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PubMed2025

Comprehensive pan-cancer analysis of HSPG2 as a marker for prognosis.

BACKGROUND: In recent years, several studies have shown that HSPG2 is associated with the prognosis of specific cancers. The aim of this study was to investigate the prognostic value of HSPG2 in pan-cancer and to analyze its possible mechanisms. METHODS: We used The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to explore the expression of HSPG2 in 33 tumors and corresponding controls. Univariate Cox regression and Kaplan-Meier survival analysis were applied to detect the effects of HSPG2 on overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in patients with these tumors, and to analyze the relationship between HSPG2 and clinical characteristics. And we further analyzed the relationship between HSPG2 and immune infiltration, DNA methylation and single cell function. And GO and KEGG enrichment analyses were performed using HSPG2 co-expressed genes. Finally, we explored the diagnostic efficacy of HSPG2 for diseases of interest and validated it using qPCR experiment. RESULTS: HSPG2 was lowly expressed in 17 cancers and highly expressed in 11 cancers, and was correlated with patient's clinical characteristics in many cancers. Multivariate regression analysis showed that HSPG2 was an independent prognostic factor for DSS, OS, and PFI in bladder urothelial carcinoma (BLCA) and Mesothelioma (MESO). HSPG2 was correlated with DNA methylation, single-cell function, and immune infiltration in a variety of cancers. HSPG2 exhibited a good diagnostic efficacy for BLCA and MESO. qPCR and western blot results showed that HSPG2 expression was increased in mesothelioma compared to normal controls. CONCLUSION: These findings suggest that HSPG2 could be considered as a potential diagnostic and prognostic marker for BLCA and MESO.

Humans↗

PubMed2025

CCT8 drives colorectal cancer progression via the RPL4-MDM2-p53 axis and immune modulation.

PURPOSE: Colorectal cancer (CRC) ranks high in global mortality, emphasizing the need for effective interventions. The aim of the research is to elucidate the oncogenic role of CCT8 in CRC and its interaction with RPL4 in the RPL4-MDM2-p53 axis. METHODS: TIMER 2.0, TCGA, and GTEx databases were used to analyze CCT8 expression patterns in CRC. Immunohistochemistry was performed to examine CCT8 distribution in CRC tissues and adjacent non-tumor tissues. Functional assays, including CCK-8, transwell, wound-healing, and flow cytometry, were conducted using DLD-1 and HCT116 cell lines to assess the effects of CCT8 on cell proliferation, migration, invasion, and apoptosis. Gene set enrichment analysis, protein-protein interaction network analysis, and co-immunoprecipitation were performed to explore the interaction between CCT8 and RPL4 and their role in the RPL4-MDM2-p53 pathway. Additionally, gene set variation analysis was applied to investigate the relationship between CCT8/RPL4 expression and immune infiltration patterns in CRC. RESULTS: CCT8 was significantly upregulated in CRC and associated with tumor progression. Mechanistically, CCT8 potentially synergizes with RPL4 concluded from their positive correlation and similar immune infiltration patterns, influencing the RPL4-MDM2-p53 axis and contributing to p53 ubiquitination and degradation. CONCLUSION: These findings underscore the oncogenic significance of CCT8 in CRC and shed light on its molecular mechanisms, paving the way for potential therapeutic applications.

Humans↗

PubMed2026

LDHC4 promotes ovarian cancer progression through H4K12 lactylation to regulate PGK1 expression and modulate glycolysis.

