Translational cancer research最新文献

{"title":"The m6A/METTL3 modifies USP11 to promote Warburg effect progression of colon cancer undifferentiated by HIF-1α/YAP signaling pathway.","authors":"Yingying Wang, Feifei Zhu, Xiaolei Chen, Lei Feng, Zhichen Pu","doi":"10.21037/tcr-2026-1567","DOIUrl":"https://doi.org/10.21037/tcr-2026-1567","url":null,"abstract":"<p><strong>Background: </strong>In China, colorectal cancer (CLC) is the third most common malignancy, following lung cancer and thyroid cancer. This study attempts to reveal the protective mechanism of ubiquitin-specific peptidase 11 (USP11) on Warburg effect of CLC.</p><p><strong>Methods: </strong>The study used single-cell analysis and the Chinese Glioma Genome Atlas (CGGA) dataset. HCT-116 cells were transfected with the USP11 or small interfering (si)-USP11 plasmid, respectively.</p><p><strong>Results: </strong>The USP11 messengerRNA(mRNA) expression in patients with CLC was elevated compared to the normal group. Patients with high USP11 expression exhibited poorer survival than those with low USP11 expression in CLC. USP11 was expressed in the epithelial cells of CLC patients. USP11 promoted Warburg effect to induce cell growth progression of CLC. USP11 upregulates the expression of HIF-1α and YAP in CLC. Mechanistically, HIF-1α mediates the effect of USP11 in CLC by driving the progression of the Warburg effect. USP11 physically interacts with the HIF-1α complex and stabilizes HIF-1α protein in CLC cells via suppressing HIF-1α ubiquitination. Additionally, the N6-methyladenosine (m6A) methyltransferase METTL3 enhances USP11 stability in a YTHDF1-dependent manner.</p><p><strong>Conclusions: </strong>This study demonstrates that the METTL3-mediated m6A modification facilitates USP11 stabilization by YTHDF1 in CLC models, providing novel mechanistic insights and potential therapeutic targets for the clinical management of CLC and other malignant tumors.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"696"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-derived habitat radiomics integrated with transformer deep learning for preoperative prediction of high CD9 expression: a novel prognostic marker in adult-type diffuse gliomas.","authors":"Bingxin Pang, Mengyuan Yuan, Ying Yan, Jie Li, Jing Xue, Lina Jing, Bo Jiang, Shengjun Sun","doi":"10.21037/tcr-2026-1847","DOIUrl":"https://doi.org/10.21037/tcr-2026-1847","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;High CD9 expression is significantly associated with shorter overall survival (OS) in patients with adult-type diffuse gliomas (ADGs). Concurrently, drug screening analyses have identified emetine as a potential targeted therapeutic agent for gliomas exhibiting high CD9 expression, indicating that CD9 may serve as a novel biomarker with the potential to transform glioma diagnosis and treatment. This study aimed to develop an artificial intelligence prediction model based on preoperative magnetic resonance imaging (MRI) images to facilitate the early identification of high CD9 expression in patients with ADGs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We retrospectively collected CD9 expression data from 262 patients with ADGs in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. Using preoperative MRI sequences [contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI)], Habitat features were extracted, using K-means clustering algorithm, together with radiomic features. Additionally, multi-channel 2.5-dimensional deep learning (2.5D_DL) data were constructed, based on neural network architectures, including ResNet101, DenseNet121, and SimpleViT, and DL features were extracted. Seven classifiers were utilized to construct individual Radiomics, Habitat, and 2.5D_DL predictive models. Subsequently, a Stacking model was developed via an ensemble fusion strategy to predict high CD9 expression. Model performance was evaluated using receiver operating characteristic (ROC) curves, and clinical efficacy was assessed by decision curve analysis (DCA). Shapley Additive Explanations (SHAP) analysis was performed to explore the interpretation of the models, enhancing the transparency of the machine learning predictions. In addition, Patients were stratified into high- and low-risk groups based on Stacking model scores, and survival differences were compared using the Kaplan-Meier analysis and the log-rank test.