Among the 100 IBC differentially expressed proteins, 37 were found to be specific to this type of cancer only. Additionally, four proteins were specifically expressed in DCIS and four in fibroadenoma. Compared to corresponding adjacent tissues and normal breast tissues, 18 step-changing proteins were differentially expressed in IBC, 14 in DCIS, and 13 in fibroadenoma, respectively. Compared to DCIS and normal breast tissues, 65 proteins were differentially expressed in IBC with growing levels of malignancy.
 
  The identified potential protein biomarkers may be used as diagnostic and/or therapeutic targets in breast tumors.
 The identified potential protein biomarkers may be used as diagnostic and/or therapeutic targets in breast tumors.Esophageal squamous cell carcinoma (ESCC) is a deadly disease with a low 5-year survival rate. Anti-epidermal growth factor receptor (EGFR) therapy has been widely used in the treatment of malignancies, and chemotherapy regimens that include nimotuzumab have been confirmed to have satisfactory efficacy among esophageal carcinoma (EC) patients. However, a subpopulation of patients may develop resistance to nimotuzumab. Here, we report an advanced ESCC patient who experienced hyperprogressive disease induced by immune checkpoint inhibitors and was then treated with a chemotherapy regimen containing nimotuzumab. NGS examination of this patient demonstrated that PIK3CA mutation and a RICTOR amplification might participate in primary and acquired resistance to nimotuzumab, respectively, via the PI3K/AKT/mTOR signaling pathway.
  Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners.
 
  The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and ificantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model.
 
  Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.
 Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.Cancers are among the difficult-to-treat diseases despite advances in diagnosis and treatment. Although newer effective targets remain to be discovered, targeted therapy has emerged as a promising field. In the last decade, contactin 1 (CNTN1) has surfaced as an important cancer-related molecule.  https://www.selleckchem.com/products/oicr-9429.html  CNTN1 is a neuronal membrane glycoprotein, which, if overexpressed, is found in different cancer cell lines, cancer tissues, and transgenic mice. It is positively associated with lymphatic invasion, metastasis, late TNM stage, and a short overall survival time. However, the role of CNTN1 in cancer cell proliferation remains unclear. In addition, CNTN1 is involved in cancer cell invasion, migration, metastasis, and chemoresistance by promoting epithelial-mesenchymal transition and mediating several signal transduction pathways. Several studies suggest CNTN1 as a new therapeutic target for cancers. This review aims to summarize the research developments on CNTN1 in various cancers, to establish its role in epithelial-mesenchymal transition and signal transduction pathways, and to identify promising areas for further investigation.
  Family with sequence similarity 111 member A (FAM111A), as a replication factor required for proliferating cell nuclear antigen (PCNA) loading, has been demonstrated a possible association with carcinogenesis. However, the role of FAM111A in lower-grade glioma (LGG) remains unclear. We aim at investigating the expression and function of FAM111A in lower-grade glioma at the molecular and clinical levels.
 
  In total, 711 lower-grade glioma samples were analyzed in our research, including 182 RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) dataset and 529 RNA-seq data from The cancer Genome Atlas (TCGA) dataset. R language and the GraphPad software were used for the majority of statistical analysis and graphical work.
 
  FAM111A expression was overexpressed in WHO grade III and IDH-wildtype lower-grade glioma. FAM111A was significantly downregulated in the IDHmut-Codel molecular subtype. Univariate and multivariate Cox analysis demonstrated that FAM111A was an independent prognostic factor in LGG patients. Functional characterization of FAM111A revealed that it was associated with inflammatory response and immune response to tumor cells. FAM111A could also act as an indicator of the stromal and immune population, especially for monocytic lineage, myeloid dendritic cells and fibroblasts. It was positively correlated with macrophages, especially the M2 macrophage cells. Furthermore, FAM111A revealed predictive value for the immune subtypes and immune checkpoint blockade therapy.
 
  FAM111A expression was closely related to the malignant phenotype, molecular pathology and immune response of lower-grade glioma. It might be a promising target for LGG immunotherapeutic strategies.
 FAM111A expression was closely related to the malignant phenotype, molecular pathology and immune response of lower-grade glioma. It might be a promising target for LGG immunotherapeutic strategies.
  To develop a radiomics nomogram that incorporates contrast-enhanced spectral mammography (CESM)-based radiomics features and clinico-radiological variables for identifying benign and malignant breast lesions of sub-1 cm.
 
  This retrospective study included 139 patients with the diameter of sub-1 cm on cranial caudal (CC) position of recombined images. Radiomics features were extracted from low-energy and recombined images on CC position. The variance threshold, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal predictive features. Radiomics signature (Rad-score) was calculated by a linear combination of selected features. The independent predictive factors were identified by ANOVA and multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability of lesions. The performance and clinical utility of the nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).