Gene network analysis reveals a novel 22-gene signature of carbon metabolism in hepatocellular carcinoma

MiR-122-5p regulates erastin-induced ferroptosis via CS in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a tumor that occurs in the nasopharynx. Although advances in detection and treatment have improved the prognosis of NPC the treatment of advanced NPC remains challenging. Here, we explored the effect of microRNA (miR)-122-5p on erastin-induced ferroptosis in NPC cells and the role of ferroptosis in the development of NPC. The effect of miR-122-5p silencing and overexpression and the effect of citrate synthase on erastin-induced lipid peroxidation in NPC cells was analyzed by measuring the amounts of malondialdehyde, Fe2+, glutathione, and reactive oxygen species and the morphological alterations of mitochondria. The malignant biological behavior of NPC cells was examined by cell counting kit-8, EDU, colony formation, Transwell, and wound healing assays. The effects of miR-122-5p on cell proliferation and migration associated with ferroptosis were examined in vivo in a mouse model of NPC generated by subcutaneous injection of NPC cells. We found that erastin induced ferroptosis in NPC cells. miR-122-5p overexpression inhibited CS, thereby promoting erastin-induced ferroptosis in NPC cells and decreasing NPC cell proliferation, migration, and invasion.

Hub genes and pathways related to caries-free dental biofilm: clinical metatranscriptomic study

OBJECTIVE

This study aimed to evaluate the microbial functional profile of biofilms related to caries-free (CF, n = 6) and caries-arrested (CI, n = 3) compared to caries-active (CA, n = 5) individuals.

MATERIALS AND METHODS

A metatranscriptomic was performed in supragingival biofilm from different clinical conditions related to caries or health. Total RNA was extracted and cDNAs were obtained and sequenced (Illumina HiSeq3000). Trimmed data (SortMeRNA) were submitted to the SqueezeMeta pipeline in the co-assembly mode for functional analysis and further differential gene expression analysis (DESeq2) and weighted gene co-expression network analysis (WCGNA) to explore and identify gene modules related to these clinical conditions.

RESULTS

A total of 5303 genes were found in the metatranscriptomic analysis. A co-expression network identified the most relevant modules strongly related to specific caries status. Correlation coefficients were calculated between the eigengene modules and the clinical conditions (CA, CI, and CF) discriminating multiple modules. CA and CI showed weak correlation coefficient strength across the modules, while the CF condition presented a very strong positive correlation coefficient (r = 0.9, p value = 4 × 10-9). Pearson's test was applied to further analyze the module membership and gene significance in CF conditions, and the most relevant were HSPA1s-K03283, Epr- K13277, and SLC1A-K05613. Gene Ontology (GO) shows important bioprocesses, such as two-component system, fructose and mannose metabolism, pentose and glucuronate interconversions, and flagellar assembly (p-adjust < 0.05). The ability to use different carbohydrates, integrate multiple signals, swarm, and bacteriocin production are significant metabolic advantages in the oral environment related to CF.

CONCLUSIONS

A distinct functional health profile could be found in CF, where co-occurring genes can act in different pathways at the same time. Genes HSPA1s, Epr, and SLC1A may be appointed as potential biomarkers for caries-free biofilms.

CLINICAL RELEVANCE

Potential biomarkers for caries-free biofilms could contribute to the knowledge of caries prevention and control.

The Role of Mass Spectrometry in Hepatocellular Carcinoma Biomarker Discovery

Hepatocellular carcinoma (HCC) is the main liver malignancy and has a high mortality rate. The discovery of novel biomarkers for early diagnosis, prognosis, and stratification purposes has the potential to alleviate its disease burden. Mass spectrometry (MS) is one of the principal technologies used in metabolomics, with different experimental methods and machine types for different phases of the biomarker discovery process. Here, we review why MS applications are useful for liver cancer, explain the MS technique, and briefly summarise recent findings from metabolomic MS studies on HCC. We also discuss the current challenges and the direction for future research.

Identified Three Interferon Induced Proteins as Novel Biomarkers of Human Ischemic Cardiomyopathy

Ischemic cardiomyopathy is the most frequent type of heart disease, and it is a major cause of myocardial infarction (MI) and heart failure (HF), both of which require expensive medical treatment. Precise biomarkers and therapy targets must be developed to enhance improve diagnosis and treatment. In this study, the transcriptional profiles of 313 patients' left ventricle biopsies were obtained from the PubMed database, and functional genes that were significantly related to ischemic cardiomyopathy were screened using the Weighted Gene Co-Expression Network Analysis and protein-protein interaction (PPI) networks enrichment analysis. The rat myocardial infarction model was developed to validate these findings. Finally, the putative signature genes were blasted through the common Cardiovascular Disease Knowledge Portal to explore if they were associated with cardiovascular disorder. Three interferon stimulated genes (IFIT2, IFIT3 and IFI44L), as well as key pathways, have been identified as potential biomarkers and therapeutic targets for ischemic cardiomyopathy, and their alternations or mutations have been proven to be strongly linked to cardiac disorders. These novel signature genes could be utilized as bio-markers or potential therapeutic objectives in precise clinical diagnosis and treatment of ischemic cardiomyopathy.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Genomic Landscape of HCC

