Translational cancer research最新文献

{"title":"Ovarian teratomas causing anti-N-methyl-D-aspartate receptor encephalitis: a case series from west China.","authors":"Jiaxin Gu, Xiumei Xu, Qian Chen, Min Feng, Ruiqi Duan","doi":"10.21037/tcr-24-2126","DOIUrl":"10.21037/tcr-24-2126","url":null,"abstract":"<p><strong>Background: </strong>The incidence of ovarian teratomas (OT) causing anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is low, and pathological studies of related cases are limited. This study aimed to analyze the clinical data of such patients and to investigate the expression of N-methyl-D-aspartate receptor (NMDAR) and lymphocytes in teratoma tissues to initially investigate the pathogenesis of the disease.</p><p><strong>Methods: </strong>Clinical data were collected and analyzed. Immunohistochemistry was applied to detect the expression of NMDAR subunits and T/B lymphocytes in 46 patients, including 8 OT patients with encephalitis and 38 regular OT patients. Immunohistochemical expression of NMDARs and T/B lymphocytes in OT tissues in patients with or without anti-NMDARE.</p><p><strong>Results: </strong>Teratomas causing encephalitis mostly occur in young women. The degree of positive expression of NMDAR, CD4 and CD20 in the encephalitis group differed statistically from the control group (P<0.05). Although there was no linear relationship between the CD4/CD20 expression and the NMDAR expression, clusters of lymphocytes were observed clearly around the squamous epithelium positive for NMDAR expression only in the encephalitis group.</p><p><strong>Conclusions: </strong>OT leading to encephalitis seems to be associated with high expression of NMDARs in the squamous epithelium of the tissue, where clusters of lymphocytes infiltration is also a unique pathological feature. This is the first multi-case group study about OTs causing anti-NMDARE in southwestern China.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2603-2614"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317949","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 prognostic and immune significance of fibronectin type III domain-containing 1 gene in pan-cancer and its relationship with proliferation and migration of stomach adenocarcinoma.","authors":"Minying Deng, Wen Huang, Rongkui Luo, Huimei Wang, Zixiang Yu, Benting Ma, Lei Xu, Xiaolei Zhang, Jieakesu Su, Chen Xu, Yingyong Hou","doi":"10.21037/tcr-2024-2279","DOIUrl":"10.21037/tcr-2024-2279","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Fibronectin type III domain containing 1 (FNDC1) exhibits emerging roles in tumorigenesis, yet its pan-cancer implications and mechanistic contributions to stomach adenocarcinoma (STAD) remain underexplored. This study systematically evaluates FNDC1's prognostic relevance, immune interactions, and functional impact in STAD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Multi-omics analyses integrated FNDC1 expression, mutation profiles, and immune associations across 33 cancers using The Cancer Genome Atlas (TCGA) data. Immunohistochemistry assessed FNDC1, mismatch repair (MMR) protein, and human epidermal growth factor receptor 2 (HER2), and clinicopathological information was collected for statistical analysis. Finally, we conducted in vitro experiments to assess the effects of FNDC1 knockdown on STAD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In various cancers, the main genetic alterations of &lt;i&gt;FNDC1&lt;/i&gt; are mutations and deep deletions, with a mutation frequency of 10% observed primarily in malignant melanoma and endometrial carcinoma. The expression levels of &lt;i&gt;FNDC1&lt;/i&gt; messenger RNA (mRNA) in breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and STAD are significantly higher than those in adjacent normal tissues (P&lt;0.05). In STAD, FNDC1 shows significant correlations with cell infiltrations such as endothelial cells, eosinophils, granulocyte-monocyte progenitors, hematopoietic stem cells, macrophage M1, macrophage M2, monocytes, myeloid dendritic cells, and activated myeloid dendritic cells. In STAD, FNDC1 exhibits significant positive correlations with immune checkpoints HAVCR2 and PDCD1LG2. Proteins with similar expression patterns to FNDC1 and ranking in the top 100 include GNAS, GNB1, MXRA5, COL3A1, COL10A1, ASPN, SFRP2, SFRP4, FXYD2, and GNG2. Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis shows that in STAD, &lt;i&gt;FNDC1&lt;/i&gt;-related genes are involved in pathways such as neuroactive ligand-receptor interaction, calcium signaling, cAMP signaling, vascular smooth muscle contraction, and pancreatic secretion. Gene Ontology (GO) functional enrichment analysis in STAD shows that FNDC1-related genes are involved in pathways related to the muscle system process, collagen-containing extracellular matrix, and receptor ligand activity. Clinical sample analysis demonstrates that FNDC1 protein is upregulated in STAD compared to adjacent normal tissues (P&lt;0.05). Age, tumor size, tumor differentiation, Lauren classification, lymphovascular invasion, neural invasion, tumor deposit, postoperative recurrence, T stage, N stage, M stage, tumor-node-metastasis (TNM) stage, HER2 expression, and MMR protein expression are relevant risk factors for poor prognosis in STAD patients, with age, tumor size, Lauren classification, lymphovascular invasion, neural invasion","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3069-3095"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317963","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":"When is belzutifan the right option for von Hippel-Lindau disease-associated hemangioblastomas?-a critical review of LITESPARK-004 results.","authors":"Ugur Sener, Taylor Galloway","doi":"10.21037/tcr-2024-2478","DOIUrl":"10.21037/tcr-2024-2478","url":null,"abstract":"","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2558-2562"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317967","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 and functional analysis of <i>GNAI1</i> as a biomarker associated with immune-related genes in pediatric acute myeloid leukemia.","authors":"Li Liu, Hongping Yang, Yan Zhou, Na Li, Qi Nie, Xinmiao Liu, Chunhui Yang, Xiaoyan Mao, Yue Tian, Qulian Guo, Xin Tian","doi":"10.21037/tcr-24-1595","DOIUrl":"10.21037/tcr-24-1595","url":null,"abstract":"<p><strong>Background: </strong>Immunotherapy is a pivotal approach in combating acute myeloid leukemia (AML), with the identification of immunomarkers being imperative. This investigation aimed to delineate biomarkers linked with immune-related genes (IRGs) in AML, thereby providing a theoretical framework for AML therapeutics.</p><p><strong>Methods: </strong>This research utilized AML-specific datasets [GSE9476 and The Cancer Genome Atlas (TCGA)-AML] alongside 1,793 IRGs. Initially, weighted gene co-expression network analysis (WGCNA) was employed to identify module genes using an integrative and systematic methodology. Differential gene expression analyses were conducted on GSE9476 and aggregated AML data from the University of California Santa Cruz (UCSC) Xena platform, alongside the Genotype-Tissue Expression (GTEx) database, to identify differentially expressed genes (DEGs). These DEGs were then intersected with WGCNA module genes and IRGs to isolate potential candidate genes. Kaplan-Meier (K-M) survival curves were subsequently utilized to identify pivotal genes with significant survival disparities. The prognostic significance of these genes was further assessed through both univariate and multivariate Cox regression analyses to pinpoint biomarkers. Finally, analyses focusing on functional enrichment associated with the identified biomarkers.</p><p><strong>Results: </strong>Using WGCNA, a cohort of 3,611 modular genes was identified. Intersection analysis involving WGCNA, DEGs, and IRGs led to the identification of eight promising candidate genes. Subsequent K-M survival assessments distilled these to six paramount genes, all of which underwent rigorous independent prognostic evaluation. Notably, <i>GNAI1</i> emerged as a potential biomarker, demonstrating marginal significance with a P value of 0.056. Enrichment analyses elucidated that <i>GNAI1</i> predominantly participates in key signaling pathways, notably oxidative phosphorylation and ubiquitin-mediated proteolysis. Comprehensive immunological profiling revealed a significant association of <i>GNAI1</i> with the 10 distinct immune cell types. Specifically, CD56dim natural killer (NK) cells and type T helper 17 (Th17) cells exhibited a pronounced negative correlation with <i>GNAI1</i>. Conversely, an array of eight other immune cell types, including type T helper 2 (Th2) cells and activated B cells, demonstrated a robust positive correlation with <i>GNAI1</i>.</p><p><strong>Conclusions: </strong><i>GNAI1</i>, associated with IRGs in AML, was identified as a biomarker, providing a basis for understanding AML pathogenesis and offering new avenues for therapeutic strategies.