Discov Oncol. 2025 Jan 17;16(1):57. doi: 10.1007/s12672-025-01798-8.

ABSTRACT

Gastric cancer (GC), a prevalent malignancy worldwide, encompasses a multitude of biological processes in its progression. Recently, ferroptosis, a novel mode of cell demise, has become a focal point in cancer research. The microenvironment of gastric cancer is composed of diverse cell populations, yet the specific gene expression profiles and their association with ferroptosis are not well understood. Our study employed single-cell RNA sequencing to thoroughly investigate the transcriptomic profiles and identify differential gene expression in gastric cancer, offering fresh insights into the cellular diversity and underlying molecular mechanisms of this disease. We discovered a set of significantly differentially expressed genes in GC, which may serve as valuable leads for future functional investigations. Subsequent analyses, including gene set intersection and functional enrichment, pinpointed genes implicated in ferroptosis and conducted comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to elucidate their biological roles. In the gene selection and model validation section, critical genes were identified using machine learning algorithms, constructing a model with high predictive accuracy. Besides, distorted immune landscapes were further identified in RBL using ssGSEA analysis such that the complex association of gene expression features and its interaction networks as well as infiltration by various types of immune cells can be more clearly understood. Correlation analysis with different immune cell subtypes showed CTSB as an important regulator in the distributions of cancer infiltrating cells. Single-cell RNA sequencing analysis was utilized to map the cellular composition and gene expression profiles of cells in the gastric cancer microenvironment, which provide critical information for elucidating cellular heterogeneity as well as tumor microenvironment regulation in GC. Moreover, the distribution of FTH1, ZFP36 and CIRBP at different expression levels show new research prospects for functional information of these promoters in tumor microenvironment. In summary, the present study augments our knowledge of molecular mechanisms underlying gastric tumorigenesisa and provide scientific basis for identifing new targets and biomarkers in therapeutic diagnosis.

PMID:39831925 | PMC:PMC11747029 | DOI:10.1007/s12672-025-01798-8

Am J Hum Genet. 2025 Jan 6:S0002-9297(24)00456-7. doi: 10.1016/j.ajhg.2024.12.014. Online ahead of print.

ABSTRACT

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole-genome sequencing (WGS) of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. We show that haplotype-based calling methods can be used with WGS data to successfully identify mLOY events. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European (EUR) ancestry group compared to other ancestries. We identify multiple loci associated with mLOY susceptibility and show that subsets of human hematopoietic stem cells are enriched for the activity of mLOY susceptibility variants. Finally, we found that certain alleles on chromosome Y are more likely to be lost than others in detectable mLOY clones.

PMID:39809269 | DOI:10.1016/j.ajhg.2024.12.014

Cancer Lett. 2025 Jan 12;612:217462. doi: 10.1016/j.canlet.2025.217462. Online ahead of print.

ABSTRACT

A user-friendly integrated database, RMethyMD (http://www.tmliang.cn/rnamethy), was developed to provide a comprehensive analysis of methylation regulators aimed at facilitating the exploration of molecular features in tumorigenesis and clinical implications in cancer diagnosis and treatment via a multiomics approach. Subsequently, molecular landscapes and a robust constructed m6A-based prognostic model using coxBoost + RSF algorithms in lung cancer highlighted m6A as a suitable marker to guide therapeutic strategy. RMethyMD provides a comprehensive resource and multiomics analysis to explore m6A-based prognostic and clinical values, thereby contributing to aiding personalized cancer therapy.

PMID:39809358 | DOI:10.1016/j.canlet.2025.217462

Radiother Oncol. 2025 Jan 10;204:110715. doi: 10.1016/j.radonc.2025.110715. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Quantifying tumor heterogeneity from various dimensions is crucial for precise treatment. This study aimed to develop and validate multi-omics models based on the computed tomography images, pathological images, dose and clinical information to predict treatment response and overall survival of non-small cell lung cancer (NSCLC) patients undergoing chemotherapy and radiotherapy.

MATERIALS AND METHODS: This retrospective study included 220 NSCLC patients from three centers. Following feature extraction and selection, single-omics and multi-omics models were built for treatment response and overall survival prediction. The performance of treatment response models was evaluated using the area under the curve (AUC) and box plots. For overall survival analysis, the model's evaluation included AUC, concordance index (C-index), Kaplan-Meier curves, and calibration curves. Shapley values were used to assess the contribution of different features to multi-omics models.

RESULTS: Multi-omics models consistently exhibited superior discriminative ability compared to single-omics models in predicting both treatment response and overall survival. For treatment response, the three all-modality models achieved AUC values of 0.87, 0.91, and 0.82 in the external validation set, respectively. In overall survival analysis, the three all-modality models demonstrated AUC values and C-index of 0.73/0.72, 0.80/0.77, 0.79/0.78 in the external validation set, respectively.

