Mi-So Jeong, Jeong-Yeon Mun, Gi-Eun Yang, Seung-Woo Baek, Sang-Yeop Lee, Sung Ho Yun, Seung Il Kim, Jae-Jun Kim, Seo-Yeong Yoon, Jong-Kil Nam, Yung-Hyun Choi, Hyeok Jun Goh, Tae-Nam Kim, Sun-Hee Leem
<p>Dear Editor,</p><p>Research on liquid biopsy markers is actively ongoing as an alternative diagnostic method for patients with bladder cancer (BC), which frequently recurs.<span><sup>1-3</sup></span> Our study shows that LCN2 expression is linked to BC progression and may serve as a valuable urinary biomarker for identifying early-stage patients and predicting outcomes.</p><p>To identify secreted proteins linked to BC progression, we analysed stepwise 5637 gemcitabine-resistant cell (GRC) lines established in our previous study.<span><sup>4</sup></span> The conditioned media (CM) from highly motile GRC sublines enhanced invasion and migration (Figure S1A,B). Liquid Chromatography-Tandem Mass Spectrometry (LC‒MS/MS) analysis of concentrated CM revealed 408 differentially expressed proteins, with 178 upregulated in the highly mobile P7 cell line. Ingenuity pathway analysis identified 56 proteins associated with three motility-related pathways, 17 of which overlapped between expression and pathway analyses (Figure 1A). Notably, LCN2 demonstrated the strongest differential expression in RNA sequencing data, prompting further investigation due to its established correlation with motility.<span><sup>4</sup></span> LCN2 has been associated with tumour progression and metastasis and has been proposed as a non-invasive prognostic indicator,<span><sup>5-8</sup></span> although its precise role in BC remains unclear.</p><p>The GSE13057 dataset confirmed elevated LCN2 expression in BC tissues compared to normal tissues (Figure S1C). In the 5637GRC model, both intracellular levels and secretion of LCN2 increased at P3 and P7 stages, which were characterised by higher motility (Figure S1D‒H). Analysis of three NMIBC datasets (UROMOL2021, GSE163209 and GSE32894) revealed that 197 genes consistently correlated with LCN2 expression (Figure 1B). Pathway enrichment analysis linked these genes to biological processes involved in cell motility (Figure 1C). High LCN2 expression was associated with activation of Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB), Janus Kinase-Signal Transducer and Activator of Transcription (JAK‒STAT) and Phosphoinositide 3-Kinase (PI3K) signalling pathways, supporting its correlation with aggressive phenotypes (Figure 1D).</p><p>Clinical analyses highlighted the prognostic significance of LCN2. In the GSE163209 cohort, the LCN2 signature predicted progression to metastatic disease (AUC = .714; Figure 1E). Kaplan‒Meier survival analyses across three NMIBC cohorts revealed significantly poorer progression-free survival for patients with high LCN2 expression. In GSE163209, elevated LCN2 levels were also linked to a higher rate of metastatic progression (Figure 1F-I). Likewise, in The Cancer Genome Atlas (TCGA) and GSE13507 cohorts, elevated LCN2 expression was associated with poor cancer-specific survival and increased metastasis (Figure 1J-M). These results underscore LCN2 as a secreted protein linked to BC progre
{"title":"Lipocalin 2 as a potential liquid biopsy marker for early detection of bladder cancer","authors":"Mi-So Jeong, Jeong-Yeon Mun, Gi-Eun Yang, Seung-Woo Baek, Sang-Yeop Lee, Sung Ho Yun, Seung Il Kim, Jae-Jun Kim, Seo-Yeong Yoon, Jong-Kil Nam, Yung-Hyun Choi, Hyeok Jun Goh, Tae-Nam Kim, Sun-Hee Leem","doi":"10.1002/ctm2.70540","DOIUrl":"10.1002/ctm2.70540","url":null,"abstract":"<p>Dear Editor,</p><p>Research on liquid biopsy markers is actively ongoing as an alternative diagnostic method for patients with bladder cancer (BC), which frequently recurs.<span><sup>1-3</sup></span> Our study shows that LCN2 expression is linked to BC progression and may serve as a valuable urinary biomarker for identifying early-stage patients and predicting outcomes.</p><p>To identify secreted proteins linked to BC progression, we analysed stepwise 5637 gemcitabine-resistant cell (GRC) lines established in our previous study.<span><sup>4</sup></span> The conditioned media (CM) from highly motile GRC sublines enhanced invasion and migration (Figure S1A,B). Liquid Chromatography-Tandem Mass Spectrometry (LC‒MS/MS) analysis of concentrated CM revealed 408 differentially expressed proteins, with 178 upregulated in the highly mobile P7 cell line. Ingenuity pathway analysis identified 56 proteins associated with three motility-related pathways, 17 of which overlapped between expression and pathway analyses (Figure 1A). Notably, LCN2 demonstrated the strongest differential expression in RNA sequencing data, prompting further investigation due to its established correlation with motility.<span><sup>4</sup></span> LCN2 has been associated with tumour progression and metastasis and has been proposed as a non-invasive prognostic indicator,<span><sup>5-8</sup></span> although its precise role in BC remains unclear.</p><p>The GSE13057 dataset confirmed elevated LCN2 expression in BC tissues compared to normal tissues (Figure S1C). In the 5637GRC model, both intracellular levels and secretion of LCN2 increased at P3 and P7 stages, which were characterised by higher motility (Figure S1D‒H). Analysis of three NMIBC datasets (UROMOL2021, GSE163209 and GSE32894) revealed that 197 genes consistently correlated with LCN2 expression (Figure 1B). Pathway enrichment analysis linked these genes to biological processes involved in cell motility (Figure 1C). High LCN2 expression was associated with activation of Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB), Janus Kinase-Signal Transducer and Activator of Transcription (JAK‒STAT) and Phosphoinositide 3-Kinase (PI3K) signalling pathways, supporting its correlation with aggressive phenotypes (Figure 1D).</p><p>Clinical analyses highlighted the prognostic significance of LCN2. In the GSE163209 cohort, the LCN2 signature predicted progression to metastatic disease (AUC = .714; Figure 1E). Kaplan‒Meier survival analyses across three NMIBC cohorts revealed significantly poorer progression-free survival for patients with high LCN2 expression. In GSE163209, elevated LCN2 levels were also linked to a higher rate of metastatic progression (Figure 1F-I). Likewise, in The Cancer Genome Atlas (TCGA) and GSE13507 cohorts, elevated LCN2 expression was associated with poor cancer-specific survival and increased metastasis (Figure 1J-M). These results underscore LCN2 as a secreted protein linked to BC progre","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145707486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Dear Editor,</p><p>Post-stroke cognitive impairment (PSCI) remains a prevalent and debilitating complication that profoundly impacts stroke survivors’ quality of life and long-term outcomes.