Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

In silico identification of novel pre-microRNA genes in Rift valley fever virus suggest new pathomechanisms for embryo-fetal dysgenesis

MicroRNAs (miRNAs) are small non-coding RNAs that regulate the post-transcriptional expression of target genes. Virus-encoded miRNAs play an important role in the replication of viruses, modulate gene expression in both the virus and host, and affect their persistence and immune evasion in hosts. This renders viral miRNAs as potential targets for therapeutic applications, especially against pathogenic viruses that infect humans and animals. Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic RNA virus that causes severe disease in both humans and livestock. High mortality among newborn lambs and abortion storms are key characteristics of an RVF outbreak. To date, limited information is available on RVFV-derived miRNAs. In this study, computational methods were used to analyse the RVFV genome for putative pre-miRNA genes, which were then analysed for the presence of mature miRNAs. We detected 19 RVFV-encoded miRNAs and identified their potential mRNAs targets in sheep (Ovis aries), the most susceptible host. The identification of significantly enriched O. aries genes in association with RVFV miRNAs will help elucidate the molecular mechanisms underlying RVFV pathogenesis and potentially uncover novel drug targets for RVFV.

Long non-coding intergenic RNA, LINC00273 induces cancer metastasis and stemness via miRNA sponging in triple negative breast cancer

LncRNAs and miRNAs, being the master regulators of gene expression, are crucial functional mediators in cancer. Our study unveils the critical regulatory role of the metastatic long non-coding RNA LINC00273 as the master regulator of oncogenes involved in cancer metastasis, stemness, and chemoresistance via its miRNA sponging mechanism. M2 (a salt of bis-Schiff base) mediated G quadruplex (G4) stabilization at the LINC00273 gene promoter remarkably inhibits LINC00273 transcription. Therefore, low-level LINC00273 transcripts are unable to efficiently sponge the miRNAs, which subsequently become available to bind and downregulate their target oncogenes. We have observed significantly different global transcriptomic scenarios in LINC00273 upregulated and downregulated circumstances in MDA-MB-231 triple-negative breast cancer model. Additionally, we have found the G4 sequence in the LINC00273 RNA to play a critical role in miRNA sequestration. miRNAs (miR-6789-5p, miR200b, miR-125b-5p, miR-4268, miR3978) have base pairing complementarity within the G4 region of LINC00273 RNA and the 3'-UTR (untranslated region) of MAPK12, TGF-β1, and SIX-1 transcripts. We have reported TGF-β1, SIX-1, and MAPK12 to be the direct downstream targets of LINC00273. The correlation between abnormal expression of lncRNA LINC00273 and TNBC aggressiveness strongly evidenced in our study shall accelerate the development of lncRNA-based anti-metastatic therapeutics.

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

Introducing novel key genes and transcription factors associated with rectal cancer response to chemoradiation through co-expression network analysis

Preoperative radiochemotherapy is a promising therapeutic method for locally advanced rectal cancer patients. However, the response of colorectal cancer (CRC) patients to preoperative radiotherapy varies widely. In this study, we aimed to identify novel biomarkers that could predict the response of colorectal tumors to treatment using a systems biology approach. We applied the Weighted Gene Co-Expression Network Analysis to construct co-expression networks and evaluated the correlation of these networks with radiation using the module-trait relationship. We then identified hub genes and related transcription factors in the selected co-expression module. Our analysis of seven constructed modules revealed that one module, which contained 113 nodes and 6066 edges, had the strongest correlation with radiation effects on CRC (correlation = 0.85; p-value = 6e-7). By analyzing the selected module with the CytoHubba plugin, we identified four hub genes, including ZEB2, JAM2, NDN, and PPAP2A. We also identified seven important transcription factors, including KLF4, SUZ12, TCF4, NANOG, POU5F1, SOX2, and SMARCA4, which may play essential roles in regulating the four hub genes. In summary, our findings suggest that ZEB2, JAM2, NDN, and PPAP2A, along with the seven transcription factors related to these hub genes, may be associated with the response of colorectal tumors to chemoradiotherapy.

