Journal of Translational Medicine最新文献

{"title":"Joint prediction of glioma molecular marker status based on GDI-PMNet.","authors":"Hong Zhu, Fengning Liang, Teng Zhao, Yaru Cao, Ying Chen, Houru Yan, Xiang Xiao","doi":"10.1186/s12967-025-07021-0","DOIUrl":"10.1186/s12967-025-07021-0","url":null,"abstract":"<p><strong>Background: </strong>Determining the status of glioma molecular markers is a problem of clinical importance in medicine. Current medical-imaging-based approaches for this problem suffer from various limitations, such as incomplete fine-grained feature extraction of glioma imaging data and low prediction accuracy of molecular marker status.</p><p><strong>Methods: </strong>To address these issues, a deep learning method is presented for the simultaneous joint prediction of multi-label statuses of glioma molecular markers. Firstly, a Gradient-aware Spatially Partitioned Enhancement algorithm (GASPE) is proposed to optimize the glioma MR image preprocessing method and to enhance the local detail expression ability; secondly, a Dual Attention module with Depthwise Convolution (DADC) is constructed to improve the fine-grained feature extraction ability by combining channel attention and spatial attention; thirdly, a hybrid model PMNet is proposed, which combines the Pyramid-based Multi-Scale Feature Extraction module (PMSFEM) and the Mamba-based Projection Convolution module (MPCM) to achieve effective fusion of local and global information; finally, an Iterative Truth Calibration algorithm (ITC) is used to calibrate the joint state truth vector output by the model to optimize the accuracy of the prediction results.</p><p><strong>Results: </strong>Based on GASPE, DADC, ITC and PMNet, the proposed method constructs the Gradient-Aware Dual Attention Iteration Truth Calibration-PMNet (GDI-PMNet) to simultaneously predict the status of glioma molecular markers (IDH1, Ki67, MGMT, P53), with accuracies of 98.31%, 99.24%, 97.96% and 98.54% respectively, achieving non-invasive preoperative prediction, thereby capable of assisting doctors in clinical diagnosis and treatment.</p><p><strong>Conclusions: </strong>The GDI-PMNet method demonstrates high accuracy in predicting glioma molecular markers, addressing the limitations of current approaches by enhancing fine-grained feature extraction and prediction accuracy. This non-invasive preoperative prediction tool holds significant potential to assist clinicians in glioma diagnosis and treatment, ultimately improving patient outcomes.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1030"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated single-cell RNA-seq analysis reveals that EZH2 regulates the MIF-CD74 axis to modulate T cell activation and exhaustion in hepatocellular carcinoma.","authors":"Yangyang Zhou, Yi Xu, Mengfan Ye, Jiayi Xing, Di Chen, Al-Ameer Wail Hussein Ahmed, Al-Rahabi Rehab Abdullah Mohammed Ali, Chenwei Pan, Xiangchou Yang, Zan Shen","doi":"10.1186/s12967-025-07071-4","DOIUrl":"10.1186/s12967-025-07071-4","url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) is a highly lethal malignancy characterized by a complex pathological mechanism involving multiple genes and progressive stages. The efficacy of targeted and immunotherapy remains limited, highlighting the urgent need for a reliable model to predict prognosis and response to immune checkpoint inhibitors (ICIs).</p><p><strong>Methods: </strong>We developed an integrated model based on genes related to autophagy, senescence, dormancy, mitochondrial function, and tumor stemness. The predictive capability of this model for HCC prognosis and ICI response was evaluated. Single-cell transcriptomic analysis and immunocompetent mouse models were further utilized to elucidate the role of model-associated genes in regulating the tumor immune microenvironment.</p><p><strong>Results: </strong>A 16-gene integrated model was constructed using genes associated with mitochondrial function, autophagy, dormancy, stemness, and senescence. This model demonstrated robust predictive power for HCC prognosis and ICI responsiveness. Single-cell trajectory analysis revealed that EZH2 plays a crucial role in immune cell infiltration, activation, and HCC progression. Additionally, in vivo mouse models further indicated that EZH2 may regulate CD8⁺ T cell activation and exhaustion through the MIF-CD74 signaling pathway.</p><p><strong>Conclusion: </strong>The integrated model holds potential as a prognostic and predictive tool for HCC immunotherapy. EZH2 may influence CD8⁺ T cell activation and exhaustion via the MIF-CD74 axis, providing insights for patient stratification and potential therapeutic strategies to enhance immunotherapy efficacy.