Copy number alteration (CNA) is a major type of cancer genome alteration that drives cancer progression. CNA signature analysis can reveal underlying etiology and provide biomarkers for cancer treatment, and existing CNA signature analyses are all performed using bulk tissue samples. However, CNA usually affects a large proportion of genome, and the CNA profile of bulk sample does not reflect the actual CNA profiles of the individual cancer cells of the sample, especially in tumors with high heterogeneity, such as hepatocellular carcinoma (HCC). Furthermore, the evolutionary trajectory of CNA mutational processes still remains elusive. Here we build a method to comprehensively analyze the CNA signatures of HCC from single-cell and bulk sample perspective, revealing patterns and potential noise signals from the usually performed bulk tissue CNA signature analysis. Single-cell signature analysis delineates the evolutionary trajectory of HCC CNA signatures, and different CNA signatures consistently emerge in different HCC evolution stages. Single-cell CNA signatures show robust performance in patient prognosis and drug sensitivity prediction. This work not only reveals specific considerations in analyzing CNA signature derived from bulk tissue but also depicts CNA evolution process and provides potential biomarkers for the prognosis and treatment of HCC patients.

Head and neck cancers represent a critical global health issue, contributing to substantial morbidity and mortality. Recent research has explored the role of microRNAs (miRNAs) in these cancers by constructing miRNA-associated disease networks using bipartite graphs. Graph attention networks (GATs) have emerged as a powerful tool for predicting disease associations within such biological networks, offering enhanced accuracy in identifying potential miRNA-disease relationships. This study employs GATs to uncover and predict potential miRNA contributors to head and neck cancers. Data on miRNA-disease associations were sourced from the HMDD v4.0 database, a platform based on SQLite and Django. The head and neck neoplasms dataset included miRNA, disease, causality, category, and PubMed ID (PMID). GATs were applied to analyze the network, leveraging their ability to capture the significance and interdependencies of nodes and edges. The model used a learnable weight matrix to compute attention coefficients, normalize them, and aggregate information from neighboring nodes for edge prediction. The GAT model, integrating graph neural networks with attention mechanisms, achieved an accuracy of 83% in predicting miRNA-disease associations for head and neck neoplasms. This study highlights the potential of graph-based deep learning models, particularly GATs, in accurately predicting miRNA-disease associations. A functional enrichment analysis revealed significant involvement of miRNAs in oral cancer pathways, notably highlighting the critical roles of the TGF-beta and PI3K-Akt signaling pathways in tumor progression and cell survival. These findings offer a pathway to better understanding the molecular mechanisms underlying head and neck cancers. Future improvements in dataset size, model evaluation, and interpretability could further enhance prediction accuracy, potentially advancing diagnostic and therapeutic strategies for these cancers.

Immune checkpoint inhibitors (ICI) benefit some cancer patients but de novo resistance remains poorly understood. Analyzing transcriptional data from two clinical trial cohorts, GO30140 and IMbrave150, we find B cell lymphoma 9 (BCL9), a Wnt/β-catenin co-factor, associated with resistance. We develop a BCL9-targeting peptide, hsBCL9Z96, which suppresses tumor growth in combination with anti-PD-L1 ab in preclinical hepatocellular carcinoma (HCC) mouse models. Multi-omics analyses implicate targeting BCL9 inhibits BMP4 secretion and downregulates CD24 on tumor cells, reprogramming macrophages toward a tumor-suppressive phenotype and promoting macrophage phagocytosis. This in turn rejuvenates T cell immunity via enhanced macrophage-mediated antigen presentation. Our data extend our understanding of how tumor-derived Wnt/β-catenin signaling impedes the innate and adaptive immune responses in the tumor microenvironment and provide preliminary evidence that targeting BCL9 is a promising preclinical strategy to mitigate ICI resistance in HCC.

