Cancer Informatics最新文献

{"title":"iCRCexp: An Integrative Database for Colorectal Cancer-Associated Gene Expression Profiles.","authors":"Yan Yuan, Bi-Jin Cao, Zhi-Kai Qian, Ze-Kun Liu, Wei-Wei Xiao, Xing-Yang Li, Zhi-Xiang Zuo, Ze-Xian Liu, Yuan-Hong Gao","doi":"10.1177/11769351261454653","DOIUrl":"10.1177/11769351261454653","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is a leading cause of tumor-related mortality. Recent studies have shown that the transcriptome plays an important role in the development and occurrence of CRC. However, a comprehensive repository of CRC transcriptome sequencing data is unavailable. In the present study, we constructed a colorectal database (iCRCexp; http://icrcexp.omicsbio.info/).</p><p><strong>Method: </strong>We collected CRC-related transcriptome datasets from The Cancer Genome Atlas (TCGA) and National Center for Biotechnology Information (NCBI) Gene Ontology Omnibus (GEO) databases up to 2022. The sequencing data were preprocessed through a unified pipeline and subsequently analyzed. CRC-related genes and drugs were identified via text mining of the PubMed abstracts.</p><p><strong>Results: </strong>A total of 18 466 tissue samples from 231 studies, 2429 CRC-related genes, and 1852 CRC-related drugs were collected and integrated into iCRCexp. Among these studies, 251 CRC-related datasets were identified with abundant characteristic information, including tissue source, baseline characteristics, therapeutic responses, recurrence and metastasis, and survival. We conducted differential correlation and survival analyses. We predicted potential target drugs for CRC-related genes by calculating connectivity scores. Consequently, we integrated these analysis results through network construction and presented them in a CRC database.</p><p><strong>Conclusion: </strong>A comprehensive resource, including CRC-related gene and medication information and an expression analysis platform, was constructed for the CRC community.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261454653"},"PeriodicalIF":2.1,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13222402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148144785","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":"Clinical Significance of SDC2 in Gastric Cancer: Insights From a Multi-Center Cohort Study.","authors":"Yihang Xu, Zisong Wang, Xuanyu Wang, Yuxin Chen, Jian Xu, Fan Wang, Sufang Tian, Xiaoping Liu","doi":"10.1177/11769351261452672","DOIUrl":"10.1177/11769351261452672","url":null,"abstract":"<p><strong>Objectives: </strong>Gastric Cancer (GC) poses a significant global health challenge, necessitating effective biomarkers for early detection and prognosis. This study investigates the relationship between SDC2 expression and GC patient outcomes.</p><p><strong>Methods: </strong>We analyzed SDC2 expression in GC and its association with patient outcomes using Kaplan-Meier survival and Cox regression analyses. A pan-cancer analysis was performed to assess cross-tumor SDC2 expression patterns. Validation included immunohistochemistry and single-cell data analyses to confirm SDC2 expression in GC tissues and cell types, with findings supported by independent cohort studies.</p><p><strong>Results: </strong>Elevated SDC2 expression correlates with poor prognosis in GC patients, marked by lower survival rates, enhanced tumor microenvironment heterogeneity, decreased tumor mutational burden, and reduced immunotherapy efficacy. Kaplan-Meier and Cox regression analyses confirm that higher SDC2 expression is associated with shorter overall survival, establishing it as an independent prognostic risk factor. Pan-cancer analysis reveals consistent SDC2 expression patterns across multiple cancers, indicating broad clinical relevance. Validation through immunohistochemistry and single-cell data analysis confirms SDC2 expression in GC tissues and cell types. Independent cohort studies further support these findings.</p><p><strong>Conclusion: </strong>In summary, this study underscores the potential of SDC2 as a promising target for early diagnosis and therapeutic intervention in GC, with implications for other malignancies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261452672"},"PeriodicalIF":2.1,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13222410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148144808","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 Ferroptosis and Immune Features Are Associated With Overall Survival in Bladder Urothelial Carcinoma: A TCGA-Based Analysis.","authors":"Lei Zhang, Zhengzuo Sheng","doi":"10.1177/11769351261453955","DOIUrl":"10.1177/11769351261453955","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the prognostic value of ferroptosis-related and immune-related transcriptional features in bladder urothelial carcinoma (BUC) and to evaluate whether their integration provides incremental prognostic information beyond conventional clinical factors.