Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.02.13.638207

Bioinformatics analysis of next generation sequencing data reveals novel biomarkers and signaling pathways associated with recurrent implantation failure

Vastrad, B.·Vastrad, C. M.
View original source ↗Export metadata ↓

Abstract

Recurrent implantation failure (RIF) is a cases in which women have had three fruitless in vitro fertilization (IVF) bid with positive quality embryos. The RIF originates from uterine endometrium microbiota has been implicated in reproductive failure, and poor prognosis and lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of RIF. To identify key genes and signaling pathways in RIF, the next genetation sequencing data GSE243550 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between RIF and normal controls samples were identified using t-tests in the limma R bioconductor package. Using the DEGs, we further performed a series of gene ontology (GO) and REACTOME pathway enrichment analyses. Protein-protein interaction network was derived using the IMex interactome database and visualized using Cytoscape software. The most significant modules from the PPI network were selected for GO and pathway enrichment analysis. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed depending on key hub genes and visualized using Cytoscape software. A receiver operating characteristic curve (ROC) analysis was plotted to diagnose RIF. In total, 958 DEGs were identified, of which 479 were up regulated genes and 479 were down regulated genes. GO and REACTOME pathway enrichment analysis results revealed that the upregulated genes were mainly enriched in multicellular organismal process, membrane, small molecule binding and extracellular matrix organization, whereas downregulated genes were mainly enriched in organonitrogen compound metabolic process, intracellular anatomical structure, catalytic activity and translation. Through analyzing the PPI network, we screened hub genes APP, HSP90AA1, CAND1, CUL1, HSP90AB1, SIRT7, SRC, CDKN1A, ISG15 and RPS16 by the Cytoscape software. The regulatory network analysis revealed that microRNAs (miRNAs) include hsa-miR-574-3p and hsa-mir-208a-3p, and transcription factors (TFs) include SREBF1 and RELA might be involved in the development of RIF. Receiver operating characteristic curve analysis demonstrated that the hub genes screened for RIF were of good diagnostic significance. Overall, these results thus highlight a range of novel signaling pathways and genes that are linked to the incidence and progression of RIF, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of RIF.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Vastrad, B., Vastrad, C. M.. 2025-02-17. Bioinformatics analysis of next generation sequencing data reveals novel biomarkers and signaling pathways associated with recurrent implantation failure. https://doi.org/10.1101/2025.02.13.638207

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

deepTools4: Faster, Scalable, and Modernized Genomic Data Analysis

The deepTools suite is widely used for quality control, summarization, and visualization of high-throughput sequencing data. However, as genomic datasets grow in scale and depth, existing tools often become computational bottlenecks. We present deepTools4, a modernized framework designed for high performance, scalability, and enhanced usability, while maintaining seamless integration with the Galaxy platform. Core functionality has been rewritten and thoroughly benchmarked, achieving substantial computational speed-ups. Visualization tools have been modernized to provide more intuitive, high-quality graphical outputs. In addition, our testing framework has been expanded to facilitate continuous development and ensure robustness. We demonstrate the utility of deepTools4 across diverse epigenomics and transcriptomics datasets, highlighting its ability to summarize genome-wide signals, apply a wide range of common normalization methods, and visualize enrichment patterns. The enhanced deepTools4 framework delivers a faster, more scalable solution for reproducible genome-wide analysis and interpretation across many sequencing modalities. Availability: https://github.com/deeptools/deepTools/

bioinformatics↗
bioRxiv2026

Reproducibility and cross-dataset concordance of drug-induced transcriptional responses

Drug-perturbation transcriptomic resources are expanding, but the reproducibility and cross-source consistency of the responses they measure remain critical to their use. We compared six resources using independent replicates, matched-drug analyses, and response decomposition. High agreement in treated expression did not translate into equally repeatable drug-induced changes. Cross-source response concordance was generally limited, including under matched cell and treatment conditions, although some comparisons retained drug-specific information. In Tahoe, a control-expression-only baseline left substantial drug-specific variation unresolved. Measured drug main effects - the average responses across cell lines - provided information for predicting cell-specific interactions, with this relationship retained in a cross-plate validation. Exploratory analyses of structure, target, and external-response representations identified modest signals in selected pipelines. Together, these analyses characterize the reliability and transferability of current perturbation resources and identify prediction of drug main effects as a key bottleneck in the framework examined, motivating shared reference measurements and better recovery of drug-specific responses.

bioinformatics↗
bioRxiv2026

Propensity-Adjusted AUC under Nuisance-Model Misspecification

Propensity adjustment compares medical-image detector scores within groups with similar measured properties, such as stain or focus. We examine its sensitivity to the nuisance model that forms those groups. Under Gaussian latent-index assumptions and exact linear-index strata, a closed-form correlation gives an exact boundary for adjusted AUC to fall below one half. Quadrature matches 36 simulation cells within 0.008. Inflation and attenuation relative to correct adjustment are empirical findings, not general ordering results. An overlap screen and a self-stratified control introduced for this study miss 140 of 141 simulated cells with absolute error above 0.05: checking the fitted propensity cannot certify completeness of the adjustment set. On real pathology corpora, logistic and boosted-tree models using the same 16 features disagree. Among splits where both pass both checks, mean adjusted-AUC gaps are 0.057 on acid-fast bacilli (AFB), 0.048 on Kaggle, and 0.049 on PCam, over three, five, and three splits respectively. These gaps measure model sensitivity; neither estimate is known to be correct. A controlled colour shift moves adjusted AUC relative to model-specific references in the predicted direction in 78 of 80 label-dependent cells. Broader balance and label-fit diagnostics favour trees without certifying their adjusted AUC. We recommend reporting sensitivity across nuisance models alongside these diagnostics. Reversal relative to known truth occurs in constructed stress tests; its frequency in practice remains unknown.

bioinformatics↗