Eur Respir J. 2025 Jul 31:2402537. doi: 10.1183/13993003.02537-2024. Online ahead of print.

ABSTRACT

BACKGROUND: Allograft injury in the early post-transplant period is a known risk factor of death after lung transplantation. However, the recipient tissue injury profile and its association with outcomes remain unexplored. This study leverages cell-free DNA (cfDNA) to test this association.

METHODS: The prospective cohort multicenter study included lung transplant recipients (GRAfT, NCT02423070) with serial plasma measurements of recipient-derived (rd)-cfDNA using digital droplet PCR. Non-transplant healthy controls were recruited as the comparator. Whole-genome bisulfite sequencing identified tissue sources of cfDNA. Mean rd-cfDNA levels within 30 days post-transplant was computed. Multivariable regression models were used to assess the association between rd-cfDNA tertiles and the primary outcome of death and secondary outcomes.

RESULTS: The study included 215 patients with 2530 cfDNA values, including 675 cfDNA assessments in the first 30 days. Median rd-cfDNA levels in the first 30 days post-transplant were ∼16-fold higher than cfDNA for healthy controls. Patients in the highest tertile rd-cfDNA group had lower lung function post-transplant, and increased risk of death (HR: 3.15, 95% CI: 1.59-6.24, p<0.001) and acute rejection (HR 2.33, 95% CI: 1.33-4.08, p=0.03), compared to the low/middle tertile group. Tissue-specific cfDNA sources were also distinct cfDNA in the highest versus lowest rd-cfDNA tertiles, with cfDNA from innate immune cells serving as the strongest predictor of mortality.

CONCLUSION: Post-transplant recipient tissue injury varies between lung transplant patients and is associated with increased risk of acute rejection and mortality.

PMID:40744691 | DOI:10.1183/13993003.02537-2024

Eur J Radiol. 2025 Jul 22;191:112327. doi: 10.1016/j.ejrad.2025.112327. Online ahead of print.

ABSTRACT

The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.

PMID:40743874 | DOI:10.1016/j.ejrad.2025.112327

J Liq Biopsy. 2025 Jul 16;9:100312. doi: 10.1016/j.jlb.2025.100312. eCollection 2025 Sep.

ABSTRACT

Breast cancer (BC) is the most frequent cancer and the leading cause of cancer-related death among women worldwide. It represents a heterogeneous group of diseases with distinct morphological, immunophenotypic, and molecular profiles, which significantly impact clinical behavior and therapeutic response. Moreover, under treatment pressure, tumor cells may undergo molecular changes and phenotypic plasticity, leading to resistance and therapeutic failure. Although tissue biopsy remains the gold standard for diagnosis and molecular characterization, it has several limitations, including invasiveness, sampling bias, and the inability to dynamically capture tumor evolution over time. Hence, a non-invasive and repeatable approach capable of real-time monitoring is increasingly needed. Liquid biopsy (LB), through the analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has emerged as a powerful tool to complement tissue biopsy. It allows for longitudinal assessment of tumor burden, detection of minimal residual disease, and identification of molecular alterations relevant to targeted therapies. Despite promising results, the integration of LB into clinical practice is still limited by methodological heterogeneity, standardization gaps, and regulatory issues. Nonetheless, LB represents a key advancement toward precision oncology and may become essential in the personalized management of BC patients. In this review, we explore the current applications, benefits, and technical limitations of LB in different BC settings. We provide a comprehensive overview of the biological and clinical significance of CTCs and ctDNA, emphasizing their diagnostic, prognostic, and predictive roles. Finally, we present an updated summary of ongoing clinical trials that incorporate LB for clinical decision-making.

PMID:40740670 | PMC:PMC12308030 | DOI:10.1016/j.jlb.2025.100312

World J Gastrointest Surg. 2025 Jul 27;17(7):107110. doi: 10.4240/wjgs.v17.i7.107110.

ABSTRACT

BACKGROUND: Gastrointestinal (GI) malignancies, including gastric and colorectal cancers, remain one of the primary contributors to cancer-related illness and death globally. Despite the availability of conventional diagnostic tools, early detection and personalized treatment remain significant clinical challenges. Integrated multi-omics methods encompassing genomic, transcriptomic, proteomic, metabolomic, and microbiome profiles have emerged as powerful tools for advancing precision oncology, improving diagnostic accuracy, and informing therapeutic strategies.

