bims-fragic 2025-05-18 papers

Issue of 2025–05–18
three papers selected by
Laura Mannarino, Humanitas Research

Cureus. 2025 Apr;17(4): e82215

Maternal Cell-Free DNA Analysis in a Fetus Affected by Beckwith-Wiedemann Syndrome: Potential for Prenatal Diagnosis.

Ryutaro Yamamoto, Takeshi Umazume, Hiroshi Asano, Satoko Asai, Hidemichi Watari.

Beckwith-Wiedemann syndrome (BWS) is a condition present from birth that involves excessive growth and is linked to changes in specific genes located on chromosome 11p15.5. Prenatal diagnosis is mainly based on imaging findings such as macrosomia, macroglossia, and omphalocele, but detection remains difficult. We report a case of a fetus suspected of having BWS based on prenatal ultrasound and MRI. A female infant was delivered via cesarean section at 37 weeks and one day of gestation, showing macrosomia, macroglossia, and other clinical features consistent with BWS. To explore potential biomarkers for prenatal diagnosis of BWS, maternal blood was collected at 36 and 37 weeks of gestation and postpartum days 1 and 5. Cell-free DNA (cfDNA) analysis revealed a bimodal fragment size distribution with peaks at 144 and 166 bp during pregnancy. After delivery, the 144 bp peak disappeared, resulting in a unimodal pattern. The fetal fraction was elevated during pregnancy (33.9-34.5%) and decreased rapidly postpartum (to 3.4%). These findings suggest an increased release of fetal-derived cfDNA with BWS-affected fetuses. This case highlights the potential utility of cfDNA analysis as a noninvasive biomarker for BWS.

Keywords: beckwith-wiedemann syndrome; biomarker; cell-free dna; fetal fraction; fragment size

DOI: https://doi.org/10.7759/cureus.82215

Diagnostics (Basel). 2025 May 01. pii: 1156. [Epub ahead of print]15(9):

EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.

Zhi-Yang Zhao, Chang-Ling Huang, Tong-Min Wang, Shi-Hao Zhou, Lu Pei, Wen-Hui Jia, Wei-Hua Jia.

Background and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis. The end-motifs of cfDNA serve as significant cancer biomarkers, offering compelling prospects for cancer diagnosis. This study proposes EM-DeepSD, a signal decomposition deep learning framework based on cfDNA end-motifs, which is aimed at improving the accuracy of cancer diagnosis and adapting to different sequencing modalities. Materials and Methods: This study included 146 patients diagnosed with cancer and 122 non-cancer controls. EM-DeepSD comprises three core modules. Initially, it utilizes a signal decomposition module to decompose and reconstruct the input end-motif profiles, thereby generating multiple regular subsequences that optimize the subsequent modeling process. Subsequently, both a machine learning module and a deep learning module are employed to improve the accuracy of cancer diagnosis. Furthermore, this paper compares the performance of EM-DeepSD with that of existing benchmarked methods to demonstrate its superiority. Based on the EM-DeepSD framework, we developed the EM-DeepSSA model and compared it with two benchmarked methods across different cfDNA sequencing datasets. Results: In the internal validation set, EM-DeepSSA outperformed the two benchmark methods for cancer diagnosis (area under the curve (AUC), 0.920; adjusted p value < 0.05). Meanwhile, EM-DeepSSA also exhibited the best performance on two independent external testing sets that were subjected to 5-hydroxymethylcytosine sequencing (5hmCS) and broad-range cell-free DNA sequencing (BR-cfDNA-Seq), respectively (test set-1: AUC = 0.933; test set-2: AUC = 0.956; adjusted p value < 0.05). Conclusions: In summary, we present a new framework which can achieve high classification performance in cancer diagnosis and which is applicable to different sequencing modalities.

Keywords: cancer diagnosis; cell-free DNA; deep neural network; signal decomposition

DOI: https://doi.org/10.3390/diagnostics15091156

Bioinformatics. 2025 May 12. pii: btaf293. [Epub ahead of print]

GCfix: A Fast and Accurate Fragment Length-Specific Method for Correcting GC Bias in Cell-Free DNA.

Chowdhury Rafeed Rahman, Zhong Wee Poh, Anders Jacobsen Skanderup, Limsoon Wong.

MOTIVATION: Cell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its non-invasive nature. However, cfDNA fragmentomics and copy number analysis can be complicated by GC bias. There is a lack of GC correction software based on rigorous cfDNA GC bias analysis. Furthermore, there is no standardized metric for comparing GC bias correction methods across large sample sets, nor a rigorous experiment setup to demonstrate their effectiveness on cfDNA data at various coverage levels.
RESULTS: We present GCfix, a method for robust GC bias correction in cfDNA data across diverse coverages. Developed following an in-depth analysis of cfDNA GC bias at the region and fragment length levels, GCfix is both fast and accurate. It works on all reference genomes and generates correction factors, tagged BAM files, and corrected coverage tracks. We also introduce two orthogonal performance metrics for (1) comparing the fragment count density distribution of GC content between expected and corrected samples, and (2) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics.
AVAILABILITY AND IMPLEMENTATION: GCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.
SUPPLEMENTARY INFORMATION: All Supplementary figures and data are available online through Bioinformatics.

Keywords: Coverage; Fragment Length; GC Correction; Single Position Method

DOI: https://doi.org/10.1093/bioinformatics/btaf293