Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co-expression network analysis

The Expression Regulation and Cancer-Promoting Roles of RACGAP1

RACGAP1 is a Rho-GTPase-activating protein originally discovered in male germ cells to inactivate Rac, RhoA and Cdc42 from the GTP-bound form to the GDP-bound form. GAP has traditionally been known as a tumor suppressor. However, studies increasingly suggest that overexpressed RACGAP1 activates Rac and RhoA in multiple cancers to mediate downstream oncogene overexpression by assisting in the nuclear translocation of signaling molecules and to promote cytokinesis by regulating the cytoskeleton or serving as a component of the central spindle. Contradictorily, it was also reported that RACGAP1 in gastric cancer could inactivate Rac and RhoA. In addition, studies have revealed that RACGAP1 can be a biomarker for prognosis, and its role in reducing doxorubicin sensitivity poses difficulties for treatment, while the current drug targets mainly focus on its downstream molecule. This article mainly reviews the expression regulation of RACGAP1 and its cancer-promoting functions through oncogene expression mediation and Rho-GTPase activation.

Identification of hub genes in digestive system of mandarin fish (Siniperca chuatsi) fed with artificial diet by weighted gene co-expression network analysis

Mandarin fish (Siniperca chuatsi) is a carnivorous freshwater fish and an economically important species. The digestive system (liver, stomach, intestine, pyloric caecum, esophagus, and gallbladder) is an important site for studying fish domestication. In our previous study, we found that mandarin fish undergoes adaptive changes in histological morphology and gene expression levels of the digestive system when subjected to artificial diet domestication. However, we are not clear which hub genes are highly associated with domestication. In this study, we performed WGCNA on the transcriptomes of 17 tissues and 9 developmental stages and combined differentially expressed genes analysis in the digestive system to identify the hub genes that may play important functions in the adaptation of mandarin fish to bait conversion. A total of 31,657 genes in 26 samples were classified into 23 color modules via WGCNA. The modules midnightblue, darkred, lightyellow, and darkgreen highly associated with the liver, stomach, esophagus, and gallbladder were extracted, respectively. Tan module was highly related to both intestine and pyloric caecum. The hub genes in liver were cp, vtgc, c1in, c9, lect2, and klkb1. The hub genes in stomach were ghrl, atp4a, gjb3, muc5ac, duox2, and chia2. The hub genes in esophagus were mybpc1, myl2, and tpm3. The hub genes in gallbladder were dyst, npy2r, slc13a1, and slc39a4. The hub genes in the intestine and pyloric caecum were slc15a1, cdhr5, btn3a1, anpep, slc34a2, cdhr2, and ace2. Through pathway analysis, modules highly related to the digestive system were mainly enriched in digestion and absorption, metabolism, and immune-related pathways. After domestication, the hub genes vtgc and lect2 were significantly upregulated in the liver. Chia2 was significantly downregulated in the stomach. Slc15a1, anpep, and slc34a2 were significantly upregulated in the intestine. This study identified the hub genes that may play an important role in the adaptation of the digestive system to artificial diet, which provided novel evidence and ideas for further research on the domestication of mandarin fish from molecular level.

Characteristic Analysis of Featured Genes Associated with Cholangiocarcinoma Progression

The noninvasive diagnosis of cholangiocarcinoma (CCA) is insufficiently accurate. Therefore, the discovery of new prognostic markers is vital for the understanding of the CCA mechanism and related treatment. The information on CCA patients in The Cancer Genome Atlas database was used for weighted gene co-expression network analysis. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to analyze the modules of interest. By using receiver operating characteristic (ROC) analysis to analyze the Human Protein Atlas (HPA), the featured genes were subsequently verified. In addition, clinical samples and GSE119336 cohort data were also collected for the validation of these hub genes. Using WGCNA, we identified 61 hub genes that regulated the progression and prognosis of CCA. Eight hub genes (VSNL1, TH, PCP4, IGDCC3, RAD51AP2, MUC2, BUB1, and BUB1B) were identified which exhibited significant interactions with the tumorigenic mechanism and prognosis of CCA. In addition, GO and KEGG clarified that the blue and magenta modules were involved with chromosome segregation, mitotic and oocyte meiosis, the cell cycle, and sister chromatid segregation. Four hub genes (VSNL1, PCP4, BUB1, and BUB1B) were also verified as featured genes of progression and prognosis by the GSE119336 cohort data and five human tissue samples.

