Search Results (2,300)

The aim of this study was to investigate the prognostic and predictive properties of four specific genes in triple-negative breast cancer (TNBC). We focused on ADIPOQ, GAS5, GATA4, and YAP1, which are known for their roles in key molecular pathways related to tumorigenesis, such as adipokine signaling, lncRNA regulation, transcriptional control, and Hippo signaling, but have not been sufficiently explored in the context of epigenetic regulation in breast cancer. Using the methylospecific PCR (MSP) method, we analyzed the methylation of the four genes in the tumor tissues collected from 57 TNBC patients. We evaluated their association with response to neoadjuvant treatment and clinicopathological characteristics. Additionally, we performed a bioinformatic analysis of methylation and expression data from The Cancer Genome Atlas (TCGA) TNBC cohort to explore their relationships with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), progression-free interval (PFI), and relapse-free survival (RFS). No significant associations were observed between methylation patterns and clinicopathological characteristics in the patients. However, in silico analysis of the TNBC cohort identified ADIPOQ methylation as having the most significant associations, correlating with all five survival endpoints, including OS, DSS, DFI, PFI, and RFS. GAS5 methylation was significantly associated with OS, DSS, and RFS, and GATA4 methylation showed significant associations with PFI, whereas YAP1 methylation was significantly associated with OS and RFS. In addition, GAS5 expression was linked to DSS, DFI and RFS. This study highlights the potential prognostic significance of the epigenetic regulation of ADIPOQ in TNBC. The in silico findings shed light on the molecular pathways associated with TNBC progression and warrant further investigation to validate their role in clinical outcomes and underlying biological mechanisms. Full article

Background: N6-methyladenosine (m⁶A) modification plays a crucial role in tumor biology; however, the function of the methyltransferase adaptor WTAP in melanoma remains poorly understood. Methods: We analyzed WTAP expression and its clinical relevance using TCGA-SKCM and GTEx datasets, followed by immunohistochemical validation in melanoma tissues. The biological effects of WTAP were assessed through gain- and loss-of-function experiments in melanoma cell lines. Weighted gene co-expression network analysis (WGCNA) and LASSO regression were used to identify key WTAP-related genes. Results: WTAP expression was significantly decreased in melanoma compared with normal skin and was negatively correlated with tumor progression and poor survival. Functionally, WTAP overexpression suppressed melanoma cell proliferation and migration, whereas its knockdown produced the opposite effects. Bioinformatic analyses and rescue experiments identified KLF9 as a potential downstream effector of WTAP. WTAP depletion reduced KLF9 mRNA and protein levels, while overexpression restored them. Moreover, MeRIP-qPCR confirmed that WTAP promotes m⁶A enrichment on KLF9 mRNA, suggesting a post-transcriptional regulatory mechanism. Conclusions: Our findings reveal a novel WTAP–KLF9 axis that mediates melanoma suppression through m⁶A-dependent regulation. This study provides new insight into the context-specific role of WTAP in melanoma and suggests it may serve as a potential biomarker or therapeutic target. Full article

Targeted therapies, including treatment with inhibitors of BRAF^V600 and MEK kinases, have improved outcomes in advanced melanoma. However, most patients relapse due to acquired resistance, underscoring the need for new drug targets. This study evaluated PRI-724, a CBP/β-catenin inhibitor, in patient-derived drug-naïve melanoma cells and their trametinib- or vemurafenib-resistant counterparts. While PRI-724 has demonstrated efficacy in preclinical models and clinical trials in different cancer types, its potential in melanoma has not been previously assessed. We found that treatment with PRI-724 downregulated survivin and other CBP/β-catenin target proteins, reduced invasiveness, and induced apoptosis in drug-naïve and trametinib- and vemurafenib-resistant cells. Trametinib-resistant melanoma cells showed the greatest sensitivity to PRI-724, indicating that CBP/β-catenin transcriptional activity may represent a new therapeutic vulnerability. Transcriptomic and immunoblotting analyses revealed the highest survivin levels in vemurafenib-resistant cells, which may underlie their reduced responsiveness to PRI-724. Bioinformatic analyses (TCGA and GSE50509) confirmed that a high survivin level predicts poor prognosis and reduced response to treatment. The results of the study point to the potential of PRI-724 as a chemotherapeutic agent for the treatment of melanoma. Its efficacy might depend on CBP/β-catenin transcriptional activity in melanoma cells, and further evaluation of this signaling with survivin as a biomarker is therefore warranted. Full article

