Translational cancer research最新文献

{"title":"The prognostic and immune significance of <i>SNHG3</i> in clear cell renal cell carcinoma.","authors":"Cheng Li, Pengnan Hu, Chenglong Fan, Hua Mi","doi":"10.21037/tcr-24-1509","DOIUrl":"10.21037/tcr-24-1509","url":null,"abstract":"<p><strong>Background: </strong>Long non-coding RNA (lncRNA) small nucleolar RNA host gene 3 (<i>SNHG3</i>) has been reported to be involved in the pathological process of a variety of tumors, including clear cell renal cell carcinoma (ccRCC). However, whether <i>SNHG3</i> can be used as a prognostic biomarker and its correlation with immune infiltration in ccRCC remain unclear, warranting further research. This study aims to explore the relationship between <i>SNHG3</i> and immune infiltration in ccRCC and confirm the potential of <i>SNHG3</i> to predict survival of ccRCC patients.</p><p><strong>Methods: </strong>The Cancer Genome Atlas (TCGA) database was used to assess the expression of <i>SNHG3</i> in ccRCC, evaluate clinicopathological characteristics, assess prognosis, and conduct functional enrichment analysis. The ccRCC microenvironment and immune infiltration were investigated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithms, respectively. We additionally investigated the relationships between <i>SNHG3</i> and immunological checkpoints. Drug sensitivity of <i>SNHG3</i> was investigated in R. The expression of <i>SNHG3</i> was verified in the Gene Expression Omnibus (GEO) database, ccRCC cell lines, and tissues. Wound healing and Methylthiazolyldiphenyl-tetrazolium bromide (MTT) assays were used to evaluate tumor cell migration and proliferation. Fluorescence in situ hybridization (FISH) assay was conducted to localize <i>SNHG3</i> in ccRCC cells.</p><p><strong>Results: </strong><i>SNHG3</i> expression was significantly upregulated in ccRCC cells and tissues and associated with several clinicopathological features and poor prognosis of ccRCC patients. <i>SNHG3</i> was correlated with immune cells infiltration in ccRCC and exhibited sensitivity to various targeted and chemotherapy drugs. Knockdown of <i>SNHG3</i> significantly reduced the proliferation and migration of ccRCC. FISH results showed that <i>SNHG3</i> was located in the cell nucleus.</p><p><strong>Conclusions: </strong>Overall, this study demonstrates that <i>SNHG3</i> is a prognostic biomarker correlated with immune infiltration in ccRCC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1008-1023"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658636","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":"FAM60A promotes proliferation and invasion of colorectal cancer cells by regulating the Wnt/β-catenin signaling pathway.","authors":"Zhikun Dong, Shuwen Jin, Kan Tang, Xiaomei Li, Yonglin Chen","doi":"10.21037/tcr-24-1608","DOIUrl":"10.21037/tcr-24-1608","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is one of the most detrimental tumors to human health. Although multimodal therapeutic approaches can improve patient survival rates, the prognosis for advanced-stage patients remains poor. It has been reported that family with sequence similarity 60, member A (FAM60A), a component of the SIN3 transcription regulator family member A (SIN3A)/histone deacetylase (HDAC) complex, plays a significant role in tumorigenesis. However, the precise function and mechanisms of action of FAM60A in CRC have not been fully elucidated. In this study, we aim to further delineate the role of FAM60A in CRC by assessing the protein expression levels of FAM60A and β-catenin in CRC tissues and to explore the potential mechanisms by which FAM60A may promote CRC cell proliferation and invasion through a suite of cellular assays.</p><p><strong>Methods: </strong>Tumor tissues of 195 CRC patients and 65 adjacent non-neoplastic tissues were collected to construct tissue microarrays. The expression levels of FAM60A, c-Myc, cyclin D1, and β-catenin were detected using immunohistochemistry (IHC) staining, and the relationship between the results and the patients' clinicopathological characteristics and prognosis was analyzed. HCT116 and HT-29 cell lines with overexpression/knockdown of FAM60A were constructed. Western blot (WB) was used to detect the protein expression of FAM60A and β-catenin. Cell proliferation, apoptosis rate, cell cycle, and cell migration and invasion abilities were assessed using cell counting kit-8 (CCK-8) assay, flow cytometry, wound healing assay, and transwell assay, respectively.