OBJECTIVE: Ovarian cancer (OC) pathogenesis involves metabolic and epigenetic alterations, yet the underlying mechanisms remain unclear. Here, we sought to investigate the role and regulatory mechanism of lactate dehydrogenase C4 (LDHC4) in OC progression. METHODS: Multi-omics approaches were employed, including analyses of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts, tissue microarrays, molecular biology experiments, and in vivo mouse models. LDHC4 expression was modulated in OC cell lines (A2780 and ES-2) to assess its effects on proliferation, migration, invasion, and metastasis. Lactylproteomic profiling, cleavage under targets and tagmentation (CUT&Tag), and chromatin analyses were conducted to explore epigenetic mechanisms. The functional role of the downstream glycolytic enzyme phosphoglycerate kinase 1 (PGK1) was examined using pharmacological inhibition. Molecular docking and xenograft models were used to evaluate the therapeutic potential of targeting H4K12lac (lactylation of histone H4 at lysine 12). RESULTS: LDHC4 was significantly overexpressed in OC tissues and associated with poor overall survival (OS) (hazard ratio [HR] = 4.017, 95% confidence interval [CI]: 2.308–6.989, P < 0.0001). It promoted proliferation, migration, invasion, and metastasis in vitro and in vivo. LDHC4 overexpression increased global lactylation, notably upregulating H4K12lac, which was enriched at the PGK1 promoter. Inhibition of PGK1 with CBR-470-1 (half-maximal inhibitory concentration [IC₅₀] = 14.56 µM) suppressed OC growth and metastasis. Importantly, Elbasvir, identified as a high-affinity H4K12lac inhibitor, significantly reduced tumor burden in mouse xenografts across multiple doses (10–20 mg·kg⁻¹) and downregulated H4K12lac and Ki-67 expression. CONCLUSION: We conclude that LDHC4 promotes OC progression via lactylation-mediated epigenetic upregulation of PGK1. Targeting this pathway through H4K12lac inhibitors such as Elbasvir thus emerges as a viable therapeutic approach for OC.

Female↗

PubMed2026

Increased IL4I1 expression predicts poor survival and modulates the immune microenvironment in acute myeloid leukemia.

BACKGROUND: The immunometabolic enzyme Interleukin-4-induced-1 (IL4I1) is implicated in cancer pathogenesis, yet its specific function and clinical relevance in acute myeloid leukemia (AML) remain unclear. METHODS: Comparative analysis of IL4I1 mRNA levels between AML patients and normal controls was performed using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of IL4I1. Functional insights were derived from analyses of differentially expressed genes (DEGs), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Immune infiltration was evaluated using the ssGSEA, ESTIMATE, quanTIseq and single-cell RNA sequencing (scRNA-seq) analysis. Finally, in vitro and in vivo functional experiments were perfromed to explore the impact of IL4I1 on AML progression and immunoregulation. RESULTS: IL4I1 expression was significantly elevated in AML compared to normal controls (p = 0.0004) and associated with poorer overall survival (p = 0.003). Bioinformatic analysis revealed that IL4I1 was linked to immune-related pathways—including humoral immune response, leukocyte interactions, and chemokine signaling—and to cellular amino acid metabolism. Its expression correlated with immune cell infiltration and checkpoint molecule expression. Experimentally, IL4I1 promoted leukemia cell proliferation in vitro and in vivo (p < 0.05). Furthermore, silencing IL4I1 suppressed M2 macrophage polarization and reduced secretion of inflammatory factors (p < 0.05). CONCLUSIONS: IL4I1 may serve as a potential biomarker for poor prognosis and an attractive target for immune-based therapeutic interventions in AML.

Humans↗

PubMed2026

Tackling non-canonical splicing in arrhythmogenic cardiomyopathy to reduce the uncertain significance variants burden.

