Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia

Patient-specific analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

MiRNAs in Hematopoiesis and Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most common kind of pediatric cancer. Although the cure rates in ALL have significantly increased in developed countries, still 15–20% of patients relapse, with even higher rates in developing countries. The role of non-coding RNA genes as microRNAs (miRNAs) has gained interest from researchers in regard to improving our knowledge of the molecular mechanisms underlying ALL development, as well as identifying biomarkers with clinical relevance. Despite the wide heterogeneity reveled in miRNA studies in ALL, consistent findings give us confidence that miRNAs could be useful to discriminate between leukemia linages, immunophenotypes, molecular groups, high-risk-for-relapse groups, and poor/good responders to chemotherapy. For instance, miR-125b has been associated with prognosis and chemoresistance in ALL, miR-21 has an oncogenic role in lymphoid malignancies, and the miR-181 family can act either as a oncomiR or tumor suppressor in several hematological malignancies. However, few of these studies have explored the molecular interplay between miRNAs and their targeted genes. This review aims to state the different ways in which miRNAs could be involved in ALL and their clinical implications.

LncRNA Uc003xsl.1-Mediated Activation of the NFκB/IL8 Axis Promotes Progression of Triple-Negative Breast Cancer

Aberrant activation of NFκB orchestrates a critical role in tumor carcinogenesis; however, the regulatory mechanisms underlying this activation are not fully understood. Here we report that a novel long noncoding RNA (lncRNA) Uc003xsl.1 is highly expressed in triple-negative breast cancer (TNBC) and correlates with poor outcomes in patients with TNBC. Uc003xsl.1 directly bound nuclear transcriptional factor NFκB-repressing factor (NKRF), subsequently preventing NKRF from binding to a specific negative regulatory element in the promoter of the NFκB-responsive gene IL8 and abolishing the negative regulation of NKRF on NFκB-mediated transcription of IL8. Activation of the NFκB/IL8 axis promoted the progression of TNBC. Trop2-based antibody-drug conjugates have been applied in clinical trials in TNBC. In this study, a Trop2-targeting, redox-responsive nanoparticle was developed to systematically deliver Uc003xsl.1 siRNA to TNBC cells in vivo, which reduced Uc003xsl.1 expression and suppressed TNBC tumor growth and metastasis. Therefore, targeting Uc003xsl.1 to suppress the NFκB/IL8 axis represents a promising therapeutic strategy for TNBC treatment.

SIGNIFICANCE

These findings identify an epigenetic-driven NFκB/IL8 cascade initiated by a lncRNA, whose aberrant activation contributes to tumor metastasis and poor survival in patients with triple-negative breast cancer.

Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs

Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.

Investigation of lncRNA-mRNA co-expression network in ETV6-RUNX1-positive pediatric B-cell acute lymphoblastic leukemia

ETV6/RUNX1 gene fusion is the most common chromosomal translocation abnormality occurred in pediatric B-cell acute lymphoblastic leukemia (B-ALL). Compared with ETV6-RUNX1-negative patients, ETV6-RUNX1-positive patients possess more improved treatment strategies but higher risk to relapse. In this research, the potential gene interaction networks were constructed intending for elucidating the pathogenesis of B-ALL. We performed the weighted gene co-expression network analysis (WGCNA) to assess the involvement of lncRNA-mRNA pairs in B-ALL patients consisting of 24 ETV6-RUNX1-positive patients and 18 ETV6-RUNX1-negative patients and found a module that was significantly associated with positive/negative trait. Gene Ontology analysis showed that mRNAs in this module were enriched in the positive regulation of MAPK cascade, positive regulation of JNK cascade, and myeloid cell differentiation pathway. To further investigate the relationship between lncRNAs and mRNAs in this significant module, we constructed the lncRNA-mRNA co-expression network. 3 lncRNAs (RP11-170J3.2, RP11-135F9.1 and RP1-151B14.9) were found at the core of the lncRNA-mRNA co-expression network, which had the most co-expression connections with mRNAs. And several related mRNAs (ACTN1, TNFRSF21 and NLRP3) had a significant correlation with the patient survival prediction. Our findings may explicate the pathogenesis of B-ALL, and the disease-associated genes could provide clues to find novel biomarkers for prognosis.

Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

Pathogenic Roles of S100A8 and S100A9 Proteins in Acute Myeloid and Lymphoid Leukemia: Clinical and Therapeutic Impacts

Deregulations of the expression of the S100A8 and S100A9 genes and/or proteins, as well as changes in their plasma levels or their levels of secretion in the bone marrow microenvironment, are frequently observed in acute myeloblastic leukemias (AML) and acute lymphoblastic leukemias (ALL). These deregulations impact the prognosis of patients through various mechanisms of cellular or extracellular regulation of the viability of leukemic cells. In particular, S100A8 and S100A9 in monomeric, homodimeric, or heterodimeric forms are able to modulate the survival and the sensitivity to chemotherapy of leukemic clones through their action on the regulation of intracellular calcium, on oxidative stress, on the activation of apoptosis, and thanks to their implications, on cell death regulation by autophagy and pyroptosis. Moreover, biologic effects of S100A8/9 via both TLR4 and RAGE on hematopoietic stem cells contribute to the selection and expansion of leukemic clones by excretion of proinflammatory cytokines and/or immune regulation. Hence, the therapeutic targeting of S100A8 and S100A9 appears to be a promising way to improve treatment efficiency in acute leukemias.

Targeting critical kinases and anti-apoptotic molecules overcomes steroid resistance in MLL-rearranged leukaemia

BACKGROUND

Acute lymphoblastic leukaemia with mixed lineage leukaemia gene rearrangement (MLL-ALL) frequently affects infants and is associated with a poor prognosis. Primary refractory and relapsed disease due to resistance to glucocorticoids (GCs) remains a substantial hurdle to improving clinical outcomes. In this study, we aimed to overcome GC resistance of MLL-ALL.

METHODS

Using leukaemia patient specimens, we performed bioinformatic analyses to identify target genes/pathways. To test inhibition of target pathways in vivo, we created pre-clinical therapeutic mouse patient-derived xenograft (PDX)-models by transplanting human MLL-ALL leukaemia initiating cells (LIC) into immune-deficient NSG mice. Finally, we conducted B-cell lymphoma-2 (BCL-2) homology domain 3 (BH3) profiling to identify BH3 peptides responsible for treatment resistance in MLL-leukaemia.

FINDINGS

Src family kinases (SFKs) and Fms-like tyrosine kinase 3 (FLT3) signaling pathway were over-represented in MLL-ALL cells. PDX-models of infant MLL- ALL recapitulated GC-resistance in vivo but RK-20449, an inhibitor of SFKs and FLT3 eliminated human MLL-ALL cells in vivo, overcoming GC-resistance. Further, we identified BCL-2 dependence as a mechanism of treatment resistance in MLL-ALL through BH3 profiling. Furthermore, MLL-ALL cells resistant to RK-20449 treatment were dependent on the anti-apoptotic BCL-2 protein for their survival. Combined inhibition of SFKs/FLT3 by RK-20449 and of BCL-2 by ABT-199 led to substantial elimination of MLL-ALL cells in vitro and in vivo. Triple treatment combining GCs, RK-20449 and ABT-199 resulted in complete elimination of MLL-ALL cells in vivo.

INTERPRETATION

SFKs/FLT3 signaling pathways are promising targets for treatment of treatment-resistant MLL-ALL. Combined inhibition of these kinase pathways and anti-apoptotic BCL-2 successfully eliminated highly resistant MLL-ALL and demonstrated a new treatment strategy for treatment-resistant poor-outcome MLL-ALL.

