Search Results (427)

Background: Morphological subclassification may refine prognosis after curative pancreaticoduodenectomy (PD) for periampullary cancers. Methods: We conducted a single-center retrospective cohort including 120 consecutive PDs performed between 2005 and 2022. Tumors were classified as intestinal (INT), pancreatobiliary (PB), or pancreatic ductal adenocarcinoma (PAN). Clinicopathologic variables included T stage, margin status, lymphovascular and perineural invasion, and lymph node ratio (LNR; cutoff 0.154 determined by ROC/Youden). Overall survival (OS) was the primary endpoint and was analyzed using Kaplan–Meier with log-rank tests and multivariable Cox regression. Results: INT tumors were associated with earlier T stage, fewer adverse histologic features, and higher R0 resection rates compared with PB and PAN. In multivariable analysis, mortality risk was higher for PB (HR 4.41; 95% CI 1.25–15.53) and PAN (HR 13.96; 95% CI 3.99–48.75) relative to INT. LNR ≥ 0.154 independently predicted worse OS (HR 1.93; 95% CI 1.11–3.35). Mean OS was 108.8 months for INT, 62.0 months for PB, and 22.7 months for PAN (log-rank p < 0.001). Conclusions: Morphological subtype and LNR are independent prognostic factors after PD for periampullary malignancies. Integrating morphology and nodal burden into risk models may improve postoperative stratification and guide adjuvant therapy. Full article

Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. While early-stage disease has favorable outcomes, advanced or recurrent EC remains associated with poor prognosis. Novel prognostic markers are needed to refine risk stratification. Systemic inflammation-based indices such as Pan-Immune Inflammation Value (PIV), Systemic Inflammation Response Index (SIRI), and Systemic Immune Inflammation Index (SII) have shown prognostic potential in solid tumors. Methods: We retrospectively evaluated 78 patients with endometrioid EC who had undergone hysterectomy with adnexectomy and lymphadenectomy. Demographic, clinicopathological, and laboratory data were extracted from electronic medical records. PIV, SII, and SIRI were calculated from the preoperative complete blood counts. Survival was assessed using Kaplan–Meier analysis, while prognostic factors were determined using univariate and multivariate Cox regression analyses. Results: The median age was 59 years, and 64.1% of the patients presented with early-stage disease. A high PIV (≥802) was significantly associated with a shorter overall survival (64 vs. 111 months, p < 0.001). PIV demonstrated the highest discriminatory accuracy (AUC = 0.776), followed by the SII (0.747) and SIRI (0.718). Univariate analysis identified that age, grade, LVSI, PNI, stage, distant metastasis, and high PIV, SII, SIRI, and NLR were predictors of poor survival. Multivariate analysis confirmed grade, distant metastasis and SIRI ≥ 1.5 as independent prognostic factors. Conclusions: Inflammation-based indices, particularly PIV and SIRI, correlated with survival outcomes in patients with EC. The SIRI retained an independent prognostic value, whereas PIV showed a strong discriminatory capacity. Incorporating these indices into established risk models may improve prognostic precision and support individualized management. Full article