BACKGROUND: Splice-altering variants (SAVs), particularly those outside canonical splice sites, are an underappreciated contributor to inherited cardiovascular diseases. In arrhythmogenic cardiomyopathy (ACM), these variants frequently remain classified as of uncertain significance (VUS) due to limited predictive power and lack of transcript-level evidence, constraining genetic yield and clinical management. Our study aimed to determine the functional impact of SAVs in ACM genes and refine their classification using ACMG/AMP and ClinGen SVI criteria. METHODS: SAVs identified in 200 ACM probands underwent SpliceAI prediction, GTEx cardiac exon-usage annotation, and functional assessment using pSPL3-based minigene assays. Aberrant transcripts were quantified using Percent Splicing Alteration (PSA). Segregation data and ACMG/AMP criteria refined by ClinGen SVI were applied to integrate functional and clinical evidence for classification. RESULTS: Aberrant splicing was confirmed in 9/20 variants (45%), including synonymous, missense, and non-canonical intronic changes. SpliceAI scores correlated strongly with PSA values (R²=0.86). Case-control burden testing revealed significant enrichment of splice-altering variants in DSP, DSG2, DSC2 and FLNC. Integrating predictive algorithms with experimental validation and segregation analysis markedly enhances reclassification of 16/20 variants (80%). CONCLUSION: Splicing defects beyond canonical sites significantly shape ACM genetic landscape. Integrating predictive models with experimental validation clarifies uncertain variants bridging the gap between genomic uncertainty and clinical decision-making.

Humans↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2025

Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.

Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer's disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.

Humans↗

PubMed2024

SPOP expression is associated with tumor-infiltrating lymphocytes in pancreatic cancer.

BACKGROUND: Speckle Type POZ Protein (SPOP), despite its tumor type-dependent role in tumorigenesis, primarily as a tumor suppressor gene is associated with a variety of different cancers. However, its function in pancreatic cancer remains uncertain. METHODS: SPOP expression and the association between its expression and patient prognosis and immune function were evaluated using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), The Tumor Immune Estimation Resource 2.0 (TIMER2.0) database, cBioportal, and various bioinformatic databases. Enrichment analysis of SPOP and the association between SPOP expression with clinical stage and grade were analyzed using the R software package. Then immunohistochemistry (IHC) was used to estimate the correlation between SPOP and tumor-infiltrating lymphocytes (TILs) in patients with pancreatic cancer. RESULTS: As part of our study, we assessed that SPOP was anomalously expressed in kinds of cancers, associated with clinical stage and outcomes. Meanwhile, SPOP also played a crucial role in the tumor microenvironment (TME). The expression level of SPOP was significantly correlated to tumor-infiltrating immune cells (TICs) in pancreatic cancer. CONCLUSIONS: Our study uncovered the potential corrections in SPOP with TICs, suggesting that SPOP may act as a biomarker for immunotherapy in pancreatic cancer.

Humans↗

PubMed2026

Integrated single-cell transcriptomics, Mendelian randomization, and machine learning identify CEBPZ as an immune-related biomarker in oral lichen planus.

BACKGROUND: Oral lichen planus (OLP) is a chronic, immune-mediated oral mucosal disease with complex pathophysiology and potential for malignant transformation. Understanding its molecular basis is critical for the development of precise diagnostic and therapeutic strategies. OBJECTIVES: We aimed to identify key immune-related biomarkers and characterize cellular dynamics in OLP, with a particular focus on the role of CEBPZ in disease pathogenesis. MATERIAL AND METHODS: We analyzed single-cell RNA sequencing (scRNA-seq) data from OLP lamina propria samples (GSE211630) to identify disease-specific T-cell subpopulations using high-dimensional weighted gene co-expression network analysis (hdWGCNA) for oxidative stress-related gene modules.-data-based Mendelian randomization (SMR) integrated FinnGen genome-wide association study (GWAS; 342,499 Europeans) data with Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) data to identify causal genes. Machine learning (ML) models (least absolute shrinkage and selection operator (LASSO) and convolutional neural network (CNN)) were developed using bulk RNA-seq datasets (GSE52130 and GSE38616) for diagnostic purposes. RESULTS: We identified OLP-specific T-cell populations (clusters 0, 3, 5, 7, 13, and 15) with enhanced migration inhibition factor (MIF) pathway signaling toward B cells and monocytes. Two oxidative stress-associated modules contained hub genes, including CEBPZ. Summary-data-based Mendelian randomization analysis identified 231 OLP-associated genes, with CEBPZ uniquely intersecting LASSO-selected markers (odds ratio (OR) = 1.057, 95% confidence interval (95% CI) = 1.013-1.102, p = 0.010). Machine learning models achieved area under the curve (AUC) values ranging from 0.653 to 0.745, with the CNN model reaching a validation accuracy of 0.735. CEBPZ showed elevated expression in OLP T cells and correlated with enhanced MIF-(CD74+CXCR4) signaling. CONCLUSIONS: This integrative approach identifies CEBPZ as a pivotal biomarker linking genetic susceptibility, oxidative stress, and immune dysregulation in OLP. Our diagnostic models offer promising tools for OLP management.

