Biomolecules. 2025 Jul 26;15(8):1081. doi: 10.3390/biom15081081.

ABSTRACT

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model's performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model's diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy.

PMID:40867526 | PMC:PMC12383841 | DOI:10.3390/biom15081081

Cancers (Basel). 2025 Aug 15;17(16):2668. doi: 10.3390/cancers17162668.

ABSTRACT

Background/Objectives: Esophageal and esophagogastric junction adenocarcinoma (EADC-EGJA), which mainly develops from Barrett's esophagus (BE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD), has a poor prognosis and several unmet clinical needs, among which is the detection of minimal residual disease (MRD) after endoscopic/surgical resection. Long interspersed nuclear element-1 (LINE-1), a surrogate marker of global methylation, is considered an emerging biomarker for MRD monitoring. The aim of this study was to determine, by LINE-1 methylation analysis, at which carcinogenesis step global methylation is affected and whether this biomarker could be followed in longitudinal to monitor the disease behavior post-surgery. Methods: Cell-free DNA of 90 patients with non-dysplastic Barrett's esophagus (NDBE), HGD/early EADC-EGJA, or locally advanced/advanced EADC-EGJA were analyzed for LINE-1 methylation, by Methylation-Sensitive Restriction Enzyme droplet digital PCR (MSRE-ddPCR). Twenty-six patients were longitudinally studied by repetitive blood sampling. Results: Global hypomethylation increased during carcinogenesis, with significant difference between locally advanced/advanced EADC-EGJA and NDBE patients (p = 0.028). Longitudinal cases confirmed the rareness of hypomethylation in NDBE cases. The majority of HGD/early EADC-EGJA and locally advanced/advanced EADC-EGJA patients showed methylation changes after resection according to clinical status. Conclusions: This study suggests that global hypomethylation occurs just prior to cancer invasiveness and that it is a promising biomarker to monitor MRD.

PMID:40867295 | PMC:PMC12384112 | DOI:10.3390/cancers17162668

J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.

ABSTRACT

BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification.

RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10^-7 to 7.5×10^-5. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10^-6 and 9×10^-5. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method.

CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .

PMID:40866952 | PMC:PMC12382282 | DOI:10.1186/s12967-025-06993-3