RNA sequencing of exosomes revealed differentially expressed long noncoding RNAs in early-stage esophageal squamous cell carcinoma and benign esophagitis

Epigenomics ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 525-541
Author(s):  
Li Tian ◽  
Lin Yang ◽  
Wenjing Zheng ◽  
Yinqing Hu ◽  
Peikun Ding ◽  
...  

Aim: To explore the roles of exosomal long noncoding RNAs (lncRNAs) in early-stage esophageal squamous cell carcinoma (ESCC) and benign esophagitis. Materials & methods: Exosomal lncRNAs were analyzed using RNA-seq and validated by quantitative real-time PCR, loss-of-function, co-culture and RNA pulldown assays. Results: Exosomal lncRNAs displayed tighter tissue-specificity, higher expression level and lower splicing efficiency than that of mRNAs. A total of 152 exosomal lncRNAs were differentially expressed between ESCC and controls. A total of 124 exosomal lncRNAs were dysregulated between ESCC and esophagitis. Knockdown of 13 ESCC-associated lncRNAs modified proliferation, migration, and apoptosis of ESCC cells. A novel lncRNA RP5-1092A11.2 was highly expressed in ESCC-derived exosomes, ESCC cells and tumor tissues. Exosomes released from RP5-1092A11.2-knockdown cells inhibited ESCC cell proliferation. Conclusion: Dysregulated exosomal lncRNAs were functionally associated with different disease status in esophagus.

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Xueting Hu ◽  
Duoguang Wu ◽  
Xiaotian He ◽  
Huiying Zhao ◽  
Zhanghai He ◽  
...  

Abstract Background Circular RNAs (circRNAs), a novel class of noncoding RNAs, have recently drawn much attention in the pathogenesis of human cancers. However, the role of circRNAs in esophageal squamous cell carcinoma (ESCC) remains unclear. In this study, we aimed to identify novel circRNAs that regulate ESCC progression and explored their regulatory mechanisms and clinical significance in ESCC. Methods Differentially expressed circRNAs between ESCC and paired adjacent normal tissues were identified using microarrays. The effects of a specific differentially expressed circRNA (circGSK3β) on tumor progression were explored in vitro and in vivo. Plasma samples from patients with ESCC, benign lesions and healthy controls were subjected to droplet digital PCR (ddPCR) analyses for circGSK3β, and the detection rates of plasma circGSK3β for ESCC were investigated. Results We demonstrated that upregulated expression of circGSK3β was positively associated with advanced clinical stage and poor outcome in patients with ESCC. We further revealed that circGSK3β promoted ESCC cell migration and invasion via direct interaction with GSK3β and inhibiting GSK3β activity, providing a novel mechanism of circRNA in cancer progression. Importantly, we identified that circGSK3β expression in plasma was a biomarker for detection of ESCC and early stage of ESCC with the area under curve (AUC) of 0.782 and 0.793, respectively. Conclusions CircGSK3β exerts critical roles in promoting ESCC metastasis and may serve as a novel therapeutic target for ESCC patients. The plasma level of circGSK3β have potential to serve as a novel diagnostic and prognostic biomarker for ESCC detection.


Author(s):  
Min Zhang ◽  
Jixia Wang ◽  
Yichun Li ◽  
Lei Qin ◽  
Ruijuan Fan ◽  
...  

Evidence indicates that the long noncoding RNAs are involved in the metformin-mediated anti-cancer processes. However, the potential effects of the long noncoding RNAs in metformin-mediated anti-tumor processes in esophageal squamous cell carcinomas (ESCC) are still elusive. This study uncovered that metformin decreases the level of long noncoding RNAs CCAT1 and SPRY4-IT1 thereby contributing to the down-regulation of c-Myc and vimentin. Also, the RNA level test of human ESCC tissue confirmed the positive correlation between CCAT1 and c-Myc. These findings demonstrated that metformin facilitated anti-cancer effects by targeting the 2 long noncoding RNAs (CCAT1 and SPRY4-IT1) and their consequential targets c-Myc and vimentin. Therefore, the CCAT1 and SPRY4-IT1 might act as novel molecular targets that mediate the anti-tumor effects in esophageal squamous cell carcinoma. This helps in predicting the treatment response of metformin in patients diagnosed with esophageal squamous cell carcinoma.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaofeng Duan ◽  
Xiaobin Shang ◽  
Jie Yue ◽  
Zhao Ma ◽  
Chuangui Chen ◽  
...  

Abstract Background A nomogram was developed to predict lymph node metastasis (LNM) for patients with early-stage esophageal squamous cell carcinoma (ESCC). Methods We used the clinical data of ESCC patients with pathological T1 stage disease who underwent surgery from January 2011 to June 2018 to develop a nomogram model. Multivariable logistic regression was used to confirm the risk factors for variable selection. The risk of LNM was stratified based on the nomogram model. The nomogram was validated by an independent cohort which included early ESCC patients underwent esophagectomy between July 2018 and December 2019. Results Of the 223 patients, 36 (16.1%) patients had LNM. The following three variables were confirmed as LNM risk factors and were included in the nomogram model: tumor differentiation (odds ratio [OR] = 3.776, 95% confidence interval [CI] 1.515–9.360, p = 0.004), depth of tumor invasion (OR = 3.124, 95% CI 1.146–8.511, p = 0.026), and tumor size (OR = 2.420, 95% CI 1.070–5.473, p = 0.034). The C-index was 0.810 (95% CI 0.742–0.895) in the derivation cohort (223 patients) and 0.830 (95% CI 0.763–0.902) in the validation cohort (80 patients). Conclusions A validated nomogram can predict the risk of LNM via risk stratification. It could be used to assist in the decision-making process to determine which patients should undergo esophagectomy and for which patients with a low risk of LNM, curative endoscopic resection would be sufficient.


2019 ◽  
Vol 89 (6) ◽  
pp. AB576-AB577
Author(s):  
Sophie L. Brigstocke ◽  
Vaishali Patel ◽  
Saurabh Chawla ◽  
Parit Mekaroonkamol ◽  
Steven Keilin ◽  
...  

2019 ◽  
Vol 11 (9) ◽  
pp. 3980-3990 ◽  
Author(s):  
Xiangyang Yu ◽  
Rusi Zhang ◽  
Tianzhen Yang ◽  
Mengqi Zhang ◽  
Kexing Xi ◽  
...  

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