</p><p><strong>Methods: </strong>Transcriptomic and clinical data of patients with BUC were obtained from The Cancer Genome Atlas (TCGA) as the training cohort. Ferroptosis and immune scores were constructed based on predefined gene sets using a <i>z</i>-score-based averaging method. Associations with overall survival were evaluated using Kaplan-Meier analysis and multivariable Cox regression with adjustment for age and pathological stage. Model performance was assessed using concordance index (C-index), likelihood ratio testing, and bootstrap internal validation. External validation was performed in independent cohorts (GSE13507 and GSE32894). Nonlinear relationships were examined using restricted cubic spline analysis, and sensitivity analyses were conducted by incorporating additional clinicopathologic variables.</p><p><strong>Results: </strong>Neither ferroptosis nor immune scores alone were significantly associated with overall survival in Kaplan-Meier analyses. Multivariable Cox regression showed a directionally protective effect of immune score and a non-significant risk trend for ferroptosis score. Integration of ferroptosis and immune features resulted in modest improvement in prognostic performance compared with clinical models alone. These findings were consistent across external validation cohorts. Restricted cubic spline analysis revealed a nonlinear association for immune score but not for ferroptosis score. Sensitivity analyses incorporating tumor grade yielded similar results, supporting robustness of the findings.</p><p><strong>Conclusion: </strong>Ferroptosis and immune transcriptional features provide limited but consistent incremental prognostic information in bladder urothelial carcinoma. These findings are exploratory and hypothesis-generating, suggesting that integration of ferroptosis and immune biology may offer complementary insights but is not yet sufficient for clinical application.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261453955"},"PeriodicalIF":2.1,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139192","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":"Decitabine Induces Subtype-Specific Epigenomic Remodeling and Perturbs Age-Associated Regulatory CpGs in Breast Cancer.","authors":"Areez Shafqat, Itika Arora, Arshiya Akbar, Mohammed Alfuwais, Safiah Aldubaisi, Mohammad Imran Khan, Ahmed Abu-Zaid, Firoz Ahmed, Ahmed Yaqinuddin","doi":"10.1177/11769351261452670","DOIUrl":"10.1177/11769351261452670","url":null,"abstract":"<p><strong>Objectives: </strong>Breast cancer (BC) subtypes such as HR+, HER2+, and triple-negative (TNBC) show distinct molecular features, treatment responses, and outcomes. DNA methylation is a key, targetable epigenetic regulator in BC. This study examined whether the DNA methyltransferase inhibitor decitabine (DAC) produces subtype-specific epigenomic and transcriptional effects in breast cancer cell lines representing distinct molecular subtypes.</p><p><strong>Methods: </strong>Gene expression and DNA methylation data from DAC-treated and untreated T-47D (Luminal-A) and JIMT-1 (HER2-amplified, trastuzumab-resistant with a TNBC-like phenotype) breast cancer cell lines were obtained from a published dataset. Differential expressions were assessed using <i>limma</i>, and methylation changes were defined using β-value thresholds. Integrated epigenomic-transcriptional analysis, functional enrichment, Horvath clock CpG evaluation, and survival analysis were performed in the METABRIC and TCGA cohorts.</p><p><strong>Results: </strong>In JIMT-1, DAC caused hypomethylation at 1195 CpG sites and upregulation of 187 genes, including <i>TFAP2E</i>, an age-associated locus selectively hypomethylated after DAC. In T-47D, DAC induced hypomethylation at 1937 CpGs and upregulated 248 genes. Amongst these, <i>KRT20</i> was upregulated despite promoter hypermethylation, indicating a subtype-specific regulatory architecture. DAC-responsive genes in JIMT-1 were enriched for cytokine signaling and piRNA-mediated epigenetic silencing, whereas T-47D showed enrichment for extracellular matrix organization, collagen dynamics, and piRNA processing pathways. Horvath clock CpG analysis showed selective perturbation of age-associated sites. Survival analysis identified 114 DAC-responsive genes associated with overall survival in ER/PR-positive BC and 8 in the JIMT-1-derived gene set.