AIM: To investigate the application of multi-omics approaches in the early detection, risk stratification, treatment optimization, and biomarker discovery of GI malignancies.

METHODS: The systematic review process was conducted in accordance with the PRISMA 2020 guidelines. Five databases, PubMed, ScienceDirect, Scopus, ProQuest, and Web of Science, were searched for studies published in English from 2015 onwards. Eligible studies involved human subjects and focused on multi-omics integration in GI cancers, including biomarker identification, tumor microenvironment analysis, tumor heterogeneity, organoid modeling, and artificial intelligence (AI)-driven analytics. Data extraction included study characteristics, omics modalities, clinical applications, and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument.

RESULTS: A total of 17196 initially identified articles, 20 met the inclusion criteria. The findings highlight the superiority of multi-omics platforms over traditional biomarkers (e.g., carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers. Key applications include the identification of circulating tumor DNA, extracellular vesicles, lipidomic and proteomic signatures, and the adoption of AI algorithms to enhance diagnostic precision. Multi-omics analysis has also revealed the mechanisms of immune modulation, tumor microenvironment regulation, metastatic behavior, and drug resistance. Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization.

CONCLUSION: Multi-omics approaches offer significant advancements in the early diagnosis, prognostic evaluation, and personalized treatment of GI malignancies. Their integration with AI analytics, organoid biobanking, and microbiota modulation provides a pathway for precision oncology research.

PMID:40740914 | PMC:PMC12305287 | DOI:10.4240/wjgs.v17.i7.107110

Drug Dev Res. 2025 Aug;86(5):e70131. doi: 10.1002/ddr.70131.

ABSTRACT

Since lung cancer accounts for approximately 20% of cancer-related fatalities globally, it is one of the most common and deadly cancers, necessitating the discovery of innovative, potent, and less toxic treatment agents as imperative. Opportunistically, phytoflavonoids (PFs), a specific class of phytochemicals, display promising anticancer activity through their multimodal apoptosis-inducing properties. Based on existing evidence, the present study employs an integrative multi-omics approach to assess the target-specific binding efficacy and drug-ability outlines of PFs against lung cancer. We selected two of the most likely lung cancer targets using the core part of PFs: carbonic anhydrase IX (PDB ID: 3DAZ) and poly(A) binding protein cytoplasmic 1 (PDB ID: 3KUJ). Another two key targets, glutathione S-transferase P1 (PDB ID: 3GSS) and 17β-hydroxysteroid dehydrogenase 1 (HSD17B1, 3HB4), were also included in our study based on recent literature. The potency of 66 PFs against four targets was assessed through a molecular docking study using PyRx 0.8-AutoDock 4.2 software. PF15, PF43, PF6, and PF26 were the lead candidates. Further, physicochemical profiles through standard Lipinski rule of five parameters and toxicity and drug-ability profiles suggested that PF43 (naringenin) is the most ideal lead candidate among them. Molecular dynamics (MD) simulation studies were performed at 200 ns to observe the kinetic behaviors of CA9-PF43 and CA9-U-1014 docking complexes along with the calculated free energy through the MM/PBSA method. From both analyses, PF43 showed higher stability and lower free energy, expressing its potency over the standard drug. We also investigated the structure-activity relationship and frontier molecular orbitals to highlight the drug chemistry of lead PFs. The integrative multi-omics investigation suggested that using PF43 for lung cancer treatment could increase the chances of experimental success. Overall, the systematic computational analyses provide a platform for lead identification and pave the way for precision phytotherapy in current drug discovery.

PMID:40741887 | DOI:10.1002/ddr.70131

World J Clin Oncol. 2025 Jul 24;16(7):106981. doi: 10.5306/wjco.v16.i7.106981.

