Translational cancer research最新文献

{"title":"Construction and validation of a clinical prognostic model for frontal glioblastoma: a real-world clinical study based on radiation therapy.","authors":"Shuai Hao, Jialing Liu, Jingjing Tuo, Li Wang, Wei Li, Ming Liu, Pengzhan Shuang, Nan Li","doi":"10.21037/tcr-24-2058","DOIUrl":"10.21037/tcr-24-2058","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma has high malignancy, treatment challenge, poor prognosis and survival. It takes place mostly in the frontal lobe, and it significantly impacts late-life activities. Therefore, the establishment of a survival model for frontal glioblastoma patients is of great significance for optimizing the treatment for patients. The aim of this study is to identify risk factors for frontal glioblastoma, to construct survival models, and to provide strong evidence for patients and doctors to apply radiotherapy to frontal glioblastoma.</p><p><strong>Methods: </strong>Independent risk factors for frontal glioblastoma patients were identified and survival models were constructed based on information obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Clinical data on patients pathologically diagnosed with frontal glioblastoma were screened. A nomogram was constructed based on the training group to verify the clinical validity of the model.</p><p><strong>Results: </strong>A total of 2,063 patients were included. There were 1,444 patients assigned to the training group, according to a random number method, and the remaining 619 patients were included in the validation group. Cox multivariate analysis based on 1,444 data from the training group showed that age, tumor hemiorism, metastasis, surgery, chemotherapy and radiotherapy were significantly correlated with the prognosis, with P values less than 0.05. In the training group, the concordance index (C-index) for overall survival (OS) and cancer-specific survival (CSS) of the cohort was 0.712 and 0.710, respectively. Calibration, receiver operating characteristic curve and decision curve analysis for OS showed a good agreement between the actual and predicted probability of survival. A total of 225 cases were screened out for analysis after 1:1 matching with a caliper value of 0.02. The median survival time of patients receiving radiotherapy was 7 months and that of those without radiotherapy was 5 months, hazard ratio =1.067, P values less than 0.05.</p><p><strong>Conclusions: </strong>Age over 60 years old, space-occupying lesions across the midline, surgery not performed, radiotherapy not performed, and without chemotherapy are poor prognostic factors for frontal glioblastoma patients. Radiation therapy can significantly improve OS and CSS in frontal glioblastoma patients. The nomogram developed in this study has the potential for clinical application.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2661-2676"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317926","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 neuronal synapse-related signatures and potential therapeutic drugs in colorectal cancer based on machine learning algorithms and molecular docking.","authors":"Wen-Jing Wu, Kan Wang, Yang Vivian Yang, Xiaoning Yang","doi":"10.21037/tcr-24-1988","DOIUrl":"10.21037/tcr-24-1988","url":null,"abstract":"<p><strong>Background: </strong>Nervous system-cancer interactions can regulate tumorigenesis, invasion, and metastasis. However, specific biomarkers for targeting neuron synapse in colorectal cancer (CRC) remain unexplored. This study aims to develop a neuronal synapse-related signature (NSRS) to predict survival in CRC patients, identify potential therapeutic drugs, and explore its clinical applications.</p><p><strong>Methods: </strong>We collected neuronal synapse genes (NSGs) from the Molecular Signatures Database (MSigDB) and published mass spectrometry data. Using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator Cox regression (LASSO-Cox), we identified prognostic NSGs and constructed a NSRS through multivariate Cox regression. Functional enrichment analysis revealed the molecular characteristics of NSRS subgroups. Additionally, xCell and ESTIMATE algorithms quantified the abundance of 54 cell subtypes and assessed the tumor immune microenvironment (TIME) of the two NSRS subgroups. Finally, drug prediction and molecular docking identified candidate drugs with therapeutic potential.