Computational and systems oncology最新文献

{"title":"Correction to \"Time Management and Personal Efficiency in the Age of Computational and Systems Oncology\".","authors":"","doi":"10.1002/cso2.70014","DOIUrl":"https://doi.org/10.1002/cso2.70014","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1002/cso2.70001.].</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 ","pages":"e70014"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824260","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":"Experiment-Anchored Abundance–Coverage Frontiers Define Conditional RHSVV Exposure Requirements in TP53-Missense Tumors","authors":"Takehiro Ishikawa","doi":"10.1002/cso2.70027","DOIUrl":"https://doi.org/10.1002/cso2.70027","url":null,"abstract":"<p>Mutant p53 accumulation may complement conformation-sensitive RHSVV recognition, but the required exposure discrimination depends on tumor coverage and normal-cell stress. We analyzed 809 tumors and 523 adjacent-normal specimens from eight CPTAC cohorts. <i>R</i> denotes p53 abundance relative to the cohort-normal median; <i>q</i> denotes high-state target/background epitope-exposure contrast; and π is the assumed target mixture weight in the high-exposure state. Native exposure was not measured. Among 268 quantified sequence-compatible missense tumors, requiring <i>R</i> ≥ 3 retained 90 (33.6%), whereas <i>R</i> ≥ 4 retained 54 (20.1%). At π = 0.90 and a primary 2.1-fold normal-p53 stress multiplier derived from a western blot in irradiated wild-type mouse embryonic fibroblasts, <i>R</i> ≥ 3 required <i>q</i> = 1.25 for both the pooled target criterion and the criterion requiring every one of seven fixed cohorts to pass. At <i>R</i> ≥ 4, abundance alone passed (<i>q</i> = 1), showing that exposure discrimination expands modeled coverage rather than being required at every landmark. For <i>R</i> ≥ 3, the required contrast under the seven-cohort criterion increased to 2.00, 3.00, and 6.00 at normal-stress multipliers of 3, 5, and 10. In an exploratory comparison restricted to selected hotspots, observed median <i>R</i> was lower in the conformational-proxy group than in the DNA-contact-proxy group (2.39 versus 3.22; <i>n</i> = 38 and 31). This subset-specific association does not establish a general mutation-class difference; class labels could not identify native exposure or π. Internal resampling revealed threshold sensitivity and cohort attrition and did not constitute validation. The resulting frontier specifies conditional experimental requirements across stress scenarios, not evidence of tumor killing or clinical safety.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146832","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":"Experiment-Anchored Abundance–Coverage Frontiers Define Conditional RHSVV Exposure Requirements in TP53-Missense Tumors","authors":"Takehiro Ishikawa","doi":"10.1002/cso2.70027","DOIUrl":"https://doi.org/10.1002/cso2.70027","url":null,"abstract":"<p>Mutant p53 accumulation may complement conformation-sensitive RHSVV recognition, but the required exposure discrimination depends on tumor coverage and normal-cell stress. We analyzed 809 tumors and 523 adjacent-normal specimens from eight CPTAC cohorts. <i>R</i> denotes p53 abundance relative to the cohort-normal median; <i>q</i> denotes high-state target/background epitope-exposure contrast; and π is the assumed target mixture weight in the high-exposure state. Native exposure was not measured. Among 268 quantified sequence-compatible missense tumors, requiring <i>R</i> ≥ 3 retained 90 (33.6%), whereas <i>R</i> ≥ 4 retained 54 (20.1%). At π = 0.90 and a primary 2.1-fold normal-p53 stress multiplier derived from a western blot in irradiated wild-type mouse embryonic fibroblasts, <i>R</i> ≥ 3 required <i>q</i> = 1.25 for both the pooled target criterion and the criterion requiring every one of seven fixed cohorts to pass. At <i>R</i> ≥ 4, abundance alone passed (<i>q</i> = 1), showing that exposure discrimination expands modeled coverage rather than being required at every landmark. For <i>R</i> ≥ 3, the required contrast under the seven-cohort criterion increased to 2.00, 3.00, and 6.00 at normal-stress multipliers of 3, 5, and 10. In an exploratory comparison restricted to selected hotspots, observed median <i>R</i> was lower in the conformational-proxy group than in the DNA-contact-proxy group (2.39 versus 3.22; <i>n</i> = 38 and 31). This subset-specific association does not establish a general mutation-class difference; class labels could not identify native exposure or π. Internal resampling revealed threshold sensitivity and cohort attrition and did not constitute validation. The resulting frontier specifies conditional experimental requirements across stress scenarios, not evidence of tumor killing or clinical safety.