Construction of artificial neural network (ANN) based on predictive value of prognostic nutritional index (PNI) and neutrophil-to-lymphocyte ratio (NLR) in patients with cervical squamous cell carcinoma.

To explore the analytical worth of prognostic nutritional index (PNI) and neutrophil-to-lymphocyte ratio (NLR) in patients with cervical squamous cell carcinoma. The clinical data of 539 patients with cervical cancer in the Affiliated Tumor Hospital of Nantong University from December 2007 to October 2016 were analyzed retrospectively. The ROC is used to select the best cutoff values of PNI and NLR, which are 48.95 and 2.4046. Cox regression analysis was used for univariate and multivariate analysis. Survival differences were assessed by Kaplan-Meier (KM) survival method. Finally, a 3-layer artificial neural network (ANN) model is established. In cervical squamous cell carcinoma, the KM survival curve showed that the overall survival (OS) rate of high-level PNI group was significantly higher than that of low-level PNI group (P < .001), while the OS rate of low-level NLR group was significantly higher than that of high-level NLR group (P = .002). In non-squamous cell carcinoma, there was no significant difference in OS between the 2 groups (P > .005). According to Cox multivariate analysis, preliminary diagnosed PNI and NLR were independent prognostic factors of cervical squamous cell carcinoma (P < .001, P = .008), and pathological type and International Federation of Gynecology and Obstetrics (FIGO) stage also had a certain impact on tumor progression (P = .042, P = .048). The increase of PNI and the decrease of NLR will help patients with cervical squamous cell carcinoma live longer. ANN showed that PNI and NLR were of great importance in predicting survival. Preoperative PNI and NLR are independent predictors of cervical squamous cell carcinoma patients related to clinicopathological features, and have particular value in judging prognosis.

CRTC2 promotes paclitaxel resistance by inducing autophagy in ovarian cancer in part via the PI3K-AKT signaling axis.

Background: Ovarian cancer is the most malignant gynecological disease, which seriously threatens female physical and mental health. Paclitaxel is a first-line chemotherapy drug in the clinical treatment of ovarian cancer, but drug resistance has become an important factor affecting the survival of ovarian cancer patients. However, the main mechanism of chemotherapy resistance in ovarian cancer remains unclear. In this study, we analyzed the Integrated Gene Expression Database (GEO) dataset using comprehensive bioinformatics tools to provide new therapeutic strategies and search for prognostic targets for ovarian cancer. Methods: Ovarian cancer related genes were extracted from GSE18520 by bioinformatics method. Differentially expressed genes (DEGs) were obtained by differential analysis, and related genes and functions were elucidated. The key gene CRTC2 was identified by prognostic analysis. Immunohistochemistry was used to detect the expression of CRTC2 in chemotherapy-resistant and chemotherapy-sensitive ovarian cancer tissues. Functional analysis (cell assay) confirmed the role of CRTC2 in paclitaxel resistance. Autophagy related proteins were detected by Western blot. Autophagy flux analysis was performed using the GFP/RFP-LC3 adenovirus reporter. Results: A total of 3,852 DEGs were identified in the GEO microarray dataset. Key genes were screened by prognostic analysis. We found that CRTC2 was highly expressed in chemoresistant tissues of ovarian cancer. In 110 patients with ovarian cancer, high expression of CRTC2 was associated with poorer prognostic factors and shorter survival. At the same time, we found that CRTC2 can promote the proliferation and invasion ability of ovarian cancer cells. In addition, CRTC2 can affect the expression of PI3K, AKT, autophagic flux and sensitivity to paclitaxel chemotherapy in ovarian cancer. Conclusion: CRTC2 can affect autophagy partially through PI3K-AKT signaling pathway, and then affect the sensitivity of ovarian cancer to paclitaxel chemotherapy. CRTC2 may be a potential predictor or target for ovarian cancer therapy.

Identification and validation of novel biomarker TRIM8 related to cervical cancer.

