Cancer Informatics最新文献

{"title":"GATES: A Lightweight Tool Automating Pathogenic Variant Discovery From Raw Whole-Exome Sequencing Data.","authors":"Nicholas E Bambach, Julio C Ricarte-Filho, Erin R Reichenberger, Aime T Franco","doi":"10.1177/11769351261485924","DOIUrl":"10.1177/11769351261485924","url":null,"abstract":"<p><strong>Background: </strong>Whole-exome sequencing is a widely used technology to identify pathogenic variants in cancer. Although sequencing itself has become increasingly accessible, downstream analysis remains computationally complex, presenting a challenge for many researchers. Existing pipelines lack integrated support for somatic and germline variant detection and still require significant computational resources.</p><p><strong>Methods: </strong>We developed GATES (GATK Automated Tool for Exome Sequencing), a lightweight pipeline that automates data preprocessing, variant calling, and variant annotation directly from raw paired-end FASTQ files through a simplified command-line interface. GATES implements the GATK Best Practices for somatic and germline variant detection and leverages Ensembl's Variant Effect Predictor for functional annotation, outputting the results in a human-readable tab-separated values (TSV) file. We evaluated the pipeline's performance using the SEQC-II benchmarking dataset and demonstrated its application using a clinical sample harboring known pathogenic germline and somatic variants.</p><p><strong>Results: </strong>GATES was run on a standard laptop and performed end-to-end variant analysis for each sample within a few hours. In benchmarking with SEQC-II samples, germline and tumor-normal somatic variant calling modes demonstrated high concordance with their respective truth sets. Tumor-only somatic mode showed decreased accuracy, consistent with expected germline contamination. GATES demonstrated high performance across various hardware configurations and compared to the established nf-core/sarek pipeline. GATES further successfully performed somatic and germline analysis of a >100X clinical sample in under 7 hours. Importantly, the pipeline accurately distinguished the known <i>KRAS</i> p.G12V and <i>KEAP1</i> p.S338L as somatic and germline, respectively.</p><p><strong>Conclusion: </strong>By lowering the technical barriers to exome sequencing analysis, GATES provides a practical solution for pathogenic variant discovery for researchers both with and without computational expertise.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261485924"},"PeriodicalIF":2.1,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148888876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Bioinformatic and Molecular Characterization of the miR-4303/miR-4291-MAPK1 Axis in Oral Squamous Cell Carcinoma.","authors":"Sara Bagheri Farahani, Hossein Noghrei, Faranak Jamshidian","doi":"10.1177/11769351261484638","DOIUrl":"10.1177/11769351261484638","url":null,"abstract":"<p><strong>Objective: </strong>Oral squamous cell carcinoma (OSCC) is characterized by high recurrence rates and an unclear underlying molecular basis, highlighting the need for improved early diagnostic strategies to enhance treatment outcomes and reduce healthcare burden. Given the regulatory role of microRNAs (miRNAs) in oral cancer progression through post-transcriptional control of target genes, this study aimed to investigate the diagnostic potential of miR-4303 and miR-4291 in OSCC.</p><p><strong>Methods: </strong>Using TargetScan, MAPK1 was predicted as a common target of both miR-4303 and miR-4291. Expression levels of miR-4303, miR-4291, and MAPK1 were quantified by qRT-PCR in 30 paired OSCC and adjacent normal tissue samples. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.</p><p><strong>Results: </strong>MAPK1 and both miRNAs (miR-4303 and miR-4291) were significantly upregulated in OSCC tissues compared to adjacent normal tissues (P < 0.0001). MAPK1 expression showed a significant association with higher tumor grade (P = 0.003) and advanced clinical stage (P = 0.005). ROC curve analysis demonstrated strong diagnostic performance, with AUC values of 0.92 for MAPK1, 0.731 for miR-4303 (P = 0.0001), and 0.707 for miR-4291 (P < 0.0001), indicating their potential utility in distinguishing OSCC from normal tissue.</p><p><strong>Conclusion: </strong>These findings suggest that miR-4303 and miR-4291, together with MAPK1, may serve as promising diagnostic biomarkers in OSCC. However, further validation in larger, independent cohorts and functional studies is required to confirm their clinical applicability.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261484638"},"PeriodicalIF":2.