Zhaozheng Hou, Ping Feng, Chi-Leung Chiang, Kazi Anisha Islam, Songran Liu, Ying Wang, Yingpei Zhang, Michael King-Yung Chung, Ngar-Woon Kam, Zilu Huang, Victor Ho-Fun Lee, Anne Wing-Mui Lee, Dora Lai-Wan Kwong, Wai Tong Ng, Jason Wing Hon Wong, Yunfei Xia, Wei Dai
<div> <section> <h3> Background</h3> <p>Nasopharyngeal carcinoma (NPC) patients who develop distant metastasis have significantly reduced survival rates. Therefore, understanding of metastasis and identifying high-risk patients are important, and a robust predictive model for accurately assessing the distant-metastasis risk before treatment is needed for personalised treatment.</p> </section> <section> <h3> Methods</h3> <p>NPC patients diagnosed at four Hong Kong public hospitals and at Sun Yat-Sen University Cancer Center in Guangzhou were selected. Patients were divided into two training cohorts (n = 77 and 30, respectively) and one testing cohort (n = 70). Two independent NPC cohorts collected from Sun Yat-Sen University Cancer Center (n = 88), and a randomised phase III trial (NPC-0501) in Hong Kong (n = 81) were used for external validation of the model-based risk prediction.</p> </section> <section> <h3> Results</h3> <p>Our RNA-based risk score could stratify the patient groups into high and low risk of metastasis and disease progression in two independent external validation cohorts. In predicting NPC 3-year distant metastasis, the score significantly improved the area under the curve from 84.8% to 90.4% when combined with the known prognostic clinical parameters. This RNA-based risk score was highly associated with dysregulated functions of B cells and T helper 17 cells and reduced plasma B cells and tertiary lymphoid structure (TLS) formation. The analysis of biopsy samples revealed a significant enrichment of the TLS in non-metastatic NPC patients.</p> </section> <section> <h3> Conclusions</h3> <p>This study improved the accuracy of NPC metastasis prediction and highlight the potential association of TLS against metastatic NPC, encouraging future studies to understand how TLS interacts with NPC to prevent distant metastasis. Furthermore, the multi-cohort Pareto-optimisation-based feature selection approach offers a practical method to explicitly avoid model overfitting and achieve a more robust model.</p> </section> <section> <h3> Novelty and Impact</h3> <p>In this multicentre study, we established a new and robust predictive model for NPC distant metastasis using markers selected by a Pareto optimisation approach designed for multi-cohort data. When combined with clinical parameters, our RNA-based risk score significantly improved the area under the curve to 90.4%. This study revealed that reduced B-cell immunity, and TLS formation, may
{"title":"Tertiary lymphoid structure-related RNA indicator as metastasis risk factor in nasopharyngeal carcinoma","authors":"Zhaozheng Hou, Ping Feng, Chi-Leung Chiang, Kazi Anisha Islam, Songran Liu, Ying Wang, Yingpei Zhang, Michael King-Yung Chung, Ngar-Woon Kam, Zilu Huang, Victor Ho-Fun Lee, Anne Wing-Mui Lee, Dora Lai-Wan Kwong, Wai Tong Ng, Jason Wing Hon Wong, Yunfei Xia, Wei Dai","doi":"10.1002/ctm2.70539","DOIUrl":"10.1002/ctm2.70539","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Nasopharyngeal carcinoma (NPC) patients who develop distant metastasis have significantly reduced survival rates. Therefore, understanding of metastasis and identifying high-risk patients are important, and a robust predictive model for accurately assessing the distant-metastasis risk before treatment is needed for personalised treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>NPC patients diagnosed at four Hong Kong public hospitals and at Sun Yat-Sen University Cancer Center in Guangzhou were selected. Patients were divided into two training cohorts (n = 77 and 30, respectively) and one testing cohort (n = 70). Two independent NPC cohorts collected from Sun Yat-Sen University Cancer Center (n = 88), and a randomised phase III trial (NPC-0501) in Hong Kong (n = 81) were used for external validation of the model-based risk prediction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our RNA-based risk score could stratify the patient groups into high and low risk of metastasis and disease progression in two independent external validation cohorts. In predicting NPC 3-year distant metastasis, the score significantly improved the area under the curve from 84.8% to 90.4% when combined with the known prognostic clinical parameters. This RNA-based risk score was highly associated with dysregulated functions of B cells and T helper 17 cells and reduced plasma B cells and tertiary lymphoid structure (TLS) formation. The analysis of biopsy samples revealed a significant enrichment of the TLS in non-metastatic NPC patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study improved the accuracy of NPC metastasis prediction and highlight the potential association of TLS against metastatic NPC, encouraging future studies to understand how TLS interacts with NPC to prevent distant metastasis. Furthermore, the multi-cohort Pareto-optimisation-based feature selection approach offers a practical method to explicitly avoid model overfitting and achieve a more robust model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Novelty and Impact</h3>\u0000 \u0000 <p>In this multicentre study, we established a new and robust predictive model for NPC distant metastasis using markers selected by a Pareto optimisation approach designed for multi-cohort data. When combined with clinical parameters, our RNA-based risk score significantly improved the area under the curve to 90.4%. This study revealed that reduced B-cell immunity, and TLS formation, may ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12705341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145762403","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}