Identification of key genes in bovine muscle development by co-expression analysis

Background

Skeletal muscle is not only an important tissue involved in exercise and metabolism, but also an important part of livestock and poultry meat products. Its growth and development determines the output and quality of meat to a certain extent, and has an important impact on the economic benefits of animal husbandry. Skeletal muscle development is a complex regulatory network process, and its molecular mechanism needs to be further studied.

Method

We used a weighted co-expression network (WGCNA) and single gene set enrichment analysis (GSEA) to study the RNA-seq data set of bovine tissue differential expression analysis, and the core genes and functional enrichment pathways closely related to muscle tissue development were screened. Finally, the accuracy of the analysis results was verified by tissue expression profile detection and bovine skeletal muscle satellite cell differentiation model in vitro (BSMSCs).

Results

In this study, Atp2a1, Tmod4, Lmod3, Ryr1 and Mybpc2 were identified as marker genes in muscle tissue, which are mainly involved in glycolysis/gluconeogenesis, AMPK pathway and insulin pathway. The assay results showed that these five genes were highly expressed in muscle tissue and positively correlated with the differentiation of bovine BSMSCs.

Conclusions

In this study, several muscle tissue characteristic genes were excavated, which may play an important role in muscle development and provide new insights for bovine molecular genetic breeding.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

CENP-A is a potential prognostic biomarker and correlated with immune infiltration levels in glioma patients

Background: Centromeric protein A (CENP-A), an essential protein involved in chromosomal segregation during cell division, is associated with several cancer types. However, its role in gliomas remains unclear. This study examined the clinical and prognostic significance of CENP-A in gliomas.

Methods: Data of patients with glioma were collected from the Cancer Genome Atlas. Logistic regression, the Kruskal–Wallis test, and the Wilcoxon signed-rank test were performed to assess the relationship between CENP-A expression and clinicopathological parameters. The Cox regression model and Kaplan–Meier curve were used to analyze the association between CENP-A and survival outcomes. A prognostic nomogram was constructed based on Cox multivariate analysis. Gene set enrichment analysis (GSEA) was conducted to identify key CENP-A-related pathways and biological processes.

Results:CENP-A was upregulated in glioma samples. Increased CENP-A levels were significantly associated with the world health organization (WHO) grade [Odds ratio (OR) = 49.88 (23.52–129.06) for grade 4 vs. grades 2 and 3], primary therapy outcome [OR = 2.44 (1.64–3.68) for progressive disease (PD) and stable disease (SD) vs. partial response (PR) and complete response (CR)], isocitrate dehydrogenase (IDH) status [OR = 13.76 (9.25–20.96) for wild-type vs. mutant], 1p/19q co-deletion [OR = 5.91 (3.95–9.06) for no codeletion vs. co-deletion], and age [OR = 4.02 (2.68–6.18) for > 60 vs. ≤ 60]. Elevated CENP-A expression was correlated with shorter overall survival in both univariate [hazard ratio (HR): 5.422; 95% confidence interval (CI): 4.044–7.271; p < 0.001] and multivariate analyses (HR: 1.967; 95% CI: 1.280–3.025; p < 0.002). GSEA showed enrichment of numerous cell cycle-and tumor-related pathways in the CENP-A high expression phenotype. The calibration plot and C-index indicated the favorable performance of our nomogram for prognostic prediction in patients with glioma.

Conclusion: We propose a role for CENP-A in glioma progression and its potential as a biomarker for glioma diagnosis and prognosis.

Screening of Prognostic Markers for Hepatocellular Carcinoma Patients Based on Multichip Combined Analysis

Methods

GSE (14520, 36376, 57957, 76427) datasets were accessed from GEO database. 55 differential mRNAs (DEGs) were obtained by differential analysis based on the datasets. GO and KEGG analysis results indicated that the DEGs were enriched in xenobiotic metabolic process and other pathways. Expression profiles and clinical data of TCGA-LIHC mRNAs were from TCGA database. We established a prognostic model of HCC through univariate and multivariate Cox risk regression analyses. ROC curve analysis was used to examine the prognostic model performance. GSEA analysis was performed between the high- and low-risk score sample groups.

Results

A 4-gene HCC prognostic model was constructed, in which the gene expressions correlated to HCC patients' survival. The AUC value presented 0.734 in the ROC analysis for the prognostic model.

