Identification of Hub Genes Associated With Development and Microenvironment of Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis and Differential Gene Expression Analysis

Integrated multi-omics and bioinformatic methods to reveal the mechanisms of sinomenine against diabetic nephropathy

OBJECTIVES

Diabetic Nephropathy (DN) is a serious complication of diabetes, the diagnosis and treatment of DN is still limited. Sinomenine (SIN) is an active extract of herbal medicine and has been applied into the therapy of DN.

METHODS

In the part of bioinformatic analyses, network pharmacology and molecular docking analyses were conducted to predict the important pathway of SIN treatment for DN. In-vivo study, DN rats were randomized to be treated with vehicle or SIN (20 mg/kg or 40 mg/kg) daily by gavage for 8 weeks. Then, the pharmacological effect of SIN on DN and the potential mechanisms were also evaluated by 24 h albuminuria, histopathological examination, transcriptomics, and metabolomics.

RESULTS

Firstly, network pharmacology and molecular docking were performed to show that SIN might improve DN via AGEs/RAGE, IL-17, JAK, TNF pathways. Urine biochemical parameters showed that SIN treatment could significantly reduce 24 h albuminuria of DN rats. Transcriptomics analysis found SIN could affect DN progression via inflammation and EMT pathways. Metabolic pathway analysis found SIN would mainly involve in arginine biosynthesis, linoleic acid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism to affect DN development.

CONCLUSIONS

We confirmed that SIN could inhibit the progression of DN via affecting multiple genes and metabolites related pathways.

Identify and validate RUNX2 and LAMA2 as novel prognostic signatures and correlate with immune infiltrates in bladder cancer

Background

Muscle-invasive bladder cancer (MIBC) develops lymph node (LN) metastasis or distant metastasis, leading to recurrence and poor prognosis. The five-year survival rate of MIBC with LN or distant metastasis is only 8.1%; therefore, there is an urgent need to identify reliable biomarkers for prognosis and treatment regimen for patients with bladder cancer (BLCA).

Methods

SEER database was used to select important clinical characteristics for MIBC. Then, weighted gene co-expression network analysis (WGCNA) was employed to identify differentially expressed genes (DEGs) to recognize significant co-expression modules by calculating the correlation between the modules and clinical data. Furthermore, Cox regression and lasso analysis were applied to screen prognostic hub genes and establish the risk predictive model. Bladder cancer cell lines (UMUC3 and 5637) were used for experimental validation in vitro.

Results

Cox analysis of 122,600 MIBC patients showed that the N stage was the most important clinical factor. A total of 4,597 DEGs were calculated between N0 and N+ patients, and WGCNA with these DEGs in 368 samples revealed that expression of turquoise was positively and strongly correlated with the N stage. Eight genes were identified as important prognostic candidates using lasso regression based on Cox analysis and STRING database. Combining GEO datasets, literature, and clinical factors, we identified LAMA2 and RUNX2 as novel prognostic biomarkers. CCK8 assay showed that depletion of LAMA2 or RUNX2 significantly inhibited the proliferation of BLCA cells, and flow cytometry indicated that knockdown of LAMA2 or RUNX2 induced the apoptosis of BLCA cells. Transwell assay also showed that silencing of LAMA2 or RUNX2 weakened the migration and invasiveness of BLCA cells.

Conclusions

We constructed a new eight-gene risk model to provide novel prognostic biomarkers and therapeutic targets for BLCA.

Upregulated ENC1 predicts unfavorable prognosis and correlates with immune infiltration in endometrial cancer

A better knowledge of the molecular process behind uterine corpus endometrial carcinoma (UCEC) is important for prognosis prediction and the development of innovative targeted gene therapies. The purpose of this research is to discover critical genes associated with UCEC. We analyzed the gene expression profiles of TCGA-UCEC and GSE17025, respectively, using Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. From four sets of findings, a total of 95 overlapping genes were retrieved. On the 95 overlapping genes, KEGG pathway and GO enrichment analysis were conducted. Then, we mapped the PPI network of 95 overlapping genes using the STRING database. Twenty hub genes were evaluated using the Cytohubba plugin, including NR3C1, ATF3, KLF15, THRA, NR4A1, FOSB, PER3, HLF, NTRK3, EGR3, MAPK13, ARNTL2, PKM2, SCD, EIF5A, ADHFE1, RERGL, TUB, and ENC1. The expression levels of NR3C1, PKM2, and ENC1 were shown to be adversely linked with the survival time of UCEC patients using univariate Cox regression analysis and Kaplan-Meier survival calculation. ENC1 were also overexpressed in UCEC tumor tissues or cell lines, as shown by quantitative real-time PCR and Western blotting. Then we looked into it further and discovered that ENC1 expression was linked to tumor microenvironment and predicted various immunological checkpoints. In conclusion, our data indicate that ENC1 may be required for the development of UCEC and may serve as a future biomarker for diagnosis and therapy.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

