Bioinformatics Research Research Articles

Discovery of the first Tn630 member and the closest homolog of IS630 from viruses

IS630/Tc1/mariner (ITm) represents the most widely distributed superfamily of DNA transposons in nature. Currently, bioinformatics research on ITm members primarily involves collecting data of existing and emerging members and organizing them into new groups or families. In the present study, our survey revealed that Tc1 and IS630 members have a broad host range, spanning across all six biological kingdoms (bacteria, fungi, plantae, animalia, archaea and protista) and viruses. The primary discoveries include the first Tn630 member—Tn630-NC1 and the closest homolog of IS630 from viruses—Tc1-C#1. By incorporating our discoveries into existing knowledge, we proposed a model to elucidate the formation of composite transposons. Organization of Tc1 and IS630 members into groups across biological kingdoms facilitates data collection for future research, particularly on their horizontal transfer between different kingdoms. The formation of composite transposons may result from asymmetric of terminal inverted repeats. IS630 should be merged with Tc1 into a single family IS630/Tc1. Furthermore, IS630 and its homologs constitute a valuable resource for studying horizontal gene transfer between gut bacteria and phages, opening up new avenues for research in this field.

SIPSC-Kac: Integrating swarm intelligence and protein spatial characteristics for enhanced lysine acetylation site identification

Elucidation of post-translational modifications (PTMs), such as lysine acetylation (Kac), is crucial for understanding protein function and regulation. Although traditional experimental methods for identifying Kac sites are accurate, they are time-consuming and costly, leading to incomplete acetylome mapping. Computational approaches, particularly those incorporating machine learning, offer a rapid alternative, but face challenges owing to dataset imbalance, limited feature space, and the need for more effective feature-selection algorithms. To address these challenges, we present SIPSC-Kac, a novel computational method that integrates swarm intelligence algorithms with protein spatial characteristics to enhance the prediction of Kac sites. We used the AlphaFold system for spatial feature extraction and employed swarm intelligence for optimal feature selection, outperforming existing methods in terms of accuracy and computational efficiency. SIPSC-Kac demonstrated superior performance across multiple bacterial species, which was validated by its high performance in evaluation metrics. Our web server provides researchers with a user-friendly platform for Kac site prediction, thereby contributing to the advancement of bioinformatics and proteomic research. The SIPSC-Kac code and web server are accessible, thereby promoting broad applications in the scientific community.

Genome-wide identification and expression analysis of the OSC gene family in Platycodon grandiflorus

Platycodon grandiflorus stands as one of the most extensively utilized traditional Chinese medicinal herbs, with triterpenoids and their derivatives serving as its primary medicinal components. Oxido squalene cyclase (OSC), serving as a crucial enzyme in the triterpenoid synthesis pathway, has the capability to enzymatically generate significant quantities of sterols and triterpenoid intermediates. While the OSC gene family has been identified in numerous species, bioinformatics research on this family remains scant. Presently, the specific members of this gene family in Platycodon grandiflorus have yet to be definitively determined. In this study, we successfully identified a total of 15 PgOSC genes within the genome of Platycodon grandiflorus by conducting homology comparisons. These genes were discovered to be unevenly distributed across the five chromosomes of the species, organized in the form of gene clusters. Subsequently, we conducted a thorough analysis of the OSC gene family’s evolutionary relationship by constructing a phylogenetic tree. Other characteristics of PgOSC family members, including gene structure, conserved motifs, protein three-dimensional structure, subcellular localization, and cis-acting elements were thoroughly characterized. Furthermore, We analyzed the expression of PgOSC gene in different tissues of Platycodon grandiflorus by qRT-PCR, and found that the expression of PgOSC genes in root was higher than that in stem and leaf. Upon comparing the effects of salt, heat, and drought treatments, we observed a significant induction of PgOSC gene expression in Platycodon grandiflorus specifically under salt stress conditions. In summary, this study comprehensively identified and analyzed the OSC gene family, aiming to provide basic biological information for exploring the members of PgOSC gene family.

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Predicting and Characterizing piRNAs and their functions Using an Integrated Machine Learning Approach

One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%.

A comprehensive review of approaches for spatial domain recognition of spatial transcriptomes.