Background: Sodium/iodide symporter (NIS) is a membrane protein involved in iodide transport into cells, making it a key component of thyroid physiology and radioiodine therapy for thyroid cancer. Although NIS is expressed in many extrathyroidal tissues, including breast tumors, its functional role and prognostic significance in these contexts remain a subject of active investigation. Understanding the mechanisms regulating NIS, its influence on cellular processes such as migration and metastasis, and its connection with transcription factors like FOXA1 could contribute to the development of new therapeutic strategies for breast cancer treatment. This study aims to investigate the correlation between sodium/iodide symporter (NIS) expression and response to neoadjuvant chemotherapy in patients with triple-negative breast cancer (TNBC). Methods: The current retrospective study included 161 TNBC patients who received neoadjuvant chemotherapy followed by mastectomy. NIS expression was assessed via immunohistochemistry, graded semi-quantitatively from 0 to 3+. The Residual Cancer Burden (RCB) scale was used to evaluate the response to chemotherapy. Statistical analysis included Lilliefors tests and Kendall’s tau correlation coefficient. Publicly available Cancer Genome Atlas datasets were analyzed to assess the relationship between NIS and FOXA1 expression. Results: NIS immunopositivity was observed in 69.5% of TNBC samples compared to 63.3% GATA-3-positive and 31.0% of Mammaglobin-positive samples. While no significant correlation was found between NIS expression and age, TNM stage, or Ki-67, a statistically significant moderate positive correlation (τ = 0.481, p < 0.01) was identified between NIS expression and RCB index, indicating that higher NIS expression was associated with a poorer response to neoadjuvant chemotherapy. TCGA data analysis revealed a statistically significant increase in NIS mRNA expression in FOXA1-mutated TNBC samples compared to FOXA1-wild-type samples (p < 0.05). Younger patients exhibited higher Ki-67 levels (τ = −0.416, p < 0.05). Conclusions: Higher NIS expression correlates with chemoresistance to neoadjuvant chemotherapy in TNBC patients. This phenomenon may be linked to FOXA1 activity, suggesting that NIS may represent a potential biomarker for chemoresistance in TNBC. The inverse correlation between patient age and Ki-67 levels may be associated with a different mutational landscape in younger patients. Full article

Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0–C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion. Full article

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. This study investigates multi-omics lung cancer data obtained from The Cancer Genome Atlas (TCGA) repository. Differentially expressed genes were identified using four statistical approaches: LIMMA, T-test, Canonical Correlation Analysis (CCA), and the Wilcoxon test applied across gene expression (GE), DNA methylation, and microRNA (miRNA) datasets. Kernel Machine Regression (KMR) was subsequently employed to perform data fusion across the multi-modal datasets. The empirical results highlight notable interactions among GE, miRNA expression, and DNA methylation in lung cancer. Our analysis identified 38 genes that show significant associations with lung cancer. Among these, 8 genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, and ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that may offer promising therapeutic potential against lung cancer. The effectiveness of these candidate drugs is further reinforced by evidence from independent research studies, which emphasize their potential in lung cancer treatment. Full article

