IET Systems Biology最新文献

{"title":"Transcriptomic and Machine Learning-Based Classification of Unprovoked Versus Provoked Venous Thromboembolism Using Public Data","authors":"Yajing Li,&nbsp;Hongru Deng,&nbsp;Yongquan Gu","doi":"10.1049/syb2.70058","DOIUrl":"10.1049/syb2.70058","url":null,"abstract":"<p>Venous thromboembolism (VTE) comprises provoked and unprovoked forms; accurate classification informs anticoagulation duration and recurrence risk but is limited by clinical phenotyping. We analysed the GSE48000 whole-blood transcriptomes to identify differentially expressed genes (DEGs) between provoked and unprovoked VTE. DEGs underwent GO and KEGG enrichment. Random forest ranked features, and an artificial neural network (ANN) built on the top 30 genes was trained and evaluated discrimination using stratified 10-fold cross-validation with receiver operating characteristic (ROC) analysis. A 30-gene signature cleanly separated the two subtypes. Most genes showed lower expression in unprovoked VTE, with a notable upregulation of GDF2, LGALS2, and LOC100130229. Enrichment analyses highlighted immune regulation and vesicle-transport pathways. The ANN achieved an AUC of 0.799 in this dataset. Transcriptomic profiling coupled with machine learning distinguished provoked from unprovoked VTE with excellent discrimination, supporting the feasibility of artificial intelligence (AI)-based molecular diagnostics for classification and risk assessment. Prospective validation in larger, independent cohorts is warranted.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12949607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147322469","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":"Pan-Cancer Analysis of CLDN3 and Its Contribution to 5-FU Resistance in Colorectal Cancer","authors":"Xi Zeng,&nbsp;Lu Zhang,&nbsp;Qing Chen,&nbsp;Yan Zeng,&nbsp;Guanglei Yang,&nbsp;Dan Feng,&nbsp;Bin Han","doi":"10.1049/syb2.70059","DOIUrl":"10.1049/syb2.70059","url":null,"abstract":"<p>The expression patterns and potential regulatory correlates of CLDN3 in cancers remain insufficiently characterised, necessitating further investigation. We employed R software alongside bioinformatics platforms to analyse the aberrant expression of CLDN3. Experiments in vitro, including proliferation, wound healing, cell cycle progression and apoptosis assays, were conducted to evaluate the role of CLDN3 in CRC. Co-immunoprecipitation (CO-IP) and immunofluorescence analyses were conducted to investigate the interaction between CLDN3 and TRIM28. Western blotting was employed to evaluate the effect of TRIM28 on CLDN3 SUMOylation and protein stability. CLDN3 was found to be overexpressed in several cancers. Genomic alterations and promoter hypomethylation were identified as key contributors to CLDN3 dysregulation. Bioinformatic analysis suggests that CLDN3 is associated with tumour progression and poor prognosis by influencing pathways, it also contributes to immune dysregulation and chemo-resistance mechanisms. Knockdown of CLDN3 in CRC cells decreased proliferation and migration. CLDN3 overexpression was shown to reduce the sensitivity to 5-FU in CRC cells. CO-IP and immunofluorescence confirmed a direct interaction between CLDN3 and TRIM28. Western blot analysis demonstrated that TRIM28 mediates CLDN3 SUMOylation and degradation. CLDN3 influences the growth and chemotherapy resistance of CRC cells, its interaction with TRIM28 makes the TRIM28/CLDN3 axis as a promising therapeutic target for CRC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12949608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318840","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFS-Unet: A Multi-Path Vision Mamba Network for Precise Thyroid Nodule Segmentation","authors":"Shaoqiang Wang,&nbsp;Zhongran Liu,&nbsp;Guiling Shi,&nbsp;Chengye Li,&nbsp;Linhao Zhang,&nbsp;Tiyao Liu,&nbsp;Yawu Zhao,&nbsp;Yuchen Wang,&nbsp;Qiang Li,&nbsp;Xiaochun Cheng","doi":"10.1049/syb2.70044","DOIUrl":"10.1049/syb2.70044","url":null,"abstract":"<p>The automated segmentation of thyroid nodules from ultrasound images holds significant value in clinical diagnosis and treatment. However, achieving precise segmentation remains a substantial challenge due to issues such as blurred nodule boundaries, variable scales, image noise, and inaccurate annotations. To address these difficulties, this paper proposes a novel medical image segmentation network named MFS-Unet. The network introduces three innovative modules to enhance segmentation performance. First, we designed the multi-path vision mamba (MPV) module, which leverages the advantages of state space models (SSMs) to efficiently capture global contextual information and multi-scale features with linear computational complexity, effectively addressing the problem of significant variations in nodule size. Second, a feature gating (FG) module is deployed in the skip connections between the encoder and decoder. Through an attention mechanism, it dynamically screens and enhances features transmitted from the encoder, suppressing background noise and reinforcing key boundary information of the nodules. Finally, we propose a supervised label rectification (SLR) module, aimed at proactively handling the prevalent issue of label noise in training data. By dynamically adjusting loss weights during training, it guides the model to learn more robust feature representations. We conducted extensive experiments on three public thyroid ultrasound datasets: DDTI, TG3K, and TN3K. The results demonstrate that MFS-Unet achieves superior performance across all evaluation metrics compared with various state-of-the-art segmentation methods, proving its effectiveness and significant potential for precise thyroid nodule segmentation in complex ultrasound environments.