BMC Medical Genomics最新文献

Objective: Integrin alpha-3 (ITGA3) has been implicated in tumor metastasis in various cancers, but its role in epithelial ovarian cancer (EOC)-associated liver metastasis (LM) remains unclear. This study aimed to investigate its role in LM in primary EOC patients.

Methods: It was a retrospective study with a sample size of n = 235 receiving surgical resection at Puren Hospital Affiliated to Wuhan University of Science and Technology between January 2020 and December 2021, including 98 LM (LM group) and 137 non-LM cases (N-LM group). ITGA3 expression was assessed by immunohistochemistry. ROC curves were used for predictive performance analysis, Kaplan-Meier curves for survival analysis, and Cox regression analysis for identification of risk factors.

Results: Markedly elevated ITGA3 expression in tumor tissues was found in the LM group (P < 0.001), which demonstrated strong predictive value for LM in EOC patients (area under the curve (AUC) = 0.881, sensitivity = 70.41%, specificity = 87.59%, P < 0.001), and strongly correlated with tumor size and postoperative residual lesions (both P < 0.05). Compared with the L-ITGA3 group, the H-ITGA3 group had a higher incidence of postoperative LM (P < 0.001) and showed a left-shifted curve in Kaplan-Meier analysis (P < 0.001). ITGA3 expression in tumor tissues (HR = 5.977), tumor grade (HR = 1.441), and postoperative residual lesions (HR = 1.697) were identified as independent risk factors for postoperative LM.

Conclusions: ITGA3 expression in tumor tissue significantly aids in predicting LM in EOC patients and is independently and closely related to adverse clinicopathological outcomes.

Background: Low-pass genome sequencing (LP GS) has been widely used for the detection of copy number variations (CNVs). As a key algorithmic parameter of LP GS, window selection may influence the performance of LP GS. However, limited studies have investigated this parameter for the detection of small CNVs.

Methods: To evaluate of the impact of sliding window on true positive rate, additional interpretation workload and resolution, 40 simulated samples with 19 pre-defined CNVs of various read amounts were simulated. Fifty-seven clinical cases with previously ascertained CMA results (27 positive cases and 30 negative cases) were used to further evaluate the influence of sliding window for detection sensitivity and specificity.

Results: In general, the true positive rate increased with the increase of sequencing depth for simulated samples. The algorithm by sliding a 10-Kb window in 1-Kb increments showed higher true positive rate, especially for CNVs < = 30 Kb. For deletions of 30 Kb, the algorithm by sliding a 10-Kb window in 1-Kb increments showed a true positive rate of 100% for all read amounts, while the algorithm by sliding a 50-Kb window in 5-Kb increments had a detection sensitivity of 80.0% even with 100 M read amount. The results of overlap analysis showed that the algorithm by sliding a 10-Kb window in 1-Kb increments showed less variability for both deletions and duplications (especially for CNVs < = 30 Kb), indicating higher detection resolution. Further combining the potential introduction of the additional interpretation workload by 10-Kb window in 1-Kb increments, 50 M reads is recommended for detecting most small CNVs. For the 57 clinical cases, the algorithm by sliding a 50-Kb window in 5-Kb increments and the algorithm by sliding a 10-Kb window in 1-Kb increments showed detection sensitivity of 85.19% (23/27) and 96.30% (26/27), respectively. The algorithm by sliding a 10-Kb window in 1-Kb increments detected all the CNVs missed by sliding a 50-Kb window in 5-Kb increments except for one 25.8 Kb deletion. The specificity for both algorithms was calculated as 96.67% (29/30).

Conclusion: Window selection, together with sequencing depth, could influence CNV detection sensitivity and resolution of LP GS for small CNVs. This study provided a set of evaluation methods and pathways based on simulated samples and clinical cases. For CNVs < = 30 kb, 10-Kb window in 1-Kb increments and >= 50 M reads were recommended for LP GS. It would be advisable for clinical labs conducting LP GS to determine the range of sensitivity and resolution for different sliding windows and sequencing depth for CNV detection.