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The Habitat model exhibited superior ROC curve [area under the ROC (AUC)] values than the Radiomics model. Among the multi-channel 2.5D_DL models, the SimpleViT model achieved slightly better performance than the convolutional neural network (CNN)-based models (ResnNet101 and DenseNet121). The final Stacking model, which integrated the outputs from Habitat, Radiomics, and 2.5D_DL models exhibited superior AUC values. The AUC reached 0.999, 0.822, and 0.728 in the training, validation, and test sets, respectively. DeLong testing confirmed the statistically significant differences in the AUC values of each model in the training set, but no statistically significant differences were observed among the other sets. In addition, enhanced net clinical benefit across a more extensive range of risk thresholds for this integrated model was verified by DCA. Prognostic stratification by Stacking model revealed significant differences in survival outcomes between hig","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"703"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-omics analysis of GBP2 as a prognostic and immune-associated biomarker in papillary thyroid carcinoma.","authors":"Zhanwei Du, Liu Wang, Yixiao Zhang, Jing Han, Zhijun Ma","doi":"10.21037/tcr-2026-1440","DOIUrl":"https://doi.org/10.21037/tcr-2026-1440","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Guanylate-binding protein 2 (GBP2) is dysregulated in several malignancies and participates in immune regulation. This study aimed to characterize its expression, prognostic relevance, cellular distribution, immune associations, and biological role in papillary thyroid carcinoma (PTC).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We integrated multi-omics data from The Cancer Genome Atlas thyroid carcinoma project (TCGA-THCA), Gene Expression Omnibus (GEO), and the Human Protein Atlas (HPA). The TCGA-THCA cohort primarily comprises differentiated thyroid carcinoma (DTC), with PTC as the predominant histologic subtype; single-cell RNA-sequencing (scRNA-seq) data from PTC samples were also analyzed. Kaplan-Meier survival analysis and univariable and multivariable Cox regression were used to evaluate the prognostic association of GBP2. ESTIMATE, CIBERSORT, and TIMER2.0 were used to assess associations between GBP2 and the tumor immune microenvironment and immune-cell infiltration. Immune-checkpoint expression and immunophenoscores (IPSs) from The Cancer Immunome Atlas (TCIA) were used to characterize immune-related phenotypes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore potential functions. scRNA-seq data were used to characterize the PTC microenvironment and cell-type-specific GBP2 expression, identify GBP2-associated differentially expressed genes, and infer potential intercellular communication with CellChat. Finally, GBP2 was transiently silenced with small interfering RNA (siRNA) in TPC-1 cells, followed by cell counting kit-8 (CCK-8), wound-healing, Transwell invasion, and flow-cytometric apoptosis assays.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;GBP2 mRNA and protein expression were higher in PTC than in normal thyroid tissue. Patients with high GBP2 expression had longer overall survival (OS), and multivariable Cox regression showed an adjusted association between GBP2 expression and a lower risk of death [hazard ratio (HR), 0.23; 95% confidence interval (CI): 0.08-0.65; P=0.006]. GBP2 expression was negatively associated with age and clinical stage and positively associated with N stage. Stratified survival analyses and interaction testing did not indicate a significant interaction between GBP2 and N stage. High GBP2 expression was associated with higher immune and stromal scores and with infiltration by several immune-cell populations, including CD8+ T cells, B cells, and dendritic cells. GBP2-related genes were enriched in T-cell activation, antigen processing and presentation, and interferon-response pathways. scRNA-seq analysis showed that GBP2 was detectable in T cells, malignant thyrocytes, and myeloid cells, with the highest average expression in T cells. In malignant thyrocytes, higher GBP2 expression was associated with enrichment of major histocompatibility complex (MHC) and immune-related functions. CellChat suggested a potential H","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"699"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pathomics model for predicting lactate-related prognostic risk groups and their biological mechanisms in lung adenocarcinoma.","authors":"Lingling Zhou, Wen Liu, Ye Pan, Wan Lei, Lingfei Yan, Jingfang Zou, Kejian Qian, Fei Wang","doi":"10.21037/tcr-2026-1520","DOIUrl":"https://doi.org/10.21037/tcr-2026-1520","url":null,"abstract":"<p><strong>Background: </strong>Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity in postoperative survival, underscoring the need for robust prognostic biomarkers. Given the emerging role of lactate metabolism in cancer progression, this study aimed to develop a pathomics model for prognostic stratification by predicting lactate-related prognostic risk groups.