CONCLUSION: Multi-omics prediction models demonstrated superior predictive ability with robustness and interpretability. By predicting treatment response and overall survival in NSCLC patients, these models have the potential to assist clinician optimizing treatment plans, supporting individualized treatment strategies, improving the tumor control probability and prolonging the patients' survival.

PMID:39800269 | DOI:10.1016/j.radonc.2025.110715

Global Spine J. 2025 Jan;15(1_suppl):16S-28S. doi: 10.1177/21925682231222012.

ABSTRACT

STUDY DESIGN: Narrative review.

OBJECTIVES: This article aims to provide a narrative review of the current state of research for liquid biopsy in spinal tumors and to discuss the potential application of liquid biopsy in the clinical management of patients with spinal tumors.

METHODS: A comprehensive review of the literature was performed using PubMed, Google Scholar, Medline, Embase and Cochrane databases, and the review was limited to articles of English language. All the relevant articles which were identified to be related to liquid biomarker study in spinal tumors, were studied in full text.

RESULTS: Liquid biopsy has revolutionized the field of precision medicine by guiding personalized clinical management of cancer patients based on the liquid biomarker status. In recent years, more research has been done to investigate its potential utilization in patients with tumors from the spine. Herein, we review the liquid biomarkers that have been proposed in different spine malignancies including chordoma, chondrosarcoma, Ewing sarcoma, osteosarcoma, astrocytoma and ependymoma. We also discuss the wide window of opportunity to utilize these liquid biomarkers in diagnosis, treatment response, monitoring, and detection of minimal residual disease in patients with spinal tumors.

CONCLUSIONS: Liquid biomarkers, especially blood-derived circulating tumor DNA, has a promising clinical utility as they are disease-specific, minimally invasive, and the procedure is repeatable. Prospective studies with larger populations are needed to fully establish its use in the setting of spinal tumors.

PMID:39801114 | PMC:PMC11726521 | DOI:10.1177/21925682231222012

Clin Transl Med. 2025 Jan;15(1):e70160. doi: 10.1002/ctm2.70160.

ABSTRACT

BACKGROUND: Plasma protein has gained prominence in the non-invasive predicting of lung cancer. We utilised Zeolite Zotero NaY-based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3).

METHODS: A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.

RESULTS: For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model.

CONCLUSIONS: This study comprehensively profiled the NaY-based plasma proteome biomarker, laying the foundation for a high-performance blood test for predicting multiple events in lung cancer.

KEY POINTS: Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

PMID:39783847 | PMC:PMC11714244 | DOI:10.1002/ctm2.70160

Int J Med Sci. 2025 Jan 1;22(2):341-356. doi: 10.7150/ijms.104437. eCollection 2025.

ABSTRACT

Background: Gastric cancer (GC) remains a significant global health challenge. This study aimed to comprehensively analyze GC epidemiology and risk factors to inform prevention and intervention strategies. Methods: We analyzed the Global Burden of Disease Study 2021 data, conducted 16 different machine learning (ML) models of NHANES data, performed Mendelian randomization (MR) studies on disease phenotypes, dietary preferences, microbiome, blood-based markers, and integrated differential gene expression and expression quantitative trait loci (eQTL) data from multiple cohorts to identify factors associated with GC risk. Results: Global age-standardized disability-adjusted life year rates (ASDR) for GC declined from 886.24 to 358.42 per 100,000 population between 1990 and 2030, with significant regional disparities. Despite this decline, total disability-adjusted life years show a concerning upward trend from 2015, rising from approximately 22.9 million to a projected 24.3 million by 2030. The slope index of inequality shifted from 87 in 1990 to -184 in 2021, indicating a reversal in GC burden distribution, with higher ASDR now associated with lower socio-demographic index countries. The ML models analysis identified higher levels of clinical characteristics such as phosphorus, calcium, eosinophils percent, and triglycerides, as well as lower levels of iron and monocyte percent, may be associated with an increased risk of GC. MR analyses revealed causal associations between GC risk and disease phenotypes such as Helicobacter pylori infection, chronic gastritis, obesity, depression, and dietary preferences such as dairy and processed meats. Gut microbiome analysis showed associations with microbiome such as Phascolarctobacterium and Ruminococcaceae species. Blood-based markers analysis identified protective and risk effects for cortisol, glutamate, nicotinamide, Natural Killer %lymphocyte, CD4-CD8- T cell Absolute Count, Phosphatidylcholine (16:0_18:1), and Interleukin-1-alpha. Integrated genomic analysis identified 10 genes significantly associated with GC risk, with strong evidence for colocalization in genes such as CCR6 and PILRB. Conclusions: This systematic analysis reveals complex global trends in GC burden and identifies novel clinical, disease phenotypes, dietary preferences, microbial, blood-based, and genetic risk factors. These findings provide potential targets for improved risk stratification, prevention, and intervention strategies to reduce the global burden of GC.

