Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Up‐regulation of LIMK1 expression in prostate cancer is correlated with poor pathological features,lymph node metastases and biochemical recurrence

This study aimed to explore the association between LIM domain kinase 1 (LIMK1) expression in prostate cancer (PCa) tissues with advanced pathological features, lymph node metastases and biochemical recurrence. A total of 279 PCa specimens from patients who underwent radical prostatectomy and 50 benign prostatic hyperplasia (BPH) specimens were collected to construct tissue microarray, which were subjected to immunohistochemical staining for LIMK1 expression subsequently. Logistic and Cox regression analysis were used to evaluate the relationship between LIMK1 expression and clinicopathological features of patients with PCa. Immunohistochemical staining assay demonstrated that LIMK1 expression was significantly higher in PCa than BPH specimens (77.1% vs 26.0%; P < .001). LIMK1 expression was significantly higher in positive lymph node specimens than corresponding PCa specimens (P = .002; P < .001). Up‐regulation of LIMK1 was associated with prostate volume, prostate‐specific antigen, prostate‐specific antigen density, Gleason score, T stage, lymph node metastases, extracapsular extension and seminal vesicle invasion, and positive surgical margin. Multivariate logistic regression analysis demonstrated that LIMK1 was an independent risk factor for PCa lymph node metastasis (P < .05). Multivariate Cox regression analysis revealed that the up‐regulation of LIMK1 was an independent risk factor for biochemical recurrence. Kaplan‐Meier analysis indicated that up‐regulation LIMK1 was associated with shortened biochemical‐free survival (BFS) after radical prostatectomy (P < .001). In conclusion, LIMK1 was significantly up‐regulated in PCa and positive lymph node specimens and correlated with lymph node metastasis and shortened BFS of PCa. The underlying molecular mechanism of LIMK1 in PCa should be further evaluated.     

Identification of IMPA2 as the hub gene associated with colorectal cancer and liver metastasis by integrated bioinformatics analysis

Background and ObjectivesColorectal cancer (CRC) is one of the most common malignant tumors worldwide with high incidence and mortality rate, while colorectal liver metastasis (CRLM) is one of the major causes of cancer-related deaths. Therefore, the present study aims to identify the hub gene associated with CRC carcinogenesis and liver metastasis, and then explore its diagnostic and prognostic value as well as the potential regulation mechanism.MethodsThe overlapping differential co-expression genes among CRC, CRLM, and normal tissues were explored on the GSE49355 and GSE81582 datasets from the Gene Expression Omnibus (GEO) database by integrated bioinformatics analysis. Then, the hub prognostic genes were selected from the overlapping genes by univariate Cox proportional hazard analysis and online database Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Subsequently, the clinical value of the hub genes was evaluated in the TCGA and GSE39582 cohorts. Finally, the underlying mechanisms of the hub gene regulating CRC carcinogenesis and metastasis were explored by Gene function annotation and DNA methylation analysis.ResultsInositol mono-phosphatase 2 (IMPA2) was identified as the hub gene associated with CRC carcinogenesis and liver metastasis. IMPA2 had an excellent diagnostic efficiency, and its expression was significantly decreased in CRC and liver metastasis samples, being positively correlated with poor prognosis. Moreover, its low expression was associated with AJCC stage III+IV, T4, N1+2, and M1. In addition, our results revealed that the potential mechanisms used by IMPA2 to mediate CRC carcinogenesis and metastasis could be associated with lipid metabolism and epithelial mesenchymal transition (EMT). Finally, IMPA2 expression could be regulated by DNA methylation.ConclusionsIMPA2 was identified and reported for the first time as a hub gene biomarker in the diagnosis and prognosis of CRC, which could regulate CRC carcinogenesis and liver metastasis through the regulation of lipid metabolism, EMT, and DNA methylation.     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Joint analysis of WES and RNA-Seq identify signature genes related to metastasis in prostate cancer

