Discover. Oncology最新文献

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer with a high metastatic rate and high mortality rate. The molecular mechanism of ccRCC development, however, needs further study. Aurora kinase B (AURKB) functions as an important oncogene in various tumors; therefore, in the present study, we aimed to explore the mechanism by which AURKB affects ccRCC development.

Methods: We performed bioinformatics analysis, CCK-8 assay, RNA sequencing, RT-PCR and Western blot to analyze the function and mechanism of AURKB in ccRCC.

Results: TIMER2.0 showed that AURKB was overexpressed in Kidney Renal Clear Cell Carcinoma (KIRC), the UALCAN database showed the survival rate of KIRC patients with different expression levels of AURKB and different gender indicated in the same gender, high AURKB expression predicts lower survival rate. Silencing of AURKB expression inhibits the proliferation of ccRCC cells. RNA-seq data suggested that AURKB is involved in fatty acid metabolism. Silencing of AURKB inhibited the expression of fatty acid synthase (FASN). FASN is a key gene involved in fatty acid metabolism. TIMER2.0 showed that FASN is upregulated in KIRC. Silencing of FASN inhibited the proliferation of ccRCC cells.

Conclusions: AURKB induces the proliferation of ccRCC cells by regulating fatty acid metabolism.

Gastric cancer is an aggressive malignancy characterized by significant clinical heterogeneity arising from complex genetic and environmental interactions. This study employed single-cell RNA sequencing, using the 10 × Genomics platform, to analyze 262,532 cells from gastric cancer samples, identifying 32 distinct clusters and 10 major cell types, including immune cells (e.g., T cells, monocytes) and epithelial subpopulations. Among 27 epithelial subgroups, five malignant subpopulations were identified, each defined by unique marker gene expressions and playing diverse roles in tumor progression. Developmental trajectory analysis revealed potential stem-like characteristics in certain clusters, suggesting their involvement in therapeutic resistance and disease recurrence. Cell-cell communication analysis uncovered a dynamic network of interactions within the tumor microenvironment, potentially influencing tumor growth and metastasis. Differential gene expression analysis identified key genes (LDHA, GPC3, MIF, CD44, and TFF3) that were used to construct a prognostic risk score model. This model demonstrated robust predictive power, achieving AUC values of 0.77, 0.77, and 0.76 for 1-, 3-, and 5-year overall survival in the TCGA training dataset, with validation across independent cohorts. These findings deepen our understanding of gastric cancer's cellular and molecular heterogeneity, offering insights into potential therapeutic targets and biomarkers. By facilitating the development of targeted therapies and personalized treatment strategies, these results hold promise for improving clinical outcomes in gastric cancer patients.

Objectives: This study aims to use Mendelian randomisation to identify the causal relationship between a spectrum of 41 inflammatory cytokines and the development of oropharyngeal cancer.

Methods: This study investigated genetic variants that have been associated with oral and oropharyngeal cancer using data from a large GWAS. Inflammatory cytokine data were obtained from 8293 asymptomatic individuals. The study primarily used inverse variance weighted and MR-Egger methods to determine the causal relationship between inflammatory cytokines and cancer incidence, complemented by a series of sensitivity analyses including MR-Egger, simple mode, weighted mode, weighted median and leave-one-out approaches.

Results: Our study demonstrates that higher levels of interleukin-7 (IL-7) and interleukin-5 (IL-5) slightly increase the odds of oropharyngeal cancer by 0.07% [OR: 1.0007, p = 0.005] and 0.04% [OR: 1.0004, p = 0.015], corresponding to a modest increase. Similarly, increased PDGF-bb and CTACK levels are modestly associated with increased odds of oral and oropharyngeal cancer by 0.22% [OR: 1.0022, p = 0.031] and 0.17% [OR: 1.0017, p = 0.043], respectively.

Conclusion: This investigation posits that IL-5 and IL-7 may be pertinent factors in the etiology of Oropharyngeal cancer, while PDGF bb and CTACK are likely implicated in the pathogenesis of both oral and oropharyngeal cancers.

