Weighted gene co-expression network analysis in identification of endometrial cancer prognosis markers

Weighted gene co-expression network analysis reveals that CXCL10, IRF7, MX1, RSAD2, and STAT1 are related to the chronic stage of spinal cord injury

Background

The process of spinal cord injury involves acute, subacute, and chronic stages; however, the specific pathological mechanism remains unclear. In this study, weighted gene co-expression network analysis (WGCNA) was used to clarify specific modules and hub genes that associated with SCI.

Methods

The gene expression profiles GEO Series (GSE)45006 and GEO Series (GSE)2599 were downloaded, and the co-expression network modules were identified by the WGCNA package. The protein-protein interaction (PPI) network and Venn diagram were constructed to identify hub genes. Quantitative real-time polymerase chain reaction (QRT-PCR) was used to quantify the degree of the top five candidate genes. Correlation analysis was also carried out between hub genes and immune infiltration.

Results

In total, 14,402 genes and seven modules were identified. The brown module was considered to be the most critical module for the chronic stage of SCI, which contained 775 genes that were primarily associated with various biological processes, including extracellular structure organization, lysosome, isoprenoid biosynthesis, response to nutrients, response to wounding, sulfur compound metabolic process, cofactor metabolic process, and ossification. Furthermore, C-X-C motif chemokine ligand 10 (CXCL10), myxovirus (influenza virus) resistance 1 (MX1), signal transducer and activator of transcription 1 (STAT1), interferon regulatory factor 7 (IRF7) and radical S-adenosyl methionine domain containing 2 (RSAD2) were identified as the hub genes in the PPI and Venn diagram network, and verified by qRT-PCR. Immune infiltration analysis revealed that CD8+ T cells, macrophages, neutrophils, plasmacytoid dendritic cells, helper T cells, Th2 cells, and tumor-infiltrating lymphocytes may be involved in the SCI process.

Conclusions

There were significant differences among the five hub genes (CXCL10, IRF7, MX1, RSAD2, and STAT1) of the brown module, which may be potential diagnostic and prognostic markers of SCI, and immune cell infiltration may play an important role in the chronic stage of SCI.

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers

The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.

Weighted gene coexpression network reveals downregulation of genes in bronchopulmonary dysplasia

BACKGROUND

Bronchopulmonary dysplasia (BPD) is a serious lung disease observed in premature infants, known to cause considerable morbidity and mortality. Its prognosis is influenced by a complex network of genetic interactions. In this study, we determined the potential key factors in the pathogenesis of this condition.

METHODS

We constructed scale-free gene coexpression network using weighted gene coexpression network analysis. The analysis was carried out on the GSE8586 dataset, which contains the expression profiles of umbilical cord tissue homogenates from 20 neonates with BPD and 34 unaffected controls.

RESULTS

Our analysis identified one significantly downregulated coexpression module related to the BPD phenotype. It was significantly enriched in genes related to human T-cell leukemia virus infection and the mitogen-activated protein kinase pathway. In this module, the expression of the following four hub genes in infants with BPD was significantly decreased: Fos proto-oncogene (FOS), BTG antiproliferation factor 2 (BTG2), Jun proto-oncogene (JUN), and early growth response protein 1 (EGR1). The downregulation of these hub genes was verified in clinical samples derived from blood and umbilical cord tissue.

CONCLUSION

The decreased expression of FOS, BTG2, JUN, and EGR1 is associated with BPD and, therefore, could be used as biomarkers to diagnose early BPD.