</p><p><strong>Results: </strong>Seven key prognostic NSGs were identified, and an independent, stable NSRS model was constructed. Kaplan-Meier survival curves indicated that the high NSRS group had poorer outcomes (log-rank test, P<0.05). Functional enrichment analysis revealed significant enrichment of epithelial-mesenchymal transition, hypoxia, and inflammation features in the high NSRS group. xCell and ESTIMATE analyses showed a more complex TIME and lower tumor purity in the high NSRS group, highlighting the role of neuro-tumor interactions in CRC. Drug prediction and molecular docking suggested alprostadil, dihydroergocristine, and nocodazole as candidate drugs for CRC treatment.</p><p><strong>Conclusions: </strong>This is the first study to develop neuron synapse-related biomarkers from the perspective of neuron-cancer interactions using machine learning. We constructed a robust NSRS model and identified candidate drugs targeting prognostic NSGs, providing new insights into CRC prognosis and treatment.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2737-2757"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317929","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":"<i>GJB2</i> enhances cancer stem cell properties by modulating <i>SOX2</i> expression via NF-κB pathway activation in lung adenocarcinoma.","authors":"Qi You, Tao Huang, Zhijie He, Xinlu Tao, Yan Zhang, Haotian Zhang, Shaojin Zhu","doi":"10.21037/tcr-24-2075","DOIUrl":"10.21037/tcr-24-2075","url":null,"abstract":"<p><strong>Background: </strong>Lung adenocarcinoma (LUAD) is a prevalent malignancy characterized by low survival rates and poor prognosis. Gap junction beta-2 protein (<i>GJB2</i>) is overexpressed in various tumors and is associated with cancer stem cell (CSC) properties. However, its role in LUAD remains unclear. This study explored the regulatory mechanism of <i>GJB2</i> in promoting CSC properties of LUAD.</p><p><strong>Methods: </strong>Differentially expressed genes (DEGs) related to CSC properties were analyzed using the Gene Expression Omnibus (GEO) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) databases. Protein expression was assessed via western blotting, and gene expression was quantitative real-time polymerase chain reaction (qRT-PCR) in LUAD cell lines. Loss-of-function experiments were performed to evaluate the impact of <i>GJB2</i> on cell proliferation, apoptosis, and migration. CSC properties were confirmed through sphere formation assay and flow cytometry of CD133+/CD44+cells. Promoter activity was examined using a dual-luciferase reporter assay.</p><p><strong>Results: </strong>GJB2 was associated with the CSC properties in LUAD, with its expression upregulated in LUAD cell lines. GJB2 downregulation impaired proliferation, reduced migration, and induced apoptosis in A549 and H1299 cells. Mechanistically, <i>GJB2</i> activated the nuclear factor kappa-B (NF-κB) pathway, facilitating the nuclear translocation of p65 and enhancing sex-determining region Y-Box 2 (<i>SOX2</i>) transcription.</p><p><strong>Conclusions: </strong><i>GJB2</i> promotes <i>SOX2</i> transcription and enhances CSC properties in LUAD by modulating NF-κB pathway activity.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2648-2660"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317923","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":"Deep learning for fine-grained molecular-based colorectal cancer classification.","authors":"Junyu Bian, Yansong Li, Yamei Dang, Yonglin Chen","doi":"10.21037/tcr-2024-2348","DOIUrl":"10.21037/tcr-2024-2348","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is one of the most common malignancies globally and a major cause of cancer-related deaths. In the molecular diagnosis of CRC, microsatellite instability (MSI) status and mutations in genes such as <i>BRAF</i>, <i>KRAS</i>, and <i>NRAS</i> are important molecular markers. Traditional molecular detection methods are costly and time-consuming. Therefore, this study proposes a fine-grained classification method for CRC based on hematoxylin and eosin (H&E) stained tissue section images combined with deep learning (DL) technology, aiming to provide new insights into the molecular diagnosis of CRC.