<span><sup>1</sup></span> Building upon our previous report that 78.7% of Chinese patients with first-ever ischemic stroke developed PSCI,<span><sup>2</sup></span> we conducted a prospective cohort study to establish an interpretable, multidimensional prediction framework using multiple machine learning (ML) algorithms.</p><p>A total of 518 acute ischemic stroke (AIS) patients were recruited at Xuanwu Hospital between January and December 2022. Following rigorous screening, 437 patients completed a 3-month cognitive follow-up using the Telephone Interview for Cognitive Status-40 (TICS-40), and 190 (43.5%) were identified as having PSCI (see Figure S1 for details). We collected 89 clinical, neuroimaging, and serological variables (see Table S1 for details). The dataset was split 8:2 into training and test sets. All preprocessing steps, namely outlier removal, imputation, and normalisation, were applied exclusively to the training set. Using 10-fold cross-validation combined with Recursive Feature Elimination (RFE), six ML algorithms, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), were trained and optimised. Model performance was subsequently evaluated on the test set (see Supplementary Information for details).</p><p>Through the feature selection process, a set of twenty features was identified across the six models (see Figure 1 and Figure S2 for details). Baseline characteristics were compared between the PSCI and non-PSCI groups in Table S2. Comparative analysis revealed that the gradient boosting models (LightGBM, XGBoost and CatBoost) consistently demonstrated superior comprehensive predictive performance compared to LR, DT and RF across key metrics, including area under the curve (AUC) (0.73–0.77), precision-recall balance, and clinical net benefit in decision curve analysis (DCA) (see Table 1 and Figure 2 for details). LightGBM and XGBoost excelled in computational efficiency and scalability, whereas CatBoost offered superior stability on limited and imbalanced data. This functional diversity highlights ensemble methods as a robust and adaptable framework for developing clinically viable PSCI predictors.</p><p>All models consistently identified age and education level as core determinants of PSCI (see Figure 1 for details). Ageing is a primary non-modifiable risk factor that promotes the accumulation of neuropathological proteins, neuroinflammation, lipid dysregulation, and neurodegeneration, culminating in cognitive decline.<span><sup>3</sup></span> In contrast, higher education confers protection, plausibly via mechanisms of cognitive reserve and socioeconomic advantage, sustaining compensatory neural effi
{"title":"Development of a multidimensional machine learning framework for predicting post-stroke cognitive impairment: A prospective cohort study","authors":"Aini He, Houlin Lai, Xuefan Yao, Benke Zhao, Wenjing Yan, Wei Sun, Xiao Wu, Kehui Ma, Yuan Wang, Haiqing Song","doi":"10.1002/ctm2.70546","DOIUrl":"10.1002/ctm2.70546","url":null,"abstract":"<p>Dear Editor,</p><p>Post-stroke cognitive impairment (PSCI) remains a prevalent and debilitating complication that profoundly impacts stroke survivors’ quality of life and long-term outcomes.<span><sup>1</sup></span> Building upon our previous report that 78.7% of Chinese patients with first-ever ischemic stroke developed PSCI,<span><sup>2</sup></span> we conducted a prospective cohort study to establish an interpretable, multidimensional prediction framework using multiple machine learning (ML) algorithms.</p><p>A total of 518 acute ischemic stroke (AIS) patients were recruited at Xuanwu Hospital between January and December 2022. Following rigorous screening, 437 patients completed a 3-month cognitive follow-up using the Telephone Interview for Cognitive Status-40 (TICS-40), and 190 (43.5%) were identified as having PSCI (see Figure S1 for details). We collected 89 clinical, neuroimaging, and serological variables (see Table S1 for details). The dataset was split 8:2 into training and test sets. All preprocessing steps, namely outlier removal, imputation, and normalisation, were applied exclusively to the training set. Using 10-fold cross-validation combined with Recursive Feature Elimination (RFE), six ML algorithms, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), were trained and optimised. Model performance was subsequently evaluated on the test set (see Supplementary Information for details).</p><p>Through the feature selection process, a set of twenty features was identified across the six models (see Figure 1 and Figure S2 for details). Baseline characteristics were compared between the PSCI and non-PSCI groups in Table S2. Comparative analysis revealed that the gradient boosting models (LightGBM, XGBoost and CatBoost) consistently demonstrated superior comprehensive predictive performance compared to LR, DT and RF across key metrics, including area under the curve (AUC) (0.73–0.77), precision-recall balance, and clinical net benefit in decision curve analysis (DCA) (see Table 1 and Figure 2 for details). LightGBM and XGBoost excelled in computational efficiency and scalability, whereas CatBoost offered superior stability on limited and imbalanced data. This functional diversity highlights ensemble methods as a robust and adaptable framework for developing clinically viable PSCI predictors.</p><p>All models consistently identified age and education level as core determinants of PSCI (see Figure 1 for details). Ageing is a primary non-modifiable risk factor that promotes the accumulation of neuropathological proteins, neuroinflammation, lipid dysregulation, and neurodegeneration, culminating in cognitive decline.<span><sup>3</sup></span> In contrast, higher education confers protection, plausibly via mechanisms of cognitive reserve and socioeconomic advantage, sustaining compensatory neural effi","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145707520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pallabi Pal, Michele Carrer, Lan Weiss, Olga G. Jaime, Cheng Cheng, Alyaa Shmara, Victoria Boock, Danae Bosch, Marwan Youssef, Yasamin Fazeli, Megan Afetian, Tamar R. Grossman, Michael R. Hicks, Paymaan Jafar-nejad, Virginia Kimonis
