To investigate long non-coding RNA (lncRNA) MIR210HG expression in high-grade serous ovarian cancer (HGSOC) and its correlation with clinicopathological features, clinical outcomes, and prognosis. Tissue samples were collected from 84 patients with HGSOC undergoing surgical treatment at the Affiliated Hospital of Qingdao University from 2019 to 2021. Fifty-seven patients undergoing total hysterectomy and bilateral adnexectomy resection for uterine fibroids and adenomyosis during the same period were also included. MIR210HG expression was detected via quantitative reverse transcription polymerase chain reaction (qRT-PCR). Univariate and multivariate logistic regression analyses were used to assess correlation with advanced FIGO stage and clinical outcomes. The Kaplan-Meier plotter was used to predict the correlation between MIR210HG expression and prognosis, considering pathological classification, clinical stage, and TP53 status. MIR210HG expression was significantly upregulated in HGSOC compared to normal ovarian and fallopian tube tissues. MIR210HG expression was moderately and weakly correlated with CA125 (r = 0.515, P < 0.001) and HE4 levels (r = 0.325, P = 0.001), respectively. Advanced clinical stage was associated with higher MIR210HG expression. Patients with lymph node metastasis and ascites also exhibited higher MIR210HG expression (P < 0.05). MIR210HG was associated with FIGO stage, while CA125 ≤ 35 U/ml and FIGO stage II were independent protective factors for composite poor clinical outcomes. Patients with low MIR210HG expression had a significantly longer 5-year progression-free survival (PFS) compared to those with high expression. What’s more, MIR210HG expression was significantly correlated with both 5-year PFS and overall survival (OS) in TP53-mutant patients, as opposed to only OS in TP53 wild-type patients. MIR210HG expression is significantly upregulated in HGSOC and is associated with clinical stage, lymph node metastasis, and ascites. MIR210HG expression exhibited survival associations based on TP53 status, suggesting biomarker potential for HGSOC.
Cervical small cell neuroendocrine carcinoma (SCNEC) admixed with squamous cell carcinoma (SCC) is a rare malignancy associated with a poor prognosis. While histological features of this mixed tumor have been described, liquid-based cytology (LBC) findings remain largely undocumented, despite prior multi-center studies describing pure SCNEC. Herein, we report a case of mixed cervical SCNEC-SCC in a 69-year-old woman. The lesion was initially suspected via LBC screening and subsequently confirmed using limited cervical biopsy specimens, no surgical resection specimen was available. Cytologically, the SCNEC component presented as small round cells in loose clusters or isolated single cells, exhibiting a high nuclear-to-cytoplasmic ratio. These cells typically featured delicate yet irregular nuclear membranes, hyperchromatic nuclei, inconspicuous nucleoli, scant cytoplasm, frequent crush artifacts, scattered mitoses, and focal tumor necrosis. In contrast, other cells displayed slightly more abundant cytoplasm with visible nucleoli, representing the SCC component. We quantitatively describe component proportions using a multi‑pathologist consensus‑based cell‑counting approach and elaborate on characteristic cytologic features, including tumor necrosis, mitotic activity, and crush artifacts, to facilitate differential diagnosis. Owing to sampling limitations intrinsic to small size biopsy specimens, histologically derived tumor component percentages should be interpreted prudently, as they may not reflect the true composition of the entire lesion. Careful evaluation of atypical malignant cells on LBC is essential to avoid misdiagnosis of this aggressive neoplasm. Importantly, although LBC can identify dual-lineage malignant cells during screening, this single case study cannot establish causal or correlative links between cytological detection and overall survival. Early cytological recognition failed to improve clinical outcomes in this patient, who presented with widespread radiologically suspected metastatic disease at initial diagnosis; therefore, no generalized survival conclusions can be drawn from this single case.