</p><p><strong>Methods: </strong>Using data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project, we performed non-negative matrix factorization to stratify patients based on lactate-related genes. A lactate-related prognostic model was constructed via least absolute shrinkage and selection operator (LASSO)-Cox regression. Patients were then randomly split into training and validation sets to develop and evaluate an XGBoost-based pathomics model for prognostic risk groups prediction. Gene set variation analysis (GSVA), immune cell infiltration analysis, and mutational landscape analysis, were further conducted to explore the underlying mechanisms.</p><p><strong>Results: </strong>A four-gene lactate-related prognostic signature (<i>GAPDH</i>, <i>PGK1</i>, <i>SOCS3</i>, and <i>CXCL9</i>) was constructed. The pathomics model achieved an area under the receiver operating characteristic curve (AUC) of 0.825 [95% confidence interval (CI): 0.773-0.877] in the training set and 0.700 (95% CI: 0.592-0.808) in the validation set for predicting lactate-related prognostic risk groups. The calibration curve indicated excellent agreement between the pathomics model's predictions and actual observations. Moreover, the high-pathomics score (PS) phenotype, characterized by elevated M2 macrophage infiltration and a higher <i>TP53</i> mutation rate, exhibited significant enrichment of tumor-related pathways.</p><p><strong>Conclusions: </strong>We developed a pathomics model for predicting lactate-related prognostic risk groups in LUAD. The model showed moderate predictive performance, suggesting potential utility for prognostic stratification and treatment planning, though further external validation is needed.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"704"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and temporal validation of nomograms and machine learning models for predicting overall and cancer-specific survival in primary distal cholangiocarcinoma: a SEER-based study.","authors":"Wentao Shang, Xibin Duan","doi":"10.21037/tcr-2026-0780","DOIUrl":"https://doi.org/10.21037/tcr-2026-0780","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Distal cholangiocarcinoma (dCCA) differs from intrahepatic and perihilar cholangiocarcinoma in anatomical origin, surgical management, and molecular profile, yet most existing prognostic tools either pool all biliary tract cancers together or rely on anatomical Tumor Node Metastasis (TNM) staging alone. A dCCA-specific model integrating clinical, treatment, and tumor-burden variables is therefore needed. We aimed to develop and evaluate nomograms and machine learning models for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with primary dCCA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We identified 1,515 patients with primary dCCA diagnosed between 2010 and 2017 in the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed in 2010-2015 were randomly divided into a training cohort (70%, n=773) and an internal testing cohort (30%, n=331); patients diagnosed in 2016-2017 (n=411) formed a temporal validation cohort with no calendar-year overlap. Independent predictors were selected by univariable and multivariable Cox regression for OS and by Fine-Gray subdistribution hazard modeling for CSS, and were used to construct nomograms predicting 1-, 3-, and 5-year survival. Four machine learning survival models-decision tree, random survival forest, gradient boosting survival analysis, and an XGBoost-based accelerated failure time model (XGBAFT)-were trained for OS prediction and interpreted with SHapley Additive exPlanations (SHAP). Performance was assessed by Harrell's concordance index (C-index), time-dependent area under the curve (AUC), calibration plots, and decision curve analysis, and benchmarked against the eighth-edition American Joint Committee on Cancer (AJCC) TNM staging system.