Human hepatocellular carcinomas (HCCs) with cancer stem cell (CSC) features are a subclass of therapeutically challenging cancers. We recently showed that retrodifferentiation of hepatic cancer cells into CSC-like cells leads to metabolic reprogramming and chemoresistance. The molecular mechanisms whereby differentiated cancer cells switch towards a CSC phenotype are poorly understood. By studying metabolic reprogramming associated with HCC cell plasticity, we identified an unsuspected role of peroxisome proliferator-activated receptor (PPAR)γ in hepatic CSC phenotype acquisition. Gene expression and metabolic analyses performed throughout the cell differentiation/retrodifferentiation process of human HepaRG and HBG-BC2 HCC cells show that metabolic reprogramming in hepatic CSCs is associated with a fragmented mitochondrial network, decreased respiration, de novo lipogenesis, and fatty acid oxidation, but increased glycolysis and lipid storage. Mitochondrial genes downregulated in HepaRG-CSCs are also downregulated in the STEM HCC subclass. While PPARα is the main isoform in differentiated hepatic cells, we find high PPARγ expression in hepatic CSCs. Accordingly, nuclear localization of PPARγ is detected in human HCC tumors, and PPARγhigh/PPARαlow expression is associated with the STEM HCC subclass and a poor outcome in human HCC cohorts. PPARγ silencing or/and inhibition of its target gene pyruvate dehydrogenase kinase 4 reactivates cell respiration, increases reactive oxygen species production and sensitizes hepatic CSCs to chemotherapy. Conversely, PPARα activation synergizes with chemotherapy to induce cell death. Targeting PPARγ, a key regulator of metabolic reprogramming and stemness in hepatic CSCs, or modulating the PPARγ/PPARα balance that finely tunes the differentiation/retrodifferentiation process in HCC deserves further investigation for anti-tumor therapy.

Ovarian cancer remains one of the most lethal malignancies affecting women, with its high mortality rate primarily attributed to the aggressive metastatic nature of the disease, leading to late-stage diagnoses. The challenges posed by tumor metastasis and treatment resistance significantly complicate disease management and substantially reduce survival rates. Thus, elucidating the mechanisms underlying ovarian cancer metastasis is crucial for developing targeted therapies and improving patient outcomes. In this study, through single-nucleus RNA sequencing and analysis of clinical samples, we identify PABPC3 as a key regulator of ovarian cancer metastasis and patient survival. Functional experiments reveal that PABPC3 knockdown markedly inhibits ovarian cancer cell proliferation and migration, whereas its overexpression exerts the opposite effects. Furthermore, in vivo models confirm that PABPC3 overexpression significantly enhances metastatic potential. Mechanistically, PABPC3 promotes tumor metastasis by modulating the expression of CLDN1, a critical component of tight junctions. PABPC3 knockdown leads to a significant upregulation of CLDN1, while simultaneous CLDN1 knockdown partially rescues the migration-inhibitory effects induced by PABPC3 depletion. Additionally, clinical analyses demonstrate that high PABPC3 expression correlates with shorter overall survival, even among patients receiving chemotherapy. Notably, increased PABPC3 protein levels in metastatic lesions are associated with reduced progression-free survival. In conclusion, this study underscores the pivotal role of PABPC3 in ovarian cancer metastasis and patient prognosis, highlighting it as a potential therapeutic target for improving clinical outcomes.

Cancer metastasis to sentinel lymph nodes (LNs) is often the first marker of potential disease progression. Although it is recognized that tumor-induced lymphangiogenesis facilitates metastasis into LNs in murine models, tumor-induced alterations in human lymphatic vessels remain obscure. Here we use single-cell RNA sequencing and high-resolution spatial transcriptomics to profile lymphatic endothelial cell (LEC) subsets in paired metastatic and non-metastatic LNs obtained from female patients with treatment-naïve breast cancer. Tumor metastasis decreases immunoregulatory LEC subsets, such as PD-L1+ subcapsular sinus LECs, while inducing an increase in capillary-like CD200+ HEY1+ LECs. Matrix Gla protein (MGP) is the most upregulated gene in metastatic LN LECs, and its expression on LECs is TGF-β and VEGF dependent. Upregulated MGP promotes cancer cell adhesion to LN lymphatics. Thus, breast cancer cell metastasis to LNs remodels LEC subsets in human LNs and escalates MGP expression, potentially facilitating cancer cell dissemination through the lymphatic system.