CEBPZ↗

PubMed2026

Shared genetic architecture of smoking dependence and Crohn's disease: A cross-trait analysis of GWAS summary statistics.

INTRODUCTION: Smoking dependence (SD) and Crohn's disease (CD) are epidemiologically associated, but whether this relationship reflects shared genetic susceptibility remains unclear. METHODS: We conducted a cross-trait genetic analysis of SD and CD using publicly available genome-wide association study (GWAS) summary statistics from European-ancestry populations. Genome-wide genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Pleiotropic variants were identified using PLACO and mapped to genomic loci using FUMA. Regional signal sharing was assessed by Bayesian colocalization. Functional analyses included stratified LDSC, Multi-marker Analysis of GenoMic Annotation (MAGMA), GTEx tissue analysis, and Metascape. Expression-linked candidate genes were prioritized using expression quantitative trait locus (eQTL)-based summary-data-based Mendelian randomization (SMR) with heterogeneity in dependent instruments (HEIDI) testing. Genetically informed spatial mapping of cells for complex traits (gsMap) was used for spatial mapping. RESULTS: SD and CD showed positive genetic correlation by LDSC (rg=0.2090, p=0.0008) and HDL (rg=0.3817, p=0.00106). PLACO identified 81 genome-wide significant pleiotropic SNPs, which were mapped by FUMA to three loci at 1p31.3, 5p13.1, and 12q12, represented by rs11209031, rs1395152, and rs17467116, respectively. MAGMA identified 22 FDR-significant genes, four of which remained Bonferroni significant: LRRK2, TNFRSF6B, ZGPAT, and RP4-583P15.15. Cross-trait tissue analysis showed significant enrichment of the shared genetic signal in whole blood and small intestine, while gene-set analysis highlighted inflammatory response (pbon=1.86×10-5) and T-helper 17 cell differentiation (pbon=7.37×10-4). SMR/HEIDI analysis further prioritized RPS6KB1 as a shared expression-linked candidate. Spatial mapping revealed a prominent signal in the embryonic gastrointestinal tract and gene-specific regional patterns involving LRRK2 and SLC2A13 in the adult mouse brain. CONCLUSIONS: SD and CD showed measurable shared genetic susceptibility, with convergent evidence from pleiotropic loci, immune-inflammatory pathway enrichment, tissue-level associations, and spatial transcriptomic mapping.

Crohn's disease↗

PubMed2026

A cuproptosis-related lncRNAs-based risk signature for predicting prognosis and immune status in glioma.

BACKGROUND: Glioma is one of the most prevalent primary malignant brain tumors, characterized by poor prognosis and limited treatment options. Recent studies have identified cuproptosis, a novel copper-dependent form of regulated cell death, as a critical mechanism involved in tumor progression. However, the role of cuproptosis-related long non-coding RNAs (lncRNAs) in glioma remains not fully clarified. This study aimed to develop and validate a prognostic model based on cuproptosis-associated lncRNAs to predict patient outcomes and guide individualizing therapeutic strategies. METHODS: Transcriptomic profiles and clinical data were obtained from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Cuproptosis -related prognostic lncRNAs were filtered via univariate and multivariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analyses, which were selected to establish a prognostic model for glioma. Samples were divided into high- and low-risk groups, and the predictive performance of the prognostic model was evaluated based on receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival curves, and a nomogram. In addition, immune cell infiltration, tumor mutational burden (TMB), immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE) and drug sensitivity were analyzed. Expression levels of selected lncRNAs and proteins were validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting. RESULTS: An 11-lncRNA signature associated with cuproptosis was established, and the risk score derived from this model was identified as an independent prognostic factor for glioma. The model exhibited excellent predictive ability, with area under the curve (AUC) values of 0.880, 0.913, and 0.866 for 1-, 3-, and 5-year survival, respectively. Higher TMB, immune checkpoint expression, and IPS were observed in the high-risk group and no significant difference was observed in TIDE between risk groups. Drug sensitivity analysis identified TPCA-1, KIN001-135, and ispinesib mesylate as potential therapeutic agents. Expression validation in glioma cells further supported the biological relevance of the selected lncRNAs. CONCLUSIONS: This cuproptosis-related lncRNA-based signature demonstrates strong prognostic value and may serve as a promising tool for glioma risk stratification and personalized treatment selection.

