ESMO Real World Data Digit Oncol. 2026 Jan 14;11:100652. doi: 10.1016/j.esmorw.2025.100652. eCollection 2026 Mar.

ABSTRACT

BACKGROUND: Malignant tumors release circulating tumor DNA (ctDNA) into the bloodstream, providing insights into tumor-specific mutations and pathways driving cancer progression. ctDNA testing is currently approved as a type of liquid biopsy to monitor disease burden and detect minimal residual disease (MRD). This study aimed to evaluate the adoption of ctDNA testing in a community oncology practice and assess the overall diagnostic performance of ctDNA and its association with disease progression in stage IV colorectal cancer (CRC), as determined by imaging studies.

PATIENTS AND METHODS: This retrospective study analyzed the medical records of 88 patients with gastrointestinal cancers (80 CRC, 5 gastric, 3 esophageal) who underwent ctDNA molecular testing between January 2020 and April 2022. Electronic medical records from patients aged ≥21 years who had two or more ctDNA tests with concurrent imaging studies or a pathology-confirmed CRC, gastric cancer, or esophageal cancer diagnosis were evaluated.

RESULTS: At baseline, 47 (53.4%) patients had negative and 41 (46.6%) had positive results. Most patients had CRC (90.1%). In stage IV CRC, ctDNA was increasing before radiologic progression in all documented cases (100%), with a median lead time of 2.5 months (range 0.5-15 months). In early-stage CRC (I-III), ctDNA preceded radiologic progression in 40% of cases, with a median lead time of 6 months (range 6-10 months).

CONCLUSIONS: Using real-world data, we report the first-time results of the ctDNA testing adoption in a community oncology setting among patients with gastrointestinal cancers, predominantly CRC. Our findings suggest that integration of ctDNA testing may support disease monitoring in routine clinical practice.

PMID:41930304 | PMC:PMC13040887 | DOI:10.1016/j.esmorw.2025.100652

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

Front Immunol. 2026 Mar 18;17:1791707. doi: 10.3389/fimmu.2026.1791707. eCollection 2026.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a complex tumor ecosystem that contributes to its progression. Deubiquitinases (DUBs) are vital regulators in cancer. However, the overall activity of DUBs and their role in driving PDAC progression within immune microenvironment remain largely unknown.

METHODS: We employed an integrative multi-omics strategy combining machine learning (ML) on bulk transcriptomic data, single-cell RNA sequencing and spatial transcriptomic profiling. We applied Coxnet and Fuzzy SVM for prognostic modeling, inferCNV for malignant cell identification, SCENIC for transcription factor regulon analysis, LIANA⁺ for inferring inter-cellular communication networks and cell2location for spatial deconvolution. USP54 expression was detected by real-time quantitative PCR, western blotting and immunohistochemistry. USP54 function was validated through in vitro and in vivo assays.

RESULTS: ML-based pathway analysis revealed post-translational modification as a major prognostic category, within which elevated DUBs activity emerged as an independent adverse prognostic factor. At the single-cell level, USP54 was upregulated along the trajectory of malignant ductal cells and correlated with an inflamed tumor microenvironment. Cell-cell communication analysis predicted signaling from monocytes/macrophages to tumor cells via the THBS1-integrin ligand-receptor pair. This immune-derived signaling potentially converged on KLF5-positive tumor cells, with KLF5 identified as a putative transcriptional activator of USP54. Spatial transcriptomics validated the co-localization of USP54 expression, elevated DUB activity, and KRAS signaling within specific tumor niches adjacent to THBS1-enriched immune regions. High USP54 expression was frequently observed in PDAC tissues and associated with poor patient survival. More importantly, in both BxPC-3 and PANC-1 cell lines, USP54 knockdown suppressed cell proliferation and metastasis, whereas its overexpression enhanced these malignant phenotypes. Subcutaneous xenograft growth and tail vein injection experiments validated these findings in vivo.

CONCLUSIONS: Our comprehensive multi-omics analysis and experimental validation identify the deubiquitinase USP54 as a novel promoter of PDAC progression within a spatially organized tumor-immune microenvironment. These findings suggest USP54 as both a candidate prognostic biomarker and a potential therapeutic target for this lethal malignancy.

