Omics A Journal of Integrative Biology最新文献

Entropy-Guided Sample-Specific Feature Selection for Robust Incomplete Multi-Omics Learning in Gut Microbiome Disease Prediction and Biomarker Discovery. 熵引导样本特异性特征选择在肠道微生物疾病预测和生物标志物发现中的鲁棒不完全多组学学习。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-11-01 Epub Date: 2026-07-23 DOI: 10.1177/15578100261472595

Min Li, Kaixin Cheng, Mingzhu Lou, Peng Wang, Wengan Xu

{"title":"Entropy-Guided Sample-Specific Feature Selection for Robust Incomplete Multi-Omics Learning in Gut Microbiome Disease Prediction and Biomarker Discovery.","authors":"Min Li, Kaixin Cheng, Mingzhu Lou, Peng Wang, Wengan Xu","doi":"10.1177/15578100261472595","DOIUrl":"10.1177/15578100261472595","url":null,"abstract":"The rapid advancement of multi-omics integration facilitates deep insights into complex diseases. However, incomplete modalities, heterogeneity, and high dimensionality hinder robust analysis. To address these limitations, we propose entropy-guided sample-specific feature selection for robust incomplete multi-omics learning (ESSFS-IMO), a novel framework for accurate disease prediction and interpretable biomarker discovery under missing-data conditions. It combines instance-wise feature selection, entropy-adaptive optimization, and variational representation learning. Specifically, a Gumbel-Softmax-based selector performs per-sample differentiable feature selection, guided by an entropy-based annealing strategy that dynamically adjusts selection sharpness. Selected features are integrated via an information-bottlenecked variational backbone with variance-weighted fusion, enabling robust classification despite missing modalities. Experiments on inflammatory bowel disease datasets demonstrate that ESSFS-IMO outperforms state-of-the-art baselines in accuracy, F1-score, and area under the receiver operating characteristic curve. The model maintains high performance across missing patterns and yields biologically coherent biomarkers, effectively linking microbial, transcriptional, and metabolic profiles to immune regulation. In conclusion, ESSFS-IMO provides a robust, interpretable solution for incomplete multi-omics learning. By integrating entropy-guided selection and variational information bottlenecks, it achieves superior predictive power and resilience while identifying meaningful signatures associated with intestinal inflammation, holding promise for broader biomedical applications.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"631-640"},"PeriodicalIF":2.0,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Driven Multiomics Biomarkers for Precision Oncology: Navigating the Translational Gap and Regulatory Hurdles. 人工智能驱动的精确肿瘤学多组学生物标志物：跨越翻译差距和监管障碍。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-11-01 Epub Date: 2026-08-06 DOI: 10.1177/15578100261474653

Ujwal Havelikar, Atharv Shinde, Hrushikesh Mhaismale, Pratik Pawar, Nilesh Chaudhari, Raosaheb Shendge, Gowtham Menon, Sandeep Kumar

{"title":"AI-Driven Multiomics Biomarkers for Precision Oncology: Navigating the Translational Gap and Regulatory Hurdles.","authors":"Ujwal Havelikar, Atharv Shinde, Hrushikesh Mhaismale, Pratik Pawar, Nilesh Chaudhari, Raosaheb Shendge, Gowtham Menon, Sandeep Kumar","doi":"10.1177/15578100261474653","DOIUrl":"10.1177/15578100261474653","url":null,"abstract":"The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"658-668"},"PeriodicalIF":2.0,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence-Driven Multiomics Integration in Lung Cancer: From Data Convergence to Precision Phenomics. 人工智能驱动的肺癌多组学整合：从数据聚合到精确表型组学。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-11-01 Epub Date: 2026-08-07 DOI: 10.1177/15578100261472236