Glioma↗

PubMed2025

AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.

In recent years, numerous genome AI models have been developed to elucidate the relationship between DNA sequence and gene expression. However, these models have faced criticism for their limited accuracy in predicting individual-specific gene expression. AlphaGenome, the current state-of-the-art in genome AI, achieves exceptional performance across a range of sequence-based predictive tasks, but its utility for personal expression prediction has not yet been assessed. In this study, we evaluate AlphaGenome's ability to predict personal gene expression and find that it significantly outperforms its predecessor. Using GTEx data, AlphaGenome improves the prediction of expression direction over Enformer, achieving an odds ratio of 3.0. In some cases, it even reverses previously observed negative correlations into positive ones. Moreover, AlphaGenome demonstrates improved performance for genes with known nonlinear sequence-expression relationships, though it uncovers mechanisms distinct from those identified by tree-based models.

deep learning↗

PubMed2026

NPLOC4 Constructs Tumor Immunosuppressive Microenvironment in Pan-cancer and Hepatocellular Carcinoma.

INTRODUCTION: NPLOC4 (nuclear protein localization 4 homolog) is mainly involved in DNA damage, cell cycle, and ubiquitination promotion. Nonetheless, the role of NPLOC4 in the tumor immune microenvironment (TIME) and its potential as a promising tumor therapeutic target remains unclear. METHODS: Therefore, analyses of NPLOC4 mRNA and protein expression, RNA subcellular localization, and patient prognosis associated with NPLOC4 expression were conducted across multiple tumor types. Additionally, the correlations between NPLOC4 and immune cells, non-immune cells, and immune molecules within the tumor immune microenvironment (TIME) were investigated. These analyses utilized data from various public resources, including the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), The Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), TIMER2.0, KM-Plotter, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), and Tumor Immune Single-cell Hub 2 (TISCH2). Subsequently, we utilized hepatocellular carcinoma (HCC) patients' cancer and adjacent tissues plus tumor cell lines to verify the differential RNA and protein expression of NPLOC4 via qRT-PCR and immunohistochemistry (IHC). Then, the relationship of NPLOC4 expression level with immune infiltration score, infiltration of effector immune cells, suppressive immune cells, and several vital immune checkpoints was analyzed in HCC immune microenvironment. Furthermore, the distribution of expression of NPLOC4 in various cells in the HCC microenvironment was determined through single-cell sequencing analysis. RESULTS: We discovered that NPLOC4 was up-regulated in a variety of tumors and was correlated with poor prognosis. NPLOC4 not only had the potential as a tumor prognostic marker and therapeutic target but also was strongly linked to immune cells, immune checkpoints, and immune-related molecules and pathways in HCC immune microenvironment. CONCLUSION: In summary, NPLOC4 may serve as a promising target for immunotherapy.

Humans↗

PubMed2026

CDC20B Dysregulation: Links to Tumor Prognosis and Immunity.