OBJECTIVE

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

METHODS

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

RESULTS

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

CONCLUSION

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Exploring novel independent prognostic biomarkers for hepatocellular carcinoma based on TCGA and GEO databases

BACKGROUND

Hepatocellular carcinoma (HCC) has become the fifth most common cancer globally, with the second-highest mortality rate and poor survival outcomes. In our research, we aimed to use The Cancer Genome Atlas and gene expression omnibus databases to identify potential genetic biomarkers to predict and improve the survival rate of HCC patients.

METHODS

In GSE60502, GSE76427, and GSE84402, we performed differential expression analysis to obtain differentially expressed genes (DEGs). In the The Cancer Genome Atlas database, the FPKM expression profile was subjected to weighted gene co-expression analysis to obtain modules closely related to HCC. We received common genes by intersecting the genes in the module with the differential genes. Then, we fused the common genes' expression profiles, survival time, and survival status for univariate, Least Absolute Shrinkage and Selection Operator, and multivariate COX regression analysis to obtain prognostic genes. Predictive genes were performed in K-M survival analysis and combined with clinical data for independent predictive analysis.

RESULTS

After differential expression analysis, GSE60502 obtained 1107 DEGs, GSE76427 obtained 424 DEGs, and GSE84402 obtained 1668 DEGs. Through weighted gene co-expression analysis analysis, we can see that the blue and brown modules were closely associated with HCC. After single and multivariate COX regression analysis, we found that suppressor of cytokine signaling 2 (SOCS2) and SERPINF2 were independent prognostic genes for HCC. After survival analysis, HCC patients with high expression of SOCS2 and SERPINF2 had a longer survival time. These 2 genes in normal liver tissues were higher than in HCC at the transcriptional level.

CONCLUSION

SOCS2 and SERPINF2 were new independent prognostic genes of HCC. So, they may provide new treatment methods and measures for diagnosing HCC.

Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis

BACKGROUND

Colorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management.

METHODS

Microarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis. Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened.

RESULTS

Thirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM.

CONCLUSIONS

Our study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.

Identification of key pathways and genes that regulate cashmere development in cashmere goats mediated by exogenous melatonin

The growth of secondary hair follicles in cashmere goats follows a seasonal cycle. Melatonin can regulate the cycle of cashmere growth. In this study, melatonin was implanted into live cashmere goats. After skin samples were collected, transcriptome sequencing and histological section observation were performed, and weighted gene co-expression network analysis (WGCNA) was used to identify key genes and establish an interaction network. A total of 14 co-expression modules were defined by WGCNA, and combined with previous analysis results, it was found that the blue module was related to the cycle of cashmere growth after melatonin implantation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the first initiation of exogenous melatonin-mediated cashmere development was related mainly to the signaling pathway regulating stem cell pluripotency and to the Hippo, TGF-beta and MAPK signaling pathways. Via combined differential gene expression analyses, 6 hub genes were identified: PDGFRA, WNT5A, PPP2R1A, BMPR2, BMPR1A, and SMAD1. This study provides a foundation for further research on the mechanism by which melatonin regulates cashmere growth.

An integrative bioinformatics investigation and experimental validation of critically involved genes in high-grade gliomas

Background

Lack of knowledge around underlying mechanisms of gliomas mandates intense research efforts to improve the disease outcomes. Identification of high-grade gliomas pathogenesis which is known for poor prognosis and low survival is of particular importance. Distinguishing the differentially expressed genes is one of the core approaches to clarify the causative factors.