Sanjukta Dasgupta, Debapriya De

{"title":"Artificial Intelligence-Driven Multiomics Integration in Lung Cancer: From Data Convergence to Precision Phenomics.","authors":"Sanjukta Dasgupta, Debapriya De","doi":"10.1177/15578100261472236","DOIUrl":"10.1177/15578100261472236","url":null,"abstract":"Lung cancer remains a leading cause of cancer-related mortality worldwide due to its extensive molecular heterogeneity, late-stage diagnosis, and therapeutic resistance. Advances in high-throughput omics technologies have enabled comprehensive characterization of tumors across multiple biological layers, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, single-omics analyses provide only fragmented insights into tumor biology, highlighting the need for integrative multiomics approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for integrating heterogeneous datasets and uncovering biologically and clinically relevant patterns. This review summarizes recent advances in AI-driven multiomics integration for lung cancer, highlighting its applications in molecular subtyping, biomarker discovery, prognosis prediction, therapeutic response modeling, and precision oncology. We also discuss current challenges, including data heterogeneity, model interpretability, reproducibility, and clinical translation, together with emerging strategies for integrating multimodal data such as radiomics and digital pathology. Finally, we introduce precision phenomics as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management. Collectively, AI-driven multiomics integration has the potential to transform lung cancer research and improve patient outcomes.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"641-657"},"PeriodicalIF":2.0,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148689432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in Single-Cell Metabolomics and Their Applications in Disease Biology. 单细胞代谢组学研究进展及其在疾病生物学中的应用

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-11-01 Epub Date: 2026-08-26 DOI: 10.1177/15578100261480571

Jie Du, Huihui Tao, Yujie Zhang, Mengyao Wu, Erjiao Hao, Tianyuan Li, Min Feng, Feng Zhu, Ruijie Yang, Ning Ni, Yong Dai

{"title":"Advances in Single-Cell Metabolomics and Their Applications in Disease Biology.","authors":"Jie Du, Huihui Tao, Yujie Zhang, Mengyao Wu, Erjiao Hao, Tianyuan Li, Min Feng, Feng Zhu, Ruijie Yang, Ning Ni, Yong Dai","doi":"10.1177/15578100261480571","DOIUrl":"10.1177/15578100261480571","url":null,"abstract":"Single-cell metabolomics (SCM) provides a functional view of cellular heterogeneity by measuring metabolic states at cellular or subcellular resolution. Unlike bulk metabolomics, SCM can reveal rare metabolic cell states, spatially restricted metabolic niches, dynamic pathway activity, and treatment-associated metabolic adaptations. This review summarizes major SCM strategies, including spatial mass spectrometry imaging (MSI), isolated single-cell mass spectrometry (MS), isotope-assisted approaches, fluorogenic probes, and vibrational spectroscopy-based methods. Rather than treating these platforms as interchangeable technologies, we organize the review around a question-driven framework linking biological questions to platform selection, data structures, computational workflows, and interpretation boundaries. We also discuss major analytical challenges, including limited sample amount, missing values, ion suppression, batch effects, metabolite annotation uncertainty, and incomplete standardization. Disease-related studies suggest that SCM can identify recurrent metabolic programs, candidate biomarkers, and intervention-relevant metabolic nodes, but most applications remain at the discovery or early translational stage. Future progress will require standardized workflows, improved quantitative confidence, multimodal integration, and validation in clinically relevant models and cohorts.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"669-685"},"PeriodicalIF":2.0,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiomics Characterization of HAO2 Expression and Its Clinical Associations in Clear Cell Renal Cell Carcinoma. 透明细胞肾细胞癌中HAO2表达的多组学特征及其临床相关性

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-15 DOI: 10.1177/15578100261495845

Haiting Chen, Minghui Ge, Mengxiao Wang, Qin Zhang, Ningning Luo, Dongsheng Chen, Weigang Bian

