Early Esophageal Cancer: What the Gastroenterologist Needs to Know

Endoscopic ultrasound-based radiomics for predicting pathologic upgrade in esophageal low-grade intraepithelial neoplasia

BACKGROUND

There is no consensus on managing patients with endoscopic suspicion of early esophageal squamous cell carcinoma (ESCC) but biopsy-confirmed low-grade intraepithelial neoplasia (LGIN). The aim of this study is to evaluate the utility of an endoscopic ultrasound (EUS)-based radiomics nomogram for predicting esophageal LGIN pathological progression before diagnostic endoscopic submucosal dissection (ESD).

METHODS

In the development phase, EUS images of 535 patients who had biopsy-confirmed LGIN and were undergoing ESD were retrospectively included. Concurrently, 251 patients were prospectively included for independent model validation. A radiomics signature (RS) was constructed using Pearson test and the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics nomogram was then developed with multivariate logistic regression to predict pathologic upgrade before ESD. Model performance was assessed with receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

RESULTS

Following stepwise multivariate logistic regression analysis, statistically significant clinical features were incorporated into the clinical predictive model. From EUS images, 105 radiomic features were extracted, with 11 key features selected for RS development. The RS showed strong predictive performance in identifying pathologic upgrade (AUC = 0.786). Moreover, when integrated with the clinical model (AUC = 0.648), the RS performance remarkably improved (AUC = 0.818). These results were subsequently validated in the prospective test cohort (RS: AUC = 0.792; Clinical model: AUC = 0.669; Combined model: AUC = 0.821). The combined model presented as a nomogram also excelled in calibration tests and DCA, underlining its potential for clinical application.

CONCLUSION

The EUS-based radiomics nomogram showed potential for predicting pathologic upgrade in esophageal LGIN, which helps to distinguish high-risk from low-risk cases and assists clinicians in assessing the necessity of diagnostic ESD.

Exosomes loaded with the anti-cancer molecule mir-1-3p inhibit intrapulmonary colonization and growth of human esophageal squamous carcinoma cells

BACKGROUND

The overall prognosis of patients with esophageal cancer (EC) is extremely poor. There is an urgent need to develop innovative therapeutic strategies. This study will investigate the anti-cancer effects of exosomes loaded with specific anti-cancer microRNAs in vivo and in vitro.

METHODS

Specific miRNAs that were significantly down-regulated in EC tissues were screened using the miRNAs profiling data of human EC tissue samples in TCGA, and the role of their exogenous expression in the proliferation and migration of human EC cell lines, KYSE150 and Eca109, were detected using CCK-8 and Transwell assays. Exosomes were loaded with miRNAs using electroporation.

RESULTS

The expression of miR-1-3p was significantly down-regulated in human EC tissues with potential anti-cancer effects. Exosomes loaded with miR-1-3p significantly inhibited the proliferation, migration and invasion of KYSE150 and Eca109 cells in vitro, as well as the intrapulmonary colonization and growth of KYSE150 cells in vivo. In addition, miR-1-3p could directly bind to the 3'UTR of the transcription factor E2F5 mRNA, down-regulate the protein expression of E2F5, and inhibit the activation of the MAPK/ERK signaling pathway.

CONCLUSION

Exosomes loaded with miR-1-3p may be applicable to the treatment of EC.

Apatinib Mesylate Inhibits Cell Proliferation and the Metastasis of Esophageal Squamous Cell Carcinoma Through ERK/ELK-1/Snail Pathway

This study aimed to evaluate the impact of apatinib (APT) mesylate on the growth, migration ability, and underlying mechanisms in esophageal squamous cell carcinoma (ESCC) cell lines Kyse30 and Kyse150. Additionally, the anti-metastatic effects of APT mesylate were further validated in a nude mouse xenograft metastasis model. In vitro, APT mesylate treatment significantly reduced cell viability and migration ability in both cell lines in a dose- and time-dependent manner. Western blot analysis showed that APT mesylate inhibited the expression of proteins involved in the ERK/ELK-1/Snail signaling pathway, including ERK1/2, Snail, N-cadherin, and Vimentin, while upregulating E-cadherin expression. In vivo, APT mesylate administration notably decreased the number of pulmonary metastatic nodules in nude mice, with higher doses showing more pronounced effects. The 200 mg/kg high-dose group exhibited a significantly lower number of metastatic nodules compared to the cisplatin (CIS) group. The results suggest that APT mesylate inhibits ESCC cell proliferation and migration primarily by suppressing the ERK/ELK-1/Snail signaling pathway, which mediates epithelial-mesenchymal transition (EMT) and reduces metastasis and invasiveness. This study provides experimental evidence for the potential clinical application of APT mesylate in targeted therapy for ESCC, indicating its promising clinical value.

Effect of vinegar supplementation on patients with esophageal lesions lightly stained with Lugol's iodine solution: Prospective single-centre trial

BACKGROUND

Esophageal chromoendoscopy with iodine solution is important for detecting early esophageal cancer. The effect of routine treatment for lesions lightly stained with Lugol's iodine solution is limited, and the addition of natural substances to a regular diet is becoming increasingly common. Vinegar has antitumor effects as reported in previous studies.

AIM

To evaluate whether vinegar supplementation could improve the prognosis of patients with lightly stained esophageal lesions.

METHODS

This prospective single-centre trial included consecutive patients with lightly stained lesions between June 2020 and April 2022. Patients in the experimental group received increased amounts of vinegar for 6 months. The primary outcome of the study was the clinical therapeutic effect. Complications related to vinegar ingestion and adverse events were also recorded in detail.