</p><p><strong>Methods: </strong>In this study, we first collected H&E-stained tissue section images of 383 CRC patients from The First Hospital of Lanzhou University (LZUFH) and constructed the LZUFH_CRC dataset. Then, we proposed a hybrid DL model combining Convolutional Neural Network (CNN) and Vision Transformer (ViT) for fine-grained classification tasks in CRC. The model consists of three parts: a feature extractor, an aggregator, and a classification head. A two-stage training strategy was adopted for model training. Finally, we evaluated the performance of the model on the LZUFH_CRC dataset and compared it with other methods.</p><p><strong>Results: </strong>The results showed that the proposed model achieved an overall accuracy (ACC) of 0.524 and area under the receiver operating characteristic curve (AUC) of 0.791 on the LZUFH_CRC dataset. Among them, the grouping names MSI and NRAS had better classification performance, with F1-scores of 0.724 and 0.514, respectively. Additionally, the study visualized the feature activation maps to show the regions of interest of the model for different input images, finding that the model paid more attention to the transitional areas between tumor and non-tumor regions and the mesenchymal areas of the tumor. Meanwhile, comparisons among different clinical characteristic groups showed that the model did not exhibit significant biases in terms of gender, age and tumor location.</p><p><strong>Conclusions: </strong>This study proposed a fine-grained classification method for CRC based on DL technology, which combines H&E-stained tissue section images with DL technologies such as CNN and ViT, providing new insights into the molecular diagnosis of CRC. Although the performance of the model needs further improvement, the results indicate that DL technology has potential in the molecular detection of CRC. In the future, the research team will continue to optimize the model to improve the ACC and efficiency of fine-grained classification in CRC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3035-3046"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317939","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 validation of prognostic models based on cell cycle-related signatures for predicting the prognosis of patients with lung adenocarcinoma.","authors":"Yuanping Huang, Yanfei Zhao, Yinghui Guan","doi":"10.21037/tcr-24-1479","DOIUrl":"10.21037/tcr-24-1479","url":null,"abstract":"<p><strong>Background: </strong>Lung adenocarcinoma (LUAD) represents the most prevalent histological subtype within lung cancer. Nevertheless, the risk of postoperative metastasis and recurrence remains a substantial concern. We aimed to build the cell cycle-related competing endogenous RNA (ceRNA) networks and potential prognosis prediction models of LUAD, which might provide a valuable reference for studying the prognosis of LUAD.</p><p><strong>Methods: </strong>The RNA sequencing data of LUAD were procured from The Cancer Genome Atlas (TCGA) database and the differentially expressed RNAs were identified from the Ensembl genome browser 96 database [P<0.05 and |log2 fold change (FC)| >1]. The gene expression profile data were acquired from the Gene Expression Omnibus (GEO) repository. A gene set variation analysis was carried out to determine the differentially expressed genes (DEGs) (P<0.05) and a cell cycle-related ceRNA network of LUAD was constructed based on the DEGs. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to acquire the optimized gene combination, a risk score (RS) prognostic risk prediction model was generated subsequently, and a Kaplan-Meier curve was developed to evaluate the efficacy of the RS model. Moreover, we constructed the 3- and 5-year prognostic models of nomogram using R3.6.1 \"rms\" package, the C-index was counted for accessing predictive capacity. Receiver operating characteristic (ROC) curves were used to evaluate the multiple prognostic risk prediction model.</p><p><strong>Results: </strong>In total, we identified 240 DEGs and constructed the cell cycle-related ceRNA network of LUAD from datasets GSE50081 and GSE37745. Six optimal genes (<i>ADRB2</i>, <i>IL1A</i>, <i>PIK3R2</i>, <i>CKD1</i>, <i>CCNB1</i> and <i>CHRNA5</i>) related to prognostic were obtained. The C-index values for 3- and 5-year prognostic nomogram models were 0.7665 and 0.7104, respectively, indicating highly accurate predictive capabilities. The area under the curve (AUC) of the combination of RS and clinical factors prognostic risk prediction model was 0.869 in TCGA and 0.770 in GSE50081 dataset.