</p><p><strong>Results: </strong>FAM60A demonstrated elevated expression in CRC tissues and was positively correlated with tumor infiltration depth, Ki67 proliferation index, and poor prognosis in patients. A positive correlation was observed between FAM60A and the expression of β-catenin, c-Myc, and cyclin D1, and patients with co-expression of FAM60A and β-catenin had a significantly higher rate of distant metastasis. The knockdown of FAM60A markedly reduced the proliferation, migration, and invasive capabilities of HCT116 cells, induced cell cycle arrest, and enhanced apoptosis, whereas its overexpression produced the converse effects. In HT-29 cells, FAM60A knockdown also reduced cell proliferation and impaired wound healing, with overexpression showing opposing outcomes. WB analysis revealed that modulation of FAM60A influenced β-catenin protein levels, suggesting a regulatory link between the two proteins.</p><p><strong>Conclusions: </strong>FAM60A may be a key regulator factor that modulates proliferation and invasion in CRC cells via the Wnt/β-catenin signaling pathway. Elevated FAM60A expression is associated with an adverse prognosis in CRC, underscoring its potential as a prognostic biomarker.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1171-1189"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658650","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":"Diagnostic accuracy of pleural fluid carbohydrate antigen 72-4 for malignant pleural effusion: a systematic review and meta-analysis.","authors":"Xi-Shan Cao, Chang-Mei Feng, Li Yan, Lei Zhang, Wei Jiao, Mei-Ying Wang, Wen-Qi Zheng, Zhi-De Hu","doi":"10.21037/tcr-24-1664","DOIUrl":"10.21037/tcr-24-1664","url":null,"abstract":"<p><strong>Background: </strong>Several studies have evaluated the diagnostic accuracy of pleural fluid carbohydrate antigen 72-4 (CA72-4) for malignant pleural effusion (MPE), but the results were diverse. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of pleural fluid CA72-4 for MPE.</p><p><strong>Methods: </strong>The PubMed and Web of Science databases were searched to verify potential studies investigating the diagnostic accuracy of pleural fluid CA72-4 for MPE. The last search date was August 2024. The quality of the eligible studies was assessed by this study using the revised diagnostic accuracy study quality assessment tool-2 to assess the quality of the eligible studies. This study used a summary receiver operating characteristic (sROC) curve and a bivariate model to pool the findings and their 95% confidence intervals (CIs) of available studies.</p><p><strong>Results: </strong>Eight studies with 828 cases of MPEs and 963 cases of benign pleural effusion (BPE) were included in the present meta-analysis. The pooled sensitivity (95% CI) and specificity (95% CI) were 0.47 (0.39-0.55) and 0.98 (0.95-0.99). The area under sROC curves was 0.77 (95% CI: 0.73-0.80). The primary design weaknesses of the included studies were the representativeness of the participants and the data-driven threshold to define positive CA72-4. A significant publication bias was observed across the eligible studies.</p><p><strong>Conclusions: </strong>Pleural fluid CA72-4 is an auxiliary diagnostic marker for MPE. However, its diagnostic accuracy may be overestimated by available studies.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1237-1245"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658718","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":"The TROP2 paradox: enhancing precision in immunotherapy for advanced non-small cell lung cancer patients-a commentary.","authors":"Saqib Raza Khan, Saurav Verma, Daniel Breadner, Jacques Raphael","doi":"10.21037/tcr-24-1633","DOIUrl":"10.21037/tcr-24-1633","url":null,"abstract":"","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"660-666"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658726","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":"Expression profile, regulatory mechanism and prognostic potential of MBNL2 in esophageal squamous cell carcinoma.","authors":"Shenglai Zhang, Xiaoqin Chu, Yan Zhang, Jianwei Qiu, Liuhong Pan, Liugen Gu, Haifeng Kang, Lin Wang","doi":"10.21037/tcr-24-1933","DOIUrl":"10.21037/tcr-24-1933","url":null,"abstract":"<p><strong>Background: </strong>It remains to refresh the understanding about the pathogenic mechanism of esophageal squamous cell carcinoma (ESCC). This study aimed to profile the expression of muscleblind like protein 2 (MBNL2), as well as its associations with ESCC behaviors.