Toward predictive R-loop computational biology: genome-scale prediction of R-loops reveals their association with complex promoter structures, G-quadruplexes and transcriptionally active enhancers.

R-loops are three-stranded RNA:DNA hybrid structures essential for many normal and pathobiological processes. Previously, we generated a quantitative R-loop forming sequence (RLFS) model, quantitative model of R-loop-forming sequences (QmRLFS) and predicted ∼660 000 RLFSs; most of them located in genes and gene-flanking regions, G-rich regions and disease-associated genomic loci in the human genome. Here, we conducted a comprehensive comparative analysis of these RLFSs using experimental data and demonstrated the high performance of QmRLFS predictions on the nucleotide and genome scales. The preferential co-localization of RLFS with promoters, U1 splice sites, gene ends, enhancers and non-B DNA structures, such as G-quadruplexes, provides evidence for the mechanical linkage between DNA tertiary structures, transcription initiation and R-loops in critical regulatory genome regions. We introduced and characterized an abundant class of reverse-forward RLFS clusters highly enriched in non-B DNA structures, which localized to promoters, gene ends and enhancers. The RLFS co-localization with promoters and transcriptionally active enhancers suggested new models for in cis and in trans regulation by RNA:DNA hybrids of transcription initiation and formation of 3D-chromatin loops. Overall, this study provides a rationale for the discovery and characterization of the non-B DNA regulatory structures involved in the formation of the RNA:DNA interactome as the basis for an emerging quantitative R-loop biology and pathobiology.

Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro-oncogenic pathways in primary tumors (PT) and adjacent non-malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10-6 ) and cross-cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross-validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, quantitative RT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non-malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence-based therapeutic interventions, that aim to reduce the risk of post-surgery relapse in HCC patients.

NotI microarrays: novel epigenetic markers for early detection and prognosis of high grade serous ovarian cancer.

Chromosome 3-specific NotI microarray (NMA) containing 180 clones with 188 genes was used in the study to analyze 18 high grade serous ovarian cancer (HGSOC) samples and 7 benign ovarian tumors. We aimed to find novel methylation-dependent biomarkers for early detection and prognosis of HGSOC. Thirty five NotI markers showed frequency of methylation/deletion more or equal to 17%. To check the results of NMA hybridizations several samples for four genes (LRRC3B, THRB, ITGA9 and RBSP3 (CTDSPL)) were bisulfite sequenced and confirmed the results of NMA hybridization. A set of eight biomarkers: NKIRAS1/RPL15, THRB, RBPS3 (CTDSPL), IQSEC1, NBEAL2, ZIC4, LOC285205 and FOXP1, was identified as the most prominent set capable to detect both early and late stages of ovarian cancer. Sensitivity of this set is equal to (72 ± 11)% and specificity (94 ± 5)%. Early stages represented the most complicated cases for detection. To distinguish between Stages I + II and Stages III + IV of ovarian cancer the most perspective set of biomarkers would include LOC285205, CGGBP1, EPHB1 and NKIRAS1/RPL15. The sensitivity of the set is equal to (80 ± 13)% and the specificity is (88 ± 12)%. Using this technique we plan to validate this panel with new epithelial ovarian cancer samples and add markers from other chromosomes.

Genetic and epigenetic analysis of non-small cell lung cancer with NotI-microarrays.

This study aimed to clarify genetic and epigenetic alterations that occur during lung carcinogenesis and to design perspective sets of newly identified biomarkers. The original method includes chromosome 3 specific NotI-microarrays containing 180 NotI clones associated with genes for hybridization with 40 paired normal/tumor DNA samples of primary lung tumors: 28 squamous cell carcinomas (SCC) and 12 adenocarcinomas (ADC). The NotI-microarray data were confirmed by qPCR and bisulfite sequencing analyses. Forty-four genes showed methylation and/or deletions in more than 15% of non-small cell lung cancer (NSCLC) samples. In general, SCC samples were more frequently methylated/deleted than ADC. Moreover, the SCC alterations were observed already at stage I of tumor development, whereas in ADC many genes showed tumor progression specific methylation/deletions. Among genes frequently methylated/deleted in NSCLC, only a few were already known tumor suppressor genes: RBSP3 (CTDSPL), VHL and THRB. The RPL32, LOC285205, FGD5 and other genes were previously not shown to be involved in lung carcinogenesis. Ten methylated genes, i.e., IQSEC1, RBSP3, ITGA 9, FOXP1, LRRN1, GNAI2, VHL, FGD5, ALDH1L1 and BCL6 were tested for expression by qPCR and were found downregulated in the majority of cases. Three genes (RBSP3, FBLN2 and ITGA9) demonstrated strong cell growth inhibition activity. A comprehensive statistical analysis suggested the set of 19 gene markers, ANKRD28, BHLHE40, CGGBP1, RBSP3, EPHB1, FGD5, FOXP1, GORASP1/TTC21, IQSEC1, ITGA9, LOC285375, LRRC3B, LRRN1, MITF, NKIRAS1/RPL15, TRH, UBE2E2, VHL, WNT7A, to allow early detection, tumor progression, metastases and to discriminate between SCC and ADC with sensitivity and specificity of 80-100%.