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The development cohort was predominantly male (53.2%) and aged over 65 years (61.6%); 28.9% underwent radical resection of the primary site and 47.6% received chemotherapy, with a median observed survival of 8 months. For OS, the nomogram achieved a C-index of 0.754 and 1-, 3-, and 5-year AUCs of 0.854, 0.868, and 0.871 in the internal testing cohort, and a C-index of 0.742 with AUCs of 0.830, 0.836, and 0.843 in the temporal validation cohort. For CSS, the corresponding C-indices were 0.726 and 0.710, with AUCs of 0.813/0.797/0.760 and 0.792/0.712/0.702. Both nomograms outperformed the eighth-edition TNM system (OS C-index, 0.585; CSS C-index, 0.635) and showed good calibration and net clinical benefit. Among the machine learning models, XGBAFT achieved the highest discrimination (AUCs, 0.861/0.834/0.831 internally and 0.807/0.803/0.789 temporally). SHAP identified chemotherapy, primary-site radical surgery, regional lymph node surgery as the most influential predictors. The nomogram stratified patients into low-, intermediate-, and high-risk groups with significantly divergent survival curves (P&lt;0.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Within the SEER pop","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"705"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: DDR1 in esophageal squamous cell carcinoma: bioinformatics discovery and experimental validation.","authors":"","doi":"10.21037/tcr-20262-9","DOIUrl":"https://doi.org/10.21037/tcr-20262-9","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.21037/tcr-2026-0437.].</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"722"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disulfidptosis-related genes and long non-coding RNAs define a prognostic model for liver cancer.","authors":"Wenqi Zhang, Xiangran Gu, Zhengrong Yuan, Haiyan Zhang","doi":"10.21037/tcr-2026-0908","DOIUrl":"https://doi.org/10.21037/tcr-2026-0908","url":null,"abstract":"<p><strong>Background: </strong>Liver cancer is a prevalent malignancy with high mortality. Owing to the liver's capacity to conceal tumors until advanced stages, it is often challenging to detect them early. This study investigated a newly discovered form of cell death, disulfidptosis, and its prognostic value for liver cancer through analysis of disulfidptosis-related genes and long non-coding RNAs (lncRNAs).</p><p><strong>Methods: </strong>Data from 406 liver cancer cases and 58 normal cases obtained from The Cancer Genome Atlas (TCGA) database were analyzed with R and Perl software for differential expression, survival analysis, and Cox regression analysis. Candidate genes were further validated using the Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. The real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the messenger RNA (mRNA) expression of candidate genes in liver cancer cell lines. Co-expression analysis was conducted to identify disulfidptosis-related lncRNAs, and a prognostic risk model was constructed via least absolute shrinkage and selection operator (LASSO)-Cox regression. The predictive performance of the model was evaluated by internal cohort validation, time-dependent receiver operating characteristic (ROC) curves, concordance index (C-index), and a prognostic nomogram.</p><p><strong>Results: </strong>Initially, 113 disulfidptosis-related genes were screened, among which 45 showed significant results. After Cox regression analysis, 26 genes were retained, and 739 lncRNAs co-expressed with these genes were detected. Six disulfidptosis-related genes (<i>AAAS, IPO7, NCKAP1, PCBP2, RNH1,</i> and <i>SMPD4</i>) were identified as independent prognostic factors. Protein-level expression of these genes was validated using the HPA and CPTAC datasets. RT-qPCR results demonstrated that <i>NCKAP1</i>, <i>PCBP2</i>, <i>RNH1</i> and <i>SMPD4</i> were highly expressed in liver cancer cell lines (MHCC97-L, MHC97-H, and HCC-LM3) in contrast to normal hepatocytes (THLE2). A lncRNA-based risk-score prognostic model was further constructed. Three pivotal lncRNAs were screened to build the prognostic signature. This signature effectively stratified patients into high- and low-risk groups with distinct survival outcomes and was confirmed to be an independent prognostic indicator.