<div> <section> <h3> Background</h3> <p>Valosin-containing protein (VCP) related disease, also known as multisystem proteinopathy 1 (MSP1), is an autosomal dominant disease caused by gain-of-function pathogenic variants of the <i>VCP</i> gene. The disease presents with variable combinations of inclusion body myopathy, early-onset Paget's disease of bone, frontotemporal dementia and may also overlap with familial amyotrophic lateral sclerosis. There is currently no treatment for this progressive disease associated with early demise resulting from proximal limb girdle and respiratory muscle weakness. We hypothesise that regulating VCP hyperactivity to normal levels can reduce the disease pathology.</p> </section> <section> <h3> Main topics covered</h3> <p>In this study, we assessed the effect of antisense oligonucleotides (ASOs) specifically targeting the human <i>VCP</i> gene in the patient (R155H) iPSC-derived skeletal muscle progenitor cells (SMPCs). ASOs were well tolerated up to a concentration of 5 µM and significantly reduced VCP protein expression in the SMPCs by 48% (95% CI [39–56]). We also treated the transgenic mouse model of VCP disease with the overexpressed humanised VCP severe A232E pathogenic gene variant (VCP A232E mice) with weekly subcutaneous ASO injections starting from 6 months of age for 3 months. In the skeletal muscle of transgenic mice, ASOs resulted in 30% (95% CI [27–32]) knockdown of VCP protein compared with control ASO. The ASO-mediated reduction of VCP expression in muscle tissue was associated with improvement in autophagy flux and reduction in TAR DNA binding protein 43 (TDP-43) expression, hallmarks of VCP related MSP1. In addition, ASO-treated VCP A232E mice showed improvements in functional tests of muscle strength, such as rotarod and inverted screen test compared with mice treated with control ASO.</p> </section> <section> <h3> Conclusions</h3> <p>These results suggest that targeting VCP could be beneficial in preventing the progression of the VCP myopathy and hold promise for the treatment of patients with VCP related MSP1.</p> </section> <section> <h3> Key points</h3> <div> <ol> <li> <p>VCP multisystem proteinopathy 1 is caused by gain-of-function pathogenic variants of the VCP gene.</p> </li> <li> <p>VCP targeting ASOs were well tolerated and significantly reduced VCP, TAR DNA binding protein 43 (TDP 43), and autophagy protein expression
{"title":"Antisense oligonucleotides targeting valosin-containing protein ameliorate muscle pathology and molecular defects in cell and mouse models of multisystem proteinopathy","authors":"Pallabi Pal, Michele Carrer, Lan Weiss, Olga G. Jaime, Cheng Cheng, Alyaa Shmara, Victoria Boock, Danae Bosch, Marwan Youssef, Yasamin Fazeli, Megan Afetian, Tamar R. Grossman, Michael R. Hicks, Paymaan Jafar-nejad, Virginia Kimonis","doi":"10.1002/ctm2.70530","DOIUrl":"10.1002/ctm2.70530","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Valosin-containing protein (VCP) related disease, also known as multisystem proteinopathy 1 (MSP1), is an autosomal dominant disease caused by gain-of-function pathogenic variants of the <i>VCP</i> gene. The disease presents with variable combinations of inclusion body myopathy, early-onset Paget's disease of bone, frontotemporal dementia and may also overlap with familial amyotrophic lateral sclerosis. There is currently no treatment for this progressive disease associated with early demise resulting from proximal limb girdle and respiratory muscle weakness. We hypothesise that regulating VCP hyperactivity to normal levels can reduce the disease pathology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Main topics covered</h3>\u0000 \u0000 <p>In this study, we assessed the effect of antisense oligonucleotides (ASOs) specifically targeting the human <i>VCP</i> gene in the patient (R155H) iPSC-derived skeletal muscle progenitor cells (SMPCs). ASOs were well tolerated up to a concentration of 5 µM and significantly reduced VCP protein expression in the SMPCs by 48% (95% CI [39–56]). We also treated the transgenic mouse model of VCP disease with the overexpressed humanised VCP severe A232E pathogenic gene variant (VCP A232E mice) with weekly subcutaneous ASO injections starting from 6 months of age for 3 months. In the skeletal muscle of transgenic mice, ASOs resulted in 30% (95% CI [27–32]) knockdown of VCP protein compared with control ASO. The ASO-mediated reduction of VCP expression in muscle tissue was associated with improvement in autophagy flux and reduction in TAR DNA binding protein 43 (TDP-43) expression, hallmarks of VCP related MSP1. In addition, ASO-treated VCP A232E mice showed improvements in functional tests of muscle strength, such as rotarod and inverted screen test compared with mice treated with control ASO.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These results suggest that targeting VCP could be beneficial in preventing the progression of the VCP myopathy and hold promise for the treatment of patients with VCP related MSP1.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ol>\u0000 \u0000 <li>\u0000 <p>VCP multisystem proteinopathy 1 is caused by gain-of-function pathogenic variants of the VCP gene.</p>\u0000 </li>\u0000 \u0000 <li>\u0000 <p>VCP targeting ASOs were well tolerated and significantly reduced VCP, TAR DNA binding protein 43 (TDP 43), and autophagy protein expression ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12683293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145699945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>One of the central challenges in oncology is understanding why tumours stop responding to therapy. Clinicians see this repeatedly: an initial response that gives way to relapse, often driven by the tumour's ability to adapt at the molecular level. These adaptations are not static. They unfold over hours, days, and weeks, and they vary across different regions of the same tumour.</p><p>This means that the molecular programmes that enable a tumour to escape treatment are <i>dynamic, spatially organised, and highly patient-specific</i>. Yet the tools we use today, bulk sequencing, fixed-tissue analysis, and endpoint assays, capture only isolated moments in time. They fall short when clinicians need to know how a tumour changes during treatment, where resistance emerges within the tissue, and when a vulnerable state might be present. This gap calls for a new paradigm for tumour profiling: capturing molecular dynamics directly in living tissue, in both space and time (Figure 1).</p><p>Emerging spatial and temporal omics technologies are heralding this paradigm, but each captures only part of the picture.