Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis

DriverMP enables improved identification of cancer driver genes

BACKGROUND

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.

FINDINGS

We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.

CONCLUSIONS

The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Potential Prognostic Biomarkers of NIMA (Never in Mitosis, Gene A)-Related Kinase (NEK) Family Members in Breast Cancer

Breast cancer remains the most common malignant cancer in women, with a staggering incidence of two million cases annually worldwide; therefore, it is crucial to explore novel biomarkers to assess the diagnosis and prognosis of breast cancer patients. NIMA-related kinase (NEK) protein kinase contains 11 family members named NEK1-NEK11, which were discovered from Aspergillus Nidulans; however, the role of NEK family genes for tumor development remains unclear and requires additional study. In the present study, we investigate the prognosis relationships of NEK family genes for breast cancer development, as well as the gene expression signature via the bioinformatics approach. The results of several integrative analyses revealed that most of the NEK family genes are overexpressed in breast cancer. Among these family genes, NEK2/6/8 overexpression had poor prognostic significance in distant metastasis-free survival (DMFS) in breast cancer patients. Meanwhile, NEK2/6 had the highest level of DNA methylation, and the functional enrichment analysis from MetaCore and Gene Set Enrichment Analysis (GSEA) suggested that NEK2 was associated with the cell cycle, G2M checkpoint, DNA repair, E2F, MYC, MTORC1, and interferon-related signaling. Moreover, Tumor Immune Estimation Resource (TIMER) results showed that the transcriptional levels of NEK2 were positively correlated with immune infiltration of B cells and CD4+ T Cell. Collectively, the current study indicated that NEK family genes, especially NEK2 which is involved in immune infiltration, and may serve as prognosis biomarkers for breast cancer progression.

DNA methylation and breast cancer-associated variants

BACKGROUND

A breast cancer polygenic risk score (PRS) comprising 313 common variants reliably predicts disease risk. We examined possible relationships between genetic variation, regulation, and expression to clarify the molecular alterations associated with these variants.

METHODS

Genome-wide methylomic variation was quantified (MethylationEPIC) in Asian breast cancer patients (1152 buffy coats from peripheral whole blood). DNA methylation (DNAm) quantitative trait loci (mQTL) mapping was performed for 235 of the 313 variants with minor allele frequencies > 5%. Stability of identified mQTLs (p < 5e-8) across lifetime was examined using a public mQTL database. Identified mQTLs were also mapped to expression quantitative trait loci (eQTLs) in the Genotype-Tissue Expression Project and the eQTLGen Consortium.

RESULTS

Breast cancer PRS was not associated with DNAm. A higher proportion of significant cis-mQTLs were observed. Of 822 significant cis-mQTLs (179 unique variants) identified in our dataset, 141 (59 unique variants) were significant (p < 5e-8) in a public mQTL database. Eighty-six percent (121/141) of the matched mQTLs were consistent at multiple time points (birth, childhood, adolescence, pregnancy, middle age, post-diagnosis, or treatment). Ninety-three variants associated with DNAm were also cis-eQTLs (35 variants not genome-wide significant). Multiple loci in the breast cancer PRS are associated with DNAm, contributing to the polygenic nature of the disease. These mQTLs are mostly stable over time.

CONCLUSIONS

Consistent results from DNAm and expression data may reveal new candidate genes not previously associated with breast cancer.

Super-delta2: An Enhanced Differential Expression Analysis Procedure for Multi-Group Comparisons of RNA-seq Data

MOTIVATION

We developed super-delta2, a differential gene expression analysis pipeline designed for multi-group comparisons for RNA-seq data. It includes a customized one-way ANOVA F-test and a post-hoc test for pairwise group comparisons; both are designed to work with a multivariate normalization procedure to reduce technical noise. It also includes a trimming procedure with bias-correction to obtain robust and approximately unbiased summary statistics used in these tests. We demonstrated the asymptotic applicability of super-delta2 to log-transformed read counts in RNA-seq data by large sample theory based on Negative Binomial Poisson (NBP) distribution.

RESULTS

We compared super-delta2 with three commonly used RNA-seq data analysis methods: limma/voom, edgeR, and DESeq2 using both simulated and real datasets. In all three simulation settings, super-delta2 not only achieved the best overall statistical power, but also was the only method that controlled type I error at the nominal level. When applied to a breast cancer dataset to identify differential expression pattern associated with multiple pathologic stages, super-delta2 selected more enriched pathways than other methods, which are directly linked to the underlying biological condition (breast cancer).

CONCLUSIONS

By incorporating trimming and bias-correction in the normalization step, super-delta2 was able to achieve tight control of type I error. Because the hypothesis tests are based on asymptotic normal approximation of the NBP distribution, super-delta2 does not require computationally expensive iterative optimization procedures used by methods such as edgeR and DESeq2, which occasionally have convergence issues.