This research was designed to investigate the functional impact of miR-217 on proliferative activity and apoptotic processes in pancreatic carcinoma cells, and to explore its associated molecular mechanisms. Five matched pairs of pancreatic ductal adenocarcinoma specimens and corresponding adjacent non-neoplastic tissues were obtained. Quantitative real-time PCR (qRT-PCR) was utilized to assess the expression levels of miR-217. In the PANC-1 cellular model, gain-of-function and loss-of-function strategies were implemented through transfection with an miR-217 mimic or an antisense oligonucleotide (ASO), respectively. Plasmid-based overexpression and short hairpin RNA (shRNA)-mediated knockdown were utilized to modulate ENO1 expression. Cell proliferation was assessed using the MTT and colony formation assays. Apoptotic rates were quantified using flow cytometric analysis. Western immunoblotting was performed to determine protein expression of ENO1, AKT1, N-cadherin, E-cadherin, and Vimentin. The direct binding of miR-217 to the 3′-untranslated region (3′-UTR) of ENO1 mRNA was confirmed using a dual-luciferase reporter system. Functional rescue studies were conducted to evaluate the regulatory relationship between miR-217 and ENO1. miR-217 expression was markedly downregulated in pancreatic tumor tissues. Ectopic overexpression of miR-217 suppressed PANC-1 cell proliferation and induced apoptosis. At the mechanistic level, miR-217 upregulation led to reduced expression of ENO1, AKT1, N-cadherin, and Vimentin, accompanied by elevated E-cadherin levels. The dual-luciferase assay confirmed direct binding of miR-217 to the 3′-UTR of ENO1. Restoration of ENO1 expression counteracted the suppressive influence of miR-217 on proliferation and its promotive effect on apoptosis. miR-217 is underexpressed in pancreatic cancer and functions as a tumor suppressor by directly targeting ENO1. This regulation is associated with altered expression of AKT1 and epithelial–mesenchymal transition (EMT) markers, which may contribute to the inhibition of proliferation and promotion of apoptosis in pancreatic cancer cells.
Gastric cancer (GC) ranks fifth in global incidence and third in cancer-related mortality. Omics technologies have become essential for decoding intratumoral heterogeneity and the spatial architecture of tumors, leading to a surge in omics-based research on gastric cancer. This study systematically maps the landscape, evolving trends, and emerging frontiers of omics applications in gastric cancer through a comprehensive bibliometric analysis. Publications were retrieved from the Web of Science Core Collection and Scopus databases, covering the period from the inception of these databases to December 31, 2025. A total of 1,382 documents were analyzed using CiteSpace, VOSviewer, and the Bibliometrix R package, focusing on six dimensions: country/region, institution, author, source journal, reference, and keyword. The annual publication output has consistently increased, with China, the United States, and Japan at the forefront of productivity. High-frequency keywords such as gastric cancer, radiomics, biomarker, and proteomics delineate the core thematic structure of the research. Co-occurrence clustering analysis revealed six distinct clusters, prominently featuring radiomics, epigenetics, and gastric cancer. Additionally, keyword burst detection analysis identified machine learning and radiomics as current research frontiers, while citation analysis underscored therapeutic strategies as a central hotspot in the field. The number of publications in the field of GC omics research has shown a sustained increase, reflecting the growing interest in omics technologies. With the advancement of omics technologies, research efforts have gradually expanded from single-omics analysis to encompass epigenetics, radiomics, and multi-omics integration, with genomics and proteomics consistently playing central roles. In this context, the development of novel biomarkers through omics analysis provides key evidence for elucidating the mechanisms underlying GC initiation and progression, predicting disease risk, and optimizing therapeutic strategies. Current research priorities include biomarker screening and exploration of pathogenic mechanisms based on omics technologies to enable early GC screening, facilitate drug development, and enable efficacy evaluation. The integration of multi-omics data with deep learning to construct early risk prediction models for GC, with subsequent application to targeted therapy and immunotherapy, represents a promising research direction.