ABSTRACT

Cancer remains a major global health concern, with escalating incidence and mortality rates underscoring the urgent need for novel diagnostic and therapeutic strategies. Increasing evidence has identified the oral microbiota as a critical contributor to tumorigenesis, thereby expanding the understanding of cancer pathogenesis beyond conventional risk factors such as tobacco use and genetic predisposition. This review summarizes recent progress in elucidating the complex relationship between the oral microbiota and various malignancies, particularly oral squamous cell carcinoma, esophageal adenocarcinoma, and pancreatic ductal adenocarcinoma. Pathogenic bacteria, including Porphyromonas gingivalis and Fusobacterium nucleatum, have been implicated in promoting tumor progression through mechanisms involving chronic inflammation, the production of metabolic toxins, and immune evasion. The dysbiosis of the oral microbiota, often driven by lifestyle factors such as poor diet, tobacco use, and alcohol consumption, further exacerbates these carcinogenic processes. Emerging therapeutic approaches including probiotics, oral microbiota transplantation, and CRISPR-based bacterial editing are under investigation for their potential to restore microbial homeostasis and suppress pathogenic species. Additionally, saliva-based microbial biomarkers have shown promise for non-invasive cancer screening. The integration of multi-omics technologies and artificial intelligence-driven platforms is further advancing the development of precision oncology. This review aims to consolidate fragmented findings concerning the oral microbiota-cancer axis and address existing gaps in mechanistic understanding. The review's significance lies in the translational potential of microbial research to clinical applications, offering opportunities to reduce the global cancer burden through early detection and microbiota-targeted therapies.

PMID:40741186 | PMC:PMC12304933 | DOI:10.5306/wjco.v16.i7.106981

Front Mol Biosci. 2025 Jul 15;12:1614288. doi: 10.3389/fmolb.2025.1614288. eCollection 2025.

ABSTRACT

Recent advances in spatial transcriptomics (ST) and spatial proteomics (SP) technologies have enabled high-dimensional molecular profiling at single-cell resolution, providing deeper insights into the tumour-immune microenvironment. However, these modalities are typically applied to separate tissue sections, limiting direct comparisons across molecular layers. We developed a wet-lab and computational framework to perform and integrate ST and SP from the same tissue section, as demonstrated on human lung cancer samples. Applying ST, SP, and hematoxylin and eosin (H&E) staining from the same section ensured consistency in tissue morphology and spatial context. Computational registration using Weave software allowed accurate alignment and annotation transfer across modalities. This co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealed segmentation accuracy and transcript-protein correlation analyses within individual cells. Notably, we observed systematic low correlations between transcript and protein levels-consistent with prior findings-now resolved at cellular resolution. Our approach highlights the feasibility and utility of performing spatially-resolved multi-omics analysis on the same section without compromising data quality, facilitating concordance studies and region-specific analysis of immune and tumour markers, and ultimately advancing our understanding of disease heterogeneity at the molecular level.

PMID:40735471 | PMC:PMC12304548 | DOI:10.3389/fmolb.2025.1614288

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1^-/- and DLK1^-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

Diabetologia. 2025 Jul 30. doi: 10.1007/s00125-025-06462-y. Online ahead of print.

ABSTRACT

This article summarises the current understanding of the heterogeneity of type 1 diabetes from a June 2024 international Expert Forum organised by the editors of Diabetes, Diabetes Care, and Diabetologia. The Forum reviewed key factors contributing to the development and progression of type 1 diabetes and outlined specific, high-priority research questions. Knowledge gaps were identified and, notably, opportunities to harness disease heterogeneity to develop personalised therapies were outlined. Herein, we summarise our discussions and review the heterogeneity of genetic risk and immunologic and metabolic phenotypes that influence and characterise type 1 diabetes progression (presented as a palette of risk factors). We discuss how these age-related factors determine disease aggressiveness (along gradients) and describe how variable immunogenetic pathways aggregate (into networks) to affect beta cell and other pancreatic pathologies to cause clinical disease at different ages and with variable severity (described as disease-related thresholds). Heterogeneity of pathogenesis and clinical severity opens avenues to prevention and intervention, including the potential of disease-modifying immunotherapy and islet cell replacement. We conclude with a call for (1) continued research to identify more factors contributing to the disease, both overall and in specific subgroups; (2) investigations focusing on both individuals who surpass metabolic and immune thresholds and develop diabetes and those who remain disease free with the same level of immunogenetic risk; and (3) efforts to identify where the current type 1 diabetes staging system may fall short and determine how it can be improved to capture and leverage heterogeneity in prevention and intervention strategies.

PMID:40736750 | DOI:10.1007/s00125-025-06462-y

Biomedicines. 2025 Jul 14;13(7):1717. doi: 10.3390/biomedicines13071717.

