A variety of pathogenic microorganisms can promote the occurrence and development of malignant tumors by colonizing in the body. It has been shown that Porphyromonas gingivalis (P. gingivalis) can be colonized for a long time in upper gastrointestinal tumors and is closely related to the occurrence and development of esophageal cancer in previous studies of our team. Because the esophagus and trachea are closely adjacent and P. gingivalis can instantly enter and colonize in cells, we speculate that P. gingivalis may be colonized in lung cancer cells through oral or blood, promoting the malignant progression of lung cancer. In this study, we investigated P. gingivalis infection in lung carcinoma tissues and adjacent lung tissues, and found that the colonization rate of P. gingivalis in carcinoma tissues was significantly higher than that in adjacent lung tissues. Therefore, we propose that the microenvironment of cancer cells is more conducive to the survival of P. gingivalis. Then, we analyzed the correlation between P. gingivalis infection and clinicopathological features and survival prognosis of patients with lung cancer. It was found that P. gingivalis infection was closely related to smoking, drinking, lymph node metastasis and clinical stage. Moreover, the survival rate and median survival time of patients with P. gingivalis infection were significantly shortened. Therefore, we put forward the view that long term smoking and drinking will cause a bad oral environment, increasing the risk of P. gingivalis infection, then P. gingivalis infection will promote the malignant progression of lung cancer.

The key-stone-pathogen, Porphyromonas gingivalis associates not only with periodontal diseases but with a variety of other chronic diseases such as cancer. We previously reported an association between the presence of Porphyromonas gingivalis in esophageal squamous cell carcinoma (ESCC) and its progression. We now report the diagnostic and prognostic potential of serum immunoglobulin G and A antibodies (IgG/A) against Porphyromonas gingivalis for ESCC. An enzyme-linked immunosorbent assay (ELISA) was used to determine the serum levels of Porphyromonas gingivalis IgG and IgA in 96 cases with ESCC, 50 cases with esophagitis and 80 healthy controls. The median serum levels of IgG and IgA for P. gingivalis were significantly higher in ESCC patients than non-ESCC controls. P. gingivalis IgG and IgA in serum demonstrated sensitivities/specificities of 29.17%/96.90% and 52.10%/70.81%, respectively, and combination of IgG and IgA produced a sensitivity/specificity of 68.75%/68.46%. The diagnostic performance of serum P. gingivalis IgA for early ESCC was superior to that of IgG (54.54% vs. 20.45%). Furthermore, high serum levels of P. gingivalis IgG or IgA were associated with worse prognosis of ESCC patients, in particular for patients with stage 0-IIor negative lymphnode metastasis, and ESCC patients with high levels of both IgG and IgA had the worst prognosis. Multivariate analysis revealed that lymph node status, IgG and IgA were independent prognostic factors. The IgG and IgA for P. gingivalis are potential serum biomarkers for ESCC and combination of IgG and IgA improves the diagnostic and prognostic performance. Furthermore, serum P. gingivalis IgG and IgA can detect early stage ESCC.

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.

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.

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.