[Predictive significance of serum IL-6, VEGF, and CRP in gastric adenoma and mucosal carcinoma before endoscopic submucosal dissection]

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

BACKGROUND

Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors.

METHODS

This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six  ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation.

RESULTS

This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer.

CONCLUSIONS

The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

Effects of Polygonatum sibiricum Polysaccharides (PSP) on Human Esophageal Squamous Cell Carcinoma (ESCC) via NF-κB Signaling Pathway

Objective. To explore the effects of different concentrations of Polygonatum sibiricum polysaccharides (PSP) on human esophageal squamous cell carcinoma (ESCC) cell line Eca109 and explore the new approach for the treatment of ESCC. Methods. Eca109 cells were divided into 5 groups, including one control group and 4 experimental groups where the concentrations of PSP used were 50, 100, 200, and 400 μg/mL. The proliferation rate of Eca109 cells in each group was measured with the CCK8 assay, and the apoptosis rate in each group was analyzed by flow cytometry; the in vitro scratch assay was used to determine the migration ability of Eca109 cells after PSP treatment; the expression levels of IL-1, IL-6, IL-10, TNF-α, and TGF-β were measured by RT-PCR, and the expression levels of TLR4 and proteins that are related to NF-κB signaling pathways were determined by Western blot. Results. PSP significantly inhibited the proliferation of Eca109 cells (p<0.05) on a time- and dose-dependent manner; the apoptosis rates of Eca109 cells in experimental groups were significantly increased after 48 h of culture (p<0.05); PSP significantly reduced the migration and invasion ability of Eca109 cells (p<0.05); RT-PCR results showed that the expression of IL-10 in Eca109 cells increased significantly after treatment with PSP (p<0.05), while the expression of IL-1, IL-6, TNF-α, and TGF-β decreased significantly (p<0.05). Compared with the control group, the expression level of TLR4, NF-κB/p50, and NF-κB/p65 protein in each experimental group was significantly lower than that in the control group (p<0.05). Conclusions. PSP significantly inhibited the proliferation, invasion, and migration of Eca109 cells and promoted cell apoptosis. These observed effects were probably due to the PSP’s inhibition on the NF-κB signaling pathway in Eca109 cells via the regulation of the TLR4 expression.

Application of data mining methods to improve screening for the risk of early gastric cancer

BACKGROUND

Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC.

METHODS

The dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms.

RESULTS

The three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on.

CONCLUSIONS

The three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.

High-sensitivity C-reactive protein and cancer

BACKGROUND

High-sensitivity C-reactive protein (hs-CRP) is a commonly used inflammatory marker. The association between hs-CRP and cancer is less consistent than that between hs-CRP and cardiovascular diseases. This study explored the association between hs-CRP and cancer, using a large database of Korean health examination records.

METHODS

A total of 80,781 Koreans who visited the health promotion center of a general hospital were included. There were 729 cases of cancer of any primary site during a 3-year period. Subjects with a known cancer or a condition capable of affecting hs-CRP were excluded.

RESULTS

Serum hs-CRP was significantly higher in cancer cases (2.9 mg/L) than in non-cases (1.4 mg/L; P < 0.0001). With the lowest hs-CRP category (<1 mg/L) as reference, the crude odds ratios (ORs) for cancer were 1.36 (95% confidence interval [CI] = 1.16-1.62) for the second highest category (1-3 mg/L) and 2.49 (95% CI = 2.02-3.07) for the highest category (>3 mg/L), and the adjusted ORs for cancer were 1.16 (95% CI = 0.95-1.42) for the second highest category and 1.94 (95% CI = 1.51-2.51) for the highest category. After excluding cancer cases detected within 1 year after the check-up, the associations remained, although the reduced number of cancer cases (n = 88) attenuated the significance of the associations.

CONCLUSIONS

Serum hs-CRP was positively associated with the risk of cancer, although causality cannot be inferred in this cross-sectional study. The results support the hypothesis that chronic inflammation plays a role in cancer.

Comparison between clinical significance of serum proinflammatory proteins (IL-6 and CRP) and classic tumor markers (CEA and CA 19-9) in gastric cancer

Gastric cancer (GC) is a second most common cause of cancer-related death and represents an inflammation-driven malignancy. It has been suggested that interleukin 6 (IL-6) and C-reactive protein (CRP) play a potential role in the growth and progression of GC. The aim of the present study was to compare clinical significance of IL-6 and CRP with classic tumor markers—carcinoembryonic antigen (CEA) and carbohydrate antigen (CA 19-9) in GC patients. The study included 92 patients with GC and 70 healthy subjects. The serum concentrations of IL-6, CEA and CA 19-9 were determined using immunoenzyme assays, whereas CRP using immunoturbidimetric method. We defined the diagnostic criteria and prognostic value for proteins tested. In GC patients, the serum concentrations of all the proteins tested were significantly higher than in healthy subjects. The IL-6, CEA and CA 19-9 levels correlated with nodal metastases, while CRP with tumor stage, gastric wall invasion, presence of nodal and distant metastases. Diagnostic sensitivity of IL-6 was higher (85%) than those of other markers (CRP 66%, CA 19-9 34%, CEA 22%) and increased in combined use with CRP or CEA (88%). The area under ROC curve for IL-6 was larger than those of CRP and classic tumor markers (CEA and CA 19-9). None of the proteins tested was independent prognostic factor for the survival of GC patients. Our findings indicate better usefulness of serum proinflammatory proteins—IL-6 and CRP than classic tumor markers—CEA and CA 19-9 in the diagnosis of GC.