Activation of Fas signaling promotes the migration of GC cells.

<p>(A) The Fas expression in AGS and MNK-45 cells were detected by real-time PCR (upper) and Western blot (down). (B) Susceptibility of AGS and MNK-45 cells to Fas-induced apoptosis was measured by staining with Annexin V and PI after both cells were stimulated with anti-Fas or ISO at the indicated concentrations for 24 h (left) and the apoptotic cells were statistically analyzed (right) (n = 3). (C) After stimulated with 5 μg/ml anti-Fas or ISO for 2 h, the AGS cells were collected and seeded into the top chamber. Forty-eight hours later, the number of cells on the bottom of the Transwell filter was imaged (left) and quantified (right) (n = 5). Magnification: 200×. (D) The proliferation of AGS cells was measured by CCK8 assay after stimulation with 5 μg/ml of anti-Fas or ISO in the indicated timepoint. Data are representative of three independent experiments. (*p <0.05, ***p <0.001)</p

Activation of Fas signaling upregulated Fascin expression in AGS cells through activation of STAT3.

<p>The AGS cells were stimulated with 5 μg/ml of anti-Fas in the indicated times. (A) The phosphorylated STAT3 was detected by Western blot. (B) The expression of Fascin mRNA was assayed by real-time PCR. (C) After stimulation with 5 μg/ml anti-Fas for 24 h, the protein level of phosphorylated STAT3 and Fascin in AGS cells was detected by Western blot. (D) The AGS cells were pre-treated with 10 μM of Stattic for 2 h and followed by 5 μg/ml of anti-Fas stimulation for 24 h; the protein level of phosphorylated STAT3 and Fascin was detected by Western blot. After transfection with STAT3 siRNA or NC siRNA for 36 h, (E) the STAT3 expression in the AGS cells was detected by Western blot; (F) the AGS cells were then stimulated with 5 μg/ml of anti-Fas for 2 h, and the Fascin expression in the cells was detected by Western blot. Data are representative of three independent experiments.</p

Correlation of the mRNA levels of Fas and Fascin in tumor tissues from GC patients.

<p>Fas and Fascin mRNA expression was measured by real-time PCR and normalized to β-actin mRNA (n = 23). Positive correlation was obtained by Spearman correlation analysis.</p

Fas signaling promotes AGS cell metastasis <i>in vivo</i> through STAT3/Fascin pathway.

<p>2 × 10⁶ AGS tumor cells pre-stimulated with anti-Fas or ISO for 2 h were intravenously injected nude mice. (A) The number of lung tumor foci was counted (n = 5). (B) The expression of Fascin in tumor tissues from lung was detected by immunohistochemistry. 2 × 10⁶ AGS tumor cells were intravenously injected into nude mice and 24 h later, the mice received intravenous injection of S3I-201 at 5 mg/kg every 2 days for total 3 times. (C) The number of lung tumor foci was counted (n = 5). (D) The expression of Fascin in tumor tissues from lung was detected by immunohistochemistry (magnification: ×100). Data are representative of two independent experiments. (*p <0.05, ***p <0.001)</p

Patient characteristics.

<p>NOTE: Data are mean ± standard deviation.</p><p>*According to American Joint Committee on Cancer.</p><p>Abbreviation: ECOG PS, Eastern Cooperative Oncology Group Performance Status.</p><p>Patient characteristics.</p

Fas signaling promoted AGS cell migration dependent on STAT3/Fascin pathway.

<p>(A) AGS cells were transfected with Fascin siRNA or NC siRNA for 36 h, and Fascin expression in the cells was detected by Western blot. After (B) inhibition of Fascin expression by siRNA; or (C) treated with 10 μM Stattic for 2 h; or (D) inhibition of STAT3 expression by siRNA, and stimulated with 5 μg/ml of anti-Fas for 2 h, the number of AGS cells which migrated to the bottom of the Transwell filter was quantified (n = 5). Data are representative of three independent experiments. (**p <0.01)</p

Supplemental Material - Differential expression profile of microRNAs in the lung tissues of coal workers with pneumoconiosis and patients with silicosis

Supplemental Material for Differential expression profile of microRNAs in the lung tissues of coal workers with pneumoconiosis and patients with silicosis by Yilin Tian, Xiuqing Cui, Xin Guan, Xiang Meng, Min Zheng, Xin Wang, Guoping Cheng, Ying Xia and Meng Ye in Toxicology and Industrial Health</p

DataSheet_1_Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.docx

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.</p