Expansion of Human-Specific GGC Repeat in Neuronal Intranuclear Inclusion Disease-Related Disorders.

Neuronal intranuclear inclusion disease (NIID) is a slowly progressing neurodegenerative disease characterized by eosinophilic intranuclear inclusions in the nervous system and multiple visceral organs. The clinical manifestation of NIID varies widely, and both familial and sporadic cases have been reported. Here we have performed genetic linkage analysis and mapped the disease locus to 1p13.3-q23.1; however, whole-exome sequencing revealed no potential disease-causing mutations. We then performed long-read genome sequencing and identified a large GGC repeat expansion within human-specific NOTCH2NLC. Expanded GGC repeats as the cause of NIID was further confirmed in an additional three NIID-affected families as well as five sporadic NIID-affected case subjects. Moreover, given the clinical heterogeneity of NIID, we examined the size of the GGC repeat among 456 families with a variety of neurological conditions with the known pathogenic genes excluded. Surprisingly, GGC repeat expansion was observed in two Alzheimer disease (AD)-affected families and three parkinsonism-affected families, implicating that the GGC repeat expansions in NOTCH2NLC could also contribute to the pathogenesis of both AD and PD. Therefore, we suggest defining a term NIID-related disorders (NIIDRD), which will include NIID and other related neurodegenerative diseases caused by the expanded GGC repeat within human-specific NOTCH2NLC.

A sample model established by S-index predicting overall survival after curative resection of primary hepatocellular carcinoma.

PURPOSE: Prognostic prediction after curative resection of primary hepatocellular carcinoma (PHCC) remains an arduous task. The S-index calculated from γ-glutamyl transpeptidase, albumin, and platelets is reported to predict the severity of liver fibrosis. We constructed a nomogram for predicting the survival probability of PHCC based on a new indicator, the S-index, combined with other routine clinical parameters. PATIENTS AND METHODS: We selected 490 patients with PHCC postradical surgery at the First Affiliated Hospital of Wenzhou Medical University between January 2007 and January 2014. The subjects were randomly allocated into the training cohort and the validation cohort in the ratio 7:3 by the digital method. Important variables screened by univariate analysis were included in multivariate analysis to obtain independent risk factors for predicting the prognosis of PHCC. The construction of the nomogram was based on Cox proportional hazard regression models. The concordance index (C-index) was used in the nomogram for evaluating the model performance for prognosis. We drew time-dependent receiver operating characteristic curves to compare our model with other staging systems. RESULTS: The nomogram based on six independent risk factors after multivariate analyses had good predictive power after radical surgery of PHCC. In the training and validation groups, the C-index of the nomogram was highly consistent for evaluating survival from PHCC. Compared with the traditional scoring system, the areas under time-dependent receiver operating characteristic curves were 0.7382, 0.7293, and 0.7520 for 1-, 3-, and 5-year overall survival, respectively. In summary, the nomogram showed excellent results in terms of prognosis of PHCC. CONCLUSION: Based on the S-index and the other clinical indicators, we developed a precise nomogram that predicts the survival probability of patients with PHCC after radical surgery. This tool can provide effective information for surgeons and patients.