</p><p>Spatial omics platforms provide detailed maps of fixed biopsy material, yet remain static snapshots that cannot show how these patterns change once treatment begins. Temporal profiling methods offer insight into dynamic responses but rely on sequential biopsies and cannot reveal where within the tissue those changes arise. Lineage tracing,<span><sup>1</sup></span> metabolic labelling,<span><sup>2</sup></span> and live-tissue imaging<span><sup>3</sup></span> each contribute fragments of the picture, but they either require destructive processing, genetic manipulation, or provide only limited molecular depth.</p><p>The core challenge remains: we can map a tumour's landscape or track its evolution, but not capture both in living tissue. This limits our ability to detect early resistance and make timely, biology-guided decisions.</p><p>A key barrier in studying treatment response is that most molecular analyses require destroying the tissue. This makes it impossible to follow how the same piece of patient-derived material changes over time. Nanotechnology now offers a way around this by enabling longitudinal molecular sampling: the ability to extract small amounts of intracellular material from living tissue without compromising its viability.</p><p>Pioneering work using single-probe technologies such as nanopipettes<span><sup>4</sup></span> and FluidFM<span><sup>5</sup></span> showed that it is possible to take ‘live-cell biopsies’: tiny samples of RNA, proteins, or metabolites from the same living cell at multiple timepoints. These studies proved the concept that molecular pathways can be monitored dynamically in living systems, a breakthrough step for temporal omics. However, these approaches work cell-by-cell and are not scalable to tissue-level analysis or to most types of patient-derived samples used in clinical research.</p><p>Nanoneedle a
{"title":"A new paradigm for tumour profiling: Spatiotemporal omics in living tissue","authors":"Ciro Chiappini","doi":"10.1002/ctm2.70547","DOIUrl":"10.1002/ctm2.70547","url":null,"abstract":"<p>One of the central challenges in oncology is understanding why tumours stop responding to therapy. Clinicians see this repeatedly: an initial response that gives way to relapse, often driven by the tumour's ability to adapt at the molecular level. These adaptations are not static. They unfold over hours, days, and weeks, and they vary across different regions of the same tumour.</p><p>This means that the molecular programmes that enable a tumour to escape treatment are <i>dynamic, spatially organised, and highly patient-specific</i>. Yet the tools we use today, bulk sequencing, fixed-tissue analysis, and endpoint assays, capture only isolated moments in time. They fall short when clinicians need to know how a tumour changes during treatment, where resistance emerges within the tissue, and when a vulnerable state might be present. This gap calls for a new paradigm for tumour profiling: capturing molecular dynamics directly in living tissue, in both space and time (Figure 1).</p><p>Emerging spatial and temporal omics technologies are heralding this paradigm, but each captures only part of the picture.</p><p>Spatial omics platforms provide detailed maps of fixed biopsy material, yet remain static snapshots that cannot show how these patterns change once treatment begins. Temporal profiling methods offer insight into dynamic responses but rely on sequential biopsies and cannot reveal where within the tissue those changes arise. Lineage tracing,<span><sup>1</sup></span> metabolic labelling,<span><sup>2</sup></span> and live-tissue imaging<span><sup>3</sup></span> each contribute fragments of the picture, but they either require destructive processing, genetic manipulation, or provide only limited molecular depth.</p><p>The core challenge remains: we can map a tumour's landscape or track its evolution, but not capture both in living tissue. This limits our ability to detect early resistance and make timely, biology-guided decisions.</p><p>A key barrier in studying treatment response is that most molecular analyses require destroying the tissue. This makes it impossible to follow how the same piece of patient-derived material changes over time. Nanotechnology now offers a way around this by enabling longitudinal molecular sampling: the ability to extract small amounts of intracellular material from living tissue without compromising its viability.</p><p>Pioneering work using single-probe technologies such as nanopipettes<span><sup>4</sup></span> and FluidFM<span><sup>5</sup></span> showed that it is possible to take ‘live-cell biopsies’: tiny samples of RNA, proteins, or metabolites from the same living cell at multiple timepoints. These studies proved the concept that molecular pathways can be monitored dynamically in living systems, a breakthrough step for temporal omics. However, these approaches work cell-by-cell and are not scalable to tissue-level analysis or to most types of patient-derived samples used in clinical research.</p><p>Nanoneedle a","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145707560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wanxin Duan, Mingjie Wang, Yifei Liu, Celine Desoyer, Christian Baumgartner, Xiangdong Wang
Precision medicine has evolved through distinct phases, from the origins of the Human Genome Project to mutation-based targeted therapies. This editorial posits that “stereological cell biomedicine” could be a new approach promoting the development of the next generation of precision medicine. This emerging discipline transitions the focus from genomic data to the multi-dimensional and spatiotemporal complexity of single cells. Driven by advances in Stereo single-cell multi-omics (Stereo Cell-seq), spatial transcriptomics (Stereo-seq), and single-cell surfaceomics (sc-surfaceome), this approach aims to capture the stereologically dynamic interactions between organelles within a cell and between cells in the tissue. We argue that understanding the spatiotemporal location of molecules, particularly protein interactions at organelle interfaces and on the cell surface, is as critical as their abundance for defining cellular function in health and disease. Integrating these high-resolution measurements with artificial intelligence and computational modelling will bridge the gap between advanced omics and pathology. Initiatives such as the newly established European Stereo Cell Center (ESCC) signal a global shift towards this new paradigm, which promises to unlock novel diagnostic biomarkers and therapeutic targets for truly multi-factorial and dynamic precision medicine.