</p><p><strong>Conclusions: </strong>This study suggested that these six genes could potentially serve as valuable prognostic indicators for liver cancer, and the developed risk assessment framework using correlated lncRNAs might assist in the clinical evaluation and personalized treatment of liver cancer.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"676"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of podoplanin in tongue squamous cell carcinoma and the regulation of malignant cellular behaviors via the ERK/MMP-2 signaling axis.","authors":"Xiaowei Sheng, Liping Ding, Qiang Zhang, Yong Lu, Jun Guo","doi":"10.21037/tcr-2026-2027","DOIUrl":"https://doi.org/10.21037/tcr-2026-2027","url":null,"abstract":"<p><strong>Background: </strong>The prognostic significance and functional role of podoplanin (PDPN) in tongue squamous cell carcinoma (TSCC) remain incompletely defined. This study investigated PDPN expression, prognostic relevance, effects on malignant cellular behaviors, and involvement of the ERK/MMP-2 signaling axis.</p><p><strong>Methods: </strong>PDPN levels in TSCC and matched surrounding noncancerous tissues were detected via immunohistochemistry. The link between PDPN levels and clinicopathological parameters was analyzed, and the prognostic value of PDPN was evaluated. Functional assays were conducted via <i>PDPN</i> knockdown and overexpression models in SCC-9 and CAL-27 cells, respectively. Extracellular matrix (ECM) adhesion, wound healing, cell proliferation, and Transwell migration and invasion analyses were conducted. Bioinformatics prediction, Western blot analysis, and treatment with a specific ERK inhibitor were integrated to clarify the regulatory effects of PDPN on MMP-2 via the MAPK-ERK axis. Furthermore, a xenograft model was established in nude mice to verify the tumor-promoting effects of PDPN <i>in vivo</i>.</p><p><strong>Results: </strong>PDPN was significantly upregulated in TSCC tissues and correlated with poor differentiation, advanced tumor-node-metastasis (TNM) stage, and lymphatic invasion. Multivariate Cox regression analysis identified PDPN as an independent unfavorable prognostic factor for patients with TSCC. Functional assays revealed that PDPN promoted cell migration, proliferation, and invasion, which were mediated through the ERK/MMP-2 pathway. <i>In vivo</i>, <i>PDPN</i> knockdown reduced tumor growth and decreased MMP-2 expression.</p><p><strong>Conclusions: </strong><i>PDPN</i> is overexpressed in TSCC and possesses independent prognostic value. PDPN promotes TSCC progression via the ERK/MMP-2 signaling axis, which suggests its utility as a prognostic biomarker and therapeutic target for patients with TSCC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"713"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A nomogram for suicide-specific mortality risk stratification in patients with tongue cancer: a Surveillance, Epidemiology, and End Results (SEER)-based study, 2004-2017.","authors":"Sha-Sha Du, Hai-Ping You, Yu-Ling Zhang, Nan-Jun Chen, Chen Ren, Heng-Wen Sun, Fei Xu, Xin Hua","doi":"10.21037/tcr-2026-1088","DOIUrl":"https://doi.org/10.21037/tcr-2026-1088","url":null,"abstract":"<p><strong>Background: </strong>Although suicide represents an important cause of mortality among cancer patients, prediction models specifically developed for tongue cancer patients remain unavailable. This study aimed to identify suicide-related risk factors and develop an individualized risk stratification model for tongue cancer patients. This study aimed to identify factors associated with suicide-specific mortality and develop an internally validated risk stratification model for patients with tongue cancer.</p><p><strong>Methods: </strong>Patients diagnosed with tongue cancer between 2004 and 2017 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Eligible patients were randomly allocated to training and validation cohorts at a ratio of 7:3. Cox proportional hazards regression was used to identify potential predictors and construct a nomogram. Model discrimination was evaluated using the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves with corresponding area under the curve (AUC) values. Calibration was assessed by comparing predicted and observed suicide-specific mortality.