Upper gastrointestinal tract microbiota with oral origin in relation to oesophageal squamous cell carcinoma.

Introduction: Poor oral hygiene is linked to high risks of many systemic diseases, including cancers. Oral dysbiosis is closely associated with poor oral hygiene, causing tooth loss, gingivitis, and periodontitis. We provide a summary of studies and discuss the risk factors for oesophageal squamous cell carcinoma (ESCC) from a microbial perspective in this review.Methods: A literature search of studies published before December 31, 2022 from PubMed, Web of Science, and The Cochrane Library was performed. The search strategies included the following keywords: (1) oral care, oral health, oral hygiene, dental health, dental hygiene, tooth loss, teeth loss, tooth absence, missing teeth, edentulism, tooth brushing, mouthwash, and tooth cleaning; (2) esophageal, esophagus, oesophagus, and oesophageal; (3) cancer, carcinoma, tumor, and neoplasm.Discussion: Poor oral health, indicated by infrequent tooth brushing, chronic periodontitis, and tooth loss, has been associated with an increased risk of squamous dysplasia and ESCC. Oral microbial diversity and composition are profoundly dysregulated during oesophageal tumorigenesis. Similar to the oral microbiota, the oesophageal microbiota varies distinctly in multiple bacterial taxa in ESCC and gastric cardia adenocarcinoma, both of which have high co-occurrence rates in the "Oesophageal Cancer Belt". In addition, the potential roles of oncogenic viruses in ESCC have also been discussed. We also briefly explore the potential mechanisms underlying the tumor-promoting role of dysregulated microbiota for the development of therapeutic targeting strategies.Conclusion: Poor oral health is an established risk indicator of ESCC. The dysbiosis of microbiota in upper gastrointestinal tract that highly resembles the oral microbial ecosystem but with distinct features at individual sites contributes to the development and progression of ESCC.

Porphyromonas gingivalis predicts local recurrence after endoscopic submucosal dissection of early esophageal squamous cell carcinoma or precancerous lesion.

BACKGROUND: Porphyromonas gingivalis plays an oncogenic role in development and progression of esophageal squamous cell carcinoma (ESCC). However, the impact of P. gingivalis on local recurrence of early ESCC or precancerous lesion after ESD treatment remains unknown. The present study aimed to evaluate the impact of P. gingivalis on local recurrence after ESD treatment of early ESCC or high-grade dysplasia (HGD). METHODS: The amount of P. gingivalis was assessed by immunohistochemistry in 205 patients with early ESCC or HGD. Univariate and multivariate Cox regression analyses were performed to determine the effect of P. gingivalis on local recurrence. Propensity score matching analysis was performed to reduce the imbalance of baseline characteristics. A nomogram integrating significant prognostic factors was built for local recurrence prediction. RESULTS: The amount of P. gingivalis increased significantly in neoplasms that invaded up to muscularis mucosa and submucosa compared with lesions confined to epithelium or lamina propria. Overabundance of P. gingivalis was positively associated with invasion depth, post-ESD stricture and local recurrence. Univariate and multivariate Cox regression analyses revealed that P. gingivalis, longitudinal length of lesion and lymphovascular invasion were independent predictors for post-ESD recurrence. A nomogram comprising P. gingivalis, lymphovascular involvement, and lesion length performed well for prediction of post-ESD local recurrence with the concordance indices of 0.72 (95%CI, 0.62 to 0.80), 0.72 (95%CI, 0.63 to 0.80), and 0.74 (95%CI, 0.65 to 0.83) in the validation cohort, the entire cohort, and the subcohort after PSM, respectively. CONCLUSION: P. gingivalis overabundance is a risk factor and a potential predictor for local recurrence of early ESCC or HGD after ESD treatment. Thus, clearance of P. gingivalis represents an attractive strategy for prognosis improvement and for prevention of ESCC.

N-linked glycoproteomic profiling in esophageal squamous cell carcinoma.

BACKGROUND: Mass spectrometry-based proteomics and glycomics reveal post-translational modifications providing significant biological insights beyond the scope of genomic sequencing. AIM: To characterize the N-linked glycoproteomic profile in esophageal squamous cell carcinoma (ESCC) via two complementary approaches. METHODS: Using tandem multilectin affinity chromatography for enrichment of N-linked glycoproteins, we performed N-linked glycoproteomic profiling in ESCC tissues by two-dimensional gel electrophoresis (2-DE)-based and isobaric tags for relative and absolute quantification (iTRAQ) labeling-based mass spectrometry quantitation in parallel, followed by validation of candidate glycoprotein biomarkers by Western blot. RESULTS: 2-DE-based and iTRAQ labeling-based quantitation identified 24 and 402 differentially expressed N-linked glycoproteins, respectively, with 15 in common, demonstrating the outperformance of iTRAQ labeling-based quantitation over 2-DE and complementarity of these two approaches. Proteomaps showed the distinct compositions of functional categories between proteins and glycoproteins with differential expression associated with ESCC. Western blot analysis validated the up-regulation of total procathepsin D and high-mannose procathepsin D, and the down-regulation of total haptoglobin, high-mannose clusterin, and GlcNAc/sialic acid-containing fraction of 14-3-3ζ in ESCC tissues. The serum levels of glycosylated fractions of clusterin, proline-arginine-rich end leucine-rich repeat protein, and haptoglobin in patients with ESCC were remarkably higher than those in healthy controls. CONCLUSION: Our study provides insights into the aberrant N-linked glycoproteome associated with ESCC, which will be a valuable resource for future investigations.

A Prognostic Model Based on mRNA Expression Analysis of Esophageal Squamous Cell Carcinoma.

Background: The aim of this study was to identify prognostic markers for esophageal squamous cell carcinoma (ESCC) and build an effective prognostic nomogram for ESCC. Methods: A total of 365 patients with ESCC from three medical centers were divided into four cohorts. In the discovery phase of the study, we analyzed transcriptional data from 179 cancer tissue samples and identified nine marker genes using edgeR and rbsurv packages. In the training phase, penalized Cox regression was used to select the best marker genes and clinical characteristics in the 179 samples. In the verification phase, these marker genes and clinical characteristics were verified by internal validation cohort (n = 58) and two external cohorts (n = 81, n = 105). Results: We constructed and verified a nomogram model based on multiple clinicopathologic characteristics and gene expression of a patient cohort undergoing esophagectomy and adjuvant radiochemotherapy. The predictive accuracy for 4-year overall survival (OS) indicated by the C-index was 0.75 (95% CI, 0.72-0.78), which was statistically significantly higher than that of the American Joint Committee on Cancer (AJCC) seventh edition (0.65). Furthermore, we found two marker genes (TM9SF1, PDZK1IP) directly related to the OS of esophageal cancer. Conclusion: The nomogram presented in this study can accurately and impersonally predict the prognosis of ESCC patients after partial resection of the esophagus. More research is required to determine whether it can be applied to other patient populations. Moreover, we found two marker genes directly related to the prognosis of ESCC, which will provide a basis for future research.

Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma.

BACKGROUND: A plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using machine learning algorithms. METHODS: Biomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to recurrence or prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of identified molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival. RESULTS: A total of 48 clinically proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131,071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts. CONCLUSION: The occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.