Background/Objectives: The mTOR serine/threonine kinase coordinates protein translation, cell growth, and metabolism, and its dysregulation promotes tumorigenesis. We present a reproducible, pan-cancer, network-aware framework that integrates curated resources with genomics to move beyond pathway curation, yielding falsifiable hypotheses and prioritized candidates for mTOR axis biomarker validation. Materials and Methods: We assembled MTOR-related genes and interactions from GeneCards, KEGG, STRING, UniProt, and PathCards and harmonized identifiers. We formulated a concise working model linking genotype → pathway architecture (mTORC1/2) → expression-level rewiring → phenotype. Three analyses operationalized this model: (i) pan-cancer alteration mapping to separate widely shared drivers from tumor-specific nodes; (ii) expression-based activity scoring to quantify translational/nutrient-sensing modules; and (iii) topology-aware network propagation (personalized PageRank/Random Walk with Restart on a high-confidence STRING graph) to nominate functionally proximal neighbors. Reproducibility was supported by degree-normalized diffusion, predefined statistical thresholds, and sensitivity analyses. Results: Gene ontology analysis demonstrated significant enrichment for mTOR-related processes (TOR/TORC1 signaling and cellular responses to amino acids). Database synthesis corroborated disease associations involving MTOR and its partners (e.g., TSC2, RICTOR, RPTOR, MLST8, AKT1 across selected carcinomas). Across cohorts, our framework distinguishes broadly shared upstream drivers (PTEN, PIK3CA) from lineage-enriched nodes (e.g., RICTOR-linked components) and prioritizes non-mutated, network-proximal candidates that align with mTOR activity signatures. Conclusions: This study delivers a transparent, pan-cancer framework that unifies curated biology, genomics, and network topology to produce testable predictions about the mTOR axis. By distinguishing shared drivers from tumor-specific nodes and elevating non-mutated, topology-inferred candidates, the approach refines biomarker discovery and suggests architecture-aware therapeutic strategies. The analysis is reproducible and extensible, supporting prospective validation of prioritized candidates and the design of correlative studies that align pathway activity with clinical response. Full article

Background: The role of CREBBP and EP300 mutations in hypermutation and immunotherapy response in gastroesophageal adenocarcinomas is poorly defined and needs further investigation. Methods: We conducted an in silico analysis of 12 publicly available studies (n = 1871; cBioPortal), stratifying samples by CREBBP/EP300 status to assess associations with TMB-High, MSI, co-mutation patterns, and mutation localization. Clinical validation was performed in an independent pan-cancer cohort treated with ICIs (n = 1610) and a gastric cancer cohort with WES data (n = 55). Results: Coding mutations in CREBBP and/or EP300 were significantly associated with TMB-high and MSI-high phenotypes (p < 0.001). All studied samples carrying coding mutations in both CREBBP and EP300 exhibited a TMB-high status. PTVs in functional HAT and bromodomain regions were exclusively associated with TMB-high. Incorporating CREBBP and/or EP300 mutation status improved identification of ultra-hypermutated tumors compared with single-gene biomarkers (p < 0.001). Clinically, these mutations predicted improved overall survival in the pan-cancer cohort (median OS 34 vs. 17 months; HR = 0.68, 95% CI 0.52–0.87, p = 0.0026), as well as in bladder (HR = 0.55, p = 0.0337) and gastrointestinal cancer cohorts (HR = 0.31, p = 0.0021) treated with ICIs. In the gastric cancer validation cohort, all tumors with PTVs demonstrated a partial response to anti-PD-1 therapy. Conclusions: We report CREBBP and EP300 coding mutations as novel potential surrogate biomarkers for hypermutation in gastroesophageal adenocarcinomas and demonstrate their association with favorable immunotherapy outcomes, supporting their potential clinical utility for patient stratification. Full article

High-grade serous carcinoma (HGSC) of the ovary is characterized by two major histological patterns: a classic papillary/micropapillary architecture and a solid pseudo-endometrioid transitional (SET) variant. We investigated whether the distinct morphologic subtypes are underpinned by transcriptomic differences in the tumor microenvironment (TME). We profiled 21 HGSC tumors (7 SET, 14 classic) using a 770-gene NanoString PanCancer Progression panel. Differential expression analysis revealed ~20 genes with significantly different expression (>4-fold, adjusted p < 0.01) between SET and classic tumors. Unsupervised clustering partially separated SET and classic tumors, suggesting that global gene expression patterns correlate with histologic subtype. SET tumors exhibited upregulation of cell-cycle and epithelial genes (e.g., PTTG1, TRAIL, HER3) and downregulation of genes involved in epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) organization, and angiogenesis (e.g., TWIST2, FGF2, decorin) relative to classic tumors. Notably, PTTG1 and TRAIL were upregulated ~6–9-fold in SET tumors, whereas TWIST2 was ~7-fold downregulated, consistent with reduced EMT in SET tumors. Pathway analysis indicated that SET tumors appear to have an immune-active, stroma-poor microenvironment, in line with an “immunoreactive” phenotype, whereas classic tumors showed a mesenchymal, stroma-rich profile. These molecular distinctions could have diagnostic utility and may inform therapeutic stratification, with key dysregulated genes (e.g., HER3, TRAIL, FGF2) representing potential prognostic or predictive biomarkers. For example, high HER3 expression in SET tumors might predict sensitivity to ERBB3/PI3K inhibitors, whereas stromal factors (e.g., FGF2) enriched in classic HGSC could be targeted with microenvironment-modulating therapies. These preliminary findings require validation before translation into pathology practice via immunohistochemical (IHC) assays (e.g., for HER3 or TRAIL), potentially enabling improved classification and personalized treatment of HGSC. We report effect sizes as log₂ fold change with 95% confidence intervals and emphasize FDR-adjusted q-values. Given the small sample size and the absence of outcome data (OS/PFS/PFI), results are preliminary and hypothesis-generating. Orthogonal protein-level validation and replication in larger, independent cohorts are required before any translational inference. Full article

