Hypertension induced by obstructive sleep apnea (OSA) may be multifactorial in origin, and systemic inflammation is one of the major factors. However, OSA patients do not always have the identical probability with hypertension even in patients with the same history and degree of OSA. The aim of this study was to compare the levels of inflammation and insulin resistance in two groups of patients who had the same degree as well as the same long history of OSA, but with/without hypertension. OSA patients (Apnea Hyponea Index, AHI ≥ 40/h, n = 70) were examined by polysomnography and blood analysis for the measurements of fasting plasma glucose, serum insulin (FINS), high-sensitivity C-reactive protein (CRP), peptide C,TNF-α, IL-6, and IL-10. Patients with hypertension (n = 40) had higher level of LDL-C and lower HDL-C levels than patients without hypertension. Almost half (16/40) of OSA patients with hypertension had family history of hypertension. Moreover in OSA patients with hypertension, the levels of TNF-α, IL-6, and CRP were higher, but IL-10 was lower than those without hypertension. FINS, peptide C, HOMA-IR, and HOMA-islet were also higher in OSA patients with hypertension. OSA patients with hypertension have higher level of inflammation and insulin resistance. Systemic inflammation and insulin resistance are both important factors for the development of hypertension in OSA patients.

Proline-rich protein 11 (PRR11) together with its upstream adjacent gene, spindle and kinetochore associated 2 (SKA2), represent a classic, head-to-head gene pair. The role of the PRR11 and SKA2 gene pair has been described in various types of cancer, including breast cancer, non-small cell lung cancer, hepatocellular carcinoma and ovarian carcinoma. However, its role in esophageal carcinoma (ESCC) remains unclear. The mRNA expression levels of PRR11 and SKA2 were examined in ESCC surgical specimens. In addition, the role of PRR11 and SKA2 in the proliferation and migratory and invasive capacities of EC9706 and EC109 cell lines was examined. The results from the present study demonstrated that PRR11 and SKA2 expression levels were upregulated in ESCC tissues compared with adjacent normal tissues. Furthermore, PRRl1 and SKA2 knockdown significantly inhibited the proliferation and migratory and invasive capacities of ESCC cells. Conversely, PRRl1 and SKA2 overexpression significantly promoted the proliferation and migratory and invasive capacities of ESCC cell lines via activation of the AKT signaling pathway and certain markers of epithelial-mesenchymal transition, including Snail and N-cadherin. The results from the present study suggested that the PRR11 and SKA2 gene pair may represent a potential target in the diagnosis and treatment of ESCC.

BackgroundAlcohol-related hepatocellular carcinoma (HCC) was reported to be diagnosed at a later stage, but the mechanism was unknown. This study aimed to identify special key genes (SKGs) during alcohol-related HCC development and progression.MethodsThe mRNA data of 369 HCC patients and the clinical information were downloaded from the Cancer Genome Atlas project (TCGA). The 310 patients with certain HCC-related risk factors were included for analysis and divided into seven groups according to the risk factors. Survival analyses were applied for the HCC patients of different groups. The patients with hepatitis B virus or hepatitis C virus infection only were combined into the HCC-V group for further analysis. The differentially expressed genes (DEGs) between the HCCs with alcohol consumption only (HCC-A) and HCC-V tumors were identified through limma package in R with cutoff criteria│log2 fold change (logFC)|>1.0 and p < 0.05. The DEGs between eight alcohol-related HCCs and their paired normal livers of from the Gene Expression Omnibus (GEO) were identified through GEO2R (a built-in tool in GEO database) with cutoff criteria |logFC|> 2.0 and adj.p < 0.05. The intersection of the two sets of DEGs was considered SKGs which were then investigated for their specificity through comparisons between HCC-A and other four HCC groups. The SKGs were analyzed for their correlations with HCC-A stage and grade and their prognostic power for HCC-A patients. The expressional differences of the SKGs in the HCCs in whole were also investigated through Gene Expression Profiling Interactive Analysis (GEPIA). The SKGs in HCC were validated through Oncomine database analysis.ResultsPathological stage is an independent prognostic factor for HCC patients. HCC-A patients were diagnosed later than HCC patients with other risk factors. Ten SKGs were identified and nine of them were confirmed for their differences in paired samples of HCC-A patients. Three (SLC22A10, CD5L, and UROC1) and four (SLC22A10, UROC1, CSAG3, and CSMD1) confirmed genes were correlated with HCC-A stage and grade, respectively. SPP2 had a lower trend in HCC-A tumors and was negatively correlated with HCC-A stage and grade. The SKGs each was differentially expressed between HCC-A and at least one of other HCC groups. CD5L was identified to be favorable prognostic factor for overall survival while CSMD1 unfavorable prognostic factor for disease-free survival for HCC-A patients and HCC patients in whole. Through Oncomine database, the dysregulations of the SKGs in HCC and their clinical significance were confirmed.ConclusionThe poor prognosis of HCC-A patients might be due to their later diagnosis. The SKGs, especially the four stage-correlated genes (CD5L, SLC22A10, UROC1, and SPP2) might play important roles in HCC development, especially alcohol-related HCC development and progression. CD5L might be useful for overall survival and CSMD1 for disease-free survival predication in HCC, especially alcohol-related HCC.

Objective To investigate the in vitro and in vivo anticancer effects of a chalcone against KYSE-4 esophageal cancer cells. Methods A chalcone was synthesized via the molecular hybridization strategy based on the anticancer activity of chalcone and dithiocarbamate scaffolds. The anticancer effects of different concentrations of the chalcone derivative were compared in esophageal cancer cells. Results This chalcone displayed strong inhibitory effects on esophageal cancer cell growth with an IC50 of 1.06 μM in KYSE-4 cells. Analysis of the mechanism revealed that the derivative obviously inhibited KYSE-4 cell growth, migration, and invasion in a concentration-dependent manner. Furthermore, the compound regulated migration-related biomarkers (E-cadherin, N-cadherin, and Slug) and inhibited the Wnt/β-catenin pathway. According to western blotting, this chalcone suppressed the expression of proline-rich protein 11 (PRR11) in a concentration- and time-dependent manner. Conclusions This chalcone might be a leading candidate for suppressing the growth and metastasis of esophageal cancer by downregulating PRR11 expression and inhibiting Wnt/β-catenin signaling.

Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate of lung cancer patients. Through computer technology, a comprehensive evaluation of genetic testing results and basic clinical information of lung cancer patients could effectively diagnose early lung cancer and indicate cancer risks. This study retrospectively collected 70 pairs of lung cancer tissue samples and normal human tissue samples. The methylation frequencies of 6 genes (FHIT, p16, MGMT, RASSF1A, APC, DAPK) in lung cancer patients, the basic clinical information, and tumor marker levels of these patients were analyzed. Then, the python package “sklearn” was employed to build a support vector machine (SVM) classifier which performed 10-fold cross-validation to construct diagnostic models that could identify lung cancer risk of suspected cases. Receiver operation characteristic (ROC) curves were drawn, and the performance of the combined diagnostic model based on several factors (clinical information, tumor marker level, and methylation frequency of 6 genes in blood) was shown to be better than that of models with only one pathological feature. The AUC value of the combined model was 0.963, and the sensitivity, specificity, and accuracy were 0.900, 0.971, and 0.936, respectively. The above results revealed that the diagnostic model based on these features was highly reliable, which could screen and diagnose suspected early lung cancer patients, contributing to increasing diagnosis rate and survival rate of lung cancer patients.