Wang Peng, Hai Huang, Yuchong Zhao, Qiaodan Zhou, Mengdie Cao, Luyao Liu, Jingwen Liang, Haochen Cui, Shiru Chen, Wei Chen, Si Xiong, Bin Cheng, Shuya Bai
<div> <section> <h3> Background</h3> <p>Oncofetal reprogramming—the reactivation of fetal-like gene programmes in malignant cells—has been implicated in the progression and stemness of hepatocellular carcinoma (HCC), yet its protein landscape and connection to tumour stemness remain incompletely defined.</p> </section> <section> <h3> Methods</h3> <p>We integrated multi-omics datasets to derive an oncofetal reprogramming-based prognostic signature (oncoScore) and validated it across multiple independent HCC cohorts. Candidate oncofetal proteins were validated in murine fetal liver and HCC tissue microarrays. Functional characterization of dual-specificity phosphatase 9 (DUSP9) was performed using gain- and loss-of-function studies, including sphere and colony formation, proliferation, migration and invasion assays, sorafenib resistance testing, and limiting-dilution tumourigenicity assays. Mechanistic studies employed Oil Red O staining, co-immunoprecipitation, chromatin immunoprecipitation, pharmacologic inhibition and genetic rescue experiments.</p> </section> <section> <h3> Results</h3> <p>The oncoScore demonstrated robust prognostic value across multiple independent HCC cohorts. DUSP9 emerged as a key regulator of stemness, promoting self-renewal and aggressive phenotypes, enhancing sphere and colony formation, proliferation, migration, invasion, sorafenib resistance, and tumourigenicity. Mechanistically, DUSP9 drives lipid metabolism by upregulating stearoyl-CoA desaturase (SCD) through the ERK1/2peroxisome proliferator-activated receptor gamma (PPARG) signalling axis.</p> </section> <section> <h3> Conclusion</h3> <p>Our results establish the oncoScore as a reliable prognostic marker for HCC and identify a DUSP-9ERK1/2-PPARG-SCD pathway that links lipid metabolism to stemness. Targeting the oncofetal protein DUSP9 may offer a therapeutic avenue for aggressive, stemness-driven HCC.</p> </section> <section> <h3> Key points</h3> <div> <ul> <li>Oncofetal reprogramming-based prognostic signature robustly stratifies HCC patient survival across independent cohorts.</li> <li>DUSP9 is identified as a core oncofetal regulator that drives stem-like traits in HCC.</li> <li>Mechanistically, DUSP9 suppresses ERK1/2-phosphorylation, stabilizes PPARG, and transcriptionally activates SCD.</li>
{"title":"Oncofetal dual‑specificity phosphatase 9 drives stem-like properties through ERK1/2-PPARG-SCD axis-mediated lipid metabolism in hepatocellular carcinoma","authors":"Wang Peng, Hai Huang, Yuchong Zhao, Qiaodan Zhou, Mengdie Cao, Luyao Liu, Jingwen Liang, Haochen Cui, Shiru Chen, Wei Chen, Si Xiong, Bin Cheng, Shuya Bai","doi":"10.1002/ctm2.70550","DOIUrl":"10.1002/ctm2.70550","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Oncofetal reprogramming—the reactivation of fetal-like gene programmes in malignant cells—has been implicated in the progression and stemness of hepatocellular carcinoma (HCC), yet its protein landscape and connection to tumour stemness remain incompletely defined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We integrated multi-omics datasets to derive an oncofetal reprogramming-based prognostic signature (oncoScore) and validated it across multiple independent HCC cohorts. Candidate oncofetal proteins were validated in murine fetal liver and HCC tissue microarrays. Functional characterization of dual-specificity phosphatase 9 (DUSP9) was performed using gain- and loss-of-function studies, including sphere and colony formation, proliferation, migration and invasion assays, sorafenib resistance testing, and limiting-dilution tumourigenicity assays. Mechanistic studies employed Oil Red O staining, co-immunoprecipitation, chromatin immunoprecipitation, pharmacologic inhibition and genetic rescue experiments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The oncoScore demonstrated robust prognostic value across multiple independent HCC cohorts. DUSP9 emerged as a key regulator of stemness, promoting self-renewal and aggressive phenotypes, enhancing sphere and colony formation, proliferation, migration, invasion, sorafenib resistance, and tumourigenicity. Mechanistically, DUSP9 drives lipid metabolism by upregulating stearoyl-CoA desaturase (SCD) through the ERK1/2peroxisome proliferator-activated receptor gamma (PPARG) signalling axis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our results establish the oncoScore as a reliable prognostic marker for HCC and identify a DUSP-9ERK1/2-PPARG-SCD pathway that links lipid metabolism to stemness. Targeting the oncofetal protein DUSP9 may offer a therapeutic avenue for aggressive, stemness-driven HCC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>Oncofetal reprogramming-based prognostic signature robustly stratifies HCC patient survival across independent cohorts.</li>\u0000 \u0000 <li>DUSP9 is identified as a core oncofetal regulator that drives stem-like traits in HCC.</li>\u0000 \u0000 <li>Mechanistically, DUSP9 suppresses ERK1/2-phosphorylation, stabilizes PPARG, and transcriptionally activates SCD.</li>\u0000 \u0000 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145740796","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}