FUNDING

This study was supported by RIKEN (RIKEN President's Discretionary Grant) for FI, Japan Agency for Medical Research and Development (the Basic Science and Platform Technology Program for Innovative Biological Medicine for FI and by NIH CA034196 for LDS. The funders had no role in the study design, data collection, data analysis, interpretation nor writing of the report.

Co-expression Network Analysis Reveals Key Genes Related to Ankylosing spondylitis Arthritis Disease: Computational and Experimental Validation

BACKGROUND

Ankylosing spondylitis (AS) is a type of arthritis which can cause inflammation in the vertebrae and joints between the spine and pelvis. However, our understanding of the exact genetic mechanisms of AS is still far from being clear.

OBJECTIVE

To study and find the mechanisms and possible biomarkers related to AS by surveying inter-gene correlations of networks.

MATERIALS AND METHODS

A weighted gene co-expression network was constructed among genes identified by microarray analysis, gene co-expression network analysis, and network clustering. Then receiver operating characteristic (ROC) curves were conducted to identify a significant module with the genes implicated in the AS pathogenesis. Real-time PCR was performed to validate the results of microarray analysis.

RESULTS

In the significant module obtained from the network analysis there were eight AS related genes (LSM3, MRPS11, NSMCE2, PSMA4, UBL5, RPL17, MRPL22 and RPS17) which have been reported in previous studies as hub genes. Further, in this module, eight significant enriched pathways were found with adjusted p-values < 0.001 consisting of oxidative phosphorylation, ribosome, nonalcoholic fatty liver disease, Alzheimer's, Huntington's, and Parkinson's diseases, spliceosome, and cardiac muscle contraction pathways which have been linked to AS. Furthermore, we identified nine AS related genes (UQCRB, UQCRH, UQCRHL, UQCRQ, COX7B, COX5B, COX6C, COX6A1 and COX7C) in these pathways which can play essential roles in controlling mitochondrial activity and pathogenesis of autoimmune diseases. Real-time PCR results showed that three genes including UQCRH, MRPS11, and NSMCE2 in AS patients were significantly differentially expressed compared with normal controls.

CONCLUSIONS

The results of the present study may contribute to understanding of AS molecular pathogenesis, thereby aiding the early prognosis, diagnosis, and effective therapies of the disease.

Roles of S100 family members in drug resistance in tumors: Status and prospects

Chemotherapy and targeted therapy can significantly improve survival rates in cancer, but multiple drug resistance (MDR) limits the efficacy of these approaches. Understanding the molecular mechanisms underlying MDR is crucial for improving drug efficacy and clinical outcomes of patients with cancer. S100 proteins belong to a family of calcium-binding proteins and have various functions in tumor development. Increasing evidence demonstrates that the dysregulation of various S100 proteins contributes to the development of drug resistance in tumors, providing a basis for the development of predictive and prognostic biomarkers in cancer. Therefore, a combination of biological inhibitors or sensitizers of dysregulated S100 proteins could enhance therapeutic responses. In this review, we provide a detailed overview of the mechanisms by which S100 family members influence resistance of tumors to cancer treatment, with a focus on the development of effective strategies for overcoming MDR.

Integrated bioinformatics analysis of the crucial candidate genes and pathways associated with glucocorticoid resistance in acute lymphoblastic leukemia