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2858-2873"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317917","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":"\"Prime and pull\" vaccination strategy for treatment of cervical pre-cancer: not quite ready for prime time.","authors":"Katherine Cooke, Deanna G K Teoh","doi":"10.21037/tcr-2024-2615","DOIUrl":"10.21037/tcr-2024-2615","url":null,"abstract":"","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2544-2547"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317922","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 a novel prognostic gene signature in pleural mesothelioma: a study based on The Cancer Genome Atlas database and experimental validation.","authors":"Xinmeng Wang, Yongqin Yang, Wenzhong Yang, Xi Yang, Jinsong Li, Yaru Lin, Zhengliang Li, Jiangyan Li, Wei Xiong","doi":"10.21037/tcr-2024-2531","DOIUrl":"10.21037/tcr-2024-2531","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Early detection and prognostic prediction are crucial in improving the survival of patients with pleural mesothelioma (PM). Therefore, this study aimed to develop a gene prognostic risk model for PM patients based on The Cancer Genome Atlas (TCGA) database analysis and experimental validations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Obtaining gene expression data and clinical information of PM from the TCGA database, the dataset was divided into a training set and a testing set. Univariate Cox regression analysis, robust testing, and multivariate Cox regression analysis were performed on the training set to establish a prognostic risk model. Risk scores were calculated for each patient, and the dataset was stratified into high- and low-risk groups. The predictive efficacy and accuracy of the model were evaluated using Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves. The messenger RNA (mRNA) expression levels of genes in the prognostic model in clinical samples and PM cell lines were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Gene expression validation in the prognostic model was conducted using samples from the TCGA and the Genotype-Tissue Expression (GTEx) project databases. The University of ALabama at birmingham CANcer data analysis portal (UALCAN) database was utilized to explore the expression patterns of genes in the prognostic model. Finally, gene set enrichment analysis (GSEA) was performed on genes in the prognostic model to explore their potential biological functions and signaling pathways.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A prognostic risk assessment model consisting of three genes, ubiquitin like with PHD and ring finger domain 1 (&lt;i&gt;UHRF1&lt;/i&gt;), kinesin family member 4A (&lt;i&gt;KIF4A&lt;/i&gt;), and never in mitosis gene A-related kinase 2 (&lt;i&gt;NEK2&lt;/i&gt;) was constructed. The risk score of the prognostic model is calculated as follows: risk score = Expression level of UHRF1 × 1.4525 - Expression level of KIF4A × 1.3270 + Expression level of NEK2 × 1.4167. Patients were further stratified into high- and low-risk groups at this optimal cutoff point. Kaplan-Meier curves demonstrate that, compared to patients in the high-risk group, those in the low-risk group exhibited significantly prolonged overall survival. Visualization of the model through a forest plot revealed a Log-Rank P&lt;0.0001 for the entire model, indicating its potential as an independent prognostic marker for PM. The mRNA expression levels of three genes in the prognostic model significantly elevated in tumor samples and PM cell lines than in non-tumorigenic tissues and cell lines as detected by qRT-PCR. Additionally, these genes exhibited significant differences in expression among PM patients of different stages, tumor subtypes, ages, and metastatic statuses. The overexpressed group of these three genes was significantly enriched in pathways such as DNA replication, mRNA surveillance path","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2981-2998"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317928","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":"Increased expression of <i>RIPOR1</i> predicts the poor prognosis of colorectal cancer patients.","authors":"Xiaolei Yue, Junyu Bian, Zhikun Dong, Yonglin Chen","doi":"10.21037/tcr-24-2029","DOIUrl":"10.21037/tcr-24-2029","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is considered one of the main causes of cancer-related deaths. Rho family-interacting cell polarization regulator 1 (<i>RIPOR1</i>) is a new Rho effector protein, and its abnormal expression may be related to various tumors. However, its expression and significance in CRC are still unclear. We aimed to explore its role and potential value in CRC, and to provide a new theoretical basis of prognostic evaluation and targeted therapy for CRC patients.