Chun Yan, Fangmei Feng, Chaoting Lan, Gang Luo, Xiaotao Jiang, Huijuan Wang, Yinchun Chen, Yuling Yang, Liangqiong Deng, Xiaoli Huang, Yuxin Wu, Wenxiong Chen, Yufeng Liu
<div> <section> <h3> Background</h3> <p>Autism spectrum disorder (ASD) is increasingly recognized as a neurodevelopmental condition with systemic immunological involvement, yet the underlying immune mechanisms remain incompletely defined.</p> </section> <section> <h3> Aims</h3> <p>To delineate the peripheral immune landscape in ASD using integrated multi-omics profiling and to determine how immune and immunometabolic alterations relate to clinical severity.</p> </section> <section> <h3> Materials & Methods</h3> <p>Circulating immune cells from individuals with ASD were profiled using multicolor flow cytometry, single-cell RNA sequencing, and bulk RNA sequencing. Plasma proteomic and metabolomic analyses were performed to identify immune-related and metabolic biomarkers. Immune features were evaluated for associations with clinical severity measures.</p> </section> <section> <h3> Results</h3> <p>Multi-omics profiling revealed marked immune dysregulation in ASD, with significant shifts in immune cell subsets and inflammatory signatures that correlated with clinical severity. T cell abnormalities included reduced frequencies and a skewed Th1/Th2 balance, consistent with a chronic inflammatory milieu. Natural killer (NK) cells showed increased activation but impaired cytotoxic capacity, accompanied by expansion of an atypical NK subset. Myeloid-derived suppressor cells (MDSCs) and hyperinflammatory CD56+ monocytes were elevated. Transcriptomic analyses corroborated broad immune activation, prominently implicating interferon-driven and antiviral signaling pathways. Plasma metabolomics and proteomics further indicated disruptions in purine metabolism and oxidative phosphorylation, alongside increased inflammatory markers, which were significantly associated with symptom severity.</p> </section> <section> <h3> Discussion</h3> <p>These findings support a systemic immunometabolic framework in ASD characterized by concurrent immune activation and altered myeloid/NK cell states, providing mechanistic context for peripheral biomarkers linked to clinical phenotype.</p> </section> <section> <h3> Conclusion</h3> <p>Integrated multi-omics profiling identifies robust peripheral immune and metabolic disturbances in ASD. The dysregulated immune subsets, activated immune pathways, and plasma biomarker signatures highlight potential avenues for biomarker-driven str
{"title":"Comprehensive multi-omics mapping of immune perturbations in autism spectrum disorder","authors":"Chun Yan, Fangmei Feng, Chaoting Lan, Gang Luo, Xiaotao Jiang, Huijuan Wang, Yinchun Chen, Yuling Yang, Liangqiong Deng, Xiaoli Huang, Yuxin Wu, Wenxiong Chen, Yufeng Liu","doi":"10.1002/ctm2.70552","DOIUrl":"10.1002/ctm2.70552","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Autism spectrum disorder (ASD) is increasingly recognized as a neurodevelopmental condition with systemic immunological involvement, yet the underlying immune mechanisms remain incompletely defined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Aims</h3>\u0000 \u0000 <p>To delineate the peripheral immune landscape in ASD using integrated multi-omics profiling and to determine how immune and immunometabolic alterations relate to clinical severity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials & Methods</h3>\u0000 \u0000 <p>Circulating immune cells from individuals with ASD were profiled using multicolor flow cytometry, single-cell RNA sequencing, and bulk RNA sequencing. Plasma proteomic and metabolomic analyses were performed to identify immune-related and metabolic biomarkers. Immune features were evaluated for associations with clinical severity measures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Multi-omics profiling revealed marked immune dysregulation in ASD, with significant shifts in immune cell subsets and inflammatory signatures that correlated with clinical severity. T cell abnormalities included reduced frequencies and a skewed Th1/Th2 balance, consistent with a chronic inflammatory milieu. Natural killer (NK) cells showed increased activation but impaired cytotoxic capacity, accompanied by expansion of an atypical NK subset. Myeloid-derived suppressor cells (MDSCs) and hyperinflammatory CD56+ monocytes were elevated. Transcriptomic analyses corroborated broad immune activation, prominently implicating interferon-driven and antiviral signaling pathways. Plasma metabolomics and proteomics further indicated disruptions in purine metabolism and oxidative phosphorylation, alongside increased inflammatory markers, which were significantly associated with symptom severity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>These findings support a systemic immunometabolic framework in ASD characterized by concurrent immune activation and altered myeloid/NK cell states, providing mechanistic context for peripheral biomarkers linked to clinical phenotype.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Integrated multi-omics profiling identifies robust peripheral immune and metabolic disturbances in ASD. The dysregulated immune subsets, activated immune pathways, and plasma biomarker signatures highlight potential avenues for biomarker-driven str","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145740331","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}
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}
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