Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma.

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10 years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

Shc3 facilitates breast cancer drug resistance by interacting with ErbB2 to initiate ErbB2/COX2/MDR1 axis.

Multidrug resistance (MDR) is a primary limitation of breast cancer chemotherapy. The common mechanism of MDR is various anticancer drugs can be effluxed by the cell membrane protein P-glycoprotein (P-gp). Here, we found that ectopic overexpression of Shc3 was detected specifically in drug-resistant breast cancer cells, consequently reducing sensitivity to chemotherapy and promoting cell migration by mediating P-gp expression. However, the molecular mechanism underlying the interplay between P-gp and Shc3 in breast cancer is unknown. We reported an additional resistance mechanism involving an increase in the active form of P-gp after Shc3 upregulation. MCF-7/ADR and SK-BR-3 cells can be sensitive to doxorubicin after knockdown of Shc3. Our results indicated that the interaction between ErbB2 and EphA2 is indirect and regulated by Shc3, and also, this complex is essential for activation of the MAPK and AKT pathways. Meanwhile, Shc3 promotes ErbB2 nuclear translocation, followed by a subsequent increase of the COX2 expression through ErbB2 binding to the COX2 promoter. We further demonstrated that COX2 expression was positively correlated with P-gp expression and the Shc3/ErbB2/COX2 axis upregulates P-gp activity in vivo. Our results show the crucial roles of Shc3 and ErbB2 in modulating P-gp efficacy in breast cancer cells and suggest that Shc3 inhibition may enhance the sensitivity to chemotherapeutic drugs that target oncogene addiction pathways.

Multiclassifier Radiomics Analysis of Ultrasound for Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma in Children.

Objective: To explore extrathyroidal extension (ETE) in children and adolescents with papillary thyroid carcinoma using a multiclassifier ultrasound radiomic model. Methods: In this study, data from 164 pediatric patients with papillary thyroid cancer (PTC) were retrospectively analyzed and patients were randomly divided into a training cohort (115) and a validation cohort (49) in a 7:3 ratio. To extract radiomics features from ultrasound images of the thyroid, areas of interest (ROIs) were delineated layer by layer along the edge of the tumor contour. The feature dimension was then reduced using the correlation coefficient screening method, and 16 features with a nonzero coefficient were chosen using Lasso. Then, in the training cohort, four supervised machine learning radiomics models (k-nearest neighbor, random forest, support vector machine [SVM], and LightGBM) were developed. ROC and decision-making curves were utilized to compare model performance, which was validated using validation cohorts. In addition, the SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model. Results: In the training cohort, the average area under the curve (AUC) was 0.880 (0.835-0.927), 0.873 (0.829-0.916), 0.999 (0.999-1.000), and 0.926 (0.892-0.926) for the SVM, KNN, random forest, and LightGBM, respectively. In the validation cohort, the AUC for the SVM was 0.784 (0.680-0.889), for the KNN, it was 0.720 (0.615-0.825), for the random forest, it was 0.728 (0.622-0.834), and for the LightGBM, it was 0.832 (0.742-0.921). Generally, the LightGBM model performed well in both the training and validation cohorts. From the SHAP results, original_shape_MinorAxisLength,original_shape_Maximum2DDiameterColumn, and wavelet-HHH_glszm_SmallAreaLowGrayLevelEmphasis have the most significant effect on the model. Conclusions: Our combined model based on machine learning and ultrasonic radiomics demonstrate the excellent predictive ability for extrathyroidal extension (ETE) in pediatric PTC.

B3GALT4 remodels the tumor microenvironment through GD2-mediated lipid raft formation and the c-met/AKT/mTOR/IRF-1 axis in neuroblastoma.

BACKGROUND: Beta-1,3-galactosyltransferase-4 (B3GALT4) plays a critical regulatory role in tumor biology. However, the role of B3GALT4 in modulating the tumor microenvironment (TME) of neuroblastoma (NB) remains unknown. METHODS: Public datasets and clinical NB samples were collected to evaluate the expression and clinical significance of GD2 and B3GALT4 in NB patients. CCK-8, colony formation, and transwell assays and experiments in tumor-bearing mouse models were conducted to investigate the function of B3GALT4. Flow cytometry, ELISA, immunohistochemistry, immunofluorescence, western blotting, and chemotaxis assays were conducted to ascertain the immunomodulatory mechanism of B3GALT4. The combined therapeutic effect of the lipid raft inhibitor MßCD and anti-GD2 mAb was validated in a murine model of NB. RESULTS: GD2 was overexpressed in NB tissues and high expression of GD2 was associated with poor prognosis in NB patients. B3GALT4 was downregulated in NB tissues, and low expression of B3GALT4 indicated poor prognosis in NB patients. Silencing B3GALT4 significantly enhanced tumor progression both in vitro and in vivo. Meanwhile, the overexpression of B3GALT4 increased the recruitment of CD8+ T lymphocytes via the chemokines CXCL9 and CXCL10. Additionally, B3GALT4 regulated NB-cell GD2 expression and lipid raft formation. Mechanistically, B3GALT4 regulated the expression of CXCL9 and CXCL10 via the c-Met signaling in the lipid rafts and the downstream AKT/mTOR/IRF-1 pathway. The lipid raft inhibitor, MßCD, attenuated B3GALT4 deficiency-induced tumor progression and immune evasion. Last, MßCD combined with anti-GD2 mAb treatment significantly enhanced the antitumor effect and the infiltration of CD8+ T cells. CONCLUSIONS: Upregulation of B3GALT4 promotes the secretion of CXCL9 and CXCL10 to recruit CD8+ T lymphocytes via the GD2-mediated lipid rafts and the c-Met/AKT/mTOR/IRF-1 pathway. Moreover, lipid raft inhibitors may enhance the efficacy of anti-GD2 immunotherapy for NB.