Identifying the critical states of complex diseases by the dynamic change of multivariate distribution

The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.

Prognostic significance of Human epidermal growth factor receptor-2 expression in patients with resectable gastric adenocarcinoma

BACKGROUND

The purpose of this study was to investigate the correlation between human epidermal growth factor receptor 2 (HER2) overexpression and clinicopathologic factors and overall survival rate in patients who underwent curative gastrectomy for gastric adenocarcinoma.

METHODS

Among patients who underwent curative gastrectomy for gastric adenocarcinoma at Inje University Paik Hospital from January 2012 to December 2015, 782 patients underwent an immunohistochemical analysis to evaluate HER2 expression levels. Clinicopathologic records that were collected from a gastric cancer database were retrospectively reviewed to identify clinicopathologic factors and survival rates of the patients.

RESULTS

HER2 overexpression was detected in 166 patients (21.2%). There was a statistically significant correlation between HER2 expression level and sex (p = 0.013), histologic differentiation (p < 0.001), Lauren classification (p < 0.001), and T pathologic stage (p = 0.022). There were no statistically significant relationships between HER2 expression level and overall 5-year survival rate (p = 0.775) and overall 5-year survival rate of gastric adenocarcinoma classified according to the TNM stage (stage I: p = 0.756, stage II: p = 0.571, stage III: p = 0.704). The HER2 expression level was not affected by the overall 5-year survival rate in the uni- and multivariate analyses.

CONCLUSIONS

In this study, the HER2 overexpression rate in gastric adenocarcinoma was 21.2% and was observed in well- and moderately differentiated types according to histologic differentiation, intestinal type according to the Lauren classification, male, and low T stage. There was no correlation between HER2 expression level and overall 5-year survival rate, and HER2 expression level was not associated with independent prognostic factors.

Prognostic implication of TSC1 and mTOR expression in gastric carcinoma

BACKGROUND

Gastric adenocarcinoma is the sixth most common and third most lethal cancer in the world. Except for HER2-targeted therapy, targeted agents against specific molecules participating in gastric carcinogenesis, including those in the mechanistic target of rapamycin (serine/threonine kinase) (mTOR) pathway have not been proved to be effective. However, some studies have suggested that dysfunction of TSC1 may augment mTOR inhibitor activity.

METHODS

We studied p-mTOR and TSC1 status by immunohistochemical analysis of gastric carcinoma samples using a tissue microarray method and expression values adopted from The Cancer Genome Atlas.

RESULTS

High p-mTOR and low TSC1 expression status is associated with adverse clinicopathologic parameters. Patients with high p-mTOR levels showed poor survival. Patients with low TSC1 levels showed unfavorable survival status in the overall patients group. The combination of p-mTOR status and TSC1 status provided more strong survival information than using each parameter alone.

CONCLUSIONS

In gastric cancer, high p-mTOR expression level is a statistically significant parameter in multivariate and Kaplan-Meier analyses (log-rank test). In addition to p-mTOR, TSC1 expression provided additional information to predict survival. We therefore suggest that evaluation of both p-mTOR and TSC1 status may be helpful in clinical trials related to mTOR inhibitors.