Chronological age may influence immune function, but individuals of the same age may differ in biological aging. We evaluated the association between biological age and clinical outcomes in patients with solid tumors treated with immune checkpoint inhibitor monotherapy. This retrospective two-center study included 302 patients with advanced solid tumors receiving ICI monotherapy. Biological age was estimated using the Levine PhenoAge model. Age-adjusted biological aging (PhenoAgeAccel) was defined as the residual from regression of PhenoAge on chronological age. Survival outcomes were compared according to PhenoAgeAccel status. The median age was 67 years; most patients had non–small cell lung cancer (78.5
Pancreatic cancer (PC) has a poor prognosis, and treatment is largely ineffective because of its unique tumor microenvironment (TME). We identified key cell types in PC using the GSE197177 dataset generated through single-cell RNA sequencing (scRNA-seq) analysis. Using TCGA-PAAD data, prognosis-related cell subtypes were identified by BayesPrism and Cox regression. Hub cell subtype-related prognostic genes were screened by integrating high-dimensional weighted gene co-expression network analysis (hdWGCNA), differential expression analysis, univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression. The upstream regulatory factors of prognostic genes were predicted. A risk model and nomogram were generated and validated, with risk scores used to evaluate pathways, the TME, immunotherapy, and drug sensitivity. Fibroblasts were identified as the key cell type in PC. TPM1+ myofibroblastic cancer-associated fibroblasts (CAFs) (myCAFs) were considered the prognosis-related hub cell subtype. AHNAK2, ARHGAP32, EVL, PRKCI, PTGES, and S100A16 were identified as prognostic genes. Regulatory factors, including DPF2 and hsa-miR-107, were predicted to target the prognostic genes. The constructed risk model and nomogram showed promise in prognostic performance, although further validation in diverse clinical subgroups is needed. Risk scores were associated with pathways, including the cell cycle, infiltration of immune cell types such as T follicular helper cells, response to immune checkpoint blockade therapy, and sensitivity to drugs such as lapatinib. A risk model based on six TPM1+ myCAF-related prognostic genes was constructed, which exhibited robust predictive ability. Our results provide novel insights into TPM1+ myCAF-related mechanisms and PC prognostic prediction, although the model’s generalizability to all patient subgroups requires further examination.
Pancreatic cancer (PC) is marked by a unique tumor microenvironment driven by chronic hypoxia and dysregulated lactate metabolism, but the combined prognostic utility of these factors remains unclear. Via integrated transcriptomic analysis of the TCGA-PAAD and GSE183795 datasets, we identified a six-gene signature (FHL2, ITGA3, KCNJ3, KLF5, MSLN, and S100A16) using weighted gene co-expression network, least absolute shrinkage and selection operator, and Cox regression analyses. This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival (p < 0.0001). High-risk tumors exhibited an immunosuppressive microenvironment, an increased abundance of M0 macrophages, an elevated tumor mutational burden, and hypothesis-generating evidence of greater sensitivity to Hsp90 inhibitors. Mechanistic exploration suggested a hypothetical lncRNA–miRNA regulatory axis that warrants further experimental verification. This hypoxia–lactate metabolism-related signature offers a robust tool for prognostic stratification and generates therapeutic hypotheses that merit further preclinical and clinical investigation.
The vitamin D receptor (VDR) plays a critical role in cancer biology, regulating cell proliferation, differentiation, and immune responses. Despite growing research interest, no systematic bibliometric analysis has mapped the global landscape of VDR-related cancer research. This study analyzed 2,567 publications on VDR and cancer from 2003 to 2025 using the Web of Science Core Collection. Visualization and analysis were performed using VOSviewer (version 1.6.20), CiteSpace (version 6.4.R1), the biblioshiny R package (version 5.1.0), and SciExplorer (online platform). The analysis included 69 countries, 1,952 institutions, and 11,771 authors. The USA leads with 748 publications, followed by China (382). Harvard University is the most influential institution (45 publications). Journal of Biological Chemistry ranks first in co-citations (4,667), while Journal of Steroid Biochemistry and Molecular Biology publishes the most articles (66). Sun J is the most prolific author (30 articles). Keyword clustering identifies seven research hotspots: breast cancer cell, receptor gene polymorphism, cardiovascular disease, nuclear receptor, pancreatic cancer, colon cancer, and predisposition. Burst detection reveals “d deficiency” (strength 10.67), “farnesoid X receptor” (8.79), and “oxidative stress” (2018–2025) as emerging frontiers. Research foci include VDR signaling mechanisms, gene polymorphisms, inflammation, and FXR-VDR metabolic axis. VDR-cancer research has matured into an interdisciplinary field bridging molecular mechanisms and clinical translation. Future directions emphasize genotype-guided prevention, VDR-based combination immunotherapy, and precision medicine approaches integrating multi-omics biomarkers.