This study aimed to investigate the relationship between methylation quantitative trait loci (meQTL) and lung adenocarcinoma (LUAD) susceptibility. Candidate SNPs linked to differentially methylated CpG sites in LUAD were identified through meQTL datasets. Genome-wide association study (GWAS) data were analyzed to assess the correlation between selected meQTLs and LUAD risk. The effects of target genes on malignant LUAD phenotypes were examined through both in vitro and in vivo experiments. Additionally, machine learning and radiomics models were employed to evaluate the association of target genes on LUAD progression. The variant A allele of rs939408 was associated with decreased methylation levels of cg09596674 in LRRC2 (β < 0, P < 0.001). While cg09596674 was highly methylated, LRRC2 showed lower expression in LUAD tumor tissues. Consistently, a negative correlation was observed between methylation of cg09596674 and LRRC2 expression (r = -0.32, P < 0.001), indicating that lower methylation of cg09596674 modulated by rs939408 may reduce non-smoking LUAD risk (OR = 0.89, P = 0.019). Increased LRRC2 expression inhibited LUAD cell line malignancy and suppressed tumor growth in mice. Furthermore, lower LRRC2 expression was linked to metastasis (P = 0.02) and higher levels of two poorer survival-related imaging features (P = 0.03). The meQTL rs939408 may modulate DNA methylation of LRRC2, thereby influencing its expression and potentially affecting non-smoking LUAD risk. These findings offer valuable insights into the role of meQTLs in LUAD carcinogenesis.

Radiotherapy is essential in the treatment of colorectal cancer (CRC), but the presence of drug resistance leads to poor prognosis for CRC patients. Identifying targets and mechanisms for regulating radiotherapy resistance has high clinical value. This study identifies CCR4-NOT transcription complex subunit 7 (CNOT7) as a key factor mediating radiotherapy resistance in CRC by stabilizing XRCC6 protein and enhancing non-homologous end joining (NHEJ) mediated DNA damage repair (DDR) pathway. Proteomic analysis of 45 CRC tissues revealed that elevated CNOT7 expression correlates with poorer responses to neoadjuvant radiotherapy and lower disease control rate (DCR). We demonstrated that CNOT7 knockdown enhances radiosensitivity by impairing NHEJ mediated double-strand breaks (DSBs) repair and promoting apoptosis in vitro and in vivo. Mechanistically, CNOT7 interacts with XRCC6 to stabilize its protein levels by inhibiting TRIM21-mediated K48-linked ubiquitination at lysine 526, thereby facilitating efficient DNA repair. CNOT7 accelerates degradation of TRIM21 mRNA through its deadenylase activity. Additionally, the combination of STL127705, an inhibitor of the XRCC6/XRCC5 heterodimer, with radiotherapy notably suppressed tumor growth in patient-derived xenograft (PDX) and cell line mouse transplant tumor models, especially in the context of CNOT7 deficiency. These findings elucidate the function of CNOT7 in promoting DNA repair and radiotherapy resistance in CRC, highlighting that targeting the CNOT7-TRIM21-XRCC6 axis provides a promising therapeutic approach to overcome radiotherapy resistance and improve clinical outcomes for CRC patients.

Immune checkpoint blockade (ICB) therapy, which has revolutionized cancer treatment, has been approved for the treatment of triple-negative breast cancer (TNBC). Unfortunately, most patients with TNBC are either not eligible for treatment or exhibit resistance, resulting in limited overall survival benefits. There is an urgent need to elucidate the mechanisms of resistance and enhance therapeutic efficacy. Here, via CRISPR activation (CRISPRa) screening, we identified family with sequence similarity 114 member A1 (FAM114A1) as a key mediator of immune evasion and ICB resistance in TNBC. Mechanistically, FAM114A1 binds p85α to disrupt the p85α/p110α protein complex, thus activating the PI3K/AKT pathway and simultaneously preventing condensate formation of E2F Transcription Factor 4 (E2F4) to promote E2F4-driven Metadherin (MTDH) transcription. Upregulation of these FAM114A1-mediated pathways suppresses tumor antigen presentation and consequently attenuates antitumor immunity in TNBC. Moreover, targeting FAM114A1 improves the therapeutic effectiveness of anti-PD-1 therapy in mouse models, and a FAM114A1-based signature shows strong predictive performance for identifying patients with TNBC who may benefit from ICB. Collectively, our findings not only reveal that FAM114A1 is an immune evasion driver but also highlight it as a promising biomarker and therapeutic target. Our study provides new insights into TNBC immune evasion and outlines a potential avenue to improve the effectiveness of ICB.