Background: Determining the underlying cause of developmental delay or intellectual disability (DD/ID) is challenging yet crucial. Establishing a genetic basis for cases of unexplained DD/ID is integral to informing clinical decisions and anticipating patient outcomes. In this report, we share our institutional insights derived from employing whole-genome sequencing (WGS) to investigate unexplained DD/ID in pediatric populations.

Methods: A retrospective study was conducted on 115 children aged 1 month to 16 years with unexplained DD/ID who underwent WGS. We analyzed demographic profiles and catalogued genetic variants identified, in conjunction with examining clinical variables potentially associated with diagnostic yield.

Results: WGS data from 115 pediatric patients identified a total of 33 pathogenic or likely pathogenic single nucleotide variants and small insertions/deletions, of which 22 were classified as diagnostic cases and 11 as carriers. In addition, 11 pathogenic or likely pathogenic copy number variations were detected. Clinical attributes such as gender, age at diagnosis, gestational maturity, birth weight, perinatal complications (anoxia, jaundice), comorbid symptoms, hereditary background, neuromuscular function (muscle tone and strength), presence of epilepsy, neuroimaging, and electroencephalography patterns did not show a significant association with WGS findings. Nonetheless, a noteworthy association emerged between earlier age at diagnosis and increased diagnostic yield via WGS.

Conclusions: WGS serves as a powerful tool for uncovering genetic etiologies in children with unexplained DD/ID, with meaningful implications for clinical care, genetic counseling for families, and long-term management planning.

Background: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether integrating genome sequencing, expanded metabolite profiling, and artificial intelligence/machine learning (AI/ML) could improve the accuracy of NBS.

Methods: We analyzed dried blood spots (DBS) from 119 screen-positive cases identified by the California NBS program across four disorders: GA-I, PA/MMA, OTCD, and VLCADD. Genome sequencing was performed to identify variants in condition-related genes using ACMG guidelines, and an AI/ML classifier trained on previously generated metabolomic data was applied to differentiate true and false positives.

Results: Genome sequencing confirmed 89% (31/35) of true positives based on the presence of two reportable variants. Among 84 false positives, 74% (62) had no variant, while 26% (22) carried a pathogenic/likely pathogenic variant or rare VUS in a condition-related gene. For VLCADD, half of false positives (15/29) were ACADVL variant carriers (P = 4.66 × 10⁻⁷). VLCADD biomarker levels were highest in patients, intermediate in carriers, and lowest in non-carriers, indicating that ACADVL variants elevate biomarker levels and increase false-positive rates. Metabolomics with AI/ML detected all true positives (100% sensitivity), while genome sequencing reduced false positives by 98.8%.

Conclusion: Targeted metabolomics with AI/ML showed high sensitivity for identifying true positives, though its ability to reduce false positives varied by condition. Genome sequencing effectively reduced false positives but lacked sufficient sensitivity as a standalone test. The elevated false-positive rate among pathogenic variant carriers underscores the potential value of parental or prenatal carrier screening to improve NBS accuracy. Integrating genomic and metabolomic data may enhance NBS precision and enable earlier diagnosis and intervention for rare diseases.

Background: Next-Generation short-read sequencing has limited diagnostic utility in phasing distantly separated variants and analysing genomic regions with high homology. Determining the phase of variants from parental chromosomes is critical for accurate identification of compound heterozygosity. Long-read sequencing technology is able to overcome these limitations through the analysis of long haplotypes of separated variants. This study has developed and validated a robust, end-to-end workflow for phasing and localising variants using long-range PCR (LR-PCR) and targeted Nanopore sequencing for clinical implementation.

Methods: NA24385 (HG002) reference DNA was used for all tests. Four PCR kits were tested to optimise LR-PCR for targets between 1 and 20 kb. Amplicons were barcoded and sequenced on Flongle flow cells, with up to eight amplicons on each flow cell. An in-house bioinformatic pipeline was developed to analyse the amplicons. This pipeline is capable of detecting chimeric reads (a known PCR artefact), and incorporating Clair3 for variant calling, and WhatsHap and HapCUT2 for phasing.