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1040"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Loss of histone methyltransferase Smyd3 triggers WAT browning and adaptive thermogenesis via enhancing PPARγ expression in a H4K20me3-dependent manner.","authors":"Meiling Shu, Youzhen Ma, Dandan Zhao, Chen Meng, Lang Chen, Leihao Wang, Jing Liu, Di Yang","doi":"10.1186/s12967-025-07072-3","DOIUrl":"10.1186/s12967-025-07072-3","url":null,"abstract":"<p><p>Adaptive thermogenesis driven by brown/beige adipose tissue has gained attention as a promising strategy for combating obesity. Histone methyltransferase SET and MYND Domain Containing 3 (Smyd3) is strongly associated with metabolic and cardiovascular diseases, however, its role in adaptive thermogenesis has not been well characterized. Here, we demonstrate that Smyd3 is abundant and closely involved in adipocyte thermogenic programming. However, genetic ablation of Smyd3 or pharmacological inhibition with the specific inhibitor EPZ031686 robustly enhanced adaptive thermogenesis in mice. Conversely, Smyd3 overexpression attenuated white adipose tissue (WAT) browning both in vivo and in vitro. Mechanistically, we found that loss of Smyd3 (pharmacological inhibition by EPZ031686, knockdown by Smyd3 siRNA and genetic ablation by Smyd3-KO mice) decreased the trimethylation of histone H4 lysine 20 (H4K20) within the promoter region of the transcription factor Peroxisome proliferator-activated receptor gamma (Pparg) gene and released the transcription suppression, thereby upregulating PPARγ expression, which initiates the transcription of thermogenic genes such as Uncoupling protein 1 (Ucp1), ultimately promoting the protein expression of UCP1 in the cytoplasm and triggering the adaptive thermogenesis program. Collectively, our findings identify Smyd3 as a potential therapeutic target for modulating adaptive thermogenesis. Pharmacological inhibition of Smyd3 with EPZ031686 represents a promising strategy to promote WAT browning and adaptive thermogenesis, offering a potential therapeutic avenue for combating obesity.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1041"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG microstates, spectral analysis, and risk prediction in epilepsy comorbid with mild cognitive impairment: alteration in intrinsic brain activity.","authors":"Shenzhi Fang, Shenggen Chen, Lizhen Chen, Hanbin Lin, Changyun Liu, Chunhui Che, Wenting Xiong, Yuying Zhang, Juan Li, Luyan Wu, Xinming Huang, Huapin Huang, Wanhui Lin, Chaofeng Zhu","doi":"10.1186/s12967-025-07023-y","DOIUrl":"10.1186/s12967-025-07023-y","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to investigate the differences in electroencephalogram (EEG) microstates and power spectrum between patients with epilepsy (PWE) comorbid with (without) mild cognitive impairment (MCI) and to develop a machine learning model to predict the risk of MCI comorbidity in PWE.</p><p><strong>Method: </strong>Participants were classified into PWE comorbid with MCI (EPMCI) and PWE comorbid without MCI (EPNMCI). The microstate parameters and power spectral density (PSD) of both groups were compared. We combined different types of variables and constructed models using Support Vector Machine (SVM), Neural Network (NNET), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). An ideal predictive model was selected to evaluate the risk of MCI comorbidity in PWE.</p><p><strong>Result: </strong>A total of 627 PWE were included in this study, of whom 106 had MCI and 521 did not. Significant differences were observed between the two groups of patients in microstates A, B, C, D, and PSD. Among various machine learning models and multiple variable groups, we selected the NNET model based on microstate variables as the optimal model. It demonstrated the second-highest ROCAUC value (0.93), the highest accuracy (0.89), the lowest standard error (0.11), and superior calibration metrics, including the highest discrimination index (D = 0.724), the lowest Brier score (0.084), and the smallest unreliability index (U = 0.006). Finally, we compared this model with the traditional MMSE decision curve analysis (DCA) and found that it exhibited a wider range of applicable thresholds and a greater overall net benefit, demonstrating enhanced clinical utility.</p><p><strong>Conclusion: </strong>Differences in EEG microstates analysis and spectral analysis provide evidence for the mechanisms and dynamic changes associated with epilepsy comorbid with MCI. The development of a predictive model offers guidance for the assessment of MCI in specific populations with epilepsy.