PMID:41929495 | PMC:PMC13038871 | DOI:10.3389/fimmu.2026.1791707

J Physiol Pharmacol. 2026 Feb;77(1):xxx. doi: 10.26402/jpp.2026.1.04. Epub 2026 Apr 2.

ABSTRACT

Helicobacter pylori infection remains one of the most common chronic bacterial infections worldwide and represents a major etiological factor in diseases of the upper gastrointestinal tract, including chronic gastritis, peptic ulcer disease, and gastric cancer. Despite continuous refinement of eradication regimens based on antibiotics and proton pump inhibitors, treatment efficacy has progressively declined, primarily due to increasing antimicrobial resistance and the ability of H. pylori to form biofilm structures. Accumulating evidence indicates that biofilm formation, bacterial virulence, and modulation of host immune responses constitute an interconnected network of mechanisms that collectively promote bacterial persistence and therapeutic failure. This review outlines an integrated pathogenic framework for H. pylori, focusing on the functional interplay between key virulence determinants - including CagA, VacA, neutrophil-activating protein (NAP), high-temperature requirement A (HtrA), IceA, DupA, urease, catalase, and adhesins - and their contribution to biofilm development, epithelial barrier disruption, and sustained gastric inflammation. Biofilm formation is highlighted as a central adaptive strategy that not only limits antibiotic penetration but also induces metabolic dormancy, enhances efflux pump activity, and increases tolerance to oxidative stress and immune-mediated clearance, thereby significantly reducing the effectiveness of standard eradication therapies. In addition, the review incorporates novel insights derived from recent high-throughput omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, which have advanced the understanding of H. pylori pathogenicity, adaptive responses, and resistance mechanisms at a systems level. A major emphasis is placed on recent advances in therapeutic strategies that extend beyond conventional antibiotic-based regimens. The review summarizes current pharmacological approaches, including the use of more potent acid-suppressive agents such as vonoprazan, susceptibility-guided and personalized eradication therapies, and emerging anti-biofilm interventions, including antimicrobial peptides, phytochemicals, small-molecule inhibitors, and enzymatic degradation of the extracellular polymeric matrix. In addition, nanotechnology-based drug delivery systems are discussed as promising tools to improve antibiotic stability, bioavailability, and targeted release within the hostile gastric environment. In conclusion, effective management of H. pylori infection requires a mechanistically informed and multidisciplinary approach that integrates bacterial virulence, biofilm biology, host immune modulation, and regional antimicrobial resistance profiles. The combination of established pharmacological therapies with innovative anti-biofilm and nanomedicine-based strategies represents a promising direction for improving eradication outcomes and limiting the further development of antimicrobial resistance.

PMID:41931732 | DOI:10.26402/jpp.2026.1.04

PLoS One. 2026 Apr 3;21(4):e0346173. doi: 10.1371/journal.pone.0346173. eCollection 2026.

ABSTRACT

Cancer remains a leading cause of mortality worldwide and a significant barrier to improving quality of life across all populations. The protein kinase D family, including PRKD3, has been demonstrated to play a crucial role in cancer development through its involvement in regulating key cellular processes. Although growing evidence highlights the role of PRKD3 in the tumorigenesis of certain cancers, a comprehensive pan-cancer analysis of PRKD3 remains unavailable. To address this, we performed an integrative pan-cancer analysis of PRKD3 using multi-omics datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and cBioPortal. We examined PRKD3 expression, copy number variation, mutation, and DNA methylation, and evaluated their associations with clinicopathological features, patient survival, and diagnostic potential across 33 cancer types. Immune relevance was further assessed through correlations with immune infiltration, checkpoint gene expression, and immunotherapy response-related genomic biomarkers. Our results revealed that PRKD3 expression was highly heterogeneous, showing significant upregulation in liver cancer, gastric cancer, and adrenocortical carcinoma, and downregulation in others. Elevated expression was consistently associated with poor prognosis and increased stromal, neutrophil, and cancer-associated fibroblast infiltration in adrenocortical carcinoma, liver cancer, and stomach cancer, whereas paradoxical associations with favorable outcomes were observed in kidney clear cell carcinoma. PRKD3 expression also correlated with immune checkpoint molecules including PD-1, PD-L1, and CTLA-4, supporting an immunosuppressive role, while context-dependent associations with TMB and MSI highlighted its potential influence on tumor immunogenicity and responsiveness to immune checkpoint blockade. Collectively, these findings identify PRKD3 as a potential context-dependent modulator of tumor biology, prognosis, and immune interactions, underscoring its potential as a biomarker of diagnostic, prognostic, and therapeutic relevance in precision oncology.