The zinc finger protein 32 (ZNF32) has been associated with high expression in various cancers, underscoring its significant function in both cancer biology and immune response. To further elucidate the biological role of ZNF32 and identify potential immunotherapy targets in cancer, we conducted an in-depth analysis of ZNF32. We comprehensively investigated the expression of ZNF32 across tumors using diverse databases, including TCGA, CCLE, TIMER2.0, KM-Plotter, cBioPortal, ImmuCellAI. We investigated correlations between ZNF32 expression and various factors such as prognosis, immune infiltration, immunotherapy, DNA methylation, and biological functions. Furthermore, we performed in vitro research to validate the significance of ZNF32 in head and neck cancer (HNSC). Our study revealed that ZNF32 was high in various types of cancer, including ACC, BRCA, and others, indicating its important potential as a prognostic biomarker. Significant changes in CNA and DNA methylation were associated with high ZNF32 expression. ZNF32 was notably linked to various immune characteristics, including immune cell infiltration, MSI, TMB and immune checkpoint gene expression, indicating its potential in informing immunotherapy approaches. Interestingly, in FaDu and CAL27 cell lines, the group with elevated ZNF32 expression exhibited increased levels of immune checkpoint markers, such as CTLA-4 and PD-L1. Overexpression of ZNF32 significantly enhanced proliferation and migration in FaDu and CAL27 cell lines, as demonstrated through CCK-8 assays, colony formation, flow cytometry, Transwell migration, and Boyden invasion assays. Our in vitro experiments confirmed that ZNF32 promotes malignant behavior by driving HNSC cell proliferation and migration. These results imply that ZNF32 might be a promising target for tumor prognosis and immunotherapy. Our results highlight the important role of ZNF32 in tumorigenesis and provide novel perspectives for potential cancer treatment strategies.

This study aims to investigate the expression of seven cancer testis antigens (MAGE-A1, MAGE-A4, MAGE-A10, MAGE-A11, PRAME, NY-ESO-1 and KK-LC-1) in pan squamous cell carcinoma and their prognostic value, thus assessing the potential of these CTAs as immunotherapeutic targets. The protein expression of these CTAs was evaluated by immunohistochemistry in 60 lung squamous cell carcinoma (LUSC), 62 esophageal squamous cell carcinoma (ESCA) and 62 head and neck squamous cell carcinoma (HNSC). The relationship between CTAs expression and progression-free survival (PFS) was assessed. PD-L1 expression and tumor-infiltrating lymphocytes were also collected and correlated with CTAs expression. The prognostic impact of CTAs gene expression was evaluated using the Kaplan-Meier Plotter website. CTAs expression was 0-48% in ESCA, 3%-77% in LUSC, and 3%-71% in HNSC. Analysis of PFS showed that MAGE-A1 expression in HNSC (**p < 0.01), PRAME in LUSC (p = 0.008, **p < 0.01), MAGE-A10 (p = 0.012, *p < 0.05) and PRAME (p = 0.021, *p < 0.05) in ESCA were significantly correlated with PFS. In all three cancers, coexpression of three CTAs was used as a cutoff value for grouping, and the results showed a significant difference in PFS between these two groups. Moreover, CTAs expression was significantly correlated with PD-L1 expression and T cell infiltration. These findings indicate a high incidence of CTA expression in HNSC, LUSC and ESCA, which was correlated with PD-L1 expression, T cell infiltration, and tumor progression. The results suggest that cancer testis antigens could be feasible vaccine targets in squamous cell carcinoma.

The established protocol for the management of acute myeloid leukemia (AML) has traditionally involved the administration of induction chemotherapy, followed by consolidation chemotherapy, and subsequent allogeneic stem cell transplantation for eligible patients. However, the prognosis for individuals with relapsed and refractory AML remains unfavorable. In response to the necessity for more efficacious therapeutic modalities, targeted immunotherapy has emerged as a promising advancement in AML treatment. This comprehensive review article specifically examines classical unconjugated and toxin-conjugated monoclonal antibodies, which are currently in the preclinical phase or undergoing evaluation in clinical trials. The review delves into the proposed mechanisms through which these monoclonal antibodies elicit anti-tumor activity and identifies the challenges associated with designing targeted immunotherapy. The review focuses on targeting specific antigens in AML, including FLT3/CD125, CLL-1, CD33, CD38, CD47, CD70, and CD123.