Background and Objective: Neuromodulators such as neuropeptide, neurotrophic factors and neurotransmitters are increasingly reported to be involved in glioblastoma (GBM) progression. Nonetheless, the association between neuromodulation-related genes (NMRGs) and GBM prognosis remains elusive. Hence, this study aims to identify clinically significant NMRGs that can form a prognostic gene signature for GBM patients. Methods and Results: Differential expression analysis of transcriptomic profiles extracted from GSE147352, GSE165595, TCGA and CGGA determined 272 differentially expressed NMRGs (deNMRGs) in GBM compared to normal brain tissue. The subsequent Kaplan–Meier survival analysis and Cox proportional hazard model further identified ten common deNMRGs (IGF2, RETN, EDNRB, C3AR1, CLCF1, NTRK1, OSMR, KCNN4, SLC18A3 and HTR7), forming a 10-NMRG signature. This signature stratifies GBM patients and consistently predicts poorer survival outcomes for the high-risk score group compared to the low-risk score group in the TCGA and CGGA cohorts. The gene set enrichment analysis and active-subnetwork-oriented enrichment analysis identified a connection between immunomodulation and tumour-associated hallmarks with the high-risk GBM patient group. Next, the correlation proportionality analysis identified a positive association between the signature genes with immune activators, immune suppressors and pro-motility genes. Additionally, high expressions of the 10-NMRGs were noted in the mesenchymal GBM subtype. Conclusions: Collectively, our analysis highlights the potential use of the 10-NMRG signature to stratify the high-risk GBM group with a strong association of immunomodulation and tumour-associated hallmarks that can contribute to the poor survival outcomes. Full article

Mitochondrial transcription factor A (TFAM) is essential for mitochondrial DNA (mtDNA) maintenance and function, but its role in glioblastoma (GBM) remains largely unexplored. Analysis of patient astrocytomas and TCGA datasets has revealed progressive TFAM downregulation with increasing malignancy, with the lowest expression in glycolytic/plurimetabolic (GPM) subtypes. Functional and transcriptomic profiling of mesenchymal GBM cell lines showed that TFAM silencing in GPM-type U87MG cells enhanced proliferation, S-phase entry, reactive oxygen species (ROS) production, and adhesion, while reducing motility. These changes were correlated with upregulation of LDHC and TRAF2 and downregulation of androgen receptor-linked motility genes and LOXL2. By contrast, TFAM loss in mitochondrial (MTC)-type A172 cells caused minimal phenotypic alterations, associated with elevated SOD1 expression and activation of antioxidant, mitochondrial membrane, and survival pathways, alongside suppression of oxidative phosphorylation and vesicle-trafficking genes. TFAM overexpression reduced proliferation in U87MG but had a limited impact on A172 cells. Taken together, these findings establish TFAM as a subtype-specific regulator of GBM cell proliferation, redox balance, and motility. TFAM loss drives a proliferative, ROS-sensitive phenotype in GPM-type cells, while eliciting adaptive, stress-resilient programs in MTC-type cells. This study identifies TFAM and downstream effectors, TRAF2 and LOXL2, as potential therapeutic targets, supporting the development of metabolic subtype-tailored strategies for GBM treatment. Full article

MicroRNAs (miRNAs) are key regulators of gene expression with critical roles in oncogenic signaling. Endometrial cancer (EC) has been redefined with the identification of POLE-ultramutated tumors which, despite their hypermutated phenotype, show more favorable prognosis. We profiled miRNA expression in tumor tissues from forty (40) EC patients and twenty (20) healthy controls using qPCR panels. POLE exonuclease domain mutations (P286R, V411L) were genotyped, and subgroup analyses were conducted between POLE-mutated (n = 7) and POLE-wild-type (n = 33) tumors. Bioinformatic analyses included validated miRNA–mRNA interactions, target enrichment, and Gene Ontology (GO) pathway mapping. Comparison of EC versus healthy endometrium revealed 50 significantly dysregulated (∣log₂ (FoldReg)∣ > 1 and BH FDR < 0.05) miRNAs, including up-regulation of the oncogenic hsa-miR-181a-5p, hsa-miR-23a-3p, hsa-miR-200c-3p, and down-regulation of tumor-suppressive let-7 family members. Target enrichment implicated canonical oncogenic regulators such as MYC, TP53, and VEGFA. POLE-mutated tumor analysis demonstrated a miRNA signature, with 19 miRNAs significantly down-regulated, including let-7f-5p and hsa-miR-200b-3p. Findings for the EC versus healthy endometrium comparison were validated against TCGA-UCEC sequencing data which confirmed concordant dysregulation of key miRNAs across platforms. Our findings reveal that EC is characterized by widespread miRNA deregulation, with a unique global down-regulation signature in POLE-mutated tumors. These results highlight the potential of miRNAs as complementary biomarkers for classification and potential targets in EC. Full article

