NXNL2 Promotes Colon Cancer Proliferation and Metastasis by Regulating AKT Pathway.

This study aimed to explore the role of nucleoredoxin-like 2 (NXNL2) in colon cancer (CC). The GEPIA and UALCAN databases were analyzed to explore genes involved in the prognosis of CC patients. DLD1 cells were treated with the DNA methylation inhibitor 5-azacitidine to validate the above findings. The methyltransferase DNMT (DNA methylation) was further knocked down by shRNA, then the expression of NXNL2 was assessed by qPCR. The role of NXNL2 on cell proliferation and metastasis was examined using corresponding assays. NXNL2 was found to exhibit the greatest impact on the prognosis of CC patients. High NXNL2 correlated with poor survival outcomes of CC. The expression of NXNL2 was regulated by DNA methylation. NXNL2 promoted CC cell proliferation and metastasis. Also, NXNL2 promoted the AKT pathway activity. In conclusion, NXNL2 could affect the cancer cell proliferation and metastasis, and has a poor survival prognosis in CC.

Oncogenic signaling pathway mediated by Notch pathway-related genes induces immunosuppression and immunotherapy resistance in hepatocellular carcinoma.

The Notch pathway is a highly conserved signaling pathway involved in the regulation of cell proliferation and differentiation. However, the relationships between Notch pathway-related genes (NPRGs), immunosuppression, and immunotherapy resistance of hepatocellular carcinoma (HCC) remain unclear. Gene expression data and clinical information were extracted from GSE14520, GSE36376, GSE76427, LIRI-JP, TCGA-LIHC, GSE20140, GSE27150, and IMvigor210 datasets. A consensus clustering analysis based on 10 NPRGs was performed to determine the molecular subtypes, and then a notchScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, a notchScore was constructed to quantify the Notch index of each patient with HCC. Next, we investigated the correlations between the clinical characteristics and the notchScore using logistic regression. Furthermore, multivariate Cox analysis showed that a high notchScore was an independent predictor of poor overall survival (OS) in the TCGA and LIRI-JP datasets and was associated with higher pathological stages. Additionally, a high notchScore was associated with higher immune cells, higher ESTIMATE score, higher immune score, higher stromal score, higher immune checkpoint, and lower tumor purity, which was consistent with the "immunity tidal model theory." Importantly, a high notchScore was sensitive to immunotherapy. Additionally, GSEA indicated that several GO and KEGG items associated with apoptosis, immune-related pathways, and cell cycle signal pathways were significantly enriched in the high notchScore phenotype pathway. Our findings propose that a high notchScore is a prognostic biomarker and correlates with immune infiltration and sensitivity to immunotherapy in HCC.

Antidepressant use and colorectal cancer morbidity and mortality: A dose-response meta analysis.

The risk of colorectal cancer associated to antidepressant use remains unclear. The purpose of this meta-analysis was to investigate the risk of colorectal cancer associated to antidepressant use.Medline, Embase, Web of Science, and Cochrane Database were accessed from the dates of their establishment to October 2018, to collect study of antidepressant use and colorectal cancer morbidity and mortality. Then a meta-analysis was conducted using Stata 12.0 software.A total of 11 publications involving 109,506 participants were included. The meta-analysis showed that antidepressant use was not associated with colorectal cancer morbidity (relevant risk (RR): 0.97; 95% confidence interval (CI): 0.94-1.01) and mortality (RR: 1.08; 95% CI: 0.99-1.17). Subgroup analysis showed selective serotonin reuptake inhibitor (RR: 0.99; 95% CI: 0.96-1.03) or serotonin norepinephrine reuptake inhibitor (RR: 1.04; 95% CI: 0.86-1.26) were not associated with colorectal cancer risk; however, TCA was associated with colorectal cancer risk decrement (RR: 0.92; 95% CI: 0.87-0.98). Furthermore, the results also showed that antidepressant use was not associated with colorectal cancer risk in Europe and North America (RR: 0.97; 95% CI: 0.92-1.02) and Asia (RR: 1.00; 95% CI: 0.95-1.26). Additionally, a dose-response showed per 1 year of duration of antidepressant use incremental increase was not associated with colorectal cancer risk (RR: 0.96; 95% CI: 0.87-1.09).Evidence suggests that antidepressant use was not associated with colorectal cancer morbidity and mortality. The cumulative duration of antidepressant use did not utilized played critical roles.