{"title":"Multiomics Characterization of HAO2 Expression and Its Clinical Associations in Clear Cell Renal Cell Carcinoma.","authors":"Haiting Chen, Minghui Ge, Mengxiao Wang, Qin Zhang, Ningning Luo, Dongsheng Chen, Weigang Bian","doi":"10.1177/15578100261495845","DOIUrl":"https://doi.org/10.1177/15578100261495845","url":null,"abstract":"Renal cell carcinoma (RCC) exhibits extensive metabolic reprogramming and tumor microenvironmental heterogeneity, contributing to variable outcomes and treatment responses. Hydroxyacid oxidase 2 (HAO2) has been implicated in metabolic regulation, but its clinical and biological relevance in RCC remains incompletely characterized. We integrated transcriptomic, proteomic, genomic, and single-cell RNA-sequencing data to characterize HAO2 expression, clinicopathological associations, metabolic and immune features, and treatment-related outcomes. Exploratory drug-sensitivity associations were evaluated using the Genomics of Drug Sensitivity in Cancer database and pRRophetic. HAO2 expression was consistently reduced across multiple RCC datasets and showed tumor-normal discrimination with area under the curves ranging from 0.73 to 0.97. Low HAO2 expression was associated with advanced stage and shorter overall survival. HAO2 expression was positively associated with metabolic programs and distinct immune microenvironmental features. In the CheckMate-025 cohort, higher HAO2 expression was associated with longer overall survival in both treatment arms. Single-cell analysis showed reduced HAO2 expression in malignant renal epithelial cells. Exploratory pharmacogenomic analysis identified associations between HAO2 expression and drug sensitivity. HAO2 downregulation is associated with advanced disease, unfavorable survival, and distinct metabolic and immune features in RCC. HAO2 represents a candidate tissue-associated biomarker warranting further experimental and prospective validation.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495845"},"PeriodicalIF":2.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149195950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Pharmacology and Bioinformatic Prediction of Five Medicinal-Food Homologous Plants Against the Progression from Chronic Atrophic Gastritis to Gastric Cancer. 五种药食同源植物抗慢性萎缩性胃炎向胃癌发展的网络药理学及生物信息学预测。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-15 DOI: 10.1177/15578100261495835

Menglin Chen, Houshu Tu, Chengming Yang, Cui Xu, Yaqian Huang, Ling He

{"title":"Network Pharmacology and Bioinformatic Prediction of Five Medicinal-Food Homologous Plants Against the Progression from Chronic Atrophic Gastritis to Gastric Cancer.","authors":"Menglin Chen, Houshu Tu, Chengming Yang, Cui Xu, Yaqian Huang, Ling He","doi":"10.1177/15578100261495835","DOIUrl":"https://doi.org/10.1177/15578100261495835","url":null,"abstract":"Chronic atrophic gastritis (CAG) progresses through distinct pathological stages to gastric cancer. This computational study employed network pharmacology and bioinformatics to predict the active compounds, targets, and pathways of five medicinal-food homologous plants (Houttuyniae Herba, Mume Fructus, Rhizoma Dioscoreae, Portulacae Herba, and Curcumae longae Rhizoma) against this progression. We identified 28 active compounds and 242 targets from TCMSP. differentially expressed genes from GSE130823 and GSE55696 were integrated with weighted gene co-expression network analysis, yielding 129 candidate targets. Machine learning (LASSO and random forest) prioritized AR, EIF6, GSTM2, and FOSB as potential diagnostic biomarkers distinguishing CAG from cancer. Enrichment analysis predicted involvement of IL-17, TNF, and p53 pathways. Compound-target-pathway network analysis identified quercetin and kaempferol as multi-target compounds. Molecular docking predicted binding affinities ranging from weak-to-moderate for core genes (-2.60 to -5.83 kcal/mol) and moderate-to-strong for inflammatory targets (up to -7.25 kcal/mol for quercetin-TNF). These findings computationally predict candidate mechanisms of these five plants, offering a rationale for future experimental validation and therapeutic development.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495835"},"PeriodicalIF":2.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149195910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Expression and Functional Characterization of NR1D2 in Gastric Cancer. NR1D2在胃癌中的表达及功能特征

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-12 DOI: 10.1177/15578100261495866

Yongyuan Chen, Zhipeng Zhuang, Ruifeng Ye, Caiming Weng, Qin Gao, Chen Lin, Hu Zhao, Shaoquan Chen

