scholarly journals Artificial Neural Network Model Using Immune-infiltration Modules for Endometrial Receptivity Assessment of Implantation Failure

Author(s):  
Bohan Li ◽  
Hua Duan ◽  
Sha Wang ◽  
Jiajing Wu ◽  
Yazhu Li

Abstract Objectives: This study was anchored on the state of local immune-infiltration in the endometrium, which acts as critical factors affecting embryonic implantation, and aimed at establishing novel approaches to assess endometrial receptivity for patients with IVF failure.Methods: Immune-infiltration levels in the GSE58144 dataset (n=115) from GEO were analyzed by digital deconvolution and validated by immunofluorescence (n=30), illustrating that dysregulation of the ratio of Mf1 to Mf2 is an important factor contributing to implantation failure. Then, modules most associated with M1/M2 macrophages (Mfs) and their hub genes were then selected by weighted gene co-expression network and univariate analyses, then validated by GSE5099 macrophage dataset, qPCR analysis (n=16), and western blot. It revealed that closely related gene modules dominated three biological processes in macrophages: antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification, respectively. Their hub genes were significantly altered in patients and related with ribosomal, lysosome, and proteasomal pathways. Finally, the artificial neural network (ANN) and nomogram models were established from hub genes, of which efficacy was compared and validated in the GSE165004 dataset (n=72). Models established by the selected hub genes exhibited excellent predictive values in both datasets, and ANN performed best with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1). Conclusions: Macrophages, proven to be essential for endometrial receptivity, were regulated by gene modules dominating antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification. Selected hub genes can effectively assess endometrial dysfunction receptivity for IVF outcomes by the ANN approach.

2021 ◽  
Author(s):  
Bohan Li ◽  
Hua Duan ◽  
Sha Wang ◽  
Jiajing Wu ◽  
Yazhu Li

Abstract ObjectivesThis study was anchored on the state of local immune-infiltration in the endometrium, which acts as critical factors affecting embryonic implantation, and aimed at establishing novel approaches to assess endometrial receptivity for patients with IVF failure.MethodsImmune-infiltration levels in the GSE58144 dataset (n=115) from GEO were analyzed by digital deconvolution and validated by immunofluorescence (n=30), illustrating that dysregulation of the ratio of Mf1 to Mf2 is an important factor contributing to implantation failure. Then, modules most associated with M1/M2 macrophages (Mfs) and their hub genes were then selected by weighted gene co-expression network and univariate analyses, then validated by GSE5099 macrophage dataset, qPCR analysis (n=16), and western blot. It revealed that closely related gene modules dominated three biological processes in macrophages: antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification, respectively. Their hub genes were significantly altered in patients and related with ribosomal, lysosome, and proteasomal pathways. Finally, the artificial neural network (ANN) and nomogram models were established from hub genes, of which efficacy was compared and validated in the GSE165004 dataset (n=72). Models established by the selected hub genes exhibited excellent predictive values in both datasets, and ANN performed best with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1). ConclusionsMacrophages, proven to be essential for endometrial receptivity, were regulated by gene modules dominating antigen presentation, interleukin−1−mediated signalling pathway, and phagosome acidification. Selected hub genes can effectively assess endometrial dysfunction receptivity for IVF outcomes by the ANN approach.


2021 ◽  
Author(s):  
Bohan Li ◽  
Hua Duan ◽  
Sha Wang ◽  
Jiajing Wu ◽  
Yazhu Li

