Cancer Informatics最新文献

{"title":"<i>Clostridium</i>-Derived p-Cresyl Metabolites Induce Inflammation and Apoptosis in Biliary Epithelial Cells.","authors":"Haiyan Yu, Haiyan Fu, Jiamin Xu, Yina Yang, Xin Ai, Rongfang Tu, Weimin Bao, Yingmei Tang","doi":"10.1177/11769351261472440","DOIUrl":"10.1177/11769351261472440","url":null,"abstract":"<p><strong>Objective: </strong>Primary biliary cholangitis (PBC) is an autoimmune cholestatic liver disease, and the effects of p-cresyl sulfate (PCS) and p-cresyl glucuronide (PCG) in PBC remain unclear. This study aimed to evaluate the pro-apoptotic and pro-inflammatory effects of PCS and PCG.</p><p><strong>Methods: </strong>Human intrahepatic biliary epithelial cells (HIBEpiCs) were treated with PCS or PCG, and apoptosis was assessed by flow cytometry. Female C57BL/6 mice received intraperitoneal PCS or PCG. Liver inflammation was evaluated by H&E staining, apoptosis-related proteins (caspase-3, Bax, Bcl-2) by Western blotting, and serum TGF-β and IFN-γ by ELISA.</p><p><strong>Results: </strong>PCS and PCG increased apoptosis in HIBEpiCs. <i>In vivo</i>, PCS and PCG treated mice displayed portal lymphocytic infiltration and elevated serum IFN-γ and TGF-β. Hepatic caspase-3 and Bax were upregulated, whereas Bcl-2 was downregulated in a time-dependent manner.</p><p><strong>Conclusions: </strong>PCS and PCG promote intrahepatic inflammation and BEC apoptosis, suggesting that gut microbial metabolites may contribute to hepatobiliary immune injury.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261472440"},"PeriodicalIF":2.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13392342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580516","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":"Trends and Disparities in Mortality from Malignant Parotid Gland Tumors in the United States, 1999-2023: A Population-Based Analysis.","authors":"Qianqian Xu, Sunjun Li, Fangyuan Liao, Donghao Lou, Tongbin Zhang","doi":"10.1177/11769351261471527","DOIUrl":"10.1177/11769351261471527","url":null,"abstract":"<p><strong>Introduction: </strong>Malignant parotid gland tumors account for less than 1% of all cancers yet represent a substantial proportion of head and neck malignancies. Long-term mortality trends and demographic disparities remain poorly defined. This study examined mortality trends and disparities across demographic and geographic subgroups in the United States.</p><p><strong>Methods: </strong>This repeated cross-sectional study analyzed CDC WONDER death certificate data for malignant parotid gland tumors (ICD-10-CM C07) from 1999 to 2023. AAMRs were calculated using the 2000 US standard population. Joinpoint regression estimated APCs and AAPCs with 95% CIs. Mortality was stratified by age, sex, race/ethnicity, and geography.</p><p><strong>Results: </strong>During the study period, 15,133 deaths from malignant parotid gland tumors were recorded. Annual deaths increased from 491 in 1999 to 696 in 2023, but the overall AAMR remained stable (AAPC = -0.09, 95% CI, -0.43 to 0.25). Declines were observed among females, males, adults aged 45 to 74 years, the Northeast region, Florida, and New York. Mortality increased among adults aged 85 years or older (AAPC = 1.22, 95% CI, 0.67 to 1.77). Trends for NH White and NH Black populations were stable; smaller race/ethnicity groups had limited or unstable estimates.</p><p><strong>Conclusion: </strong>Overall mortality from malignant parotid gland tumors remained stable from 1999 to 2023. This stable national pattern masked declines in selected strata and increasing mortality among adults aged 85 years or older. Because CDC WONDER lacks clinical, treatment, socioeconomic, and tumor-specific variables, these findings should be interpreted as descriptive mortality patterns rather than causal evidence.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261471527"},"PeriodicalIF":2.1,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148550849","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":"Development and Validation of a Six-Gene Signature of Myeloid Antigen Presentation Dysfunction Based on Single-Cell and Multi-Cohort Transcriptomics for Predicting Prognosis and Recurrence of Hepatocellular Carcinoma.","authors":"Ye Tan, Jingxuan Xiang, Zehan Wang, Yu Wang, Aidong Chen, Qiwen Wu","doi":"10.1177/11769351261466300","DOIUrl":"10.1177/11769351261466300","url":null,"abstract":"<p><strong>Background: </strong>Myeloid antigen-presentation dysfunction is a central but incompletely translated immune state in hepatocellular carcinoma (HCC). This study aimed to develop a compact prognostic and recurrence model directly anchored in single-cell-defined myeloid antigen-presentation loss.