A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies

A potential tumor marker: Chaperonin containing TCP‑1 controls the development of malignant tumors (Review)

Due to concealment, high invasiveness and a lack of indicators, malignant tumors have emerged as one of the deadliest diseases worldwide and their incidence is rising yearly. Research has revealed that the chaperonin family member, chaperonin containing TCP‑1 (CCT), serves a crucial role in malignant tumors. CCT is involved in the growth of numerous malignant tumors such as lung cancer, breast cancer, hepatocellular carcinoma and colorectal cancer and assists the folding of a number of proteins linked to cancer, such as KRAS, p53 and STAT3. According to clinical data, CCT is highly expressed in a range of tumor cells and is associated with poor patient prognosis. In addition, through controlling the cell cycle or interacting with other proteins (including YAP1, HoXB2 and SMAD2), CCT has an effect on the proliferation, invasion and migration of cancer cells. As a result, it is possible that CCT will become a new tumor marker or therapeutic target, which will provide some guidance for early tumor screening or late tumor prognosis. In the present review, the molecular properties of CCT are introduced, alongside a summary of its interactions with other cancer‑related proteins and a discussion of its function in common malignant tumors. It is expected that the present review will offer fresh approaches to the treatment of cancer.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

Background: Colorectal cancer (CRC) is the third most common cancer and third leading cause of cancer-associated deaths worldwide. Diagnosing CRC patients reliably at an early and curable stage is of utmost importance to reduce the risk of mortality. Methods: We identified global differentially expressed genes with copy number alterations in patients with CRC. We then identified genes that are also expressed in blood, which resulted in a blood-based gene signature. We validated the gene signature's diagnostic and prognostic potential using independent datasets of gene expression profiling from over 800 CRC patients with detailed clinical data. Functional enrichment, gene interaction networks and pathway analyses were also performed. Results: The analysis revealed a 17-gene signature that is expressed in blood and demonstrated that it has diagnostic potential. The 17-gene SVM classifier displayed 99 percent accuracy in predicting the patients with CRC. Moreover, we developed a prognostic model and defined a risk-score using 17-gene and validated that high risk score is strongly associated with poor disease outcome. The 17-gene signature predicted disease outcome independent of other clinical factors in the multivariate analysis (HR = 2.7, 95% CI = 1.3-5.3, p = 0.005). In addition, our gene network and pathway analyses revealed alterations in oxidative stress, STAT3, ERK/MAPK, interleukin and cytokine signaling pathways as well as potentially important hub genes, including BCL2, MS4A1, SLC7A11, AURKA, IL6R, TP53, NUPR1, DICER1, DUSP5, SMAD3, and CCND1. Conclusion: Our results revealed alterations in various genes and cancer-related pathways that may be essential for CRC transformation. Moreover, our study highlights diagnostic and prognostic value of our gene signature as well as its potential use as a blood biomarker as a non-invasive diagnostic method. Integrated analysis transcriptomic data coupled with copy number aberrations may provide a reliable method to identify key biological programs associated with CRC and lead to improved diagnosis and therapeutic options.

Meta-analysis of transcriptomics data identifies potential biomarkers and their associated regulatory networks in gallbladder cancer

Aim

This study aimed to identify key genes, non-coding RNAs, and their possible regulatory interactions during gallbladder cancer (GBC).

Background

The early detection of GBC, i.e. before metastasis, is restricted by our limited knowledge of molecular markers and mechanism(s) involved during carcinogenesis. Therefore, identifying important disease-associated transcriptome-level alterations can be of clinical importance.

Methods

In this study, six NCBI-GEO microarray dataseries of GBC and control tissue samples were analyzed to identify differentially expressed genes (DEGs) and non-coding RNAs {microRNAs (DEmiRNAs) and long non-coding RNAs (DElncRNAs)} with a computational meta-analysis approach. A series of bioinformatic methods were applied to enrich functional pathways, create protein-protein interaction networks, identify hub genes, and screen potential targets of DEmiRNAs and DElncRNAs. Expression and interaction data were consolidated to reveal putative DElncRNAs:DEmiRNAs:DEGs interactions.

Results

In total, 351 DEGs (185 downregulated, 166 upregulated), 787 DEmiRNAs (299 downregulated, 488 upregulated), and 7436 DElncRNAs (3127 downregulated, 4309 upregulated) were identified. Eight genes (FGF, CDK1, RPN2, SEC61A1, SOX2, CALR, NGFR, and NCAM) were identified as hub genes. Genes associated with ubiquitin ligase activity, N-linked glycosylation, and blood coagulation were upregulated, while those for cell-cell adhesion, cell differentiation, and surface receptor-linked signaling were downregulated. DEGs-DEmiRNAs-DElncRNAs interaction network identified 46 DElncRNAs to be associated with 28 DEmiRNAs, consecutively regulating 27 DEGs. DEmiRNAs-hsa-miR-26b-5p and hsa-miR-335-5p; and DElnRNAs-LINC00657 and CTB-89H12.4 regulated the highest number of DEGs and DEmiRNAs, respectively.