GNG12 regulates PD-L1 expression by activating NF-κB signaling in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid tumors in the digestive system. A greater understanding of the pathogenesis of PDAC may facilitate the search for new therapeutic targets. Guanine nucleotide-binding protein subunit gamma-12 (GNG12) belongs to the G protein family and participates in the modulation of the inflammatory signaling cascade. However, the cancer-related function and clinical relevance of GNG12 in PDAC have not previously been reported. Here, we investigated the clinical significance of GNG12 in PDAC using the Oncomine web tool, the gene expression profiling interactive analysis tool and tissue microarray (TMA). GNG12 expression was observed to be higher in PDAC patient specimens than in nontumor pancreatic tissues, and high expression of GNG12 was associated with poor prognosis. We subsequently show that GNG12 promotes pancreatic cancer cell growth in vivo and in vitro, as evaluated using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt assays, colony formation assays and a xenograft mouse model. Furthermore, our results suggest that GNG12 activates nuclear factor-κB signaling and modulates the immune response. Collectively, our findings suggest that GNG12 may be suitable as a new prognosis-related biomarker and a promising target for treatment of pancreatic cancer.

Lymph node ratio predicts prognosis in patients with surgically resected invasive pancreatic cystic neoplasms.

BACKGROUND: In current days, the prevalence of pancreatic cystic neoplasms (PCN) is on the rise. Lymph node ratio (LNR) has emerged as a promising prognostic factor in pancreatic adenocarcinoma (PDAC). However, the prognostic value of LNR in patients with invasive PCN remains unknown. METHODS: We used Surveillance, Epidemiology, and End Results (SEER) database to retrieve the baseline characteristics and clinical tumor variables of patients diagnosed with PCN between 1988 and 2014. Survival analyses were performed using the Kaplan-Meier method. Univariate and multivariate analyses were performed to identify factors associated with patient prognosis. RESULTS: A total of 10,656 PCN cases were initially identified. Based on our exclusion criteria, our analyses included data from 1246 cases, of which 479 were patients with lymph node involvement. Patients with high LNR had shorter overall survival (OS) than patients with low LNR (median OS, 13 vs. 21 months; P=0). Our univariate and multivariate analyses identified LNR (P=0) and grade (P=0.010) as independent prognostic factors in patients with invasive PCN. CONCLUSIONS: Our findings suggest that LNR is a reliable, independent prognostic factor in patients with invasive PCN, strongly associated with OS and cancer-specific survival (CSS). LNR may represent a promising prognostic factor alternative to the AJCC (the American Joint Committee on Cancer) N stage in patients with node-positive PCN.

Tooth loss and risk of colorectal cancer: a dose-response meta-analysis of prospective cohort studies.

BACKGROUND: Previous studies have indicated that tooth loss is associated with colorectal cancer risk but have presented controversial results. METHODS: We conducted a dose-response meta-analysis in order to investigate the correlation between tooth loss and colorectal cancer risk. Up to August 2017, six eligible studies were included in this meta-analysis. RESULTS: Our results showed statistically significant association between tooth loss and colorectal cancer (OR =1.08, 95% CI: 1.03-1.15, P<0.001). In addition, we obtained the best fit at an inflection point of every two tooth loss in piecewise regression analysis, and the summary relative risk (RR) of colorectal cancer for an increase of every two tooth loss was 1.06 (95% CI: 1.02-1.11, P<0.001). Furthermore, tooth loss was significantly associated with colorectal cancer risk in Caucasia (RR: 1.18; 95% CI: 1.09-1.28; P<0.001) and Asia (RR: 1.06; 95% CI: 1.02-1.10; P<0.001). Moreover, tooth loss was significantly associated with a higher risk of colon cancer (RR: 1.09; 95% CI: 1.02-1.17; P<0.001) and rectal cancer (RR: 1.08; 95% CI: 1.01-1.17; P<0.001). CONCLUSION: Subgroup meta-analyses showed consistency with the primary findings. Considering these promising results, increasing tooth loss may be harmful to our health, and maintenance of our oral health is essential.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

BACKGROUND: RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. RESULTS: In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. CONCLUSIONS: The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?

Identification of potential druggable targets utilizing protein-protein interactions network (PPIN) has been emerging as a hotspot in drug discovery and development research. However, it remains unclear whether the currently used PPIN topological properties are enough to discriminate the drug targets from non-drug targets. In this study, three-step classification models using different network topological properties were designed and implemented using support vector machine (SVM) to compare the enrichment of known drug targets from non-targets. Surprisingly, none of the models was able to identify more than 75% of the true targets in the test set. It appears that the currently used simple PPIN topological properties are not likely robust enough for prediction of potential drug targets with high confidence, which also echoes similarly unsatisfying prediction data reported previously. However, we proposed that quality and quantity improvement of the protein-protein interactions (PPI) data for model training will help increasing the prediction accuracy.

An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

BACKGROUND: DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. RESULTS: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. CONCLUSIONS: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.