{"title":"Stereo cell: A new approach to the next generation of clinical precision medicine","authors":"Wanxin Duan, Mingjie Wang, Yifei Liu, Celine Desoyer, Christian Baumgartner, Xiangdong Wang","doi":"10.1002/ctm2.70537","DOIUrl":"https://doi.org/10.1002/ctm2.70537","url":null,"abstract":"<p>Precision medicine has evolved through distinct phases, from the origins of the Human Genome Project to mutation-based targeted therapies. This editorial posits that “stereological cell biomedicine” could be a new approach promoting the development of the next generation of precision medicine. This emerging discipline transitions the focus from genomic data to the multi-dimensional and spatiotemporal complexity of single cells. Driven by advances in Stereo single-cell multi-omics (Stereo Cell-seq), spatial transcriptomics (Stereo-seq), and single-cell surfaceomics (sc-surfaceome), this approach aims to capture the stereologically dynamic interactions between organelles within a cell and between cells in the tissue. We argue that understanding the spatiotemporal location of molecules, particularly protein interactions at organelle interfaces and on the cell surface, is as critical as their abundance for defining cellular function in health and disease. Integrating these high-resolution measurements with artificial intelligence and computational modelling will bridge the gap between advanced omics and pathology. Initiatives such as the newly established European Stereo Cell Center (ESCC) signal a global shift towards this new paradigm, which promises to unlock novel diagnostic biomarkers and therapeutic targets for truly multi-factorial and dynamic precision medicine.</p>","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"CLINICAL AND TRANSLATIONAL MEDICINE","authors":"","doi":"10.1002/ctm2.70544","DOIUrl":"https://doi.org/10.1002/ctm2.70544","url":null,"abstract":"","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>T cells are central orchestrators of adaptive immunity and play important and complex roles in chronic inflammation, despite that their roles remain even paradoxical. The dysregulations of T cells occur in chronic diseases, such as inflammation and cancer, from being protectors to potent drivers of tissue pathology.<span><sup>1-3</sup></span> Of those, the pro-inflammatory tissue-resident memory (TRM) T cells accumulate within the tissue, perpetuating a cycle of inflammation. Subsets of TRM T cells, including those producing the highly inflammatory cytokine interleulin-17 (IL-17), are directly implicated in tissue damage, to form the ectopic lymphoid tissues, remodel the microenvironment, and amplify the local response in inflammation and cancer.<span><sup>4, 5</sup></span> Reformed lymphoid alter local gradients of inflammatory mediators to trap and retain more lymphocytes and exacerbate the microenvironmental bioecology. The pre-activated TRM-like T cells harboured in lungs of smokers as the pre-existing state of a tissue can create an immune pressure that reprograms subsequent tumour evolution and response to therapy and profoundly influences disease progression.<span><sup>6</sup></span></p><p>The deep understanding of TRM T-cell phenomes and bio-behaviours provides new insights for the identification of diagnostic biomarkers and therapeutic targets. The TRM T cells as a special type of memory T cells are categorised on basis of the locations (e.g., gut-TRM, lung-TRM, brain-TRM), cell surface antigens (e.g., CD8⁺TRM, CD4⁺TRM), or cell identity gene markers measured by single-cell RNA sequencing (scRNA-seq).<span><sup>7-9</sup></span> One of biological characteristics is their long-term residence in specific tissue to take an immediate action in the initiation of immune responses to invaded pathogens and reduction infectious spreads, faster than circulating memory T cells. Of those, CD8⁺TRM T cells are the majority responsible for antiviral and anti-tumour immunity and can directly terminate infected cells and pathogen replication by releasing inflammatory mediators and enzymes. CD4⁺TRM can support other immune cells (like B cells for antibody production, macrophages for activation), and regulate local immune responses to infectious and autoimmune diseases by enhancing the synergistic effects of the immune networks. In addition, TRM T cells play critical roles in the tissue repair by controlling microenvironmental contents of inflammatory mediators and recognising abnormal cells like infected cells or cancer cells to reduce the risk of tissue damage and maintain microenvironmental immune bioecology. The molecular processes of reservable immune memory in TRM T cells can provide a number of alternatives for vaccination and immunotherapy.</p><p>Recent redefinition of redefining T-cell behaviour in inflamed or tumour microenvironment are largely driven by high-resolution techniques such as scRNA-seq, spatial transcriptomics and multi-omics integ
{"title":"Translational values of tissue-resident memory T cells in chronic inflammation and cancer","authors":"Wanxin Duan, Xiangdong Wang","doi":"10.1002/ctm2.70516","DOIUrl":"https://doi.org/10.1002/ctm2.70516","url":null,"abstract":"<p>T cells are central orchestrators of adaptive immunity and play important and complex roles in chronic inflammation, despite that their roles remain even paradoxical. The dysregulations of T cells occur in chronic diseases, such as inflammation and cancer, from being protectors to potent drivers of tissue pathology.<span><sup>1-3</sup></span> Of those, the pro-inflammatory tissue-resident memory (TRM) T cells accumulate within the tissue, perpetuating a cycle of inflammation. Subsets of TRM T cells, including those producing the highly inflammatory cytokine interleulin-17 (IL-17), are directly implicated in tissue damage, to form the ectopic lymphoid tissues, remodel the microenvironment, and amplify the local response in inflammation and cancer.<span><sup>4, 5</sup></span> Reformed lymphoid alter local gradients of inflammatory mediators to trap and retain more lymphocytes and exacerbate the microenvironmental bioecology. The pre-activated TRM-like T cells harboured in lungs of smokers as the pre-existing state of a tissue can create an immune pressure that reprograms subsequent tumour evolution and response to therapy and profoundly influences disease progression.