Prostate cancer (PCa) has a certain degree of heritability, and metastasis occurs as cancer progresses. However, its underlying mechanism remains largely unknown. We sequenced four cases of cancer without metastasis, four metastatic cancer, and four benign hyperplasia tissues as controls. A total of 1839 damaging mutations were identified. Pathway analysis, gene clustering, and weighted gene co-expression network analysis were employed to find characteristics associated with metastasis. Chr19 had the most mutation density and 1p36 had the highest mutation frequency across the genome. These mutations occurred in 1630 genes, including the most frequently mutated genes TTN and PLEC, and dozens of metastasis-related genes, such as FOXA1, NCOA1, CD34, and BRCA2. Ras signalling and arachidonic acid metabolism were uniquely enriched in metastatic cancer. Gene programmes 10 and 11 showed the signatures indicating the occurrence of metastasis better. A module (135 genes) was specifically associated with metastasis. Of them, 67.41% reoccurred in program 10, with 26 genes further retained as the signature genes related to PCa metastasis, including AGR3, RAPH1, SOX14, DPEP1, and UBL4A. Our study provides new molecular perspectives on PCa metastasis. The signature genes and pathways could be served as potential therapeutic targets for metastasis or cancer progression.     

Identification of castration‐resistant prostate cancer‐related hub genes using weighted gene co‐expression network analysis

Prostate cancer is the most common malignancy in urinary system and brings heavy burdens in men. We downloaded gene expression profile of mRNA and related clinical data of GSE70768 data set from public database. Weighted gene co‐expression network analysis (WGCNA) was used to identify the relationships between gene modules and clinical features, as well as the candidate genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were developed to investigate the potential functions of related hub genes. Importantly, basic experiments were performed to verify the relationship between hub genes and the phenotype previously identified. Lastly, copy number variation (CNV) analysis was conducted to explore the genetical alteration. WGCNA identified that black module was the most relevant module which was tightly related to castration‐resistant prostate cancer (CRPC) phenotype. KEGG and GO analysis results revealed genes in black module were mainly related to RNA splicing. Additionally, 9 genes were chosen as hub genes and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1), golgin A8 family member B (GOLGA8B) and mitogen‐activated protein kinase 8 interacting protein 3 (MAPK8IP3) were identified to be associated with PCa progression and prognosis. Moreover, all above three genes were highly expressed in CRPC‐like cells and their suppression led to hindered cell proliferation in vitro. Finally, CNV analysis found that amplification was the main type of alteration of the 3 hub genes. Our study found that HNRNPA2B1, GOLGA8B and MAPK8IP3 were identified to be tightly associated with tumour progression and prognosis, and further researches are needed before clinical application.     

Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2

The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling.In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.     

Restoration of miR-29b exerts anti-cancer effects on glioblastoma

Background

Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.

Methods

Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.

Results

Totally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine–cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell–cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.

Conclusion

In summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.

     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

Mechanisms of ferroptosis with immune infiltration and inflammatory response in rotator cuff injury

The processes driving ferroptosis and rotator cuff (RC) inflammation are yet unknown. The mechanism of ferroptosis and inflammation involved in the development of RC tears was investigated. The Gene Expression Omnibus database was used to obtain the microarray data relevant to the RC tears for further investigation. In this study, we created an RC tears rat model for in vivo experimental validation. For the additional function enrichment analysis, 10 hub ferroptosis-related genes were chosen to construct the correlation regulation network. In RC tears, it was discovered that genes related to hub ferroptosis and hub inflammatory response were strongly correlated. The outcomes of in vivo tests showed that RC tears were related to Cd68-Cxcl13, Acsl4-Sat1, Acsl3-Eno3, Acsl3-Ccr7, and Ccr7-Eno3 pairings in regulating ferroptosis and inflammatory response. Thus, our results show an association between ferroptosis and inflammation, providing a new avenue to explore the clinical treatment of RC tears.     