Purpose: Paraneoplastic syndromes (PNS) are a group of rare disorders triggered by an immune response to malignancy, characterized by diverse neurological, muscular, and systemic symptoms. This study aims to leverage machine learning to develop a predictive model for cancer diagnosis in patients with paraneoplastic autoantibodies.

Methods: Demographic data included age and sex, and presenting symptoms were recorded. Laboratory data comprised serum or cerebrospinal fluid (CSF) paraneoplastic autoantibody panels. The study included participants who tested positive for at least one autoantibody. Naive Bayes model was used to predict cancer presence. Model performance was evaluated using sensitivity, specificity, likelihood ratios, predictive values, AUC-ROC, Brier score, and overall accuracy. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. A graphical user interface (GUI)-based application was developed to facilitate model use.

Results: The study included 116 participants, with an average age of 57.1 years and a higher proportion of females (53.4%). The most common presenting symptom was ''Motor'' (40.5%), followed by ''Cognitive'' (17.2%) and ''Bulbar'' (15.5%) symptoms. Cancer was present in 23 participants (19.8%). The Naive Bayes model demonstrated high performance with a sensitivity of 85.71% and specificity of 100.00%. The AUC-ROC was 0.9795, indicating excellent diagnostic capability. Age and the presence or absence of specific autoantibodies were significant predictors of cancer.

Conclusion: Machine learning models, such as the Naive Bayes classifier developed in this study, can accurately stratify cancer risk in patients with positive paraneoplastic autoantibodies.

Objective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).

Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to train the deep learning model. Multiple deep learning models were trained to predict SDM in ccRCC patients. The results of the best machine learning model were then fused with those of the deep learning model to create a combined model.

Results: Of the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. Among the deep learning models, ResNet101 performed optimally, achieving AUC of 0.815 and 0.743 in the training and validation cohort, respectively. The combined model yielded an AUC of 0.863. Decision curve analysis suggested that the combined model offers superior clinical applicability.

Conclusion: The model integrates radiomics and deep learning, showing significant potential in predicting SDM in ccRCC patients. It holds promise for supporting clinical decision-making, reducing missed diagnoses of SDM, and guiding patients in further enhancing their systemic examinations.

Introduction: We conducted a panoramic analysis of GBN5 expression and prognosis in 33 cancers, aiming to deepen the systematic understanding of GBN5 in cancer.

Materials and methods: We employed a multi-omics approach, including transcriptomic, genomic, proteomic, single-cell cytomic, spatial transcriptomic, and genomic data, to explore the prognostic value and potential oncogenic mechanisms of GBN5 across pan-cancers from multiple perspectives.

Results: We found that GBN5 was differentially expressed in multiple tumors and showed early diagnostic value. Mutations, somatic copy number alterations, and DNA methylation lead to its aberrant expression. GBN5 expression is associated with many clinical features. GBN5 expression has been validated to be associated with many metabolic, metastatic, and immune-related pathways. We also demonstrated that GBN5 expression was significantly associated with immunomodulatory molecules and biomarkers of lymphocyte subpopulation infiltration. Methylation levels of GBN5 expression were significantly negatively correlated in a variety of tumors, and GBN5 missense mutations were the predominant SNP type in pan-cancer. In addition, GBN5 was positively correlated with multiple genomic scores, implying that higher GBN5 expression tends to imply that patients have higher chromosomal instability. More importantly, GBN5 has an important role in predicting drug sensitivity. We have also developed effective targeted drugs against GBN5.

Conclusion: GBN5 plays an important role in the genesis and progression of various tumors and is a potential tumor diagnostic and prognostic biomarker as well as an anti-cancer therapeutic target.