AVAILABILITY

Our method is implemented in a R-package, "superdelta2", freely available at: https://github.com/fhlsjs/superdelta2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LSM3, NDUFB3, and PTGS2 may be potential biomarkers for BRCA1-mutation positive breast cancer

Purpose: We aimed to find critical biomakers associated with BRCA1-mutation positive breast cancer.

Methods: The data set E-MTAB-982 was downloaded from ArrayExpress database and the data were preprocessed using R package Oligo. Differential expression analysis between BRCA1-mutation positive breast cancer patients and BRCA1-mutation positive healthy subjects were performed using limma package. Then, gene set enrichment analysis was conducted. We constructed the network for BRCA1, its related differentially expressed genes (DEGs), and the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. After that, survival analysis was performed based on the clinical data of breast cancer in TCGA database. Finally, box diagram for key genes was drawn.

Results: The network showed that LSM3, NDUFB3, GNPDA2, and PTGS2 were BRCA1 related DEGs. Furthermore, LSM3 was mainly enriched in RNA degradation pathway and spliceosome pathway. PTGS2 was enriched in arachidonic acid metabolism and VEGF signaling pathway. Survival analysis indicated that high expression of LSM3 indicated a poor prognosis of BRCA1-mutant breast cancer. Besides, box diagram showed that LSM3 was down-regulated in BRCA1-mutation positive breast cancer patients compared with that in BRCA1-mutation positive healthy subjects.

Conclusions: LSM3, NDUFB3, and PTGS2 may be biomarkers in BRCA1-mutant breast cancer, and high expression of LSM3 may indicate a poor prognosis of BRCA1-mutant breast cancer.

DeepSIBA: chemical structure-based inference of biological alterations using deep learning

Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. To make this complex problem computationally tractable, compound differences were mapped to biological effect alterations using Siamese Graph Convolutional Neural Networks. The proposed model was able to encode molecular graph pairs and identify structurally dissimilar compounds that affect similar biological processes with high precision. Additionally, by utilizing deep ensembles to estimate uncertainty, we were able to provide reliable and accurate predictions for chemical structures that are very different from the ones used during training. Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathway signature of a compound perturbation, using only its chemical structure as input, and subsequently identify which substructures influenced the predicted pathways. As a use case, this approach was used to infer important substructures and affected signaling pathways of FDA-approved anticancer drugs.

Predicted molecular targets and pathways for germacrone, curdione, and furanodiene in the treatment of breast cancer using a bioinformatics approach

Germacrone, curdione, and furanodiene have been shown to be useful in the treatment of breast cancer but the pharmacological mechanism of action is unclear. In this paper, we explored a new method to study the molecular network and function of Traditional Chinese Medicine (TCM) herbs and their corresponding ingredients with bioinformatics tools, including PubChem Compound Database, BATMAN-TCM, SystemsDock, Coremine Medical, Gene ontology, and KEGG. Eleven targeted genes/proteins, 4 key pathways, and 10 biological processes were identified to participate in the mechanism of action in treating breast cancer with germacrone, curdione, and furanodiene. The information achieved by the bioinformatics tools was useful to interpretation the molecular mechanism for the treatment of germacrone, curdione, and furanodiene on breast cancers.

Screening of the prognostic targets for breast cancer based co-expression modules analysis

The purpose of the present study was to screen the prognostic targets for breast cancer based on a co-expression modules analysis. The microarray dataset GSE73383 was downloaded from the Gene Expression Omnibus (GEO) database, including 15 breast cancer samples with good prognosis and 9 breast cancer samples with poor prognosis. The differentially expressed genes (DEGs) were identified with the limma package. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Furthermore, the co-expression analysis of DEGs was conducted with weighted correlation analysis. The interaction associations were analyzed with the Human Protein Reference Database and BioGRID. The protein-protein interactions (PPI) network was constructed and visualized by Cytoscape software. A total of 491 DEGs were identified in breast cancer samples with poor prognosis compared with those with good prognosis, and they were enriched in 85 GO terms and 4 KEGG pathways. 368 DEGs were co-expressed with others, and they were clustered into 10 modules. Module 6 was the most relevant to the clinical features, and 21 genes and 273 interaction pairs were selected out. Abnormal expression levels of required for meiotic nuclear division 5 homolog A (RMND5A) and angiopoietin-like protein 1 (ANGPTL1) were associated with a poor prognosis. It was indicated that SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily D, member 1, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily D, member 1, dihydropyrimidinase-like 2, RMND5A and ANGPTL1 were potential prognostic markers in breast cancer, and the cell cycle may be involved in the regulation of breast cancer.