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146894","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":"Bioinformatics Identification of lncRNA-Associated Molecular Signatures Potentially Related to Quercetin Response in Breast Cancer","authors":"Mahboobeh Mehrabani Natanzi,&nbsp;Zohreh Khodaii,&nbsp;Naghmeh Zhalehjoo","doi":"10.1002/cso2.70026","DOIUrl":"https://doi.org/10.1002/cso2.70026","url":null,"abstract":"<p>Breast cancer affects approximately one in every eight women globally, as reported by the World Health Organization. Among all female malignant tumors, breast cancer has the highest fatality rate, surpassed only by lung cancer. Advanced stages of this disease are almost always fatal, and despite the advancements in surgical techniques and carefully designed chemotherapy regimens, relapse remains nearly inevitable. Although there are various chemical therapies for the treatment of breast cancer that can destroy the tumor or inhibit its growth, they often come with a variety of side-effects.</p><p>The polyphenolic compound quercetin can be found in numerous food plants. Previous research strongly suggests quercetin's potential as a cancer therapy. Various types of cancer cell lines have demonstrated that quercetin can induce apoptosis and suppress the proliferation of cancer cells. Therefore, we conducted an integrative bioinformatics analysis to identify molecular signatures, signaling pathways, and lncRNA-associated gene relationships potentially related to quercetin-associated transcriptional changes in breast cancer. The identified candidates were subsequently explored using protein–protein interaction and public regulatory databases. These findings are intended to generate hypotheses regarding potential lncRNA-associated mechanisms rather than establish direct causal quercetin–lncRNA interactions.</p><p>We found several genes involved in different metabolic pathways in relation to breast cancer and quercetin treatment.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942411","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":"Bioinformatics Identification of lncRNA-Associated Molecular Signatures Potentially Related to Quercetin Response in Breast Cancer","authors":"Mahboobeh Mehrabani Natanzi,&nbsp;Zohreh Khodaii,&nbsp;Naghmeh Zhalehjoo","doi":"10.1002/cso2.70026","DOIUrl":"https://doi.org/10.1002/cso2.70026","url":null,"abstract":"<p>Breast cancer affects approximately one in every eight women globally, as reported by the World Health Organization. Among all female malignant tumors, breast cancer has the highest fatality rate, surpassed only by lung cancer. Advanced stages of this disease are almost always fatal, and despite the advancements in surgical techniques and carefully designed chemotherapy regimens, relapse remains nearly inevitable. Although there are various chemical therapies for the treatment of breast cancer that can destroy the tumor or inhibit its growth, they often come with a variety of side-effects.</p><p>The polyphenolic compound quercetin can be found in numerous food plants. Previous research strongly suggests quercetin's potential as a cancer therapy. Various types of cancer cell lines have demonstrated that quercetin can induce apoptosis and suppress the proliferation of cancer cells. Therefore, we conducted an integrative bioinformatics analysis to identify molecular signatures, signaling pathways, and lncRNA-associated gene relationships potentially related to quercetin-associated transcriptional changes in breast cancer. The identified candidates were subsequently explored using protein–protein interaction and public regulatory databases. These findings are intended to generate hypotheses regarding potential lncRNA-associated mechanisms rather than establish direct causal quercetin–lncRNA interactions.</p><p>We found several genes involved in different metabolic pathways in relation to breast cancer and quercetin treatment.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942412","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 Transcriptomic Identification of Drug-Tolerant Persister Mechanisms in Capecitabine-Treated Triple-Negative Breast Cancer","authors":"Md Naiyem Islam,&nbsp;Md Kaziul Islam Sarkar Suja,&nbsp;Alif Al Takbir,&nbsp;Md. Kaderi Kibria","doi":"10.1002/cso2.70025","DOIUrl":"https://doi.org/10.1002/cso2.70025","url":null,"abstract":"<p>Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. This lack of receptors limits targeted therapies and contributes to high rates of recurrence. A subset of tumor cells survives even intensive chemotherapy, and growing evidence suggests that drug-tolerant persister cells (DTPs) play a central role in this survival, evading treatment and later driving relapse. However, the molecular programs that support their survival across different treatment conditions remain poorly understood. To address this gap, this study investigates the transcriptional landscape of TNBC at single-cell resolution to characterize transcriptional programs associated with capecitabine-induced drug tolerance relative to the chemonaive state. Single-cell RNA sequencing (scRNA-seq) data from capecitabine-treated and chemonaive patient-derived xenograft (PDX) models were processed using a Seurat-based pipeline, including quality control, SCTransform normalization, batch correction, and data integration. Cell clustering and annotation were performed using SingleR with the Human Primary Cell Atlas reference. Differential expression analysis using the MAST model compared epithelial cells across conditions, followed by protein–protein interaction (PPI) network construction and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. After quality filtering, 42,015 high-quality