Development of a breast cancer prognostic model based on vesicle-mediated transport-related genes to predict immune landscape and clinical drug therapy.

BACKGROUND: Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis. METHODS: Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis. RESULTS: We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group. CONCLUSION: We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

Domain nucleation kinetics and polarization-texture-dependent electronic properties in two-dimensional α-In2Se3 ferroelectrics.

Two-dimensional (2D) ferroelectric semiconductors, such as α-In2Se3 with switchable spontaneous polarization and superior optoelectronic properties, exhibit large potential for functional device applications. The electric transport properties and device performance of 2D α-In2Se3 are strongly sensitive to the ferroelectric domain structures and polarization textures, but they are rarely explored at the atomic scale. Herein, by a combination of first-principles calculations and a developed domain switching theory, we report the domain nucleation kinetics and polarization-texture dependent electronic properties in α-In2Se3 ferroelectrics. Our calculated results reveal that the reversed domains characterized by armchair boundaries tend to form triangular or stripped shape. The energy barrier for propagating domain boundaries is ∼1.42 eV and can be reduced by loading external electric field, which is responsible for driving the evolution of domain structures. Moreover, the domain switching leads to notable changes in the band gap and carrier spatial distribution of α-In2Se3 monolayer, resulting in higher electric resistance of multi-polarization domain structures than that of single-polarization state. The domain structures of multilayer α-In2Se3 follow a layer-by-layer switching mechanism, which causes the transition of electronic structures from self-doped p-n junctions to type-II semiconductor homojunctions. This study not only provides an in-depth insight into the domain switching mechanisms of α-In2Se3 but also opens up the possibility to tailor their electronic and transport properties.

Construction of a prognostic risk assessment model for HER2 + breast cancer based on autophagy-related genes.

Although breast cancer (BC) has a low mortality rate relative to other cancers, it prominently affects the survival of patients with human epidermal growth factor receptor-2 (HER2 +) BC due to its high recurrence rate. By far, it has been found that autophagy can affect various tumor occurrence and development, as well as patients' prognosis. HER2 + BC patient samples and autophagy-related genes (ARGs) were acquired from a public database, least absolute shrinkage and selection operator (LASSO) and Cox analyses (including univariate and multivariate analyses) were utilized to construct a 9-ARGs model, which was verified by using HER2 + BC patient samples in The Cancer Genome Atlas (TCGA) dataset. Sample risk score was worked out based on characteristic genes, and prominent differences in overall survival were tracked down between high- and low-risk groups. Predictive ability of the model was validated by drawing receiver operating characteristic (ROC) curves and then calculating the area under the curves (AUC) value. Results showed good accuracy and prediction ability of the model in both validation set and training set. For the purpose of facilitating model application in clinical practice, we constructed a nomogram combing clinical factors and risk scores to evaluate 1-year, 3-year and 5-year survival of HER2 + BC patients. In addition, we assessed the correlation of risk score with tumor mutational burden and tumor immune infiltration. Results exhibited that in a high-risk group, tumor mutation was relatively high, while tumor immune infiltration was relatively poor. Overall, based on ARGs, the prognostic signature in this study can tellingly evaluate prognoses of HER2 + BC patients and provide a reference for clinicians.