Hepatocellular carcinoma continues to be a predominant contributor to oncological fatalities, characterized by restricted treatment alternatives. Although berberine exhibits anti-neoplastic capabilities, the underlying molecular pathways in hepatic malignancy require clarification. A comprehensive computational framework was established, incorporating transcriptomic data analysis, multiple machine learning methodologies, weighted gene co-expression network analysis (WGCNA), and molecular simulation techniques to elucidate berberine’s therapeutic pathways. Transcriptomic datasets from the Cancer Genome Atlas (TCGA) underwent examination to detect differentially expressed genes (DEGs). Ten machine learning methodologies screened critical targets, subsequently validated through molecular docking and 100 ns molecular dynamics simulations. Transcriptomic examination revealed 531 DEGs (341 exhibiting upregulation, 190 demonstrating downregulation) alongside 173 putative berberine interaction targets, yielding 17 intersecting candidates. Machine learning approaches consistently recognized AURKA and CDK1 as principal targets, subsequently confirmed by WGCNA as central genes. Elevated expression of both targets demonstrated correlation with unfavorable survival outcomes (p < 0.05). Computational docking analysis demonstrated robust binding interactions (AURKA: −8.2 kcal/mol; CDK1: −8.4 kcal/mol), with interaction stability validated through molecular dynamics simulations. Functional enrichment analysis unveiled targeting of cell cycle modulation, chromosome segregation, and p53 signaling networks. Berberine manifests anti-hepatocellular carcinoma activities primarily via coordinated targeting of AURKA and CDK1, essential cell cycle modulators. These discoveries provide molecular insights supporting berberine’s potential as adjunctive hepatic cancer therapy. Full article

Spermidine/spermine N1-Acetyltransferase 2 (SAT2), belonging to the spermidine/spermine N1-Acetyltransferase family, has been increasingly recognized for its potential effects on tumor occurrence and development. Nonetheless, little is known about its biological function and clinical value for pancreatic cancer (PC). The present work focused on investigating its expression pattern, prognostic value, molecular mechanisms, and immune relevance in PC. SAT2 expression within PC samples and its prognostic significance were analyzed by retrieving the relevant data from the TCGA, CPTAC, and HPA databases. The biological function of SAT2 was investigated through GO and KEGG enrichment analyses. The association of SAT2 with immune cell infiltration in tumors was assessed by CIBERSORT. Additionally, in vitro experiments were performed to examine how SAT2 expression affected the PC cell proliferation, invasion, and migration abilities. An in vivo xenograft tumor model was employed for investigating how SAT2 expression affected the PC cell-derived tumor growth. The expression of SAT2 within the PC tissue exhibited a significant decrease in comparison with a non-carcinoma sample. Such observation was validated within PC cells. In addition, SAT2 expression showed a close relation to both tumor growth (T stage) and prognosis. SAT2 primarily participates in pathways, including the PI3K/Akt and MAPK pathways. Furthermore, we demonstrated a significant association between SAT2 expression and immune cell infiltration into tumors. The in vitro experiments confirmed that elevated SAT2 expression significantly suppressed PC cell viability, invasion, and migration through modulating the PI3K/Akt and MAPK pathways. The in vivo experimental results suggested the role of SAT2 overexpression in inhibiting xenograft tumor growth. Our investigation confirms the role of SAT2 in PC development through involvement in the PI3K/Akt and MAPK pathways. The correlation between SAT2 expression levels, immune infiltration, and checkpoint regulation provides valuable insights for immunotherapy strategies targeting PC. Full article

