Front Pharmacol. 2025 Oct 17;16:1697608. doi: 10.3389/fphar.2025.1697608. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a leading cause of cancer death, and recovery after therapy is shaped by heterogeneous etiologies, genomes and microenvironments. Targeted and immunotherapy combinations have broadened first-line options; yet durable benefit is uneven, and serum/imaging anchors (AFP, AFP-L3%, PIVKA-II, LI-RADS/mRECIST) incompletely resolve residual disease or functional restoration. In this review we summarise AI-enabled radiology, digital pathology and multi-omic/liquid-biopsy analytics that test and refine traditional biomarkers and drug-target readouts, and appraise translational opportunities in composite surveillance and recovery forecasting. We also discuss enduring challenges-including assay standardisation, spectrum bias, data leakage, domain shift and limited prospective external validation-that temper implementation. By integrating established anchors (AFP/AFP-L3%, PIVKA-II, ALBI, contrast-enhanced hallmarks) with AI-derived signals (radiomics/pathomics, cfDNA methylation) and pathway contexts (VEGF-VEGFR, WNT/β-catenin), emerging strategies align predictions with clinical endpoints, individualise therapy and chart hepatic function. Our synthesis provides an appraisal of AI-traditional integration in liver cancer recovery and outlines pragmatic standards-analytical robustness, transparent reporting and prospective, guideline-conformant evaluation-required for clinical adoption. We hope these insights will aid researchers and clinicians as they implement more effective, individualised monitoring and treatment pathways.

PMID:41181592 | PMC:PMC12575369 | DOI:10.3389/fphar.2025.1697608

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.

ABSTRACT

Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.

PMID:40600435 | DOI:10.1021/jasms.4c00505

Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.

ABSTRACT

PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.

METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.

RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.

CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.

PMID:40536584 | DOI:10.1007/s00520-025-09670-9

ACS Sens. 2025 Apr 25;10(4):3101-3111. doi: 10.1021/acssensors.5c00314. Epub 2025 Apr 7.

ABSTRACT

Early screening of individuals with high-risk lung nodules can significantly improve the prognosis of lung cancer patients, and accurate identification of lung nodule subtypes can provide guidance for medical treatment. Exhaled breath (EB) analysis via eNoses offers a quick and noninvasive approach, but current eNose technology lacks quality control and solid validation in large population studies. Herein, an eNose platform integrated with a metal ion-decorated graphene sensor array and a breath sampling accessory was established. EB samples from 427 healthy subjects and 2586 subjects with lung nodules, including various benign and malignant subtypes, were collected through the breath sampling accessory for quality control. The large-cohort clinical EB samples were analyzed by the eNose platform to acquire the cross-reactive resistance response. Breathprint analysis for high-risk lung nodules using SVM and age-matched training sets yielded strong and robust performance. Combined with baseline data, the model achieved an AUC of 0.93 (95% CI, 0.89-0.96) on the external test set, with 97% sensitivity and 73% specificity. Moreover, dimensionality reduction analysis of breathprints demonstrated separability across different lung nodule subtypes. This study demonstrates the reliability of the graphene eNose platform to identify high-risk lung nodules and classify lung nodule subtypes in a noninvasive and rapid method.

PMID:40193324 | DOI:10.1021/acssensors.5c00314

Sci Data. 2025 Apr 1;12(1):545. doi: 10.1038/s41597-025-04912-1.

ABSTRACT

Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.

PMID:40169596 | PMC:PMC11961589 | DOI:10.1038/s41597-025-04912-1

Bioinformatics. 2024 Nov 1;40(11):btae623. doi: 10.1093/bioinformatics/btae623.

ABSTRACT

MOTIVATION: Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.

RESULTS: In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network.

AVAILABILITY AND IMPLEMENTATION: PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.

PMID:39418180 | PMC:PMC11549024 | DOI:10.1093/bioinformatics/btae623