Translational cancer research最新文献

{"title":"Narrative review of 3D bioprinting for the construction of <i>in vitro</i> tumor models: present and prospects.","authors":"Jia-Yu Tao, Jun Zhu, Yu-Qiong Gao, Min Jiang, Hong Yin","doi":"10.21037/tcr-2025-128","DOIUrl":"10.21037/tcr-2025-128","url":null,"abstract":"<p><strong>Background and objective: </strong>The conventional in vitro research on tumor mechanisms is typically based on two-dimensional (2D) culture of tumor cells, which has many limitations in replicating <i>in vivo</i> tumorigenesis processes. In contrast, the three-dimensional (3D) bioprinting has paved the way for the construction of more biomimetic in vitro tumor models. This article comprehensively elucidates the features of 3D bioprinting and meticulously summarizes its applications in several selected tumors, aiming to offer valuable insights for future relevant studies.</p><p><strong>Methods: </strong>A literature search was conducted in the databases of PubMed and Web of Science for articles on 3D bioprinting for <i>in vitro</i> tumor model construction.</p><p><strong>Key content and findings: </strong>This article introduces various 3D bioprinting technologies for <i>in vitro</i> tumor model construction, focusing on their pros and cons, principles, and protocols. Several <i>in vitro</i> tumor models are presented, detailing their utility in tumorigenesis research and their constraints. To date, 3D bioprinting has been widely applied in oncology, addressing the limitation of traditional 2D tumor cell culture in replicating tumor microenvironment (TME).</p><p><strong>Conclusions: </strong>Advanced 3D bioprinting technology accurately replicates the complex TME and the heterogeneity of intratumor structures, enabling further <i>in vitro</i> tumor studies. It significantly fuels our understanding of tumor pathophysiology and offers new hope for cancer patients.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1479-1491"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658192","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":"Identification of Fanconi anemia pathway genes as novel prognostic biomarkers and therapeutic targets for breast cancer.","authors":"Yunyong Wang, Xiaohang Lu, Hongsheng Lin, Yangling Zeng, Jiaqian He, Jinna Tan, Mingfen Li","doi":"10.21037/tcr-24-772","DOIUrl":"10.21037/tcr-24-772","url":null,"abstract":"<p><strong>Background: </strong>Globally, breast cancer is one of the most common cancers with poor prognosis. The Fanconi anemia (FA) pathway genes maintain genome stability and play important roles in human diseases, including cancer. However, the prognostic values and biological roles of FA pathway genes in breast cancer have not been clarified. This study aims to investigate the potential of FA pathway genes as prognostic biomarkers and therapeutic targets in breast cancer.</p><p><strong>Methods: </strong>In this study, the Oncomine Cancer Microarray (ONCOMINE), University of ALabama at Birmingham Cancer (UALCAN), Kaplan-Meier plotter, cBio Cancer Genomics Portal (cBioPortal), Gene Expression Profiling Interactive Analysis (GEPIA), Gene Multi-Association Network Integration Algorithm (GeneMANIA), the Database for Annotation, Visualization and Integrated Discovery (DAVID) and Tumor Immune Estimation Resource (TIMER) databases were used to investigate the transcriptional and survival data of FA pathway genes in patients with breast cancer.</p><p><strong>Results: </strong>Most of the FA pathway genes were found to be significantly upregulated in breast cancer tissues when compared to normal tissues. Additionally, the elevated expression levels of FA pathway genes were significantly associated with poor survival outcomes in breast cancer patients. Through functional enrichment analysis, the FA pathway genes were positively associated with cell cycle and nucleoplasm and negatively correlated with signal recognition particle-dependent co-translational protein targeting to membrane and ribosome. Furthermore, the expression levels of FA pathway genes exhibited a significant positive association with immune infiltration.</p><p><strong>Conclusions: </strong>The FA pathway genes are potential prognostic biomarkers for breast cancer and may offer effective as well as new strategies for cancer management.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"843-864"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657909","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":"Genome-wide profiling of a prognostic RNA-binding protein signature in esophageal cancer.","authors":"Shaowu Sun, Junya Wang, Yujie Zhang, Yuetong Li, Yanan Guo, Chunyao Huang, Alfredo Tartarone, Pierlorenzo Pallante, Kaiyuan Li, Guoqing Zhang, Xue Pan, Xiangnan Li","doi":"10.21037/tcr-2024-2561","DOIUrl":"10.21037/tcr-2024-2561","url":null,"abstract":"<p><strong>Background: </strong>RNA-binding proteins (RBPs) are known to be involved in the initiation and development of malignant tumors, but the roles of RBPs in esophageal cancer (EC) remain unclear. This study aims to establish a prognostic signature based on RBPs through genome-wide analysis to predict the prognosis of EC patients and provide new insights into chemoresistance.