Results: The UltraRun LongRange PCR Kit performed with a 90% success rate for DNA amplification up to 22 kb. All 15 tested heterozygous Single Nucleotide Variant (SNV) pairs, and 10 small InDels, with inter-variant distances from 5.8 to 21.4 kb, were phased with 100% concordance to known phase. Furthermore, SNV calling within six low-mappability genes demonstrated precision and sensitivity of 1 against benchmark data. The median proportion of chimeric reads was maintained at 2.80% (range 1.79-16.12%) under optimised conditions.

Conclusions: This study establishes a reliable and affordable clinical diagnostic workflow for accurate phasing of variants separated by up to ~ 20 kb and for variant localisation in genomic regions not able to be sequenced by short-read sequencing. This integrated approach enables implementation in diagnostic settings to resolve complex genetic findings and improve variant interpretation. The bioinformatic pipeline and documentation are available at https://github.com/j-jamshidi/ONT_amp_phase .

Objective: This study is conducted to investigate whether serum microRNA (miR)-141-3p can serve as a biomarker for early-stage diagnosis of endometriosis.

Methods: A total of 246 patients who underwent laparoscopic examination and were diagnosed with endometriosis at our hospital between October 2020 and October 2022 were retrospectively enrolled as the Endometriosis group. This group was further allocated into Early-Endometriosis (Stage I-II) and Severe-Endometriosis (Stage III-IV) groups. Additionally, 87 healthy women with normal clinical parameters during the same period were selected as the control group. miR-141-3p expression in the serum of endometriosis patients were detected using RT-qPCR. The relationship of serum miR-141-3p expression with EHP-30 score in endometriosis patients was examined using Spearman. The diagnostic value of serum miR-141-3p for endometriosis was assessed by ROC analysis. Further ROC analysis was conducted to evaluate the diagnostic value of combined serum miR-141-3p and CA125 levels for early-stage endometriosis.

Results: Serum miR-141-3p expression was significantly lower in endometriosis patients and was negatively correlated with clinical staging. Serum miR-141-3p demonstrated excellent diagnostic performance for endometriosis (AUC = 0.916) and retained a high diagnostic value for early-stage endometriosis (AUC = 0.858). The diagnostic efficacy was further improved when combined with CA125 (AUC = 0.985).

Conclusion: Serum miR-141-3p expression decreases with disease progression in endometriosis patients and shows high clinical utility for the early-stage diagnosis of endometriosis. miR-141-3p expression may serve as a potential marker for diagnosis of endometriosis.

Exome and genome sequencing have greatly improved the diagnosis of rare genetic disorders but remain limited in their ability to identify and classify non-coding variants, including intronic variants, cryptic splice-site alterations, and disruptions in regulatory regions. RNA sequencing (RNA-seq) has emerged as a powerful tool to bridge this gap by providing functional insights into genomic variants that disrupt splicing or gene expression, thereby aiding in variant interpretation and classification. We retrospectively reviewed 30 cases from the Utah Penelope Program and the Undiagnosed Diseases Network over a three-year period, in which RNA-seq was performed on whole blood and/or fibroblasts following either negative DNA sequencing or the identification of candidate variants requiring functional assessment. In these cases, RNA-seq identified exon skipping, cryptic splice-site activation, and intron retention, leading to transcript disruption. Additionally, positional enrichment analysis clarified X-inactivation patterns and dosage effects, confirming the pathogenicity of copy number variants. By detecting these transcript-level alterations, RNA-seq provided functional evidence supporting the reclassification of multiple variants of uncertain significance, contributing to diagnostic resolution in selected cases. This study underscores the clinical utility of RNA-seq in detecting splicing and regulatory defects that DNA sequencing and predictive tools alone cannot resolve. Integrating RNA-seq into clinical workflows can support variant classification, aid in diagnostic resolution for selected cases, and provide mechanistic insights into genetic disorders, contributing to patient care and genetic counseling.

Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated with the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.