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BackgroundColorectal cancer (CRC) is a common cancer worldwide. The main cause of death in CRC includes tumor progression and metastasis. At molecular level, these processes may be triggered by epithelial-mesenchymal transition (EMT) and necessitates specific alterations in cell metabolism. Although several EMT-related metabolic changes have been described in CRC, the mechanism is still poorly understood.ResultsUsing CrossHub software, we analyzed RNA-Seq expression profile data of CRC derived from The Cancer Genome Atlas (TCGA) project. Correlation analysis between the change in the expression of genes involved in glycolysis and EMT was performed. We obtained the set of genes with significant correlation coefficients, which included 21 EMT-related genes and a single glycolytic gene, HK3. The mRNA level of these genes was measured in 78 paired colorectal cancer samples by quantitative polymerase chain reaction (qPCR). Upregulation of HK3 and deregulation of 11 genes (COL1A1, TWIST1, NFATC1, GLIPR2, SFPR1, FLNA, GREM1, SFRP2, ZEB2, SPP1, and RARRES1) involved in EMT were found. The results of correlation study showed that the expression of HK3 demonstrated a strong correlation with 7 of the 21 examined genes (ZEB2, GREM1, TGFB3, TGFB1, SNAI2, TWIST1, and COL1A1) in CRC.ConclusionsUpregulation of HK3 is associated with EMT in CRC and may be a crucial metabolic adaptation for rapid proliferation, survival, and metastases of CRC cells.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4477-4) contains supplementary material, which is available to authorized users.

Background Prostate cancer is one of the most common and socially significant cancers among men. The aim of our study was to reveal changes in miRNA expression profiles associated with lymphatic dissemination in prostate cancer and to identify the most prominent miRNAs as potential prognostic markers for future studies. Methods High-throughput miRNA sequencing was performed for 44 prostate cancer specimens taken from Russian patients, with and without lymphatic dissemination (N1 – 20 samples; N0 – 24 samples). Results We found at least 18 microRNAs with differential expression between N0 and N1 sample groups: miR-182-5p, miR-183-5p, miR-96-5p, miR-25-3p, miR-93-5p, miR-7-5p, miR-615-3p, miR-10b, miR-1248 (N1-miRs; elevated expression in N1 cohort; p < 0.05); miR-1271-5p, miR-184, miR-222-3p, miR-221-5p, miR-221-3p, miR-455-3p, miR-143-5p, miR-181c-3p and miR-455-5p (N0-miRs; elevated expression in N0; p < 0.05). The expression levels of N1-miRs were highly correlated between each other (the same is applied for N0-miRs) and the expression levels of N0-miRs and N1-miRs were anti-correlated. The tumor samples can be divided into two groups depending on the expression ratio between N0-miRs and N1-miRs. Conclusions We found the miRNA expression signature associated with lymphatic dissemination, in particular on the Russian patient cohort. Many of these miRNAs are well-known players in either oncogenic transformation or tumor suppression. Further experimental studies with extended sampling are required to validate these results.

BackgroundCarotid body tumor (CBT) is a form of head and neck paragangliomas (HNPGLs) arising at the bifurcation of carotid arteries. Paragangliomas are commonly associated with germline and somatic mutations involving at least one of more than thirty causative genes. However, the specific functionality of a number of these genes involved in the formation of paragangliomas has not yet been fully investigated.MethodsExome library preparation was carried out using Nextera® Rapid Capture Exome Kit (Illumina, USA). Sequencing was performed on NextSeq 500 System (Illumina).ResultsExome analysis of 52 CBTs revealed potential driver mutations (PDMs) in 21 genes: ARNT, BAP1, BRAF, BRCA1, BRCA2, CDKN2A, CSDE1, FGFR3, IDH1, KIF1B, KMT2D, MEN1, RET, SDHA, SDHB, SDHC, SDHD, SETD2, TP53BP1, TP53BP2, and TP53I13. In many samples, more than one PDM was identified. There are also 41% of samples in which we did not identify any PDM; in these cases, the formation of CBT was probably caused by the cumulative effect of several not highly pathogenic mutations. Estimation of average mutation load demonstrated 6–8 mutations per megabase (Mb). Genes with the highest mutation rate were identified.ConclusionsExome analysis of 52 CBTs for the first time revealed the average mutation load for these tumors and also identified potential driver mutations as well as their frequencies and co-occurrence with the other PDMs.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0327-0) contains supplementary material, which is available to authorized users.

Older age is one of the main risk factors for cancer development. The incidence of prostate cancer, as a multifactorial disease, also depends upon demographic factors, race, and genetic predisposition. Prostate cancer most frequently occurs in men over 60 years of age, indicating a clear association between older age and disease onset. Carcinogenesis is followed by the deregulation of many genes, and some of these changes could serve as biomarkers for diagnosis, prognosis, prediction of drug therapy efficacy, as well as possible therapeutic targets. We have performed a bioinformatic analysis of a The Cancer Genome Atlas (TCGA) data and RNA-Seq profiling of a Russian patient cohort to reveal prognostic markers of locally advanced lymph node-negative prostate cancer (lymph node-negative LAPC). We also aimed to identify markers of the most common molecular subtype of prostate cancer carrying a fusion transcript TMPRSS2-ERG . We have found several genes that were differently expressed between the favorable and unfavorable prognosis groups and involved in the enriched KEGG pathways based on the TCGA ( B4GALNT4 , PTK6 , and CHAT ) and Russian patient cohort data ( AKR1C1 and AKR1C3 ). Additionally, we revealed such genes for the TMPRSS2-ERG prostate cancer molecular subtype ( B4GALNT4 , ASRGL1 , MYBPC1 , RGS11 , SLC6A14 , GALNT13 , and ST6GALNAC1 ). Obtained results contribute to a better understanding of the molecular mechanisms behind prostate cancer progression and could be used for further development of the LAPC prognosis marker panel.