</p><p><strong>Methods: </strong>In this prognostic prediction-model development and validation study, GSE149614 was used as the single-cell discovery cohort. Tumor and normal myeloid cells were extracted, antigen-presentation (AP) module scores were calculated, and AP-loss candidate genes were identified using pseudobulk aggregation. Candidate genes were projected to TCGA-LIHC, GSE14520, and GSE76427 bulk transcriptomic cohorts. Survival-oriented model refinement was performed in GSE14520 using overall survival (OS) and recurrence-free survival (RFS), followed by validation with Kaplan-Meier analysis, Cox regression, fixed-time AUC, clinicopathological comparison, pathway scoring, targeted cell-cell communication analysis, and nomogram construction.</p><p><strong>Results: </strong>A total of 13,784 myeloid cells were analyzed, including 8,209 tumor-infiltrating myeloid cells and 5,575 normal myeloid cells. Tumor-associated myeloid cells showed lower AP scores than normal myeloid cells (P = 0.04054). A compact six-gene signature consisting of SMOX, CSF1, AQP9, FLNB, COL7A1, and MXI1 was established. In GSE14520, the signature predicted poor OS (HR = 1.94, 95% CI: 1.41-2.65, P = 3.73e-05) and poor RFS (HR = 1.60, 95% CI: 1.23-2.08, P = 4.63e-04). In TCGA-LIHC, it also predicted inferior OS (HR = 1.49, 95% CI: 1.12-1.99, P = 5.83e-03). High-risk tumors showed enhanced glycolysis, hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, and IL6-JAK-STAT3 activity, with reduced antigen-presentation and IFNG response signals.</p><p><strong>Conclusions: </strong>We report a compact single-cell-derived myeloid AP-loss signature for HCC prognosis and recurrence stratification. The model links clinical risk to a biologically interpretable myeloid dysfunction state and broader tumor microenvironment remodeling.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261466300"},"PeriodicalIF":2.1,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13333059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148391827","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":"GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas.","authors":"Morteza Hadizadeh, Seyedeh Zahra Mousavi, Sorayya Ghasemi","doi":"10.1177/11769351261460625","DOIUrl":"10.1177/11769351261460625","url":null,"abstract":"<p><strong>Objectives: </strong>Glioma tumors, especially glioblastoma (GBM), are among the most heterogeneous brain cancers, and this characteristic is considered the main cause of treatment resistance and disease recurrence. The aim of this study was to investigate the cellular dynamics and transcriptional pathways dependent on the GFAP gene in three clinical conditions, including grade III glioma, primary glioblastoma, and recurrent glioma.</p><p><strong>Methods: </strong>Single-cell RNA sequencing (scRNA-seq) data from the GSE103224 collection were merged and clustered using the Seurat and Harmony software packages. Then, the Monocle3 tool was used to model cellular developmental pathways, and the Metascape database was used for functional enrichment of genes.</p><p><strong>Results: </strong>The results showed that malignant cells, microglia, immune cells, and glial progenitors formed distinct clusters in all conditions, and the transition of radial glial cells to cancer cells was observed in all three conditions. Differential gene expression analysis identified six genes, APOE, AQP4, CLU, GFAP, ID3, and ID4 as stable and common genes between all tumor stages, whose expression was directly related to tumor persistence and recurrence. Functional enrichment also identified steroid hormone response pathways, NGF signaling, and the VEGF pathway as common processes in glioma progression.</p><p><strong>Conclusion: </strong>Overall, these results indicate that GFAP and its associated co-expression network play an important role in tumor persistence and cellular rearrangement in glioma and can be used as molecular markers and novel therapeutic targets for precise and personalized treatment of patients.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261460625"},"PeriodicalIF":2.1,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346694","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":"Widening Health Inequality and Causal Metabolic Drivers in Global Colorectal Cancer: A Multi-Dimensional Study.","authors":"Song Gao, Mingying Peng, Susu Han, Yufei Tang, Juan Ren, Yi Cen, Fenggang Hou, Lianyu Chen, Xiaoling Yin","doi":"10.1177/11769351261454666","DOIUrl":"10.1177/11769351261454666","url":null,"abstract":"<p><strong>Objectives: </strong>To describe the geographical distribution of global colorectal cancer (CRC) and the time trend of its disease burden from 1990 to 2021, and to predict its future development.</p><p><strong>Methods: </strong>Data were derived from the Global Burden of Disease Study 2021 (GBD 2021, IHME database), covering 204 countries and 21 GBD regions. Analyses were conducted both globally and stratified by socio-demographic index (SDI) region. Three-stage analysis was carried out around incidence, mortality and disability-adjusted life years (DALYs): trend analysis by Joinpoint regression and age-period-cohort model; decomposition of disease burden drivers by Das Gupta decomposition and data envelopment analysis; 15-year forecasting based on the Bayesian age-period-cohort analysis. In addition, the causal effect of BMI on CRC risk was assessed using the Mendelian randomization (MR) method.