Systematic analysis of genes involved in oral cancer metastasis to lymph nodes

Oral cancer remains a deadly disease worldwide. Lymph node metastasis and invasion is one of the causes of death from oral cancer. Elucidating the mechanism of oral cancer lymph node metastasis and identifying critical regulatory genes are important for the treatment of this disease. This study aimed to identify differentially expressed genes (gene signature) and pathways that contribute to oral cancer metastasis to lymph nodes. The GSE70604-associated study compared gene profiles in lymph nodes with metastasis of oral cancer to those of normal lymph nodes. The GSE2280-associated study compared gene profiles in primary tumor of oral cancer with lymph node metastasis to those in tumors without lymph node metastasis. There are 28 common differentially expressed genes (DEGs) showing consistent changes in both datasets in overlapping analysis. GO biological process and KEGG pathway analysis of these 28 DEGs identified the gene signature CCND1, JUN and SPP1, which are categorized as key regulatory genes involved in the focal adhesion pathway. Silencing expression of CCND1, JUN and SPP1 in the human oral cancer cell line OECM-1 confirmed that those genes play essential roles in oral cancer cell invasion. Analysis of clinical samples of oral cancer found a strong correlation of these genes with short survival, especially JUN expression associated with metastasis. Our study identified a unique gene signature – CCND1, JUN and SPP1 – which may be involved in oral cancer lymph node metastasis.     

Proteomic profiling of proteins associated with lymph node metastasis in colorectal cancer

Lymph node metastasis (LNM) is associated with poor prognosis in colorectal cancer (CRC). The presence or absence of lymph node metastases is a strong independent prognostic factor for CRC survival. Investigation of proteins associated with the process of lymph node metastasis (LNM) is crucial for understanding of the molecular mechanisms underlying the LNM process and for predicting the CRC prognosis. In the present study, proteins from CRC tissues and adjacent normal mucosa (NMC) were examined using two‐dimensional gel electrophoresis coupled with MALDI‐TOF‐MS. The expression levels of Ferritin Heavy Chain (FHC) were decreased in LNM CRC as compared to those in non‐LNM CRC, while the expression of Cathepsin D and Ubiquitin C‐terminal hydrolase‐L1 (UCH‐L1) were increased in LNM CRC. The results were confirmed by Western blotting and immunohistochemical staining. Furthermore, in vitro cell invasion assay showed that the overexpression of UCH‐L1 through gene transfection increased the invasive ability of HCT8 cells, suggesting that UCH‐L1 is not only a biomarker for LNM in CRC, but also a functional protein that may play a significant role in cell migration. The proteins identified in the present study should further our understanding of the LNM process of CRC and may become useful markers for diagnosis and targets for therapeutic interventions. J. Cell. Biochem. 110: 1512–1519, 2010. © 2010 Wiley‐Liss, Inc.     

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R ² = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors. In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms.     

PRC2/EED-EZH2 Complex Is Up-Regulated in Breast Cancer Lymph Node Metastasis Compared to Primary Tumor and Correlates with Tumor Proliferation In Situ

Background

Lymph node metastasis is a key event in the progression of breast cancer. Therefore it is important to understand the underlying mechanisms which facilitate regional lymph node metastatic progression.

Methodology/Principal Findings

We performed gene expression profiling of purified tumor cells from human breast tumor and lymph node metastasis. By microarray network analysis, we found an increased expression of polycomb repression complex 2 (PRC2) core subunits EED and EZH2 in lymph node metastatic tumor cells over primary tumor cells which were validated through real-time PCR. Additionally, immunohistochemical (IHC) staining and quantitative image analysis of whole tissue sections showed a significant increase of EZH2 expressing tumor cells in lymph nodes over paired primary breast tumors, which strongly correlated with tumor cell proliferation in situ. We further explored the mechanisms of PRC2 gene up-regulation in metastatic tumor cells and found up-regulation of E2F genes, MYC targets and down-regulation of tumor suppressor gene E-cadherin targets in lymph node metastasis through GSEA analyses. Using IHC, the expression of potential EZH2 target, E-cadherin was examined in paired primary/lymph node samples and was found to be significantly decreased in lymph node metastases over paired primary tumors.

Conclusions/Significance

This study identified an over expression of the epigenetic silencing complex PRC2/EED-EZH2 in breast cancer lymph node metastasis as compared to primary tumor and its positive association with tumor cell proliferation in situ. Concurrently, PRC2 target protein E-cadherin was significant decreased in lymph node metastases, suggesting PRC2 promotes epithelial mesenchymal transition (EMT) in lymph node metastatic process through repression of E-cadherin. These results indicate that epigenetic regulation mediated by PRC2 proteins may provide additional advantage for the outgrowth of metastatic tumor cells in lymph nodes. This opens up epigenetic drug development possibilities for the treatment and prevention of lymph node metastasis in breast cancer.     

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein–protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.