Search Results (797)

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Replication timing (RT), the temporal order of DNA replication during S phase, influences regional mutation rates, yet the mechanistic basis for RT-associated mutagenesis remains incompletely defined. To identify drivers of RT-dependent mutation biases, we analyzed whole-genome sequencing data from cells with disruptions in DNA replication/repair genes or exposed to mutagenic compounds. Mutation distributions between early- and late-replicating regions were compared using bootstrapping and statistical modeling. We identified 14 genes that exhibit differential effects in early- or late-replicating regions, encompassing multiple DNA repair pathways, including mismatch repair (MLH1, MSH2, MSH6, PMS1, and PMS2), trans-lesion DNA synthesis (REV1) and double-strand break repair (DCLRE1A and PRKDC), DNA polymerases (POLB, POLE3, and POLE4), and other genes central to genomic instability (PARP1 and TP53). Similar analyses of mutagenic compounds revealed 19 compounds with differential effects on replication timing. These results establish replication timing as a critical modulator of mutagenesis, with distinct DNA repair pathways and exogenous agents exhibiting replication timing-specific effects on genomic instability. Our systematic bioinformatics approach identifies new DNA repair genes and mutagens that exhibit differential activity during the S phase. These findings pave the way for further investigation of factors that contribute to genome instability during cancer transformation. Full article

Sub-Saharan Africa is experiencing the highest mortality rates for several cancer types. While cancer research globally has entered the genomic era and advanced the deployment of precision oncology, Africa has largely been excluded and has received few benefits from tumour profiling. Through a thorough literature review, we identified only five whole cancer genome databases that include patients from Sub-Saharan Africa, covering four cancer types (breast, esophageal, prostate, and Burkitt lymphoma). Irrespective of cancer type, these studies report higher tumour genome instability, including African-specific cancer drivers and mutational signatures, suggesting unique contributory mechanisms at play. Reviewing bioinformatic tools applied to African databases, we carefully select a workflow suitable for large-scale African resources, which incorporates cohort-level data and a scalable design for time and computational efficiency. Using African genomic data, we demonstrate the scalability achieved by high-level parallelism through physical data or genomic interval chunking strategies. Furthermore, we provide a rationale for improving current workflows for African data, including the adoption of more genomic techniques and the prioritisation of African-derived datasets for diverse applications. Together, these enhancements and genomic scaling strategies serve as practical computational guidance, lowering technical barriers for future large-scale African-inclusive research and ultimately helping to reduce the disparity gap in cancer mortality rates across Sub-Saharan Africa. Full article

The recent identification of early-onset mutational signatures with geographic variations by Diaz-Gay et al. is a significant finding, since early-onset colorectal cancer has emerged as an alarming public health challenge in the past two decades, and the pathomechanism remains unclear. Environmental risk factors, including lifestyle and diet, are highly suspected. The identification of colibactin from Escherichia coli as a potential pathogenic source is a major step forward in addressing this public health challenge. Therefore, the following opinion manuscript aims to outline the likely onset of the pathomechanism and the critical role of acquired Piezo2 channelopathy in early-onset colorectal cancer, which skews proton availability and proton motive force regulation toward E. coli within the microbiota–host symbiotic relationship. In addition, the colibactin produced by the pks island of E. coli induces host DNA damage, which likely interacts at the level of Wnt signaling with Piezo2 channelopathy-induced pathological remodeling. This transcriptional dysregulation eventually leads to tumorigenesis of colorectal cancer. Mechanotransduction converts external physical cues to inner chemical and biological ones. Correspondingly, the proposed quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling, initiated by Piezo2, seems to be the principal and essential underlying novel oscillatory signaling that could be lost in colorectal cancer onset. Hence, Piezo2 channelopathy not only contributes to cancer initiation and impaired circadian regulation, including the proposed hippocampal ultradian clock, but also to proliferation and metastasis. Full article

Sporadic colorectal cancer (CRC), the third leading cause of cancer-related death globally, arises through a continuum from normal tissue to adenomas, progressing from low-grade (LGD) to high-grade dysplasia (HGD); yet, the early epigenetic drivers of this transition remain unclear. To investigate these events, we profiled LGD and HGD adenomas using EM-seq, and identified a consensus differential methylation signature (DMS) of 626 regions through two independent bioinformatics pipelines. This signature effectively distinguished LGD from HGD in both tissue and plasma-derived cell-free DNA (cfDNA), highlighting specific methylation patterns. Functional annotation indicated enrichment for regulatory elements associated with transcription factor activity and cell signaling. Applying the DMS to the TCGA CRC dataset revealed three tumor subtypes with increasing hypermethylation and one normal cluster. The most hypermethylated subtype exhibited poor survival, high mutation burden, and disrupted transcriptional networks. While overlapping with classical CpG Island Methylator Phenotype (CIMP) categories, the DMS captured a broader spectrum of methylation alterations. These findings suggest that the DMS captures functionally relevant, antecedent epigenetic alterations in CRC progression, enabling the robust stratification of dysplasia severity and tumor subtypes. This signature holds promise for enhancing preclinical detection and molecular classification, and warrants further evaluation in larger prospective cohorts. Full article