</p><p><strong>Results: </strong>In 2021, 2.19 million new CRC cases were reported globally (age-standardized incidence rate 25.61/100 000). From 1990 to 2021, the incidence rate continued to rise, while the mortality rate and DALYs declined - a divergence likely attributable to improved screening and treatment, particularly in high-SDI regions. Population aging is the primary driving factor for the increased CRC global burden (47.44%), while population growth is dominant in low SDI regions. Health inequality increased, and the slope index rose from 437 in 1990 to 575 in 2021. Projections showed that the incidence rate will increase slightly (+0.56%) by 2036, while the mortality rate (-9.5%) and DALYs (-8.1%) will continue to decline. MR analysis supported a causal association between BMI and increased CRC risk.</p><p><strong>Conclusion: </strong>The global CRC burden remains substantial, driven by population aging, the obesity epidemic, and widening regional disparities. Strategies should prioritize age-targeted screening in high-SDI regions, expand early detection in low-SDI areas, and integrate obesity control into national non-communicable disease prevention frameworks.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261454666"},"PeriodicalIF":2.1,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346665","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":"Translating Data Into Clinical Tools: An Integrative Strategy for Precision Biomarker Identification in Soft Tissue Sarcoma Diagnosis and Prognosis.","authors":"Masoume Avateffazeli, Rahem Rahmati, Abdolreza Mohammadnia, Maryam Hajimoradi, Elham Nazari, Shadi Shafaghi","doi":"10.1177/11769351261457913","DOIUrl":"10.1177/11769351261457913","url":null,"abstract":"<p><strong>Objectives: </strong>Soft tissue sarcomas (STSs) are rare, heterogeneous cancers with over 70 subtypes, often diagnosed late due to diagnostic complexity, leading to poor outcomes. We aimed to identify and validate novel transcriptomic biomarkers for the diagnosis and prognosis of STS using an integrative machine learning and bioinformatics framework applied to publicly available cohorts.</p><p><strong>Methods: </strong>RNA-seq and clinical data from 261 STS samples were obtained from The Cancer Genome Atlas (TCGA). A multi-step analytical pipeline was implemented, including differential expression analysis, functional enrichment, protein-protein interaction network construction, and clinical correlation assessment. Machine learning algorithms were employed for feature selection and model development. Diagnostic performance was evaluated using receiver operating characteristic curve analysis, and prognostic value was assessed using Kaplan-Meier survival analysis.</p><p><strong>Results: </strong>We identified a 26-gene prognostic signature significantly associated with overall survival (15 upregulated and 11 downregulated genes). For diagnosis, <i>A1CF</i> alone showed an AUC of 0.70, while the combinations <i>A1CF-ATP6V0D2</i> and <i>A1CF-LECT2</i> achieved AUCs of 0.743 and 0.796, respectively. External validation confirmed dysregulation of A1CF in tumor tissues compared with controls.</p><p><strong>Conclusions: </strong>This study identifies a novel 26-gene prognostic signature and A1CF-based diagnostic panels for STS using computational methods. These biomarkers represent exploratory candidates for future investigation in STS diagnosis and prognosis; however, experimental and prospective clinical validation are required before their potential use in early detection, risk stratification, or personalized management.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261457913"},"PeriodicalIF":2.1,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148219971","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":"Multi-Scale Cross-Attention Multiple Instance Learning Network for Automated Classification of Colorectal Polyps.","authors":"Wisdom Ikezogwo, Yongjun Liu, Kareem Hosny, Jonathan Henriksen, Robert Ricciotti, Meenal Rawlani, Paul Swanson, Patrick C Mathias, Noah G Hoffman, Geoffrey Baird, Luis F Gonzalez-Cuyar, Linda Shapiro, Deepti Reddi","doi":"10.1177/11769351261459985","DOIUrl":"10.1177/11769351261459985","url":null,"abstract":"<p><strong>Objectives: </strong>The Gastrointestinal (GI) Pathology subspecialty at our medical center is the highest-volume service in our Anatomic Pathology division, with colon polyps constituting 40% of specimens. 