PMID:41931575 | PMC:PMC13048501 | DOI:10.1371/journal.pone.0346173

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

J Intern Med. 2026 Apr 3. doi: 10.1111/joim.70092. Online ahead of print.

ABSTRACT

Tuberculosis (TB) remains a major global health challenge, with multi-drug antibiotic regimens as the current standard of care. While effective at killing Mycobacterium tuberculosis, these treatments do not resolve persistent inflammation, prevent lung damage, or reverse immune dysregulation that contribute to poor outcomes and disease recurrence. Precision medicine offers a promising alternative but requires deeper insight into disease mechanisms to enable tailored interventions. This comprehensive review introduces the concept of immune endotyping to define the underlying disease mechanisms as tools to decode clinical and immunological heterogeneity in TB. TB displays a wide spectrum of clinical phenotypes, from latent or asymptomatic infection to mild or severe disease with characteristic non-cavitary or cavitary lung pathology. Instead, distinct immune endotypes capture the diverse biological pathways that shape disease progression and treatment response. Similar clinical presentations may arise from different immune dysfunctions, underscoring the need to move beyond broad phenotypic classifications. Advances in multi-omics and computational analyses uncover immune signatures that enable stratification for host-directed therapies (HDTs) targeting hyperinflammation, immunosuppression, coagulopathy or metabolic exhaustion. Integrating clinical, radiological, and immunological data through multimodal profiling is essential for developing personalized interventions. We also explore how endotyping has transformed treatment in other diseases, offering valuable insights for TB. Additionally, we present examples of how putative immune endotypes may be targeted with appropriate HDTs. In summary, this review underscores the potential of immune endotypes to advance precision medicine in TB, moving beyond one-size-fits-all treatment to improve outcomes, especially in severe and drug-resistant cases.

PMID:41930636 | DOI:10.1111/joim.70092

Oncol Res. 2026 Mar 23;34(4):11. doi: 10.32604/or.2026.076176. eCollection 2026.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality worldwide, primarily driven by metabolic reprogramming and immune evasion mechanisms within tumor cells. To adapt to the nutrient-deprived tumor microenvironment (TME), lung cancer cells undergo profound metabolic reprogramming, characterized by enhanced glycolysis (the Warburg effect), increased glutamine dependency (mediated by GLS1), and accelerated lipid synthesis (involving enzymes such as FASN). These metabolic alterations not only remodel the TME but also dampen antitumor immune responses by promoting immunosuppressive cell populations (e.g., Tregs and M2 macrophages) and inhibiting effector functions of CD8⁺ T cells and natural killer (NK) cells. Critically, a bidirectional crosstalk operates between tumor cell metabolism and the immunosuppressive TME: metabolic reprogramming drives immune suppression through metabolite accumulation, whereas the immunosuppressive TME, in turn, promotes tumor cell adaptability-thus forming a positive feedback loop that reinforces immune evasion and therapy resistance. This review elucidates key molecular pathways governing metabolic reprogramming in lung cancer-spanning glucose, amino acid, and lipid metabolism-and their dynamic crosstalk with immune regulation, including epigenetic modifications and non-coding RNA-mediated mechanisms. Additionally, it evaluates emerging therapeutic strategies targeting the metabolic-immune axis, such as inhibitors of HK2 or GLS1 combined with anti-PD-1/PD-L1 agents, which aim to reverse immunosuppression and improve clinical outcomes. By synthesizing recent advances, this work provides a theoretical framework for precision oncology interventions, highlighting the potential of metabolic immunotherapies and future directions integrating AI and multi-omics data to overcome resistance in lung cancer.

PMID:41930159 | PMC:PMC13040304 | DOI:10.32604/or.2026.076176

Oncol Res. 2026 Mar 23;34(4):27. doi: 10.32604/or.2026.070208. eCollection 2026.