RESULTS

A total of 166 patients were included in the final analysis. There was no significant difference in the baseline data between the two groups. Intention-to-treat (ITT) analysis demonstrated that the rates at which endoscopic characteristics improved were 33.72% in the experimental group and 20.00% in the conventional group (P = 0.007); and the rates at which biopsy pathology improved were 19.77% and 8.75%, respectively (P = 0.011). Additional vinegar consumption had a statistically protective effect on the rate at which endoscopic characteristics improved [hazard ratio (HR) ITT = 2.183, 95%CI: 1.183-4.028; HRper-protocol (PP) = 2.307, 95%CI: 1.202-4.426] and biopsy pathology improved (HRITT = 2.931, 95%CI: 1.212-7.089; HRPP = 3.320, 95%CI: 1.295-8.507). No statistically significant effect of increased vinegar consumption on preventing high-grade intraepithelial neoplasia or early cancer was observed (HRITT = 0.382, 95%CI: 0.079-1.846; HRPP = 0.382, 95%CI: 0.079-1.846). The subgroup analyses indicated that the overall therapeutic improvement of endoscopic characteristics and biopsy pathology seemed more obvious in older (age > 60) male patients with small lesions (lesion size ≤ 0.5 cm). Three patients in the experimental group reported acid regurgitation and heartburn. No adverse event during gastroscopy were recorded during follow-up.

CONCLUSION

A moderately increased ingestion of vinegar could not directly reduce the risk of esophageal cancer in the mucosa dysplasia population, but it improved the endoscopic characteristics and ameliorated the biopsy pathology to a certain extent. Further research is needed to verify the effect of nutritional intervention on precancerous esophageal lesions.

Esophageal cancer detection via non-contrast CT and deep learning

Background

Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images.

Methods

In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted.

Results

In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test).

Conclusion

The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

From LncRNA to metastasis: The MALAT1-EMT axis in cancer progression

Cancer is a complex disease that causes abnormal genetic changes and unchecked cellular growth. It also causes a disruption in the normal regulatory processes that leads to the creation of malignant tissue. The complex interplay of genetic, environmental, and epigenetic variables influences its etiology. Long non-coding RNAs (LncRNAs) have emerged as pivotal contributors within the intricate landscape of cancer biology, orchestrating an array of multifaceted cellular processes that substantiate the processes of carcinogenesis and metastasis. Metastasis is a crucial driver of cancer mortality. Among these, MALAT1 (Metastasis-Associated Lung Adenocarcinoma Transcript 1) has drawn a lot of interest for its function in encouraging metastasis via controlling the Epithelial-Mesenchymal Transition (EMT) procedure. MALAT1 exerts a pivotal influence on the process of EMT, thereby promoting metastasis to distant organs. The mechanistic underpinning of this phenomenon involves the orchestration of an intricate regulatory network encompassing transcription factors, signalling cascades, and genes intricately associated with the EMT process by MALAT1. Its crucial function in transforming tumor cells into an aggressive phenotype is highlighted by its capacity to influence the expression of essential EMT effectors such as N-cadherin, E-cadherin, and Snail. An understanding of the MALAT1-EMT axis provides potential therapeutic approaches for cancer intervention. Targeting MALAT1 or its downstream EMT effectors may reduce the spread of metastatic disease and improve the effectiveness of already available therapies. Understanding the MALAT1-EMT axis holds significant clinical implications. Therefore, directing attention towards MALAT1 or its downstream mediators could present innovative therapeutic strategies for mitigating metastasis and improving patient prognosis. This study highlights the importance of MALAT1 in cancer biology and its potential for cutting back on metastatic disease with novel treatment strategies.

miR-18a-5p and ATM Expression in Esophageal Squamous Cell Carcinoma and Their Correlations with Clinicopathological Features

Objective. To investigate miR-18a-5p and ataxia telangiectasia muted (ATM) expression in esophageal squamous cell carcinoma (ESCC) and their correlations with clinicopathological features. Methods. The subjects of this study were 62 ESCC patients (research group, RG) and 57 healthy controls (control group, CG) presented to our hospital between July 2019 and April 2020. Peripheral blood (PB) miR-18a-5p and ATM levels in these participants were quantified via qRT-PCR, and the correlations of the two genes with ESCC patients’ clinicopathological characteristics were investigated. In addition, a two-year follow-up was performed on ESCC patients to understand their survival, so as to further determine the prognostic utility of miR-18a-5p and ATM in ESCC. Factors influencing patient outcomes were identified by COX analysis. Results. PB miR-18a-5p expression was higher in RG compared with CG, while ATM was lower, suggesting an inverse connection between the two genes in ESCC ( $P<0.001$ ). miR-18a-5p and ATM levels were determined to be strongly linked to TNM stage, differentiation degree, and lymph node metastasis in ESCC patients ( $P<0.001$ and $P=0.007$ ). The patients who succumbed to the disease exhibited higher miR-18a-5p and lower ATM than the survival ( $P<0.05$ ). ROC analysis suggested favorable evaluation effects of miR-18a-5p and ATM on the occurrence and prognostic death of ESCC ( $P<0.001$ ). Further, these two genes were identified by the COX analysis to be factors independently affecting the prognosis of ESCC. Conclusion. miR-18a-5p is highly expressed in ESCC, and ATM is underexpressed, both of which are closely linked to the pathological process of ESCC and have a good evaluation effect on the occurrence and prognosis of ESCC, which may become a breakthrough in future diagnosis and treatment of ESCC.

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis

BACKGROUND

Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images.

METHODS

A comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I² test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test.

RESULTS

Seven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82-0.94) and 0.91 (95% CI: 0.79-0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8-24.8) and the LR- was 0.11 (95% CI: 0.06-0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93-0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images.

CONCLUSIONS

Based on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.