Lupeol alters ER stress-signaling pathway by downregulating ABCG2 expression to induce Oxaliplatin-resistant LoVo colorectal cancer cell apoptosis.

Colorectal cancer (CRC) is one of the most common cancers and causes of cancer-related death. There are several first-line chemotherapeutic drugs used to treat CRC. Oxaliplatin (OXA) is an alkylating cytotoxic agent that is usually combined with other chemotherapeutic drugs to treat stage II and stage III CRC. However, cancer cells commonly acquire multidrug resistance (MDR), which is a major obstruction to cancer treatment. Recent studies have shown that natural components from traditional Chinese medicine or foods that have many biological functions may be new adjuvant therapies in clinical trials. We challenged LoVo CRC cell lines with OXA in a dose-dependent manner to create an OXA-resistant model. The expression of ABCG2 was significantly higher, and levels of endoplasmic reticulum (ER) stress markers were lower than those Parental cells. However, Lupeol, which is found in fruits and vegetables, has been shown to have bioactive properties, including anti-tumor properties that are relevant to many diseases. In our study, Lupeol downregulated cell viability and activated cell apoptosis. Moreover, Lupeol decreased the expression of ABCG2 and activated ER stress to induce OXA-resistant cell death. Importantly, the anti-tumor effect of Lupeol in OXA-resistant cells was higher than that of LoVo Parental cells. In addition, we also confirmed our results with a xenograft animal model, and the tumor size significantly decreased after Lupeol injections. Our findings show that Lupeol served as a strong chemoresistant sensitizer and could be a new adjuvant therapy method for chemoresistant patients.

beta-Naphthoflavone protects from peritonitis by reducing TNF-alpha-induced endothelial cell activation.

ß-Naphthoflavone (ß-NF), a ligand of the aryl hydrocarbon receptor, has been shown to possess anti-oxidative properties. We investigated the anti-oxidative and anti-inflammatory potential of ß-NF in human microvascular endothelial cells treated with tumor necrosis factor-alpha (TNF-α). Pretreatment with ß-NF significantly inhibited TNF-α-induced intracellular reactive oxygen species, translocation of p67(phox), and TNF-α-induced monocyte binding and transmigration. In addition, ß-NF significantly inhibited TNF-α-induced ICAM-1 and VCAM-1 expression. The mRNA expression levels of the inflammatory cytokines TNF-α and IL-6 were reduced by ß-NF, as was the infiltration of white blood cells, in a peritonitis model. The inhibition of adhesion molecules was associated with suppressed nuclear translocation of NF-κB p65 and Akt, and suppressed phosphorylation of ERK1/2 and p38. The translocation of Egr-1, a downstream transcription factor involved in the MEK-ERK signaling pathway, was suppressed by ß-NF treatment. Our findings show that ß-NF inhibits TNF-α-induced NF-kB and ERK1/2 activation and ROS generation, thereby suppressing the expression of adhesion molecules. This results in reduced adhesion and transmigration of leukocytes in vitro and prevents the infiltration of leukocytes in a peritonitis model. Our findings also suggest that ß-NF might prevent TNF-α-induced inflammation.

Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography.

OBJECTIVE: To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS: CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency. RESULTS: 129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSION: The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE: We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.