biostudies-arrayexpressluyan zhangHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-16559Background: Osteoporosis and diabetes represent major global public health challenges. Neutrophil extracellular traps (NETs) serve as key components of the innate immune system by capturing bacteria, fungi, and parasites, thereby trapping them in local environments with high concentrations of antimicrobial agents leading to their elimination. This study aimed to identify biomarkers associated with NETs in osteoporosis with diabetes, and to explore the underlying molecular mechanisms. Methods: A transcriptomic sequencing dataset was obtained for osteoporosis combined with diabetes. The NETs-related genes (NETs-RGs) were obtained from previous literature. Biomarkers were identified through differential analysis, machine learning, and receiver operating characteristic (ROC). The identified biomarkers were further validated by qRT-PCR and ELISA. Subsequently, molecular regulatory network construction, immune infiltration analysis, enrichment analysis, and drug prediction were conducted. Results: S100A12 and SLC25A37 were identified as biomarkers. Their significant upregulation at the protein level was further confirmed by experimental validation in an independent cohort. Enrichment analysis indicated that S100A12 was significantly enriched in 68 pathways, including \"ECM receptor interaction\", \"maturity onset diabetes of the young\", and others. SLC25A37 was significantly enriched in 54 pathways, primarily including \"ribosome\", \"leishmania infection\", and \"Toll-like receptor signaling pathway\". A total of 7 immune cell types exhibited significant differences between the two groups, including neutrophils and regulatory T cells. A total of 59 miRNAs and 43 lncRNAs were predicted. Additionally, XIST-hsa-miR-146a-5p-S100A12 and XIST-hsa-miR-7-5-SLC25A37 were suggested to have potential regulatory roles in osteoporosis with diabetes. Drugs such as rimegepant and eptinezumab were associated with biomarkers. Conclusion: S100A12 and SLC25A37 were identified as biomarkers associated with NETs in osteoporosis with diabetes, providing a theoretical foundation for developing targeted treatments for osteoporosis with diabetes.biostudies-arrayexpressSample Collection - Peripheral blood samples were collected from patients who underwent bone mineral density (BMD) testing at the People‘s Hospital of Gansu Province. The cohort included 15 patients with osteoporosis combined with diabetes and 15 control subjects. Osteoporosis was defined as a spinal BMD T-score ≤ -2.5 SD, while the control group had a T-score ≥ -1.0 SD.Nucleic Acid Extraction - Total RNA was isolated and purified from peripheral blood samples using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The quantity and integrity of the extracted total RNA were assessed prior to library construction.Sequencing - Paired-end sequencing (PE150) was performed on the Illumina NovaSeq™ 6000 platform (LC Bio Technology Co., Ltd., Hangzhou, China).Library Construction - The RNA library was prepared following these steps: mRNA with PolyA tails was captured and fragmented. First-strand cDNA was synthesized using reverse transcriptase, followed by second-strand synthesis using E. coli DNA polymerase I (NEB, Cat. No. M0209) and RNase H (NEB, Cat. No. M0297). The double-stranded DNA was then purified, and the fragment size was selected. The UDG enzyme (NEB, Cat. No. M0280) was used for digestion. Finally, PCR amplification was performed to generate sequencing libraries with a fragment size of 300 bp ± 50 bp.OrganizationMINSEQE ScoreAssays and DataMAGE-TAB FilesData Transformation - Raw sequencing reads (FASTQ files) were first subjected to quality control and adapter trimming using fastp with default parameters. The cleaned reads were then aligned to the human reference genome (Homo sapiens, GRCh38) using HISAT2. Transcript assembly and quantification were performed using StringTie, and gene expression levels were estimated as Fragments Per Kilobase of transcript per Million mapped reads (FPKM). For downstream analysis and generation of the processed count matrix (count.matrix.csv), differential expression analysis was performed using the DESeq2 package (version 1.38.0). The raw count data were normalized using the DESeq2 median-of-ratios method to account for library size differences. This normalized count matrix represents the final processed data file submitted.UnknownTranscriptomicsIllumina NovaSeq 6000RNA-seq of coding RNAHomo sapiensluyan zhangfalseTranscriptome profiling identifies NETs-associated biomarkers in osteoporosis with diabetesBackground: Osteoporosis and diabetes represent major global public health challenges. Neutrophil extracellular traps (NETs) serve as key components of the innate immune system by capturing bacteria, fungi, and parasites, thereby trapping them in local environments with high concentrations of antimicrobial agents leading to their elimination. This study aimed to identify biomarkers associated with NETs in osteoporosis with diabetes, and to explore the underlying molecular mechanisms. Methods: A transcriptomic sequencing dataset was obtained for osteoporosis combined with diabetes. The NETs-related genes (NETs-RGs) were obtained from previous literature. Biomarkers were identified through differential analysis, machine learning, and receiver operating characteristic (ROC). The identified biomarkers were further validated by qRT-PCR and ELISA. Subsequently, molecular regulatory network construction, immune infiltration analysis, enrichment analysis, and drug prediction were conducted. Results: S100A12 and SLC25A37 were identified as biomarkers. Their significant upregulation at the protein level was further confirmed by experimental validation in an independent cohort. Enrichment analysis indicated that S100A12 was significantly enriched in 68 pathways, including \"ECM receptor interaction\", \"maturity onset diabetes of the young\", and others. SLC25A37 was significantly enriched in 54 pathways, primarily including \"ribosome\", \"leishmania infection\", and \"Toll-like receptor signaling pathway\". A total of 7 immune cell types exhibited significant differences between the two groups, including neutrophils and regulatory T cells. A total of 59 miRNAs and 43 lncRNAs were predicted. Additionally, XIST-hsa-miR-146a-5p-S100A12 and XIST-hsa-miR-7-5-SLC25A37 were suggested to have potential regulatory roles in osteoporosis with diabetes. Drugs such as rimegepant and eptinezumab were associated with biomarkers. Conclusion: S100A12 and SLC25A37 were identified as biomarkers associated with NETs in osteoporosis with diabetes, providing a theoretical foundation for developing targeted treatments for osteoporosis with diabetes.2026-01-11T00:00:00Z2026-01-21T15:50:29.496Z2026-01-21T15:49:56.708ZE-MTAB-16559ERP188004EFO_0002944EFO_0004170EFO_0005518EFO_0003816EFO_0003738EFO_0004184