<span><sup>6</sup></span></p><p>The deep understanding of TRM T-cell phenomes and bio-behaviours provides new insights for the identification of diagnostic biomarkers and therapeutic targets. The TRM T cells as a special type of memory T cells are categorised on basis of the locations (e.g., gut-TRM, lung-TRM, brain-TRM), cell surface antigens (e.g., CD8⁺TRM, CD4⁺TRM), or cell identity gene markers measured by single-cell RNA sequencing (scRNA-seq).<span><sup>7-9</sup></span> One of biological characteristics is their long-term residence in specific tissue to take an immediate action in the initiation of immune responses to invaded pathogens and reduction infectious spreads, faster than circulating memory T cells. Of those, CD8⁺TRM T cells are the majority responsible for antiviral and anti-tumour immunity and can directly terminate infected cells and pathogen replication by releasing inflammatory mediators and enzymes. CD4⁺TRM can support other immune cells (like B cells for antibody production, macrophages for activation), and regulate local immune responses to infectious and autoimmune diseases by enhancing the synergistic effects of the immune networks. In addition, TRM T cells play critical roles in the tissue repair by controlling microenvironmental contents of inflammatory mediators and recognising abnormal cells like infected cells or cancer cells to reduce the risk of tissue damage and maintain microenvironmental immune bioecology. The molecular processes of reservable immune memory in TRM T cells can provide a number of alternatives for vaccination and immunotherapy.</p><p>Recent redefinition of redefining T-cell behaviour in inflamed or tumour microenvironment are largely driven by high-resolution techniques such as scRNA-seq, spatial transcriptomics and multi-omics integ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <section> <p>Acute myeloid leukaemia (AML) remains the most common type of leukaemia in adults. Despite advances in conventional therapies, high relapse rates persist, underscoring the need for novel approaches such as chimeric antigen receptor T (CAR-T) cell therapy. C-type lectin-like molecule-1 (CLL1)-targeted CAR-T emerges as a promising treatment for relapsed/refractory (R/R) AML. Although approximately 70% patients achieved remission, only a subset achieved minimal residual disease-negative remission, which still has much room for improvement. The main reasons for the failure of CLL1 CAR-T-cell therapy include: (1) persistence of CLL1-negative AML cells persist due to antigen escape; (2) downregulation of interleukin (IL)-12 and other cytokines by the immunosuppressive tumour microenvironment (TME), contributing to the exhaustion of both endogenous T cells and CLL1 CAR-T cells.</p> <p>We synthesise a combination of CAR-engineered dendritic cells (CAR-DCs) and CLL1 CAR-T cells to overcome current limitations. CAR-DCs enhance antigen cross-presentation to activate endogenous T cells against antigen-negative clones, secrete immunostimulatory cytokines (e.g., IL-12) to sustain CAR-T activity, and remodel the TME. Key challenges involve optimising CAR designs (e.g., incorporating Fms-like tyrosine kinase 3 ligand [FLT-3L] or CD40 signalling domains), mitigating toxicity and establishing clinical administration protocols.</p> <p>In this review, a focused discussion was provided on the specific challenges limiting CLL1-targeted CAR-T-cell therapy in R/R AML, namely antigen escape and the TME, and a novel combination strategy of CAR-DCs with CLL1 CAR-T cells was proposed as a promising approach to mitigate these barriers. Here, the rationale, current research advances, and future perspectives of this synergistic strategy were critically examined.</p> </section> <section> <h3> Highlights</h3> <div> <ul> <li> <p>Our earlier clinical trials showed that C-type lectin-like molecule-1 (CLL1)-targeted therapy for refractory/relapse acute myeloid leukaemia (AML) was validated, which still has a considerable room for improvement.</p> </li> <li> <p>We summarise the clinical trials and basic research on the dendritic cell (DC) therapy and chimeric antigen receptor-engineered DC (CAR-DC) therapy.</p> </li> <li> <p>We explored the synergistic mechanism and prospects of CLL1 CAR-DC cells combined with CLL1 CAR-T cells in AML.</p> </li>
{"title":"CAR-DC combined with CAR-T therapy for relapsed/refractory acute myeloid leukaemia: Research progress and future perspectives","authors":"Rui Zhang, Jinlin Zhang, Hongkai Zhang, Mingfeng Zhao","doi":"10.1002/ctm2.70536","DOIUrl":"https://doi.org/10.1002/ctm2.70536","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Acute myeloid leukaemia (AML) remains the most common type of leukaemia in adults. Despite advances in conventional therapies, high relapse rates persist, underscoring the need for novel approaches such as chimeric antigen receptor T (CAR-T) cell therapy. C-type lectin-like molecule-1 (CLL1)-targeted CAR-T emerges as a promising treatment for relapsed/refractory (R/R) AML. Although approximately 70% patients achieved remission, only a subset achieved minimal residual disease-negative remission, which still has much room for improvement. The main reasons for the failure of CLL1 CAR-T-cell therapy include: (1) persistence of CLL1-negative AML cells persist due to antigen escape; (2) downregulation of interleukin (IL)-12 and other cytokines by the immunosuppressive tumour microenvironment (TME), contributing to the exhaustion of both endogenous T cells and CLL1 CAR-T cells.</p>\u0000 \u0000 <p>We synthesise a combination of CAR-engineered dendritic cells (CAR-DCs) and CLL1 CAR-T cells to overcome current limitations. CAR-DCs enhance antigen cross-presentation to activate endogenous T cells against antigen-negative clones, secrete immunostimulatory cytokines (e.g., IL-12) to sustain CAR-T activity, and remodel the TME. Key challenges involve optimising CAR designs (e.g., incorporating Fms-like tyrosine kinase 3 ligand [FLT-3L] or CD40 signalling domains), mitigating toxicity and establishing clinical administration protocols.</p>\u0000 \u0000 <p>In this review, a focused discussion was provided on the specific challenges limiting CLL1-targeted CAR-T-cell therapy in R/R AML, namely antigen escape and the TME, and a novel combination strategy of CAR-DCs with CLL1 CAR-T cells was proposed as a promising approach to mitigate these barriers. Here, the rationale, current research advances, and future perspectives of this synergistic strategy were critically examined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Highlights</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>\u0000 <p>Our earlier clinical trials showed that C-type lectin-like molecule-1 (CLL1)-targeted therapy for refractory/relapse acute myeloid leukaemia (AML) was validated, which still has a considerable room for improvement.</p>\u0000 </li>\u0000 \u0000 <li>\u0000 <p>We summarise the clinical trials and basic research on the dendritic cell (DC) therapy and chimeric antigen receptor-engineered DC (CAR-DC) therapy.</p>\u0000 </li>\u0000 \u0000 <li>\u0000 <p>We explored the synergistic mechanism and prospects of CLL1 CAR-DC cells combined with CLL1 CAR-T cells in AML.</p>\u0000 </li>\u0000 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pei Zhang, Dan Liu, Tonghui Yu, Yanlin Zhang, Lu Zhong, Xiao Ouyang, Qin Xia, Lei Dong