Conclusion

The four-gene model could be introduced as an independent prognostic factors to assess HCC patients' survival status.

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Central Genes Associated With Bovine Subcutaneous Adipose Tissue

Background

Fat deposition is an important economic trait in livestock and poultry production. However, the relationship between various genes and signal pathways of fat deposition is still unclear to a large extent. The purpose of this study is to analyze the potential molecular targets and related molecular pathways in bovine subcutaneous adipose tissue.

Results

We downloaded the GSE116775 microarray dataset from Gene Expression Omnibus (GEO). The weighted gene co-expression network (WGCNA) was used to analyze the gene expression profile, and the key gene modules with the highest correlation with subcutaneous adipose tissue were identified, and the functional enrichment of the key modules was analyzed. Then, the “real” Hub gene was screened by in-module analysis and protein–protein interaction network (PPI), and its expression level in tissue samples and adipocytes was verified. The study showed that a total of nine co-expression modules were identified, and the number of genes in these modules ranged from 101 to 1,509. Among them, the blue module is most closely related to subcutaneous adipose tissue, containing 1,387 genes. These genes were significantly enriched in 10 gene ontologies including extracellular matrix organization, biological adhesion, and collagen metabolic process, and were mainly involved in pathways including ECM-receptor interaction, focal adhesion, cAMP signaling pathway, PI3K-AKT signaling pathway, and regulation of lipolysis in adipocytes. In the PPI network and coexpression network, five genes (CAV1, ITGA5, COL5A1, ABL1, and HSPG2) were identified as “real” Hub genes. Analysis of Hub gene expression by dataset revealed that the expression of these Hub genes was significantly higher in subcutaneous adipose tissue than in other tissues. In addition, real-time fluorescence quantitative PCR (qRT-PCR) analysis based on tissue samples and adipocytes also confirmed the above results.

Conclusion

In this study, five key genes related to subcutaneous adipose tissue were discovered, which laid a foundation for further study of the molecular regulation mechanism of subcutaneous adipose tissue development and adipose deposition.

Identification of key genes associated with papillary thyroid microcarcinoma characteristics by integrating transcriptome sequencing and weighted gene co-expression network analysis

OBJECTIVE

Papillary thyroid microcarcinoma (PTMC) is the most prevalent histological type of thyroid carcinoma. Despite the overall favorable prognosis of PTMC, some cases exhibit aggressive phenotypes. The identification of robust biomarkers may improve early PTMC diagnosis. In this study, we integrated high-throughput transcriptome sequencing, bioinformatic analyses and experimental validation to identify key genes associated with the malignant characteristics of PTMC.

METHODS

Total RNA was extracted from 24 PTMC samples and 7 non-malignant thyroid tissue samples, followed by RNA sequencing. The differentially expressed genes (DEGs) were identified and used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction networks were constructed. Key modules and hub genes showing a strong correlation with the malignant characteristics of PTMC were identified and validated.

RESULTS

The green-yellow and turquoise modules generated by WGCNA were strongly associated with the malignant characteristics of PTMC. Functional enrichment analysis revealed that genes in the green-yellow module participated in cell motility and metabolism, whereas those in the turquoise module participated in several oncogenic biological processes. Nine real hub genes (FHL1, NDRG2, NEXN, SYNM, COL1A1, FN1, LAMC2, POSTN, and TGFBI) were identified and validated at the transcriptional and translational levels. Our preliminary results indicated their diagnostic potentials in PTMC.

CONCLUSIONS

In this study, we identified key co-expression modules and nine malignancy-related genes with potential diagnostic value in PTMC.

CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2

Background

Ovarian cancer (OV) is a highly lethal disease, and the fifth leading cause of all cancer-related deaths in women. The study aimed to identify potential key genes associated with the proliferation and prognosis of OV.

Methods

Differentially expressed genes (DEGs) between ovarian cancer and normal tissues were screened by the robust rank aggregation (RRA) method. The expression of CENPA and MYBL2 were examined in SKOV3 and A2780 ovarian cancer cell lines and tumor tissues by qRT-PCR and western blot. Small RNA interference assays, plasmid overexpression assays and EdU assays were used to validate the proliferative effect of the MYBL2-CENPA axis in ovarian cancer cell lines. The ChIP assay was used to verify the direct regulation of MYBL2 on CENPA.