</p><p><strong>Methods: </strong>The gene expression profiles and clinical data of patients with EC were downloaded from the Xena database. Candidate genes were obtained by taking the intersection of RBP genes, Kyoto Encyclopedia of Genes and Genomes pathway-related genes, and differentially expressed RBP genes from cluster analysis. Hub genes were extracted via protein-protein interaction network construction. A Cox proportional hazards regression model with seven prognostic RBPs (TRMT2A, PDHA1, MPRIP, KRI1, IL17A, HSPA1A, and HIST1H4J) was built. The risk score of each patient in internal and external dataset cohorts was calculated, and then the patients were divided into two groups based on the median value.</p><p><strong>Results: </strong>There were significant differences in survival curves between the two risk groups in the internal and external dataset cohorts (P<0.05). In terms of chemotherapy, there was a significant association between RBP risk score and response to chemotherapy, with low-risk patients being more likely to achieve complete response. Finally, univariate and multivariate analyses indicated that the risk score was significantly correlated with overall survival (P<0.05), and pathological stage could also be used independently to predict the prognosis of EC.</p><p><strong>Conclusions: </strong>Our study indicated that the RBP signature could serve as a prognostic biomarker of EC and provided new insights into the chemoresistance of this disease.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1428-1446"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658651","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":"Radiomics models using machine learning algorithms to differentiate the primary focus of brain metastasis.","authors":"Yuping Xie, Xuanzi Li, Shuai Yang, Fujie Jia, Yuanyuan Han, Mingsheng Huang, Lei Chen, Wei Zou, Chuntao Deng, Zibin Liang","doi":"10.21037/tcr-24-1355","DOIUrl":"10.21037/tcr-24-1355","url":null,"abstract":"<p><strong>Background: </strong>Brain metastases are common brain tumors in adults. Brain metastases from different primary tumors have special magnetic resonance imaging (MRI) features. As a new technology that can extract and quantify medical image data, and with the rapid development of artificial intelligence, the machine learning model based on radiology has been successfully applied to the diagnosis and differentiation of tumors. This study aimed to develop radiomics models from post-contrast T1-weighted images using machine learning algorithms to differentiate lung cancer from breast cancer brain metastases.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 118 lung cancer brain metastases patients and 62 breast cancer brain metastases patients confirmed by surgery pathology or combined clinical and imaging diagnosis at The Fifth Affiliated Hospital of Sun Yat-sen University from August 2015 to September 2023. Patients were randomly divided into a training set (126 cases) and a validation set (54 cases) at a 7:3 ratio. Enhanced T1-weighted images of all patients were imported into ITK-SNAP software to manually delineate the region of interest (ROI). Radiomic features were extracted based on the ROI and feature selection was performed using the least absolute shrinkage and selection operator. Significant features were used to develop models using logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multilayer perceptron (MLP), and light gradient boosting machine (LightGBM). The diagnostic performance of the models was assessed using the receiver operating characteristic (ROC) curve.</p><p><strong>Results: </strong>The LightGBM radiomics model exhibited the best diagnostic performance, with an area under the curve (AUC) of 0.875 [95% confidence interval (CI): 0.819-0.931] in the training set and 0.866 (95% CI: 0.740-0.993) in the validation set.</p><p><strong>Conclusions: </strong>The enhanced MRI radiomics model, especially the LightGBM model, can accurately predict the primary lesion types of brain metastases from lung cancer and breast cancer origins.