IET Systems Biology最新文献

{"title":"Causality-Preserving Generative Adversarial Networks for the Diagnosis of Neurological Disorders","authors":"Junhong Ren,&nbsp;Wensheng Zhang","doi":"10.1049/syb2.70085","DOIUrl":"10.1049/syb2.70085","url":null,"abstract":"<p>The brain network has proven to be an effective tool for recognising neurological disorders. However, current brain network modelling algorithms often rely on numerous assumptions when evaluating the interaction relationships between brain regions, which are frequently shown to be inaccurate. For instance, some studies assume that interactions among brain regions are linear. Additionally, some research posits that other brain regions do not influence the evaluation outcomes when assessing causal effects among paired brain regions. To address this challenge, we propose an innovative method for modelling brain networks that estimates interaction relationships among brain regions from a causal perspective. Notably, during the evaluation process, this method comprehensively considers all relevant brain regions rather than examining individual relationships in isolation. To validate the efficacy of our approach, we conducted extensive experimental verifications using publicly available datasets. The experimental results indicate that our proposed brain network modelling method not only demonstrates superior performance in the dementia identification task but also successfully identifies several new biomarkers, providing novel perspectives and insights for dementia research.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFCPred: Advancing circRNAs-Encoded Peptides Prediction Through Cluster Purity-Guided Resampling and Fuzzy Voting","authors":"Siyuan Zhao,&nbsp;Bin Yu,&nbsp;Lingling Liu","doi":"10.1049/syb2.70084","DOIUrl":"10.1049/syb2.70084","url":null,"abstract":"<p>Circular RNAs (circRNAs) have recently been shown to possess coding potential via a cap-independent translation mechanism, challenging their traditional classification as noncoding RNAs and implicating it directly in the pathogenesis of various diseases. Despite growing evidence of the functional roles of circRNAs-encoded peptides (circPEPs) in disease mechanisms, computational methods for predicting these peptides remain underdeveloped. In this study, we introduce CFCPred, a novel tool for circPEPs prediction that combines cluster purity-guided resampling with fuzzy voting (FV) within a traditional machine learning framework. To address the scarcity of known circPEPs, we employ a resampling strategy that mitigates class-imbalance in the training data by generating synthetic samples based on relationships among individual samples, their nearest neighbours and their cluster assignments. Additionally, to tackle class overlap in the testing data, FV enables adaptive model selection and dynamic weight adjustment across the ensemble. This adaptive ensemble integrates predictions from multiple base models to produce a robust consensus classification. Experimental validation across multiple independent datasets demonstrates that CFCPred achieves strong predictive performance. Overall, the tool provides a computational framework that can accelerate research into circPEPs and their roles in disease.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13449297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Modelling, Single-Cell Landscape Profiling and Spatial Transcriptomics Provide New Insights Into SUMOylation in Head and Neck Squamous Cell Carcinoma","authors":"Zhe Fang,&nbsp;Kai Mei,&nbsp;Jiaqi Liu,&nbsp;Wei Zhou,&nbsp;Tingjing Li,&nbsp;Hai Zhang,&nbsp;Chuangjie Cao","doi":"10.1049/syb2.70082","DOIUrl":"https://doi.org/10.1049/syb2.70082","url":null,"abstract":"<p>SUMOylation is implicated in the regulation of multiple malignancies. However, its potential roles in head and neck squamous cell carcinoma (HNSCC) remain insufficiently characterised. By integrating bulk RNA-seq, scRNA-seq and stRNA-seq datasets, we systematically interrogated the biological relevance of SUMOylation in HNSCC. Key markers from the signature were further validated using in vitro functional assays. A recognition model was established and validated using 692 HNSCC and 178 non-HNSCC samples. SROC analysis demonstrated robust performance across eight datasets (AUC = 0.92). Functional enrichment and scRNA-seq analyses indicated that SUMOylation may exert its effects in HNSCC primarily through cell-cycle regulation. Among the model features, <i>SAE1</i> was markedly overexpressed in HNSCC (SMD = 1.07, 95% CI 0.56–1.59, <i>p</i> &lt; 0.05). In vitro