Results

133 up-regulated genes and 158 down-regulated genes were identified, and the up-regulated genes mainly enrichment in cell cycle. The three up-regulated gene with DNA separation (CENPA, CENPF and CEP55) might be tightly correlated with proliferation and prognosis of OV. Knockdown CENPA expression inhibited the proliferation of A2780 and SKOV3 cells After the knockout of MYBL2, the expression of CENPA significantly decreased. MYBL2 directly binds to the promoter region of CENPA.

Conclusions

The MYBL2-CENPA pathway plays an important role in the proliferation of ovarian cancer cells, suggesting that this pathway may be a potential target for the treatment of ovarian cancer.

Identification of condition-specific biomarker systems in uterine cancer

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer

Background

Breast cancer (BRCA) is a malignant tumor with a high mortality rate and poor prognosis in patients. However, understanding the molecular mechanism of breast cancer is still a challenge.

Materials and Methods

In this study, we constructed co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene-expression profiles and clinical data were integrated to detect breast cancer survival modules and the leading genes related to prognostic risk. Finally, we introduced machine learning algorithms to build a predictive model aiming to discover potential key biomarkers.

Results

A total of 42 prognostic modules for breast cancer were identified. The nomogram analysis showed that 42 modules had good risk assessment performance. Compared to clinical characteristics, the risk values carried by genes in these modules could be used to classify the high-risk and low-risk groups of patients. Further, we found that 16 genes with significant differential expressions and obvious bridging effects might be considered biological markers related to breast cancer. Single-nucleotide polymorphisms on the CYP24A1 transcript induced RNA structural heterogeneity, which affects the molecular regulation of BRCA. In addition, we found for the first time that ABHD11-AS1 was significantly highly expressed in breast cancer.

Conclusion

We integrated clinical prognosis information, RNA sequencing data, and drug targets to construct a breast cancer–related risk module. Through bridging effect measurement and machine learning modeling, we evaluated the risk values of the genes in the modules and identified potential biomarkers for breast cancer. The protocol provides new insight into deciphering the molecular mechanism and theoretical basis of BRCA.

Screening Hub Genes of Hepatocellular Carcinoma Based on Public Databases

Tumor recurrence and metastasis often occur in HCC patients after surgery, and the prognosis is not optimistic. Hence, searching effective biomarkers for prognosis of is of great importance. Firstly, HCC-related data was acquired from the TCGA and GEO databases. Based on GEO data, 256 differentially expressed genes (DEGs) were obtained firstly. Subsequently, to clarify function of DEGs, clusterProfiler package was used to conduct functional enrichment analyses on DEGs. Protein-protein interaction (PPI) network analysis screened 20 key genes. The key genes were filtered via GEPIA database, by which 11 hub genes (F9, CYP3A4, ASPM, AURKA, CDC20, CDCA5, NCAP, PRC1, PTTG1, TOP2A, and KIFC1) were screened out. Then, univariate Cox analysis was applied to construct a prognostic model, followed by a prediction performance validation. With the risk score calculated by the model and common clinical features, univariate and multivariate analyses were carried out to assess whether the prognostic model could be used independently for prognostic prediction. In conclusion, the current study screened HCC prognostic gene signature based on public databases.

Analysis and verification of N6-methyladenosine-modified genes as novel biomarkers for clear cell renal cell carcinoma

N⁶-methyladenosine (m⁶A) has been involved in diverse biological processes in cancer, but its function and clinical value in clear cell renal cell carcinoma (ccRCC) remain largely unknown. In this study, we found that 1453 m⁶A-modified differentially expressed genes (DEGs) of ccRCC were mainly enriched in cell cycle, PI3K-AKT, and p53 signaling pathways. Then we constructed a co-expression network of the 1453 m⁶A-modified DEGs and identified a most clinically relevant module, where NUF2, CDCA3, CKAP2L, KIF14, and ASPM were hub genes. NUF2, CDCA3, and KIF14 could combine with a major RNA m⁶A methyltransferase METTL14, serving as biomarkers for ccRCC. Real-time quantitative PCR assay confirmed that NUF2, CDCA3, and KIF14 were highly expressed in ccRCC cell lines and ccRCC tissues. Furthermore, these three genes were modified by m⁶A and negatively regulated by METTL14. This study revealed that NUF2, CDCA3, and KIF14 were m⁶A-modified biomarkers, representing a potential diagnostic, prognostic, and therapeutic target for ccRCC.