Conclusion

The current study has identified meaningful transcriptome-level changes and gene-miRNA-lncRNA interactions during GBC and laid a platform for future studies on novel prognostic and diagnostic markers in GBC.

Novel Insight Into the Association Between Obesity and Hepatocellular Carcinoma Occurrence and Recurrence: High-Throughput Microarray Data Set Analysis of Differentially Expressed Genes

PURPOSE

This study aims to identify potential biomarkers of hepatocellular carcinoma (HCC) occurrence/recurrence and obesity, along with the molecular mechanisms that involve these biomarkers.

METHODS

Three microarray data sets, namely GSE18897, GSE25097, and GSE36376 (genetic suppressor elements associated with obesity, tumor, and recurrence, respectively), were downloaded from Gene Expression Omnibus database to be investigated for their expression as differentially expressed genes (DEGs) in HCC and obesity. The functional and pathway enrichment analysis of these DEGs were identified by the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction network analysis was performed with STRING online tool and Cytoscape software.

RESULTS

One hundred sixty common DEGs were screened. We found that these genes were associated with certain pathways such as metabolic pathways, terpenoid backbone biosynthesis, and adipocytokine signaling pathway. The involvements of 10 genes, including RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, EGR1, FDPS, and MCM4, were identified in the subnetwork. HNRNPA2B1 and RPS7 in the GSE18897 data set, RPS16, RPS7, CCT3, HNRNPA2B1, PSMC4, NHP2, FDPS, and MCM4 in the GSE25097 data set, and RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, FDPS, and MCM4 in the GSE36376 data set exhibited positive fold changes.

CONCLUSION

These DEGs and pathways could be of diagnostic value as potential biomarkers involved in the pathogenesis of HCC, pertaining to both obesity and HCC occurrence/recurrence.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Underlying mechanism of sorafenib resistance in hepatocellular carcinoma: a bioinformatics study based on validated resistance-related genes

Background

Sorafenib, the first approved targeted therapy for advanced hepatocellular carcinoma (HCC), is often reported to comprised survival-benefit due to resistance. An underlying mechanism of resistance was proposed using bioinformatics analysis based on differentially expressed genes (DEGs) from microarrays. However, most DEGs were invalidated at both the expression level, and the role in causing resistance. Therefore, we conducted a bioinformatics analysis based on experimentally determined sorafenib-resistance-related genes (SRRGs) to elucidate the mechanism of sorafenib resistance.

Methods

The SRRGs, which have been experimentally determined to promote or inhibit resistance, were collected from published studies. The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform Gene Ontology (GO) and pathway enrichment analysis, respectively. A corresponding protein-protein interaction network (PPI) was created using the Cytoscape software program, and network hub genes were proposed.

Results

A total of 145 SRRGs, with 117 promoting and 28 inhibiting resistance, were identified. Cell proliferation, migration, development, response to oxygen levels, epithelial-to-mesenchymal transition (EMT), cell skeleton, protein function, and autophagy were all proposed as crucial gene functions related to resistance. The pathways related to cell proliferation or apoptosis, immune function, endocrine metabolism, stem cell function, and differentiation were identified as key resistance-related pathways. A total of 81 hub genes were proposed, including the following top 10 genes: TP53, AKT1, EGFR, STAT3, VEGFA, JUN, MAPK1, IL6, PTEN, and CTNNB1.

Conclusions

In conclusion, this study gathered experimentally validated genes that determine sorafenib resistance in HCC, provided an overview of the underlying mechanisms of resistance, and further validated sorafenib resistance in HCC.

PLGA nanoparticles containing α-fetoprotein siRNA induce apoptosis and enhance the cytotoxic effects of doxorubicin in human liver cancer cell line

Hepatocellular carcinoma (HCC) is one of the most common cancers and is a leading cause of death. Delivery of therapeutic molecules, e.g., siRNA, to HCC cells could potentially be an alternative treatment for HCC. In this study, the siRNA targeting α-fetoprotein (AFP) mRNA was found to specifically induce apoptosis and significant cell death in HepG2 cells. It also enhanced the cytotoxic effects of doxorubicin by about two-fold, making it the candidate therapeutic molecule for HCC treatment. To deliver the siRNAs into HCC cells, the AFP siRNAs were loaded into the nanoparticles based on poly (lactic-co-glycolic) acid (PLGA). These nanoparticles induced apoptosis in HepG2 cells and synergistically increased the cytotoxicity of doxorubicin. In summary, the delivery of the AFP siRNA-loaded PLGA nanoparticles in combination with doxorubicin could be a very promising approach for the treatment of HCC.