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"731-742"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658336","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":"Specific effects of hypoxia-immune core gene <i>ARHGAP11A</i> on lung adenocarcinoma.","authors":"Kang Sun, Luyao Wang, Xueying Zhang, Huili Chen, Ziqiang Wang, Jing Zhang, Xiaojing Wang, Chaoqun Lian","doi":"10.21037/tcr-24-224","DOIUrl":"10.21037/tcr-24-224","url":null,"abstract":"<p><strong>Background: </strong>The changes in tumor microenvironment (TME) are closely related to the regulation of immunity and hypoxia. This study aimed to investigate the specific effects of <i>ARHGAP11A</i> on the prognosis, immunity, and hypoxia of lung adenocarcinoma (LUAD).</p><p><strong>Methods: </strong>The core gene <i>ARHGAP11A</i> related to immunity and hypoxia was obtained from a variety of databases, including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), the Search Tool for the Retrieval of Interacting Genes (STRING), HALLMARK gene set, and various analysis methods (differences and single factor Cox analysis). The relationship between the expression level of <i>ARHGAP11A</i>, survival prognosis, immune invasion, and hypoxia regulation was analyzed.</p><p><strong>Results: </strong><i>ARHGAP11A</i> was associated with poor patient prognosis and was strongly associated with immune and hypoxic-related signal pathways. We also found that knocking down the expression of <i>ARHGAP11A</i> can affect the proliferation, glycolysis, migration, invasion, and anti-apoptotic ability of tumor cells. The changes of apoptosis-related proteins (BCL2, BAX, and Caspase-3), cell cycle protein E1, D1 (cyclin D1, cyclin E1), matrix metalloproteinase 2 and 9 (MMP2, MMP9), and P-Phosphatidylinositol 3-kinase and protein kinase B (P-PI3K and P-AKT) in the knockdown group, were verified by Western blot (WB). We also found that interfering with the expression of <i>ARHGAP11A</i> can reduce the expression of programmed cell death ligand 1 (PDL1) in LUAD cells. Through the induction of tumor cells by cobalt chloride (CoCL2), we established a hypoxic microenvironment, and found that interfering with <i>ARHGAP11A</i> can significantly reduce the expression of hypoxia-inducible factor 1A (<i>HIF1A</i>), downstream molecular vascular endothelial growth factor A (VEGFA), and lactate dehydrogenase A (LDHA).</p><p><strong>Conclusions: </strong>The expression of <i>ARHGAP11A</i> is highly correlated with immunity, hypoxia, poor prognosis, and tumor cell development. Therefore, the study of <i>ARHGAP11A</i> can provide more ideas on comprehensive treatment and prognosis management of LUAD.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"778-795"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658363","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":"Clinical characterization and prognostic modeling of bladder cancer patients with a history of prior tumors: a SEER database analysis.","authors":"Zhengli Liu, Haojie Zhang, Rongtuan Luo, Bin Wang, Tengfei Li, Baoshou Zheng","doi":"10.21037/tcr-24-1530","DOIUrl":"10.21037/tcr-24-1530","url":null,"abstract":"<p><strong>Background: </strong>Bladder cancer is one of the most prevalent malignancies within the urinary system, with incidence and mortality rates showing a global upward trend. This study aims to examine the clinical characteristics of bladder cancer patients with a history of prior malignancies and to develop a prognostic model using extensive data from the Surveillance, Epidemiology, and End Results (SEER) database to inform clinical treatment strategies.</p><p><strong>Methods: </strong>Data from bladder cancer patients diagnosed between 2011 and 2015 were extracted using SEER*Stat software. Statistical analyses, including Kaplan-Meier survival curves, and Cox regression, were conducted using R software version 3.6.1 to develop a nomogram model. The predictive performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and the concordance index (C-index).</p><p><strong>Results: </strong>A total of 12,260 bladder cancer patients were analyzed, including 8,959 individuals with no prior tumor history and 3,301 individuals with a history of previous tumors. The mean survival duration for patients with a prior tumor history was 56.04±39.96 months, significantly lower than the 70.28±39.36 months for patients without a prior tumor history (P<0.001). Significant differences were observed between the two groups across various clinical characteristics, such as age, race, gender, marital status, tumor location, tumor stage, and tumor grade. Multifactorial analysis identified age, race, gender, marital status, tumor grade, tumor stage, tumor histological type, surgical intervention, radiotherapy, chemotherapy, and prior tumor history as independent prognostic factors influencing survival. A nomogram was subsequently developed to predict overall mortality risk and 3- and 5-year survival rates, demonstrating robust predictive performance with a C-index and AUC exceeding 0.70.