Oncogenic and tumor suppressor function of MEIS and associated factors

MEIS proteins are historically associated with tumorigenesis, metastasis, and invasion in cancer. MEIS and associated PBX-HOX proteins may act as tumor suppressors or oncogenes in different cellular settings. Their expressions tend to be misregulated in various cancers. Bioinformatic analyses have suggested their upregulation in leukemia/lymphoma, thymoma, pancreas, glioma, and glioblastoma, and downregulation in cervical, uterine, rectum, and colon cancers. However, every cancer type includes, at least, a subtype with high MEIS expression. In addition, studies have highlighted that MEIS proteins and associated factors may function as diagnostic or therapeutic biomarkers for various diseases. Herein, MEIS proteins and associated factors in tumorigenesis are discussed with recent discoveries in addition to how they could be modulated by noncoding RNAs or newly developed small-molecule MEIS inhibitors.

Identification of microRNA-mRNA regulatory networks and pathways related to retinoblastoma across human and mouse

AIM

To explore the mRNA and pathways related to retinoblastoma (RB) genesis and development.

METHODS

Microarray datasets GSE29683 (human) and GSE29685 (mouse) were downloaded from NCBI GEO database. Homologous genes between the two species were identified using WGCNA, followed by protein-protein interaction (PPI) network construction and gene enrichment analysis. Disease-related miRNAs and pathways were retrieved from miR2Disease database and Comparative Toxicogenomics Database (CTD), respectively.

RESULTS

A total of 352 homologous genes were identified. Two pathways including "cell cycle" and "pathway in cancer" in CTD and enrichment analysis were identified and seven miRNAs (including hsa-miR-373, hsa-miR-34a, hsa-miR-129, hsa-miR-494, hsa-miR-503, hsa-let-7 and hsa-miR-518c) were associated with RB. miRNAs modulate "cell cycle" and "pathway in cancer" pathways via regulating 13 genes (including CCND1, CDC25C, E2F2, CDKN2D and TGFB2).

CONCLUSION

These results suggest that these miRNAs play crucial roles in RB genesis through "cell cycle" and "pathway in cancer" pathways by regulating their targets including CCND1, CDC25C, E2F2 and CDKN2D.

Identification of potential novel biomarkers to differentiate malignant thyroid nodules with cytological indeterminate

Background

The fine-needle aspiration (FNA) biopsy was broadly applied to clinical diagnostics evaluation for thyroid carcinomas nodule, while companioning with higher uncertainty rate (15~30%) to identify malignancy for cytological indeterminate cases. It is requirement to discover novel molecular biomarkers to differentiate malignant thyroid nodule more precise.

Methods

We employed weighted gene co-expression network analysis (WGCNA) to discover genes significantly associated with malignant histopathology for cytological indeterminate nodules. In addition, identified significantly genes were validated through another independently investigations of thyroid carcinomas patient’s samples via cBioportal and Geipa. The key function pathways of significant genes involving were blast through GenClip.

Results

Twenty-four signature genes were identified significantly related to thyroid nodules malignancy. Furthermore, five novel genes with missense mutation, FN1 (R534P), PROS1((K200I), (Q571K)), SCEL (T320S), SLC34A2(T688M) and TENM1 (S1131F), were highlighted as potential biomarkers to rule out nodules malignancy. It was identified that the key functional pathways involving in thyroid carcinomas.

Conclusion

These results will be helpful to better understand the mechanism of thyroid nodules malignant transformation and characterize the potentially biomarkers for thyroid carcinomas early diagnostics.

Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway "natural killer cell-mediated cytotoxicity" were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the "natural killer cell-mediated cytotoxicity" pathway.

Differential clinical significance of COL5A1 and COL5A2 in tongue squamous cell carcinoma

BACKGROUND

Type V collagen (COL5), in the functional heterotrimer [α1(V)₂ α2(V)] isoform, participates in the malignancies of various cancers. However, its role in tongue squamous cell carcinoma (TSCC) remains unclear.

MATERIALS AND METHODS

The expression levels of COL5A1 and COL5A2 polypeptide chains were examined using the tissue microarray from 245 TSCC patients with immunohistochemistry. Paired t test and Wilcoxon signed-rank test were performed for comparisons among the groups. Survival rates were estimated by using the Kaplan-Meier method and compared with log-rank tests. A Cox proportional hazards model was used to evaluate the impact of protein expression level on survival rate.