OBJECTIVE: This study aimed to clarify the pan-cancer expression pattern, upstream regulatory mechanisms, prognostic relevance, and immune associations of CDC20B. METHOD: Using public databases (GTEx, GEO, and TCGA), we examined CDC20B expression and its associations with prognosis and tumor immunity across multiple cancers. Immunohistochemistry (IHC) on an independent clinical cohort was performed to validate CDC20B upregulation in tumor tissues. Promoter methylation, genetic alterations, and immune infiltration were analyzed using bioinformatics tools (cBioPortal, UALCAN, TIMER2.0, ESTIMATE). Functional enrichment was assessed by GSEA and single-cell state analysis (CancerSEA). RESULTS: CDC20B was markedly upregulated in most tumor types (p < 0.001), with strong diagnostic efficiency (AUC > 0.7 in 15 cancers) and potential regulation by promoter hypomethylation. IHC confirmed its overexpression in clinical tumor tissues. However, the prognostic impact of CDC20B was cancer-type-specific: high expression correlated with poor overall survival in UCS, LGG, KIRC, and OV, but with favorable survival in BRCA, LUAD, and PAAD. CDC20B expression was associated with immune infiltration patterns, showing negative correlations with ImmuneScore in most cancers but positive correlations with CD8+ T cells in PAAD. Functional analyses indicated involvement in EMT, KRAS/NF-κB signaling, and DNA damage response pathways. DISCUSSION: The dual prognostic role of CDC20B suggests context-dependent functions, likely influenced by tumor microenvironment composition and underlying oncogenic programs. Promoter hypomethylation emerges as a potential epigenetic driver of overexpression. The associations with immune modulation and genomic instability suggest that CDC20B is a candidate biomarker, though causal relationships require experimental validation. CONCLUSION: CDC20B may contribute to tumor progression in a context-dependent manner, with its prognostic impact varying across cancer types. Its role in tumor immunity and oncogenic pathways warrants further investigation, particularly in stratified patient populations.

CDC20B↗

PubMed2026

KLK6 is Associated with a Neutrophil-Dominant Immunosuppressive Microenvironment and Epigenetic Deregulation in Lung Adenocarcinoma.

INTRODUCTION: Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer and is associated with poor survival despite advances in targeted therapies. Kallikrein-related peptidase 6 (KLK6) has been implicated in several malignancies, but its expression pattern, clinical relevance, and biological function in LUAD remain incompletely characterized. This study aimed to evaluate KLK6 expression and its associations with prognosis, epigenetic regulation, immune infiltration, and migratory phenotypes in LUAD. METHODS: RNA-seq expression and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. KLK6 expression was analyzed in relation to clinicopathological parameters, survival outcomes, promoter methylation status (via UALCAN), and tumor-infiltrating immune cell abundance (via TIMER2.0). In vitro, KLK6 was knocked down using shRNA in A549 and H1299 LUAD cell lines. Cell migration was assessed by transwell assays, and the expression of Epithelial-Mesenchymal Transition (EMT)- and Wnt signaling-related markers was examined by qRT-PCR and Western blotting. RESULTS: KLK6 expression was significantly upregulated in LUAD tissues compared with normal lung tissues. High KLK6 expression was associated with poorer overall survival (HR = 1.52, P = 0.009) and disease-specific survival (HR = 1.55, P = 0.03). ROC analysis showed that KLK6 discriminated stage I LUAD from normal tissues with an AUC of 0.73. Promoter hypomethylation was observed in LUAD tumors and correlated with increased KLK6 expression. Immune infiltration analysis revealed that KLK6-high tumors exhibited reduced B-cell infiltration and increased neutrophil infiltration. Functional experiments demonstrated that KLK6 knockdown significantly suppressed cell migration, accompanied by increased E-cadherin and decreased N-cadherin, Vimentin, Wnt5a, and β-catenin expression. DISCUSSION: These findings suggest that KLK6 overexpression in LUAD is driven in part by promoter hypomethylation and is closely linked to a neutrophil-dominant immunosuppressive microenvironment. Furthermore, KLK6 appears to promote LUAD cell migration through EMT- and Wnt-related signaling pathways. Collectively, these multi-layered data position KLK6 as a potential driver of aggressive tumor behavior and a candidate biomarker for risk stratification. CONCLUSION: KLK6 is aberrantly overexpressed in LUAD and is associated with poor prognosis and enhanced migratory capacity. It may serve as a promising prognostic biomarker and a potential therapeutic target for LUAD.