Pancreatic cancer has a poor prognosis. This study investigated the prognostic role of the keratin gene family and aimed to develop a keratin-based biomarker. Bioinformatics analysis was performed using RNA-seq data from gemcitabine-treated patients in the TCGA-PAAD cohort (discovery set, n = 62). Differential expression and protein interaction analyses identified resistance-associated keratin genes. A Keratin Switch Score (KSS) was constructed via LASSO-Cox regression based on the KRT13/KRT4 expression ratio. Its prognostic value was validated in the full TCGA cohort (n = 183) and an independent GEO dataset (GSE205154, n = 289) using Kaplan–Meier and multivariate Cox analyses. Associations with pathways, tumor mutational burden, and immune microenvironment were assessed using GSEA, GSVA, and immune deconvolution algorithms. Multiple keratin genes were upregulated in chemotherapy-resistant tumors. The KRT13/KRT4-based KSS was an independent prognostic factor for worse overall survival in both validation cohorts. The high-KSS subtype was enriched for E2F targets, hypoxia, epithelial-mesenchymal transition, and p53 pathway activity, and exhibited higher tumor mutational burden. Its tumor immune microenvironment was characterized by functional suppression (e.g., downregulated T-cell exhaustion genes). KSS did not stably correlate with in vitro sensitivity to gemcitabine or other chemotherapeutics. The KRT13/KRT4 ratio defines a novel, high-risk pancreatic cancer subtype with integrated aggressive tumor cell phenotypes and an immunosuppressive microenvironment. KSS is a robust, independent prognostic biomarker that may guide risk stratification and combination therapy strategies.
Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with poor prognosis and pronounced heterogeneity. Mechanical stimuli within the tumor microenvironment (TME) regulate HCC cell behavior through mechanosensitive-related genes (MSRGs); however, their prognostic value and underlying mechanisms remain incompletely characterized. RNA-seq and clinical data for HCC patients were retrieved from The Cancer Genome Atlas (TCGA-LIHC), and single-cell RNA-seq data (GSE149614) were obtained from the Gene Expression Omnibus (GEO). MSRGs were collected from the Molecular Signatures Database (MSigDB) using the Gene Ontology term GO:0009612 (“response to mechanical stimulus”). Univariate Cox regression was applied to identify OS-associated MSRGs. Consensus clustering was employed to stratify HCC into molecular subtypes. A LASSO-Cox regression model was constructed and validated by Kaplan-Meier analysis, time-dependent ROC curves, and an independent external cohort. Functional enrichment, immune microenvironment characterization, genomic variation analysis, and drug sensitivity prediction were performed. Single-cell transcriptomic analysis mapped the risk signature to specific cell types, and SHAP analysis together with in vitro experiments validated the key driver gene. Thirty-nine OS-associated MSRGs were identified in HCC, and patients were stratified into two molecular subtypes with distinct prognostic and immune features. A six-gene risk model (HPN, ENDOG, UCN, FYN, ETV1, KCNQ3) was constructed, demonstrating robust discriminatory performance (1-, 3-, and 5-year AUCs: 0.74, 0.74, and 0.73, respectively) in the TCGA-LIHC cohort and consistent performance in an independent external validation cohort. High-risk patients exhibited shorter OS, a more immunosuppressive TME, and distinct genomic alteration patterns. Computational drug sensitivity analysis revealed differential predicted responses between risk groups, although these remain hypothesis-generating. Single-cell analysis demonstrated cell-type specificity of the risk signature. SHAP analysis identified KCNQ3 as the key driver gene. In vitro experiments in Huh7 and Hep3B cells confirmed that KCNQ3 knockdown significantly suppressed HCC cell proliferation and clonogenicity. Western blot analysis further indicated that KCNQ3 knockdown attenuated PI3K-AKT signaling. MSRGs are closely associated with prognosis and the immune microenvironment in HCC. The six-gene MSRG-based risk model demonstrates reliable predictive value for HCC patient survival, and KCNQ3 represents a candidate prognostic biomarker and potential therapeutic target, offering new insights for personalized HCC treatment.
