Gastric cancer : epidemiology, risk factors, classification, genomic characteristics and treatment strategies

Gastric cancer (GC) is one of the most common malignancies worldwide and it is the fourth leading cause of cancer-related death. GC is a multifactorial disease, where both environmental and genetic factors can have an impact on its occurrence and development. The incidence rate of GC rises progressively with age; the median age at diagnosis is 70 years. However, approximately 10% of gastric carcinomas are detected at the age of 45 or younger. Early-onset gastric cancer is a good model to study genetic alterations related to the carcinogenesis process, as young patients are less exposed to environmental carcinogens. Carcinogenesis is a multistage disease process specified by the progressive development of mutations and epigenetic alterations in the expression of various genes, which are responsible for the occurrence of the disease

High-throughput sequencing of gastric cancer patients : unravelling genetic predispositions towards an early-onset subtype

Background: Gastric cancer is the fourth most common cause of cancer-related death. Currently, it is broadly accepted that the molecular complexity and heterogeneity of gastric cancer, both inter- and intra-tumor, display important barriers for finding specific biomarkers for the early detection and diagnosis of this malignancy. Early-onset gastric cancer is not as prevalent as conventional gastric carcinoma, but it is a preferable model for studying the genetic background, as young patients are less exposed to environmental factors, which influence cancer development. Aim: The main objective of this study was to reveal age-dependent genotypic characteristics of gastric cancer subtypes, as well as conduct mutation profiling for the most frequent alterations in gastric cancer development, using targeted next-generation sequencing technology. Patients and methods: The study group included 53 patients, consisting of 18 patients with conventional gastric cancer and 35 with an early-onset subtype. The DNA of all index cases was used for next-generation sequencing, employing a panel of 94 genes and 284 single nucleotide polymorphisms (SNPs) (TruSight Cancer Panel, Illumina), which is characteristic for common and rare types of cancer. Results: From among the 53 samples processed for sequencing, we were able to identify seven candidate genes (STK11, RET, FANCM, SLX4, WRN, MEN1, and KIT) and nine variants among them: one splice_acceptor, four synonymous, and four missense variants. These were selected for the age-dependent differentiation of gastric cancer subtypes. We found four variants with C-Score ≥ 10, as 10% of the most deleterious substitutions: rs1800862 (RET), rs10138997 (FANCM), rs2230009 (WRN), and rs2959656 (MEN1). We identified 36 different variants, among 24 different genes, which were the most frequent genetic alterations among study subjects. We found 16 different variants among the genes that were present in 100% of the total cohort: SDHB (rs2746462), ALK (rs1670283), XPC (rs2958057), RECQL4 (rs4925828; rs11342077, rs398010167; rs2721190), DDB2 (rs326212), MEN1 (rs540012), AIP (rs4930199), ATM (rs659243), HNF1A (rs1169305), BRCA2 (rs206075; rs169547), ERCC5 (rs9514066; rs9514067), and FANCI (rs7183618). Conclusions: The technology of next-generation sequencing is a useful tool for studying the development and progression of gastric carcinoma in a high-throughput way. Our study revealed that early-onset gastric cancer has a different mutation frequency profile in certain genes compared to conventional subtype

Patterns of gene expression characterize T1 and T3 clear cell renal cell carcinoma subtypes

Renal carcinoma is the 20th most common cancer worldwide. Clear cell renal cell carcinoma is the most frequent type of renal cancer. Even in patients diagnosed at an early stage, characteristics of disease progression remain heterogeneous. Up-to-date molecular classifications stratify the ccRCC samples into two clusters. We analyzed gene expression in 23 T1 or T3 ccRCC samples. Unsupervised clustering divided this group into three clusters, two of them contained pure T1 or T3 samples while one contained a mixed group. We defined a group of 36 genes that discriminate the mixed cluster. This gene set could be associated with tumor classification into a higher stage and it contained significant number of genes coding for molecular transporters, channel and transmembrane proteins. External data from TCGA used to test our findings confirmed that the expression levels of those 36 genes varied significantly between T1 and T3 tumors. In conclusion, we found a clustering pattern of gene expression, informative for heterogeneity among T1 and T3 tumors of clear cell renal cell carcinoma

How much of the predisposition to Hashimoto's thyroiditis can be explained based on previously reported associations?

Purpose Our insight in the genetics of Hashimoto’s thyroiditis (HT) has become clearer through information provided by genome-wide association studies and candidate gene studies, but remains still not fully understood. Our aim was to assess how many different genetic risk variants contribute to the development of HT. Methods 147 HT cases (10.2% men) and 147 controls (13.6% men) were qualified for the analysis. Intrinsic and environmental factors were controlled for. Polymorphisms (SNP) were chosen based on the literature and included markers of the genes PTPN22, CTLA4, TG, TPO among others, and of genomic regions pointed by GWAS studies. SNP were typed on a microarray. Variants in the HLA-DRB1 gene were identified by Sanger sequencing. Results Multivariate predisposition to HT was modeled. Based on the investigated group, a model of seven variables was obtained. The variability explained by this model was assessed at only 5.4821% (p = 2 × 10-6), which indicates that many dozens of factors are required simultaneously to explain HT predisposition. Conclusions We analyzed genetic regions commonly and most significantly associated with autoimmune thyroid disorders in the literature, on a carefully selected cohort. Our results indicated a lack of possibility to predict the risk of HT development, even with a multivariate model. We therefore conclude that strong associations of single genetic regions with HT should be interpreted with great caution. We believe that a change in the attitude towards genetic association analyses of HT predisposition is necessary. Studies including multiple factors simultaneously are needed to unravel the intricacies of genetic associations with HT

The Pattern of Signatures in Gastric Cancer Prognosis

Gastric cancer is one of the most common malignancies worldwide and it is a fourth leading cause of cancer-related death. Carcinogenesis is a multistage disease process specified by the gradual procurement of mutations and epigenetic alterations in the expression of different genes, which finally lead to the occurrence of a malignancy. These genes have diversified roles regarding cancer development. Intracellular pathways are assigned to the expression of different genes, signal transduction, cell-cycle supervision, genomic stability, DNA repair, and cell-fate destination, like apoptosis, senescence. Extracellular pathways embrace tumour invasion, metastasis, angiogenesis. Altered expression patterns, leading the different clinical responses. This review highlights the list of molecular biomarkers that can be used for prognostic purposes and provide information on the likely outcome of the cancer disease in an untreated individual