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1035"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SET8 modulates prognosis and radiotherapeutic efficacy by regulating radiation-induced migration in lung adenocarcinoma.","authors":"Quan Li, Qi Wang, Zhihao Qi, Shuhua Yang, Aihua Shen, Junfang Yan, Burong Hu","doi":"10.1186/s12967-025-07059-0","DOIUrl":"10.1186/s12967-025-07059-0","url":null,"abstract":"<p><strong>Background: </strong>Tumor migration in lung adenocarcinoma (LUAD) contributes to a poor prognosis by allowing malignant cells to escape the localized effects of radiotherapy, diminishing its overall efficacy. This study investigated the role of SET8, a methyltransferase, in LUAD migration and radiotherapy.</p><p><strong>Methods: </strong>In vitro experiments, including CRISPR/Cas9-mediated SET8 knockout, wound healing assays, and transwell migration assays, were used to assess the impact of SET8 on radiation-induced migration in LUAD cells. Bioinformatics analyses, such as differential expression analysis, clustering, functional enrichment, and CpG island methylation analysis, were performed using LUAD patient data from TCGA to examine the broader relationship between SET8, LUAD migration, and prognosis. Statistical methods, including Cox regression and LASSO regression, were employed to establish a prognostic model for radiotherapy outcomes, and drug sensitivity analysis was used to identify potential therapeutic agents.</p><p><strong>Results: </strong>Ionizing radiation induced migration in LUAD cells, coupled with altered SET8 expression. SET8 was found to engage in IR-induced migration through the PTTG1-PI3K-AKT signaling axis. Furthermore, elevated SET8 expression was more prevalent in LUAD patients with metastasis and correlated with adverse prognosis. Under equivalent X-ray irradiation doses, SET8 depletion significantly inhibited the migratory capability of LUAD cells. Finally, SET8-associated migration genes could predict the survival rate, radiation responsiveness, and drug sensitivity of radiotherapy patients.</p><p><strong>Conclusion: </strong>SET8 facilitates radiation-induced migration in LUAD through the PTTG1-PI3K-AKT pathway, and SET8-associated genes may act as valuable markers for predicting radiotherapeutic efficacy in LUAD patients.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1024"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material.","authors":"Eliseos J Mucaki, Wenhan Zhang, Aryamaan Saha, Sabina Trebinjac, Sharon Nofech-Mozes, Eileen Rakovitch, Vanessa Dumeaux, Michael T Hallett","doi":"10.1186/s12967-025-07031-y","DOIUrl":"10.1186/s12967-025-07031-y","url":null,"abstract":"<p><strong>Background: </strong>Formalin-fixed paraffin embedded (FFPE) samples suffer from the degradation of nucleic acids, a problem that becomes particularly acute with samples stored for extended periods. It remains challenging to profile FFPE using high-throughput sequencing technologies including RNA-sequencing, and the resulting FFPE RNA-seq (fRNA-seq) data has a high rate of transcript dropout, a property shared with single cell RNA-seq. Transcript counts also have high variance and are prone to extreme values, together making downstream analyses extremely challenging.</p><p><strong>Methods: </strong>We introduce the PaRaffin Embedded Formalin-FixEd Cleaning Tool (PREFFECT), a probabilistic framework for the analysis of fRNA-seq data. PREFFECT uses generative models to fit distributions to observed expression counts while adjusting for technical and biological variables. The framework can exploit multiple expression profiles generated from matched tissues for a single sample (e.g., a tumor and morphologically normal tissue) in order to stabilize profiles and impute missing counts. PREFFECT can also leverage sample-sample adjacency networks that assist graph attention mechanisms to identify the most informative correlations in the data.</p><p><strong>Results: </strong>We evaluated the distribution of transcript counts across a compendium of fRNA-seq datasets, finding the negative binomial distribution best fits the data with little evidence supporting zero-inflated extensions. We use this knowledge in the design of PREFFECT. We show that PREFFECT can accurately impute missing values from fRNAseq count matrices and adjust for batch effects. The inclusion of sample-sample adjacency networks and multiple tissues were shown to enhance sample clustering.