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.

Historically, there have been many outbreaks of viral diseases that have continued to claim millions of lives. Research on human-virus protein-protein interactions (PPIs) is vital to understanding the principles of human-virus relationships, providing an essential foundation for developing virus control strategies to combat diseases. The rapidly accumulating data on human-virus PPIs offer unprecedented opportunities for bioinformatics research around human-virus PPIs. However, available detailed analyses and summaries to help use these resources systematically and efficiently are lacking. Here, we comprehensively review the bioinformatic tools used in human-virus PPIs research, discuss and compare the function, performance, and limitations of these web resources. This study aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.

Lightweight technology stacks for assistive linked annotations

This report presents the findings of a project from the 8th Biomedical Linked Annotation Hackathon (BLAH) to explore lightweight technology stacks to enhance assistive linked annotations. Using modern JavaScript frameworks and edge functions, in-browser Named Entity Recognition (NER), serverless embedding and vector search within web interfaces, and efficient serverless full-text search were implemented. Through this experimental approach, a proof of concept to demonstrate the feasibility and performance of these technologies was demonstrated. The results show that lightweight stacks can significantly improve the efficiency and cost-effectiveness of annotation tools and provide a local-first, privacy-oriented, and secure alternative to traditional server-based solutions in various use cases. This work emphasizes the potential of developing annotation interfaces that are more responsive, scalable, and user-friendly, which would benefit bioinformatics researchers, practitioners, and software developers.

Evolution of the biochemistry underpinning purine alkaloid metabolism in plants.

Purine alkaloids are naturally occurring nitrogenous methylated derivatives of purine nucleotide degradation products, having essential roles in medicine, food and various other aspects of our daily lives. They are generated through convergent evolution in different plant species. The pivotal reaction steps within the purine alkaloid metabolic pathways have been largely elucidated, and the convergent evolution of purine alkaloids has been substantiated through bioinformatic, biochemical and other research perspectives within S-adenosyl-ʟ-methionine-dependent N-methyltransferases. Currently, the biological and ecological roles of purine alkaloids, further refinement of the purine alkaloid metabolic pathways and the investigation of purine alkaloid adaptive evolutionary mechanisms continue to attract widespread research interest. The exploration of the purine alkaloid metabolic pathways also enhances our comprehension of the biochemical mechanism, providing insights into inter-species interactions and adaptive evolution and offering potential value in drug development and agricultural applications. Here, we review the progress of research in the distribution, metabolic pathway elucidation and regulation, evolutionary mechanism and ecological roles of purine alkaloids in plants. The opportunities and challenges involved in elucidating the biochemical basis and evolutionary mechanisms of the purine alkaloid metabolic pathways, as well as other research aspects, are also discussed. This article is part of the theme issue 'The evolution of plant meta-bolism'.

Optimized Spectral Clustering Methods For Potentially Divergent Biological Sequences

Various recent researches in bioinformatics demonstrated that clustering is a very efficient technique for sequence analysis. Spectral clustering is particularly efficient for highly divergent sequences and GMMs (Gaussian Mixture Models) are often able to cluster overlapping groups if given an adequately designed embedding. The current study used spectral embedding and Mixture Models for clustering potentially divergent biological sequences. The research approach resulted in a pipeline consisting of the following four steps. The first step consists of aligning the biological sequences. The pairwise affinity of the sequences is computed in the second step. Then the Laplacian Eigenmap embedding of the data is performed in the third step. Finally, the last step consists of a GMM-based clustering. Improving the quality of the generated clustering and the performance of this approach is directly related to the enhancement of each one of these four steps. The main contribution is proposing four GMM-based algorithms for automatically selecting the optimal number of clusters and optimizing the clustering quality. A clustering quality assessment method, based on phylogenetic trees, is also proposed. Moreover, a performance study and analysis have been conducted while testing different clustering methods and GMM implementations. Experimental results demonstrated the superiority of using the BIC (Bayesian Information Criterion) for selecting the optimal GMM configuration. Significant processing speed improvements were also recorded for the implementation of the proposed algorithms.

Bioinformatics in Russia: history and present-day landscape.