Background/Objectives: Endometrial carcinoma is an emerging challenge for public health systems globally, especially in countries with a high development index. Traditionally, histopathological staging and grading have been the main criteria informing treatment modalities. More recently, clinically actionable molecular targets have been developed, following observations from the TCGA project and the ProMisE cohort. Although promising, the cost of these methods is an obstacle for some countries that lack well developed theranostics infrastructure in their public systems. This study aimed to contextualize our center’s diagnostic experience from the perspective of histopathological diagnosis. Methods: This is a retrospective study that selected 109 cases of already diagnosed endometrial carcinoma from the interval of 2017–2023. We analyzed traditional parameters related to staging and grading, using the FIGO 2009 system as well as basic histological parameters (lymphovascular invasion, perineural invasion, necrosis). Excel and SPSS 26 were used for database management and correlations. Findings were contextualized using the more recent studies that reported on similar parameters. Results: Higher-grade tumors were associated with lymphovascular invasion (p = 0.04) and lymph node involvement (p = 0.0006), as well as deeper myoinvasion (p = 0.0018). Myoinvasion (p = 0.013) and lymphovascular invasion (p = 0.0001) were associated with advanced disease (FIGO III and IV). Our cohort showed a relative paucity (6.5%) of non-endometrioid endometrial carcinoma and presence of lymphovascular invasion (9.2%). Perineural invasion was found in 3 cases with extrauterine involvement. Conclusions: Histopathological diagnosis represents an integral component in informing clinical management for endometrial carcinoma and should serve as a means of triage for more expensive molecular techniques. It nevertheless presents reproducibility issues. Further efforts should focus on resolving such issues or possibly introducing less-researched parameters like perineural invasion. Full article

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive subtype of kidney cancer. This study aimed to construct a prognostic model for ccRCC based on glycosyltransferase genes, which play important roles in cell processes like proliferation, apoptosis. Glycosyltransferase genes were collected from four public databases and analyzed using RNA-seq data with clinical information from three ccRCC datasets. Prognostic models were constructed using eight machine learning algorithms, generating a total of 117 combinatorial algorithm models, and the StepCox[forward]+Ridge model with the highest predictive accuracy (C-index = 0.753) which selected and named the Glycosyltransferases Risk Score (GTRS) model. The GTRS effectively stratified patients into high- and low-risk groups with significantly different overall survival and maintained robust performance across TCGA, CPTAC, and E-MTAB1980 cohorts (AUC > 0.75). High-risk patients exhibited higher tumor mutational burden, immunosuppressive microenvironment, and poorer response to immunotherapy. TYMP and GCNT4 were experimentally validated as key genes, functioning as oncogenic and tumor-suppressive factors. In conclusion, GTRS serves as a reliable prognostic tool for ccRCC and provides mechanistic insights into glycosylation-related tumor progression. Full article

Complex human diseases, including cancer, are linked to genetic factors. Genome-wide association studies (GWASs) are powerful for identifying genetic variants associated with cancer but are limited by their reliance on case–control data. We propose approaches to expanding GWAS by using tumor and paired normal tissues to investigate somatic mutations. We apply penalized maximum likelihood estimation for single-marker analysis and develop a Bayesian hierarchical model to integrate multiple markers, identifying SNP sets grouped by genes or pathways, improving detection of moderate-effect SNPs. Applied to breast cancer data from The Cancer Genome Atlas (TCGA), both single- and multiple-marker analyses identify associated genes, with multiple-marker analysis providing more consistent results with external resources. The Bayesian model significantly increases the chance of new discoveries. Full article

Enhancer RNAs (eRNAs) are abundant in most human cells and tissues, and quantifying eRNAs has become a robust approach for biomarker discovery. While eRNAs play crucial roles in regulating biological processes and cancer progression, their functions in lung adenocarcinoma (LUAD) remain poorly understood. Here, we developed a LUAD prognostic model based on eRNA expression data from The Cancer Genome Atlas (TCGA). Through rigorous validation, a 7-eRNA signature was identified, which robustly stratified LUAD patients into high-risk and low-risk groups in both training and testing sets. Functional analyses revealed distinct enrichment of pathways related to amino acid biosynthesis, ribosome biogenesis, and proteasome activity in high-risk patients. Somatic mutation profiling highlighted TP53 and TTN as frequently mutated genes, while drug sensitivity prediction identified four potential therapeutic agents (including AZD4547 and Nutlin-3a) for high-risk individuals. Collectively, this study constructed a 7-eRNA prognostic model for LUAD, providing a powerful tool for clinical risk assessment and uncovering eRNA-mediated regulatory mechanisms. Full article