</p><p><strong>Methods: </strong>Bioinformatic tools were used to mine The Cancer Genome Atlas (TCGA) database for the expression data of MBNL2 in ESCC. The expression of MBNL2 in tissue microarray of 179 ESCC patients was determined by immunohistochemistry (IHC), and the relationship of MBNL2 with patients' clinical and pathological characteristics was analyzed. The expression of MBNL2 was tested in fresh ESCC and adjacent normal tissues <i>in vitro</i>. Experiments about cellular invasion, migration and proliferation were performed to detect the impacts of silencing MBNL2 on the biological behaviors of ESCC, and the positive results were checked <i>in vivo</i>.</p><p><strong>Results: </strong>In the TCGA database, the expression of MBNL2 in ESCC was higher than that in adjacent tissues (P<0.05). The protein level of MBNL2 in the tissue microarray of 179 ESCC patients was positively correlated with tumor stage and lymph node metastasis, and negatively correlated with the prognosis of patients. The expression of MBNL2 was significantly upregulated in five fresh ESCC tissues, compared to that in adjacent tissues. In functional experiments, knocking down MBNL2 significantly inhibited the migration and invasion of ESCC cell lines KYSE150 and Eca109, but had no significant effect on their proliferation. Finally, silencing MBNL2 inhibited the epithelial-mesenchymal transition (EMT) of ESCC cells, as evidenced by the upregulation of E-cadherin, the downregulation of Snail and Slug.</p><p><strong>Conclusions: </strong>MBNL2 is highly expressed in ESCC and associated with its Tumor Node Metastasis (TNM) stage, lymph node metastasis and prognosis. MBNL2 may promote ESCC progression through facilitating EMT.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"717-730"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658731","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":"CD69 predicts prognosis through immune cell infiltration and decitabine treatment response in acute myeloid leukemia.","authors":"Jie Zhou, Hao Wu, Bing Li, Lixin Lv, Shunli Zhu, Aibin Liang, Jianfei Fu","doi":"10.21037/tcr-24-1550","DOIUrl":"10.21037/tcr-24-1550","url":null,"abstract":"<p><strong>Background: </strong>Acute myeloid leukemia (AML) is a heterogeneous myeloid neoplasm. Recent studies have focused on unraveling the complexities of the tumor microenvironment (TME) and its impact on AML, with a specific emphasis on CD69, a potential TME regulator. However, the precise relationship between CD69 and AML is yet to be fully elucidated. This study aimed to analyze the heterogeneous gene expression landscape of AML patients using public databases, and to elucidate the relationship between CD69 expression and the pathophysiology of AML.</p><p><strong>Methods: </strong>Three gene datasets from Gene Expression Omnibus (GEO), ribonucleic acid (RNA) sequence data from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and tumor cell lines data from Cancer Cell Line Encyclopedia (CCLE) were used. The Cox proportional hazards regression model was employed to assess the impact of differentially expressed genes on the overall survival (OS) rate of AML. Spearman's rank correlation coefficient analysis was conducted to determine the relationship between CD69 and immune cell infiltration in AML patients. Western blot analysis was utilized to verify CD69 expression in AML cell lines.</p><p><strong>Results: </strong>(I) Gene expression: 13 differentially expressed genes were identified in AML. (II) Impact on survival: CD69 expression was inversely related to OS of AML patients, with lower CD69 levels correlating with improved survival outcomes. (III) Independent risk factors: CD69, ITGB7, SCD and age were identified as independent risk factors in AML. (IV) Immune cell infiltration: a higher expression of CD69 was associated with reduced infiltration of CD8+ T cells and macrophages in AML. (V) Effect of decitabine (DA) treatment: AML patients treated with DA exhibited decreased CD69 expression.