Discover. Oncology最新文献

Background: Cancer is a major global health issue. Aurora kinase B (AURKB) is known to regulate cell division and linked to poor prognosis in some cancers, but its role across all cancer types is unclear.

Objective: The goal of this study is to examine how AURKB expression, epigenetic regulation, and immune cell infiltration relate to its potential as a prognostic biomarker.

Methods: We conducted a comprehensive analysis of transcriptomic and clinical data from TCGA, HPA, and CCLE to assess AURKB expression in various cancers. RNA-seq data processing involved TCGAbiolinks, normalization to TPM, and filtering based on the completeness of survival data and sequencing quality. The maxstat method was used to categorize groups into high and low expression. The prognostic significance was evaluated using Cox regression and Kaplan-Meier analysis for OS, DFS, PFI, and DSS. UALCAN was used to analyze promoter methylation, and immune infiltration was assessed with TIMER2 and CIBERSORT. Using the GDSC and CTRP datasets, researchers carried out functional enrichment (KEGG, GO) and examined drug sensitivity correlations. With more than 11,000 cases, the sample size provided enough statistical power.

Results: AURKB was found to be significantly overexpressed in multiple malignancies, including breast (BRCA), liver (LIHC), kidney (KIRP), and lung (LUAD) cancers, with high expression linked to advanced clinical stages and poorer prognosis. Elevated AURKB levels were associated with significantly reduced OS, DFS, PFI and DSS, particularly in cancers like KIRP (HR = 2.04 for OS, p < 4.2e-10). ROC analysis demonstrated the high diagnostic potential of AURKB, with Area Under the Curve (AUC) values of 1.000 for CHOL and GBM, indicating strong sensitivity and specificity. Additionally, AURKB overexpression was often accompanied by promoter hypomethylation, suggesting an epigenetic mechanism underlying its dysregulation. Immune infiltration analysis revealed that increased AURKB expression correlated with reduced CD8 + T cell infiltration and enhanced immune suppression, particularly in aggressive subtypes. Drug sensitivity analysis showed a negative correlation between AURKB expression and chemotherapy response, suggesting that AURKB overexpression contributes to drug resistance.

Conclusion: AURKB is a significant prognostic biomarker and potential therapeutic target, with overexpression linked to poor outcomes and chemotherapy resistance. The association with hypomethylation suggests an epigenetic role, supporting targeted therapies. AURKB's influence on immune suppression highlights its role in the tumor microenvironment. Clinical integration of AURKB analysis could enhance personalized treatment, and future research should explore combination therapies with AURKB inhibitors.

Background: To investigate the independent risk factors for liver metastasis-specific mortality in patients with gallbladder carcinoma and also to develop a competing risk model.

Methods: Patients diagnosed with gallbladder carcinoma between 2000 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Patients' death of liver metastases was regarded as an outcome event and death of other causes as a competing event. Feature selection was performed using univariate and multivariate Fine & Gray regression. The area under the receiver operating character (ROC) curve (AUC) was calculated, and calibration curves were plotted to assess the performance of the constructed predictive model.

Results: The study ultimately enrolled 8964 patients, with 6266 in the training cohort and 2698 in the validation cohort. The median follow-up duration for the total study cohort was 12 months, with a total of 1105 patients who died of liver metastases. The results of feature selection showed that histological type, median of household income, SEER stage, tumor sequence, sex, tumor size, and whether surgery was performed (Surgery) were independent factors influencing the liver metastasis-specific mortality in patients with gallbladder carcinoma. Result of calibration curve analysis showed that the model performed well and did not significantly overestimate or underestimate the risk of death.

Conclusion: The predictive model we constructed can help surgeons assess the risk of liver metastasis-specific death in patients with gallbladder carcinoma and help clinicians predict the prognosis of patients and make more rational clinical decisions.

Background: Epithelial ovarian cancer (EOC) is an aggressive malignancy marked by late diagnosis, recurrence, and treatment resistance. Tumor-associated macrophages (TAMs) play pivotal role in EOC progression, particularly through polarization into the pro-tumorigenic M2 phenotype. Ubiquitin-specific protease 19 (USP19), a deubiquitinase implicated in oncogenesis, regulates immune responses via the NLRP3 inflammasome. However, its role in EOC remains unclear. This study aims to investigate the role of USP19 in EOC progression and TAM polarization.

Methods: The expression levels of USP19 and its subcellular localization were evaluated in human EOC cell lines C13K, SW626, and A2780 using RT-qPCR, Western blot, and immunofluorescence. USP19 knockdown models were established in A2780 cells to assess the effects of USP19 on cell proliferation, migration, invasion, and macrophage polarization in co-culture systems. Cytokine levels, macrophage markers, and epithelial-mesenchymal transition (EMT) marker expression were analyzed using ELISA, flow cytometry, and Western blot, respectively.

Results: USP19 was significantly upregulated in EOC cells and predominantly localized in the cytoplasm, co-localizing with NLRP3. Knockdown of USP19 inhibited proliferation, migration, invasion, and EMT marker expression in A2780 cells. Additionally, USP19 suppression promoted TAM polarization towards the pro-inflammatory M1 phenotype by enhancing the expression of IL-12 and CXCL10 while reducing M2 markers such as CD206. Dual-conditioned medium from USP19-deficient cells significantly mitigated the malignant phenotypes of EOC cells, indicating a disrupted TAM-mediated feedback loop.