{"title":"Expression and Functional Characterization of NR1D2 in Gastric Cancer.","authors":"Yongyuan Chen, Zhipeng Zhuang, Ruifeng Ye, Caiming Weng, Qin Gao, Chen Lin, Hu Zhao, Shaoquan Chen","doi":"10.1177/15578100261495866","DOIUrl":"https://doi.org/10.1177/15578100261495866","url":null,"abstract":"Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, and identification of novel molecular markers remains an important research priority. The nuclear receptor NR1D2 has been implicated in tumor progression in several cancer types, but its role in GC remains incompletely characterized. In this study, we integrated bioinformatic analyses of public transcriptomic datasets with in vitro cell-based assays to evaluate the expression, clinical correlates, and cellular functions of NR1D2 in GC. We found that NR1D2 expression was significantly higher in GC tissues than in adjacent nontumor tissues. High NR1D2 expression was associated with shorter overall survival, whereas disease-free and progression-free survival showed no statistically significant differences. Transcriptomic enrichment analyses suggested associations between NR1D2 expression and lipid metabolism, oncogenic signaling pathways, and immune-related gene signatures. In vitro, siRNA-mediated silencing of NR1D2 was associated with reduced cell viability, increased apoptosis, G0/G1 cell cycle arrest, decreased wound closure, and fewer cells crossing Transwell membranes in two GC cell lines. These findings support further investigation of NR1D2 in GC, although its molecular mechanisms, independent prognostic value, and clinical utility require further validation.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495866"},"PeriodicalIF":2.0,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Proteomic and Phosphoproteomic Profiling Reveals Dysregulated Inflammatory Signaling Networks in Rheumatoid Arthritis. 综合蛋白质组学和磷酸化蛋白质组学分析揭示类风湿关节炎炎症信号网络失调。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-09 DOI: 10.1177/15578100261495875

Jinglin Tang, Yixi Li, Bin Tan, Chunyan Huang, Jinzhe Tan, Xuemian Song, Junping Liu, Lin Zhao, Wei Zhang

{"title":"Integrated Proteomic and Phosphoproteomic Profiling Reveals Dysregulated Inflammatory Signaling Networks in Rheumatoid Arthritis.","authors":"Jinglin Tang, Yixi Li, Bin Tan, Chunyan Huang, Jinzhe Tan, Xuemian Song, Junping Liu, Lin Zhao, Wei Zhang","doi":"10.1177/15578100261495875","DOIUrl":"https://doi.org/10.1177/15578100261495875","url":null,"abstract":"Aberrant protein phosphorylation orchestrates sustained inflammatory immune signaling in rheumatoid arthritis (RA), yet the coordinated remodeling of proteomic and phosphoproteomic landscapes in peripheral blood mononuclear cells (PBMCs) remains poorly resolved via system-level multi-omics integration. In this article, we integrated quantitative proteomics, phosphoproteomics, and public transcriptomic datasets from PBMCs of RA patients and healthy controls to unravel multilayered molecular circuits driving RA autoimmune pathology. Differentially expressed proteins were enriched in perturbed fatty acid metabolism and RNA processing pathways, while phosphoproteome quantification uncovered a striking inverse correlation between protein phosphorylation stoichiometry and total protein abundance. Network topological analyses prioritized actin beta (ACTB) and HSP90AA1 as core phosphoprotein hubs controlling cytoskeletal dynamics and mRNA splicing. Cross-omics regulatory integration identified HDAC1, together with transcription factors CBFB, PML, and STAT1, as master mediators bridging phospho-signaling cascades to inflammatory transcriptional programs. Kinome activity inference further revealed disease-reconfigured signaling networks centered on protein kinase C alpha and protein kinase C delta in RA immune cells. Collectively, this integrative multiomics study delineates a phosphorylation-inflammation pathogenic axis in RA, pinpoints druggable hub proteins and kinases as promising therapeutic candidates, and provides a comprehensive systems-level framework to decipher immune metabolic and signaling dysregulation in chronic autoimmune inflammatory disorders.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495875"},"PeriodicalIF":2.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Triadic Carrier-Free Nanomedicines: Natural Product-Based Nanoarchitectures for Cancer Therapy. 三元无载体纳米药物：用于癌症治疗的天然产物纳米结构。

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-08 DOI: 10.1177/15578100261495868

Diyar Salahuddin Ali, Hazha Omar Othman, Rebwar Omar Hassan, Suren Azad Ramadhan, Soran Mahmoud Hameed, Kovan Dilawer Issa, Esra Tariq Anwer

