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Passionfruit (Passiflora edulis) is a significant fruit crop in the commercial sector, owing to its high nutritional and medicinal value. The advent of high-throughput genomics sequencing technology has led to the publication of a vast amount of passionfruit omics data, encompassing complete genome sequences and transcriptome data under diverse stress conditions. To facilitate the efficient integration, storage, and analysis of these large-scale datasets, and to enable researchers to effectively utilize these omics data, we developed the first passionfruit genome database (PGD). The PGD platform comprises a diverse range of functional modules, including a genome browser, search function, heatmap, gene expression patterns, various tools, sequence alignment, and batch download, thereby providing a user-friendly interface. Additionally, supplementary practical tools have been developed for the PGD, such as gene family analysis tools, gene ontology (GO) terms, a pathway enrichment analysis, and other data analysis and mining tools, which enhance the data's utilization value. By leveraging the database's robust scalability, the intention is to continue to collect and integrate passionfruit omics data in the PGD, providing comprehensive and in-depth support for passionfruit research. The PGD is freely accessible via http://passionfruit.com.cn .
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http://dx.doi.org/10.1186/s12864-024-10069-9 | DOI Listing |
Front Genet
August 2025
Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Background: Prostatic diseases, consisting of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa), pose significant health challenges. While single-omics studies have provided valuable insights into the role of mitochondrial dysfunction in prostatic diseases, integrating multi-omics approaches is essential for uncovering disease mechanisms and identifying therapeutic targets.
Methods: A genome-wide meta-analysis was conducted for prostatic diseases using the genome-wide association studies (GWAS) data from FinnGen and UK Biobank.
Oncol Res
September 2025
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Studies have reported the special value of PANoptosis in cancer, but there is no study on the prognostic and therapeutic effects of PANoptosis in bladder cancer (BLCA). This study aimed to explore the role of PANoptosis in BLCA heterogeneity and its impact on clinical outcomes and immunotherapy response while establishing a robust prognostic model based on PANoptosis-related features. Gene expression profiles and clinical data were collected from public databases.
View Article and Find Full Text PDF3 Biotech
October 2025
Department of Oncology, The Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China.
Unlabelled: By integrating single-cell and bulk RNA-sequencing data for esophageal cancer (ESCA), we developed and validated a seven-macrophage-gene prognostic signature (FCN1, SCARB2, ATF5, PHLDA2, GLIPR1, CHORDC1, and BCKDK). This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival, achieving area under the curve (AUC) values greater than 0.7 for 1-, 2-, and 3-year survival prediction.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Blood Transfusion, Huashan Hospital, Fudan University, Shanghai, China.
Background: Aging is accompanied by profound changes in immune regulation and epigenetic landscapes, yet the molecular drivers underlying these alterations are not fully understood.
Methods: Transcriptional profiles of peripheral blood samples from young and elderly individuals, together with aging-associated methylation probe data, were used to identify aging biomarkers. Transcriptomics and chromatin immunoprecipitation sequencing (ChIP-Seq) were conducted to explore potential regulatory mechanisms.
NAR Genom Bioinform
September 2025
Mathématiques et Informatique Appliquées Paris-Saclay, AgroParisTech, INRAE, Université Paris-Saclay, 91120 Palaiseau, France.
Composite hypothesis testing using summary statistics is a well-established approach for assessing the effect of a single marker or gene across multiple traits or omics levels. Numerous procedures have been developed for this task and have been successfully applied to identify complex patterns of association between traits, conditions, or phenotypes. However, existing methods often struggle with scalability in large datasets or fail to account for dependencies between traits or omics levels, limiting their ability to control false positives effectively.
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