</p><p><strong>Conclusion: </strong>DAC induces subtype-dependent epigenomic and transcriptional remodeling, selectively disrupts age-associated regulatory programs, and underscores the need for subtype-stratified evaluation of epigenetic therapies in breast cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261452670"},"PeriodicalIF":2.1,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139189","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 Machine Learning Approach to Prognostication in Oral Cancer: Analysis of the Surveillance, Epidemiology, and End Results Database.","authors":"Philips Okeagu, Saranya Murukutla, Afamdi Iwuchukwu, Chukwuebuka Ogwo","doi":"10.1177/11769351261442875","DOIUrl":"10.1177/11769351261442875","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate machine learning models to predict 5-year survival in oral cancer using a large population-based registry and to rank prognostic factors.</p><p><strong>Methods: </strong>We analyzed Surveillance, Epidemiology, and End Results (SEER) data from 1992 to 2020. After applying inclusion criteria, 39 904 patients with complete data on 21 variables were included from 53 611 cases (25.6% exclusion). Selection bias was assessed by comparing included and excluded patients. The outcome was binary 5-year survival. Four models were trained and evaluated using nested fivefold cross-validation: XGBoost, LASSO, Random Forest, and logistic regression. Performance was assessed using the Brier score, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Calibration was evaluated using slope and intercept with 95% confidence intervals. Model explainability used permutation feature importance and SHAP values.</p><p><strong>Results: </strong>Random Forest showed the best discrimination (AUC 77.6%, accuracy 71.4%, Brier 0.186) and was selected for risk stratification. However, it overestimated risk in lower deciles. Logistic regression and LASSO showed better calibration, with slopes near 1.0, but slightly lower discrimination (AUCs 75.5% and 76.9%). SHAP analysis identified localized stage as the strongest protective factor (importance 100.0), followed by age (91.1) and chemotherapy (29.3). Excluded patients had more unstaged tumors (3.9% vs 1.7%, <i>P</i> < .001).</p><p><strong>Conclusion: </strong>Random Forest provides strong risk stratification, but miscalibration limits its use for absolute risk prediction. Logistic regression and LASSO may be preferable when accurate probabilities are needed. External validation is required before clinical use.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261442875"},"PeriodicalIF":2.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148043992","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":"Hesperidin Inhibits Proliferation and Metastasis of Human Breast Cancer Cells by Suppressing MMP-2/9 Activity and Reversing Epithelial-Mesenchymal Transition.","authors":"Hong Hu, Chao Zhang, Zhiyong Liu","doi":"10.1177/11769351261456238","DOIUrl":"10.1177/11769351261456238","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the mechanism by which Hesperidin (Hsp) inhibits the proliferation and metastasis of the triple-negative breast cancer cell line MDA-MB-231.</p><p><strong>Methods: </strong>The effect of Hsp on cell viability was analyzed using the MTT assay. The anti-migration and anti-invasion abilities of Hesperidin were evaluated using the scratch test and Transwell migration assay. The activity of matrix metalloproteinases (MMP)-2/MMP-9 was analyzed using gelatin zymography. The expression of epithelial-mesenchymal transition (EMT)-related proteins (Vimentin, Snail, ZO-1) was detected by Western blot.</p><p><strong>Results: </strong>Low concentrations of Hsp (2-10 μmol/L) showed no significant cytotoxicity; 20-40 μmol/L significantly reduced cell survival rate (p<0.01). The scratch test showed that Hsp inhibited wound healing in a concentration-dependent manner. The Transwell migration assay showed that the number of migrating cells decreased with increasing Hsp concentration. Gelatin zymography results indicated that MMP-2/MMP-9 activity decreased with increasing Hsp concentration. Western blotting results showed that Hsp downregulated the metastasis-related proteins Vimentin and Snail and upregulated the adhesion protein ZO-1.</p><p><strong>Conclusion: </strong>Our findings suggest that Hsp inhibits tumor cell invasion and metastasis by potentially reducing MMP-2/MMP-9 hydrolase activity, blocking extracellular matrix degradation, and reversing the EMT process (downregulating Vimentin/Snail and upregulating ZO-1). Hsp may represent a promising candidate for adjuvant therapy in breast cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261456238"},"PeriodicalIF":2.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148043944","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":"Exon-Specific Targeted Analysis of <i>BRCA1</i> and <i>BRCA2</i> Mutations in Bangladeshi Breast Cancer Patients.","authors":"Shuvra Dutta, S M Mahbubur Rashid, Shihabul Islam, Sk Farid Ahmed, Mustak Ibn Ayub","doi":"10.1177/11769351261445625","DOIUrl":"10.1177/11769351261445625","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is a leading cause of death among women. Pathogenic variations (PVs) in <i>BRCA1</i> and <i>BRCA2</i> genes increase the risk of hereditary and Triple-negative breast cancer (TNBC). Despite its great importance, NGS-based genetic testing of <i>BRCA1</i> and <i>BRCA2</i> in Bangladesh is out of reach of most patients because of its high cost.