Abbreviations: m⁶A: N⁶-methyladenosine; ccRCC: clear cell renal cell carcinoma; DEGs: differentially expressed genes; NUF2: NUF2 component of NDC80 kinetochore complex; CDCA3: cell division cycle associated 3; CKAP2L: cytoskeleton associated protein 2 like; KIF14: kinesin family member 14; ASPM: assembly factor for spindle microtubules; METTL14: methyltransferase 14; OS: overall survival; FPKM: fragments per kilobase million; GEO: gene expression omnibus; TCGA: the Cancer Genome Atlas; RMA: robust multi-array average expression measure; WGCNA: weighted gene co-expression network analysis; GO: gene ontology; KEGG: kyoto encyclopedia of genes and genomes; ROC: receiver operating characteristic curve; AUC: area under the curve; RIP: RNA immunoprecipitation; qPCR: real-time quantitative PCR.

Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma

Metabolic pattern reconstruction is an important factor in tumor progression. Metabolism of tumor cells is characterized by abnormal increase in anaerobic glycolysis, regardless of high oxygen concentration, resulting in a significant accumulation of energy from glucose sources. These changes promotes rapid cell proliferation and tumor growth, which is further referenced a process known as the Warburg effect. The current study reconstructed the metabolic pattern in progression of cancer to identify genetic changes specific in cancer cells. A total of 12 common types of solid tumors were included in the current study. Gene set enrichment analysis (GSEA) was performed to analyze 9 glycolysis-related gene sets, which are implicated in the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes showed high area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). The findings of the current study showed that 8 genes (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were correlated with overall survival (OS) and recurrence-free survival (RFS). Further analysis showed that the prediction model accurately distinguished between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics showed good discrimination based on internal and external cohorts. These findings indicate that changes in expression level of metabolic genes implicated in glycolysis can contribute to reconstruction of tumor-related microenvironment.

Correlation AnalyzeR: functional predictions from gene co-expression correlations

BACKGROUND

Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills.

RESULTS

We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses.

CONCLUSIONS

Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .

The roles of the cell division cycle-associated gene family in hepatocellular carcinoma

Background

The members of the cell division cycle-associated (CDCA) gene family are significant regulators of cell proliferation known to play key roles in various cancers. However, the function of CDCA genes in hepatocellular carcinoma (HCC) is unclear. The aim of this research was to clarify the roles of CDCA family members in HCC using bioinformatics analysis tools.

Methods

We studied data on the mRNA and protein expression of CDCA genes and survival in patients with HCC using the Oncomine, UALCAN, HPA, CCLE, LinkedOmics, cBioPortal, and Metascape databases.

Results

Significant overexpression of all CDCA members was found in HCC tissues. The expression levels of CDCAs were related to the tumor stage, and high expression levels were correlated with a low survival rate in patients with HCC. Also, we observed a high mutation rate (45%) of CDCAs in the HCC samples, which manifested as deep deletion, amplification, or increased mRNA expression. In the correlation analysis, we found that any 2 CDCA members were significantly positively correlated with each other. Cycle-related genes including AHCTF1, AKT1, BIRC5, CENPF, CENPL, and CENPQ were closely associated with CDCA gene alterations.

Conclusions

The findings of this study indicate that CDCAs may be potential therapeutic targets and prognostic indicators for patients with HCC.

Identification and Validation of MSX1 as a Key Candidate for Progestin Resistance in Endometrial Cancer

Purpose

Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, studies to acquire a more comprehensive understanding of the mechanisms are urgent. However, the pivotal molecules are still unexplored.

Materials and Methods

We downloaded GSE121367 from the GEO database. The “limma” R language package was applied to identify differentially expressed genes (DEGs). We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). Protein–protein interaction was constructed by STRING and visualized in Cytoscape. The tumor immune microenvironment was explored by the TISIDB database. Methylation validation and overall survival analysis were conducted by the TCGA database. In addition, the upstream modulators of hub genes were predicted by miRTarBase and Network Analyst databases. The expression levels of candidate genes were validated by quantitative real-time PCR (qRT-PCR), Western blot, and immunohistochemical assay (IHC). Cell growth, clone formation, migration, invasion, and wound healing assays were studied to explore the role of MSX1 in progestin resistance in vitro.

