Translational cancer research最新文献

{"title":"Construction and validation of a nomogram prediction model for predicting the risk of chemotherapy-induced myelosuppression after chemotherapy in patients with triple-negative breast cancer: a single-center retrospective case-control study.","authors":"Haoling Xie, Rong Zhang, Chunmei Wei, Jinsong Xu, Jie Chu, Xuexing Wang","doi":"10.21037/tcr-24-1513","DOIUrl":"https://doi.org/10.21037/tcr-24-1513","url":null,"abstract":"<p><strong>Background: </strong>Triple-negative breast cancer (TNBC) has a poor prognosis due to limited targeted treatments. Chemotherapy often causes chemotherapy-induced myelosuppression (CIM), complicating treatment and raising costs, yet predictive tools for this risk are scarce. This study examined the prevalence and risk factors of CIM in TNBC patients after chemotherapy and created nomograms to predict this risk.</p><p><strong>Methods: </strong>Nomograms were developed from a retrospective study of 316 TNBC patients treated at the Anning First People's Hospital Affiliated to Kunming University of Science and Technology between 1 July 2021 and 31 May 2024. The patients were split into development and validation cohorts in an 8:2 ratio. Least absolute shrinkage and selection operator (LASSO) identified risk factors for CIM, which were used to create the nomograms. The models' accuracy, calibration, and clinical utility were evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA), with validation through bootstrapping.</p><p><strong>Results: </strong>In this study of 316 TNBC patients, 102 experienced CIM, an incidence rate of 32.28%. Patient characteristics were similar across cohorts. The development cohort had a mean age of 52.05 years, with a median hospital stay of 5 days. Myelosuppression of degree I was the most common CIM event. LASSO and logistic regression analyses linked CIM to factors like bone metastasis, platinum regimens, chemotherapy cycles, pre-chemotherapy neutrophil count, and drug combinations. The nomograms showed strong predictive accuracy with AUCs of 0.886 [95% confidence interval (CI): 0.836-0.937] and 0.905 (95% CI: 0.834-0.976) in the development and validation cohorts, respectively, and high agreement in calibration curves. DCA confirmed their clinical utility.</p><p><strong>Conclusions: </strong>This study developed a validated nomogram that accurately predicts the risk of CIM in TNBC patients, helping healthcare providers create personalized treatment plans.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"2885-2899"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317935","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":"Ovarian cancer and malnutrition: a literature review.","authors":"Zhuyan Shao, Liying Fu, Yi Lu, Haifei Zhou, Yuyang Zhu, Tao Zhu","doi":"10.21037/tcr-2025-758","DOIUrl":"https://doi.org/10.21037/tcr-2025-758","url":null,"abstract":"<p><strong>Background and objective: </strong>Among gynecological malignancies, ovarian cancer is the most fatal, with surgery and chemotherapy being the primary treatment modalities. The nutritional status of patients with ovarian cancer undergoing chemotherapy after surgery remains generally poor, with 76.1% classified as severely malnourished according to the Patient-Generated Subjective Global Assessment (PG-SGA), while only 9.0% are well-nourished and do not require nutritional intervention. The high risk of nutritional decline associated with the new treatment modality, poly (ADP ribose) polymerase (PARP) inhibitors, has not received sufficient attention. This review examined the factors contributing to malnutrition in ovarian cancer, the adverse effects of malnutrition on treatment outcomes, the importance of regular nutritional screening and assessment, and potential nutritional interventions.</p><p><strong>Methods: </strong>A review of the relevant literature was conducted to analyze the prevalence of malnutrition in patients with ovarian cancer, its impact on treatment and prognosis, and the role of nutritional assessment and interventions in improving patient outcomes.</p><p><strong>Key content and findings: </strong>Malnutrition is highly prevalent among patients with ovarian cancer and is associated with worsened treatment side effects, reduced quality of life, and decreased survival rates. The use of PARP inhibitors may pose an additional risk for nutritional decline, but this possibility has not been sufficiently studied. Regular nutritional screening and assessment are essential for the early detection and management of malnutrition. Nutritional interventions have been investigated for their potential to support patients with ovarian cancer through treatment, but further research is needed to determine the most effective strategies.</p><p><strong>Conclusions: </strong>Malnutrition is a major concern for ovarian cancer patients, especially those receiving chemotherapy or PARP inhibitors. Regular nutritional assessment and timely interventions may improve treatment tolerance and overall prognosis. Further prospective studies and large randomized controlled trials are necessary to establish effective nutritional strategies for high-risk patients.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3239-3254"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317948","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":"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":"https://doi.org/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":"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":"https://doi.org/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":"https://doi.org/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":"The role of radiotherapy in distant nasopharyngeal carcinoma: a SEER analysis.","authors":"Long Chen, Dun-Chang Mo, Jin-Nian He, Ke-Jiang Du, Li-Ping Peng, Shi-Hua Yin","doi":"10.21037/tcr-2024-2332","DOIUrl":"https://doi.org/10.21037/tcr-2024-2332","url":null,"abstract":"<p><strong>Background: </strong>Prior research, characterized by small patient cohorts, has yielded limited understanding of the effects of radiotherapy (RT) on distant nasopharyngeal carcinoma (dNPC). Therefore, we aim to examine the impact of RT on survival outcomes in dNPC utilizing a large-scale population database.</p><p><strong>Methods: </strong>Clinical data from 1,171 dNPC patients in the Surveillance, Epidemiology, and End Results (SEER) registry [2004-2019] was retrospectively analyzed. Among them, 227 (19%) did not receive RT, while 944 (81%) did. The primary outcome was overall survival (OS). Kaplan-Meier analysis was used to assess the survival of patients, and differences between treatment groups were evaluated using the Log-rank test and Cox's regression model. Nomogram was established to predict patient survival time and the model was evaluated using receiver operating characteristic (ROC) curves.</p><p><strong>Results: </strong>The median survival duration for all patients was 33 months, with a range spanning from 0 to 215 months. RT was associated with significantly improved OS [hazard ratio (HR): 0.35, 95% confidence interval (CI): 0.29-0.43, P<0.001]. According to the Cox proportional hazards model, the HR for patients who did not receive RT was 2.83 times higher compared to those who underwent RT. The nomograms for predicting 1-, 3-, and 5-year OS demonstrated moderately good calibration and discrimination, with a concordance index of 0.71. The areas under the curves (AUCs) of ROC for 1-, 3-, and 5-year OS were 0.762, 0.763, and 0.750 respectively.</p><p><strong>Conclusions: </strong>Our research revealed the significance of RT in the treatment of dNPC, and RT can serve as an independent predictor of survival, which might contribute to the creation of guidelines for dNPC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 5","pages":"3047-3056"},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317965","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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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}