</p><p><strong>Conclusions: </strong>The vast majority of studies to date contain at most a few hundred profiles, making it challenging to correctly infer good statistical fits for each transcript especially in complex cohorts, given the noisy, incomplete and heterogeneous nature of the data. The integrative and generative approach of PREFFECT provides better and more specific model fits than generic bulk RNA-seq tools, especially when more advanced PREFFECT models provide matched profiles are included in the analysis. The transformed data can be directly used with many well-established tools for downstream analysis tasks, empowering its use in clinical biomarker studies and diagnostics.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1023"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmembrane protein TMEM98 as a multifunctional regulator in cancer: from signaling pathways to translational implications.","authors":"Xiaoling Xu, Xiaojun Xie","doi":"10.1186/s12967-025-06998-y","DOIUrl":"10.1186/s12967-025-06998-y","url":null,"abstract":"<p><p>Transmembrane (TMEM) protein family members increasingly feature as clinically actionable regulators of cellular physiology and pathology, most notably in cancer biology. Several family members (e.g., TMEM16A/ANO1) have already progressed into first‑in‑human trials or pre‑market diagnostic kits, underscoring the druggability of the TMEM class. Among them, TMEM98 is a multifaceted protein implicated in pivotal processes like cell growth, migration, adhesion, and intracellular signaling. TMEM98 has recently been shown to be involved in major oncogenic pathways like Wnt/β-catenin and AKT/GSK3β, and interacts with transcription factors like MYRF and NF90. Clinically, aberrant TMEM98 expression (e.g., high expression detected in 67.8% of hepatocellular carcinoma specimens, which was associated with early tumor recurrence and poorer overall and disease-free survival) correlates significantly with prognosis, tumor aggressiveness, chemoresistance, and responsiveness to therapy, making it a promising candidate for biomarker-driven personalized oncology. Such findings highlight TMEM98's role in tumor initiation as well as tumor progression. This review integrates current information on TMEM98's functional roles in various malignancies, ranging from lung, gastric, hepatic, ovarian, to head and neck cancer. We further discuss implications of TMEM98 gene mutations, its regulation by non-coding RNA, and its prospective role as a marker and therapeutic target within the translational pipeline. By correlating outcomes of functional assays and clinical cohorts, our goal is to reveal the TMEM98-centered regulation landscape and identify its oncological relevance.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1021"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell RNA sequencing reveals the adverse role of cDC3s in the response of ulcerative colitis patients to anti-TNF-α therapy.","authors":"Zhangqin Li, Ruijie Ma, Zhongshun Nie, Youxu Ren, Yunxing Li, Yinglei Miao, Jie Jia, Jiarong Miao","doi":"10.1186/s12967-025-06909-1","DOIUrl":"10.1186/s12967-025-06909-1","url":null,"abstract":"<p><strong>Background: </strong>Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease that belongs to the inflammatory bowel disease (IBD). The complex etiology of UC contributes to heterogeneous clinical outcomes in treatment. In clinical practice, approximately 30% of UC patients do not respond to first-line treatment with anti-TNF-α therapy.</p><p><strong>Methods: </strong>In this study, we performed single-cell sequencing of intestinal mucosal tissue before (pre) and after (post) anti-TNF-α therapy in UC patients and analyzed it in relation to therapy response (-R) and non-response (-NR).</p><p><strong>Results: </strong>We found that immune cell profiles differed between the pre-R and post-R groups. Specifically, the proportion of type 3 conventional dendritic cells (cDC3s) with distinct transcriptomes was lower in the post-R group than in the pre-R group and was not different between the pre-NR and post-NR groups. Cell trajectory analysis revealed that the number of cells differentiated into cDC3s significantly decreased in the post-R group, and the genes related to the MAPK signaling pathway obviously increased in these cells. Additionally, the interaction analysis of ligands and receptors revealed that the interactions between HLA-DPA1/DPB1 in fibroblasts and TNFSF9 in cDC3s and between CD44 in fibroblasts and TYROBP in cDC3s were significantly weakened in the post-R group compared to the pre-R group.