A transcriptome profile in gallbladder cancer based on annotation analysis of microarray studies

The purpose of the present study was to identify aberrantly expressed genes for gallbladder cancer based on the annotation analysis of microarray studies and to explore their potential functions. Differential gene expression was investigated in cholesterol polyps, gallbladder adenoma and gallbladder cancer using microarrays. Subsequently, microarray results were comprehensively analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the affected biological processes or pathways. Differentially expressed genes (DEGs) of cholesterol polyps, gallbladder adenoma and gallbladder cancer were identified. Following comprehensive analysis, 14 genes were found to be differentially expressed in the gallbladder wall of both gallbladder cancer and gallbladder adenoma. The 20 most significantly upregulated genes were only upregulated in the gallbladder wall of gallbladder cancer, but not in the gallbladder wall of cholesterol polyps and gallbladder adenoma. In addition, 182 DEGs were upregulated in the gallbladder wall of gallbladder adenoma compared with the gallbladder wall of cholesterol polyps. A total of 20 most significant DEGs were found in both the tumor and gallbladder wall of gallbladder cancer. In addition, the most significant DEGs that were identified were only upregulated in the tumor of gallbladder cancer. GO and KEGG analysis indicated that the aforementioned DEGs could participate in numerous biological processes or pathways associated with the development of gallbladder cancer. The present findings will help improve the current understanding of tumorigenesis and the development of gallbladder cancer.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

NACC-1 regulates hepatocellular carcinoma cell malignancy and is targeted by miR-760

Hepatocellular carcinoma (HCC) is the most prominent form of presentation in liver cancer. It is also the fourth most common cause of cancer-associated deaths globally. The role of nucleus accumbens associated protein-1 (NACC-1) has been evaluated in several cancers. This protein is a transcriptional regulator that regulates a number of significant cellular processes. In the current study, we aimed to understand the role of NACC-1 in HCC. Primarily, we measured the expression of NACC-1 using quantitative real time polymerase chain reaction and western blot analysis. We knocked down the expression of NACC-1 in HCC cell lines Huh7 and HepG2 by transferring a commercially synthesized small interfering RNA and explored the impact of NACC-1 knockdown on cellular growth, migration, invasion, and chemoresistance to doxorubicin. Through bioinformatic analysis, we identified NACC-1 as a potential target of miR-760. Using a dual reporter luciferase assay, we confirmed the predicted target and assessed miR-760-mediated regulation of NACC-1 and rescue of tumorigenic phenotypes. We observed increased expression of NACC-1 in HCC. Furthermore, knockdown of NACC-1 resulted in reduced cell proliferation and invasion and increased susceptibility to doxorubicin-mediated chemosensitivity. Overexpression of miR-760 in HCC cell lines rescued NACC-1-mediated migration and invasion. We revealed that miR-760 regulated NACC-1 expression in HCC. Our data indicated that both miR-760 and NACC-1 could be used as prognostic markers, and miR-760 may have therapeutic benefits for HCC and other cancers.

Identification of pathogenic genes and transcription factors in vitiligo

Our study aimed to identify the key genes and upstream regulators in vitiligo. To screen the pathogenic genes of vitiligo, an integrated analysis was performed by using the microarray datasets in vitiligo derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We constructed a vitiligo-specific transcriptional regulatory network to identify crucial transcriptional factors that target the DEGs in vitiligo. From two GEO datasets, we identified 1863 DEGs (744 downregulated DEGs and 1,119 upregulated DEGs [false discovery rate < 0.05, |Combined.ES| > 1]) between lesional tissues and nonlesional tissues. GO and KEGG analyses revealed that ubiquitin-mediated proteolysis and the endoplasmic reticulum were significantly enriched pathways for DEGs. The expressions of premelanosome (PMEL), melan-A (MLANA), dopachrome tautomerase (DCT), SRY-boxtranscription factor 10 (SOX10), tyrosinase-related protein 1 (TYRP1), and melanocortin 1 receptor (MC1R) were shown to be involved in the pathogenesis of vitiligo. We concluded that PMEL, MLANA), DCT, SOX10, TYRP1, and MC1R may play a role in vitiligo, among which TYRP1 and MC1R are regulated by forkhead box J2 (FOXJ2). Our finding may contribute to the development of new potential biomarkers, reveal the underlying pathogenesis of vitiligo, and identify novel therapeutic targets for vitiligo.