<div> <section> <h3> Background</h3> <p>Gene expression-based molecular subtypes in glioblastoma from The Cancer Genome Atlas Network (TCGA-GBM) unraveled the pathological origins by identifying tumour cell driver genes. However, the causal inference between molecular subtype origins and their therapeutic efficacy remains obscure.</p> </section> <section> <h3> Methods</h3> <p>We integrated TCGA-GBM multi-omics (DNA, mRNA, and protein profiles) using correlation analysis to identify <i>cis-</i>regulation. We analyzed the exposure-mediated base substitution-level mutations and their potential triggers. Importantly, we performed Consensus Clustering based on the MSigDB database with Silhouette-correction to identify prognostically relevant pathway-based MSig subtypes. The tumour driver mutations (co-occurrence mutation pattern), aberrant pathways (tumour hallmarks), immune microenvironment (<i>xCell</i>), and pseudo-time analysis (<i>dyno</i>) were used to characterize the MSig subtype landscape. Furthermore, we evaluated potential drug sensitivities across MSig subtypes using the Genomics of Drug Sensitivity in Cancer database.</p> </section> <section> <h3> Results</h3> <p>We classified five MSig subtypes, characterized by neural-like, tumour-driving, low tumour evolution, immune-inflamed, and classical tumour features. We observed several key features in ‘tumour-driving’ GBM patients: (1) mutual exclusivity between prognostic factors TP53 and EGFR; and (2) IDH1 mutations co-occurring with TP53, which account for the protective role of IDH1 in TP53 mutant patients. The immune-inflamed GBM, characterized as a ‘hot’ tumour, exhibited upregulation of immune-related pathways, including PD-1 and IFN-γ signalling responses. DNA methylation landscape revealed 14 MGMT CpG-rich regions regulating expression. Evolutionary trajectories revealed progression from a primary tumour state (close to normal tissue) to two distinct endpoints (tumour-driving and immune-inflamed subtypes).</p> </section> <section> <h3> Conclusions</h3> <p>Our findings reveal interactions between tumour cells and their surrounding immune environment, classifying GBM into two newly identified subtypes: (1) the tumour-driving subtype is characterized by multiple oncogenic mutations, while (2) the immune-blockade subtype is marked by a high presence of immune cells. We highlight the importance of integrating multi-type data (somatic mutations, DNA methylation, and RNA transcripts, etc.) to decipher GBM biology and potential therapeutic implications.</p> </section>
{"title":"Integrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi-omics","authors":"Pei Zhang, Dan Liu, Tonghui Yu, Yanlin Zhang, Lu Zhong, Xiao Ouyang, Qin Xia, Lei Dong","doi":"10.1002/ctm2.70517","DOIUrl":"https://doi.org/10.1002/ctm2.70517","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Gene expression-based molecular subtypes in glioblastoma from The Cancer Genome Atlas Network (TCGA-GBM) unraveled the pathological origins by identifying tumour cell driver genes. However, the causal inference between molecular subtype origins and their therapeutic efficacy remains obscure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We integrated TCGA-GBM multi-omics (DNA, mRNA, and protein profiles) using correlation analysis to identify <i>cis-</i>regulation. We analyzed the exposure-mediated base substitution-level mutations and their potential triggers. Importantly, we performed Consensus Clustering based on the MSigDB database with Silhouette-correction to identify prognostically relevant pathway-based MSig subtypes. The tumour driver mutations (co-occurrence mutation pattern), aberrant pathways (tumour hallmarks), immune microenvironment (<i>xCell</i>), and pseudo-time analysis (<i>dyno</i>) were used to characterize the MSig subtype landscape. Furthermore, we evaluated potential drug sensitivities across MSig subtypes using the Genomics of Drug Sensitivity in Cancer database.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We classified five MSig subtypes, characterized by neural-like, tumour-driving, low tumour evolution, immune-inflamed, and classical tumour features. We observed several key features in ‘tumour-driving’ GBM patients: (1) mutual exclusivity between prognostic factors TP53 and EGFR; and (2) IDH1 mutations co-occurring with TP53, which account for the protective role of IDH1 in TP53 mutant patients. The immune-inflamed GBM, characterized as a ‘hot’ tumour, exhibited upregulation of immune-related pathways, including PD-1 and IFN-γ signalling responses. DNA methylation landscape revealed 14 MGMT CpG-rich regions regulating expression. Evolutionary trajectories revealed progression from a primary tumour state (close to normal tissue) to two distinct endpoints (tumour-driving and immune-inflamed subtypes).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings reveal interactions between tumour cells and their surrounding immune environment, classifying GBM into two newly identified subtypes: (1) the tumour-driving subtype is characterized by multiple oncogenic mutations, while (2) the immune-blockade subtype is marked by a high presence of immune cells. We highlight the importance of integrating multi-type data (somatic mutations, DNA methylation, and RNA transcripts, etc.) to decipher GBM biology and potential therapeutic implications.</p>\u0000 </section>\u0000 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mimi Wang, Jiajun Li, Jiali Qian, Xi Zhang, Miao Li, Yingying Zhao, Zhiming Wang, Kun Guo, Dongmei Gao, Yan Zhao, Rongxin Chen, Zhenggang Ren, Taiwei Sun, Fan Wang, Jiefeng Cui
Background
Although the contribution of matrix stiffness to aggravating the malignant features of HCC cells has been well documented, the effects of matrix stiffness on chemoradiotherapy resistance and its underlying mechanism remain largely elusive.