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Isoliensinine suppressed gastric cancer cell proliferation and migration by targeting TGFBR1 to regulate TGF-β-smad signaling pathways.

Gastric cancer (GC) ranks as the fifth most prevalent cancer globally, and its pronounced invasiveness and propensity to spread provide significant challenges for therapy. At present, there are no efficacious medications available for the treatment of patients with GC. Isoliensinine (ISO), a bisbenzylisoquinoline alkaloid, was isolated from Nelumbo nucifera Gaertn. It possesses anti-tumor, antioxidant, and other physiological effects. Nevertheless, there is currently no available study on the impact of ISO on GC, and further investigation is needed to understand its molecular mechanism. ISO target points and GC-related genes were identified, and the cross-target points of ISO and GC were obtained. We then examined cross-targeting and found genes that were differentially expressed in GCs. Kaplan-Meier survival curves were used to screen target genes, and the STRING database and Cytoscape 3.9.1 were used to construct protein-protein interactions and drug-target networks. In addition, molecular docking studies confirmed the interactions between ISO screen targets. Finally, in vitro tests were used to establish the impact of ISO on GC cells. Through bioinformatics research, we have identified TGFBR1 as the target of ISO in GC. In addition, we noticed a substantial inhibition in GC cell proliferation, migration, and invasion activities following ISO treatment. Moreover, we noticed that ISO treatment effectively suppressed TGF-β-induced epithelial-mesenchymal transition (EMT) and activation of the TGF-β-Smad pathway. Furthermore, we discovered that siTGFBR1 nullified the impact of ISO on TGF-β-triggered migration, invasion, and activation of the TGF-β-Smad pathway. Our research suggests that ISO specifically targets TGFBR1 and regulates the TGF-β-Smad signaling pathway to suppress the proliferation and migration of GC cells.

A comprehensive guide to bioinformatics tools and databases

The interest in the functional aspects of the genome has increased eventually with the expansion of genomic databases. In this review, various genetic alterations within the genome, such as Single Nucleotide Polymorphisms (SNPs), disease-associated mutations, neutral polymorphisms, and small insertions or deletions (INDELs), have been identified and characterized. These polymorphisms, particularly SNPs, have become widely employed in genetic analyses, serving as valuable molecular markers. They are applied across diverse genetic studies, including the reconstruction of haplotypes. In addition to experimental methods for investigating specific genetic variants, there has been a significant surge in bioinformatics research over the past decade, aimed at the molecular consequences of these genetic alterations. Haplotypes, which represent various alleles of a gene, can be reconstructed not only for entire chromosomes but also for specific genes, utilizing the information derived from SNPs. The computational approach to SNP and haplotype discovery has gained prominence due to the continuous expansion of sequence data within public databases. This growth enables more precise identification of SNPs. This article aims to introduce easily accessible and practical online tools for researchers. In summary, SNP and haplotype identification tools are indispensable in bioinformatics for studying genetic variation, population genetics, disease associations, and personalized medicine. They provide valuable insights into the genetic underpinnings of various traits and diseases, helping advance our understanding of genetics and its applications in healthcare.

Systematic analysis and functional verification of citrus ascorbate peroxidases reveal that CsAPX01 and CsAPX02 negatively regulate citrus bacterial canker through the hydrogen peroxide regulation

Ascorbate peroxidases (APXs) are antioxidant enzymes that play vital roles in redox homeostasis in plants. Citrus is susceptible to infection by Xanthomonas citri subsp. citri (Xcc), resulting in citrus bacterial canker (CBC). The present study used bioinformatic and expression analyses to investigate the APX family in Citrus sinensis. Bioinformatic research revealed the chromosomal locations, phylogeny, gene structure, promoter elements, functional domains, conserved motifs, and most likely physicochemical properties of the sequences. Six APXs clustered in three groups were identified, with each protein containing a single peroxidase domain. The promoter regions contained a variety of transcription factor-binding and hormone-response components. Xcc infection induced different CsAPX01 and CsAPX02 expressions in the CBC-susceptible Wanjincheng and CBC-resistant Kumquat varieties. Subcellular localization and transient expression showed that CsAPX01 and CsAPX02 were expressed in the cytoplasm and nucleus and had hydrogen peroxide (H2O2)-scavenging activity. Virus-induced gene silencing (VIGS) of CsAPX01 and CsAPX02 resulted in strong resistance to CBC and H2O2 bursts without effects on the plant phenotype. The current study focused on investigating and characterizing the citrus APX family. It was found that CsAPX01 and CsAPX02 exacerbated CBC by altering the balance of H2O2. These findings emphasize the importance of APXs in enhancing plant resistance to pathogens.