RESULTS

Expression level of COL5A1 was significantly increased in tumor tissues (P < 0.001) compared to that in corresponding adjacent normal tissues. High expression level of COL5A1 was associated with advanced pathological stage (III, IV, P = 0.015) and lymph node metastasis (P = 0.005) of TSCC patients. High expression level of COL5A1 was also correlated with poor disease-specific survival (DSS, P = 0.001) and disease-free survival (DFS, P = 0.003) in TSCC patients. However, high expression level of COL5A2 was correlated with better DFS in TSCC patients (P = 0.043). Moreover, co-expression level of high (COL5A1)₂ /low (COL5A2) heterotrimer was correlated with worse DSS (P = 0.004) and DFS (P = 0.004).

CONCLUSION

COL5A1 is an unfavorable factor for tumorigenesis, clinicopathological outcomes, and prognosis, whereas COL5A2 is only a favorable factor for prognosis in TSCC. The co-expression of high (COL5A1)2/low (COL5A2) heterotrimer is a more potential unfavorable factor for prognosis in TSCC.

Involvement of heat shock protein 40 in the wing dimorphism of the house cricket Acheta domesticus

Wing dimorphism is a common phenomenon in a wide range of insect taxa. In most insects, the two morphs are macropterous and micropterous, in extreme cases of the latter, wing shedding can occur. Wing dimorphism contributes significantly to the ecological success of many insect species. However, the molecular basis of wing dimorphism is not fully understood, especially for wing-shed. Here, differentially expressed genes over eight developmental stages of the house cricket Acheta domesticus, which undergoes wing-shed dimorphism, were studied. The results revealed a wing-shed peak during adult development in which many DEGs were highly upregulated and it's influenced by cricket population density. A weighted correlation network analysis (WGCNA) grouped 21,922 DEGs among 141,456 unigenes into 18 modules of different expression patterns. The module in which the gene expression pattern correlated with the wing-shed phenotypic data was selected for further analyses with STEM and Cytoscape, and three candidate genes (AdomHSP40: Heat shock protein 40, AdomCFDP: Craniofacial development protein, AdomDIS3L: DIS3 Like 3'-5' Exoribonuclease) were identified by gene network analysis as the DEGs most relevant to wing-shed occurrence. The RNA interference of these genes together with an insulin receptor and Nylanderia fulva virus showed that the silencing of AdomHSP40 significantly decreased wing-shed occurrence, whereas the silencing of other candidate genes did not, suggesting that AdomHSP40 plays a crucial role in the wing-shed of Acheta domesticus. These findings provide insights into the molecular mechanisms underlying wing dimorphism in the house crickets, which differ from those found in other insects such as the planthopper.

Hsa-let-7g promotes osteosarcoma by reducing HOXB1 to activate NF-kB pathway

MicroRNA (miRNA) is known to be involved in regulating the proliferation, migration and apoptosis of cancer cells in osteosarcoma. In this study, We aim to explore the expression of hsa-let-7 g and its role in pathogenesis of osteosarcoma. By analyzing clinical data. We found high expression of hsa-let-7 g in patients with osteosarcoma. The patients with higher expression of hsa-let-7 g showed poorer prognosis and lower survival rate. After downregulation of hsa-let-7 g in cell model and animal model, we found that with downregulation of hsa-let-7 g, the proliferation of osteosarcoma cells was significantly reduced, the level of migration and invasion was down-regulated, the cell cycle was inhibited, and cell apoptosis was increased. Through Dual Luciferase Reporter, immunohistochemistry, western blot and other experiments, it was found that hsa-let-7 g down-regulated HOXB1 gene and activated NF-kB pathway to promote the development of osteosarcoma. In conclusion, hsa-let-7 g is highly expressed in osteosarcoma tissues, and high expression of hsa-let-7 g can promote the occurrence of osteosarcoma by down-regulating HOXB1 and activating NF-kB pathway.