PMID:39781526 | PMC:PMC11704698 | DOI:10.7150/ijms.104437

Nat Commun. 2025 Jan 8;16(1):430. doi: 10.1038/s41467-024-55428-y.

ABSTRACT

The analysis of circulating tumour DNA (ctDNA) through minimally invasive liquid biopsies is promising for early multi-cancer detection and monitoring minimal residual disease. Most existing methods focus on targeted deep sequencing, but few integrate multiple data modalities. Here, we develop a methodology for ctDNA detection using deep (80x) whole-genome TET-Assisted Pyridine Borane Sequencing (TAPS), a less destructive approach than bisulphite sequencing, which permits the simultaneous analysis of genomic and methylomic data. We conduct a diagnostic accuracy study across multiple cancer types in symptomatic patients, achieving 94.9% sensitivity and 88.8% specificity. Matched tumour biopsies are used for validation, not for guiding the analysis, imitating an early detection scenario. Furthermore, in silico validation demonstrates strong discrimination (86% AUC) at ctDNA fractions as low as 0.7%. Additionally, we successfully track tumour burden and ctDNA shedding from precancerous lesions post-treatment without requiring matched tumour biopsies. This pipeline is ready for further clinical evaluation to extend cancer screening and improve patient triage and monitoring.

PMID:39779727 | PMC:PMC11711490 | DOI:10.1038/s41467-024-55428-y

Int J Mol Sci. 2024 Dec 20;25(24):13669. doi: 10.3390/ijms252413669.

ABSTRACT

Liquid biopsy (LB) involves the analysis of circulating tumour-derived DNA (ctDNA), providing a minimally invasive method for gathering both quantitative and qualitative information. Genomic analysis of ctDNA through next-generation sequencing (NGS) enables comprehensive genetic profiling of tumours, including non-driver alterations that offer prognostic insights. LB can be applied in both early-stage disease settings, for the diagnosis and monitoring of minimal residual disease (MRD), and advanced disease settings, for monitoring treatment response and understanding the mechanisms behind disease progression and tumour heterogeneity. Currently, LB has limited use in clinical practice, primarily due to its significant costs, limited diagnostic yield, and uncertain prognostic role. The application of artificial intelligence (AI) in the medical field is a promising approach to processing extensive information and applying it to individual cases to enhance therapeutic decision-making and refine risk assessment.

PMID:39769431 | PMC:PMC11727717 | DOI:10.3390/ijms252413669

Exp Mol Med. 2025 Jan 8. doi: 10.1038/s12276-024-01382-0. Online ahead of print.

ABSTRACT

Most cancer mutation profiling studies are laboratory-based and lack direct clinical application. For clinical use, it is necessary to focus on key genes and integrate them with relevant clinical variables. We aimed to evaluate the prognostic value of the dosage of the KRAS G12 mutation, a key pancreatic ductal adenocarcinoma (PDAC) variant and to investigate the biological mechanism of the prognosis associated with the dosage of the KRAS G12 mutation. In this retrospective cohort study, we analyzed 193 surgically treated patients with PDAC between 2009 and 2016. RNA, whole-exome, and KRAS-targeted sequencing data were used to estimate the dosage of the KRAS G12 mutant. Our prognostic scoring system included the mutation dosage from targeted sequencing ( > 0.195, 1 point), maximal tumor diameter at preoperative imaging ( > 20 mm, 1 point), and carbohydrate antigen 19-9 levels ( > 150 U/mL, 1 point). The KRAS mutation dosage exhibited comparable performance with clinical variables for survival prediction. High KRAS mutation dosages activated the cell cycle, leading to high mutation rates and poor prognosis. According to prognostic scoring systems that integrate mutation dosage with clinical factors, patients with 0 points had superior median overall survival of 97.0 months and 1-year, 3-year, and 5-year overall survival rates of 95.8%, 70.8%, and 66.4%, respectively. In contrast, patients with 3 points had worse median overall survival of only 16.0 months and 1-year, 3-year, and 5-year overall survival rates of 65.2%, 8.7%, and 8.7%, respectively. The incorporation of the KRAS G12 mutation dosage variable into prognostic scoring systems can improve clinical variable-based survival prediction, highlighting the feasibility of an integrated scoring system with clinical significance.

PMID:39779977 | DOI:10.1038/s12276-024-01382-0

Sci Rep. 2025 Jan 2;15(1):129. doi: 10.1038/s41598-024-83742-4.

ABSTRACT

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions.

PMID:39747972 | PMC:PMC11696309 | DOI:10.1038/s41598-024-83742-4