assays further confirmed that <i>SAE1</i> enhanced proliferation, colony formation and migration in SAS and SCC-9 cells and validated its role in regulating cell cycle progression and apoptosis. We established a SUMOylation-related recognition model for HNSCC with consistently strong performance in multiple external validation cohorts. <i>SAE1</i> emerges as a candidate molecular biomarker for HNSCC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation","authors":"Ya Liu,&nbsp;Xiaofan Yuan,&nbsp;Hong Chen","doi":"10.1049/syb2.70080","DOIUrl":"10.1049/syb2.70080","url":null,"abstract":"<p>Mitochondrial Fission Regulator 1 (<i>MTFR1</i>) plays a critical regulatory role in various malignancies; however, its specific function and clinical significance in breast cancer (BRCA) remain unexplored. This study systematically characterised <i>MTFR1</i> by integrating multi-omics data, spatial transcriptomics, and single-cell sequencing, followed by in vitro experimental validation. <i>MTFR1</i> was significantly overexpressed in BRCA tissues and predominantly localised to malignant cells. Functionally, <i>MTFR1</i> was closely associated with cell cycle progression; in vitro knockdown significantly suppressed cell proliferation and induced cell cycle arrest. Clinically, high <i>MTFR1</i> expression served as an independent predictor of poor prognosis and was linked to chemotherapy resistance. Cross-cancer immunotherapy analyses revealed that <i>MTFR1</i> exhibited improved predictive performance for anti-PD-1 response during the on-treatment phase compared with pretreatment settings. This study elucidates the multidimensional role of <i>MTFR1</i> as a putative oncogene in BRCA, highlighting it as a promising prognostic biomarker and therapeutic target.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scGMB: A scRNA-seq Cell Classification Method Combining GCN and Mamba","authors":"Lejun Gong,&nbsp;Like Yu,&nbsp;Yimu Ji,&nbsp;Shuhua Xu","doi":"10.1049/syb2.70079","DOIUrl":"10.1049/syb2.70079","url":null,"abstract":"<p>This study proposes a single-cell RNA sequencing data classification method called scGMB, which is based on graph convolutional networks (GCN) and the Mamba model. This method constructs a cell graph to capture the topological relationships between cells and combines selective state–space models (SSMs) to extract complex features of gene expression patterns. The innovations of scGMB are as follows: (1) the GCN module captures the topological relationships between cells; (2) the Mamba module efficiently processes sparse and high-dimensional data; (3) the end-to-end architecture improves computational efficiency and interpretability of the results. We validated scGMB on five datasets: Zheng68 K, Zhengsorted, BaronHuman, BaronMouse and AMB, achieving classification accuracies of 72.2%, 91.1%, 98.9%, 99.2% and 99.5%, respectively, outperforming existing mainstream methods. Experimental results demonstrate that scGMB can effectively identify cell types, offering a high-performance and stable new tool for single-cell data analysis. Future work will expand scGMB to spatial transcriptomics and multi-omics data analysis, further optimising model performance to aid biomedical research and cell type discovery.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148354642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Chemokine-Related Genes Derived From T and NK Cells in the Tumour Microenvironment of Ovarian Cancer Based on scRNA-Seq","authors":"Liang Wang,&nbsp;Guifen Liu,&nbsp;Liying Wang,&nbsp;Yisen Cao,&nbsp;JiaYi Jiang,&nbsp;Qunhui Wang,&nbsp;Xite Lin,&nbsp;Zhenhong Wang","doi":"10.1049/syb2.70069","DOIUrl":"https://doi.org/10.1049/syb2.70069","url":null,"abstract":"<p>Ovarian cancer (OC) is characterised by high malignancy. Tumour immune microenvironment (TME) may serve as a breakthrough for therapy. Chemokines are responsible for the recruitment of diverse immune cells in TME. We identify the association between chemokines and T and NK cells. This study was conducted to screen the chemokine derived from T and NK cells in OC single-cell RNA sequencing (scRNA-seq) dataset was acquired from the GEO database. Total 64 genes were collected. The intersection of marker genes of T and NK cells and chemokine-related genes was considered as key chemokine-related genes. Then, cell-cell communication analysis, pseudotime analysis and transcription factors (TFs)–target genes regulatory network construction, were performed. Immunohistochemical staining experiments and wound healing assays are performed. The results found 21 distinct cell