Gene coexpression network analysis identified potential biomarkers in gestational diabetes mellitus progression

Background

Gestational diabetes mellitus (GDM) is one of the most common problems during pregnancy. Lack of international consistent diagnostic procedures has limit improvement of current therapeutic effectiveness. Here, we aimed to screen potential gene biomarkers that might play vital roles in GDM progression for assistance of its diagnostic and treatment.

Methods

Gene expression profiles in four GDM placentae at first trimester, four GDM placentae at second trimester, and four normal placentae were obtained from the publicly available Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) indicated two gene modules, that is, black and brown module, that was significantly positively and negatively correlated with GDM progression time points, respectively. Additionally, a significant positive correlation between module membership (MM) and degree in protein–protein interaction network of brown module genes was observed.

Results

KIF2C, CENPE, CCNA2, AURKB, MAD2L1, CCNB2, CDC20, PLK1, CCNB1, and CDK1 all have degree larger than 50 and MM larger than 0.9, so they might be valuable biomarkers in GDM. Gene set enrichment analysis inferred tight relations between carbohydrate metabolism or steroid biosynthesis‐related processes and GDM progression.

Conclusions

All in all, our study should provide several novel references for GDM diagnosis and therapeutic.

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis

Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01). The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).

ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Screening feature modules and pathways in glioma using EgoNet

Background

To investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.

Methodology

Based on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.

Results

We obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.

Conclusion

The identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.

A five-gene based risk score with high prognostic value in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently occurring malignancies worldwide. The outcomes of patients with similar clinical symptoms or at similar pathological stages remain unpredictable. This inherent clinical diversity is most likely due to the genetic heterogeneity. The present study aimed to create a predicting tool to evaluate patient survival based on genetic profile. Firstly, three Gene Expression Omnibus (GEO) datasets (GSE9348, GSE44076 and GSE44861) were utilized to identify and validate differentially expressed genes (DEGs) in CRC. The GSE14333 dataset containing survival information was then introduced in order to screen and verify prognosis-associated genes. Of the 66 DEGs, the present study screened out 46 biomarkers closely associated to patient overall survival. By Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was demonstrated that these genes participated in multiple biological processes which were highly associated with cancer proliferation, drug-resistance and metastasis, thus further affecting patient survival. The five most important genes, MET proto-oncogene, receptor tyrosine kinase, carboxypeptidase M, serine hydroxymethyltransferase 2, guanylate cyclase activator 2B and sodium voltage-gated channel a subunit 9 were selected by a random survival forests algorithm, and were further made up to a linear risk score formula by multivariable cox regression. Finally, the present study tested and verified this risk score within three independent GEO datasets (GSE14333, GSE17536 and GSE29621), and observed that patients with a high risk score had a lower overall survival (P<0.05). Furthermore, this risk score was the most significant compared with other predicting factors including age and American Joint Committee on Cancer stage, in the model, and was able to predict patient survival independently and directly. The findings suggest that this survival associated DEGs-based risk score is a powerful and accurate prognostic tool and is promisingly implemented in a clinical setting.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm

Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.

Increased Expression of Several Collagen Genes is Associated with Drug Resistance in Ovarian Cancer Cell Lines

Ovarian cancer is the most lethal gynaecological cancer. The main reason for the high mortality among ovarian cancer patients is the development of drug resistance. The expression of collagen genes by cancer cells can increase drug resistance by inhibiting the penetration of the drug into the cancer tissue as well as increase apoptosis resistance. In this study, we present data that shows differential expression levels of collagen genes and proteins in cisplatin- (CIS), paclitaxel- (PAC), doxorubicin- (DOX), topotecan- (TOP), vincristine- (VIN) and methotrexate- (MTX) resistant ovarian cancer cell lines. Quantitative real-time polymerase chain reactions were performed to determine the mRNA levels. Protein expression was detected using Western blot and immunocytochemistry assays. In the drug resistant cell lines, we observed the upregulation of eight collagen genes at the mRNA level and based on these expression levels, we divided the collagen genes into the following three groups:

1. Genes with less than a 50-fold increase in expression: COL1A1, COL5A2, COL12A1 and COL17A1. 2. Genes with greater than a 50-fold increase in expression: COL1A2, COL15A1 and COL21A1. 3. Gene with a very high level of expression: COL3A1. Expression of collagen (COL) proteins from groups 2 and 3 were also confirmed using immunocytochemistry. Western blot analysis showed very high expression levels of COL3A1 protein, and immunocytochemistry analysis showed the presence of extracellular COL3A1 in the W1TR cell line. The cells mainly responsible for the extracellular COL3A1 production are aldehyde dehydrogenase-1A1 (ALDH1A1) positive cells. All correlations between the types of cytostatic drugs and the expression levels of different COL genes were studied, and our results suggest that the expression of fibrillar collagens may be involved in the TOP and PAC resistance of the ovarian cancer cells. The expression pattern of COL genes provide a preliminary view into the role of these proteins in cytostatic drug resistance of cancer cells. The exact role of these COL genes in drug resistance requires further investigation.

Relations of Serum Visfatin and Resistin Levels with Endometrial Cancer and Factors Associated with its Prognosis

BACKGROUND

The aims of this study were compare the serum visfatin and resistin levels between endometrial cancer (EC) patients and controls and evaluate their power to predict prognosis.

MATERIALS AND METHODS

This prospective study was conducted between March 2013 to June 2014 on the Gynecologic Oncology Department of the University of Selcuk, Konya, Turkey. A total of 42 EC patients and 42 controls were included and assessed for differences in serum visfatin and resistin levels, along with prognostic factors.

RESULTS

Endometrial cancer patients had significantly higher visfatin levels than control s (p: 0.011), associated with deep myometrial invasion (p: 0.019). In contrast the serum level of resistin did not significantly differ between EC patients and controls (p: 0.362). However, high resistin level in EC patients was associated with increase lymph node metastasis (p: 0.009). On logistic regression analysis, we found that serum visfatin elevation was associated with risk of myometrial invasion (OR: 1,091; 95%CI: 1.021- 1.166; p: 0.010) and serum resistin with risk of lymph node metastasis (OR: 1.018; 95%CI: 1.000- 1.035; p: 0.046). For myometrial invasion prediction, a serum visfatin level greater than 26.8 ng/mL demonstrated a sensitivity and specificity of 66.6 % and 96.4%, respectively. For lymph node metastasis prediction, the best cut-off for serum resistin level was 599ng/mL. A serum resistin level greater than this demonstrated a sensitivity and specificity of 87.5% and 77.1%, respectively.

CONCLUSIONS

Our data suggest that serum visfatin is elevated in patients with EC and serum visfatin and resistin levels could be used to predict the risk of advance stage lesions.

Role of exon 7 PTEN Gene in Endometrial Carcinoma

BACKGROUND

Endometrial carcinoma is the most common malignant tumor of the female genital tract and the fourth most common cancer in Iranian women after breast, colorectal and lung cancers. Various genetic alterations appear to be early events in the pathogenesis of endometrial carcinoma and it seems that PTEN is the most commonly mutated gene in the endometrioid subtype. The aim of the present study was to investigate the correlation between mutations in exon 7 of PTEN gene and endometrial carcinoma.

MATERIALS AND METHODS

Seventy-five patients with endometrial carcinoma and 75 females whose underwent hysterectomy for non tumoral indication were selected for evaluation of PTEN mutations in exon 7 by PCR-SSCP and sequencing. Correlations between the frequency and type of mutation and the pathologic findings of the cancer (tumor subtype, stage and grade) were assessed.

