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Breast cancer type 1 susceptibility protein (BRCA1) is essential for homologous recombination repair of DNA double-strand breaks. Loss of BRCA1 is lethal to embryos due to extreme genomic instability and the activation of p53-dependent apoptosis. However, the apoptosis is resisted in BRCA1-deficient cancer cells even though their p53 is proficient. In this study, by analysis of transcriptome data of ovarian cancer patients bearing BRCA1 defects in TCGA database, we found that cAMP signaling pathway was significantly activated. Experimentally, we found that BRCA1 deficiency caused an increased expression of ADRB1, a transmembrane receptor that can promote the generation of cAMP. The elevated cAMP not only inhibited DNA damage-induced apoptosis through abrogating p53 accumulation, but also suppressed the proliferation of cytotoxic T lymphocytes by enhancing the expression of immunosuppressive factors DKK1. Inhibition of ADRB1 effectively killed cancer cells by abolishing the apoptotic resistance. These findings uncover a novel mechanism of apoptotic resistance in BRCA1-deficient ovarian cancer cells and point to a potentially new strategy for treating BRCA1-mutated tumors.

ObjectivesThe purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.MethodsIn this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.ResultsThe predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.ConclusionsRadiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

BackgroundCurcumin, one of the promising candidates for supplementary therapy in cancer treatment, has been demonstrated by numerous preclinical and clinical evidence to be beneficial in treating various cancers. Apart from the critical role in a deluge of pathological processes, some mRNAs, in particular, microRNAs (miRNAs), are also involved in the anti-tumor activity. Therefore, our research focused on the possible effects of curcumin on small cell lung cancer (SCLC) cells and drew a comprehensive transcriptomes profile by high throughput sequencing to understand the molecular mechanism of curcumin as an anti-tumor agent.MethodsFirst, we calculated the apoptosis rate of H446 cells (a human SCLC cell line) cultured with curcumin. The high output sequencing uncovered the altered expression profile of genes and miRNAs. KEGG analysis selected the potential signal pathway associated with the antiproliferative property of curcumin. Finally, miRNAs significantly changed, as well as the regulatory roles of those miRNAs in cell apoptosis were determined.ResultThe apoptosis rate of H446 cells increased under the elevated concentration of curcumin treatment. And cell cycle-related genes downregulated in the curcumin-treated cells. Besides, miRNA-548ah-5p of a high level acted as a negative role in the anticarcinogenic activity of curcumin.ConclusionOur findings not only enriched the understanding of anti-tumor activity initiated by curcumin through figuring out the downregulated cell cycle-related pathways but also shed light on its novel therapeutic application.

Objectives: This study compared the diagnostic ability of image-based parameters with texture parameters in the differentiation of hepatocellular carcinoma (HCC) and hepatic lymphoma (HL) by positron emission tomography–computed tomography (PET/CT). Methods: Patients with pathological diagnosis of HCC and HL were included in this study. Image-based and texture parameters were obtained by manual drawing of region of interest. Receiver operating characteristic (ROC) was used to test the diagnostic capacity of each parameter. Binary logistic regression was used to transform the most discriminative image-based parameters, texture parameters, and the combination of these parameters into three regression models. ROC was used to test the diagnostic capacity of these models. Result: Ninety-nine patients diagnosed with HCC (n = 76) and HL (n = 23, 10 primary HL, 13 secondary HL) by histological examination were included in this study (From 2011 to 2018, West China hospital). According to the AUC and p-value, 2 image-based parameters and five texture parameters were selected. The diagnostic ability of texture-based model was better than that of image-based model, and after combination of those two groups of parameters the diagnostic capacity improved. Conclusion: Texture parameters can differentiate HCC from HL quantitatively and improve the diagnostic ability of image-based parameters.