subgroups and 8 core cell types. Total 667 and 611 marker genes were identified in T and NK cells, respectively. Eight key chemokine-related genes were found. Also, 49 TFs-related to cell subtypes were obtained, and the TFs with the highest activation degree was SPI1. Eight key chemokine-related genes were mainly distributed in T cells, NK cells and monocyte cells. The pseudotime trajectory shown that key chemokine-related genes were highly expressed in T and NK cells in the later stage of differentiation. CCL5 exhibits differences in ovarian malignant tumour tissues compared with normal ovarian tissues. These findings provide novel insights for subsequent exploration in OC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling the Mechanism of Compound Kushen Injection in Treating Cervical Cancer Through Ferroptosis Regulation: An Integrated Network Pharmacology and Molecular Docking Study","authors":"Linlin Chen,&nbsp;Chunmei Li,&nbsp;Cheng Xu,&nbsp;Rong Cai,&nbsp;Jiayi Chen","doi":"10.1049/syb2.70075","DOIUrl":"https://doi.org/10.1049/syb2.70075","url":null,"abstract":"<p>This study employed network pharmacology and molecular docking techniques to investigate the potential mechanisms of Compound Ku Shen Injection in the treatment of cervical cancer (CC), focussing on ferroptosis. Active compounds and corresponding targets of CKI, as well as CC-related targets, were identified from relevant databases. Intersecting targets between CKI and CC were used to construct a protein–protein interaction (PPI) network, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Core compounds and targets were further validated by molecular docking experiments. A total of 58 active compounds and 801 overlapping targets were identified in this study. Key active compounds included matrine, quercetin, diosgenin, luteolin, sophoramine, sophocarpine, formononetin, and kushenol J_qt2. Major target genes involved were IL6, PPARG, HIF1A, TP53, STAT3, EGFR, JUN, MTOR, RELA, and SRC. KEGG highlighted C-type lectin receptor, AGE-RAGE, HIF-1, ErbB, and IL-17 pathways in regulating ferroptosis. Molecular docking confirmed strong binding of compounds to TP53, HIF1A, PPARG, and IL6. Bioinformatics showed downregulation of JUN, PPARG, and RELA in CC patients, whereas TP53 and IL6 were upregulated. The Kaplan-Meier curve suggested that high expressions of JUN and IL6 may serve as prognostic risk markers for CC. These findings suggest that CKI may exert potential anti-CC effects via multi-component, multi-target, and multi-pathway mechanisms, possibly involving ferroptosis-related regulatory processes. This study provides a preliminary computational reference for future mechanistic and experimental investigations of CKI in cervical cancer.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolic Reprogramming in Recurrent Spontaneous Abortion: Key Biomarkers Identification and Diagnostic Model Development","authors":"Fan Wu,&nbsp;Chuanmei Qin,&nbsp;Xiaowei Wei,&nbsp;Qian Li,&nbsp;Yi Yuan,&nbsp;Weihong Zeng,&nbsp;Yi Lin","doi":"10.1049/syb2.70078","DOIUrl":"10.1049/syb2.70078","url":null,"abstract":"<p>Recurrent spontaneous abortion (RSA) involves complex and often unexplained pathogenesis. This study investigated metabolic reprogramming in RSA to identify diagnostic biomarkers. We integrated GEO datasets (GSE26787, GSE165004), identified metabolic reprogramming-related differentially expressed genes, and intersected them with WGCNA modules. Module genes were refined using logistic regression, SVM-RFE, and LASSO to construct a diagnostic model validated by ROC, nomogram, calibration, and decision curve analysis. The role of <i>SREBF2</i> was explored by PCR, western blot, CCK-8, and transwell assays in vitro. Immune infiltration (ssGSEA, CIBERSORT) and single-cell RNA sequencing (GSE214607) explored immune-metabolic crosstalk. Results demonstrate critical involvement of metabolic reprogramming in RSA. Four model genes (<i>SREBF2</i>, <i>PPARG</i>, <i>SQLE</i>, <i>UCP2</i>) were upregulated in RSA and enriched in lipid/cholesterol pathways. Experimental validation confirmed <i>SREBF2</i> upregulation in RSA decidua. Its overexpression in endometrial stromal cells impaired cell proliferation, decidualization, and trophoblast migration. Immune infiltration revealed correlations between model genes and specific immune subsets. Single-cell analysis identified altered immune cell proportions and cell-type-specific expression patterns, predominantly in macrophage subsets. This study indicates that lipid-centric metabolic remodelling may concurrently affect decidual function, trophoblast behaviour, and immune tolerance in RSA, offering diagnostic and therapeutic insights.