Results

A total of 3,282 DEGs were identified and they were mostly enriched in the cell adhesion pathway. We screened out ten hub genes whose genomic alteration rates were low based on the current endometrial carcinoma sample sets. Has-miR-335-5p, has-miR-124-3p, MAZ, and TFDP1 were the most prominent upstream regulators. The methylation status of CDH1, JAG1, EPCAM, and MSX1 was decreased, corresponding to their high protein expression, which also predicted better overall survival. The homeobox protein of MSX1 showed significant tissue specificity and better prognostic value and its knockdown inhibited epithelial–mesenchymal transitions (EMT) and enhanced progesterone efficacy.

Conclusion

Our study identified that the gene of MSX1 promised to be the specific indicator and therapeutic target for progestin resistance. This would shed new light on the underlying biological mechanism to overcome progestin resistance of endometrial cancer.

Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients

The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 102 HCC serum samples collected at diagnosis. WGCNA identified 16 miRNA modules, nine of them were significantly associated with the clinical characteristics of the patient. The Red module had a significant negative correlation with patients Survival (− 0.59, p = 0.007) and albumin (− 0.52, p = 0.02), and a positive correlation with PCR (0.61, p = 0.004) and alpha-fetoprotein (0.51, p = 0.02). In the red module, 16 circulating miRNAs were significantly associated with patient survival. MiR-3185 and miR-4507 were identified as predictors of patient survival after the validation phase. At diagnosis, high expression of circulating miR-3185 and miR-4507 identifies patients with longer survival (HR 2.02, 95% CI 1.10–3.73, p = 0.0086, and HR of 1.75, 95% CI 1.02–3.02, p = 0.037, respectively). Thought a WGCNA we identified miR-3185 and miR-4507 as promising candidate biomarkers predicting a longer survival in HCC patients.

Identified IGSF9 association with prognosis and hypoxia in nasopharyngeal carcinoma by bioinformatics analysis

Background

Despite improvements in nasopharyngeal carcinoma (NPC) treatment, patients with recurrence and metastasis still have a poor prognosis. Thus, the identification of novel biomarkers is urgently needed to predict outcomes and tailor treatment for NPC.

Methods

Four data sets were downloaded from Gene Expression Omnibus, and one data set GSE68799 of which was applied to filtrate key modules and hub genes by construction of a co-expression network. Other data sets (GSE12452 and GSE53819) were used to verify hub genes. The data set GSE102349 was devoted to identify prognostic hub genes by survival analysis. To explored whether prognostic hub genes are related to hypoxia signatures in NPC, correlation analysis was carried out, and followed by functional verification experiments of those genes in vitro.

Results

By co-expression network analysis, blue module was regarded as a key module in the benign and malignant group, and IGSF9 of the blue module was identified as a prognostic hub gene. Moreover, IGSF9 is expected to be a innovative hypoxia-related gene in NPC based on the strong associativity between expression of IGSF9 and hypoxia scores of three signatures (99-gene, 26-gene and 15-gene). Further functional studies verified that down-regulated expression of IGSF9 could reduce the proliferation, migration and invasion ability of NPC cells, and hypoxia could induce the expression of IGSF9.

Conclusion

IGSF9 was identified to be relevant to prognosis and involved in hypoxia in NPC. IGSF9 might serve as one novel prognostic indicator of NPC in the future.

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.

Elevated mRNA Expression Levels of NCAPG are Associated with Poor Prognosis in Ovarian Cancer

BACKGROUND

Ovarian cancer is a major gynecologic malignancy that is often detected at a late stage due to the lack of detailed studies on its pathogenesis and reliable biomarkers for predicting its prognosis.

MATERIALS AND METHODS

Four ovarian cancer data sets GSE18520, GSE27651, GSE40595, and GSE52037 were downloaded from the Gene Expression Omnibus (GEO) database and the robust rank aggregation approach was used to find common differentially expressed genes (DEGs). Cytoscape software was used to construct and detect key models of protein-protein interaction (PPI) network. While the expression, prognostic value and potential mechanism of the hub gene non-SMC condensin I complex subunit G (NCAPG) was carried out through Gene Expression Profiling Interactive Analysis, Kaplan-Meier plotter online dataset and gene set enrichment analysis. To further investigate the role of NCAPG in ovarian cancer, in vitro experiments were carried out.