We investigated differences in circulating cell-free DNA (cfDNA) methylation between patients with cancer and those presenting with severe, nonspecific symptoms. Plasma cfDNA from 229 patients was analyzed, of whom 37 were diagnosed with a wide spectrum of cancer types within 12 months. Samples underwent enzymatic conversion, library preparation, and enrichment using the NEBNext workflow and Twist pan-cancer methylation panel, followed by sequencing. Methylation analysis was performed with nf-core/methylseq. Differentially methylated regions (DMRs) were identified with DMRichR. Machine learning with cross-validation was used to classify cancer and controls. The classifier was applied to an external validation set of 144 controls previously unseen by the model. Cancer samples showed higher overall CpG methylation than controls (1.82% vs. 1.34%, p < 0.001). A total of 162 DMRs were detected, 95.7% being hypermethylated in cancer. Machine learning identified 20 key DMRs for classification between cancer and controls. The final model achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity), while mean cross-validation performance reached an AUC of 0.73 (57.1% sensitivity, 77.5% specificity). The specificity of the classifier on unseen control samples was 79.2%. Distinct methylation differences and DMR-based classification support cfDNA methylation as a robust biomarker for cancer detection in patients with confounding conditions. Full article

Background/Objectives: Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of AI models depends critically on the quality, standardization, and fairness of the input data. The EU-funded INCISIVE project aimed to create a federated, pan-European repository of imaging and clinical data for cancer cases, with a key objective to develop a robust framework for pre-validating data prior to its use in AI development. Methods: We propose a data validation framework to assess clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness. The framework includes procedures for deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance. Results: The pre-validation process identified key data quality issues, such as missing clinical information, inconsistent formatting, and subgroup imbalances, while also demonstrating the added value of structured data entry and standardized protocols. Conclusions: This structured framework addresses common challenges in curating large-scale, multimodal medical data. By applying this approach, the INCISIVE project ensures data quality, interoperability, and equity, providing a transferable model for future health data repositories supporting AI research in oncology. Full article