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13530518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148875931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Cell Transcriptome Analysis Reveals IRF1-Driven Epithelial States and Glycosaminoglycan-Glycolysis Coupling in Cisplatin-Resistant HGSOC.","authors":"Zhaoyang Jia, Wenjing Pan, Xibo Zhao, Lianxiu Li, Wenhua Tan","doi":"10.1177/11769351261478408","DOIUrl":"10.1177/11769351261478408","url":null,"abstract":"<p><strong>Objectives: </strong>Cisplatin resistance is the principal cause of relapse in high-grade serous ovarian cancer (HGSOC), but bulk-expression signatures cannot localize resistant malignant states or the tumor-microenvironment (TME) interactions that sustain them. This study aimed to define cisplatin-resistant epithelial cell states and their regulatory and metabolic circuits by integrating multi-cohort single-cell transcriptomes with pharmacogenomic and clinical data.</p><p><strong>Methods: </strong>We assembled 159,419 cells from 32 HGSOC tumors across six public single-cell RNA-sequencing cohorts and performed harmonized integration, clustering, and lineage annotation. Cisplatin response was mapped to single cells by coupling scRNA-seq data to Genomics of Drug Sensitivity in Cancer predicted cisplatin response score values using Scissor, with AUCell-based validation. We then applied receptor-ligand-based cell-cell communication analysis (CellChat), transcription-factor (TF) activity inference (SCENIC and NetAct with TRRUST), pseudotime trajectory reconstruction (Monocle3), and pathway-level metabolic scoring. Associations with drug sensitivity and patient outcome were evaluated in ovarian cancer cell lines and The Cancer Genome Atlas (TCGA) HGSOC cohort.</p><p><strong>Results: </strong>Among 34 epithelial subclusters, 14 were significantly enriched for a cisplatin-resistant phenotype and collectively accounted for most predicted resistant cells. These states were transcriptionally characterized by stress, interferon, and apoptotic programs and formed dense communication hubs with endothelial cells, fibroblasts, myeloid cells, and T/NK cells via extracellular-matrix and adhesion pathways (for example, LAMA3-CD44, COL6A1/2-CD44, NECTIN3-NECTIN2, and CD99-CD99 interactions). TF-activity modeling converged on an IRF1-centered regulatory program that increased along an epithelial trajectory and coordinated inflammatory and apoptotic gene expression. Metabolically, resistance-enriched epithelial states showed selective up-regulation of glycosaminoglycan biosynthesis, particularly keratan sulfate, coupled to heightened glycolysis; glycolytic activity correlated with predicted cisplatin response score in cell lines and an IRF1/STAT1 readout (GBP3) stratified survival in TCGA HGSOC.</p><p><strong>Conclusion: </strong>Cisplatin resistance in HGSOC is encoded in discrete IRF1-driven epithelial states that are supported by specific TME communication networks and a glycosaminoglycan-glycolysis metabolic axis. This integrative single-cell informatics framework yields testable biomarkers and therapeutic targets for overcoming platinum resistance in ovarian cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261478408"},"PeriodicalIF":2.1,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13477523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptome-Wide Cox Regression Identifies Candidate Survival-Associated Genes in Newly Diagnosed Acute Myeloid Leukemia.","authors":"Songphol Tungjitviboonkun, Nikhil Mullapudi, Elly Gardev","doi":"10.1177/11769351261478087","DOIUrl":"10.1177/11769351261478087","url":null,"abstract":"<p><strong>Objectives: </strong>Acute myeloid leukemia (AML) exhibits substantial biological heterogeneity that is not fully captured by current prognostic systems. We aimed to identify transcriptome-wide gene expression markers associated with overall survival in newly diagnosed AML patients.</p><p><strong>Methods: </strong>Gene expression and clinical data from 399 newly diagnosed AML patients in the OHSU cohort were obtained via cBioPortal. Cox proportional hazards regression was performed independently for 22,836 genes to evaluate associations with overall survival. Analyses were restricted to genes with sufficient observations, and Bonferroni correction was applied to account for multiple testing.</p><p><strong>Results: </strong>A total of 46 genes were significantly associated with overall survival after multiple testing correction. Higher expression of <i>FAM213A</i> (hazard ratio [HR] 1.240) and <i>TJP2</i> (HR 1.439) was associated with increased mortality risk, whereas higher expression of <i>PJA2</i> (HR 0.381), <i>MICALL2</i> (HR 0.779), and <i>LTK</i> (HR 0.828) was associated with improved survival. Several identified genes, including <i>FAM213A, MICALL2</i>, and <i>PJA2</i>, have known roles in cancer biology, supporting the biological plausibility of these findings.</p><p><strong>Conclusions: </strong>This transcriptome-wide analysis identified multiple genes associated with survival in newly diagnosed AML, highlighting candidate prognostic biomarkers that may complement existing risk stratification frameworks. Further validation in independent cohorts is warranted.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261478087"},"PeriodicalIF":2.