</p><p><strong>Conclusions: </strong>Patients with a history of tumors exhibited lower survival rates and distinct clinical characteristics. The developed nomogram accurately predicts overall mortality and 3- and 5-year survival rates, offering potential for personalized prognostic assessments in clinical practice. Future research should validate the model's generalizability and include additional biological factors to enhance its predictive power.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1111-1123"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658614","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 cancer stem cell heterogeneity in intrahepatic cholangiocarcinoma.","authors":"Yiwang Zhang, Juping Xie, Xiangqi Huang, Jintian Gao, Zhiyong Xiong","doi":"10.21037/tcr-24-1286","DOIUrl":"10.21037/tcr-24-1286","url":null,"abstract":"<p><strong>Background: </strong>Intrahepatic cholangiocarcinoma (ICC) is a highly invasive bile duct cancer with poor prognosis due to frequent recurrence and limited effective treatments. Cancer stem cells (CSCs) contribute to ICC's therapeutic resistance and recurrence, driven by distinct cellular subpopulations with variable tumorigenic properties. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled a deeper exploration of cellular heterogeneity in tumors, offering insights into unique CSC subgroups that impact ICC progression and patient outcomes. This study aimed to investigate the effect of CSC heterogeneity on the prognosis of ICC.</p><p><strong>Methods: </strong>The scRNA-seq dataset GSE142784 was retrieved from the Gene Expression Omnibus (GEO) database, and Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) databases. Hallmarks and AUCell R package were adopted for analyzing the signaling pathway activity, CellChat for observing cell communication between subgroups, and SCENIC for analyzing transcription factors expression. The immune cell infiltration and drug sensitivity of the model were analyzed using the CIBERSORT algorithm and the \"pRRophetic\" R packages, respectively. And immunohistochemistry (IHC) tests were used to evaluate expression of transcription factors in ICC patients.</p><p><strong>Results: </strong>Based on scRNA-seq data, five clusters (DLK⁺, CD13⁺, CD90⁺, CD133⁺, and other cholangiocarcinoma cells) were observed in ICC, which presented different signaling pathway activities, such as HSF1 and STAT1 were highly expressed in the CD133 cluster, and consistent with the results of IHC tests. Pathways like Notch and Wnt/β-catenin signaling transferred among above subgroups. Further, subgroups favored varied immune response and drug sensitivity, and CD133⁺ subgroup patients showed significantly shortened recurrence-free survival (RFS).</p><p><strong>Conclusions: </strong>Configuring the subgroup of ICC is helpful for predicting the prognosis and drug resistance in ICC and can provide new strategies for cancer treatment.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1265-1281"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658430","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: <i>Beclin-1</i> expression serves as an important biomarker for carcinogenesis and evolution in lung adenocarcinoma presenting as ground glass opacity.","authors":"","doi":"10.21037/tcr-2024b-12","DOIUrl":"10.21037/tcr-2024b-12","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.21037/tcr-20-1001.].</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1506"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658642","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":"Constructing and validating a novel prognostic risk score model for rectal cancer based on four immune-related genes.","authors":"Ruyun Cai, Zhonghua Hong, Hezhai Yin, Huilin Chen, Mengting Qin, Yihong Huang","doi":"10.21037/tcr-24-1511","DOIUrl":"10.21037/tcr-24-1511","url":null,"abstract":"<p><strong>Background: </strong>Immunotherapy is playing an increasing role in the treatment of various cancers. However, its application in rectal cancer is very limited as only microsatellite-unstable bowel cancers with defective mismatch repair are found to benefit. The majority of rectal cancers belong to the microsatellite-stable phenotype. Therefore, the aim of this study is to explore immune-related genes within the tumor microenvironment of rectal cancer, with the objective of discovering novel biomarkers and therapeutic targets for rectal cancer, and to establish a new prognostic prediction model for rectal cancer based on these immune-related genes.