cells were retained from 57,943 cells. These cells formed 16 clusters representing 36 distinct cell types, with platelets and neutrophils comprising the largest populations and epithelial cells representing a smaller but biologically critical malignant compartment (4.2%). We identified 481 upregulated genes in treated epithelial cells, including <i>NECTIN4, ACTN4, RHOV</i>, and <i>RPN2</i>. PPI network analysis further identified highly connected hub genes, including <i>RPL18A, RPL19, RPL35, RPL8, RPS11, RPS3</i>, and <i>UBA52</i>, predominantly associated with translational and ribosomal pathways. Given the known tendency of network centrality methods to prioritize housekeeping genes, these findings are presented as candidate associations warranting further mechanistic investigation. Together, these findings define a transcriptional program associated with capecitabine-induced drug tolerance in TNBC. They also highlight candidate molecular targets for overcoming therapy resistance and reducing disease recurrence, pending functional and independent validation.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848617","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 Transcriptomic Identification of Drug-Tolerant Persister Mechanisms in Capecitabine-Treated Triple-Negative Breast Cancer","authors":"Md Naiyem Islam,&nbsp;Md Kaziul Islam Sarkar Suja,&nbsp;Alif Al Takbir,&nbsp;Md. Kaderi Kibria","doi":"10.1002/cso2.70025","DOIUrl":"https://doi.org/10.1002/cso2.70025","url":null,"abstract":"<p>Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. This lack of receptors limits targeted therapies and contributes to high rates of recurrence. A subset of tumor cells survives even intensive chemotherapy, and growing evidence suggests that drug-tolerant persister cells (DTPs) play a central role in this survival, evading treatment and later driving relapse. However, the molecular programs that support their survival across different treatment conditions remain poorly understood. To address this gap, this study investigates the transcriptional landscape of TNBC at single-cell resolution to characterize transcriptional programs associated with capecitabine-induced drug tolerance relative to the chemonaive state. Single-cell RNA sequencing (scRNA-seq) data from capecitabine-treated and chemonaive patient-derived xenograft (PDX) models were processed using a Seurat-based pipeline, including quality control, SCTransform normalization, batch correction, and data integration. Cell clustering and annotation were performed using SingleR with the Human Primary Cell Atlas reference. Differential expression analysis using the MAST model compared epithelial cells across conditions, followed by protein–protein interaction (PPI) network construction and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. After quality filtering, 42,015 high-quality cells were retained from 57,943 cells. These cells formed 16 clusters representing 36 distinct cell types, with platelets and neutrophils comprising the largest populations and epithelial cells representing a smaller but biologically critical malignant compartment (4.2%). We identified 481 upregulated genes in treated epithelial cells, including <i>NECTIN4, ACTN4, RHOV</i>, and <i>RPN2</i>. PPI network analysis further identified highly connected hub genes, including <i>RPL18A, RPL19, RPL35, RPL8, RPS11, RPS3</i>, and <i>UBA52</i>, predominantly associated with translational and ribosomal pathways. Given the known tendency of network centrality methods to prioritize housekeeping genes, these findings are presented as candidate associations warranting further mechanistic investigation. Together, these findings define a transcriptional program associated with capecitabine-induced drug tolerance in TNBC. They also highlight candidate molecular targets for overcoming therapy resistance and reducing disease recurrence, pending functional and independent validation.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848616","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":"From Association to Mechanism: Regulatory Annotation and Pathway Mapping of Genes Surrounding Breast Cancer Risk Variants","authors":"Sultana Jannat,&nbsp;Trina Mitra,&nbsp;Nafisa Nawar Fariha,&nbsp;Tasmia Tabassum,&nbsp;Kulsuma Bahar Bethi,&nbsp;Synchita Majumder Kaya,&nbsp;Md. Arif Hossen,&nbsp;Md. Saad Hossain,&nbsp;Jobaier Ibne Deen,&nbsp;Mohammed Emon,&nbsp;Laila Khaleda,&nbsp;Md. Hridoy Ahmed","doi":"10.1002/cso2.70024","DOIUrl":"https://doi.org/10.1002/cso2.70024","url":null,"abstract":"<p>Inherited factors account for a large share of breast cancer susceptibility, yet the biological consequences of most risk variants are still poorly understood. To address this gap, we studied 175 breast cancer risk variants confirmed by genome-wide association studies and gathered the genes that lie near them. Two complementary gene sets were assembled. The first collected every gene immediately flanking each risk variant, regardless of distance. The second kept only the closest gene together with genes tagged by variants in strong linkage disequilibrium within a 100 kb interval. Across both sets, the clearest biological signal pointed to androgen receptor-mediated signaling. Pathway mapping additionally implicated the Wnt/β-catenin and Hedgehog cascades, and this pattern was most evident in the flanking-gene set. With HaploReg, we annotated correlated variants at each locus and recovered features that mark active regulation. Enrichment testing returned a 4.8-fold excess of DNase I hypersensitivity in a breast cancer cell line and a 15.7-fold excess of enhancer motifs in a stem cell line. Together, these results indicate that breast cancer risk markers and their correlated variants influence the activity of neighboring genes and converge on a small number of signaling pathways central to mammary biology.