Breast cancer (BC) is a leading cause of premature death in women. However, researcher’s understanding of BC development, progression and its treatment strategies has not yet reached a satisfactory level. To address these issues, first, it is necessary to accurately identify the key molecular signatures associated with BC. This study aimed to identify genetic variants associated with BC through meta-analysis of multiple genome-wide association studies (GWAS), as meta-analysis of multiple GWAS datasets increases statistical power and improves the reliability of identified genetic associations. At first, we have identified 728 genetic variants also known as single nucleotide polymorphisms (SNPs) associated with BC using METAL software tool. These SNPs were then mapped to the corresponding genes using FUMA web-tools, and found 68 genes. These genes were analyzed via independent PPI networks. By taking the intersection of top hub genes and filtering for combined annotation dependent depletion (CADD) scores and GWAS significance, we identified 5 Key Genes (KGs) associated with BC. We have identified top-ranked functionally significant SNPs for each of KGs as rs11571833 T/C in BRCA2, rs186430430 T/C in CHEK2, rs4252685A/C and rs4252686 A/G in MDM4, rs6940919 T/G, rs35240111 C/G in ESR1, rs4751844 T/G and rs17542768 A/G in FGFR2. Top-ranked three transcription factors FOXC1, GATA2 and E2F1 and two miRNAs hsa-miR-34a-5p and hsa-let-7i-5p were identified as the transcriptional and post-transcriptional regulators of KGs. Functional enrichment analysis of KGs with GO-terms and KEGG-pathways revealed some crucial biological processes, molecular functions, cellular components and signaling pathways that might be associated with the BC. Finally, KG-guided drug prioritization identified five top-ranked compounds, including three FDA-approved drugs (Imatinib, Nilotinib, and Lynparza), one investigational drug (Masitinib), and NVP-BHG712 as a computationally prioritized repurposing candidate with limited evidence in the breast cancer literature. Experimental validation is required to confirm these computational predictions.
Glioblastoma (GBM) is the most common malignant brain tumor with a low 5-year survival rate. The search for therapeutic agents for GBM from the perspective of lncRNA has attracted widespread attention. Bioinformatics analysis was used to explore GBM-related lncRNA and target genes. GBM patient clinical samples and cells (U87MG, LN-229, T98G, U373, and U251) were used for validation. Knockdown of LINC01515 was used to evaluate its effects on GBM cells. Moreover, protein expression levels of caspase-3, cleaved-caspase-3, p21, p53, Snail, N-cadherin, and E-cadherin were analyzed. Balb/c nude mouse subcutaneous tumor formation assay and HE staining were used to observe the effect of LINC01515 on GBM. ChIP-PCR was used to explore the relationship between MEOX2 and LINC01515. Here, the potential association of LINC01515 and MEOX2 with GBM was confirmed by clinical samples and GBM cells. MEOX2 is involved in the transcriptional activation of LINC01515. In vitro cellular experiments have shown that down-regulation of LINC01515 resulted in increased apoptosis and decreased activity, invasion, and migration capacity in GBM cells LN-229 and U251. Furthermore, GBM cells showed G1-phase cell cycle arrest accompanied by high protein expression of p53 and p21 due to LINC01515 downregulation. In vivo experiments in mice demonstrated that down-regulation of LINC01515 reduced the weight and volume of tumors and was accompanied by an increase in apoptosis and a decrease in inflammatory infiltration of GBM cells. MEOX2 activates LINC01515 transcription. Down-regulation of LINC01515 promotes GBM cell apoptosis and inhibits GBM cell function and activity, improves inflammatory infiltration, and plays a therapeutic role in GBM.