</p><p><strong>Conclusions: </strong>The study established a correlation between the expression of ITGB7, SCD, CD69 and the OS in AML patients. SCD, ITGB7 and age were identified as key prognostic factors. The multifaceted role of CD69 in AML, encompassing its association with prognosis, immune cell infiltration, and response to chemotherapy, underscores its potential as a key player in the complex landscape of AML pathogenesis and treatment response.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"865-880"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658632","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 novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis.","authors":"Yali Wang, Peng Zhao, Xude Sun, Felipe Batalini, Gabriel Levin, Hooman Soleymani Majd, Hao Chen, Tingting Gao","doi":"10.21037/tcr-2025-118","DOIUrl":"10.21037/tcr-2025-118","url":null,"abstract":"<p><strong>Background: </strong>Ovarian cancer (OC) is one of the most lethal malignancies in women, primarily due to the absence of reliable predictive biomarkers and effective therapies. The complex role of immunogenic cell death (ICD) in OC remains poorly understood, despite its critical implications for enhancing immune responses against tumors. We are committed to developing and validating a novel ICD-related gene signature and producing certain guiding value for the clinical treatment of OC.</p><p><strong>Methods: </strong>We employed single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) on The Cancer Genome Atlas (TCGA)-ovarian carcinoma dataset to identify ICD-associated genes. A combination of 10 different machine learning approaches was used to construct an ICD-related signature (ICDRS), which was then validated across multiple datasets. The model's predictive power was integrated into a clinical nomogram to predict patient outcomes. Ultimately, we assessed the reaction of various risk subgroups to screen pharmaceuticals designed to address specific risk factors in the context of personalized medicine.</p><p><strong>Results: </strong>We identified 72 prognostic genes related to ICD. An unanimous ICDRS was developed using a 101-combination machine learning computational structure, demonstrating outstanding predictive accuracy for prognosis and clinical use. Patients categorized as low ICDRS varied from those of high ICDRS in terms of biological processes, mutation profiles, and immune cell penetration in the tumor microenvironment. In addition, potential medications that target specific subgroups at risk were identified.</p><p><strong>Conclusions: </strong>The ICDRS presents a significant advancement for prognostication of patients with OC, facilitating refined predictions and the exploration of personalized treatment pathways. Prospective clinical trials are necessary to validate its clinical utility and expand the application of this model to other cancer types.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1359-1374"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657936","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":"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":"Construction and validation of a prognostic model of lncRNAs associated with RNA methylation in lung adenocarcinoma.","authors":"Liren Zhang, Lei Yang, Xiaobo Chen, Qiubo Huang, Zhiqiang Ouyang, Ran Wang, Bingquan Xiang, Hong Lu, Wenjun Ren, Ping Wang","doi":"10.21037/tcr-24-1085","DOIUrl":"10.21037/tcr-24-1085","url":null,"abstract":"<p><strong>Background: </strong>Lung adenocarcinoma (LUAD) is a common type of lung cancer and one of the leading causes of cancer death worldwide. Long non-coding RNAs (lncRNAs) play a crucial role in tumors. The purpose of this study was to explore the expression of lncRNAs associated with RNA methylation modification and their prognostic value in LUAD.