Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

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Abstract

Background

Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications.

Methods

We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression.

Results

We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion.

Conclusions

Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03123-7.

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Most cited references 48

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Author and article information

Contributors

Chenghong Yin: yinchh@ccmu.edu.cn

Wentao Yue: yuewt@ccmu.edu.cn

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 30 October 2021

Publication date PMC-release: 30 October 2021

Publication date Collection: 2021

Volume: 19

Electronic Location Identifier: 454

Affiliations

GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Central Laboratory, , Beijing Obstetrics and Gynecology Hospital, Capital Medical University, ; Beijing, 100026 China

Article

Publisher ID: 3123

DOI: 10.1186/s12967-021-03123-7

PMC ID: 8557560

PubMed ID: 34717685

SO-VID: e348d9a9-dc0e-4971-8d4f-78b8f1bee0da

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 16 August 2021

Date accepted : 21 October 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: No. 81672838

Award Recipient : Wentao Yue

Funded by: FundRef http://dx.doi.org/10.13039/501100009331, Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support;

Award ID: No. XMLX201705

Award Recipient : Wentao Yue

Funded by: the Beijing Municipal Science and Technology Commission

Award ID: No. Z181100001718193

Award Recipient : Wentao Yue

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: ovarian cancer,single-cell rna-sequencing,heterogeneity,m2-like tams,malignant epithelial cluster,prognosis,il4i1

Data availability:

ScienceOpen disciplines: Medicine

Keywords: ovarian cancer, single-cell rna-sequencing, heterogeneity, m2-like tams, malignant epithelial cluster, prognosis, il4i1

Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

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Abstract

Background

Methods

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Conclusions

Supplementary Information

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Karger: Oncology

Most cited references 48

clusterProfiler: an R package for comparing biological themes among gene clusters.

GSVA: gene set variation analysis for microarray and RNA-Seq data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

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