ABSTRACT

Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet severe immune-related adverse events (irAEs) often necessitate immunotherapy discontinuation and cause life-threatening complications. Circulating plasma proteins, dynamically accessible and functionally linked to immunity, may predict and offer novel targets for irAEs. Methods: Leveraging multi-omics integration, we conducted bidirectional two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) from 4998 plasma proteins and genome-wide association data of irAE phenotypes. A causal inference framework combining colocalization analysis, multivariable MR (MVMR) adjusting for body mass index (BMI) confounding, and mediation MR elucidated BMI-independent pathways. Systems biology approaches including tissue-specific expression profiling, pathway enrichment, and protein interaction network analysis revealed spatial and functional drivers of irAE pathogenesis. Results: Proteome-wide MR mapping identified eight plasma proteins (CCL20, CSF1, CXCL9, CD40, TGFβ1, CLSTN2, TNFSF12, TGFα) causally associated with all-grade irAEs, and five (CCL20, CCL25, CXCL10, ADA, TGFα) with high-grade irAEs. Colocalization prioritized CD40/TNFSF12 (all-grade) and ADA/CCL25 (high-grade) as therapeutic targets (PPH4 > 0.7). CXCL9/TNFSF12 (all-grade) and CCL25 (high-grade) exerted BMI-independent effects, suggesting intrinsic immune dysregulation mechanisms. Tissue-specific gene expression patterns, CSF1, TGFβ1 in lung, TNFSF12 in the ileum may explain organ-specific irAE vulnerabilities. High-grade irAEs correlated with compartmentalized immune dysregulation and IL-17/immunodeficiency pathway activation. Conclusions: This study establishes the causal atlas of plasma proteins in irAE pathogenesis, bridging biomarker discovery with actionable therapeutic targets. These advances align with next-generation immunotherapy goals: maximizing efficacy while taming the immune storm.

PMID:40722787 | PMC:PMC12293052 | DOI:10.3390/biomedicines13071717

World J Clin Cases. 2025 Jul 26;13(21):105066. doi: 10.12998/wjcc.v13.i21.105066.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA)-based liquid biopsy has been found to be effective for the detection of minimal residual disease and the evaluation of prognostic risk in various solid tumors, with good sensitivity and specificity for identifying patients at high risk of recurrence. However, use of its results as a biomarker for guiding the treatment and predicting the prognosis of nasopharyngeal carcinoma (NPC) has not been reported.

CASE SUMMARY: In this case study of a patient with stage IVb NPC, we utilized ctDNA as an independent biomarker to guide treatment. Chemotherapy was administered in the early stages of the disease, and local intensity-modulated radiation therapy was added when the patient tested positive for ctDNA, while radiation therapy was stopped and the patient was observed when the ctDNA test was negative. During the follow-up period, ctDNA signals became positive before tumor progression and became negative again at the end of treatment. We also explored the potential of ctDNA in combination with Epstein-Barr virus (EBV) DNA status to predict the prognosis of NPC patients, as well as the criteria for selecting genetic mutations and the testing cycle for ctDNA analysis.

CONCLUSION: The results of ctDNA-based liquid biopsy can serve as an independent biomarker, either independently or in conjunction with EBV DNA status, to guide the treatment and predict the prognosis of NPC.

PMID:40726932 | PMC:PMC12068179 | DOI:10.12998/wjcc.v13.i21.105066

Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003049. Online ahead of print.

ABSTRACT

Fatty acid-binding proteins (FABPs) are intracellular lipid chaperones with molecular weights of approximately 14-15 kDa. By binding and transporting fatty acids and lipid-related molecules, FABPs precisely regulate metabolic pathways, signal transduction, and gene expression, playing a central role in cancer initiation and progression. The 11 identified subtypes (FABP1-FABP12; FABP11 is identical to FABP3) exhibit tissue-specific expression and influence tumor progression through metabolic reprogramming, immune microenvironment modulation, and therapy resistance. Metabolically, FABPs enhance fatty acid uptake, β-oxidation, and synthesis, meeting the high proliferative demands of tumors. In immune regulation, FABP4+ macrophages secrete IL-6 to suppress T cell activity, while FABP6 downregulates MHC-I molecule expression to reduce CD8+ T cell infiltration, fostering an immunosuppressive microenvironment. Regarding therapy resistance, FABP4 enhances mitochondrial β-oxidation to reduce apoptosis in ovarian cancer, and FABP5 promotes chemoresistance in HCC via the HIF-1α pathway. Functional heterogeneity exists among subtypes: FABP7 drives glioblastoma stem cell migration via RXRα signaling, while FABP5 exhibits context-dependent roles, promoting HCC progression but suppressing colorectal cancer (CRC) through mTOR-mediated autophagy. Clinically, FABPs serve as diagnostic biomarkers and therapeutic targets. However, challenges such as insufficient target specificity, cross-cancer heterogeneity, and normal tissue toxicity remain. Future studies should integrate multi-omics and single-cell technologies to elucidate cell-specific mechanisms and develop precise combination therapies for clinical translation.