</p><p><strong>Conclusions: </strong>This research identified six prognostic biomarkers and built the prognostic prediction models of LUAD, which may enhance the comprehension of disease biology, serve as an effective prognostic tool for LUAD and drive novel therapy development potentially.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2900-2915"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317942","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":"Microsatellite instability (MSI) and the tumor mutation burden (TMB) as biomarkers of response to immune checkpoint inhibitors in prostate cancer.","authors":"Ioannis A Voutsadakis","doi":"10.21037/tcr-2024-2516","DOIUrl":"10.21037/tcr-2024-2516","url":null,"abstract":"","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2553-2557"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317946","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":"Plasma-derived exosomal human epidermal growth factor receptor 2 (HER2) protein for distinguishing breast cancer from benign breast disease and assessing the efficacy of neoadjuvant therapy.","authors":"Xiaofang Yang, Mengdan Xu, Yu Xia, Zhaofen Ba, Chunmiao Han, Yipu Wang, Jinwen Qu, Yu Wang, Yehui Zhou, Rong Wang, Jing Lan","doi":"10.21037/tcr-2025-825","DOIUrl":"10.21037/tcr-2025-825","url":null,"abstract":"<p><strong>Background: </strong>Exosomes derived from liquid biopsy can serve as excellent biomarkers in clinical practices. Human epidermal growth factor receptor 2 (HER2) has been shown to be associated with tumor stage, clinical therapy, and prognosis. However, the clinical value of exosomal HER2 for breast cancer remains unclear. The study aimed to investigate the potential of exosomal HER2 in breast cancer diagnosis, explore its role in guiding clinicians in the selection of treatment options, and find out whether changes in exosomal HER2 levels could be used to evaluate the efficacy of neoadjuvant chemotherapy.</p><p><strong>Methods: </strong>The HER2 protein was detected by magnetic particle-based chemiluminescence immunoassay. The study enrolled 51 patients with breast cancer and 36 patients with benign breast disease to evaluate the diagnostic value of exosomal HER2. Additionally, a receiver operating characteristic (ROC) curve was drawn for HER2 immunohistochemistry (IHC) to determine the concordance between exosomal HER2 and HER2 IHC. Furthermore, the exosomal HER2 levels during neoadjuvant therapy were measured to assess the efficacy of neoadjuvant therapy.</p><p><strong>Results: </strong>Exosomal HER2 concentration in patients with breast cancer was significantly higher than that in patients with benign breast disease. The optimal cutoff value of exosomal HER2 for diagnosing breast cancer was 772.7 pg/mL, with a sensitivity of 45.1% and a specificity of 97.22%. With 743 pg/mL as the cutoff value, the concordance between exosomal HER2 levels and HER2 IHC was 74.51%, with a sensitivity of 81.25% and a specificity of 71.43%. Exosomal HER2 could be detected in patients receiving neoadjuvant therapy, and some patients (5/8) exhibited a proportional relationship between exosomal HER2 levels and clinical tumor size changes.</p><p><strong>Conclusions: </strong>Plasma-derived exosomal HER2 might serve as a promising biomarker for distinguishing breast cancer from benign breast disease, screening patients who could benefit from HER2-targeted therapy, and monitoring neoadjuvant therapy.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3186-3200"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317951","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":"In silico development and validation of a novel six-gene-derived signature in hepatocellular carcinoma.","authors":"Jin He, Binbin Li, Huize Liu, Weijian Chu, Chunhui Rao","doi":"10.21037/tcr-2024-2621","DOIUrl":"10.21037/tcr-2024-2621","url":null,"abstract":"<p><strong>Background: </strong>Pyruvate metabolism presents a novel, therapeutically targetable metabolic vulnerability in hepatocellular carcinoma (HCC). In this study, we sought to identify HCC molecular subtypes and develop prognostic signatures based on pyruvate metabolism-related genes (PMRGs) to inform personalized therapeutic approaches.