RESULTS

A total of 232 DEGs were identified in the four GEO datasets; and we detected 32 hub genes from the PPI network and 21 of these genes were associated with ovarian cancer prognosis, one of which was NCAPG. NCAPG was significantly upregulated in most of the ovarian cancer samples. High NCAPG expression was mainly involved in homologous recombination, DNA replication, proteasome, and more correlated pathways. NCAPG knockdown arrested the cell cycle, inhibited the proliferation, and attenuated the migration ability of A2780 cells. Meanwhile, silencing of NCAPG significantly promoted cisplatin-induced apoptosis thus increased the sensitivity to cisplatin.

CONCLUSION

NCAPG together with the other 31 hub genes play a vital role in the tumorigenesis of ovarian, meanwhile, the cell cycle pathway may be a potential pathway contributing to progression in OC; and NCAPG expression can be used as a promising target for the treatment of OC.

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.

Identification of Prognostic Immune-Related Genes in Pancreatic Adenocarcinoma and Establishment of a Prognostic Nomogram: A Bioinformatic Study

BACKGROUND

The prognosis of pancreatic adenocarcinoma (PAAD) is extremely poor and has not been improved. Thus, an effective method to assess the prognosis of patients must be established to improve their survival rate.

METHOD

This study investigated immune-related genes that could be used as potential therapeutic targets for PAAD. Level 3 gene expression data from the PAAD cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. For validation, other PAAD datasets (DSE62452) were downloaded from the Gene Expression Omnibus (GEO) database. The PAAD datasets from TCGA and GEO were used to screen immune-related genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping immune-related genes of the two datasets were identified. Coexpression networks of the immune-related genes were constructed.

RESULTS

A signature of three immune-related genes (CKLF, ERAP2, and EREG) was identified in patients with PAAD. The signature could be used to divide the patients with PAAD into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of PAAD. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model based on the clinical characteristics and risk score of the TCGA PAAD dataset. The accuracy of the prognostic signature was further evaluated through functional evaluation and principal component analysis.

CONCLUSIONS

The results indicated that the signature of three immune-related genes had excellent predictive value for PAAD. These findings might help improve personalized treatment and medical decisions.

Identification of potential key mRNAs and LncRNAs for psoriasis by bioinformatic analysis using weighted gene co-expression network analysis

Psoriasis is a common chronic autoimmune inflammatory skin disease that involves genetic and environmental factors. To date, psoriasis is still incurable. Thus, detection of its underlying molecular mechanisms is urgent. Weighted gene co-expression network analysis (WGCNA) was performed on the basis of the RNA-Seq data of psoriatic and normal (NN) skin tissues to detect the key mRNAs and long non-coding RNAs (LncRNAs) implicated in psoriasis and to identify psoriasis-related gene modules. Subsequently, 23 independent modules were obtained, and the pink module that contained differentially expressed 212 mRNAs and 100 LncRNAs was the most remarkable. Differentially expressed genes (DEGs) between psoriasis and healthy control in other RNA-Seq and microarray datasets were integrated to identify convinced psoriasis-associated genes. A total of 312 genes in the pink module and 613 DEGs were scanned. Eleven overlapped key mRNAs were identified, including two known genes (e.g., KRT15 and CCL27) and nine novel ones (e.g., ARSF, CLDN1, DACH1, LONRF1, PAMR1, RORC, SLC26A2, STS, UNC93A). A total of 11 key mRNAs were selected to construct a co-expression network to investigate potential candidate LncRNAs. Seventy-six pairs of LncRNA-mRNA co-expression relationships were found. To validate the findings, CCL27 and LncRNA-AL162231.4 expressions were detected in psoriatic and NN skin tissues. Result of RT-qPCR showed that CCL27 and LncRNA-AL162231.4 decreased in psoriatic lesions with statistical significance (P ≤ 0.05). Our study provides a new direction for elucidating the pathogenesis of psoriasis, but further experiments are still required.

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

OBJECTIVE

We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD).

METHODS

RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein-protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug-gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test.

RESULTS

DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug-gene network identified these hub genes as potential drug targets.

CONCLUSION

Nineteen DEG were identified between the colon and ileum of IBD patients.

