PMID- 36131919
OWN - NLM
STAT- MEDLINE
DCOM- 20220923
LR  - 20231213
IS  - 1664-3224 (Electronic)
IS  - 1664-3224 (Linking)
VI  - 13
DP  - 2022
TI  - Deciphering the endometrial immune landscape of RIF during the window of 
      implantation from cellular senescence by integrated bioinformatics analysis and 
      machine learning.
PG  - 952708
LID - 10.3389/fimmu.2022.952708 [doi]
LID - 952708
AB  - Recurrent implantation failure (RIF) is an extremely thorny issue in in-vitro 
      fertilization (IVF)-embryo transfer (ET). However, its intricate etiology and 
      pathological mechanisms are still unclear. Nowadays, there has been extensive 
      interest in cellular senescence in RIF, and its involvement in endometrial immune 
      characteristics during the window of implantation (WOI) has captured scholars' 
      growing concerns. Therefore, this study aims to probe into the pathological 
      mechanism of RIF from cellular senescence and investigate the correlation between 
      cellular senescence and endometrial immune characteristics during WOI based on 
      bioinformatics combined with machine learning strategy, so as to elucidate the 
      underlying pathological mechanisms of RIF and to explore novel treatment 
      strategies for RIF. Firstly, the gene sets of GSE26787 and GSE111974 from the 
      Gene Expression Omnibus (GEO) database were included for the weighted gene 
      correlation network analysis (WGCNA), from which we concluded that the genes of 
      the core module were closely related to cell fate decision and immune regulation. 
      Subsequently, we identified 25 cellular senescence-associated differentially 
      expressed genes (DEGs) in RIF by intersecting DEGs with cellular 
      senescence-associated genes from the Cell Senescence (CellAge) database. 
      Moreover, functional enrichment analysis was conducted to further reveal the 
      specific molecular mechanisms by which these molecules regulate cellular 
      senescence and immune pathways. Then, eight signature genes were determined by 
      the machine learning method of support vector machine-recursive feature 
      elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), 
      comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were 
      also validated in the testing gene set (GSE106602). Furthermore, distinct immune 
      microenvironment abnormalities in the RIF endometrium during WOI were 
      comprehensively explored and validated in GSE106602, including infiltrating 
      immunocytes, immune function, and the expression profiling of human leukocyte 
      antigen (HLA) genes and immune checkpoint genes. Moreover, the correlation 
      between the eight signature genes with the endometrial immune landscape of RIF 
      was also evaluated. After that, two distinct subtypes with significantly distinct 
      immune infiltration characteristics were identified by consensus clustering 
      analysis based on the eight signature genes. Finally, a "KEGG pathway-RIF 
      signature genes-immune landscape" association network was constructed to 
      intuitively uncover their connection. In conclusion, this study demonstrated that 
      cellular senescence might play a pushing role in the pathological mechanism of 
      RIF, which might be closely related to its impact on the immune microenvironment 
      during the WOI phase. The exploration of the molecular mechanism of cellular 
      senescence in RIF is expected to bring new breakthroughs for disease diagnosis 
      and treatment strategies.
CI  - Copyright (c) 2022 Zhao, Zhao, Jiang and Zhang.
FAU - Zhao, Xiaoxuan
AU  - Zhao X
AD  - Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou Hospital of 
      Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, 
      Hangzhou, China.
FAU - Zhao, Yang
AU  - Zhao Y
AD  - College of Basic Medicine, Hebei College of Traditional Chinese Medicine, 
      Shijiazhuang, China.
FAU - Jiang, Yuepeng
AU  - Jiang Y
AD  - College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, China.
FAU - Zhang, Qin
AU  - Zhang Q
AD  - Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou Hospital of 
      Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, 
      Hangzhou, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220905
PL  - Switzerland
TA  - Front Immunol
JT  - Frontiers in immunology
JID - 101560960
RN  - 0 (Chromosomal Proteins, Non-Histone)
RN  - 0 (DEK protein, human)
RN  - 0 (HLA Antigens)
RN  - 0 (Oncogene Proteins)
RN  - 0 (Poly-ADP-Ribose Binding Proteins)
RN  - EC 2.7.11.1 (Protein Serine-Threonine Kinases)
SB  - IM
MH  - Cellular Senescence/genetics
MH  - Chromosomal Proteins, Non-Histone/metabolism
MH  - *Computational Biology
MH  - *Endometrium/metabolism
MH  - Female
MH  - HLA Antigens/metabolism
MH  - Humans
MH  - Machine Learning
MH  - Oncogene Proteins
MH  - Poly-ADP-Ribose Binding Proteins/metabolism
MH  - Protein Serine-Threonine Kinases
PMC - PMC9484583
OTO - NOTNLM
OT  - bioinformatics
OT  - cellular senescence
OT  - immune landscape
OT  - machine learning
OT  - recurrent implantation failure
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/09/23 06:00
MHDA- 2022/09/24 06:00
PMCR- 2022/01/01
CRDT- 2022/09/22 03:29
PHST- 2022/05/25 00:00 [received]
PHST- 2022/08/17 00:00 [accepted]
PHST- 2022/09/22 03:29 [entrez]
PHST- 2022/09/23 06:00 [pubmed]
PHST- 2022/09/24 06:00 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fimmu.2022.952708 [doi]
PST - epublish
SO  - Front Immunol. 2022 Sep 5;13:952708. doi: 10.3389/fimmu.2022.952708. eCollection 
      2022.