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Salvianolic Acid B in Reversing Oxaliplatin Resistance of Colorectal Cancer Cells","authors":"Wei Liang,&nbsp;Bo Li,&nbsp;Huan Yang,&nbsp;Yuehong Sun,&nbsp;Hongjuan Yang,&nbsp;Xiuru Hu,&nbsp;Rujing Huang,&nbsp;Tingting Hu,&nbsp;Chao Wu,&nbsp;Xinying He,&nbsp;Yunchuan Sun","doi":"10.1049/syb2.70074","DOIUrl":"10.1049/syb2.70074","url":null,"abstract":"<p>Chemotherapy resistance of colorectal cancer (CRC) seriously affects the therapeutic effect. Salvianolic acid B (SalB) is a primary active compound found in the traditional Chinese medicine (TCM) Danshen, known for its antitumour properties and ability to reverse chemoresistance. However, the underlying mechanism remains to be elucidated. The present study aims to explore the chemoresistance-reversal effects of SalB on colon cancer cells and elucidate its potential mechanisms using both network pharmacology and experimental approaches. Accordingly, SalB- and disease-related targets were extracted from the public database. A protein–protein interaction (PPI) network was constructed to identify critical targets. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted using the DAVID database. Validation experiments were carried out using CCK-8, colony-formation, EdU, flow cytometry (Fc), and Western blot (WB) assays. Ultimately, network pharmacology analysis identified 29 overlapping targets between SalB and CRC drug resistance (DR), with key proteins such as TP53, JUN, CASP3 and MMP9. Enrichment analysis revealed that SalB addresses CRC resistance through multiple targets and pathways, notably oxidative stress and apoptosis pathways. Further cellular experiments demonstrated that SalB significantly inhibited the proliferation of HCT116/L-OHP cells. A nontoxic dose of SalB enhanced the sensitivity of these cells to oxaliplatin, promoted apoptosis, upregulated the expression of apoptotic proteins cleaved caspase-3 and Bax and downregulated the expression of antiapoptotic protein Bcl-2. Additional studies indicated that SalB increased ROS levels induced by oxaliplatin. Pretreatment with NAC, a ROS inhibitor, markedly reduced the apoptosis induced by the combined treatment of SalB and oxaliplatin. In conclusion, SalB modulates CRC drug resistance through multiple targets and pathways. SalB potentially reverses oxaliplatin resistance in CRC cells by regulating the ROS-mediated apoptotic signalling pathway.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Analysis Identifies an Anoikis-Related Gene Signature for Predicting Prognosis in Patients With Triple-Negative Breast Cancer","authors":"Bing Xue,&nbsp;Yue Bai,&nbsp;Wu-Di Yang,&nbsp;Shi-Zhen Li,&nbsp;Rui Qin,&nbsp;Hua Zhang,&nbsp;Hong-Fei Wu,&nbsp;Jun-Jian Li,&nbsp;Sen Miao","doi":"10.1049/syb2.70076","DOIUrl":"10.1049/syb2.70076","url":null,"abstract":"<p>Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options and poor prognosis. Anoikis resistance contributes to tumour dissemination, metastasis and therapeutic resistance. However, anoikis-related molecular signatures integrating single-cell and bulk transcriptomic data for prognostic prediction in TNBC remain insufficiently characterised. Bulk RNA-seq data from TCGA–TNBC and GSE58812, together with single-cell transcriptomic data, were analysed. Anoikis-related genes were identified by WGCNA and single-cell module scoring. A prognostic signature was constructed using Cox and LASSO regression. Immune features and immunotherapy-related indicators were evaluated, and STC2 was validated by tissue microarray, immunohistochemistry and in vitro anoikis assays. An anoikis-related gene signature (<i>ASS1</i>, <i>COL18A1</i>, <i>STC2</i>, <i>DHX36</i>, <i>S100B</i> and <i>TGFBI</i>) was established. High-risk patients exhibited poor overall survival, increased cancer-associated fibroblast infiltration and reduced T-cell infiltration. Additionally, high anoikis risk scores were associated with increased mutations frequencies of <i>PTEN</i>, <i>PIK3CA</i> and <i>ZEB2</i>, along with lower tumour mutation burden, microsatellite instability and PD-L1 levels. IHC staining confirmed that patients with low STC2 expression had poor prognosis. Furthermore, STC2 knockdown reduced anoikis-related apoptotic rates in an MDA-MB-231-based anoikis-mimic model in vitro. This study established an anoikis-related gene signature that may improve prognostic stratification and reflect immunotherapy-related features in TNBC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}