INTRODUCTION

Hepatocellular carcinoma (HCC) is a leading cause of cancer related mortality in the world and it has limited treatment options. Understanding the molecular drivers of HCC is important to develop novel biomarkers and therapeutics.

PURPOSE OF REVIEW

HCC arises in a complex background of chronic hepatitis, fibrosis and liver regeneration which lead to genomic changes. Here, we summarize studies that have expanded our understanding of the molecular landscape of HCC.

RECENT FINDINGS

Recent technological advances in next generation sequencing (NGS) have elucidated specific genetic and molecular programs involved in hepatocarcinogenesis. We summarize the major somatic mutations and epigenetic changes have been identified in NGS-based studies. We also describe promising molecular therapies and immunotherapies which target specific genetic and epigenetic molecular events.

SUMMARY

The genomic landscape of HCC is incredibly complex and heterogeneous. Promising new developments are helping us decipher the molecular drivers of HCC and leading to new therapies.

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

Background

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

Methods

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

Results

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

Conclusion

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

TCA Cycle Rewiring as Emerging Metabolic Signature of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy. Despite progress in treatment, HCC is still one of the most lethal cancers. Therefore, deepening molecular mechanisms underlying HCC pathogenesis and development is required to uncover new therapeutic strategies. Metabolic reprogramming is emerging as a critical player in promoting tumor survival and proliferation to sustain increased metabolic needs of cancer cells. Among the metabolic pathways, the tricarboxylic acid (TCA) cycle is a primary route for bioenergetic, biosynthetic, and redox balance requirements of cells. In recent years, a large amount of evidence has highlighted the relevance of the TCA cycle rewiring in a variety of cancers. Indeed, aberrant gene expression of several key enzymes and changes in levels of critical metabolites have been observed in many solid human tumors. In this review, we summarize the role of the TCA cycle rewiring in HCC by reporting gene expression and activity dysregulation of enzymes relating not only to the TCA cycle but also to glutamine metabolism, malate/aspartate, and citrate/pyruvate shuttles. Regarding the transcriptional regulation, we focus on the link between NF-κB-HIF1 transcriptional factors and TCA cycle reprogramming. Finally, the potential of metabolic targets for new HCC treatments has been explored.

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (-0.2491 × EXP_RRAGB) + (-0.0679 × EXP_RSPH9) + (-0.2317 × EXP_RPS6KL1) + (-0.1035 × EXP_RXFP1) + 0.1571 × EXP_RRM2 + 0.1104 × EXP_RTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.

A network-based signature to predict the survival of non-smoking lung adenocarcinoma

Background

A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.

Materials and methods

Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.

Results

Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.

Conclusion

The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management.

Co-expression analysis revealed PTCH1-3'UTR promoted cell migration and invasion by activating miR-101-3p/SLC39A6 axis in non-small cell lung cancer: implicating the novel function of PTCH1

Metastasis is the most common cause of mortality for non-small cell lung cancer (NSCLC). PTCH1, a receptor of Hedgehog (Hh) pathway, is reported to suppress cell proliferation. Interestingly, our previous study showed PTCH1 silencing promoted cell proliferation but inhibited cell migration and invasion of NSCLC cells. However, the precise mechanisms of PTCH1 regulating NSCLC metastasis remain unclear. PTCH1 has multiple splicing variants, which all share the same 3'UTR sequence, meanwhile, emerging studies have shown competing endogenous RNAs (ceRNAs) play important roles in regulating cancer progression. Therefore, we hypothesized the functions of PTCH1-3'UTR in NSCLC in present study to reveal its role as a ceRNA. Here, we find overexpression of PTCH1-3'UTR promotes cell migration, invasion and adhesion, but does not affect cell proliferation in NSCLC cells. By combining weighted correlation network analysis (WGCNA) analysis and experimental validation, we reported PTCH1-3'UTR acted as a sponge to absorb miR-101-3p and promoted SLC39A6 expression. Moreover, we observed low expression of miR-101-3p and PTCH1 and high SLC39A6 levels were positively correlated with NSCLC progression. Therefore, our results help to understand the function of PTCH1 in NSCLC tumorigenesis and provide novel insights for the prevention of NSCLC metastasis.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

Background

The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins.