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127469","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-Based Integrative Analysis Identifies SUMOylation-Related Genes Underlying the Immune Heterogeneity of Sepsis","authors":"Zeqian Li,&nbsp;Jian Yang,&nbsp;Jiale Dong,&nbsp;Zhaofei Ye,&nbsp;Chengxiang Li,&nbsp;Yang Hu,&nbsp;Han Ren,&nbsp;Shiran Li,&nbsp;Zhili Ji","doi":"10.1049/syb2.70053","DOIUrl":"10.1049/syb2.70053","url":null,"abstract":"<p>Sepsis heterogeneity poses a challenge to accurate diagnosis and treatment. The impact of SUMOylation, a post-translational modification, on sepsis is largely unexplored. We integrated three GEO datasets to construct a large-scale sepsis cohort and applied three machine learning algorithms to screen hub genes from differentially expressed genes (DEGs) associated with SUMOylation in sepsis. Unsupervised consensus clustering was performed to identify sepsis subtypes. Using single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA), we analysed the immunological and functional features of these subtypes. We assembled the regulatory network of hub genes and performed drug prediction analysis. The expression of hub genes was confirmed in a murine caecal ligation and puncture (CLP) sepsis model through qRT-PCR. Bioinformatics analysis identified a total of 43 SUMOylation-associated DEGs. The machine learning pipeline further pinpointed eight hub genes: TOP2B, HDAC4, NUP43, HNRNPK, BCL11A, RPA1, RORA and XRCC4. Each gene exhibited high diagnostic potential. Based on this eight-gene signature, sepsis patients were stratified into two subtypes. Subtype A, known as immune suppressive, was characterised by high infiltration of regulatory T cells, along with suppressed activity in immune pathways. The hyper-inflammatory subtype B displayed large infiltration of effector lymphocytes and extensive activation of inflammatory pathways. Drug prediction analysis revealed possible therapeutic compounds, particularly the epigenetic modulator vorinostat. Experimental validation ultimately confirmed the dysregulation of these hub genes. In conclusion, our study discovered a novel eight-gene signature associated with SUMOylation that supports new diagnostic strategies, and uncovers sepsis heterogeneity. The identification of two sepsis subtypes with different immunological and functional characteristics emphasises the role of SUMOylation in sepsis pathophysiology and provides a new strategy for advancing precision diagnostics and personalised therapy.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12858257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094850","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Analysis of FOXO1 as a Tumour Suppressor Biomarker Related to Immune Infiltration and Cell Proliferation of Hepatocellular Carcinoma","authors":"Senzhe Xia,&nbsp;Xueqian Qin,&nbsp;Chenggeng Pan,&nbsp;Dingwei Fan,&nbsp;Daqing Yang","doi":"10.1049/syb2.70051","DOIUrl":"10.1049/syb2.70051","url":null,"abstract":"<p>Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality, and effective biomarkers for indicating prognosis and treatment have still not been fully investigated. Forkhead box O1 (FOXO1), as a crucial transcription factor, its role remains to be elucidated in HCC. Herein, by combining bioinformatics techniques and basic experiments, the expression and function of FOXO1 in HCC were preliminarily explored. The expression profile, prognostic analysis, mutation landscape, immune infiltration abundance and tumour stemness index of FOXO1 were determined in TCGA and GEO databases. Moreover, a FOXO1-related nomogram was constructed and validated in the HCC cohort. Ultimately, the expression and function of FOXO1 in HCC were verified through basic experiments, such as western blotting, RT-qPCR, immunohistochemical analysis, CCK8 assay and clone formation assay. The expression of FOXO1 was decreased and was associated with a favourable prognosis in majority of cancers. Mutation landscapes of FOXO1 in various cancers were described and revealed the significant association between FOXO1 expression and TMB/MSI. A FOXO1-based Nomogram was constructed and verified in HCC cohort. The expression and function of FOXO1 were closely related to the HCC immune microenvironment. Moreover, there was a negative correlation between the FOXO1 expression and tumour stemness index. Finally, the expression pattern of FOXO1 in HCC and its association with tumour proliferation ability were verified through basic experiments. FOXO1 was identified to regulate the immune microenvironment and the tumour proliferation ability in HCC, demonstrating its potential as a therapeutic target for HCC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12854392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087136","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}