Background: This study aimed to develop a nomogram based on the most relevant clinical, CT, and radiomic features comprising 11 key signatures (2 clinical, 2 CT-based, and 7 radiomic) for the non-invasive prediction of the EGFR mutation status and to support the timely initiation of tyrosine kinase inhibitor (TKI) therapy in patients with non-small cell lung cancer (NSCLC) adenocarcinoma. Methods: Retrospective real-world data were collected from 521 patients with histologically confirmed NSCLC adenocarcinoma who underwent CT imaging and either surgical resection or pathological biopsy for EGFR mutation testing. Five Random Forest classification models were developed and trained on various datasets constructed by combining clinical, CT, and radiomic features extracted from CT image regions of interest (ROIs), with and without feature preselection. Results: The model trained exclusively on the most relevant clinical, CT, and radiomic features demonstrated superior predictive performance compared to the other models, with strong discrimination between EGFR-mutant and wild-type cases (AUC = 0.88; macro-average = 0.90; micro-average = 0.89; precision = 0.90; recall = 0.94; F1-score = 0.91; and accuracy = 0.87). Conclusions: A nomogram constructed using a Random Forest model trained solely on the most informative clinical, CT, and radiomic features outperformed alternative approaches in the non-invasive prediction of the EGFR mutation status, offering a promising decision-support tool for precision treatment planning in NSCLC. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

Basal cell carcinoma (BCC), the most common skin cancer, is primarily driven by Hedgehog (Hh) and TP53 pathway alterations. Although additional pathways were implicated, the mutational landscape in Asian populations, particularly Koreans, remains underexplored. We performed whole-exome sequencing of BCC tumor tissues from Korean patients and analyzed mutations in 11 established BCC driver genes (PTCH1, SMO, GLI1, TP53, CSMD1/2, NOTCH1/2, ITIH2, DPP10, and STEAP4). Mutational profiles were compared with Caucasian cohort profiles to identify ethnicity-specific variants. Ultraviolet (UV)-exposed and non-UV-exposed tumor sites were compared; genes unique to non-UV-exposed tumors were further analyzed with protein–protein interaction analysis. BCCs in Koreans exhibited distinct features, including fewer truncating and more intronic variants compared to Caucasians. Korean-specific mutations in SMO, PTCH1, TP53, and NOTCH2 overlapped with oncogenic gain-of-function/loss-of-function (GOF/LOF) variants annotated in OncoKB, with some occurring at hotspot sites. BCCs in non-exposed areas showed recurrent mutations in CSMD1, PTCH1, and NOTCH1, suggesting a UV-independent mechanism. Novel mutations in TAS1R2 and ADCY10 were exclusive to non-exposed BCCs, with protein–protein interaction analysis linking them to TP53 and NOTCH2. We found unique ethnic-specific and UV-independent mutational profiles of BCCs in Koreans. TAS1R2 and ADCY10 may contribute to tumorigenesis of BCC in non-exposed areas, supporting the need for population-specific precision oncology. Full article

Background/Objectives: The role of tertiary lymphoid structures (TLSs) in cancer prognosis is well established, yet their significance in early-stage EGFR-mutant lung adenocarcinoma remains unclear. While outcomes for early-stage lung cancer are generally better than those of late-stage disease, recurrence remains a significant challenge. This study investigates the prognostic value of TLSs and their molecular characteristics in early-stage EGFR-mutant lung adenocarcinoma. Methods: TLSs were identified in tumor samples using multiplex immunohistochemistry (IHC), and their density was quantified. The PD-L1 tumor proportion score (TPS) and TLS density were analyzed for associations with disease-free survival (DFS). Gene expression profiling was performed to compare tumor microenvironment signatures between high- and low-TLS-density groups. Results: High TLS density correlated with significantly longer DFS (43 vs. 20.5 months, p = 0.0082). No relationship was found between TLS density and PD-L1 TPS or EGFR mutation subtype. Transcriptomic analysis revealed upregulated immune response genes in the high-TLS-density group, including those involved in T and B cell activation. Low-TLS-density tumors exhibited gene signatures promoting tumor growth, such as cell cycle and WNT pathway activation. Conclusions: In summary, TLS density is a potential prognostic biomarker for DFS in early-stage EGFR-mutant lung adenocarcinoma, independent of PD-L1 TPS or EGFR mutation subtype. Enhanced immune activation in high-TLS-density tumors highlights TLSs as a potential target for improving outcomes in these patients. Full article