To assess the role of stanniocalcin 2 protein in abdominal invasion and metastasis of gastric cancer, and its molecular mechanism.

To assess the causality between other diseases of digestive system and breast cancer.

Homologous recombination repair (HRR) is a cellular pathway for high-fidelity double strand DNA break repair that uses the sister chromatid as a guide to ensure chromosomal integrity and cell viability. Deficiency in the HRR pathway (HRD) can sensitize tumors to poly (ADP-ribose) polymerase inhibitors (PARPi) and platinum-based chemotherapy, offering an avenue to identify patients who may benefit from targeted therapies. HRD signature (HRDsig) is a pan-solid-tumor biomarker on the FoundationOne®CDx (F1CDx®) assay that employs a DNA scar-based approach to calculate a score based on copy number features (e.g., segment size, oscillation patterns, and breakpoints per chromosome arm) and does not rely on HRR gene alterations, enabling detection of genomic and epigenetic mechanisms of HRD. After finalizing the HRDsig algorithm, analytical validation was conducted in a CAP-accredited, CLIA-certified laboratory on 278 solid tumor and normal tissue specimens. HRDsig results were compared with an independent HRD biomarker, defined by the presence of a reversion mutation restoring HRR gene function. In this evaluation, 100 HRD-positive and 126 HRD-negative samples showed a positive percent agreement of 90.00% and a negative percent agreement of 94.44%. The limit of detection (LoD) was estimated at 23.04% tumor purity, with the limit of blank (LoB) confirmed as zero in 60 normal tissue replicates. Reproducibility testing on 11 positive and 11 negative samples across multiple labs, reagent lots, and sequencers yielded agreement in 99.49% of positive and 99.73% of negative replicates. HRDsig status remained consistent in the presence of interfering substances, demonstrating 100% concordance in spiked samples. These validation results underscore the high analytical concordance, low false-positive rate, and overall robustness of HRDsig for reliable assessment of homologous recombination deficiency.

Prostate cancer (PCa) diagnosis is hampered by the limited specificity of current methods, necessitating more reliable biomarkers. To identify causal protein biomarkers and therapeutic targets in humans, we conducted a proteome-wide Mendelian randomization (MR) study. We first performed a meta-analysis of two independent genome-wide association studies, including 94,397 individuals with PCa and 192,372 controls, which identified five possible susceptibility loci (JAZF1, PDILM5, WDPCP, EEFSEC, TNS3) for PCa. Subsequently, MR and colocalization analyses were performed using genetic instruments for 4907 plasma proteins from deCODE Genetics (N=35,559) and 2940 plasma proteins from UK Biobank Pharma Proteomics Project (UKB-PPP) (N=54,219). Among 3722 human proteins analyzed, 193 were associated with PCa risk, with 20 high-risk proteins (including KLK3) validated across both cohorts. Functional annotation implicated immune and inflammatory responses and cell-cell interaction pathways. Druggability analyses nominated several potential drug targets for PCa, such as HSPB1, RRM2B, and PSCA. Our findings reveal novel risk loci and candidate protein biomarkers, providing new etiological insights and potential avenues for PCa early detection and therapy.