</p><p><strong>Methods: </strong>Transcriptional profiles and clinical data of HCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Consensus clustering was employed for molecular classification, while a least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for risk score calculation. The relationship between the risk score and HCC prognosis, immune landscape, gene expression, and drug sensitivity was analyzed.</p><p><strong>Results: </strong>Twenty PMRGs were identified as significantly associated with HCC prognosis. Consensus clustering of these genes revealed two distinct molecular subtypes that stratified patients into groups with favorable and unfavorable outcomes. A novel six-gene signature, comprising <i>ACACA</i>, <i>ACAT1</i>, <i>CYP1</i>, <i>DLAT</i>, <i>LDHA</i>, and <i>ME1</i>, was developed for HCC prognostication. The receiver operating characteristic (ROC) curve demonstrated robust survival prediction in all cohorts, allowing the stratification of patients into high- and low-risk groups with markedly different overall survival (OS). The signature-derived nomogram displayed appreciable clinical net benefit. Enrichment analysis revealed activation of PMRGs and enrichment of diverse metabolic processes and signaling pathways in the high-risk group. Moreover, the prognostic signature showed significant correlations with immune landscapes and therapeutic responses, enabling prediction of immunotherapy responsiveness.</p><p><strong>Conclusions: </strong>Collectively, a unique PMRG-based signature effectively predicts prognosis in HCC patients and provides valuable insights into chemotherapy and immunotherapy strategies for these individuals.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2940-2955"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317932","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":"Construction and validation of a prognostic model for glioma: an analysis based on mismatch repair-related genes and their correlation with clinicopathological features.","authors":"Tong Wang, Bohao Sun, Rui Yu, Jing Zhang, Yichen Wu, Delin Wang, Xiaoying Ni, Hao Wang","doi":"10.21037/tcr-24-2045","DOIUrl":"10.21037/tcr-24-2045","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Glioma is a prevalent and aggressive form of brain neoplasm, characterized by a 5-year survival rate of less than 10%. Despite the encouraging outcomes demonstrated by numerous prognostic models for gliomas in preliminary research, these models frequently do not meet anticipated results when subjected to external validation. Our goal is to uncover potential prognostic biomarkers and therapeutic targets by concentrating on mismatch repair-related genes (MRRGs) that are significantly linked to glioma.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We employed least absolute shrinkage and selection operator (LASSO) Cox regression to develop a multigene signature based on MRRGs. The functional implications of the &lt;i&gt;EXO1&lt;/i&gt; gene were evaluated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). We analyzed the correlation between &lt;i&gt;EXO1&lt;/i&gt; gene expression and immune cell infiltration using single-sample GSEA (ssGSEA). Moreover, we undertook a comprehensive examination of the correlation between &lt;i&gt;EXO1&lt;/i&gt; expression and several clinical parameters derived from clinical samples obtained from the TCGA database. The parameters assessed encompassed World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) wild-type status, the status of 1p/19q non-co-deletion, and patient age. Additionally, we executed a thorough prognostic evaluation of EXO1 across various subgroups defined by clinical parameters. Utilizing the \"rms\" R package, we constructed a nomogram model that amalgamates clinical characteristics and &lt;i&gt;EXO1&lt;/i&gt; expression levels. Immunohistochemical techniques were utilized to assess &lt;i&gt;EXO1&lt;/i&gt; expression in sixty glioma cases.