ABSTRACT

BACKGROUND: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression; however, their contribution to the functional classification and personalized treatment of gastric cancer remains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatment strategies for patients with gastric cancer.

METHODS: Single-cell and bulk transcriptomic analyses were integrated to characterize gastric cancer fibroblasts. "Seurat", "Slingshot", and "CellChat" were used for dimensionality reduction, trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast modules were identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct a prognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features. The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays.

RESULTS: Eight fibroblast subpopulations associated with gastric cancer metastasis exhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated that metastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed marked immunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-related pathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressed gastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis.

CONCLUSION: This study characterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis and established a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strong predictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, the findings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.

PMID:41930144 | PMC:PMC13040347 | DOI:10.32604/or.2026.070208

Chem Res Toxicol. 2026 Apr 3. doi: 10.1021/acs.chemrestox.5c00440. Online ahead of print.

ABSTRACT

Wildfire smoke exposures are increasingly common, consisting of complex mixtures of gases and particulates known to cause diverse pulmonary health effects. While health outcomes are regularly studied, quantitative links between smoke chemical composition and toxicological outcomes remain poorly defined, limiting interpretation of wildfire smoke health risks. This study explores symbolic regression (SR) as an interpretable artificial intelligence/machine learning method to generate closed-form mathematical models linking chemical exposure to biological responses relevant to wildfire smoke. Prior to application on wildfire-relevant data sets, we benchmarked three Python-based SR packages on simulated data, assessing performance across varying noise levels and operator complexities. Insights from these simulation tests, such as the importance of including necessary operators, were incorporated when applying SR to lab-generated wildland fire exposure-toxicity data. This data set included chemical characterizations of biomass smoke exposures and corresponding pulmonary responses in female CD-1 mice (n = 60). Specifically, we evaluated the ability to predict a lung injury marker using (1) targeted measures of over 80 chemicals measured in smoke (RMSE = 17.57 mg/mL) and (2) lung tissue measures of hundreds of transcripts (RMSE = 15.12 mg/mL). Resulting error metrics were comparable to Random Forest and XGBoost models. To aid model interpretation, we developed directional ensemble contribution scores (DECS), a novel feature importance scoring method that quantifies the direction and magnitude of predictor contributions across top-performing models. Expert toxicologists also contributed to model prioritization, integrating a "biologists-in-the-loop" approach. Results highlighted polycyclic aromatic hydrocarbons as drivers of lung injury and methoxyphenols as suppressors. Transcriptomic analyses highlighted a small set of genes, which have roles in metabolism, cell proliferation, immune regulation, and oncogenic processes, with MYC proto-oncogene (Myc) showing the strongest association. Overall, this study demonstrates SR and associated DECS as practical, interpretable tools for modeling environmental mixtures, such as wildfire smoke, and their toxicological effects.

PMID:41928614 | DOI:10.1021/acs.chemrestox.5c00440

PLoS One. 2026 Apr 3;21(4):e0346173. doi: 10.1371/journal.pone.0346173. eCollection 2026.

ABSTRACT

Cancer remains a leading cause of mortality worldwide and a significant barrier to improving quality of life across all populations. The protein kinase D family, including PRKD3, has been demonstrated to play a crucial role in cancer development through its involvement in regulating key cellular processes. Although growing evidence highlights the role of PRKD3 in the tumorigenesis of certain cancers, a comprehensive pan-cancer analysis of PRKD3 remains unavailable. To address this, we performed an integrative pan-cancer analysis of PRKD3 using multi-omics datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and cBioPortal. We examined PRKD3 expression, copy number variation, mutation, and DNA methylation, and evaluated their associations with clinicopathological features, patient survival, and diagnostic potential across 33 cancer types. Immune relevance was further assessed through correlations with immune infiltration, checkpoint gene expression, and immunotherapy response-related genomic biomarkers. Our results revealed that PRKD3 expression was highly heterogeneous, showing significant upregulation in liver cancer, gastric cancer, and adrenocortical carcinoma, and downregulation in others. Elevated expression was consistently associated with poor prognosis and increased stromal, neutrophil, and cancer-associated fibroblast infiltration in adrenocortical carcinoma, liver cancer, and stomach cancer, whereas paradoxical associations with favorable outcomes were observed in kidney clear cell carcinoma. PRKD3 expression also correlated with immune checkpoint molecules including PD-1, PD-L1, and CTLA-4, supporting an immunosuppressive role, while context-dependent associations with TMB and MSI highlighted its potential influence on tumor immunogenicity and responsiveness to immune checkpoint blockade. Collectively, these findings identify PRKD3 as a potential context-dependent modulator of tumor biology, prognosis, and immune interactions, underscoring its potential as a biomarker of diagnostic, prognostic, and therapeutic relevance in precision oncology.