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative Analysis of Steroid Metabolism-Based Molecular Subtypes Reveals Prognostic Subtypes and Therapeutic Implications in Gastric Cancer.","authors":"Linen Li, Huiling Zhu, Guang Gao, Hao Chen","doi":"10.1177/11769351261475376","DOIUrl":"10.1177/11769351261475376","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to identify steroid metabolism-related molecular subtypes, investigate the gene expression patterns of these subtypes, and construct a prognostic risk model as well as predict therapeutic response in gastric cancer.</p><p><strong>Methods: </strong>We analyzed 410 TCGA-STAD and 483 GSE84437 gastric cancer samples. Unsupervised consensus clustering based on steroid metabolism-related genes identified molecular subtypes. Differential expression and functional enrichment analyses (GO, KEGG, GSEA) were performed. Prognostic genes were intersected with survival-associated genes, and a Lasso-Cox regression model was used to build a seven-gene risk score. Immune infiltration, tumor mutational burden (TMB), immune checkpoint expression, and drug sensitivity were evaluated.</p><p><strong>Results: </strong>Two steroid metabolism-related gastric cancer subtypes were identified, with steroid-metabolism-poor prognosis subtype showing poorer overall survival. Differential expression analysis revealed 1,709 genes enriched in immune regulation, calcium signaling, cell adhesion, and extracellular matrix remodeling. Seven key genes (PRICKLE1, SERPINE1, APOD, RIMS1, GLP2R, CDH19, GRP) were used to construct a risk score, which correlated with advanced stage, steroid-metabolism-poor prognosis subtype subtype, and worse survival, and was an independent prognostic factor. High-risk and steroid-metabolism-poor prognosis subtype tumors displayed higher immune infiltration and immune scores, lower TMB, and upregulated immune checkpoint genes, indicating an immunosuppressive microenvironment. Drug sensitivity differed across subtypes and risk groups, suggesting potential implications for personalized therapy.</p><p><strong>Conclusions: </strong>Steroid metabolism defines molecular heterogeneity, immune features, and prognosis in gastric cancer. The seven-gene risk model provides a reliable tool for survival prediction and may guide personalized therapeutic strategies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261475376"},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextualized Malignantization: Morphometric Signatures of Breast Tissue Transformation and a Clinically Translatable Diagnostic Algorithm.","authors":"Oluwaseun Adebayo Bamodu","doi":"10.1177/11769351261476174","DOIUrl":"10.1177/11769351261476174","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The transformation from benign to malignant breast tissue represents a complex biological continuum rather than a discrete pathological switch. Current diagnostic paradigms rely heavily on binary classification systems that may inadequately capture the nuanced morphometric signatures underlying this transformation process. While machine learning classifiers have demonstrated high classification accuracy on morphometric datasets, they typically function as predictive black boxes that offer limited biological insight into the transformation mechanisms themselves.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To establish a comprehensive morphometric framework for characterizing the architectural signatures that distinguish benign from malignant breast tissue through quantitative analysis of cellular features and develop a clinically translatable diagnostic algorithm for breast cancer detection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed 569 breast tissue samples from the Wisconsin Diagnostic Breast Cancer Dataset, comprising 212 malignant and 357 benign cases. Thirty morphometric parameters were evaluated across three analytical dimensions: central tendency measures, variability indices, and extreme value characterizations. A weighted diagnostic algorithm was developed using the eight most discriminative parameters, with performance validated through receiver operating characteristic analysis and ten-fold cross-validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our analysis revealed distinct morphometric transformation signatures characterized by progressive architectural disruption. Concavity measurements demonstrated the most pronounced differentiation (3.49-fold increase in malignant tissues), followed by concave point density (3.42-fold increase) and cellular area expansion (2.11-fold increase). Categorical analysis demonstrated clear hierarchical organization of discriminative features, with boundary architecture parameters showing superior diagnostic utility compared to size or regularity measures. The developed diagnostic algorithm achieved excellent performance with 94.3% sensitivity, 91.6% specificity, and 92.6% overall accuracy. Receiver operating characteristic analysis yielded an area under the curve of 