Therapeutic resistance remains a defining challenge in oncology, limiting the durability of current therapies and contributing to disease relapse and poor patient outcomes. This review systematically integrates recent progress in understanding the molecular, cellular, and ecological foundations of drug resistance across chemotherapy, targeted therapy, and immunotherapy. We delineate how genetic alterations, epigenetic reprogramming, post-translational modifications, and non-coding RNA networks cooperate with metabolic reprogramming and tumor microenvironment remodeling to sustain resistant phenotypes. The influence of the microbiome is highlighted as an emerging determinant of therapeutic response through immune modulation and metabolic cross-talk. By summarizing key regulatory circuits, We establishe a unified framework linking clonal evolution, metabolic adaptability, and tumor ecological dynamics. We further synthesizes novel therapeutic strategies that convert resistance mechanisms into therapeutic vulnerabilities, including synthetic lethality approaches, metabolic targeting, and disruption of stem cell and stromal niches. Advances in single-cell and spatial omics, liquid biopsy, and artificial intelligence are emphasized as transformative tools for early detection and real-time prediction of resistance evolution. This review also identifies major translational gaps in preclinical modeling and proposes precision oncology frameworks guided by evolutionary principles. By bridging mechanistic understanding with adaptive clinical design, this work provides an integrated roadmap for overcoming therapeutic resistance and achieving sustained, long-term cancer control.

Castration-resistant prostate cancer (CRPC) poses a significant clinical challenge, characterized by limited therapeutic options and unfavorable prognosis, particularly among elderly men. Reactivation of androgen receptor (AR) signaling remains the principal driver of CRPC cell survival and tumor progression even under castrated levels of serum androgen. Lysine methyltransferase 2D (KMT2D) has been established as a key oncogenic driver in prostate cancer, promoting tumor progression via multiple pathways. However, its functional interaction with the AR signaling axis in the context of CRPC remains incompletely understood. In this study, we demonstrate that KMT2D substantially upregulates AR protein levels, thereby reactivating AR signaling under castration conditions. Mechanistically, KMT2D employs its histone methyltransferase function to transcriptionally enhance the expression of G3BP stress granule assembly factor 1 (G3BP1). Upregulated G3BP1 subsequently suppresses the activity of the E3 ubiquitin ligase Speckle Type BTB/POZ protein (SPOP), leading to diminished AR ubiquitination and impaired proteasomal degradation. Furthermore, we explored a novel combination therapy involving the histone methyltransferase inhibitor MI-503 and enzalutamide in AR-positive and AR splice variant-positive cell lines. Our results confirmed the synergistic therapeutic effects of this combination, which can continue to inhibit the AR signaling pathway during the CRPC stage, thereby delaying disease progression. Taken together, our findings elucidate a critical KMT2D/G3BP1/SPOP/AR regulatory axis in prostate cancer progression and propose that targeted inhibition of histone methylation in combination with anti-androgen therapy represents a promising strategy for the management of advanced prostate cancer.

MGMT promoter methylation is of importance in glioma regarding prognosis and management. Non-methylated MGMT glioblastoma patients seem to benefit more from gross total resection. MGMT status is not ultra-rapidly available in the operating room. The presents study is the first aiming to evaluate whether the novel imaging technique intraoperative hyperspectral imaging (HSI) can predict MGMT promoter methylation status in glioma patients using a novel optical scoring system.

Bovine leukemia virus (BLV), a member of the Deltaretrovirus genus, causes enzootic bovine leukosis, leading to clinical outcomes that range from asymptomatic infection to malignant lymphoma. Host genetic factors significantly influence BLV susceptibility, proviral load (PVL), immune response, and disease progression. This mini-review synthesizes evidence on genetic polymorphisms in immune-related genes such as BoLA-DRB3, Tumor necrosis factor (TNF), and immunoglobulin loci, and examines novel findings from genome-wide association studies (GWAS). Beyond classical immune genes, recent GWAS have identified novel loci including SPATA16 (spermatogenesis associated 16), ABT1 (activator of basal transcription 1), IER3 (immediate early response 3), Adaptor Related Protein Complex 4 Subunit Beta 1 (AP4B1), Tripartite Motif Containing 45 (TRIM45), Patatin Like Phospholipase Domain Containing 1 (PNPLA1), and PRRC2A (proline-rich coiled-coil 2 A) that are implicated in transcriptional regulation, stress response, RNA processing, and intracellular transport, all of which may modulate viral replication and persistence. Understanding these genetic determinants provides new insights into host-virus interactions and offers opportunities for selective breeding strategies, biomarker development, and improved BLV control programs.