Background: The ESR2 gene encodes Estrogen Receptor-β1 (ERβ1), a putative tumor suppressor in hormone-dependent malignancies. Although ERβ biology has been studied extensively at the expression level, the functional impact of nonsynonymous SNPs (nsSNPs) and untranslated-region (UTR) variants in ESR2 remains underexplored. Methods: We retrieved variants from Ensembl and performed an integrative in silico assessment using PredictSNP, I-Mutant, MUpro, HOPE, MutPred2, and CScape for pathogenicity, oncogenicity and structural stability; STRING/KEGG/GO for pathway context; RegulomeDB and polymiRTS for regulatory effects; and cBioPortal for pan-cancer clinical outcomes (breast (BRCA), endometrial (UCEC), and ovarian (OV)). We evaluated effects of nsSNPs on ERβ1 stability, ligand-binding/DNA-binding domains, co-factor recruitment, and post-transcriptional regulation. Results: Across tools, 93 missense nsSNPs were consistently predicted to be deleterious. Notably, several variants were found to destabilize ERβ1, particularly within the ligand-binding domains (LBD) and DNA-binding domains (DBD). Putative oncogenic drivers R198P and D154N showed high CScape scores and very low population frequencies, consistent with pathogenicity. Several substitutions were predicted to impair coactivator binding and disrupt interactions with key transcriptional partners, including JUN, NCOA1, and SP1. At the post-transcriptional level, rs139004885 was predicted to disrupt miRNA binding, while 3′UTR rs4986938 showed strong regulatory potential and comparatively high population frequency; by contrast, most other identified SNPs were rare. Clinically, pan-cancer survival analyses indicated worse overall survival (OS) in BRCA for ESR2-Altered cases (HR ≈ 2.25; q < 0.001), but better OS in UCEC (HR ≈ 0.24; q ≈ 0.014) and OV (HR ≈ 0.29; q < 0.001), highlighting a tumor-type-specific association. Conclusions: This integrative analysis prioritizes high-impact ESR2 variants that likely impair ERβ1 structure and shows context-dependent clinical effects. Despite their generally low frequency (except for rs4986938), prospective validation linking variant class to ERβ expression and survival outcomes is needed to support biomarker development and therapeutic applications. Full article

Phase separation is one of the mechanisms critical for protein function, and its aberrances are associated with cancer development. However, mutations that affect protein phase separation and cancer development have not been systematically identified and analyzed. In this study, we systematically identified the mutations affecting protein liquid–liquid phase separation in multiple cancers. We calculated the phase separation scores alterations for over 1,200,000 mutations across 16 cancer types using the TCGA dataset. We then performed pathway enrichment, kinase, TF enrichment, and survival analysis to identify related biological processes and clinical implications. Nearly 10% of the mutations were defined to affect phase separation in pan-cancer. These mutations occupied a consistent percentage in each cancer type. Extremely influencing mutations accumulate on stomach adenocarcinoma (STAD), uterine corpus endometrial carcinoma (UCEC), and skin cutaneous melanoma (SKCM). Moreover, proteins carrying these mutations are enriched in cancer-related pathways, including TGF-beta signaling pathways and polycomb repressive complex. Phase separation of these proteins would be regulated by kinases, including CDK1, CDK2, and EGFR, and transcription factors, including ZNF407, ZNF318, and MGA proteins, to play functions in cancer. Protein–Protein Interaction Network revealed that these phase separation proteins are highly interconnected. Finally, patients carrying mutations that positively affect the protein phase separation are associated with poor prognosis in skin cutaneous melanoma (SKCM) and lung squamous cell carcinoma (LUSC), which could be partially explained by the pathogenicity of these mutations. The study provided a pan-cancer landscape for depicting the association of phase separation and cancer mutations, which would be a rich data resource for understanding the association of cancer mutations and phase separation. Full article

Background: Urothelial carcinoma of the bladder (UCB) demonstrates considerable heterogeneity, with markedly varying outcomes between non–muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). The pan-immune-inflammation value (PIV), derived from routine hematological parameters, has emerged as a novel biomarker reflecting systemic inflammation and immune dysregulation. This pilot, exploratory analysis evaluated the prognostic relevance of the PIV in UCB and contextualized PIV against other inflammation-based indices. Methods: We retrospectively analyzed 119 patients with histologically confirmed UCB who were treated between 2019 and 2024. PIV was calculated as (neutrophils × platelets × monocytes) ÷ lymphocytes. Additional indices included the NLR, SII, SIRI, and PLR. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan–Meier analysis, and prognostic factors were assessed using Cox regression. Results: Among 119 patients (median age, 72 years; 88% male), 68 were diagnosed with NMIBC and 51 with MIBC. Elevated PIV levels were significantly associated with NMIBC progression to MIBC (p = 0.028) and strongly correlated with NLR, SII, SIRI, and PLR. Patients with high PIV exhibited shorter OS (24 vs. 45 months) and PFS (20 vs. 35 months) than those with low patients (p < 0.001). Although the prognostic value was evident in the univariate analyses, PIV did not retain significance in multivariate models. Conclusion: Elevated PIV levels predict adverse survival outcomes and progression in UCB, underscoring its potential as a cost-effective and accessible biomarker for risk stratification. Prospective validation in larger cohorts is warranted to confirm its role in personalized patient management. Full article

