differences in genetic amplification, cellular compositions, and expression modules. We revealed that LUAD and LUSC contained amplifications occurring selectively in subclusters of AT2 and basal cells, and had distinct cellular composition modules associated with poor survival of lung cancer. Malignant and stage-specific gene analyses further uncovered critical transcription factors and genes in tumor progression. Moreover, we identified subclusters with proliferating and differentiating properties in AT2 and basal cells. Overexpression assays of ten genes, including sub-cluster markers AQP5 and KPNA2, further indicated their functional roles, providing potential targets for early diagnosis and treatment in lung cancer.

SUBMITTER: Zhang L

PROVIDER: S-EPMC8758688 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer.

Zhang Li L Zhang Yiming Y Wang Chengdi C Yang Ying Y Ni Yinyun Y Wang Zhoufeng Z Song Tingting T Yao Menglin M Liu Zhiqiang Z Chao Ningning N Yang Yongfeng Y Shao Jun J Li Zhidan Z Zhou Ran R Chen Li L Zhang Dan D Zhao Yuancun Y Liu Wei W Li Yupeng Y He Ping P Lin Jing-Wen JW Wang Yuan Y Zhang Kang K Chen Lu L Li Weimin W

Signal transduction and targeted therapy 20220114 1

Lung adenocarcinoma (LUAD) and squamous carcinoma (LUSC) are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms. The heterogeneity can be attributed to genetic, transcriptional, and epigenetic parameters. Here, we established a multi-omics atlas, integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing. We investigated their differences in genetic amplification, cellular compositions, and expression modules. We revealed ...[more]

PMID: 35027529

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Project description:BackgroundTranscriptional cis-regulatory modules (for example, enhancers) play a critical role in regulating gene expression. While many individual regulatory elements have been characterized, they have never been analyzed as a class.ResultsWe have performed the first such large-scale study of cis-regulatory modules in order to determine whether they have common properties that might aid in their identification and contribute to our understanding of the mechanisms by which they function. A total of 280 individual, experimentally verified cis-regulatory modules from Drosophila were analyzed for a range of sequence-level and functional properties. We report here that regulatory modules do indeed share common properties, among them an elevated GC content, an increased level of interspecific sequence conservation, and a tendency to be transcribed into RNA. However, we find that dense clustering of transcription factor binding sites, especially homotypic clustering, which is commonly believed to be a general characteristic of regulatory modules, is rather a feature that belongs chiefly to a specific subclass. This has important implications for current computational approaches, many of which are biased toward this subset. We explore two new strategies to assess binding site clustering and gauge their performances with respect to their ability to detect all 280 modules and various functionally coherent subsets.ConclusionOur findings demonstrate that cis-regulatory modules share common features that help to define them as a class and that may lead to new insights into mechanisms of gene regulation. However, these properties alone may not be sufficient to reliably distinguish regulatory from non-regulatory sequences. We also demonstrate that there are distinct subclasses of cis-regulatory modules that are more amenable to in silico detection than others and that these differences must be taken into account when attempting genome-wide regulatory element discovery.

Dataset Information

Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer.

Publications

Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer.

Similar Datasets

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