Hybrid neurofibroma/schwannoma tumors (HNS) represent a still underrecognized, yet clinically and diagnostically significant entity within the spectrum of schwannomatosis (SWN). While classical schwannomas have been well known for decades, HNS have only recently been described as a distinct histological pattern, composed of intermixed features typical of both schwannomas and neurofibromas. Differentiating HNS from pure neurofibroma (Nf) is critical, as misclassification may lead to an incorrect diagnosis of neurofibromatosis type 1 rather than SWN. The distinction of hybrid tumors (more precisely HNS) is especially important in SWN forms outside the neurofibromatosis type 2 (NF2) spectrum (NF2-SWN), where major diagnostic criteria are less well defined, making histological differentiation even more significant. At the molecular level, HNS frequently show alterations in the genes NF2, LZTR1, and SMARCB1, often accompanied by characteristic losses of chromosome 22q. In addition, recurrent somatic mutations have been identified in genes such as ERBB2, RET, KMT2A, and CTNNA3. Methylation profiling classifies HNS within the schwannoma spectrum, supporting the hypothesis that they may be a morphological variant rather than a distinct entity, although this has not yet been conclusively confirmed. Histologically, HNS are characterized by a combination of mostly schwannoma-associated Antoni A patterns, collagen-rich neurofibroma-like areas, lymphocytic infiltrates, and, in some cases, plexiform growth. Given the diagnostic challenges, artificial intelligence-based image analysis, such as whole-slide imaging and radiomics, may offer valuable tools for more accurate identification of these tumors in the future. Initial studies in related fields have shown that such approaches can even surpass human-level accuracy. Nevertheless, an accurate histological and, if necessary, molecular evaluation remains essential-particularly for the correct classification as SWN and for ensuring appropriate genetic counseling to affected individuals.

To identify highly invasive subpopulations of tumor cells that may be responsible for early uveal melanoma (UM) metastasis at the single-cell level and to analyze their interactions with immune cells.

Food contamination poses a significant global health threat with carcinogenic potential, though the molecular pathways connecting contaminants to cancer remain poorly understood. This study sought to identify key molecular targets mediating the carcinogenic effects of nine prevalent dietary contaminants: glyphosate, perfluorooctane sulfonate, nitrosamines, pentabromodiphenyl ethers, methylmercury, dioxins, acrylamide, pyrrolizidine alkaloids, and aflatoxin. Using multiple online databases, we identified target genes associated with these contaminants and pan-cancer, then conducted protein-protein interaction (PPI) analysis and visualization on intersecting genes. Subsequent gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) functional enrichment analyses were performed to uncover potential mechanisms, with a focus on breast (BRCA), prostate (PRAD), and colon (COAD) carcinomas due to their significant pathway associations. Hub genes were prioritized through an integrative strategy combining topological algorithms in cytoscape (Centiscape, MCODE, and cytohubba's MCC), machine learning validation, and weighted gene co-expression network analysis (WGCNA). Molecular docking simulations were conducted to examine interactions between contaminants and hub genes. The study identified 69 pan-cancer-intersected targets, with enrichment analyses revealing significant cancer-associated pathways. Hub gene prioritization pinpointed JUN in BRCA, CDC42 in COAD, and MAPK14 in PRAD as critical regulatory targets. Validation using The Cancer Genome Atlas (TCGA) data confirmed statistically significant differential expression patterns (p < 0.05) for these targets across respective malignancies. Gene set enrichment analysis (GSEA) outlined pathway activation profiles consistent with tumor progression mechanisms. Molecular docking simulations demonstrated strong binding affinities (binding energy ≤ -5.0 kcal/mol) between contaminants and structural domains of the identified hub targets, suggesting potential mechanistic links between these food contaminants and cancer development.