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous cancer with a severe stromal reaction mediated by pancreatic stellate cells (PSCs), leading to increased resistance to chemotherapy and radiotherapy. Following a repurposing concept, this preclinical study investigates the potential of approved drugs, known to be modulators of cellular copper transport, in combination with cisplatin for therapeutic approaches in PDAC. Two major strategies were pursued: (i) inhibiting copper transporters ATP7A and B with tranilast (TR) and omeprazole (OM) to block the cellular copper and, potentially, also cisplatin efflux, and (ii) using the chelator elesclomol (ES) to elevate intracellular copper and cisplatin levels. Human cell lines PanC-1 (PDAC), HPaSteC (PSC), and their co-culture, as well as the hepatocellular carcinoma cell line HepG2 as a reference model, were used. In addition to an analysis of the expression of copper transport proteins, the dynamics of cellular copper uptake and transport were monitored using a [⁶⁴Cu]CuCl₂ radiotracer approach. In vitro, all drugs enhanced cellular copper uptake and/or reduced copper efflux. Moreover, all drugs contributed to the enhanced cellular anticancer activity of cisplatin, with ES being the most effective compound. The results suggest that the targeted modulation of copper transport mechanisms may offer novel adjuvant approaches for the treatment of PDAC. Full article

Background: Effusion-based lymphoma (EBL) is a rare and aggressive large B-cell lymphoma. It presents as a body cavity effusion without a solid mass, lacks HHV-8 association, and typically expresses CD20. Objectives: To better understand the biology of this entity, we performed transcriptomic profiling of eight EBL cases. Methods: We analyzed the cases with the NanoString PanCancer Immune Profiling Panel and compared the results with publicly available datasets representing follicular lymphoma (FL), mantle cell lymphoma (MCL), and large B-cell lymphoma (LBCL) subtypes. Results: Unsupervised clustering and differential expression analysis revealed that EBL cases cluster transcriptionally with the LBCL group. Lymphoma-specific signaling pathway enrichment (SignatureDB) predominantly identified non-germinal center (activated B-cell-type) pathways. In addition, KEGG pathway analyses revealed enrichment in specific inflammatory and immune response pathways that are associated with B-cell lymphoma development in the setting of chronic inflammation, including those linked to Toll-like receptor and NF-κB signaling. Conclusions: These findings support a post-germinal center origin for EBL, which arises in a background of chronic inflammation and persistent antigen stimulation. Full article

Background/Objectives: The JCHAIN (immunoglobulin-linked chain) is a multimeric IgA and IgM-linked chain whose involvement in oncogenesis and immunomodulation is unknown. The goal of this work was to conduct a comprehensive pan-cancer analysis of the JCHAIN to determine its expression profile, prognostic significance, immune infiltration, and function in diverse malignancies. Methods: We performed pan-cancer analysis of gene expression data and protein expression data of JCHAIN using multiple databases, and analysed the prognostic significance of JCHAIN in a variety of cancers using univariate Cox analysis and Kaplan–Meier tools. The relationship between JCHAIN and immune cell infiltration was analysed via the TISIDB and TIMER websites, while single-cell and spatial transcriptomic analyses were performed to analyse the relationship between JCHAIN and the immune microenvironment. Mutations in the JCHAIN and their connection with methylation were then investigated using the cBioPortal and UALCAN websites. Afterwards, the function of JCHAIN was analysed by KEGG as well as GSEA, and the function of JCHAIN in breast cancer cells was verified by in vitro experiments. Results: The expression of the JCHAIN gene shows significant differences in most cancers, and its high expression is associated with a favourable prognosis. In most cancers, JCHAIN gene expression is closely linked to immune-related genes, immune cells, and methylation, as well as to being affected by mutations. In breast cancer, we found that the JCHAIN was negatively correlated with cellular stemness. Enrichment analysis indicated that the JCHAIN was involved in immune responses, B cell activation, and JAK-STAT signalling pathways. Functional experiments showed that overexpression of the JCHAIN inhibited tumour migration and invasion, which may be closely related to the activation of the IL-2/STAT4 signalling pathway. Conclusions: We found that JCHAIN can be used as a diagnostic and prognostic marker for a variety of cancers by pan-cancer analysis and verified that JCHAIN affects breast cancer cell progression through IL-2/STAT4 by in vitro experiments. Full article

