Gene profiling reveals the role of inflammation, abnormal uterine muscle contraction and vascularity in recurrent implantation failure

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Abstract

Objective: Recurrent implantation failure (RIF) is now disturbing numerous infertile couples accepting assisted reproductive technology (ART). And the endometrial factors are crucial causes of recurrent implantation failure. However, its mechanism is still unclear. Thus, the aim of this study is to identify altered biologic processes in endometrium that may contribute to recurrent implantation failure.

Methods: We recruited two microarray datasets (GSE103465, GSE111974) from Gene Expression Omnibus database (GEO), which contain endometrium from RIF and normal women during implantation period. Using the online tools GEO2R and Venny, we identified Differentially Expressed Genes (DEGs) of selected datasets, and obtained common DEGs. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCatar pathway enrichment were conducted with Enrichr platform, “ssgsea” and “ggplot2” package of RStudio. PPI networks and hub gene related TF-gene interaction and TF-miRNA co-regulation networks were built via online tools STRING and NetworkAnalyst. Immune infiltration analysis was performed by CIBERSORT platform. Recurrent implantation failure subgroup identification was achieved through “ConsensusClusterPlus,” “tsne,” “ssgsea”, and “ggpubr” package in RStudio. Diagnostic characteristic ROC curves were constructed via “pROC” and “ggplot2” package of RStudio. Enrichr platform was utilized to find drugs targeting hub genes.

Results: 26 common DEGs were confirmed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes/BioCarta analysis determined common DEGs were mainly enriched in inflammation associated pathways including TNF, NF-κB, IL-4, IL-10, IL-6, and TGF-β signaling pathways. Five hub genes ( PTGS2, VCAM1, EDNRB, ACTA2, and LIF) and related TF-gene and TF-miRNA interactions were identified. Immune infiltration analysis indicated the importance of macrophage M2 in recurrent implantation failure patients. Importantly, subgroup identification analysis highlighted that recurrent implantation failure patients can be divided into two subgroups with different phenotypes. Moreover, the ROC curves and drugs may provide new diagnostic and therapeutic thought for recurrent implantation failure.

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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KEGG: kyoto encyclopedia of genes and genomes.

M Kanehisa (2000)

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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Is Open Access

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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Author and article information

Contributors

Xinyi Dong: URI : https://loop.frontiersin.org/people/1663152/overview

Mi Zhou: URI : https://loop.frontiersin.org/people/1004811/overview

Xinyu Li: URI : https://loop.frontiersin.org/people/1088470/overview

Huijing Huang: URI : https://loop.frontiersin.org/people/1580685/overview

Yun Sun: URI : https://loop.frontiersin.org/people/1042398/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 24 February 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 1108805

Affiliations

[1] ¹ Center for Reproductive Medicine , Renji Hospital , School of Medicine , Shanghai Jiao Tong University , Shanghai, China

[2] ² Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics , Shanghai, China

[3] ³ Department of Rheumatology , Renji Hospital , School of Medicine , Shanghai Jiaotong University , Shanghai, China

[4] ⁴ Department of Rheumatology , Zhongshan Hospital , Fudan University , Shanghai, China

Author notes

Edited by: Shuai Liu, University of Hawaii at Manoa, United States

Reviewed by: Yunting Wang, University of Houston, United States

Chen Li, Free University of Berlin, Germany

*Correspondence: Huijing Huang, fangfeijin90@ 123456163.com ; Yun Sun, syun163@ 123456163.com

[ † ]

These authors have contributed equally to this work

This article was submitted to RNA, a section of the journal Frontiers in Genetics

Article

Publisher ID: 1108805

DOI: 10.3389/fgene.2023.1108805

PMC ID: 9998698

PubMed ID: 36911409

SO-VID: 7820d2df-8a58-4ad2-ba6f-c860b1bbb9e2

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 26 November 2022

Date accepted : 15 February 2023

Funding

This study was supported by the National Natural Science Foundation of China (No. 82130046), National Natural Science Foundation of China (No. 82101708), National Natural Science Foundation of China (No. 82201976), National Key R&D Program of China (2019YFA0802604), Shanghai leading talent program, Innovative research team of high-level local universities in Shanghai (No. SHSMU-ZLCX20210201, No. SSMU-ZLCX20180401), Shanghai Jiaotong University School of Medicine Affiliated Renji Hospital Clinical Research Innovation Cultivation Fund Program (RJPY-DZX-003), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (No. 20161413).

Gene profiling reveals the role of inflammation, abnormal uterine muscle contraction and vascularity in recurrent implantation failure

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Most cited references 76

Gene Ontology: tool for the unification of biology

KEGG: kyoto encyclopedia of genes and genomes.

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

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