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Microbial dysbiosis in the upper digestive tract is linked to an increased risk of esophageal squamous cell carcinoma (ESCC). Overabundance of Porphyromonas gingivalis is associated with shorter survival of ESCC patients. We investigated the molecular mechanisms driving aggressive progression of ESCC by P. gingivalis. Intracellular invasion of P. gingivalis potentiated proliferation, migration, invasion, and metastasis abilities of ESCC cells via transforming growth factor-β (TGFβ)-dependent Drosophila mothers against decapentaplegic homologs (Smads)/Yes-associated protein (YAP)/Transcriptional coactivator with PDZ-binding motif (TAZ) activation. Smads/YAP/TAZ/TEA domain transcription factor1 (TEAD1) complex formation was essential to initiate downstream target gene expression, inducing an epithelial–mesenchymal transition (EMT) and stemness features. Furthermore, P. gingivalis augmented secretion and bioactivity of TGFβ through glycoprotein A repetitions predominant (GARP) up-regulation. Accordingly, disruption of either the GARP/TGFβ axis or its activated Smads/YAP/TAZ complex abrogated the tumor-promoting role of P. gingivalis. P. gingivalis signature genes based on its activated effector molecules can efficiently distinguish ESCC patients into low- and high-risk groups. Targeting P. gingivalis or its activated effectors may provide novel insights into clinical management of ESCC. Microbial dysbiosis in the upper digestive tract is linked to an increased risk of esophageal squamous cell carcinoma (ESCC); this study shows that invasion of the bacterium Porphyromonas gingivalis enhances the aggressive progression of ESCC via the TGFβ/Smad and TGFβ/YAP/TAZ pathways. Targeting P. gingivalis or its activated effectors may provide novel avenues into clinical management of ESCC. [ABSTRACT FROM AUTHOR]

Background: In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC., Methods: We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation., Results: The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM., Conclusions: Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1058/coif). The authors have no conflicts of interest to declare., (2023 Journal of Thoracic Disease. All rights reserved.)

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., Competing Interests: Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Jiao, Shen, Chen, Zhao, Li, Gu, Lan, Ruan, Liu, Xu, Yuan, Qi and Gao.)