Clinical significance of circulating tumor cell (CTC)-specific microRNA (miRNA) in breast cancer

As a noninvasive method, circulating tumor cell (CTC) provides ideal liquid biopsy specimens for early cancer screening and diagnosis. CTCs detection in breast cancer is correlated with patient prognosis such as disease-free survival (DFS) and overall survival (OS). Besides, accumulating evidence supported that CTCs count may be indicator for chemotherapy response as well. The functional roles of microRNA (miRNA) in breast cancer have been well-recognized for the last few years. Due to its stability in circulation, numerous studies have proven that circulating miRNA may serve as promising diagnostic and prognostic biomarkers in breast cancer. The potential ability of miRNAs in disease screening, staging or even molecular subtype classification makes them valuable tools for early breast cancer patients. It would be of great significance to characterize the miRNA expression profile in CTCs, which could provide reliable biological information originated from tumor. However, some issues need to be addressed before the utility of CTC-specific miRNAs in clinical setting. Taken together, we believe that CTC-specific miRNA detection will be trend for early breast cancer screening, diagnosis and treatment monitor in near future.

The Role of microRNAs in Multidrug Resistance of Glioblastoma

Simple Summary

Glioblastoma (GBM) is one of the most malignant types of central nervous system tumor which accounts for more than 60% of all brain tumors in adults. Owing to poor prognosis and drug resistance of most GBM, it is urged to further develop the diagnosis and treatment strategies. The aim of this article is to highlight the roles of some functional microRNAs in the diagnosis and treatment of drug-resistant GBM. Besides, we suggest effective treatment strategies based on the expression profiles of these effective miRNAs to provide an alternative solution to deal with this cancer.

Abstract

Glioblastoma (GBM) is an aggressive brain tumor that develops from neuroglial stem cells and represents a highly heterogeneous group of neoplasms. These tumors are predominantly correlated with a dismal prognosis and poor quality of life. In spite of major advances in developing novel and effective therapeutic strategies for patients with glioblastoma, multidrug resistance (MDR) is considered to be the major reason for treatment failure. Several mechanisms contribute to MDR in GBM, including upregulation of MDR transporters, alterations in the metabolism of drugs, dysregulation of apoptosis, defects in DNA repair, cancer stem cells, and epithelial–mesenchymal transition. MicroRNAs (miRNAs) are a large class of endogenous RNAs that participate in various cell events, including the mechanisms causing MDR in glioblastoma. In this review, we discuss the role of miRNAs in the regulation of the underlying mechanisms in MDR glioblastoma which will open up new avenues of inquiry for the treatment of glioblastoma.

The Role of MiRNA in Cancer: Pathogenesis, Diagnosis, and Treatment

Cancer is also determined by the alterations of oncogenes and tumor suppressor genes. These gene expressions can be regulated by microRNAs (miRNA). At this point, researchers focus on addressing two main questions: "How are oncogenes and/or tumor suppressor genes regulated by miRNAs?" and "Which other mechanisms in cancer cells are regulated by miRNAs?" In this work we focus on gathering the publications answering these questions. The expression of miRNAs is affected by amplification, deletion or mutation. These processes are controlled by oncogenes and tumor suppressor genes, which regulate different mechanisms of cancer initiation and progression including cell proliferation, cell growth, apoptosis, DNA repair, invasion, angiogenesis, metastasis, drug resistance, metabolic regulation, and immune response regulation in cancer cells. In addition, profiling of miRNA is an important step in developing a new therapeutic approach for cancer.

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.

Gene coexpression network approach to develop an immune prognostic model for pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a nonimmunogenic tumor, and very little is known about the relationship between the host immune response and patient survival. We aimed to develop an immune prognostic model (IPM) and analyze its relevance to the tumor immune profiles of patients with PAAD.

METHODS

We investigated differentially expressed genes between tumor and normal tissues in the TCGA PAAD cohort. Immune-related genes were screened from highly variably expressed genes with weighted gene correlation network analysis (WGCNA) to construct an IPM. Then, the influence of IPM on the PAAD immune profile was comprehensively analyzed.