Abstract Background A comprehensive clinical strategy for infertility involves treatment and, more importantly, post-treatment evaluation. Endometrial receptivity, as a component of assessment, does not have a validated tool. This study was anchored on immune factors, which act as critical factors affecting embryonic implantation, was aimed at establishing novel approaches for assessing endometrial receptivity to guide clinical practice. Methods Immune-infiltration levels in the GSE58144 dataset (n = 115) from GEO were analyzed by digital deconvolution and validated by immunofluorescence (n = 30). Modules that were most associated with M1/M2 macrophages and their hub genes were then selected by weighted gene co-expression network as well as univariate analyses and validated by GSE5099 macrophage dataset and qPCR analysis (n = 16). Finally, the artificial neural network model was established from hub genes and its predictive efficacy was validated in the GSE165004 dataset (n = 72). Results Dysregulation of the ratio of M1 to M2 macrophages is an important factor contributing to decreased endometrial receptivity. Selected hub genes, RPS9, DUT, and KIAA0430, were significantly altered in patients with endometrial receptivity decreased and found relating to M1/M2 through ribosomal and proteasomal pathways, and were significantly altered in patients. The model established by the selected hub genes exhibited an excellent predictive value in both datasets with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1). Conclusions M1/M2 polarization can influence endometrial receptivity by the three genes regulation, while the established ANN model can effectively assess endometrial receptivity to inform patients' pregnancy and individualized clinical management strategies.


2021 ◽  
Author(s):  
Han Wang ◽  
Jieqing Chen ◽  
Xinhui Liao ◽  
Yang Liu ◽  
Aifa Tang ◽  
...  

Abstract BACKGROUND and OBJECTIVE: A better understanding of the molecular mechanisms underlying bladder cancer is necessary to identify candidate therapeutic targets. METHODS: We screened for genes associated with bladder cancer progression and prognosis. Publicly available expression data were obtained from TCGA and GEO to identify differentially expressed genes (DEGs) between bladder cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations between hub genes and immune infiltration and immune therapy were evaluated. RESULTS: 3461 DEGs in TCGA-BC and 1069 DEGs in the GSE dataset were identified, with 87 overlapping differentially expressed genes between the bladder cancer and normal bladder groups. Hub genes in the tumour group were mainly enriched for cell proliferation-related GO terms and KEGG pathways, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. PPI networks for the genes identified in the normal and tumour groups were constructed. Based on a survival analysis, CDH19, RELN, PLP1, and TRIB3 were significantly associated with prognosis (P < 0.05). Four hub genes were significantly enriched in the MAPK signalling pathway, VEGF signalling pathway, WNT signalling pathway, cell cycle, and P53 signalling pathway based on a gene set enrichment analysis; these genes were associated with immune infiltration levels in bladder cancer. CONCLUSIONS: CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of bladder cancer and are potential therapeutic and prognostic targets.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10682
Author(s):  
Chunyang Li ◽  
Haopeng Yu ◽  
Yajing Sun ◽  
Xiaoxi Zeng ◽  
Wei Zhang

Background Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy. Methods GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm. Results Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946. Conclusions These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.


2021 ◽  
Author(s):  
Beibei Hu ◽  
Guohui Yin ◽  
Xuren Sun

Abstract Project: We here perform a systematic bioinformatic analysis to uncover the role of SNX family in clinical outcome of gastric cancer (GC). Methods: Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan-Meier Plotter. Statistic analysis was conducted with R language, and artificial neural network was constructed using Python.Results: Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein-Protein Interaction (PPI) map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related TIICs, including Cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan-Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX12/23/28 excluded. SNXs alteration contributed to MSI or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy such as mutation of TP53, ARID1A and MLH1.The multivariate cox model performed excellently in patients risk classification, for those with higher risk-score suffered from OS period and susceptibility to death as well as tumor immune infiltration. Compared to previous researches, our ANN models shown a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71, 0.94/0.66, 0.83/0.71, 0.88/0.72 corresponding to one-, three- and five-year OS and RFS estimation, but we were totally sure that those models would perform great better if given larger-size samples, which served as evidence to specific role of SNX family in prognosis assessment in GC. Conclusion: The SNX family shows great value in postoperative survival of GC, and artificial neural network models constructed using SNXs transcriptional data manifest excellent predictive power in both OS and RFS evaluation.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

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