0.974, demonstrating superior discriminative capability. The algorithm employs weighted scoring of morphometric parameters, with concavity-related features receiving highest priority (49% combined weighting) due to their biological significance in malignant transformation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Contextualized malignantization represents a morphometrically quantifiable process characterized by specific architectural transformation patterns, particularly boundary disruption signatures. The developed diagnostic algorithm successfully translates morphometric research into clinical practice, providing objective, quantitative diagnostic criteria that could enhance breast cancer detection while reducing diagnostic va","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261476174"},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Glutathione Metabolism-Related Transcriptomic Signature for Prognostic Assessment and Biological Characterization of Lung Adenocarcinoma.","authors":"Zhengzuo Sheng, Siyu Chen, Su Chen","doi":"10.1177/11769351261476162","DOIUrl":"10.1177/11769351261476162","url":null,"abstract":"<p><strong>Background: </strong>Glutathione metabolism plays an important role in redox homeostasis, oxidative stress responses, and metabolic adaptation in cancer. However, its prognostic significance in lung adenocarcinoma (LUAD) remains incompletely understood. This study aimed to develop a glutathione metabolism-related prognostic signature and investigate its associations with immune characteristics, genomic alterations, and biological pathways in LUAD.</p><p><strong>Methods: </strong>Transcriptomic, clinical, and somatic mutation data from TCGA-LUAD were analyzed, and GSE50081 was used as an independent validation cohort. Glutathione metabolism-related genes were identified through differential expression and Cox regression analyses, followed by least absolute shrinkage and selection operator (LASSO) Cox regression to construct a prognostic signature. Survival analysis, time-dependent receiver operating characteristic (ROC) analysis, Cox regression, immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), mutation profiling, tumor mutation burden (TMB) analysis, and drug-sensitivity prediction were subsequently performed.</p><p><strong>Results: </strong>A glutathione metabolism-related signature stratified patients into high- and low-risk groups with significantly different overall survival in both the TCGA training cohort and the GSE50081 validation cohort. The risk score remained an independent prognostic factor in multivariable Cox regression analysis. High-risk tumors exhibited reduced B-cell and dendritic-cell infiltration, enrichment of cell-cycle- and metabolism-related pathways, higher frequencies of TP53 and KEAP1 mutations, and elevated tumor mutation burden. Computational drug-sensitivity analysis identified differences in predicted responses to several therapeutic agents between risk groups.</p><p><strong>Conclusions: </strong>The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD. These findings provide additional insights into glutathione metabolism-related heterogeneity in LUAD and warrant further biological and clinical validation.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261476162"},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13438220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative Analyses of Individual Patient Genomic-Data to Discover Novel Biomarkers: Application to Cervical Pre-Cancer DNA-Methylation Datasets.","authors":"Innas Widiasti, Putri Wikie Novianti","doi":"10.1177/11769351261454663","DOIUrl":"10.1177/11769351261454663","url":null,"abstract":"<p><strong>Objective: </strong>The growing availability of public genomic repositories led to increased use of integrative and meta-analytic approaches to combine multi-omics datasets for biomarker discovery. Building on this framework, our study applies a molecular discovery approach to identify diagnostic biomarkers for cervical pre-cancer in high-risk HPV-positive women using aggregated genomic evidence across populations.</p><p><strong>Methods: </strong>This study performed a secondary analysis of publicly available DNA-methylation datasets identified through a systematic search of the Gene Expression Omnibus database to enable integrative analysis. After employing standard preprocessing methods across the selected DNA-methylation datasets, statistical analyses (specifically limma and meta-analysis with a random-effects model) were used to calculate the cumulative standardized effect sizes for all 9570 genes across the 4 selected studies.</p><p><strong>Results: </strong>This cross-dataset analysis led to the identification of 4 promising, non-population-specific genes, that show potential for detecting precancerous cervical lesions in high-risk HPV-positive women. Collectively, these genes reflect a molecular profile associated with Cervical Intraepithelial Neoplasia lesions undergoing malignant progression.