Conclusion: USP19 drives EOC progression by promoting M2 macrophage polarization and suppressing NLRP3-mediated inflammatory responses. Targeting USP19 may represent a novel therapeutic strategy to modulate the tumor immune microenvironment and inhibit EOC progression.

Background: Pancreatic cancer (PC) is characterized by a profoundly immunosuppressive and fibrotic tumor microenvironment, driven largely by cancer-associated fibroblasts (CAFs). While microRNAs (miRNAs) are key regulators in PC, their specific roles in stromal fibrosis remain poorly understood. This study aims to investigate the prognostic and fibrotic relevance of miR-4787-3p and miR-581 in PC.

Methods: We identified differentially expressed miRNAs from The Cancer Genome Atlas (TCGA) database. MiR-4787-3p and miR-581 were selected for their prognostic value using Cox regression and Kaplan-Meier analysis. Their expression was validated in vitro using a co-culture model of PC cells and CAFs, and in clinical blood and tissue samples via RT-PCR, with correlations to clinical-pathological features and fibrosis markers (TGF-β1, COL1A1) assessed.

Results: TCGA analysis revealed significant downregulation of both miRNAs in PC tissues, which correlated strongly with poor patient survival. Mechanistically, their expression was suppressed in PC cell lines co-cultured with CAFs and inversely correlated with TGF-β1 and COL1A1 levels. Consistent with these findings, miR-4787-3p and miR-581 levels were markedly lower in PC patient tissues and blood samples compared to controls, with the lowest expression observed in high-fibrosis tissues. Both miRNAs demonstrated high sensitivity and specificity for distinguishing PC from non-cancerous controls and for identifying fibrosis within tumors.

Conclusion: The downregulation of miR-4787-3p and miR-581 is a hallmark of pancreatic cancer, closely associated with tumor fibrosis and poor prognosis. These miRNAs represent promising circulating and tissue biomarker candidates for diagnosing PC and evaluating its fibrotic burden, suggesting potential utility for early detection and personalized treatment strategies.

Recurrence prediction in differentiated thyroid carcinoma (DTC) remains clinically challenging despite generally favorable outcomes and well-established treatment strategies. Improving early identification of patients at elevated recurrence risk may enhance individualized surveillance and therapeutic decision-making. This study evaluated the performance and clinical utility of machine-learning models for recurrence prediction using routinely collected clinicopathologic features from a publicly available cohort of 383 patients with long-term follow-up.16 variables were initially analyzed following international guideline definitions. Random Forest, XGBoost, and LightGBM models were developed using stratified training-test splits, SMOTE for class-imbalance correction, fivefold cross-validation, probability calibration, and decision-curve analysis. Shapley Additive Explanations (SHAP) was applied to quantify global and local feature contributions and to derive simplified feature subsets. Models trained with 4, 6, 8, and full feature sets were compared to assess the impact of dimensionality reduction on discrimination and interpretability. Full-feature models achieved strong performance, with Random Forest obtaining the highest AUC (0.931). Notably, a compact 4-feature Random Forest model-including Risk, N stage, T stage, and Age-maintained high discriminatory ability (AUC 0.913; accuracy 0.862; recall 0.750), demonstrating that substantial simplification preserved predictive value. Performance improvements plateaued beyond 6-8 features, indicating limited incremental benefit from larger feature sets. SHAP analysis consistently identified Risk, N, T, and Age as dominant predictors. These findings highlight that streamlined, interpretable ML models using a small number of clinically accessible features can provide accurate and explainable recurrence prediction in DTC. Such models offer advantages in computational efficiency, transparency, and real-world deployability, supporting their potential integration into electronic health record systems or point-of-care decision tools. Future work should prioritize multicenter external validation and incorporation of additional pathological or molecular markers to enhance generalizability and clinical applicability.

Histone deacetylase 6 (HDAC6) is the sole member of the histone deacetylase (HDAC) family predominantly localized in the cytoplasm, characterized by dual catalytic domains and an ubiquitin-binding domain. In recent years, it has garnered substantial attention due to its critical role in tumor initiation and progression. This review delineates the unique structural features and core biological functions of HDAC6, while further exploring its expression patterns and prognostic significance in tumors. Additionally, it elaborates on the regulatory roles of HDAC6 in key biological behaviors of tumor cells, including promoting proliferation, suppressing apoptosis, enhancing migratory and invasive potentials, and inducing epithelial-mesenchymal transition (EMT). Concomitantly, the review analyzes the impacts of HDAC6 on the tumor immune microenvironment, its modulation of tumor metabolism, and its association with tumor drug resistance. To date, research on HDAC6 in tumors has firmly established its value as a potential therapeutic target, with specific inhibitors (e.g., ACY-1215 and Tubastatin A) demonstrating significant antitumor activity in preclinical studies and several clinical trials. Focused on the implications of HDAC6 in tumors, this review not only highlights its distinct functions compared to other HDAC family members but also integrates previously unreported mechanisms of action (e.g., HDAC6 cooperates with NEDD8/p62 to sustain proteostasis) and clinical translation perspectives. Collectively, it presents an innovative review that provides valuable references for subsequent basic research and clinical practice of HDAC6-targeted tumor therapy.