Methods
To delineate the role of matrix stiffness in HCC progression, we engineered novel in vivo animal models with defined liver stiffness and a complementary tunable hydrogel culture system. This integrated approach enabled a comprehensive investigation into how biomechanical cues modulate HCC cell proliferation and DNA repair both in vitro and in vivo.
Results
High stiffness stimulation noticeably enhanced cell proliferation and cell survival from DNA damage through changing the expression and distribution of metabolic enzyme PFKFB3. Specifically, high stiffness stimulation prominently suppressed PFKFB3 ubiquitination by downregulating E3 ubiquitin ligase NEDD4, and then increased the stability of PFKFB3 protein to enhance glycolysis, ultimately promoted HCC growth. Meanwhile, high matrix stiffness stimulation also effectively strengthened the DNA damage repair ability of irradiated HCC cells, and PFKFB3 nuclear translocation mediated in matrix stiffness-regulated DNA damage repair by interacting with Ku70.
Conclusions
Our results delineate a PFKFB3-mediated pathway that underpins how increased matrix stiffness potentiates HCC growth and compromises radiotherapy efficacy. These findings not only highlight the contribution of matrix stiffness to tumor growth and DNA damage repair in HCC, but also disclose a previously unidentified nonmetabolic function of PFKFB3.
Key points
Increased matrix stiffness significantly promoted glycolysis in HCC cells via upregulating PFKFB3 expression.
High stiffness stimulation suppressed PFKFB3 ubiquitination by downregulating E3 ubiquitin ligase NEDD4 expression.
PFKFB3 participated in DNA damage repair by translocating into nuclear and interacting with Ku70, which strengthened by matrix stiffness.
{"title":"Metabolic enzyme PFKFB3 mediates matrix stiffness-potentiated tumour growth and radiotherapeutic resistance in HCC","authors":"Mimi Wang, Jiajun Li, Jiali Qian, Xi Zhang, Miao Li, Yingying Zhao, Zhiming Wang, Kun Guo, Dongmei Gao, Yan Zhao, Rongxin Chen, Zhenggang Ren, Taiwei Sun, Fan Wang, Jiefeng Cui","doi":"10.1002/ctm2.70509","DOIUrl":"https://doi.org/10.1002/ctm2.70509","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although the contribution of matrix stiffness to aggravating the malignant features of HCC cells has been well documented, the effects of matrix stiffness on chemoradiotherapy resistance and its underlying mechanism remain largely elusive.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To delineate the role of matrix stiffness in HCC progression, we engineered novel in vivo animal models with defined liver stiffness and a complementary tunable hydrogel culture system. This integrated approach enabled a comprehensive investigation into how biomechanical cues modulate HCC cell proliferation and DNA repair both in vitro and in vivo.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>High stiffness stimulation noticeably enhanced cell proliferation and cell survival from DNA damage through changing the expression and distribution of metabolic enzyme PFKFB3. Specifically, high stiffness stimulation prominently suppressed PFKFB3 ubiquitination by downregulating E3 ubiquitin ligase NEDD4, and then increased the stability of PFKFB3 protein to enhance glycolysis, ultimately promoted HCC growth. Meanwhile, high matrix stiffness stimulation also effectively strengthened the DNA damage repair ability of irradiated HCC cells, and PFKFB3 nuclear translocation mediated in matrix stiffness-regulated DNA damage repair by interacting with Ku70.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our results delineate a PFKFB3-mediated pathway that underpins how increased matrix stiffness potentiates HCC growth and compromises radiotherapy efficacy. These findings not only highlight the contribution of matrix stiffness to tumor growth and DNA damage repair in HCC, but also disclose a previously unidentified nonmetabolic function of PFKFB3.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>Increased matrix stiffness significantly promoted glycolysis in HCC cells via upregulating PFKFB3 expression.</li>\u0000 \u0000 <li>High stiffness stimulation suppressed PFKFB3 ubiquitination by downregulating E3 ubiquitin ligase NEDD4 expression.</li>\u0000 \u0000 <li>PFKFB3 participated in DNA damage repair by translocating into nuclear and interacting with Ku70, which strengthened by matrix stiffness.</li>\u0000 </ul>\u0000 </div>\u0000 </section>\u0000 </div>","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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