</p><p><strong>Results: </strong>Among the 25,024 eligible tongue cancer patients, 96 suicide-related deaths were identified during follow-up, corresponding to a cumulative suicide mortality rate of 0.38%. Multivariate Cox regression analysis revealed that sex and age (P<0.001) were independent predictors, and race, tumor (T) stage, and income (P≤0.2) were potential significant factors for suicide in tongue cancer patients. The nomogram had a good performance for the prediction of suicide-specific mortality both in the training {C-index =0.762 [95% confidence interval (CI): 0.710-0.815]} and validation sets [C-index =0.761 (95% CI: 0.673-0.849)]. ROC curve demonstrated acceptable performance in both the training and validation sets, with 3-year AUCs of 0.784 and 0.774, 5-year AUCs of 0.762 and 0.788, and 10-year AUCs of 0.729 and 0.783, respectively. Moreover, the calibration plot indicated moderate accuracy of the nomogram in both datasets.</p><p><strong>Conclusions: </strong>Overall, this study successfully identified risk factors for suicide among tongue cancer patients and developed a nomogram to offer risk assessment to mitigate the risk of suicide in this patient population.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"679"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequential chemoradiotherapy <i>vs.</i> radiotherapy alone or concurrent chemoradiotherapy after radical resection of stage IA2-IIA1 cervical cancer: a retrospective cohort study.","authors":"Jia Yang, Huili Chen, Hui Tao, Xinxin Liu, Yan Chen, Xiang Fan, Pengfeng Zhu, Chao Zhang","doi":"10.21037/tcr-2026-2410","DOIUrl":"https://doi.org/10.21037/tcr-2026-2410","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cervical cancer is one of the most common gynecologic malignancies worldwide, and the optimal adjuvant treatment strategy remains controversial for patients with early-stage cervical cancer with intermediate-risk factors who undergo radical surgery. This study aimed to identify the risk factors affecting prognosis and to evaluate the clinical efficacy of sequential chemoradiotherapy (SCRT) as adjuvant therapy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The data of 152 patients with stage IA2-IIA1 cervical cancer who underwent radical hysterectomy with postoperative adjuvant therapy at Changzhou Maternal and Child Health Care Hospital between January 2014 and December 2019 were retrospectively analyzed. The patients were divided into three groups according to the mode of postoperative adjuvant therapy: the concurrent chemoradiotherapy (CCRT) group (n=56), the SCRT group (n=50), and the radiotherapy (RT) alone group (n=46). Disease-free survival (DFS), overall survival (OS), and adverse reactions were compared among the three groups. Follow-up was performed every 3-6 months until December 2023 or death. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The 3-year DFS rates were 67.4%, 85.7%, and 92.0% in the RT, CCRT, and SCRT groups, respectively, and the 5-year DFS rates were 43.4%, 60.7%, and 76.0%, respectively. The 3-year OS rates were 87.0%, 91.1%, and 94.0%, respectively, and the 5-year OS rates were 49.7%, 66.1%, and 84.0%, respectively. The patients receiving SCRT demonstrated significantly improved DFS (P=0.002) and OS (P=0.001) compared with those receiving RT alone, whereas no significant differences were observed between the SCRT and CCRT groups (P=0.18 and P=0.11, respectively). Tumor size was imbalanced at baseline: tumors &gt;4 cm were present in 35/46 (76.09%), 39/56 (69.64%), and 26/50 (52.00%) patients in the RT, CCRT, and SCRT groups, respectively. Severe myelosuppression occurred in 2/46 (4.35%), 20/56 (35.71%), and 15/50 (30.00%) patients in the RT, CCRT, and SCRT groups, respectively (P&lt;0.05 for SCRT and CCRT &lt;i&gt;vs.&lt;/i&gt; RT), with no significant difference between the SCRT and CCRT groups (P&gt;0.05). The multivariate analysis identified pathological type and lymph node metastasis as independent prognostic factors for DFS and OS (P&lt;0.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this retrospective cohort, SCRT after radical hysterectomy was associated with longer DFS and OS than RT alone in patients with stage IA2-IIA1 cervical cancer, whereas no statistically significant difference was detected between SCRT and CCRT, which should not be interpreted as evidence of equivalence. SCRT was associated with a higher incidence of severe myelosuppression than RT alone; however, its toxicity profile was comparable to that of CCRT and remained clinically manageable. These findings suggest that SCRT may serve as a viable postoperative adjuvant t","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"15 9","pages":"716"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}