Glucocorticoids (GC) are the foundation of the chemotherapy regimen in acute lymphoblastic leukemia (ALL). However, resistance to GC is observed more frequently than resistance to other chemotherapy agents in patients with ALL relapse. Moreover, the mechanism underlying the development of GC resistance in ALL has not yet been fully uncovered. In this study, we used bioinformatic analysis methods to integrate the candidate genes and pathways participating in GC resistance in ALL and subsequently verified the bioinformatics findings with in vitro cell experiments. Ninety‐nine significant common differentially expressed genes (DEGs) associated with GC resistance were determined by integrating two gene profile datasets, including GC‐sensitive and ‐resistant samples. Using Kyoto Encyclopedia of Genes and Genomes (KEGG) and REACTOME pathways analysis, the signaling pathways in which DEGs were significantly enriched were clustered. The GC resistance‐related biologically functional interactions were visualized as DEG‐associated Protein–Protein Interaction (PPI) network complexes, with 98 nodes and 127 edges. MYC, a node which displayed the highest connectivity in all edges, was highlighted as the core gene in the PPI network. Increased C‐MYC expression was observed in adriamycin‐resistant BALL‐1/ADR cells, which we demonstrated was also resistant to dexamethasone. These results outlined a panorama in which the solitary and scattered experimental results were integrated and expanded. The potential promising target of the candidate pathways and genes involved in GC resistance of ALL was concomitantly revealed.

Differential network analysis and protein-protein interaction study reveals active protein modules in glucocorticoid resistance for infant acute lymphoblastic leukemia

BACKGROUND

Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.

METHODS

This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis.

RESULTS

Through the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL.

CONCLUSIONS

Differential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients.

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

Induction of apoptosis via proteasome inhibition in leukemia/lymphoma cells by two potent piperidones

PURPOSE

Previously, compounds containing a piperidone structure have been shown to be highly cytotoxic to cancer cells. Recently, we found that the piperidone compound P2 exhibits a potent anti-neoplastic activity against human breast cancer-derived cells. Here, we aimed to evaluate two piperidone compounds, P1 and P2, for their potential anti-neoplastic activity against human leukemia/lymphoma-derived cells.

METHODS

Cytotoxicity and apoptosis induction were evaluated using MTS, annexin V-FITC/PI and mitochondrial membrane potential polychromatic assays to confirm the mode of action of the piperidone compounds. The effects of compound P1 and P2 treatment on gene expression were assessed using AmpliSeq analysis and, subsequently, confirmed by RT-qPCR and Western blotting.

RESULTS

We found that the two related piperidone compounds P1 and P2 selectively killed the leukemia/lymphoma cells tested at nanomolar concentrations through induction of the intrinsic apoptotic pathway, as demonstrated by mitochondrial depolarization and caspase-3 activation. AmpliSeq-based transcriptome analyses of the effects of compounds P1 and P2 on HL-60 acute leukemia cells revealed a differential expression of hundreds of genes, 358 of which were found to be affected by both. Additional pathway analyses revealed that a significant number of the common genes were related to the unfolded protein response, implying a possible role of the two compounds in the induction of proteotoxic stress. Subsequent analyses of the transcriptome data revealed that P1 and P2 induced similar gene expression alterations as other well-known proteasome inhibitors. Finally, we found that Noxa, an important mediator of the activity of proteasome inhibitors, was significantly upregulated at both the mRNA and protein levels, indicating a possible role in the cytotoxic mechanism induced by P1 and P2.

CONCLUSIONS

Our data indicate that the cytotoxic activity of P1 and P2 on leukemia/lymphoma cells is mediated by proteasome inhibition, leading to activation of pro-apoptotic pathways.

Novel tumor suppressor SPRYD4 inhibits tumor progression in hepatocellular carcinoma by inducing apoptotic cell death

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-associated deaths worldwide. Although recent studies have proposed different biomarkers for HCC progression and therapy resistance, a better understanding of the molecular mechanisms underlying HCC progression and recurrence, as well as the identification of molecular markers with a higher diagnostic accuracy, are necessary for the development of more effective clinical management strategies. Here, we aimed to identify novel players in HCC progression.