Background: Epigenomic instability accelerates mutations in tumor suppressor genes and oncogenes, contributing to malignant transformation. Histone modifications, particularly methylation and acetylation, significantly influence tumor biology, with chromo-, bromo-, and Tudor domain-containing proteins mediating these changes. This study investigates how genes encoding these domain-containing proteins affect colorectal cancer (CRC) prognosis. Methods: Using CRC data from the GSE39582 and TCGA datasets, we identified domain-related genes via GeneCards and developed a prognostic signature using LASSO-COX regression. Patients were classified into high- and low-risk groups, and comparisons were made across survival, clinical features, immune cell infiltration, immunotherapy responses, and drug sensitivity predictions. Single-cell analysis assessed gene expression in different cell subsets. Results: Four domain-related genes (AKAP1, ORC1, CHAF1A, and UHRF2) were identified as a prognostic signature. Validation confirmed their prognostic value, with significant differences in survival, clinical features, immune patterns, and immunotherapy responses between the high- and low-risk groups. Drug sensitivity analysis revealed top candidates for CRC treatment. Single-cell analysis showed varied expression of these genes across cell subsets. Conclusions: This study presents a novel prognostic signature based on domain-related genes that can predict CRC severity and offer insights into immune dynamics, providing a promising tool for personalized risk assessment in CRC. Full article

We report the first case of pleural mesothelioma (PM) occurring in a child affected by NF2-related schwannomatosis (NF2-SWN) and without any history of environmental exposure to asbestos. Mesothelioma is a rare secondary tumor in brain cancer patients and the association with NF2-SWN has been described only in a few anecdotal cases and never in the pediatric field. NF2-SWN is an autosomal dominant disease caused by inactivating germline mutations of the NF2 tumor suppressor gene, one of the most common mutations associated with human primary mesothelioma too. By MLPA assay, array-CGH analysis, and NGS on blood and tumor DNA, we determined the mutation profile of this rare NF2-driven PM and we identified several atypical chromosomal aberrations in tumor cells, suggesting a different genomic signature between pediatric and adult mesothelioma. Full article

Oral squamous cell carcinoma (OSCC) remains one of the most common malignancies in the head and neck region, often preceded by a spectrum of oral potentially malignant disorders (OPMDs). Despite advances in diagnostic methods, reliable and non-invasive biomarkers for early detection and prognostic stratification are still lacking. In recent years, circulating cell-free DNA (cfDNA) has emerged as a promising liquid biopsy tool in several solid tumors, offering insights into tumor burden, heterogeneity, and molecular dynamics. However, its application in oral oncology remains underexplored. This study aims to review and discuss the current evidence on cfDNA quantification and mutation analysis (including TP53, NOTCH1, and EGFR) in patients with OPMDs and OSCC. Particular attention is given to cfDNA fragmentation patterns, methylation signatures, and tumor-specific mutations as prognostic and predictive biomarkers. Moreover, we highlight the challenges in standardizing pre-analytical and analytical workflows in oral cancer patients and explore the potential role of cfDNA in monitoring oral carcinogenesis. Understanding cfDNA dynamics in the oral cavity might offer a novel, minimally invasive strategy to improve early diagnosis, risk assessment, and treatment decision-making in oral oncology. Full article

Background: Hypoxia and immune components significantly shape the tumor microenvironment and influence prognosis and immunotherapy response in gastric cancer (GC). This study aimed to develop hypoxia- and immune-related gene signatures for prognostic evaluation in GC. Methods: Transcriptomic data from TCGA-STAD were integrated with hypoxia- and immune-related genes from InnateDB and MSigDB. A prognostic gene signature was constructed using Cox regression analyses and validated on an independent GSE84437 cohort and single-cell RNA dataset. We further analyzed immune cell infiltration, molecular characteristics of different risk groups, and their association with immunotherapy response. Single-cell RNA-seq data from the TISCH database were used to explore gene expression patterns across cell types. Results: Five genes (TGFB3, INHA, SERPINE1, GPC3, SRPX) were identified. The risk score effectively stratified patients by prognosis, with the high-risk group showing lower overall survival and lower T-cell expression. The gene signature had an association with immune suppression, ARID1A mutation, EMT features, and poorer response to immunotherapy. Gene signature, especially SRPX was enriched in fibroblasts. Conclusions: We developed a robust hypoxia- and immune-related gene signature that predicts prognosis and may help guide immunotherapy strategies for GC patients. Full article

In this study, we investigated the expression of TIM-3 and Galectin-9 (Gal-9) in colorectal cancer (CRC) and their associations with oncogenic mutations, MSI status, cytokine profiles, and transcriptional data. TIM-3 and Gal-9 protein levels were significantly increased in CRC tissues compared to matched non-tumor margins (p < 0.05 and p < 0.001, respectively). TIM-3 protein concentration was notably higher in PIK3CA-mutated tumors (p < 0.05), while no associations were found with KRAS, NRAS, BRAF, AKT1, or MSI status. Multiplex cytokine profiling revealed strong correlations between TIM-3 and Gal-9 levels and key immunomodulatory pathways, including IL-10, IL-17, and chemokine signaling. We also observed significant associations with cytokine subsets involved in protumor activity and immune regulation. Gene set enrichment analysis (GSEA) demonstrated that high TIM-3 and Gal-9 expression was associated with upregulation of cell cycle-related pathways, and downregulation of immune signatures, such as interferon responses and TNF-α/NFκB signaling. These findings suggest that increased TIM-3 and Gal-9 expression reflects a shift toward proliferative activity and immune suppression in the CRC tumor microenvironment, highlighting their potential as biomarkers of immunoevasive tumor phenotypes, especially in PIK3CA-mutant CRC tumors. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article