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753550","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":"From Association to Mechanism: Regulatory Annotation and Pathway Mapping of Genes Surrounding Breast Cancer Risk Variants","authors":"Sultana Jannat,&nbsp;Trina Mitra,&nbsp;Nafisa Nawar Fariha,&nbsp;Tasmia Tabassum,&nbsp;Kulsuma Bahar Bethi,&nbsp;Synchita Majumder Kaya,&nbsp;Md. Arif Hossen,&nbsp;Md. Saad Hossain,&nbsp;Jobaier Ibne Deen,&nbsp;Mohammed Emon,&nbsp;Laila Khaleda,&nbsp;Md. Hridoy Ahmed","doi":"10.1002/cso2.70024","DOIUrl":"https://doi.org/10.1002/cso2.70024","url":null,"abstract":"<p>Inherited factors account for a large share of breast cancer susceptibility, yet the biological consequences of most risk variants are still poorly understood. To address this gap, we studied 175 breast cancer risk variants confirmed by genome-wide association studies and gathered the genes that lie near them. Two complementary gene sets were assembled. The first collected every gene immediately flanking each risk variant, regardless of distance. The second kept only the closest gene together with genes tagged by variants in strong linkage disequilibrium within a 100 kb interval. Across both sets, the clearest biological signal pointed to androgen receptor-mediated signaling. Pathway mapping additionally implicated the Wnt/β-catenin and Hedgehog cascades, and this pattern was most evident in the flanking-gene set. With HaploReg, we annotated correlated variants at each locus and recovered features that mark active regulation. Enrichment testing returned a 4.8-fold excess of DNase I hypersensitivity in a breast cancer cell line and a 15.7-fold excess of enhancer motifs in a stem cell line. Together, these results indicate that breast cancer risk markers and their correlated variants influence the activity of neighboring genes and converge on a small number of signaling pathways central to mammary biology.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753551","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":"Cross-Cancer Profiling of CDH1 Reveals Context-Dependent EMT Decoupling, Immune Heterogeneity, and Prognostic Variability in Epithelial Cancers","authors":"Md. Abdur Rahman,&nbsp;Sm Faysal Bellah,&nbsp;Md. Mustafizur Rahman","doi":"10.1002/cso2.70023","DOIUrl":"https://doi.org/10.1002/cso2.70023","url":null,"abstract":"<p>Cadherin 1 (<i>CDH1</i>) encodes E-cadherin, a key epithelial adhesion molecule traditionally associated with tumor suppression and epithelial–mesenchymal transition (EMT). However, its roles across cancers remain incompletely understood, particularly within multilayer regulatory contexts involving genomic, epigenetic, transcriptional, and immune mechanisms. <i>CDH1</i> expression, survival associations, EMT-correlated gene profiles (<i>VIM</i>, <i>SNAI1</i>, and <i>ZEB1</i>), immune infiltration patterns, immune checkpoint correlations (<i>PDCD1</i>, <i>CD274</i>, and <i>CTLA4</i>), promoter methylation, and genomic alterations were assessed across five epithelial cancers, breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), and stomach adenocarcinoma (STAD). Cross-platform analysis was performed using The Cancer Genome Atlas (TCGA)/Genomic Data Commons (GDC) datasets, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), UALCAN, TIMER, KM Plotter, cBioPortal, and g:Profiler. <i>CDH1</i> was overexpressed but showed variable prognostic significance; TCGA/GDC analyses were nonsignificant, whereas KM Plotter showed better survival in COAD, LUAD, and STAD, worse survival in BRCA, and no association in OV. Classic inverse relationships between <i>CDH1</i> and <i>VIM</i> or <i>ZEB1</i> were evident only in STAD, and <i>SNAI1</i> showed no consistent association. Immune infiltration patterns were tumor-specific, ranging from cytotoxic T-cell dominance in LUAD to macrophage-rich profiles in OV; immune checkpoint correlations were similarly context-dependent. Cancer-specific enrichment revealed distinct trafficking- and adhesion-related profiles. Promoter methylation patterns varied by cancer, whereas genomic alterations of <i>CDH1</i> were rare. <i>CDH1</i> does not function as a universal epithelial or EMT marker across epithelial cancers. Instead, its associations with EMT, immune contexture, methylation, and prognosis are context-dependent, supporting a model of <i>CDH1</i> as a heterogeneous regulator of epithelial plasticity. These findings challenge single-function interpretations and support cancer-specific <i>CDH1</i> evaluation in translational research.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752777","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}