PMID:41931575 | PMC:PMC13048501 | DOI:10.1371/journal.pone.0346173

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

J Intern Med. 2026 Apr 3. doi: 10.1111/joim.70092. Online ahead of print.

ABSTRACT

Tuberculosis (TB) remains a major global health challenge, with multi-drug antibiotic regimens as the current standard of care. While effective at killing Mycobacterium tuberculosis, these treatments do not resolve persistent inflammation, prevent lung damage, or reverse immune dysregulation that contribute to poor outcomes and disease recurrence. Precision medicine offers a promising alternative but requires deeper insight into disease mechanisms to enable tailored interventions. This comprehensive review introduces the concept of immune endotyping to define the underlying disease mechanisms as tools to decode clinical and immunological heterogeneity in TB. TB displays a wide spectrum of clinical phenotypes, from latent or asymptomatic infection to mild or severe disease with characteristic non-cavitary or cavitary lung pathology. Instead, distinct immune endotypes capture the diverse biological pathways that shape disease progression and treatment response. Similar clinical presentations may arise from different immune dysfunctions, underscoring the need to move beyond broad phenotypic classifications. Advances in multi-omics and computational analyses uncover immune signatures that enable stratification for host-directed therapies (HDTs) targeting hyperinflammation, immunosuppression, coagulopathy or metabolic exhaustion. Integrating clinical, radiological, and immunological data through multimodal profiling is essential for developing personalized interventions. We also explore how endotyping has transformed treatment in other diseases, offering valuable insights for TB. Additionally, we present examples of how putative immune endotypes may be targeted with appropriate HDTs. In summary, this review underscores the potential of immune endotypes to advance precision medicine in TB, moving beyond one-size-fits-all treatment to improve outcomes, especially in severe and drug-resistant cases.

PMID:41930636 | DOI:10.1111/joim.70092

Oncol Res. 2026 Mar 23;34(4):11. doi: 10.32604/or.2026.076176. eCollection 2026.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality worldwide, primarily driven by metabolic reprogramming and immune evasion mechanisms within tumor cells. To adapt to the nutrient-deprived tumor microenvironment (TME), lung cancer cells undergo profound metabolic reprogramming, characterized by enhanced glycolysis (the Warburg effect), increased glutamine dependency (mediated by GLS1), and accelerated lipid synthesis (involving enzymes such as FASN). These metabolic alterations not only remodel the TME but also dampen antitumor immune responses by promoting immunosuppressive cell populations (e.g., Tregs and M2 macrophages) and inhibiting effector functions of CD8⁺ T cells and natural killer (NK) cells. Critically, a bidirectional crosstalk operates between tumor cell metabolism and the immunosuppressive TME: metabolic reprogramming drives immune suppression through metabolite accumulation, whereas the immunosuppressive TME, in turn, promotes tumor cell adaptability-thus forming a positive feedback loop that reinforces immune evasion and therapy resistance. This review elucidates key molecular pathways governing metabolic reprogramming in lung cancer-spanning glucose, amino acid, and lipid metabolism-and their dynamic crosstalk with immune regulation, including epigenetic modifications and non-coding RNA-mediated mechanisms. Additionally, it evaluates emerging therapeutic strategies targeting the metabolic-immune axis, such as inhibitors of HK2 or GLS1 combined with anti-PD-1/PD-L1 agents, which aim to reverse immunosuppression and improve clinical outcomes. By synthesizing recent advances, this work provides a theoretical framework for precision oncology interventions, highlighting the potential of metabolic immunotherapies and future directions integrating AI and multi-omics data to overcome resistance in lung cancer.