</p><p><strong>Methods: </strong>Here we investigated the expression and significance of <i>RIPOR1</i> from public databases by using bioinformatics methods. Immunohistochemistry (IHC) was performed to determine protein expression levels of <i>RIPOR1</i> in CRC patients, and the correlation between <i>RIPOR1</i> expression and clinicopathological characteristics was analyzed. <i>In vitro</i> cellular experiments were used to explore the role of <i>RIPOR1</i> in CRC cell lines.</p><p><strong>Results: </strong>Both bioinformatics analysis and IHC results demonstrated that <i>RIPOR1</i> was significantly overexpressed in CRC, which was associated with a worse prognosis. The microRNA (miRNA) database showed that hsa-miR-625-5p was significantly and negatively correlated with <i>RIPOR1</i>. <i>RIPOR1</i> may affect immune cell infiltration in CRC. Cell Counting Kit-8 (CCK-8) assay revealed that <i>RIPOR1</i> promoted cell proliferation in CRC.</p><p><strong>Conclusions: </strong><i>RIPOR1</i> has a significant impact on the development and proliferation of CRC, and may be a potential predictive biomarker.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2707-2721"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317933","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":"Developing a prognostic risk model based on circulating tumor cell genes to predict prognosis and provide potential therapeutic strategies in colorectal cancer.","authors":"Yupeng Zheng, Mian Yang, Hongyi Yi, Tao Peng, Jiaze Sun, Jiazi Yu","doi":"10.21037/tcr-2024-2268","DOIUrl":"10.21037/tcr-2024-2268","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide. Understanding the genetic and molecular alterations in CRC can improve patient outcomes. Circulating tumor cells (CTCs) are crucial in cancer metastasis and progression. Analyzing the differentially expressed genes (DEGs) between CTCs and CRC may provide us with new therapeutic strategies. Therefore, this study aims to analyze these DEGs to construct a prognostic risk model that predicts the outcomes of CRC patients and guides clinical treatment.</p><p><strong>Methods: </strong>We analyzed The Cancer Genome Atlas (TCGA) database to identify 1,727 DEGs between CRC and normal samples, and GSE82198 data to find 3,564 DEGs between CTCs and primary CRC samples. Using enrichment analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise Cox regression, we derived eight model genes to construct a prognostic risk model. Various algorithms were employed in the immune microenvironment analysis. Integrating clinical factors with risk grouping, we developed a nomogram. We assessed chemotherapy sensitivity and epithelial-mesenchymal transition (EMT) scores in high-/low-risk groups and explored model gene expression at the single-cell level.</p><p><strong>Results: </strong>We constructed a prognostic risk model for CRC based on eight DEGs of CTCs. The model effectively predicted treatment outcomes and correlated closely with actual prognosis. Through immune microenvironment analysis, we revealed differences in immune cell infiltration and checkpoint gene expression among different risk groups. Moreover, patients in the high-risk group showed higher sensitivity to chemotherapy drugs compared to those in the low-risk group.</p><p><strong>Conclusions: </strong>The prognosis model based on CTCs' DEGs can effectively predict patient outcomes, facilitating precision treatment for patients. This model holds significant guiding implications for immunotherapy and chemotherapy in CRC, offering potential strategies for the clinical treatment of CRC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3096-3112"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317940","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":"Machine learning-driven prognostic model based on sphingolipid-related gene signature in pancreatic cancer: development and validation.","authors":"Qi Zou, Hailin Jiang, Qihui Sun, Qian Peng, Jie He, Keping Xie, Fang Wei","doi":"10.21037/tcr-24-1893","DOIUrl":"10.21037/tcr-24-1893","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Pancreatic cancer, a highly malignant tumor with poor prognosis, lacks effective early diagnosis and treatment strategies. Sphingolipids have emerged as key players in tumorigenesis, with certain sphingolipid-related genes linked to patient survival. This study aims to identify prognostic glycosphingolipid (GSL)-related genes and construct a predictive model to improve survival prediction and guide personalized treatment. By providing potential biomarkers, our findings may enhance clinical decision-making and offer new insights into pancreatic cancer diagnosis and