METHODS

SPRYD4 mRNA and protein expression analyses were carried out on a normal liver-derived cell line (HL-7702) and four HCC-derived cell lines (HepG2, SMMC7721, Huh-7, BEL-7402) using qRT-PCR and Western blotting, respectively. Cell proliferation Cell Counting Kit-8 (CCK-8) assays, protein expression analyses for apoptosis markers using Western blotting, and Caspase-Glo 3/7 apoptosis assays were carried out on the four HCC-derived cell lines. Expression comparison, functional annotation, gene set enrichment, correlation and survival analyses were carried out on patient data retrieved from the NCBI Gene module, the NCBI GEO database and the TCGA database.

RESULTS

Through a meta-analysis we found that the expression of SPRYD4 was downregulated in primary HCC tissues compared to non-tumor tissues. We also found that the expression of SPRYD4 was downregulated in HCC-derived cells compared to normal liver-derived cells. Subsequently, we found that the expression of SPRYD4 was inversely correlated with a gene signature associated with HCC cell proliferation. Exogenous SPRYD4 expression was found to inhibit HCC cell proliferation by inducing apoptotic cell death. We also found that SPRYD4 expression was associated with a good prognosis and that its expression became downregulated when HCCs progressed towards more aggressive stages and higher grades. Finally, we found that SPRYD4 expression may serve as a biomarker for a good overall and relapse-free survival in HCC patients.

CONCLUSIONS

Our data indicate that a decreased SPRYD4 expression may serve as an independent predictor for a poor prognosis in patients with HCC and that increased SPRYD4 expression may reduce HCC growth and progression through the induction of apoptotic cell death, thereby providing a potential therapeutic target.

Network-Wide Screen Identifies Variation of Novel Precise On-Module Targets Using Conformational Modudaoism

Modular targeting is promising in drug research at the network level, but it is challenging to quantificationally identify the precise on-modules. Based on a proposed Modudaoism (MD), we defined conserved MD (MDc) and varied MD (MDv) to quantitatively evaluate the conformational and energy variations of modules, and thereby identify the conserved and discrepant allosteric modules (AMs). Compared to the Z_summary , MDc/MDv got an optimized result of module preserved ratio and modular structure. In the mice anti-ischemic networks, 3, 5, and 1 conserved AMs as well as 4, 1, and 3 on-modules of baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) were identified by MDc and MDv, 5 unique AMs and their characteristic actions were revealed. Besides, co-immunoprecipitation (Co-IP) experiments validated the representative modular structure. MDc/MDv method can quantitatively define the conformational variations of modules and screen the precise on-modules network-wide, which may provide a promising strategy for drug discovery.

Identification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal prognosis which is, among others, due to a lack of suitable biomarkers and therapeutic targets. Previously, basic gene expression analysis methods have been used for their identification, but recently new algorithms have been developed allowing more comprehensive data analyses. Among them, weighted gene co-expression network analysis (WGCNA) has already been applied to several cancer types with promising results.

METHODS

We applied WGCNA to miRNA expression data from PDAC patients. Specifically, we processed microarray-based expression data of 2555 miRNAs in serum from 100 PDAC patients and 150 healthy subjects. We identified network modules of co-expressed miRNAs in the healthy subject dataset and verified their preservation in the PDAC dataset. In the non-preserved modules, we selected key miRNAs and carried out functional enrichment analyses of their experimentally known target genes. Finally, we tested their prognostic significance using overall survival analyses.

RESULTS

Through WGCNA we identified several miRNAs that discriminate healthy subjects from PDAC patients and that, therefore, may play critical roles in PDAC development. At a functional level, we found that they regulate p53, FoxO and ErbB associated cellular signalling pathways, as well as cell cycle progression and various genes known to be involved in PDAC development. Some miRNAs were also found to serve as novel prognostic biomarkers, whereas others have previously already been proposed as such, thereby validating the WGCNA approach. In addition, we found that these novel data may explain at least some of our previous PDAC gene expression analysis results.

CONCLUSIONS

We identified several miRNAs critical for PDAC development using WGCNA. These miRNAs may serve as biomarkers for PDAC diagnosis/prognosis and patient stratification, and as putative novel therapeutic targets.