CD276 as a promising diagnostic and prognostic biomarker for bladder cancer through bioinformatics and clinical research.

To assess CD276 expression and explore its relationship with the clinicopathological characteristics and prognosis of patients with bladder cancer. In total, RNA-sequencing data and clinical profiles of 436 bladder cancer cases from The Cancer Genome Atlas (TCGA) were assessed using the University of California Santa Cruz Xena (UCSC) platform. We compared the CD276 levels in cancerous and adjacent normal tissues and used the R software for statistical association with the clinical stage, grade, and survival (the overall survival, disease-specific survival, and progression-free survival). A single-gene GSEA analysis on TCGA-BLCA data was performed to explore potential pathways through which CD276 might influence bladder cancer. Additionally, CD276 expression was analyzed by comparing data from 9 cancerous tissues and 3 adjacent normal tissues in the GEO dataset GSE7476. Furthermore, we analyzed 133 cancerous bladder and adjacent tissue samples from the Soochow University Hospital, collected between January 1, 2016, and September 30, 2022, to assess the CD276 protein expression using immunohistochemistry. We examined the relationship between tumor CD276 levels and clinical outcomes and prognosis of bladder cancer. Bioinformatic analysis revealed elevated CD276 expression in tumors compared to that in adjacent tissues (p<0.05), correlating with poor survival. GSEA revealed that CD276 was significantly involved in extracellular matrix-related pathways. Immunohistochemistry confirmed CD276 overexpression in tumor tissues, with higher levels linked to advanced pathological grades and worse prognosis. CD276 is markedly upregulated in bladder cancer and associated with severe pathological features, advanced disease, potential for metastasis, and diminished survival rates. It may promote bladder cancer development and progression by influencing extracellular matrix-related-related pathways, making it a viable diagnostic and prognostic biomarker for bladder cancer.

From bioinformatics to clinical applications: a novel prognostic model of cuproptosis-related genes based on single-cell RNA sequencing data in hepatocellular carcinoma

Objective and methodsTo ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database.ResultsThirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels.ConclusionA PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

Guest Editors' Introduction to the Special Section on Bioinformatics Research and Applications

Guest Editors' Introduction to the Special Section on Bioinformatics Research and Applications

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach.

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

Screening Preeclampsia Genes and the Effects of CITED2 on Trophoblastic Function.

Preeclampsia (PE) is a serious complication of obstetrics and represents a significant challenge in terms of understanding its underlying mechanism. It has been shown that a number of disorders involve dysregulation of the CBP/p300-interacting transactivator with glutamic acid/aspartic acid-rich carboxyl-terminal domain 2 (CITED2). However, the relationship between PE and CITED2 is still mostly unclear. This work aimed to confirm the hub genes linked to PE and explore the roles of CITED2 in trophoblast using experimental and bioinformatic methods. To determine the hub genes, bioinformatics research was performed on two datasets from the Gene Expression Omnibus (GEO) public database. Immune infiltration analysis and enrichment analysis were also used to identify the related pathways and immune cells. PCR and WB were then used to validate the mRNA and protein levels of CITED2 in the PE samples. Finally, the expression of CITED2 was knocked down using siRNA to investigate the function of CITED2 in trophoblast development in vitro. The study's findings showed that the NOTCH signaling pathways, glycolysis, and hypoxia were the main areas of enrichment for the six PE-related genes that were tested. The results of immune infiltration suggest that activated NK cells and regulatory T cells may play an important role in this process. CITED2 was significantly upregulated in the PE placenta. In functional tests, the knockdown of CITED2 may enhance apoptosis while suppressing migration, invasion, and proliferation of cells. This study offers important proof that CITED2 influences trophoblast cell function and may one day be a therapeutic target for PE.