PMID:40717587 | DOI:10.1097/JS9.0000000000003049

Iran J Pharm Res. 2025 May 25;24(1):e159954. doi: 10.5812/ijpr-159954. eCollection 2025 Jan-Dec.

ABSTRACT

CONTEXT: Gastric cancer (GC) is a major global health burden, with drug resistance representing a critical barrier to effective treatment. Understanding the mechanisms underlying drug resistance and leveraging advanced technologies, such as artificial intelligence (AI), are essential for developing innovative therapeutic strategies.

EVIDENCE ACQUISITION: This review systematically examines the primary mechanisms of drug resistance in GC, organized into eight categories: Reduced drug uptake, enhanced drug efflux, impaired pro-drug activation or increased inactivation, molecular target alterations, enhanced DNA damage repair, imbalance in apoptotic regulation, tumor microenvironment modifications, and phenotypic changes. Additionally, the role of AI in addressing these challenges is explored, with a focus on omics-driven insights, pathway analysis, biomarker discovery, and modeling drug-response relationships.

RESULTS: The review highlights the transformative potential of AI in advancing precision therapy for GC. Key applications include therapeutic stratification, optimization of drug combinations, adaptive therapy design, and integration with clinical workflows. Challenges such as data quality, model interpretability, and the need for interdisciplinary collaboration are identified, along with strategies to address these barriers. Future directions emphasize the development of explainable AI models, integration of multi-omics and real-time patient data, and AI-driven drug discovery targeting resistance pathways.

CONCLUSIONS: By bridging research and clinical practice, AI offers a promising path to more effective, personalized, and adaptive therapeutic strategies for GC. Overcoming existing challenges and leveraging AI's potential can significantly improve treatment outcomes and address the pressing issue of drug resistance in GC.

PMID:40708930 | PMC:PMC12285678 | DOI:10.5812/ijpr-159954

STAR Protoc. 2025 Jul 24;6(3):103980. doi: 10.1016/j.xpro.2025.103980. Online ahead of print.

ABSTRACT

Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth. For complete details on the use and execution of this protocol, please refer to Kremer et al. and other works.^{1,2,3,4,5,6,7} This protocol is an update to Cerrizuela et al.⁷.

PMID:40711871 | DOI:10.1016/j.xpro.2025.103980

bioRxiv [Preprint]. 2025 Jun 8:2025.06.08.658506. doi: 10.1101/2025.06.08.658506.

ABSTRACT

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOT's strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

PMID:40661390 | PMC:PMC12259011 | DOI:10.1101/2025.06.08.658506

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Discov Oncol. 2025 Jul 23;16(1):1391. doi: 10.1007/s12672-025-03243-2.

ABSTRACT

We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By combining single-cell RNA-seq, bulk transcriptomics, and deep neural networks (DNN), we identified a 23-gene signature validated across four cohorts (AUC 0.696–0.812). Conducted MR analysis to explore causal links between signature genes and NSCLC incidence, providing biological insights. (3) Results: A prognostic signature was developed, including 23 prognostic genes related to 19 PCD patterns and three organelle functions. The signature demonstrated powerful performance in predicting NSCLC prognosis, immune in-filtration, and therapeutic response. Established DNN models showed high value in predicting risk score groupings of NSCLC. MR analysis for combined SNP information of the 23 prognostic genes suggested a link to the high incidence of NSCLC. Individual MR analysis showed that HIF1A and SQLE expression had a causal effect on NSCLC incidence. (4) Conclusion: This signature stratifies high-risk patients with immunosuppressive microenvironments and predicts enhanced sensitivity to gemcitabine and PD-1 inhibitors, offering a roadmap for personalized NSCLC management.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-025-03243-2.

PMID:40699399 | PMC:PMC12287486 | DOI:10.1007/s12672-025-03243-2