Background: Cervical cancer, as a common gynecological disease, endangers female health. Give the lack of effective biomarkers for the diagnosis and treatment of cervical cancer, this paper aims to analyze the Gene Expression Omnibus (GEO) data sets using comprehensive bioinformatics tools, and to identify biomarkers associated with the cancer in patient samples. Methods: The bioinformatics methods were used to extract genes related to cervical cancer from GSE39001, while the GEO2R online tool to elaborate on differentially expressed genes (DEGs) in normal and cancer samples, and to clarify related genes and functions. The results were verified by IHC, WB, CCK-8, clone formation and flow cytometry experiments. Results: A total of 2,859 DEGs were identified in the GEO microarray dataset. We extracted genes associated with both ubiquitination and autophagy from the key modules of weighted gene co-expression network analysis (WGCNA), and the analysis showed that TRIM8 was of great significance for the diagnosis and prognosis of cervical cancer. Besides, experimental validation showed the high TRIM8 expression in cervical cancer, as well as its involvement in the proliferation of cervical cancer cells. Conclusion: We identified a biomarker (TRIM8) that may be related to cervical cancer through a series of analyses on the GEO dataset. Experimental verification confirmed the inhibition of cervical cancer cells proliferation by lowering TRIM8 expression. Therefore, TRIM8 can be adopted as a new biomarker of cervical cancer to develop new therapeutic targets.

Predictive value of indicator of CA125 combined with D-dimer (ICD) for lymph node metastasis in patients with ovarian cancer: A two center cohort study.

Background: The clinical serum markers CA125 and D-dimer have been reported to predict lymph node metastasis(LNM) in several malignant tumors, but the reports in ovarian cancer(OC) are still absent. The purpose of this study was to explore the value of indicator CA125 combined with D-dimer (ICD) in predicting LNM in patients with OC. Methods: A total of 447 patients diagnosed with OC from January 2008 to June 2019 were included in this retrospective study as the training set. A total of 284 patients were included in the validation set. The optimal cut-off critical value of ICD was evaluated by the receiver operating characteristic curve (ROC), and the maximum Youden index (sensitivity + specificity-1). Univariate and multivariate analysis were used to evaluate ICD as a predictor of LNM in OC. Results: According to ROC curve, area under curve (AUC) of ICD (AUC=0.706, p<0.001) was significantly larger than that of CA125 (AUC=0.671, p<0.001) and D-dimer (AUC=0.562, p=0.022) alone. Multivariate analysis showed that ICD (HR 2.651, 95% CI 1.273-5.520, p=0.009) was an independent predictor of LNM and overall survival (OS) in OC. It has also been verified in another medical center. Conclusion: ICD is an independent predictor of LNM in ovarian cancers, which is helpful for clinicians to draw up individual treatment plans.

The Number of Lymph Nodes Examined is Associated with Survival Outcomes of Neuroendocrine Tumors of the Jejunum and Ileum (siNET): Development and Validation of a Prognostic Model Based on SEER Database.

PURPOSE: The number of neuroendocrine tumors (NETs) is gradually increasing worldwide, and those located in the small intestine (siNETs) are the most common. As some biological and clinical characteristics of tumors of the jejunum and the ileum differ, there is a need to assess the prognosis of individuals with siNETs of the jejunum and ileum separately. We generated a predictive nomogram by assessing individuals with siNETs from the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: We used univariate Cox regression analysis to determine both the overall survival (OS) and the cancer-specific survival (CSS) of 2501 patients with a pathological confirmation of siNETs of the jejunum and ileum. To predict 3-, 5-, and 10-year OS of siNETs, a nomogram was generated based on a training cohort and validated with an external cohort. Accuracy and clinical practicability were evaluated separately by Harrell's C-indices, calibration plots, and decision curves. The correlation was examined between dissected lymph nodes and positive lymph nodes. RESULTS: Dissection of 7 or more lymph nodes significantly improved patient OS and was found to be a protective factor for patients with siNETs. In Cox regression analyses, age, primary site, tumor size, N stage, M stage, and regional lymph node examination were significant predictors in the nomogram. A significant positive correlation was found between dissected lymph nodes and positive lymph nodes. CONCLUSIONS: Patients with 7 or more dissected lymph nodes showed an accurate tumor stage and a better prognosis. Our nomogram accurately predicted the OS of patients with siNETs.