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A comparative analysis of the expression of 23 MRRGs between glioma and normal samples revealed that 22 MRRGs were upregulated in glioma tissues. Univariate analysis indicated that 20 of these MRRGs were significantly differentially expressed (P&lt;0.05). The LASSO algorithm reduced this set to seven key genes: &lt;i&gt;EXO1&lt;/i&gt;, &lt;i&gt;POLD2&lt;/i&gt;, &lt;i&gt;POLD4&lt;/i&gt;, &lt;i&gt;RFC1&lt;/i&gt;, &lt;i&gt;RFC2&lt;/i&gt;, &lt;i&gt;RFC4&lt;/i&gt;, and &lt;i&gt;RPA3&lt;/i&gt;. Kaplan-Meier survival analysis confirmed the association between the aberrant expression of these genes and patient survival outcomes. GO and KEGG enrichment analyses highlighted the role of &lt;i&gt;EXO1&lt;/i&gt; in crucial biological processes and pathways, including the cell cycle and DNA repair mechanisms. Increased expression of &lt;i&gt;EXO1&lt;/i&gt; was correlated with higher WHO grades, IDH wild type, 1p/19q non-codel, and poor prognosis. A nomogram that combines EXO1 with clinical parameters has been developed to assist in predicting the overall survival probabilities of patients at 1-year intervals. The calibration chart revealed that effectiveness of the nomogram was accurate (c-index =0.850). Immunohistochemical evaluations showed that &lt;i&gt;EXO1&lt;/i&gt; expression levels were significantly elevated in 60 glioma t","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2690-2706"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317936","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":"CREB3-mediated upregulation of MIR210HG transcription enhances proliferation in colon cancer cells.","authors":"Xiaoqian Wang, Aqiang Fan, Liu Hong","doi":"10.21037/tcr-24-1525","DOIUrl":"10.21037/tcr-24-1525","url":null,"abstract":"<p><strong>Background: </strong>Understanding the regulatory mechanisms behind colon cancer (CC) pathogenesis is crucial for developing effective therapeutic strategies. Long non-coding RNA (lncRNA) <i>MIR210HG</i> has been implicated in various cancers, including CC, where it may play a role in tumor progression. Additionally, the transcription factor cyclic adenosine monophosphate-responsive element-binding protein 3 (CREB3) has been suggested to regulate lncRNA expression, but its role in CC remains unclear. This study investigates the CREB3-<i>MIR210HG</i> regulatory axis, focusing on how this interaction influences CC cell proliferation and its potential as a therapeutic target for cancer treatment.</p><p><strong>Methods: </strong>To explore the CREB3-<i>MIR210HG</i> axis, bioinformatics analysis was conducted to identify the <i>MIR210HG</i> promoter and predict potential transcription factor binding sites. Expression levels of CREB3 and <i>MIR210HG</i> were analyzed using The Cancer Genome Atlas (TCGA) database, which provided a broader understanding of their correlation in human CC samples. Additionally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed in SW480 CC cells to validate these findings at the cellular level. Luciferase reporter assays and chromatin immunoprecipitation (ChIP) experiments were employed to confirm the binding of CREB3 to the <i>MIR210HG</i> promoter, providing direct evidence of their interaction. Finally, functional assays, including cell proliferation assays and knockdown experiments, were carried out to assess the impact of <i>MIR210HG</i> on CREB3-induced proliferation in CC cells.</p><p><strong>Results: </strong>Bioinformatics and experimental analysis revealed that CREB3 directly binds to the <i>MIR210HG</i> promoter, leading to significant upregulation of <i>MIR210HG</i> transcription in CC cells. Data from TCGA and RT-qPCR analyses showed a positive correlation between CREB3 and <i>MIR210HG</i> expression in CC tissues, supporting the hypothesis of a regulatory link. Functional assays demonstrated that overexpression of CREB3 enhanced CC cell proliferation, while silencing <i>MIR210HG</i> reversed this effect, indicating that <i>MIR210HG</i> mediates CREB3-induced proliferation. These results suggest that the CREB3-<i>MIR210HG</i> axis plays a critical role in CC progression.</p><p><strong>Conclusions: </strong>This study highlights the CREB3-<i>MIR210HG</i> axis as a pivotal mechanism driving CC cell proliferation. Targeting this regulatory pathway may provide a novel therapeutic strategy for CC treatment, with the potential for developing lncRNA-based therapies aimed at inhibiting this axis to slow down tumor growth and progression.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2874-2884"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317938","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}