Background: HTR1F (5-Hydroxytryptamine Receptor 1F) encodes a G protein-coupled receptor involved in serotonin signaling. Although dysregulated HTR1F expression has been implicated in certain malignancies, its biological functions and clinical significance across cancer types remain largely unexplored. Methods: We performed an integrative pan-cancer analysis of transcriptomic and pharmacogenomic datasets covering 34 cancer types (PAN-CAN cohort, N = 19,131; normal tissues, G = 60,499). Drug sensitivity and molecular docking analyses were conducted using the GSCALite database. The protein–protein interaction (PPI) network of HTR1F was constructed via the STRING database. Additionally, we evaluated the effects of HTR1F overexpression on proliferation and invasion in human lung squamous cell carcinoma (LUSC) cell lines NCI-H520 and NCI-H226. Results: HTR1F expression was significantly upregulated in 17 cancer types and was associated with poor prognosis, with LUSC showing an AUC of 0.912 for 1-year survival prediction. In LUSC, 695 genes were upregulated and 67 downregulated in response to HTR1F overexpression. HTR1F expression correlated with immune-related genes, immune checkpoints, tumor-infiltrating immune cells, tumor mutation burden (TMB), microsatellite instability (MSI), and drug responses. Genomic alterations, including amplification and deletion, were positively associated with HTR1F expression. Drug sensitivity analysis identified compounds such as sotrastaurin (−10.2 kcal/mol), austocystin D (−9.7 kcal/mol), and tivozanib (−9.3 kcal/mol) as potentially effective inhibitors based on predicted binding affinity. Functional enrichment analyses (GO, KEGG) and GSEA revealed that HTR1F is primarily involved in cell cycle regulation, DNA replication, cellular senescence, and immune-related pathways. Functional validation showed that HTR1F overexpression promotes proliferation of LUSC cells via the MAPK signaling pathway. Conclusions: Our integrative analysis highlights HTR1F as a potential biomarker associated with prognosis, immune modulation, and drug sensitivity across multiple cancer types. These findings provide a foundation for future experimental and clinical studies to explore HTR1F-targeted therapies. Full article

Detailed analysis of gene expression by real time-quantitative polymerase chain reaction (RT-qPCR) has become a widespread method. To normalize the expression of target genes, this approach relies on constitutively expressed internal controls known as housekeeping genes (HKGs). Their proper selection is a critically important methodological step, since all the studied gene expression will be recalculated based on HKG expression. This concise review aims to discuss the selection of HKGs for endometrial cancer (EC) studies. We draw attention to the fact that the commonly used gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is unsuitable as a HKG for research on the normal endometrium, EC, as well as many other tissues. In contrast, accumulating evidence suggests that GAPDH is a pan-cancer marker and an EC marker. Work on GAPDH overexpression in EC in relation to overall and relapse-free survival is lacking. Both original research and overviews indicate that at least two HKGs should be used for target gene expression recalculations, a rarely applied technical aspect of final data processing. The insufficiently careful selection in many studies of only one HKG, e.g., GAPDH, can be held responsible for broad discrepancies in published results obtained by this RT-qPCR technique. We provide an account of the discrepancies reported for sex hormone receptors expression in EC. Achieving consensus on the selection and validation of HKGs for research on this cancer is of crucial importance. Ideally, this trusted gene combination should be universal for any EC histotype and grade, irrespective of the final anatomopathological result. Full article