In silico identification of EP400 and TIA1 as critical transcription factors involved in human hepatocellular carcinoma relapse

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-associated mortality worldwide. Transcription factors (TFs) are crucial proteins that regulate gene expression during cancer progression; however, the roles of TFs in HCC relapse remain unclear. To identify the TFs that drive HCC relapse, the present study constructed co-expression network and identified the Tan module the most relevant to HCC relapse. Numerous hub TFs (highly connected) were subsequently obtained from the Tan module according to the intra-module connectivity and the protein-protein interaction network connectivity. Next, E1A-binding protein p400 (EP400) and TIA1 cytotoxic granule associated RNA binding protein (TIA1) were identified as hub TFs differentially connected between the relapsed and non-relapsed subnetworks. In addition, zinc finger protein 143 (ZNF143) and Yin Yang 1 (YY1) were also identified by using the plugin iRegulon in Cytoscape as master upstream regulatory elements, which could potentially regulate expression of the genes and TFs of the Tan module, respectively. The Kaplan-Meier (KM) curves obtained from KMplot and Gene Expression Profiling Interactive Analysis tools confirmed that the high expression of EP400 and TIA1 were significantly associated with shorter relapse-free survival and disease-free survival of patients with HCC. Furthermore, the KM curves from the UALCAN database demonstrated that high EP400 expression significantly reduced the overall survival of patients with HCC. EP400 and TIA1 may therefore serve as potential prognostic and therapeutic biomarkers.

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non-tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein-protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle-associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease-free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.

Integrated analyses identify potential prognostic markers for uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults, which has a high rate of metastases and can induce vision loss and even death to the patients. To identify suitable prognostic markers of UM for the early detection or prognosis prediction would be an essential step toward successful management of the disease. Herein, we extracted the mRNA expression data along with the clinical information from The Cancer Genome Atlas (TCGA) database. A total of eight co-expression modules were constructed by 5,000 genes based on the weighted gene co-expression network analysis (WGCNA). We found the blue and yellow modules were significantly associated with clinical stage. The Cox regression analyses found the blue, yellow, green and brown modules were significantly associated with overall survival (OS), while the blue, yellow, brown, green and pink modules were significantly associated with recurrence-free survival (RFS). Furthermore, the hallmark pathway enrichment analyses found the genes encompassed in the blue, yellow, and brown modules were significantly enriched in critical pathways involved in tumorigenesis and progression process, such as EMT and KRAS pathways. The hub-genes in these three modules were visualized by Cytoscape software and further validated by an external Gene Expression Omnibus (GEO) dataset. Besides, the OS and RFS predicting signatures were constructed based on the validated hub-genes according to the LASSO Cox regression model. The UM patients were assigned to low-/high-risk population. The survival analyses indicated high-risk patients mostly had bad OS/RFS rate compared with the low-risk population. The receiver operating characteristic (ROC) curve proved the stability and superiority of the two signatures. To sum up, our findings provide a framework of co-expression network of UM and identify a series of biomarkers, which will benefit from improving the prognosis prediction of UM patients.

MicroRNA‑181c suppresses growth and metastasis of hepatocellular carcinoma by modulating NCAPG

Purpose: Numerous studies have shown that the expression of microRNA-181c (miR-181c) is inhibited in various cancers, which suggests that it has a cancer suppressive effect. In the current study, we evaluated the regulation and characteristics of miR-181c in human hepatocellular carcinoma (HCC).

Materials and methods: Samples of tumor tissues and adjacent non-tumor tissues were collected from 52 patients with HCC, and expression levels of miR-181c in these samples were investigated via quantitative real-time polymerase chain reaction. HCC cell migration and invasion were investigated via wound healing assays and transwell assays. HCC cell apoptosis rates were assessed via flow cytometry, and HCC proliferation was assessed via 5-ethynyl-20-deoxyuridine assays. In vivo tumors were initiated by subcutaneously inoculating HCC cells into nude mice. And various biomarkers were investigated via western blotting.

Results: In microarray datasets and tumor tissues, significant downregulation of miR-181c was apparent compared with non-tumorous adjacent tissues. Expression of miR-181c in HCC cells was also significantly lower than it was in normal human liver cells. miR-181c regulated the migration, invasion, apoptosis, and proliferation of HCC cell lines in vitro, and tumor development in vivo. Observations also suggest that miR-181c regulates NCAPG in HCC cells, and its expression affects cellular invasion, migration, proliferation, and apoptosis. There was a negative correlation between miR-181c expression and NCAPG in HCC tissue samples.

Conclusion: miR-181c exhibits tumor-suppression via the regulation of NCAPG levels.