PMID:41930159 | PMC:PMC13040304 | DOI:10.32604/or.2026.076176

Discov Oncol. 2026 Apr 2. doi: 10.1007/s12672-026-04740-8. Online ahead of print.

NO ABSTRACT

PMID:41927922 | DOI:10.1007/s12672-026-04740-8

Discov Oncol. 2026 Apr 2. doi: 10.1007/s12672-026-04740-8. Online ahead of print.

NO ABSTRACT

PMID:41927922 | DOI:10.1007/s12672-026-04740-8

Oncogenesis. 2026 Apr 2. doi: 10.1038/s41389-026-00603-7. Online ahead of print.

ABSTRACT

Gallbladder cancer (GBC) is an aggressive malignancy characterized by metabolic plasticity and profound immune evasion. However, the functional role of glutathione peroxidase 3 (GPX3), a secreted antioxidant enzyme, in these processes remains unclear. Multi-omics analyses of paired GBC and adjacent non-tumor tissues revealed consistent downregulation of GPX3, which correlated with reactive oxygen species (ROS) accumulation and enhanced glycolytic activity. Functional restoration of GPX3 in GBC cells reduced intracellular ROS levels, suppressed the expression of glycolysis-related enzymes, and consequently impaired tumor proliferation, migration, and invasion. In xenograft models, GPX3 overexpression markedly attenuated tumor growth and lung metastasis. Notably, GPX3 restoration also enhanced CD8⁺ T cell infiltration and elevated pro-inflammatory cytokine production, suggesting reversal of tumor-associated immunosuppression. These findings identify GPX3 as a critical tumor suppressor that integrates redox regulation, metabolic reprogramming, and immune activation to restrict malignant progression. Targeting GPX3 or its downstream pathways may represent a promising therapeutic strategy to simultaneously suppress gallbladder cancer aggressiveness and reinforce anti-tumor immunity.

PMID:41927557 | DOI:10.1038/s41389-026-00603-7

Curr Comput Aided Drug Des. 2026 Mar 31. doi: 10.2174/0115734099433388260204214424. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with a poor prognosis. BI-1015550 is an oral phosphodiesterase 4B (PDE4B) inhibitor that has shown anti-inflammatory and anti-fibrotic effects; the exact molecular target(s) and mechanism of action in fibrosis are unknown. BI-1015550, an orally available PDE4B inhibitor with a possible anti-fibrotic effect, whose molecular mechanism of action is unknown Methods: We adopt an integrative approach that combines network pharmacology for identifying putative targets, molecular docking, and Molecular Dynamics (MD) simulations to assess the binding, ML-based target prioritization. Predicted targets/pathways were verified by Western blotting and Immunohistochemistry (IHC).

RESULTS: Network pharmacology analysis identified eight key targets: PTGS2, VCAM1, MMP1, IGF1, MMP7, CCL5, MMP13, and SELE. Docking results and MD simulation demonstrated that the predicted major targets of BI-1015550 include MMP1, PTGS2, and VCAM1. Therapeutic targets were also prioritized using machine learning methods. BI-1015550 treatment significantly decreased collagen deposition and HYP content of lung tissues in vivo. It down-regulated PTGS2, MMP1, and VCAM1 proteins via modulation of the NF-κB signaling pathway.

DISCUSSION: We presented an integrative multi-omics approach based on in silico prediction and wet-lab experiments to dissect the antifibrotic activity of BI-1015550. We showed here that BI- 1015550 mainly acts by inhibiting the NF-κB axis, resulting in downstream suppression of profibrotic and proinflammatory mediators. Our work on integrating network pharmacology with molecular simulation and ML is promising in both identifying reliable targets and providing a solid basis for further drug repurposing and mechanistic investigations. The better performance of BI-1015550 than current drugs (i.e., nintedanib and pirfenidone) demonstrates that it could be considered as an effective multi-target therapy against IPF.

CONCLUSION: BI-1015550 attenuates idiopathic pulmonary fibrosis through the suppression of the NF-kB signalling pathway and up-regulation of PTGS2, MMP1, and VCAM1. This provides some theoretical basis to treat IPF with this compound and indicates that a combination study is beneficial for revealing drug mechanisms.

PMID:41926303 | DOI:10.2174/0115734099433388260204214424

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | DOI:10.1007/s00262-026-04374-3