Nucleosomes positioning around transcriptional start site of tumor suppressor (Rbl2/p130) gene in breast cancer

The Epigenetic Regulatory Protein CBX2 Promotes mTORC1 Signalling and Inhibits DREAM Complex Activity to Drive Breast Cancer Cell Growth

Chromobox 2 (CBX2) is a chromatin-binding component of polycomb repressive complex 1, which causes gene silencing. CBX2 expression is elevated in triple-negative breast cancer (TNBC), for which there are few therapeutic options. Here, we aimed to investigate the functional role of CBX2 in TNBC. CBX2 knockdown in TNBC models reduced cell numbers, which was rescued by ectopic expression of wild-type CBX2 but not a chromatin binding-deficient mutant. Blocking CBX2 chromatin interactions using the inhibitor SW2_152F also reduced cell growth, suggesting CBX2 chromatin binding is crucial for TNBC progression. RNA sequencing and gene set enrichment analysis of CBX2-depleted cells identified downregulation of oncogenic signalling pathways, including mTORC1 and E2F signalling. Subsequent analysis identified that CBX2 represses the expression of mTORC1 inhibitors and the tumour suppressor RBL2. RBL2 repression, in turn, inhibits DREAM complex activity. The DREAM complex inhibits E2F signalling, causing cell senescence; therefore, inhibition of the DREAM complex via CBX2 may be a key oncogenic driver. We observed similar effects in oestrogen receptor-positive breast cancer, and analysis of patient datasets suggested CBX2 inhibits RBL2 activity in other cancer types. Therapeutic inhibition of CBX2 could therefore repress mTORC1 activation and promote DREAM complex-mediated senescence in TNBC and could have similar effects in other cancer types.

Four long noncoding RNAs act as biomarkers in lung adenocarcinoma

Introduction

Lung adenocarcinoma (LUAD) is currently one of the most common malignant tumors worldwide. However, there is a lack of long noncoding RNA (lncRNA)-based effective markers for predicting the prognosis of LUAD patients. We identified four lncRNAs that can effectively predict the prognosis of LUAD patients.

Methods

We used data gene expression profile for 446 patients from The Cancer Genome Atlas database. The patients were randomly divided into a training set and a test set. Significant lncRNAs were identified by univariate regression. Then, multivariate regression was used to identify lncRNAs significantly associated with the survival rate. We constructed four-lncRNA risk formulas for LUAD patients and divided patients into high-risk and low-risk groups. Identified lncRNAs subsequently verified in the test set, and the clinical independence of the lncRNA model was evaluated by stratified analysis. Then mutated genes were identified in the high-risk and low-risk groups. Enrichment analysis was used to determine the relationships between lncRNAs and co-expressed genes. Finally, the accuracy of the model was verified using external database.

Results

A four-lncRNA signature (AC018629.1, AC122134.1, AC119424.1, and AL138789.1) has been verified in the training and test sets to be significantly associated with the overall survival of LUAD patients.

Conclusions

The present study demonstrated that identified four-lncRNA signature can be used as an independent prognostic biomarker for the prediction of survival of LUAD patients.

HARVESTMAN: a framework for hierarchical feature learning and selection from whole genome sequencing data

BACKGROUND

Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additionally, variant calls may not be the optimal encoding for a given learning task, which also contributes to poor predictive capabilities. To address these issues, we present HARVESTMAN, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building.

RESULTS

We demonstrate that HARVESTMAN scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, one of the largest publicly available collection of whole genome sequences. Using breast cancer data from The Cancer Genome Atlas, we show that HARVESTMAN selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. We compare HARVESTMAN to existing feature selection methods and demonstrate that our method is more parsimonious-it selects smaller and less redundant feature subsets while maintaining accuracy of the resulting classifier.

CONCLUSION

HARVESTMAN is a hierarchical feature selection approach for supervised model building from variant call data. By building a knowledge graph over genomic variants and solving an integer linear program , HARVESTMAN automatically and optimally finds the right encoding for genomic variants. Compared to other hierarchical feature selection methods, HARVESTMAN is faster and selects features more parsimoniously.

ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning

Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu's method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo's method 'iNuc-PseKNC', the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning.

Distinct DNA Sequence Preference for Histone Occupancy in Primary and Transformed Cells

Genome-wide occupancy of several histone modifications in various cell types has been studied using chromatin immunoprecipitation (ChIP) sequencing. Histone occupancy depends on DNA sequence features like inter-strand symmetry of base composition and periodic occurrence of TT/AT. However, whether DNA sequence motifs act as an additional effector of histone occupancy is not known. We have analyzed the presence of DNA sequence motifs in publicly available ChIP-sequence datasets for different histone modifications. Our results show that DNA sequence motifs are associated with histone occupancy, some of which are different between primary and transformed cells. The motifs for primary and transformed cells showed different levels of GC-richness and proximity to transcription start sites (TSSs). The TSSs associated with transformed or primary cell-specific motifs showed different levels of TSS flank transcription in primary and transformed cells. Interestingly, TSSs with a motif-linked occupancy of H2AFZ, a component of positioned nucleosomes, showed a distinct pattern of RNA Polymerase II (POLR2A) occupancy and TSS flank transcription in primary and transformed cells. These results indicate that DNA sequence features dictate differential histone occupancy in primary and transformed cells, and the DNA sequence motifs affect transcription through regulation of histone occupancy.