</p><p><strong>Objective: </strong>To overcome this challenge, this study explored whole genome databases (cBioPortal, gnomAD, dbSNP) to investigate whether sequencing selected exons in <i>BRCA1</i> and <i>BRCA2</i> genes may provide meaningful clinical interpretation of BC risk.</p><p><strong>Methods: </strong>Exons 4 and 16 of <i>BRCA1</i> and exons 18, 23 and 25 of <i>BRCA2</i> were selected for targeted analysis. According to the aforementioned databases, 10 and 21 mutations are pathogenic in the targeted regions of <i>BRCA1</i> and <i>BRCA2</i> respectively. To evaluate the presence or absence of these germline pathogenic variations (PV) in Bangladeshi BC patients, we collected blood samples from 13 subjects, among whom, 9 were diagnosed with BC, and 4 without cancerous tumors but with family history and breast lump.</p><p><strong>Results: </strong>Analysis of 13 patient samples revealed no PVs in the targeted regions. Within the <i>BRCA1</i> gene, 5 of the 10 sites, and within the <i>BRCA2</i> gene, 10 of the 21 sites associated with pathogenic variations were successfully sequenced and clearly interpretable. However, comparison with the reference sequence confirmed the absence of PVs in the targeted regions of both <i>BRCA1</i> and <i>BRCA2</i>. In silico analysis indicated that missense mutations are the predominant type of variation in both genes.</p><p><strong>Conclusion: </strong>This study, although limited by sample size, demonstrates that even exons enriched for pathogenic mutations may not be sufficiently representative for detecting PVs through targeted sequencing of <i>BRCA1</i> and <i>BRCA2</i>. Comprehensive sequencing of the full lengths of <i>BRCA1</i> and <i>BRCA2</i> may be necessary to ensure confident clinical interpretation in BC patients.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261445625"},"PeriodicalIF":2.1,"publicationDate":"2026-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976062","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":"Explore the Heterogeneity of Glioblastoma Based on Genes Related to Glutamine Metabolism.","authors":"Ling Wang, Tong Chen, Jiajia Chen, Yiwen Yang, Linglin Sun, Chao Gu, Chenliang Cao","doi":"10.1177/11769351261449032","DOIUrl":"10.1177/11769351261449032","url":null,"abstract":"<p><strong>Objectives: </strong>To establish a glutamine metabolism (GM)-based classification for glioblastoma (GBM) and evaluate its prognostic and immunotherapeutic implications.</p><p><strong>Methods: </strong>A total of 237 GBM patients from the Chinese Glioma Genome Atlas (CGGA) database were included as the training set, and 219 patients from the Gene Expression Omnibus (GEO) database served as the validation set. Consensus clustering was performed based on the expression profiles of 13 GM-associated genes to identify robust subgroups. Differences between clusters were analyzed using clinical indices, genomic and transcriptomic biomarkers. Tumor response to immune checkpoint inhibitors (ICIs) was predicted using the tumor immune dysfunction and exclusion (TIDE) algorithm, tumor microenvironment (TME) score, T cell inflammation score, and SubMap algorithm. A GM-based classifier was subsequently developed and validated.</p><p><strong>Results: </strong>Consensus clustering of the training set revealed 2 distinct subgroups (cluster 1 and cluster 2) with significant prognostic differences; cluster 2 exhibited poorer overall survival. Immunotherapy response prediction indicated that cluster 2 had a lower likelihood of benefiting from ICIs. The newly developed GM-based classifier demonstrated high accuracy (AUC > 0.9) and maintained strong consistency with the original clustering in terms of subtype classification and immunotherapy prediction across both datasets.</p><p><strong>Conclusion: </strong>This study establishes a robust classification system for GBM based on glutamine metabolism-related genes, which effectively stratifies patients into prognostic subgroups and predicts immunotherapy response. The GM-based classifier offers a valuable tool for guiding clinical prognosis and treatment decisions in GBM.