KLK6↗

PubMed2026

ERP44 Is Associated With Poor Prognosis and Promotes Proliferation and Temozolomide Resistance in Lower-grade Glioma.

BACKGROUND/AIM: Endoplasmic reticulum resident protein 44 (ERP44), a protein disulfide isomerase family member, has been implicated in tumor biology, but its role in lower-grade glioma (LGG) remains unclear. This study investigated the prognostic significance and biological function of ERP44 in LGG, focusing on proliferation and temozolomide (TMZ) resistance. MATERIALS AND METHODS: ERP44 expression, clinicopathological associations, and prognostic value were analyzed using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) datasets. Time-dependent receiver operating characteristic (ROC) curves, Cox regression, and a prognostic nomogram were constructed. Differential expression, Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) enrichment, immune infiltration, and drug sensitivity analyses were performed. Functional validation was conducted in SW1088 and SW1783 cells using shRNA-mediated ERP44 knockdown, followed by RT-qPCR, western blotting, CCK-8, colony formation, and TMZ IC50 assays. Subcutaneous xenograft models with or without TMZ treatment were used for in vivo validation. RESULTS: ERP44 was markedly upregulated in LGG and associated with higher WHO grade, IDH wildtype status, 1p/19q non-codeletion, and poor survival in TCGA and CGGA cohorts. ERP44 showed strong prognostic performance and improved risk stratification in a multivariable nomogram. Enrichment analyses linked high ERP44 expression to immune/inflammatory pathways and reduced neuronal functional signatures. ERP44 positively correlated with immune infiltration, proliferation/stemness markers, and predicted TMZ resistance, while its knockdown inhibited proliferation and colony formation, reduced TMZ IC50, suppressed xenograft growth, enhanced TMZ efficacy, and decreased Ki67 positivity. CONCLUSION: ERP44 is a prognostic biomarker that promotes LGG proliferation and TMZ resistance, suggesting its potential as a therapeutic target.

Humans↗

PubMed2026

Integrating machine learning and GWAS for variant prioritization in the INCIPE cohort highlights ABC transporter genes in chronic kidney disease.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge, affecting approximately 674 million people worldwide and representing one of the fastest-growing causes of mortality. Since CKD is frequently asymptomatic in its early stages, the identification of novel genetic biomarkers may improve early detection and risk stratification. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with CKD and related traits; however, their performance is often limited in small and imbalanced cohorts, where reduced statistical power increases both false-positive and false-negative findings. Machine learning (ML) approaches can complement conventional GWAS by prioritizing biologically relevant genetic signals from high-dimensional genomic data. METHODS: In this study, we implemented a nested ensemble (NCBC) model composed of an undersampler and a CatBoostClassifier (CBC) to prioritize candidate genetic variants associated with CKD in the INCIPE cohort. Prioritized variants were functionally annotated and evaluated through enrichment analyses, GTEx gene expression profiling, and protein-protein interaction network analyses. Genes identified by the CKDGen Consortium were analysed as an external reference set and used to validate the biological relevance of the prioritized results. RESULTS: The NCBC model outperformed conventional ML classifiers, achieving a ROC AUC score of 87.77%, compared to 50%-53% for the other evaluated models. Among the prioritized genes, 56.25% showed protein-protein interactions with genes previously reported by the CKDGen Consortium, whereas only 1.9% of randomly generated gene sets showed interactions. DISCUSSION: Our study demonstrates that the NCBC model improves the prioritization of biologically plausible candidate variants in a small and imbalanced CKD cohort. Functional analyses suggested ABC transporter-related genes, including ABCA13, ABCA4, and ABCC4 genes, as promising candidate for future validation, with ABCA4 showing substantial expression in kidney tissues. Overall, these findings support the integration of ML with GWAS to prioritize candidate genes and investigate the genetic architecture of complex diseases.

SNP prioritization↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