Immune and Transcriptional Landscape of Endometrial Receptivity at P+5 Phase: A Triad Analysis of EIN, RIF, and Control Endometrial Profiles

This study delineates the immune and transcriptional landscape of endometrial receptivity at the P5 phase via triad analysis of Endometrial Intraepithelial Neoplasia/Early Endometrial Cancer (EIN/ECa, N = 64), Repeated Implantation Failure (RIF, N = 25), and control (Ctrl., N = 3)) endometrial profiles. RNA sequencing and deconvolution algorithms identified differential gene expression, immune cell composition, and pathway enrichments. EIN/ECa exhibited dysregulated complement/coagulation cascades and proliferation-related genes, suggesting a pro-proliferative/inflammatory microenvironment. RIF showed immune pathway perturbations, with abnormal TLR4 and CXCL10 expression disrupting maternal-fetal tolerance. Integrative analysis revealed distinct molecular signatures: EIN/ECa prioritized oncogenic proliferation, while RIF focused on immune tolerance defects.These findings illuminate phase-specific endometrial receptivity mechanisms, providing targets for improving fertility outcomes in high-risk groups. Overall design: These findings illuminate phase-specific endometrial receptivity mechanisms, providing targets for improving fertility outcomes in high-risk groups.RNA Sequencing and Data Analysis RNA was extracted from the endometrial tissue using the RaPure Total RNA Micro Kit & DNase Set (Magen). RNA quantification was performed using the Qubit RNA HS kit (Thermo Fisher Scientific). The extracted RNA samples were reverse transcribed into cDNA using the MALBAC Platinum Micro RNA Amplification Kit (Yikon Genomics). The cDNA products were purified using DNA Clean & Concentrator (Zymo Research). Library preparation involved DNA fragmentation, end repair, adaptor ligation, and PCR enrichment and purification. The resulting libraries were quantified using the Qubit® dsDNA HS Assay Kit (Thermo Fisher Scientific) and then sequenced on the Illumina MiSeq DX platform. Sequencing reads were analyzed using a computational model developed by Yikon Genomics in collaboration with Xiangya Hospital of Central South University, designed to predict endometrial receptivity status (rsERT). This model utilizes a panel of over 12,000 differentially expressed biomarker genes spanning the pre-receptive, receptive, and post-receptive phases of the endometrial window of implantation (WOI). The expression profile of each sample was mapped to the three WOI phases, and the predicted deviation from the optimal WOI was calculated. Software and Bioinformatics Analysis Processed data were uploaded to ChromGo (Yikon Genomics,Suzhou, China) for data partitioning, bioinformatics analysis, and determination of the recommended time for optimal endometrial receptivity. Experimental Design and Quality Control The experimental design encompassed RNA sequencing (RNA-seq) and established quality control procedures, including the aforementioned RNA extraction and library preparation methods. Differentially Expressed Gene (DEG) Identification Differentially expressed genes (DEGs) were identified using the following criteria: |log2FC| > 1 and adjusted p-value (padj) < 0.05. Functional Enrichment Analysis Functional enrichment analysis was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Immune Cell Deconvolution Analysis Immune cell deconvolution analysis was performed on the bulk RNA-seq data using established methods21,22.