{"title":"Triadic Carrier-Free Nanomedicines: Natural Product-Based Nanoarchitectures for Cancer Therapy.","authors":"Diyar Salahuddin Ali, Hazha Omar Othman, Rebwar Omar Hassan, Suren Azad Ramadhan, Soran Mahmoud Hameed, Kovan Dilawer Issa, Esra Tariq Anwer","doi":"10.1177/15578100261495868","DOIUrl":"https://doi.org/10.1177/15578100261495868","url":null,"abstract":"Triadic carrier-free nanomedicines are an emerging class of anticancer nanoassemblies in which therapeutic efficacy derives solely from the self-assembly and/or prodrug behavior of natural-product building blocks, without exogenous carriers. This review systematically evaluates the design principles, assembly mechanisms, biological activities, pharmacokinetics, safety, and translational status of natural-product-based triadic systems composed of two phytochemicals and a third functional modulator (peptide, nucleic acid fragment, or secondary drug). We summarize current in vitro and in vivo evidence showing that preclinical studies on these triadic carrier-free nanomedicines have reported markedly enhanced cytotoxicity, improved tumor accumulation, and reduced off-target toxicity in selected models compared with free-drug mixtures or conventional nanocarriers. However, these benefits have so far been demonstrated in a limited range of tumor types and experimental conditions and require confirmation in broader, clinically oriented investigations. The novelty of this review lies in providing a focused framework that integrates molecular self-assembly forces, multistimuli responsiveness, and mechanistic crosstalk among three natural-product-derived components, while critically outlining key barriers to manufacturability, long-term biodistribution and toxicity, and regulatory pathways that must be addressed to advance triadic carrier-free nanomedicines toward human cancer therapy.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495868"},"PeriodicalIF":2.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Immune Exclusion Index Identifies T/NK Cell Effector Deficiency and a Barrier-Like Microenvironmental State in MMRp/MSS Endometrial Cancer. 免疫排斥指数识别MMRp/MSS子宫内膜癌中T/NK细胞效应物缺乏和屏障样微环境状态

IF 2 3区生物学

Omics A Journal of Integrative Biology Pub Date : 2026-10-08 DOI: 10.1177/15578100261495854

Jie Wang, Yu Wang, Liyan Duan, Kunming Li, Yingya Li, Yanping Jia, Yu Wang

{"title":"An Immune Exclusion Index Identifies T/NK Cell Effector Deficiency and a Barrier-Like Microenvironmental State in MMRp/MSS Endometrial Cancer.","authors":"Jie Wang, Yu Wang, Liyan Duan, Kunming Li, Yingya Li, Yanping Jia, Yu Wang","doi":"10.1177/15578100261495854","DOIUrl":"https://doi.org/10.1177/15578100261495854","url":null,"abstract":"Mismatch repair-proficient or microsatellite stable (MMRp/MSS) endometrial cancer derives limited benefit from immune checkpoint inhibitor monotherapy, and immune exclusion may reflect both barrier-like tumor-stromal programs and insufficient T cell/natural killer cell (T/NK-cell) effector activity. We integrated The Cancer Genome Atlas uterine corpus endometrial carcinoma (TCGA-UCEC), Clinical Proteomic Tumor Analysis Consortium (CPTAC)-UCEC, GSE17025, GSE21882, and GSE251923 to derive and evaluate an immune exclusion index (IEI) in candidate MMRp/MSS tumors. IEI was derived from a 112-gene biologically constrained candidate pool using Cox elastic-net modeling in the TCGA candidate MMRp/MSS cohort (243 tumors, 39 death events). The weighted 9-gene IEI showed apparent association with poor overall survival in the same TCGA cohort (unadjusted hazard ratio [HR] = 5.06, 95% confidence interval [CI] 2.80-9.14, p = 7.77e-08; adjusted HR = 2.00, 95% CI 1.37-2.93, p = 3.62e-04). Median stratification gave HR = 8.15. Gene Expression Omnibus cohorts supported the adverse phenotype direction but not independent MMRp/MSS survival validation; CPTAC and GSE251923 supported RNA-protein and spot-level marker consistency. IEI provides an interpretable framework for candidate T/NK-low, barrier-like microenvironmental states.","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"15578100261495854"},"PeriodicalIF":2.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0