The emergence of cross‑resistance between anticancer agents poses a major challenge to effective sequential therapy. To determine whether resistance to one chemotherapeutic agent confers resistance to another, we examined cisplatin‑ and doxorubicin‑resistant HeLa and HepG2 models generated through iterative pulse‑selection, a process known to induce stable resistance phenotypes while preserving lineage‑specific characteristics. Drug sensitivity profiling using MTT assays showed that each resistant line exhibited elevated IC₅₀ values and reduced apoptosis exclusively toward its selecting drug, consistent with drug‑specific apoptosis evasion. However, both cisplatin‑ and doxorubicin‑resistant cells retained full sensitivity to the alternate agent, with survival curves and apoptotic responses comparable to parental controls, indicating the absence of generalized cross‑resistance. Transcriptomic analysis further revealed that the two resistance states were governed by largely non‑overlapping molecular programs. Only a small subset of differentially expressed genes was shared, whereas pathway enrichment highlighted Rap1 signaling and drug‑metabolism enzymes in doxorubicin resistance, and PI3K signaling together with H3K27 demethylase‑associated genes in cisplatin resistance. These findings demonstrate that cisplatin and doxorubicin resistance arise through distinct adaptive mechanisms and support the concept that these agents remain mutually effective even in resistant cellular contexts.
Breast cancer is one of the most prevalent malignant tumors affecting women worldwide. Although early-stage breast cancer treatments often result in favorable outcomes, recurrence and metastasis remain significant clinical challenges. Recent advancements in molecular biology and genetics have greatly enhanced our understanding of breast cancer. Epigenetics has emerged as a critical factor in the initiation, progression, and metastasis of breast cancer. Epigenetics refers to heritable changes in gene expression caused by chemical modifications that do not alter the DNA sequence, including DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA (ncRNA) regulation. Currently, numerous epigenetic drugs have been utilized in the clinical treatment and research of breast cancer. This review examines the relationship between epigenetics and breast cancer to better understand its role in pathogenesis and potential for novel therapeutic strategies.
This study is aim to develop and validate a simple, clinical diagnostic tool based on the clinical and pathological characteristics of synchronous multiple esophageal squamous cell neoplasias (ESCNs), thereby assisting clinicians in reducing the rate of missed diagnoses. This retrospective study analyzed the clinical and pathological data of 445 patients with ESCN from three medical centers. Patients were divided into a training group (n = 349) and an external validation group (n = 96) based on center of care.We developed a diagnostic nomogram model for synchronous multiple ESCN using logistic regression and Platt scaling calibration. We then compared this model with the best one built using 21 machine learning methods to identify a simpler, more stable, and more accurate model that could assist clinical practice. The incidence of synchronous multiple ESCNs was 10.6
The optimal adjuvant management of pathologically node-positive (pN1) prostate cancer following radical prostatectomy remains controversial, with conflicting retrospective evidence regarding the survival benefit of postoperative radiotherapy. This study aimed to evaluate the association between postoperative radiotherapy and long-term cancer-specific survival (CSS) and overall survival (OS) in a large population-based cohort. Patients with pN1 prostate adenocarcinoma who underwent radical prostatectomy between 2010 and 2022 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were stratified into radical prostatectomy alone (RP-only) and radical prostatectomy plus postoperative radiotherapy (RP + RT) groups. Propensity score matching (1:1) was performed to balance baseline covariates. Multivariable Cox regression, propensity score–matched analyses, and inverse probability of treatment weighting (IPTW) were used to estimate hazard ratios (HRs) for CSS and OS. Competing risk and subgroup analyses were conducted as sensitivity analyses. A total of 7,250 eligible patients were included, comprising 5,006 in the RP-only group and 2,244 in the RP + RT group. Propensity score matching yielded 1,702 well-balanced pairs. In the matched cohort, postoperative radiotherapy was significantly associated with improved CSS (HR = 0.786, 95
