Co-expression of key gene modules and pathways of human breast cancer cell lines

Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer

BACKGROUND

The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression.

METHODS

In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines.

RESULTS

Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated.

CONCLUSION

The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein-protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein-protein interactions and a unique perspective on possible novel protein complexes.

Identification of critical lncRNAs for milk fat metabolism in dairy cows using WGCNA and the construction of a ceRNAs network

As key regulators, long non-coding RNAs (lncRNAs) play a crucial role in the ruminant mammary gland. However, the function of lncRNAs in milk fat synthesis from dairy cows is largely unknown. In this study, we used the weighted gene co-expression network analysis (WGCNA) to comprehensive analyze the expression profile data of lncRNAs from the group's previous Illumina PE150 sequencing results based on bovine mammary epithelial cells from high- and low-milk-fat-percentage (MFP) cows, and identify core_lncRNAs significantly associated with MFP by module membership (MM) and gene significance (GS). Functional enrichment analysis (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes) of core_lncRNA target genes (co-localization and co-expression) was performed to screen potential lncRNAs regulating milk fat metabolism and further construct an interactive regulatory network of lipid metabolism-related competing endogenous RNAs (ceRNAs). A total of 4876 lncRNAs were used to construct the WGCNA. The MEdarkturquoise module among the 19 modules obtained was significantly associated with MFP (r = 0.78, p-value <0.05) and contained 64 core_lncRNAs (MM > 0.8, GS > 0.4). Twenty-four lipid metabolism-related lncRNAs were identified by core_lncRNA target gene enrichment analysis. TCONS_00054233, TCONS_00152292, TCONS_00048619, TCONS_00033839, TCONS_00153791 and TCONS_00074642 were key candidate lncRNAs for regulating milk fat synthesis. The 22 ceRNAs most likely to be involved in milk fat metabolism were constructed by interaction network analysis, and TCONS_00133813 and bta-miR-2454-5p were located at the network's core. TCONS_00133813_bta-miR-2454-5p_TNFAIP3, TCONS_00133813_bta-miR-2454-5p_ARRB1 and TCONS_00133813_bta-miR-2454-5p_PIK3R1 are key candidate ceRNAs associated with milk fat metabolism. This study provides a framework for the co-expression module of MFP-related lncRNAs in ruminants, identifies several major lncRNAs and ceRNAs that influence milk fat synthesis, and provides a new understanding of the complex biology of milk fat synthesis in dairy cows.

Systemic lupus erythematosus dysregulates the expression of long noncoding RNAs in placentas

Background

Systemic lupus erythematosus (SLE) can cause placental dysfunctions, which may result in pregnancy complications. Long noncoding RNAs (lncRNAs) are actively involved in the regulation of immune responses during pregnancy. The present study aimed to determine the lncRNA expression profiles in placentas from women with SLE to gain new insights into the underlying molecular mechanisms in SLE pregnancies.

Methods

RNA sequencing (RNA-seq) analysis was performed to identify SLE-dysregulated lncRNAs and mRNAs in placentas from women with SLE and normal full-term (NT) pregnancies. Bioinformatics analysis was conducted to predict the biological functions of these SLE-dysregulated lncRNAs and mRNAs.

Results

RNA-seq analysis identified 52 dysregulated lncRNAs in SLE placentas, including 37 that were upregulated and 15 downregulated. Additional 130 SLE-dysregulated mRNAs were discovered, including 122 upregulated and 8 downregulated. Bioinformatics analysis revealed that SLE-dysregulated genes were associated with biological functions and gene networks, such as regulation of type I interferon-mediated signaling pathway, response to hypoxia, regulation of MAPK (mitogen-activated protein kinase) cascade, response to steroid hormone, complement and coagulation cascades, and Th1 and Th2 cell differentiation.

Conclusions

This is the first report of the lncRNA profiles in placentas from SLE pregnancies. These results suggest that the aberrant expression and the potential regulatory function of lncRNAs in placentas may play comprehensive roles in the pathogenesis of SLE pregnancies. SLE-dysregulated lncRNAs may potentially serve as biomarkers for SLE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02825-7.

Data mining and mathematical models in cancer prognosis and prediction

Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis

Milk fat is the most important and energy-rich substance in milk, and its content and composition are important reference elements in the evaluation of milk quality. However, the current identification of valuable candidate genes affecting milk fat is limited. IlluminaPE150 was used to sequence bovine mammary epithelial cells (BMECs) with high and low milk fat rates (MFP), the weighted gene co-expression network (WGCNA) was used to analyze mRNA expression profile data in this study. As a result, a total of 10,310 genes were used to construct WGCNA, and the genes were classified into 18 modules. Among them, violet (r = 0.74), yellow (r = 0.75) and darkolivegreen (r =  − 0.79) modules were significantly associated with MFP, and 39, 181, 75 hub genes were identified, respectively. Combining enrichment analysis and differential genes (DEs), we screened five key candidate DEs related to lipid metabolism, namely PI4K2A, SLC16A1, ATP8A2, VEGFD and ID1, respectively. Relative to the small intestine, liver, kidney, heart, ovary and uterus, the gene expression of PI4K2A is the highest in mammary gland, and is significantly enriched in GO terms and pathways related to milk fat metabolism, such as monocarboxylic acid transport, phospholipid transport, phosphatidylinositol signaling system, inositol phosphate metabolism and MAPK signaling pathway. This study uses WGCNA to form an overall view of MFP, providing a theoretical basis for identifying potential pathways and hub genes that may be involved in milk fat synthesis.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data

The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations.

Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Simple Summary

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In the past few years, studies on the gene expression changes of dairy cow mammary glands were only based on transcriptome comparisons between two lactation stages. Few studies focused on the relationships between gene expression of the dairy mammary gland and lactation stage or milk composition in a lactation cycle. In this study, we detected milk yield and composition in a lactation cycle. For the first time, we constructed a gene coexpression network using WGCNA on the basis of 18 gene expression profiles during six stages of a lactation cycle by transcriptome sequencing, generating 10 specific modules. Genes in each module were performed with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Module–trait relationship analysis showed a series of potential candidates related to milk yield and composition. The current study provides an important theoretical basis for the further molecular breeding of dairy cows.

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Identification of breast cancer prognostic modules via differential module selection based on weighted gene Co-expression network analysis

Breast cancer is a complex cancer which includes many different subtypes. Identifying prognostic modules, i.e., functionally related gene networks that play crucial roles in cancer development is essential in breast cancer study. Different subtypes of breast cancer correspond to different treatment methods. The purpose of this study is to use a new method to divide breast cancer into different prognostic modules, so as to provide scientific basis for improving clinical management. The method is based on comparing similarities between modules detected from different weighted gene co-expression networks. The method was applied on genomic data of breast cancer from The Cancer Genome Atlas database and was applied to select differential modules between two groups of patients with significant differences in survival times. It was compared with a previously proposed module selection method. The result shows that our method outperforms the previously proposed one. Moreover, within the identified two differential modules, the first one is highly enriched with genes involved in hormone responds, the second one is highly related with biological process engaged in M-phase. The two modules were further validated by log-rank test in the validation dataset GSE3494. Both of the two modules show significantly different with p-values less than 0.02. The identified two modules confirmed previous findings including importance of biological networks in breast cancer involved in hormone response and M-phase. Out of the top twenty hub genes in the two modules, fifteen genes were previously shown to be prognostic markers for breast cancer.

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p = 0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.