Results

In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed.

Conclusion

We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4257-6) contains supplementary material, which is available to authorized users.

Identification of Potential Gene Network Associated with HCV-Related Hepatocellular Carcinoma Using Microarray Analysis

In order to identify potential specific gene networks of Hepatitis C virus (HCV) related hepatocellular carcinoma (HCC), weighted gene co-expression network analysis (WGCNA) was performed, which may provide an insight into the potential mechanism of the HCC development. HCV-related HCC and normal sample data were downloaded from GEO, T test of limma package was used to screen different expression genes (DEGs); KEGG pathway was used to analyze related biochemical pathways, and WGCNA was used to construct clustering trees and screen hub genes in the HCC-specific modules. A total of 1151 DEGs were authenticated between the HCC and normal liver tissue samples, including 433 upregulated and 718 downregulated genes. Among these genes, three specific modules of HCC were constructed, including Tan, Yellow and Cyan, but only Yellow module had a significant enrichment score in substance combination module with three hub genes: SLA2547, EFNA4 and MME. Although Tan and Cyan separately had four and three hub genes, but the bio-functions of them did not have significant enrichment scores (score < 2). SLA2547, EFNA4 and MME may play important roles in the substance combination of HCV-related HCC, so studying the function of this gene network may provide us a deeper understanding of HCV-related HCC.

Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis

mRNA expression profiles provide important insights on a diversity of biological processes involved in rectal carcinoma (RC). Our aim was to comprehensively map complex interactions between the mRNA expression patterns and the clinical traits of RC. We employed the integrated analysis of five microarray datasets and The Cancer Genome Atlas rectal adenocarcinoma database to identify 2118 consensual differentially expressed genes (DEGs) in RC and adjacent normal tissue samples, and then applied weighted gene co-expression network analysis to parse DEGs and eight clinical traits in 66 eligible RC samples. A total of 16 co-expressed gene modules were identified. The green-yellow and salmon modules were most appropriate to the pathological stage (R = 0.36) and the overall survival (HR =13.534, P = 0.014), respectively. A diagnostic model of the five pathological stage hub genes (SCG3, SYP, CDK5R2, AP3B2, and RUNDC3A) provided a powerful classification accuracy between localized RC and non-localized RC. We also found increased Secretogranin III (SCG3) expression with higher pathological stage and poorer prognosis in the test and validation set. The increased Homer scaffolding protein 2 (HOMER2) expression with the favorable survival prediction efficiency significantly correlated with the markedly reduced overall survival of RC patients and the higher pathological stage during the test and validation set. Our findings indicate that the SCG3 and HOMER2 mRNA levels should be further evaluated as predictors of pathological stage and survival in patients with RC.

Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival (PFS) or overall survival (OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that "glycoprotein binding" was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor (PTAFR) and feline Gardner-Rasheed (FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Relation of Transcriptional Factors to the Expression and Activity of Cytochrome P450 and UDP-Glucuronosyltransferases 1A in Human Liver: Co-Expression Network Analysis

Cytochrome P450 (CYPs) and UDP-glucuronosyltransferases (UGTs) play important roles in the metabolism of exogenous and endogenous compounds. The gene transcription of CYPs and UGTs can be enhanced or reduced by transcription factors (TFs). This study aims to explore novel TFs involved in the regulatory network of human hepatic UGTs/CYPs. Correlations between the transcription levels of 683 key TFs and CYPs/UGTs in three different human liver expression profiles (n = 640) were calculated first. Supervised weighted correlation network analysis (sWGCNA) was employed to define hub genes among the selected TFs. The relationship among 17 defined TFs, CYPs/UGTs expression, and activity were evaluated in 30 liver samples from Chinese patients. The positive controls (e.g., PPARA, NR1I2, NR1I3) and hub TFs (NFIA, NR3C2, and AR) in the Grey_sWGCNA Module were significantly and positively associated with CYPs/UGTs expression. And the cancer- or inflammation-related TFs (TEAD4, NFKB2, and NFKB1) were negatively associated with mRNA expression of CYP2C9/CYP2E1/UGT1A9. Furthermore, the effect of NR1I2, NR1I3, AR, TEAD4, and NFKB2 on CYP450/UGT1A gene transcription translated into moderate influences on enzyme activities. To our knowledge, this is the first study to integrate Gene Expression Omnibus (GEO) datasets and supervised weighted correlation network analysis (sWGCNA) for defining TFs potentially related to CYPs/UGTs. We detected several novel TFs involved in the regulatory network of hepatic CYPs and UGTs in humans. Further validation and investigation may reveal their exact mechanism of CYPs/UGTs regulation.