This study was conducted to investigate the microenvironment of multiple myeloma (MM) and predict treatment response based on the extrachromosomal DNA (ecDNA) signature. Bioinformatics was implemented to determine the expression characteristics of ecDNA-related genes to construct a prognostic signature for MM, analyze its association with the tumor immune microenvironment (TME), predict treatment regimens, and assess the core gene expression patterns at the single-cell level. The background expression of core genes in the MM cell lines RPMI and GM12878 was evaluated. Bone marrow samples were collected from six patients with MM, after which plasma cells were isolated using magnetic bead technology, and eight healthy participants were placed in the control group. QPCR analysis was conducted to detect differences in the expression of core genes between the two groups. Three key ecDNA-related genes (AKAP9, TYMS, and YEATS4) were identified via differential analysis, univariate Cox regression analysis, Kaplan–Meier (KM) survival analysis, and LASSO-Cox regression based on the bulk transcriptome to construct an ecDNA-related prognostic signature. Patients were divided into high-risk and low-risk groups based on their prognosis score, and patients with high scores had a poorer prognosis. The results of univariate and multivariate Cox regression analyses revealed that the constructed prognostic signature was an independent prognostic factor; these findings were confirmed in the independent validation set. By analyzing a single-cell subset of C5 cells that had a high expression of the AKAP9 gene and was present in low proportions in MM patients, it was found that this subset of cells may be related to the pathogenesis of MM. Cell experiments confirmed that RPMI cells expressed significantly higher levels of the AKAP9, TYMS, and YEATS4 genes than GM12878 cells. No significant difference in the expression of AKAP9 was found between the plasma cells of MM patients and those of healthy controls. The expression of TYMS and YEATS4 genes was significantly higher in the plasma cells of MM patients than in healthy controls. The genes AKAP9, TYMS, and YEATS4 are highly expressed in MM cells. The level of expression of the AKAP9 gene in plasma cells of MM patients was not significantly different from that in the bone marrow of healthy controls. The level of expression of the TYMS and YEATS4 genes was significantly higher in MM patients than in the healthy control group. Therefore, the prediction model based on ecDNA-related features can predict the treatment response and prognosis of MM. The feasibility of this method was preliminarily determined by experiments.
To address the limited discrimination of existing clinical risk assessment tools for venous thromboembolism (VTE) in patients with multiple myeloma (MM), this study aims to construct an interpretable machine learning-based VTE risk prediction model. This study retrospectively examined data from 517 MM patients, finding a 10.06
Regenerative medicine has emerged as a transformative field that can augment cancer therapy by leveraging stem cells, gene therapy, and tissue engineering to address limitations of conventional treatments. This review synthesizes recent advances in cell-based therapies, biomaterial scaffolds, gene-delivery systems, and nanoparticle platforms, and it organizes key unresolved controversies that shape research priorities: safety and tumorigenicity of stem-cell platforms; optimal TME-versus-tumor-directed strategies; durability and scalability of gene-delivery systems; balancing immune activation with off-target inflammation; and criteria for clinical translation and patient selection. We summarize updated clinical outcome data where relevant and provide recommendations to guide translational research and trial design toward safer, durable, and personalized regenerative oncology approaches.
Eyes absent (EYA) proteins, known for their critical role as regulators, are prominently expressed during embryogenesis but are typically downregulated following development. Notably, aberrant expression of Eya proteins has been observed in various tumor types. For example, studies on gastric cancer have shown that high EYA1 expression inhibits autophagy and activates the mTORC1 signaling pathway, thus promoting tumor cell growth; however, its impact on low-grade gliomas (LGGs) remains to be elucidated. This study investigated the involvement of EYA transcriptional coactivator and phosphatase 3 (EYA3) in LGG. Pan-cancer and survival analyses were performed to screen for EYA3 in LGGs. Subsequently, we systematically analyzed the association between EYA3 and multiple parameters, including prognosis, clinical characteristics, biological functions, genetic variations, and immunological characteristics of LGG. The tumor-promoting effects of EYA3 were evaluated using CCK8 and transwell assays. Western blotting and RT-qPCR were performed to assess the effects of EYA3 on the JAK2-STAT3-MYC axis. EYA3 is upregulated in various tumor types and is associated with poor prognosis. In LGG, patients with higher EYA3 levels had worse prognoses than those with lower EYA3 levels. EYA3 is associated with multiple adverse clinical traits and serves as an independent prognostic factor for LGG. Furthermore, EYA3 expression in LGG was associated with immune checkpoint genes (ICPGs), tumor mutation burden (TMB), and immune cell infiltration. Laboratory studies have confirmed that abnormally elevated levels of EYA3 are necessary for the growth and cancerous characteristics of LGGs. EYA3 could be a novel therapeutic target and predictive biomarker for patients with LGGs, with abnormal expression linked to poor outcomes. Functionally, EYA3 enhances the malignant progression of LGGs, at least in part via engagement with the pharmacologically linked JAK2-STAT3-MYC signaling axis.
