Clinicopathological characteristics of synchronous multiple primary early esophageal cancer and risk factors for multiple lesions

BackgroundWith the development of endoscopic technology, the detection rate of synchronous multiple primary early esophageal cancer (SMPEEC) is increasing; however, the risk factors remain unclear. We aimed to assess the clinicopathological characteristics of patients with SMPEEC and investigate the risk factors contributing to the development of multiple lesions.MethodsA retrospective cohort study was conducted on 911 consecutive patients who underwent Endoscopic submucosal dissection (ESD) for primary esophageal neoplasms from January 2013 to June 2021. The patients were divided into the SMPEEC group and the solitary early esophageal cancer (SEEC) group. We compared the differences in clinicopathological characteristics between the two groups and investigated the risk factors linked to multiple lesions. Additionally, we investigated the relationship between the main and accessory lesions.ResultsA total of 87 SMPEEC patients were included in this study, and the frequency of synchronous multiple lesions was 9.55% in patients with early esophageal cancer. The lesions in the SMPEEC group were mainly located in the lower segment of the esophagus (46[52.9%]), whereas those in the SEEC group were in the middle segment (412[50.0%]). The pathology type, tumor location, and circumferential rate of lesions were independent risk factors(P<0.05) for SMPEEC by logistic regression analysis. Significant positive correlations were observed between the main and accessory lesions in terms of morphologic type (r=0.632, P=0.000), tumor location(r=0.325, P=0.037), pathologic type (r=0.299, P=0.003), and depth of invasion (r=0.562, P=0.000).ConclusionPathology type, tumor location, and circumferential rate of lesions were identified as independent risk factors for SMEPPC. Understanding these risk factors and the correlation between the main and accessory lesions could significantly improve the detection rate of SMPEEC

Long Noncoding RNA FAM201A Mediates the Radiosensitivity of Esophageal Squamous Cell Cancer by Regulating ATM and mTOR Expression via miR-101

Background: The aim of the present study was to identify the potential long non-coding (lnc.)-RNA and its associated molecular mechanisms involved in the regulation of the radiosensitivity of esophageal squamous cell cancer (ESCC) in order to assess whether it could be a biomarker for the prediction of the response to radiotherapy and prognosis in patients with ESCC.Methods: Microarrays and bioinformatics analysis were utilized to screen the potential lncRNAs associated with radiosensitivity in radiosensitive (n = 3) and radioresistant (n = 3) ESCC tumor tissues. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed in 35 ESCC tumor tissues (20 radiosensitive and 15 radioresistant tissues, respectively) to validate the lncRNA that contributed the most to the radiosensitivity of ESCC (named the candidate lncRNA). MTT, flow cytometry, and western blot assays were conducted to assess the effect of the candidate lncRNA on radiosensitivity in vitro in ECA109/ECA109R ESCC cells. A mouse xenograft model was established to confirm the function of the candidate lncRNA in the radiosensitivity of ESCC in vivo. The putative downstream target genes regulated by the candidate lncRNA were predicted using Starbase 2.0 software and the TargetScan database. The interactions between the candidate lncRNA and the putative downstream target genes were examined by Luciferase reporter assay, and were confirmed by PCR.Results: A total of 113 aberrantly expressed lncRNAs were identified by microarray analysis, of which family with sequence similarity 201-member A (FAM201A) was identified as the lncRNA that contributed the most to the radiosensitivity of ESCC. FAM201A was upregulated in radioresistant ESCC tumor tissues and had a poorer short-term response to radiotherapy resulting in inferior overall survival. FAM201A knockdown enhanced the radiosensitivity of ECA109/ECA109R cells by upregulating ataxia telangiectasia mutated (ATM) and mammalian target of rapamycin (mTOR) expression via the negative regulation of miR-101 expression. The mouse xenograft model demonstrated that FAM201A knockdown improved the radiosensitivity of ESCC.Conclusion: The lncRNA FAM201A, which mediated the radiosensitivity of ESCC by regulating ATM and mTOR expression via miR-101 in the present study, may be a potential biomarker for predicting radiosensitivity and patient prognosis, and may be a therapeutic target for enhancing cancer radiosensitivity in ESCC