RESULTS

All of the samples were obtained from Iranian patients. 60 % (45 cases) of the tumors were endometriod and 40% (30 cases) were of serous type. The grade distributions of the 75 cases according to the FIGO staging system were as follows: low grade, 20 cases; high grade 55 cases, low stage, 41 cases; high stage 34 cases. For exon 7 of the PTEN gene, the analysis showed that there were no mutations in our cases.

CONCLUSIONS

Our findings in the present study suggest that exon 7 of PTEN does not play any significant role in the development of endometrial carcinoma in Iranian cases.

The role of racial genetic admixture with endometrial cancer outcomes: An NRG Oncology/Gynecologic Oncology Group study

PURPOSE

Racial genetic admixture (RGA), a measure to account for ancestral genetic background that correlates with individual's racial classification, could provide insights on causation of racial disparity in endometrial cancer (EC). Our objective is to evaluate the association of RGA with EC outcomes.

METHODS

EC patients enrolled onto the GOG-210 protocol were eligible. A randomized subcohort stratified by stage and self-reported race/ethnicity of black or white was used. Genotyping was performed using custom-selected Ancestry Informative Markers to calculate individual admixture estimates of African and European ancestral background.

RESULTS

A total of 149 patients were evaluated (self-reported race: 70 black & 79 white). Mean RGA for African ancestry for self-reported black patients was 0.65 (range 0.04-0.86); while mean RGA for European ancestry for self-reported white patients was 0.77 (range 0.12-0.88). Progression-free survival (PFS) analysis using proportional hazards models stratified by stage and race revealed that each 0.10 increase in African ancestry was associated with worse PFS with hazard ratio (HR) of 1.11 (95% CI 0.90-1.37). Each 0.10 increase in European RGA was associated with improved PFS with HR of 0.86 (95% CI 0.69-1.07). Using tertiles of African RGA showed increasing risk of progression of death with increasing African RGA (with 0-5% as reference), HR (95% CIs) for top two tertiles are: 6%-66%: 1.38 (0.64, 2.97), and 67%-86%: 2.27 (0.74, 6.95).

CONCLUSION

RGA demonstrated a trend with PFS in self-reported black and white patients with EC. Patients with increased levels of African ancestry showed a trend towards worse survival after stratifying by stage/race.

HOXB1 Is a Tumor Suppressor Gene Regulated by miR-3175 in Glioma

The HOXB1 gene plays a critical role as an oncogene in diverse tumors. However, the functional role of HOXB1 and the mechanism regulating HOXB1 expression in glioma are not fully understood. A preliminary bioinformatics analysis showed that HOXB1 is ectopically expressed in glioma, and that HOXB1 is a possible target of miR-3175. In this study, we investigated the function of HOXB1 and the relationship between HOXB1 and miR-3175 in glioma. We show that HOXB1 expression is significantly downregulated in glioma tissues and cell lines, and that its expression may be closely associated with the degree of malignancy. Reduced HOXB1 expression promoted the proliferation and invasion of glioma cells, and inhibited their apoptosis in vitro, and the downregulation of HOXB1 was also associated with worse survival in glioma patients. More importantly, HOXB1 was shown experimentally to be a direct target of miR-3175 in this study. The downregulated expression of miR-3175 inhibited cell proliferation and invasion, and promoted apoptosis in glioma. The oncogenicity induced by low HOXB1 expression was prevented by an miR-3175 inhibitor in glioma cells. Our results suggest that HOXB1 functions as a tumor suppressor, regulated by miR-3175 in glioma. These results clarify the pathogenesis of glioma and offer a potential target for its treatment.

Differential analysis of biological networks

Background

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences.

Results

We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation.

Conclusions

We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.

The polymorphism of hypoxia-inducible factor-1a gene in endometrial cancer

BACKGROUND

Endometral carcinoma is the most common malignant tumor of the female genital tract and the fourth most common cancer in women after breast, colorectal and lung cancers Hypoxia-inducible factor -1 (HIF-1) is a key transcription factor that regulates cellular response to hypoxia HIF-1 plays important roles in the development and progression of cancer through activation of various genes that are involved in crucial aspects of cancer biology, including angiogenesis, energy metabolism, vasomotor function, erythropoiesis, and cell survival. In this study, we aimed to investigate the association between HIF-1 1772 C/T polymorphisms and endometrial cancer.