Methods

Microarray datasets of the treatment-naïve gliomas were provided from the Gene Expression Omnibus considering the similar platform and batch effect removal. Interacting recovery of the top differentially expressed genes was performed on the STRING and Cytoscape platforms. Kaplan–Meier analysis was piloted using RNA sequencing data and the survival rate of glioma patients was checked considering selected genes. To validate the bioinformatics results, the gene expression was elucidated by real-time RT-qPCR in a series of low and high-grade fresh tumor samples.

Results

We identified 323 up-regulated and 253 down-regulated genes. The top 20 network analysis indicated that PTX3, TIMP1, CHI3L1, LTF and IGFBP3 comprise a crucial role in gliomas progression. The survival was inversely linked to the levels of all selected genes. Further analysis of RNA sequencing data indicated a significant increase in all five genes in high-grade tumors. Among them, PTX3, TIMP1 and LTF did not show any change in low-grade versus controls. Real-time RT-qPCR confirmed the in-silico results and revealed significantly higher expression of selected genes in high-grade samples compared to low-grade.

Conclusions

Our results highlighted the role of PTX3 and TIMP1 which were previously considered in glioma tumorigenesis as well as LTF as a new potential biomarker.

Analysis of Dormancy-Associated Transcriptional Networks Reveals a Shared Quiescence Signature in Lung and Colorectal Cancer

Quiescent cancer cells (QCCs) are a common feature of solid tumors, representing a major obstacle to the long-term success of cancer therapies. We isolated QCCs ex vivo from non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) xenografts with a label-retaining strategy and compared QCCs gene expression profiles to identify a shared “quiescence signature”. Principal Component Analysis (PCA) revealed a specific component neatly discriminating quiescent and replicative phenotypes in NSCLC and CRC. The discriminating component showed significant overlapping, with 688 genes in common including ZEB2, a master regulator of stem cell plasticity and epithelial-to-mesenchymal transition (EMT). Gene set enrichment analysis showed that QCCs of both NSCLC and CRC had an increased expression of factors related to stemness/self renewal, EMT, TGF-β, morphogenesis, cell adhesion and chemotaxis, whereas proliferating cells overexpressed Myc targets and factors involved in RNA metabolism. Eventually, we analyzed in depth by means of a complex network approach, both the ‘morphogenesis module’ and the subset of differentially expressed genes shared by NCSLC and CRC. This allowed us to recognize different gene regulation network wiring for quiescent and proliferating cells and to underpin few genes central for network integration that may represent new therapeutic vulnerabilities. Altogether, our results highlight common regulatory pathways in QCCs of lung and colorectal tumors that may be the target of future therapeutic interventions.

HAO1 negatively regulates liver macrophage activation via the NF-κB pathway in alcohol-associated liver disease

Inflammation is a key factor contributing to the progression of alcohol-associated liver disease (ALD). Accumulating data have shown that ethyl alcohol (EtOH) induced liver macrophages activation along with an inflammatory response that contributes to the development of ALD. The liver-specific peroxisomal enzyme hydroxyacid oxidase 1 (HAO1) has been found to be associated with chronic liver disease. But the role of HAO1 remains unknown in ALD. In our study, HAO1 was found to be decreased in ALD patients and EtOH-fed mice. Interestingly, HAO1 expression was reduced in primary hepatocytes, whereas HAO1 was elevated in peripheral blood monocytes from ALD patients and EtOH-fed mice liver macrophages as well as LPS-treated RAW264.7 cells. Moreover, HAO1 knockdown exacerbated the inflammatory response, while HAO1 overexpression inhibited inflammation in LPS-stimulated RAW264.7 cells. Additionally, overexpression or silencing of HAO1 in vitro significantly affected NF-κB signaling pathway. Collectively, the results revealed a key role of HAO1-mediated macrophage activation and may provide a potential target for treating ALD.

Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach

Objective

Polycystic ovary syndrome (PCOS) is a common endocrine disorder with high incidence. Recently it has been implicated as a significant risk factor for endometrial cancer (EC). Our study aims to detect shared gene signatures and biological mechanism between PCOS and EC by bioinformatics analysis.

Methods

Bioinformatics analysis based on GEO database consisted of data integration, network construction and functional enrichment analysis was applied. In addition, the pharmacological methodology and molecular docking was also performed.