therapy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study utilized 150 pancreatic cancer samples from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) and 69 from GSE62452 [Gene Expression Omnibus (GEO)] for training and validation. Cox univariate regression identified sphingolipid-related genes with prognostic value. Over 100 machine learning algorithms, including Cox models, support vector machines (SVM), and random forests (RF), were applied to construct an optimal survival prediction model for pancreatic ductal adenocarcinoma (PDAC). Model accuracy was evaluated using the concordance index (C-index). Enrichment, immune infiltration, mutation spectrum, and cell communication analyses were performed to explore sphingolipid mechanisms in pancreatic cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Using 10 machine learning algorithms, we developed over 100 models to predict sphingolipid-related survival in pancreatic cancer. A robust prognostic model was constructed, incorporating three GSL-related genes (&lt;i&gt;MET&lt;/i&gt;, &lt;i&gt;GBA2&lt;/i&gt;, &lt;i&gt;DEFB1&lt;/i&gt;), represented by the equation: weighted score = 0.469 * MET + (-0.357) * GBA2 + 0.103 * DEFB1. The model demonstrated strong predictive performance, with a C-index of 0.854 for overall survival in 150 pancreatic cancer patients from the TCGA database and 0.652 in 69 patients from the GEO validation set. Pathway enrichment analysis revealed that high-risk patients were significantly enriched in oncogenic and immune-related pathways. Mutation spectrum analysis indicated a higher mutation load in high-risk patients, with mutations concentrated in common oncogenic pathways. Immune infiltration analysis showed that the risk score positively correlated with immune-suppressive genes but negatively correlated with immune-killing cell infiltration. Cell communication analysis highlighted elevated activity in the macrophage migration inhibitory factor (MIF) pathway within high-risk groups, associated with tumor proliferation and immune escape. In conclusion, this study establishes a sphingolipid-based prognostic model with significant potential for predicting pancreatic cancer outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The sphingolipid-based model accurately predicts pancreatic cancer survival and suggests sphingolipids promote tumor progression by mediating immune-suppressive microenvironments, aiding prognostic prediction a","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2779-2796"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317945","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":"Overexpression of <i>LINC00880</i> promotes colorectal cancer growth.","authors":"Ge Gao, Peiwen Xu, Chunyu Yang, Mengyuan Qian, Qingwen Wang, Surui Yao, Yuan Yin, Zhaohui Huang, Zehua Bian","doi":"10.21037/tcr-2025-54","DOIUrl":"10.21037/tcr-2025-54","url":null,"abstract":"<p><strong>Background: </strong>Research has revealed that long non-coding RNAs (lncRNAs) are intimately associated with the occurrence, development, and metastasis of tumors through their regulation of gene expression. The lncRNA <i>LINC00880</i> is important for colorectal cancer (CRC) occurrence and development. Our research aimed to explore the roles of <i>LINC00880</i> in CRC progression.</p><p><strong>Methods: </strong>The expression of <i>LINC00880</i> in CRC cells and tissues was first measured using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the prognosis of CRC patients was then investigated using the Kaplan-Meier method. The impacts of <i>LINC00880</i> on CRC growth were evaluated by a series of <i>in vitro</i> and <i>in vivo</i> assays. Mechanistically, RNA sequencing (RNA-seq) technology and transcriptome analysis experiments were employed to validate the impact of <i>LINC00880</i> on cell cycle pathway.</p><p><strong>Results: </strong>In this study, we have demonstrated, for the first time, that <i>LINC00880</i> is significantly overexpressed in CRC and is associated with poor patient survival. Functional assays indicated that <i>LINC00880</i> promotes the growth of CRC cells both <i>in vitro</i> and <i>in vivo</i>. Furthermore, RNA-seq has revealed the impact of <i>LINC00880</i> on the cell cycle and DNA replication pathways, and identified MCM3 as a potential downstream target of <i>LINC00880</i>.</p><p><strong>Conclusions: </strong>Our findings indicate that <i>LINC00880</i> is upregulated in CRC and promotes tumor growth.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2926-2939"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317950","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}