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Background & Aims Early recurrence of hepatocellular carcinoma (HCC) after curative resection is common. However, the association between genetic mechanisms and early HCC recurrence, especially in Chinese patients, remains largely unknown. Methods We performed whole-genome sequencing (49 cases), whole-exome sequencing (18 cases), and deep targeted sequencing (115 cases) on 182 primary HCC samples. Focusing on WNK2, we used Sanger sequencing and qPCR to evaluate all the coding exons and copy numbers of that gene in an additional 554 HCC samples. We also explored the functional effect and mechanism of WNK2 on tumor growth and metastasis. Results We identified 5 genes (WNK2, RUNX1T1, CTNNB1, TSC1, and TP53) harboring somatic mutations that correlated with early tumor recurrence after curative resection in 182 primary HCC samples. Focusing on WNK2, the overall somatic mutation and copy number loss occurred in 5.3% (39/736) and 27.2% (200/736), respectively, of the total 736 HCC samples. Both types of variation were associated with lower WNK2 protein levels, higher rates of early tumor recurrence, and shorter overall survival. Biofunctional investigations revealed a tumor-suppressor role of WNK2: its inactivation led to ERK1/2 signaling activation in HCC cells, tumor-associated macrophage infiltration, and tumor growth and metastasis. Conclusions Our results delineate genomic events that characterize Chinese HCCs and identify WNK2 as a driver of early HCC recurrence after curative resection. Lay summary We applied next-generation sequencing and conducted an in-depth genomic analysis of hepatocellular carcinomas from a Chinese patient cohort. The results delineate the genomic events that characterize hepatocellular carcinomas in Chinese patients and identify WNK2 as a driver associated with early tumor recurrence after curative resection.

Wideband Spectrum Sensing (WSS) is considered as one of the challenging issues in Cognitive Radio (CR) dealing with the opportunistic utilization of a wide frequency band. Compressive sampling (CS, also known as compressive sensing) has been introduced recently with a revolutionary idea to detect the sparse wideband spectrum by using a much lower sampling rate. However, in traditional compressive sensing approach, the sparsity of a received signal is always utilized as prior knowledge, which can't be obtained in practical Cognitive Radio Networks (CRN) in advance. In this paper, an Optimized Adaptive Compressive Sampling (OACS) algorithm is proposed to recover original signals via proper sampling rate, which doesn't need any information of sparsity. Furthermore, a Cross Validation (CV) method is introduced that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the stopping criterion is satisfied, avoiding wasting hardware resources. Both theoretical analysis and experimental simulations show that the proposed algorithm can achieve satisfactory signal recovery using lower sampling rate than traditional CS recovery approaches.