Results

Totally 10 hub common genes, MRPL16, MRPL22, MRPS11, RPL26L1, ESR1, JUN, UBE2I, MRPL17, RPL37A, GTF2H3, were considered as shared gene signatures for EC and PCOS. The GO and KEGG pathway analysis of these hub genes showed that “mitochondrial translational elongation”, “ribosomal subunit”, “structural constituent of ribosome” and “ribosome” were highly correlated. Besides, associated transcription factors (TFs) and miRNAs network were constructed. We identified candidate drug molecules including fenofibrate, cinnarizine, propanil, fenthion, clindamycin, chloramphenicol, demeclocycline, hydrochloride, azacitidine, chrysene and artenimol according to these hub genes. Molecular docking analysis verified a good binding interaction of fenofibrate against available targets (JUN, ESR1, UBE2I).

Conclusion

Gene signatures and regulatory biological pathways were identified through bioinformatics analysis. Moreover, the molecular mechanisms of these signatures were explored and potential drug molecules associated with PCOS and EC were screened out.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Background

Runs of homozygosity (ROH) are continuous homozygous regions typically located in the DNA sequence of diploid organisms. Identifications of ROH that lead to reduced performance can provide valuable insight into the genetic architecture of complex traits. Here, we systematically investigated the population genetic structure of five Anhui indigenous pig breeds (AHIPs), and compared them to those of five Western commercial pig breeds (WECPs). Furthermore, we examined the occurrence and distribution of ROHs in the five AHIPs and estimated the inbreeding coefficients based on the ROHs (F_ROH) and homozygosity (F_HOM). Finally, we identified genomic regions with high frequencies of ROHs and annotated candidate genes contained therein.

Results

The WECPs and AHIPs were clearly differentiated into two separate clades consistent with their geographical origins, as revealed by the population structure and principal component analysis. We identified 13,530 ROHs across all individuals, of which 4,555 and 8,975 ROHs were unique to AHIPs and WECPs, respectively. Most ROHs identified in our study were short (< 10 Mb) or medium (10–20 Mb) in length. WECPs had significantly higher numbers of short ROHs, and AHIPs generally had longer ROHs. F_ROH values were significantly lower in AHIPs than in WECPs, indicating that breed improvement and conservation programmes were successful in AHIPs. On average, F_ROH and F_HOM values were highly correlated (0.952–0.991) in AHIPs and WECPs. A total of 27 regions had a high frequency of ROHs and contained 17 key candidate genes associated with economically important traits in pigs. Among these, nine candidate genes (CCNT2, EGR2, MYL3, CDH13, PROX1, FLVCR1, SETD2, FGF18, and FGF20) found in WECPs were related to muscular and skeletal development, whereas eight candidate genes (CSN1S1, SULT1E1, TJP1, ZNF366, LIPC, MCEE, STAP1, and DUSP) found in AHIPs were associated with health, reproduction, and fatness traits.

Conclusion

Our findings provide a useful reference for the selection and assortative mating of pig breeds, laying the groundwork for future research on the population genetic structures of AHIPs, ultimately helping protect these local varieties.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08583-9.

A pan-cancer analysis of GINS complex subunit 4 to identify its potential role as a biomarker in multiple human cancers

This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.

EBF3 reactivation by inhibiting the EGR1/EZH2/HDAC9 complex promotes metastasis via transcriptionally enhancing vimentin in nasopharyngeal carcinoma

Metastasis is the major reason for treatment failure and accounts for cancer-related death in patients with nasopharyngeal carcinoma. However, the genetic alterations and molecular mechanisms that cause nasopharyngeal carcinoma metastasis are elusive. Herein, we performed RNA sequencing in patients with or without metastasis, and found that the early B-cell factor 3 (EBF3) was significantly elevated in the samples with metastasis. Mechanistically, EBF3 promoted metastasis by directly combining with the promoter of Vimentin and transcriptionally upregulating it. In addition, EBF3 was epigenetically silenced by EGR1/EZH2/HDAC9 complexes via sustaining the high level of H3K27-Me3 at its promoter. Clinically, there was a positive correlation between EBF3 and Vimentin in nasopharyngeal carcinoma tissues. Moreover, high expression of EBF3 or Vimentin was correlated with poor overall survival, while the combination of high EBF3 and Vimentin expression was associated with more significant poor prognosis. Therefore, specific agents targeting EBF3 or stabilizing the EGR1/EZH2/HDAC9 complex could be novel therapeutic strategies for cancer metastasis.