2021 revisions to colorectal cancer screening guidelines lowered the screening age to 45, which may increase endoscopy procedures and specimen volume. We therefore sought to develop an artificial intelligence (AI) classifier to triage colorectal polyp specimens.</p><p><strong>Methods: </strong>We retrospectively searched our pathology database from 2021 to 2023 for colon polyps with following 12 final diagnoses: normal, lymphoid aggregate, inflammatory polyp, hyperplastic polyp, sessile serrated adenoma (with and without dysplasia), traditional serrated adenoma, tubular adenoma, tubulovillous adenoma, villous adenoma, high grade dysplasia (in any adenoma type), and invasive carcinoma. 1191 (759 neoplastic and 432 nonneoplastic) representative slides were scanned using a Leica Aperio LV1 Scanner at 40x magnification. Images were used to train a multi-scale cross attention multiple instance learning (MsCAMIL) network, a weakly-supervised transformer-based model to perform binary/triage (neoplastic versus nonneoplastic) and 12-way/final diagnosis classifications. Slides were randomly assigned for training (N=715, 60%), validation (N=119, 10%), and testing (N=357, 30%). An additional 40 slides collected from two subsequent clinical service days were included to represent routine clinical cases. In addition, 40 external slides from multiple outside academic and private practice institutions were scanned on both Leica Aperio GT450 LV1 and Leica Aperio ScanScope AT2 to compare performance.</p><p><strong>Results: </strong>We assessed the following diagnostic performance metrics: the macro-averaged F1-Score (F1 = 2 x (precision x recall)/(precision + recall)), micro-averaged Accuracy (mACC), and macroaveraged specificity. In binary classification, the model achieved highest accuracy and F1-score at 95.18% and 97.42% in archived and routine clinical cases, respectively, with best discriminatory performance when overlapping patches of microscopic field from 10x and 20x magnifications. In 12-way classification, the F1 score was reduced to 74% and 57% in archived and routine cases, respectively. In the external set, the binary classification performance decreased to 86%, with misclassifications occurring primarily in slides from a single outside institution.</p><p><strong>Conclusion: </strong>In daily clinical workflow, MsCAMIL networks can function as an efficient triage system by screening colon polypectomy specimens for neoplasm. In binary classification, the classifier showed >95% specificity and accuracy in both archived and routine clinical cases. Multi-institutional collaborations will be necessary to expand patient population and further validate this tool for clinical use.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261459985"},"PeriodicalIF":2.1,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206479","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":"LEPR Contributes to Lung Squamous Cell Carcinoma: Insights From Mendelian Randomization and Experimental Studies.","authors":"Wenhua Hu, Guowei Wu, Yanming Lin, Shiliang Li, Zhibin Lin, Yuting Chen, Zhen Cheng, Junxuan Zeng, Zhixiong Yang, Yongcun Wang","doi":"10.1177/11769351261459382","DOIUrl":"10.1177/11769351261459382","url":null,"abstract":"<p><strong>Background: </strong>The involvement of certain adipokines link to carcinogenesis, development, and prognosis. However, the roles of adipokines in lung cancer and its histological subtypes remains indistinct. Therefore, the aims of this study are to explore the causal relationship between adipokines and lung cancers.</p><p><strong>Methods: </strong>Summary-level data for exposures (six adipokines) and outcomes (lung cancer and its histological subtypes) were collected from the IEU OpenGWAS, International Lung Cancer Consortium (ILCCO) and lectures. Two-sample mendelian randomization (MR) was conducted to estimate the causality by employing single nucleotide polymorphisms (SNPs) as instrument variables (IVs). Human tissue microarray and immunohistochemistry (IHC) analysis validated the adipokines expression. Functional effects of LEPR in LUSC were assessed <i>in vitro</i> using NCI-H2170 and SK-MES-1 cell lines via siRNA-mediated knockdown, followed by assays for cell proliferation, invasion, and apoptosis.</p><p><strong>Results: </strong>Leptin receptor (LEPR) was associated with risk of lung squamous cell carcinoma (LUSC, OR: 1.05, 95% CI: 1.01-1.08; P < 0.0125), and no other adipokines associated with lung cancer and its histological subtypes (P > 0.05). Experimental results revealed that high expression of LEPR in LUSC tumor samples compared to adjacent normal samples, and associated with unfavorable survival status. <i>In vitro</i>, LEPR knockdown significantly inhibited proliferation and invasion while promoting apoptosis in LUSC cells.</p><p><strong>Conclusion: </strong>These findings suggest that LEPR may play a critical role in the development and progression of LUSC, providing a potential target for therapeutic intervention.