Prostate cancer (PC) is one of the most common cancers among men worldwide, and advanced PCs, such as locally advanced PC (LAPC) and castration-resistant PC (CRPC), present the greatest challenges in clinical management. Current indicators have limited capacity to predict the disease course; therefore, better prognostic markers are greatly needed. In this study, we performed a bioinformatic analysis of The Cancer Genome Atlas (TCGA) datasets, including RNA-Seq data from the prostate adenocarcinoma (PRAD; n = 55) and West Coast Dream Team – metastatic CRPC (WCDT-MCRPC; n = 84) projects, to evaluate the transcriptome changes associated with progression-free survival (PFS) for LAPC and CRPC, respectively. We identified the genes whose expression was positively/negatively correlated with PFS. In LAPC, the genes with the most significant negative correlations were ZC2HC1A, SQLE, and KIF11, and the genes with the most significant positive correlations were SOD3, LRRC26, MIR22HG, MEG3, and MIR29B2CHG. In CRPC, the most significant positive correlations were found for BET1, CTAGE5, IFNGR1, and GIMAP6, and the most significant negative correlations were found for CLPB, PRPF19, ZNF610, MPST, and LINC02001. In addition, we performed a gene network interaction analysis using STRINGdb, which revealed a significant relationship between genes predominantly involved in the cell cycle and characterized by upregulated expression in early recurrence. Based on the results, we propose several genes that can be used as potential prognostic markers.

Background CpG island methylator phenotype (CIMP) is found in 15–20% of malignant colorectal tumors and is characterized by strong CpG hypermethylation over the genome. The molecular mechanisms of this phenomenon are not still fully understood. The development of CIMP is followed by global gene expression alterations and metabolic changes. In particular, CIMP-low colon adenocarcinoma (COAD), predominantly corresponded to consensus molecular subtype 3 (CMS3, “Metabolic”) subgroup according to COAD molecular classification, is associated with elevated expression of genes participating in metabolic pathways. Methods We performed bioinformatics analysis of RNA-Seq data from The Cancer Genome Atlas (TCGA) project for CIMP-high and non-CIMP COAD samples with DESeq2, clusterProfiler, and topGO R packages. Obtained results were validated on a set of fourteen COAD samples with matched morphologically normal tissues using quantitative PCR (qPCR). Results Upregulation of multiple genes involved in glycolysis and related processes ( ENO2, PFKP, HK3, PKM, ENO1, HK2, PGAM1, GAPDH, ALDOA, GPI, TPI1, and HK1 ) was revealed in CIMP-high tumors compared to non-CIMP ones. Most remarkably, the expression of the PKLR gene, encoding for pyruvate kinase participating in gluconeogenesis, was decreased approximately 20-fold. Up to 8-fold decrease in the expression of OGDHL gene involved in tricarboxylic acid (TCA) cycle was observed in CIMP-high tumors. Using qPCR, we confirmed the increase (4-fold) in the ENO2 expression and decrease (2-fold) in the OGDHL mRNA level on a set of COAD samples. Conclusions We demonstrated the association between CIMP-high status and the energy metabolism changes at the transcriptomic level in colorectal adenocarcinoma against the background of immune pathway activation. Differential methylation of at least nine CpG sites in OGDHL promoter region as well as decreased OGDHL mRNA level can potentially serve as an additional biomarker of the CIMP-high status in COAD. Electronic supplementary material The online version of this article (10.1186/s12881-019-0771-5) contains supplementary material, which is available to authorized users.

Carotid paragangliomas (CPGLs) are rare neuroendocrine tumors often associated with mutations in SDHx genes. The immunohistochemistry of succinate dehydrogenase (SDH) subunits has been considered a useful instrument for the prediction of SDHx mutations in paragangliomas/pheochromocytomas. We compared the mutation status of SDHx genes with the immunohistochemical (IHC) staining of SDH subunits in CPGLs. To identify pathogenic/likely pathogenic variants in SDHx genes, exome sequencing data analysis among 42 CPGL patients was performed. IHC staining of SDH subunits was carried out for all CPGLs studied. We encountered SDHx variants in 38% (16/42) of the cases in SDHx genes. IHC showed negative (5/15) or weak diffuse (10/15) SDHB staining in most tumors with variants in any of SDHx (94%, 15/16). In SDHA-mutated CPGL, SDHA expression was completely absent and weak diffuse SDHB staining was detected. Positive immunoreactivity for all SDH subunits was found in one case with a variant in SDHD. Notably, CPGL samples without variants in SDHx also demonstrated negative (2/11) or weak diffuse (9/11) SDHB staining (42%, 11/26). Obtained results indicate that SDH immunohistochemistry does not fully reflect the presence of mutations in the genes; diagnostic effectiveness of this method was 71%. However, given the high sensitivity of SDHB immunohistochemistry, it could be used for initial identifications of patients potentially carrying SDHx mutations for recommendation of genetic testing.