NONMMUT140591.1 may serve as a ceRNA to regulate Gata5 in UT-B knockout-induced cardiac conduction block

We intended to explore the potential molecular mechanisms underlying the cardiac conduction block inducted by urea transporter (UT)-B deletion at the transcriptome level. The heart tissues were harvested from UT-B null mice and age-matched wild-type mice for lncRNA sequencing analysis. Based on the sequencing data, the differentially expressed mRNAs (DEMs) and lncRNAs (DELs) between UT-B knockout and control groups were identified, followed by function analysis and mRNA-lncRNA co-expression analysis. The miRNAs were predicted, and then the competing endogenous RNA (ceRNA) network was constructed. UT-B deletion results in the aberrant expression of 588 lncRNAs and 194 mRNAs. These DEMs were significantly enriched in the inflammation-related pathway. A lncRNA-mRNA co-expression network and a ceRNA network were constructed on the basis of the DEMs and DELs. The complement 7 (C7)-NONMMUT137216.1 co-expression pair had the highest correlation coefficient in the co-expression network. NONMMUT140591.1 had the highest degree in the ceRNA network and was involved in the ceRNA of NONMMUT140591.1-mmu-miR-298-5p-Gata5 (GATA binding protein 5). UT-B deletion may promote cardiac conduction block via inflammatory process. The ceRNA NONMMUT140591.1-mmu-miR-298-5p-Gata5 may be a potential molecular mechanism of UT-B knockout-induced cardiac conduction block.

AKT3 and related molecules as potential biomarkers responsible for cryptorchidism and cryptorchidism-induced azoospermia

BACKGROUND

Cryptorchidism is a common congenital malformation strongly related to future oligospermia and male infertility. Normally functioning early-stage spermatogonia are vital to ensure fertility. The present study aimed to identify new differentially expressed genes (DEGs) associated with signaling pathways related to spermatogonial stem cell (SSC) maintenance during early spermatogenesis.

METHODS

GEO2R was used to screen for genes differentially regulated in cryptorchidism using mRNA expression profiling data in the GEO database. DAVID was used to perform GO and KEGG enrichment analysis of DEGs to analyze their functions. A protein-protein interaction (PPI) network of DEGs was constructed using the STRING database. The hub genes in the PPI networks were identified using Maximal Clique Centrality (MCC) in Cytohubba, and the top 50 genes were displayed as hub genes using Cytoscape software. Then, the miRNAs targeting hub genes were predicted using miRWalk and an mRNA-miRNA interaction network was constructed using Cytoscape. We took the intersection of these target miRNAs and the differentially expressed miRNAs identified from a non-coding RNA sequencing dataset, GSE149084. Furthermore, the intersected miRNAs and their predicted target genes were validated in the testicular tissue of rats with cryptorchidism.

RESULTS

A total of 474 DEGs were identified, most of which were annotated to the PI3K-AKT-mTOR signaling pathway. Hub genes related to the pathway were predicted to be targeted by 27 miRNAs. Further miRNA mining revealed that miRNA-7-5p and miRNA-519d-3p were both dysregulated in cryptorchidism patients. Further, we found that these two miRNAs were predicted with high confidence to share a common target gene, AKT3. In the testicular tissue of rats with cryptorchidism, miRNA-519d-3p was upregulated while miRNA-7-5p and AKT3 were downregulated. We also found that AKT3 plays an essential role in regulating SSC state through the PI3K-AKT-mTOR signaling pathway and that AKT3 is one of the key genes related to SSC self-renewal, proliferation, and differentiation.

CONCLUSIONS

The PI3K-AKT-mTOR signaling pathway functions in SSC maintenance, and alterations in this pathway may explain defects in spermatogenesis. AKT3-related miRNAs, including hsa-miR-7-5p and hsa-miR-519d-3p, might be responsible for cryptorchidism and cryptorchidism-induced azoospermia and serve as potential biomarkers.

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.