</p><p><strong>Conclusion: </strong>We provide a comprehensive resource detailing the dynamic changes in immune cells during TNF-α therapy in UC patients and identify the reduction in the number of functionally distinct cDC3s as a potential biomarker for predicting anti-TNF-α therapy outcomes.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1025"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TumorXDB: an integrated multi-omics xWAS/xQTL platform for cross-ethnic pan-cancer analysis.","authors":"Zehua Dong, Yihang Cheng, Tao Mo, Wencai Mao, Wenqian Zhao, Deqiang Sun","doi":"10.1186/s12967-025-07029-6","DOIUrl":"10.1186/s12967-025-07029-6","url":null,"abstract":"<p><strong>Background: </strong>TumorXDB is a comprehensively curated tumor database integrating molecular association data (xWAS/xQTL) to explore genetic mechanisms across diverse tumors, organs, and ethnic groups. We aimed to provide a unified resource for discovering novel genetic associations and molecular mechanisms in tumors.</p><p><strong>Methods: </strong>TumorXDB integrates four molecular-wide association studies (xWAS) and 23 molecular quantitative trait locus (xQTL) types spanning 10 physiological systems, 50 organs, and 139 cancer subtypes, while incorporating data from 25 ethnic subgroups across four major ancestral populations. To ensure data harmonization, we performed batch-effect correction using ComBat and applied the Benjamini-Hochberg (BH) procedure with false discovery rate (FDR) of < 0.05 for multiple testing correction. Meta-analysis models were developed to generate unified pan-cancer datasets, which are all accessible through a user-friendly web interface ( http://www.tumor-xdb.com ) with full data download capabilities.</p><p><strong>Results: </strong>TumorXDB enabled robust integration of molecular data, revealing novel cross-cancer genetic associations through harmonized analysis.</p><p><strong>Conclusions: </strong>This resource advances precision oncology by providing batch-corrected and statistically rigorous pan-cancer data for therapeutic discovery.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1019"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extensive cross-reactive T cell epitopes across SARS-CoV-2 Omicron variant spikes with finite immune evasion mutations.","authors":"Mengze Gan, Jinge Cao, Qi Ouyang, Xinyue Xu, Xingxing Wang, Peihang Dan, Yinlong Yao, Hui Fu, Xuanyu Yao, Xiaosong Lin, Qing Lei, Xionglin Fan","doi":"10.1186/s12967-025-07076-z","DOIUrl":"10.1186/s12967-025-07076-z","url":null,"abstract":"<p><strong>Background: </strong>The impact of spike protein mutations on T cell responses, particularly in SARS-CoV-2 Omicron variants, remains incompletely elucidated.</p><p><strong>Methods: </strong>In this study, DNA vaccines encoding the spike protein of both the ancestral virus and Omicron variants were developed and administered in conjunction with a Th1-type adjuvant to BALB/c mice. Cross-reactive T cell responses to the spike proteins were assessed in the splenocytes of these mice using IFN-γ ELISPOT assays. Additionally, flow cytometry (FACS) was utilized to evaluate IFN-γ⁺ CD4⁺ and CD8⁺ T cell responses to various peptides covering the entire spike protein sequence.</p><p><strong>Results: </strong>Our study demonstrated that only a limited number of mice, along with a minor subset of their splenocytes vaccinated with the DNA vaccine targeting the original spike protein, exhibited weak cross-reactivity with Omicron variants. This observation underscores the differences in T cell epitopes between Omicron variants and the prototype spikes, indicating that at least 50 and 37 mutations in Omicron variants contribute to their evasion of CD4⁺ and CD8⁺ T cell responses, respectively. Conversely, DNA vaccines encoding spike proteins from Omicron variants successfully elicited strong cross-reactive T cell responses in the immunized mice. In particular, Vaccines targeting the BA.1, BA.5, XBB.1.5, and JN.1 variants demonstrated the most robust and comprehensive T cell immune responses among Omicron variants. This efficacy is attributed to the presence of less than eleven T cell immune evasion mutations in their spike proteins, alongside numerous mutations that enhance T cell responses.</p><p><strong>Conclusions: </strong>These findings underscore the imperative to update WHO emergency vaccine policies and contribute to the development of more effective vaccines and immunization strategies, to better control the infections caused by emerging Omicron variants.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"1027"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}