</p><p><strong>Conclusions: </strong>By leveraging aggregated data, our findings enhance the potential of generalization of biomarker signals and illustrate the strength of multi-data sets integration in advancing cervical pre-cancer triage screening strategies. Given their diagnostic promise, further experimental validation in large and diverse cohorts is warranted to evaluate their clinical applicability.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261454663"},"PeriodicalIF":2.1,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Multitalented Marvels: Exploring the Versatile Potential of Natural Products in Osteosarcoma Treatment.","authors":"Chenxu Jia, Qingtao Meng, Mingna Huo, Fengping Zhang, Jian Han","doi":"10.1177/11769351261474027","DOIUrl":"10.1177/11769351261474027","url":null,"abstract":"<p><p>Osteosarcoma (OS) is a malignant neoplasm arising from bone tissue. The OS exhibited a significant degree of heterogeneity, with a high incidence of local invasion and a high rate of metastasis. Approximately 25% of those diagnosed with OS present with metastases, with the lungs being the most prevalent site, followed by the bones or lymph nodes. The 5-year survival rate for patients with OS with lung metastasis is only 10% to 30%. Adjuvant chemotherapy regimens significantly enhanced the 5-year survival rate in patients diagnosed with non-metastatic OS. However, the same regimens did not provide a substantial improvement in survival for patients with OS who had acquired lung metastasis or had recurrence. Moreover, the extensive use of chemotherapy medications often results in the development of resistance to chemotherapy in patients with OS, which frequently culminates in treatment ineffectiveness. Natural products (NPs) are significant reservoirs of anti-cancer medications. Preclinical investigations have shown that certain NPs had substantial promise for treating OS. However, there is currently a lack of a comprehensive overview of the use of NPs in OS treatment. In this review, we provide a comprehensive overview of the NPs, which include polyphenols, alkaloids, and terpenoids, that have the potential to be effective in the treatment of OS.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261474027"},"PeriodicalIF":2.1,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association of Neutrophil-To-Lymphocyte and Platelet-To-Lymphocyte Ratios With Distant Metastasis in Vietnamese Gastric Cancer Patients.","authors":"N Tran, D N L Tran, T H D Phan, H Q Nguyen","doi":"10.1177/11769351261466304","DOIUrl":"10.1177/11769351261466304","url":null,"abstract":"<p><strong>Background: </strong>Gastric cancer (GC) remains a leading cause of mortality in Vietnam, where distant metastasis is frequently present at diagnosis. Since accessible biomarkers are scarce, we evaluated the association of Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) with distant metastasis in a Vietnamese cohort to develop a model for patient stratification.</p><p><strong>Methods: </strong>This retrospective study analyzed 114 patients, categorizing into metastatic (Stage IV) and non-metastatic groups per AJCC 8^th criteria. The optimal cutoffs were determined using receiver operating characteristic (ROC) curve analysis. The diagnostic efficacy between models was compared by DeLong's test. LASSO regression was employed to identify stable indicators. A multivariable logistic regression model was constructed, and its performance was evaluated using 1,000 bootstrap resamples. Clinical utility was assessed via Decision Curve Analysis (DCA).</p><p><strong>Results: </strong>Distant metastasis was 44.7% of cases. Optimal cutoffs of 2.0 for NLR and 181.5 for PLR were identified. Both markers were significantly higher in metastatic group and positively correlated with disease progression. LASSO identified PLR, NLR, and tumor location as the most robust determinants. In multivariable analysis, only multiple tumor location and high PLR remained independent prognostic factors. The combined model integrating clinical factors with NLR and PLR significantly outperformed clinical features alone (AUC: 0.766 vs. 0.619, p=0.0036), with an optimism-corrected C-index of 0.708. At a 0.586 threshold, the model achieved 82.5% specificity and 62.7% sensitivity, supporting a conservative \"rule-out\" strategy. Calibration slope was 0.659. DCA demonstrated superior net benefit across a 15%-90% risk range; specifically, at a 40% threshold, model spared 19 per 100 patients from unnecessary intervention.</p><p><strong>Conclusion: </strong>This study establishes the first baseline for NLR and PLR in the Vietnamese cohort, showing an association of these biomarkers with GC distant metastasis. A model integrating PLR, NLR and tumor location optimizes patient stratification and resource allocation in resource-constrained healthcare environments.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261466304"},"PeriodicalIF":2.1,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}