MATERIALS AND METHODS

75 patients with endometrial carcinoma and 75 patients whose underwent hysterectomy for non tumoral indication selected for evaluation of HIF-1 1772 C/T polymorphisms by PCR-RFLP and sequencing.

RESULTS

For the 1772 C/T polymorphism, the analysis showed that the T allele and genotype TT were significantly associated with endometrial cancer risk.

CONCLUSIONS

Our results suggest that the C1772T polymorphism of the HIF-1a may be associated with endometrial cancers.

Gene co-expression network and function modules in three types of glioma

The aim of the present study was to identify the disease‑associated genes and their functions involved in the development of three types of glioma (astrocytoma, glioblastoma and oligodendroglioma) with DNA microarray technology, and to analyze their differences and correlations. First, the gene expression profile GSE4290 was downloaded from the Gene Expression Omnibus database, then the probe‑level data were pre‑processed and the differentially expressed genes (DEGs) were identified with limma package in R language. Gene functions of the selected DEGs were further analyzed with the Database for Annotation, Visualization and Integrated Discovery. After the co‑expression network of DEGs was constructed by Cytoscape, the functional modules were mined and enrichment analysis was performed, and then the similarities and differences between any two types of glioma were compared. A total of 1151 genes between normal and astrocytoma tissues, 684 genes between normal and malignant glioma tissues, and 551 genes between normal and oligodendroglioma tissues were filtered as DEGs, respectively. By constructing co‑expression networks of DEGs, a total of 77232, 455 and 987 interactions were involved in the differentially co‑expressed networks of astrocytoma, oligodendroglioma and glioblastoma, respectively. The functions of DEGs were consistent with the modules in astrocytoma, glioblastoma and oligodendroglioma, which were mainly enriched in neuron signal transmission, immune responses and synthesis of organic acids, respectively. Model functions of astrocytoma and glioblastoma were similar (mainly related with immune response), while the model functions of oligodendroglioma differed markedly from that of the other two types. The identification of the associations among these three types of glioma has potential clinical utility for improving the diagnosis of different types of glioma in the future. In addition, these results have marked significance in studying the underlying mechanisms, distinguishing between normal and cancer tissues, and examining novel therapeutic strategies for patients with glioma.

Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas

OBJECT

Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%-5% display malignant pathological features. The key molecular pathways involved in malignant transformation remain to be determined.

METHODS

Illumina expression microarrays were used to assess gene expression levels, and Illumina single-nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant meningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones). A weighted gene coexpression network approach was used to identify coexpression modules associated with malignancy.

RESULTS

At the genomic level, malignant meningiomas had more chromosomal losses than atypical and benign meningiomas, with average length of 528, 203, and 34 megabases, respectively. Monosomic loss of chromosome 22 was confirmed to be one of the primary chromosomal level abnormalities in all subtypes of meningiomas. At the transcriptome level, the authors identified 23 coexpression modules from the weighted gene coexpression network. Gene functional enrichment analysis highlighted a module with 356 genes that was highly related to tumorigenesis. Four intramodular hubs within the module (GAB2, KLF2, ID1, and CTF1) were oncogenic in other cancers such as leukemia. A putative meningioma tumor suppressor MN1 was also identified in this module with differential expression between malignant and benign meningiomas.

CONCLUSIONS

The authors' genomic and transcriptome analysis of meningiomas provides novel insights into the molecular pathways involved in malignant transformation of meningiomas, with implications for molecular heterogeneity of the disease.

Protein-protein interaction network analyses for elucidating the roles of LOXL2-delta72 in esophageal squamous cell carcinoma

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.

DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.