Growth Inhibition and Apoptosis Induced by Osthole, A Natural Coumarin, in Hepatocellular Carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most commonly diagnosed tumors worldwide and is known to be resistant to conventional chemotherapy. New therapeutic strategies are urgently needed for treating HCC. Osthole, a natural coumarin derivative, has been shown to have anti-tumor activity. However, the effects of osthole on HCC have not yet been reported. METHODS AND FINDINGS: HCC cell lines were treated with osthole at various concentrations for 24, 48 and 72 hours. The proliferations of the HCC cells were measured by MTT assays. Cell cycle distribution and apoptosis were determined by flow cytometry. HCC tumor models were established in mice by subcutaneously injection of SMMC-7721 or Hepa1-6 cells and the effect of osthole on tumor growths in vivo and the drug toxicity were studied. NF-κB activity after osthole treatment was determined by electrophoretic mobility shift assays and the expression of caspase-3 was measured by western blotting. The expression levels of other apoptosis-related genes were also determined by real-time PCR (PCR array) assays. Osthole displayed a dose- and time-dependent inhibition of the HCC cell proliferations in vitro. It also induced apoptosis and caused cell accumulation in G2 phase. Osthole could significantly suppress HCC tumor growth in vivo with no toxicity at the dose we used. NF-κB activity was significantly suppressed by osthole at the dose- and time-dependent manner. The cleaved caspase-3 was also increased by osthole treatment. The expression levels of some apoptosis-related genes that belong to TNF ligand family, TNF receptor family, Bcl-2 family, caspase family, TRAF family, death domain family, CIDE domain and death effector domain family and CARD family were all increased with osthole treatment. CONCLUSION: Osthole could significantly inhibit HCC growth in vitro and in vivo through cell cycle arrest and inducing apoptosis by suppressing NF-κB activity and promoting the expressions of apoptosis-related genes

The synthesis of novel Mn-doped CdTe fluorescence probes and their application in the determination of luteolin

Mn-doped CdTe QDs were prepared. Luteolin can reduce their fluorescence intensity, based on which detection of luteolin can be realized.</p

CarbonAI, A Non-Docking Deep learning based small molecule virtual screening platform

Structure-based virtual screening is a promising in silico technique that integrates computational methods into drug discovery. The most extensively used method in structure-based virtual screening is molecular docking. However, the docking process is not computationally efficient and simultaneously accurate due to classic mechanics-based scoring functions. These can only approximate, but not reach, quantum mechanics precision. In order to reduce the computational cost of the protein-ligand scoring process and use data-driven approaches to boost the scoring function accuracy, deep learning non-docking methods can be used by utilizing 3D structure or 1D sequence information of the protein target. This method can minimize the error inherited from molecular docking methods and avoid the extensive computational cost of docking. Furthermore, these two methods are integrated into an easy-to-use framework, CarbonAI, that provides both choices for researchers. Graph neural network (GNN) is employed in the 3D version and BiLSTM has been adopted in the sequence version of CarbonAI, respectively. To verify our approaches, different experiments were performed on two datasets, an open dataset Directory of Useful Decoys: Enhanced (DUD.E) and an in-house proprietary dataset without computer generated artificial decoys (NoDecoy). On DUD.E we achieved a state-of-the-art AUC of 0.981 and on NoDecoy we achieved an AUC of 0.974 whereas on the conventional docking program, the respective AUC performance is less than 0.8. The CarbonAI engine also reaches a state-of-the-art enrichment factor at top 2 percent for 36.2 folds. We have also retrospectively validated the CarbonAI models with various wet lab experimental data, and the results demonstrated a consistently accurate performance. Furthermore, the inference speed of the engine was benchmarked using the openly available 2021 Enamine REAL Database (RDB), that comprises over 1.36 billion molecules in 4050 core-hours using our CarbonAI non-docking method (CarbonAI-ND). The inference speed of CarbonAI-ND is about 36000 molecule per core-hour, compared to typical docking methods\u27 speed of 20, which is about 16000 times faster than conventional docking method. Overall, the experiments indicate that CarbonAI is accurate and computationally efficient with good generalization to different molecular targets for virtual screening