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261449032"},"PeriodicalIF":2.1,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13176551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964824","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":"AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.","authors":"Charlotte Jennings, Andrew Broad, Lucy Godson, Emily Clarke, David Westhead, Darren Treanor","doi":"10.1177/11769351261434523","DOIUrl":"10.1177/11769351261434523","url":null,"abstract":"<p><p>This study systematically reviews and evaluates published research on machine learning models that integrate histopathology whole slide images and high-throughput -omic data to predict overall survival in cancer. A comprehensive search of PubMed, EMBASE, and Cochrane CENTRAL was conducted through August 12, 2024, with citation screening for additional studies. Eligible studies applied machine learning or deep learning methods to multimodal data combining pathology images and -omics. Data extraction followed the CHARMS checklist, and risk of bias was assessed using the PROBAST + AI tool. Narrative synthesis was conducted in line with PRISMA 2020 guidelines. Forty-eight studies published since 2017 met inclusion criteria, spanning 19 cancer types. All relied on The Cancer Genome Atlas dataset. Modelling approaches included regularised Cox regression (n = 4), classical machine learning (n = 13), and deep learning (n = 31). Reported concordance indices ranged from 0.550 to 0.857, with most multimodal models outperforming unimodal counterparts. However, all studies were assessed as having high or unclear risk of bias-most often due to limited external validation, insufficient reporting, and minimal assessment of clinical utility. This review highlights a rapidly evolving yet methodologically underdeveloped field. While model performance is promising, improvements in data standardisation, reporting practices, and real-world contextualisation are critical for clinical translation. This work was funded by the National Pathology Imaging Cooperative (NPIC), supported by UK Research and Innovation (Project no. 104687).</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261434523"},"PeriodicalIF":2.1,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13172687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964813","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":"Integration of Multi-Omics Data Identifies the Role of the Selenium-Related Gene PNPO and Pre-exhausted CD127^- CD8⁺ T Cells in Laryngeal Carcinoma.","authors":"Lin Xiang, Lin Chen, Cheng Gong, Yao Tang","doi":"10.1177/11769351261438477","DOIUrl":"10.1177/11769351261438477","url":null,"abstract":"<p><strong>Objective: </strong>To elucidate mechanistic links among selenium measures, selenium-related genes, and immune traits in laryngeal carcinoma (LC) by integrating Mendelian randomization, bulk transcriptomic, and single-cell analyses.</p><p><strong>Methods: </strong>Two-sample Mendelian randomization (MR) was used to evaluate the causal effects of selenium measures and genetically predicted expression of selenium-related genes instrumented by eQTLs on LC risk, and immune traits were screened as candidate mediators via a directionality-based filter. We then examined PNPO expression in relation to pathway activity and immune infiltration in the TCGA and GEO cohorts. Single-cell RNA sequencing was further analyzed to characterize PNPO-associated programs in malignant epithelial and immune cells. The scPagwas and deconvolution algorithms were applied to prioritize disease-relevant T-cell subsets, and hdWGCNA was used to identify phenotype-associated gene modules. Intercellular communication, signaling activity, developmental trajectories, and transcription factor programs were evaluated in complementary analyses. The reporting of this study conforms to the STROBE-MR guideline for Mendelian randomization studies.</p><p><strong>Results: </strong>Genetically predicted selenium measures showed no evidence of a causal association with LC risk. In contrast, PNPO showed evidence consistent with involvement in LC susceptibility, potentially involving CD127^- CD8⁺ T cells. In bulk transcriptomic cohorts, tumors with low PNPO expression were enriched for oncogenic pathways and were associated with poorer survival. PNPO expression correlated positively with inferred CD8⁺ T-cell infiltration. At single-cell resolution, PNPO^- malignant epithelial cells displayed transcriptional features consistent with stemness, drug resistance, and immune-evasion programs. We further identified a pre-exhausted CD127^- CD8⁺ T-cell subset with distinct molecular and functional characteristics.</p><p><strong>Conclusion: </strong>These analyses implicate PNPO-linked vitamin B6 metabolism and a pre-exhausted CD127^- CD8⁺ T-cell state in LC, highlighting candidates for mechanistic validation and potential therapeutic exploration.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"25 ","pages":"11769351261438477"},"PeriodicalIF":2.1,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783630","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}