</p><p><strong>Methods: </strong>The RNA sequencing and clinical data were downloaded from The Cancer Genome Atlas dataset, and the messenger RNA and lncRNAs were annotated by Ensemble. The lncRNAs related to RNA methylation regulators (RMlncRNAs) were filtered by Pearson correlation analysis between differentially expressed lncRNAs and RNA methylation regulators. Univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator regression analysis were used to construct a prognostic model. The receiver operating characteristic curve (ROC) was plotted to validate the predictive value of the prognostic model. Then, tumor mutational burden (TMB) and microsatellite instability were used to compare the immunotherapy response. Finally, to perform a drug sensitivity analysis, the half-maximal inhibitory concentration (IC₅₀) of targeted drugs was calculated using pRRophetic package.</p><p><strong>Results: </strong>In total, 18 RMlncRNAs associated with the prognosis of LUAD patients were identified. Then, six feature lncRNAs (<i>NFYC-AS1</i>, <i>OGFRP1</i>, <i>MIR4435-2HG</i>, <i>TDRKH-AS1</i>, <i>DANCR</i>, and <i>TMPO-AS1</i>) were used to construct a prognostic model. The ROC curves for training, testing, and validation sets showed that the prognosis model was effective. The subindex based on the prognostic model had a high correlation with TMB. The high-risk group might be subject to greater immune resistance according to the comparison of Tumor Immune Dysfunction and Exclusion scores. Finally, the IC₅₀ of 11 drugs had differences between high- and low-risk group, and only three of the drug's target genes (<i>ERBB4</i>, <i>CASP8</i>, and <i>CD86</i>) were differentially expressed.</p><p><strong>Conclusions: </strong>In conclusion, a prognostic model based on six feature lncRNAs (<i>NFYC-AS1</i>, <i>OGFRP1</i>, <i>MIR4435-2HG</i>, <i>TDRKH-AS1</i>, <i>DANCR</i>, and <i>TMPO-AS1</i>) was constructed by bioinformatics analysis, which might provide a new insight into the evaluation and treatment of LUAD.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"761-777"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658692","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 machine learning models for predicting lymph node metastasis in cutaneous malignant melanoma: a large population-based study.","authors":"Ling-Feng Lan, Yi-Long Kai, Xiao-Ling Xu, Jun-Kun Zhang, Guang-Bo Xu, Yan-Bi Dai, Yan Shen, Hua-Ya Lu, Ben Wang","doi":"10.21037/tcr-24-1672","DOIUrl":"10.21037/tcr-24-1672","url":null,"abstract":"<p><strong>Background: </strong>Lymph node status is essential for determining the prognosis of cutaneous malignant melanoma (CMM). This study aimed to develop a machine learning (ML) model for predicting lymph node metastases (LNM) in CMM.</p><p><strong>Methods: </strong>We gathered data on 6,196 patients from the Surveillance, Epidemiology, and End Results (SEER) database, including known clinicopathologic variables, using six ML algorithms, including logistic regression (LR), support vector machine (SVM), Complement Naive Bayes (CNB), Extreme Gradient Boosting (XGBoost), RandomForest (RF), and k-nearest neighbor algorithm (kNN), to predict the presence of LNM in CMM. Subsequently, we established prediction models. The utilization of the adaptive synthetic (ADASYN) method served to address the challenge posed by imbalanced data. We assessed prediction model performance in terms of average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA). Furthermore, employing SHapley Additive exPlanation (SHAP) analysis resulted in the creation of visualized explanations tailored to individual patients.</p><p><strong>Results: </strong>Among the 6,196 CMM cases, 19.9% (n=1,234) presented with LNM. The XGBoost model showed the best predictive performance when compared with the other algorithms (AP of 0.805). XGBoost showed that age and Breslow thickness were the two most important factors related to LNM.</p><p><strong>Conclusions: </strong>The XGBoost model predicted LNM of CMM with a high level of precision. We hope that this model could assist surgeons in accurately evaluating surgical approaches and determining the extent of surgery, while also guiding the subsequent adjuvant therapies, thereby improving the prognosis of patients.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"706-716"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658696","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}