RESULTS

A total of 4902 genes highly variably expressed among primary tumors were used to construct a weighted gene coexpression network. One hundred seventy-five hub genes in the immune-related module were used for machine learning. Then, we established an IPM with four core genes (FCGR2B, IL10RA, and HLA-DRA) to evaluate the prognosis. The risk score predicted by IPM was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. Moreover, we found that the patients in the low-risk group had higher cytolytic activity and lower innate anti-PD-1 resistance (IPRES) signatures than patients in the high-risk group.

CONCLUSIONS

Unlike the traditional methods that use immune-related genes listed in public databases to screen prognostic genes, we constructed an IPM through WGCNA to predict the prognosis of PAAD patients. In addition, an IPM prediction of low risk indicated enhanced immune activity and a decreased anti-PD-1 therapeutic response.

Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

BACKGROUND

The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules.

RESULTS

Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19.

CONCLUSIONS

Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis

Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.

Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.

A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers

Microarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed the kidney miRNA and mRNA sequence data sets for predicting cancer markers using 5 different statistical feature selection methods. In the results, we obtained 3 mRNA- and 27 miRNA-based cancer biomarkers to compare with the normal samples. In addition, we clustered the kidney cancer subtypes using a nonnegative matrix factorization method and obtained significant results of survival analysis from the 2 separate groups including miRNA-342 and its target eukaryotic translation initiation factor 5A (EIF5A).

MicroRNA biogenesis, gene silencing mechanisms and role in breast, ovarian and prostate cancer

Micro-ribonucleic acids (miRNAs) are important class of short regulatory RNA molecules involved in regulation of several essential biological processes. In addition to Dicer and Drosha, over the past few years several other gene products are discovered that regulates miRNA biogenesis pathways. Similarly, various models of molecular mechanisms underlying miRNA mediated gene silencing have been uncovered through which miRNA contribute in diverse physiological and pathological processes. Dysregulated miRNA expression has been reported in many cancers manifesting tumor suppressive or oncogenic role. In this review, critical overview of recent findings in miRNA biogenesis, silencing mechanisms and specifically the role of miRNA in breast, ovarian and prostate cancer will be described. Recent advancements in miRNA research summarized in this review will enhance the molecular understanding of miRNA biogenesis and mechanism of action. Also, role of miRNAs in pathogenesis of breast, ovarian and prostate cancer will provide the insights for the use of miRNAs as biomarker or therapeutic agents for the cancers.

HNRNPA2/B1 is upregulated in endocrine-resistant LCC9 breast cancer cells and alters the miRNA transcriptome when overexpressed in MCF-7 cells

MicroRNAs are dysregulated in breast cancer. Heterogeneous Nuclear Ribonucleoprotein A2/B1 (HNRNPA2/B1) is a reader of the N(6)-methyladenosine (m6A) mark in primary-miRNAs (pri-miRNAs) and promotes DROSHA processing to precursor-miRNAs (pre-miRNAs). We examined the expression of writers, readers, and erasers of m6A and report that HNRNPA2/B1 expression is higher in tamoxifen-resistant LCC9 breast cancer cells as compared to parental, tamoxifen-sensitive MCF-7 cells. To examine how increased expression of HNRNPA2/B1 affects miRNA expression, HNRNPA2/B1 was transiently overexpressed (~5.4-fold) in MCF-7 cells for whole genome miRNA profiling (miRNA-seq). 148 and 88 miRNAs were up- and down-regulated, respectively, 48 h after transfection and 177 and 172 up- and down-regulated, respectively, 72 h after transfection. MetaCore Enrichment analysis identified progesterone receptor action and transforming growth factor β (TGFβ) signaling via miRNA in breast cancer as pathways downstream of the upregulated miRNAs and TGFβ signaling via SMADs and Notch signaling as pathways of the downregulated miRNAs. GO biological processes for mRNA targets of HNRNPA2/B1-regulated miRNAs included response to estradiol and cell-substrate adhesion. qPCR confirmed HNRNPA2B1 downregulation of miR-29a-3p, miR-29b-3p, and miR-222 and upregulation of miR-1266-5p, miR-1268a, miR-671-3p. Transient overexpression of HNRNPA2/B1 reduced MCF-7 sensitivity to 4-hydroxytamoxifen and fulvestrant, suggesting a role for HNRNPA2/B1 in endocrine-resistance.