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261459382"},"PeriodicalIF":2.1,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206470","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 MAPK Pathway Coordinates an Immunosuppressive Microenvironment in Colorectal Cancer: A Single-Cell Guided Prognostic Model.","authors":"Yao Zhang, Xizheng Zhang, Jiayu Wei, Yelei Wang, Ying Shen, Anqi Jiang, Qian Liu","doi":"10.1177/11769351261451845","DOIUrl":"10.1177/11769351261451845","url":null,"abstract":"<p><strong>Background: </strong>The immunosuppressive tumor microenvironment in colorectal cancer is a key factor in its progression and treatment resistance, yet the coordinated mechanisms of its cellular composition and signaling networks are not fully understood. This study aims to explore the regulatory foundation of this microenvironment and construct a clinically relevant prognostic model through the integration of single-cell and multi-omics analyses.</p><p><strong>Methods: </strong>We first analyzed single-cell RNA sequencing data from 9 samples, mapping cellular landscapes and inferring intercellular communication networks. By integrating extensive transcriptomic data from public databases, we conducted weighted gene co-expression analysis to identify core functional modules. Additionally, we employed machine learning methods to integrate multi-omics features and construct a prognostic risk scoring model.</p><p><strong>Results: </strong>Analysis suggested that macrophages and fibroblasts in the tumor microenvironment play a dominant role in intercellular communication through specific chemokine signaling axes. Co-expression network analysis further identified the MAPK signaling pathway as a core module associated with immunosuppressive microenvironmental phenotype. Based on these findings, we developed and preliminarily verified a prognostic risk score comprising 17 genes.</p><p><strong>Conclusion: </strong>This score may independently predict predicts patient survival and effectively distinguishes tumor subtypes with distinct immune microenvironmental characteristics: the high-risk group shows an immunosuppressive, stroma-rich \"cold tumor\" phenotype, suggesting higher predictive sensitivity to certain chemotherapies and targeted drugs.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261451845"},"PeriodicalIF":2.1,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206103","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":"Genetically Predicted Gut Microbiota in Correlation with the Risk of Head and Neck Cancer.","authors":"Zhaoyi Lu, Liqing Zhang, Qin Zhang, Qianqian Duan, Xuewen Liu, Xiaoli Deng, Dongsheng Chen, Xi Chen","doi":"10.1177/11769351261457904","DOIUrl":"10.1177/11769351261457904","url":null,"abstract":"<p><strong>Background: </strong>Existing evidence supports observational associations between the gut microbiome composition and susceptibility to extraintestinal cancers; however, the causal relationship between gut microbiome composition and the risk of developing head and neck cancer (HNC) remains uncertain. Therefore, we conducted a two-sample Mendelian randomization (MR) analysis using publicly available genome-wide association study (GWAS) data to investigate the potential causal link between gut microbiome composition and HNC.</p><p><strong>Methods and analysis: </strong>Relevant investigations were conducted to examine the casualties of exposures and outcomes, using inverse variance weighted (IVW), weighted median (WM) and the MR-Egger method. Sensitivity analyses, including the MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis, were used to assess pleiotropy and heterogeneity. In addition, the Steiger directionality test was applied to exclude studies with potential directional biases.</p><p><strong>Results: </strong>Our study identified 16 causal relationships between the genetically predicted abundances of gut microbiota and the risk of developing HNC. After sensitivity analysis on these identified relationships, 10 [class (<i>Actinobacteria, Betaproteobacteria</i>), order <i>Bacillales</i>, family (<i>Ruminococcaceae</i>), genus (<i>Eubacterium_ventriosum_group, Coprococcus2, Coprococcus3, Desulfovibrio, Howardella, Subdoligranulum, Veillonella, Victivallis</i>)] out of the initially identified 16 causal relationships were ultimately validated as being associated with HNC tumors. Steiger directionality test indicated that the MR estimate of causal direction was accurate (All p < 0.05).</p><p><strong>Conclusion: </strong>Our MR analysis revealed that the gut microbiome composition was causally associated with the risk of developing HNC and could serve as a new biomarker for preventive and therapeutic strategies for HNC.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261457904"},"PeriodicalIF":2.1,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200250","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}