Although gastric cancer is one of the most common causes of cancer death in the world, mechanisms underlying this type of tumor have not been fully understood. In this study, we found that IQGAP3, a member of the IQGAP gene family, was significantly up-regulated in human gastric cancer starting from the early stages of tumor progression. Overexpression of IQGAP3 in 293T and NIH3T3 cells, which have no endogenous IQGAP3 expression, resulted in morphological change with multiple dendritic-like protrusions and enhanced migration. Overexpression of IQGAP3 also led to reduced cell–cell adhesion in 293T cells, likely as a result of its interactions with e-cadherin or β-catenin proteins. Additionally, IQGAP3 accumulated along the leading edge of migrating cells and at the cleavage furrow of dividing cells. In contrast, suppression of IQGAP3 by short-interfering RNA (siRNA) markedly reduced invasion and anchorage-independent growth of MKN1 and TMK-1 gastric cancer cells. We further confirmed that IQGAP3 interacted with Rho family GTPases, and had an important role in cytokinesis. Taken together, we demonstrated that IQGAP3 plays critical roles in migration and invasion of human gastric cancer cells, and regulates cytoskeletal remodeling, cell migration and adhesion. These findings may open a new avenue for the diagnosis and treatment of gastric cancer.

Chromosomal microarray (CMA) is now recognized as the first-tier genetic test for detection of copy number variations (CNVs) in patients with autism spectrum disorder (ASD). The aims of this study were to identify known and novel ASD associated-CNVs and to evaluate the diagnostic yield of CMA in Thai patients with ASD. The Infinium CytoSNP-850K BeadChip was used to detect CNVs in 114 Thai patients comprised of 68 retrospective ASD patients (group 1) with the use of CMA as a second line test and 46 prospective ASD and developmental delay patients (group 2) with the use of CMA as the first-tier test. We identified 7 (6.1%) pathogenic CNVs and 22 (19.3%) variants of uncertain clinical significance (VOUS). A total of 29 patients with pathogenic CNVs and VOUS were found in 22% (15/68) and 30.4% (14/46) of the patients in groups 1 and 2, respectively. The difference in detected CNV frequencies between the 2 groups was not statistically significant (Chi square = 1.02, df = 1, P = 0.31). In addition, we propose one novel ASD candidate gene, SERINC2, which warrants further investigation. Our findings provide supportive evidence that CMA studies using population-specific reference databases in underrepresented populations are useful for identification of novel candidate genes.

Failure to detect early-stage epithelial ovarian cancer (EOC) is a major contributing factor to its low survival rate. Increasing evidence suggests that different subtypes of EOC may behave as distinct diseases due to their different cells of origins, histology and treatment responses. Therefore, the identification of EOC subtype-specific biomarkers that can early detect the disease should be clinically beneficial. Exosomes are extracellular vesicles secreted by different types of cells and carry biological molecules, which play important roles in cell-cell communication and regulation of various biological processes. Multiple studies have proposed that exosomal miRNAs present in the circulation are good biomarkers for non-invasive early detection of cancer. In this review, the potential use of exosomal miRNAs as early detection biomarkers for EOCs and their accuracy are discussed. We also review the differential expression of circulating exosomal miRNAs and cell-free miRNAs between different biofluid sources, i.e., plasma and serum, and touch on the issue of endogenous reference miRNA selection. Additionally, the current clinical trials using miRNAs for detecting EOCs are summarized. In conclusion, circulating exosomal miRNAs as the non-invasive biomarkers have a high potential for early detection of EOC and its subtypes, and are likely to be clinically important in the future.

Copy number variation (CNV) is a major genetic polymorphism contributing to genetic diversity and human evolution. Clinical application of CNVs for diagnostic purposes largely depends on sufficient population CNV data for accurate interpretation. CNVs from general population in currently available databases help classify CNVs of uncertain clinical significance, and benign CNVs. Earlier studies of CNV distribution in several populations worldwide showed that a significant fraction of CNVs are population specific. In this study, we characterized and analyzed CNVs in 3,017 unrelated Thai individuals genotyped with the Illumina Human610, Illumina HumanOmniexpress, or Illumina HapMap550v3 platform. We employed hidden Markov model and circular binary segmentation methods to identify CNVs, extracted 23,458 CNVs consistently identified by both algorithms, and cataloged these high confident CNVs into our publicly available Thai CNV database. Analysis of CNVs in the Thai population identified a median of eight autosomal CNVs per individual. Most CNVs (96.73%) did not overlap with any known chromosomal imbalance syndromes documented in the DECIPHER database. When compared with CNVs in the 11 HapMap3 populations, CNVs found in the Thai population shared several characteristics with CNVs characterized in HapMap3. Common CNVs in Thais had similar frequencies to those in the HapMap3 populations, and all high frequency CNVs (>20%) found in Thai individuals could also be identified in HapMap3. The majorities of CNVs discovered in the Thai population, however, were of low frequency, or uniquely identified in Thais. When performing hierarchical clustering using CNV frequencies, the CNV data were clustered into Africans, Europeans, and Asians, in line with the clustering performed with single nucleotide polymorphism (SNP) data. As CNV data are specific to origin of population, our population-specific reference database will serve as a valuable addition to the existing resources for the investigation of clinical significance of CNVs in Thais and related ethnicities.

Intrahepatic cholangiocarcinoma (ICC) is the cancer of the intrahepatic bile ducts, and together with hepatocellular carcinoma (HCC), constitute the majority of primary liver cancers. ICC is a rare disorder as its overall incidence is < 1/100,000 in the United States and Europe. However, it shows much higher incidence in particular geographical regions, such as northeastern Thailand, where liver fluke infection is the most common risk factor of ICC. Since the early stages of ICC are often asymptomatic, the patients are usually diagnosed at advanced stages with no effective treatments available, leading to the high mortality rate. In addition, unclear genetic mechanisms, heterogeneous nature, and various etiologies complicate the development of new efficient treatments. Recently, a number of studies have employed high-throughput approaches, including next-generation sequencing and mass spectrometry, in order to understand ICC in different biological aspects. In general, the majority of recurrent genetic alterations identified in ICC are enriched in known tumor suppressor genes and oncogenes, such as mutations in TP53, KRAS, BAP1, ARID1A, IDH1, IDH2, and novel FGFR2 fusion genes. Yet, there are no major driver genes with immediate clinical solutions characterized. Interestingly, recent studies utilized multi-omics data to classify ICC into two main subgroups, one with immune response genes as the main driving factor, while another is enriched with driver mutations in the genes associated with epigenetic regulations, such as IDH1 and IDH2. The two subgroups also show different hypermethylation patterns in the promoter regions. Additionally, the immune response induced by host-pathogen interactions, i.e., liver fluke infection, may further stimulate tumor growth through alterations of the tumor microenvironment. For in-depth functional studies, although many ICC cell lines have been globally established, these homogeneous cell lines may not fully explain the highly heterogeneous genetic contents of this disorder. Therefore, the advent of patient-derived xenograft and 3D patient-derived organoids as new disease models together with the understanding of evolution and genetic alterations of tumor cells at the single-cell resolution will likely become the main focus to fill the current translational research gaps of ICC in the future.