</p><p><strong>Methods: </strong>The data in The Cancer Genome Atlas (TCGA) database were processed using the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to obtain differently expressed genes (DEGs). Then the DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Gene and Genomes (KEGG), Reactome function enrichment analysis, and protein-protein interaction (PPI) analysis to screen the core genes, which were utilized to compute the risk scores of individual patients. Finally, combining risk scores and clinical characteristics, a new prognostic prediction model was established by univariate and multivariate Cox analyses, and the prognostic model was validated by the Gene Expression Omnibus (GEO) database.</p><p><strong>Results: </strong>The study finally identified four core genes (<i>CYBB</i>, <i>CCR4</i>, <i>FOXP3</i>, and <i>CD80</i>), and immune cell infiltration analyses of the four core genes showed that their expression levels were positively correlated with the distribution of various immune cells. The 4-gene risk score categorized rectal cancer patients into high-risk and low-risk groups, and the results showed that the low-risk group had a stronger correlation with the immune response and had a better prognosis. A prognostic model was developed by integrating risk scores and clinical characteristics and showed a strong predictive effect.</p><p><strong>Conclusions: </strong>In patients with rectal cancer, <i>CYBB</i>, <i>CCR4</i>, <i>FOXP3</i>, and <i>CD80</i> are immune-related core genes, and low expression of each gene is associated with poor clinical prognosis. The risk score obtained on their basis is independent prognostic factors for rectal cancer, suggesting that the four core genes may provide a foundation for the development of new prognostic biomarkers for rectal cancer and the study of immunotherapy.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1053-1069"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658675","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":"Establishment of a prognosis-related predictive model for hepatocellular carcinoma patients with macrovascular invasion treated with transcatheter arterial chemoembolization combined with intensity modulated radiotherapy.","authors":"Dan Yang, Wanting Tian, Wei Wang, Xuan Zhao, Chaozhi Wang, Zhufang Ma","doi":"10.21037/tcr-24-1226","DOIUrl":"10.21037/tcr-24-1226","url":null,"abstract":"<p><strong>Background: </strong>So far, there are still few studies on the prognostic factors of hepatocellular carcinoma (HCC) patients with macrovascular invasion (MVI) treated with transcatheter arterial chemoembolization (TACE) combined with intensity modulated radiotherapy (IMRT), and no relevant model has been established to predict the prognosis of such patients. Thus, the purpose of this study was to determine the prognostic factors of HCC patients with MVI after treatment with TACE combined with IMRT, and to establish a nomogram model for forecasting 1-, 3-, 5-year overall survival (OS) of the patients.</p><p><strong>Methods: </strong>HCC patients with MVI who were diagnosed and treated at Department of Gastroenterology, 3201 Hospital between January 2010 and December 2020 were enrolled in this study according to the inclusion and exclusion criteria. The risk factors linked to patient OS were determined by performing Cox regression analysis. The nomogram for predicting 1-, 3-, 5-year OS in HCC patients with MVI was stablished and validated based on the results of the Cox regression analysis.</p><p><strong>Results: </strong>In total, 118 patients were included in the current study. The medium follow-up time was 46 months (range, 29-71 months). Univariate Cox regression analysis revealed that tumor diameter, treatment frequency of TACE, IMRT dose, Child-Pugh grade, liver cirrhosis and alpha fetoprotein (AFP) level were significantly related to the OS of the patients. Further multivariate Cox regression analysis showed that treatment frequency of TACE and Child-Pugh grade, liver cirrhosis and AFP level were the independent prognostic factors of the OS in patients who were treated with TACE combined with IMRT. The nomogram we constructed using the above independent risk factors exhibited good ability for predicting 1-, 3-, 5-year OS of the patients. The concordance-index of the nomogram was 0.727, indicating the nomogram had a good discrimination.</p><p><strong>Conclusions: </strong>Treatment frequency of TACE and Child-Pugh grade, liver cirrhosis and AFP level were independent predictors of OS in HCC patients with MVI after TACE combined with IMRT treatment. The nomogram that we developed using these predictors provided a convenient tool to predict the survival probability in HCC patients with MVI.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1214-1222"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658729","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}