Identification of pathogenic genes and transcription factors in glaucoma

Glaucoma is a group of eye diseases characterized by alterations in the contour of the optic nerve head, with corresponding visual field defects and progressive loss of retinal ganglion cells. The present study aimed to identify the key genes and upstream regulators in glaucoma. To screen the pathogenic genes involved in glaucoma, an integrated analysis was performed by using the microarray datasets in glaucoma derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were additionally examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A glaucoma‑specific transcriptional regulatory network was constructed to identify crucial transcriptional factors that target the DEGs in glaucoma. From two GEO datasets, 1,935 DEGs (951 upregulated and 984 downregulated genes) between glaucoma and normal controls were identified. GO and KEGG analyses identified that 'eye development' [false discovery rate (FDR)=0.00415533] and 'visual perception' (FDR=0.00713283) were significantly enriched pathways for DEGs. The expression of lipocalin 2 (LCN2), monoamine oxidase A (MAOA), hemoglobin subunit β (HBB), paired box 6 (PAX6), fibronectin (FN1) and cAMP responsive element binding protein 1 (CREB1) were demonstrated to be involved in the pathogenesis of glaucoma. In conclusion, LCN2, MAOA, HBB, PAX6, FN1 and CREB1 may serve roles in glaucoma, regulated by PAX4, solute carrier family 22 member 1, hepatocyte nuclear factor 4 α and ELK1, ETS transcription factor. These data may contribute to the development of novel potential biomarkers, reveal the underlying pathogenesis and additionally identify novel therapeutic targets for glaucoma.

Identification of Pathogenic Genes and Transcription Factors in Osteosarcoma

Osteosarcoma (OS) is an aggressive malignant tumor of the bones. Our study intended to identify and analyze potential pathogenic genes and upstream regulators for OS. We performed an integrated analysis to identify candidate pathogenic genes of OS by using three Gene Expression Omnibus (GEO) databases (GSE66673, GSE49003 and GSE37552). GO and KEGG enrichment analysis were utilized to predict the functional annotation and potential pathways of differentially expressed genes (DEGs). The OS-specific transcriptional regulatory network was established to study the crucial transcriptional factors (TFs) which target the DEGs in OS. From the three GEO datasets, we identified 759 DEGs between metastasis OS samples and non-metastasis OS samples. After GO and KEGG analysis, 'cell adhesion' (FDR = 1.27E-08), 'protein binding' (FDR = 1.13E-22), 'cytoplasm' (FDR = 5.63E-32) and 'osteoclast differentiation' (FDR = 0.000992221) were significantly enriched pathways for DEGs. HSP90AA1 exhibited a highest degree (degree = 32) and was enriched in 'pathways in cancer' and 'signal transduction'. BMP6, regulated by Pax-6, was enriched in the 'TGF-beta signaling pathway'. We indicated that BMP6 may be downregulated by Pax-6 in the non-metastasis OS samples. The up-regulated HSP90AA1 and down-regulated BMP6 and 'pathways in cancer' and 'signal transduction' were deduced to be involved in the pathogenesis of OS. The identified biomarkers and biological process in OS may provide foundation for further study.

Transcriptome Analysis Revealed a Highly Connected Gene Module Associated With Cirrhosis to Hepatocellular Carcinoma Development

Introduction

Cirrhosis is one of the most important risk factors for development of hepatocellular carcinoma (HCC). Recent studies have shown that removal or well control of the underlying cause could reduce but not eliminate the risk of HCC. Therefore, it is important to elucidate the molecular mechanisms that drive the progression of cirrhosis to HCC.

Materials and Methods

Microarray datasets incorporating cirrhosis and HCC subjects were identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were determined by GEO2R software. Functional enrichment analysis was performed by the clusterProfiler package in R. Liver carcinogenesis-related networks and modules were established using STRING database and MCODE plug-in, respectively, which were visualized with Cytoscape software. The ability of modular gene signatures to discriminate cirrhosis from HCC was assessed by hierarchical clustering, principal component analysis (PCA), and receiver operating characteristic (ROC) curve. Association of top modular genes and HCC grades or prognosis was analyzed with the UALCAN web-tool. Protein expression and distribution of top modular genes were analyzed using the Human Protein Atlas database.

Results

Four microarray datasets were retrieved from GEO database. Compared with cirrhotic livers, 125 upregulated and 252 downregulated genes in HCC tissues were found. These DEGs constituted a liver carcinogenesis-related network with 272 nodes and 2954 edges, with 65 nodes being highly connected and formed a liver carcinogenesis-related module. The modular genes were significantly involved in several KEGG pathways, such as “cell cycle,” “DNA replication,” “p53 signaling pathway,” “mismatch repair,” “base excision repair,” etc. These identified modular gene signatures could robustly discriminate cirrhosis from HCC in the validation dataset. In contrast, the expression pattern of the modular genes was consistent between cirrhotic and normal livers. The top modular genes TOP2A, CDC20, PRC1, CCNB2, and NUSAP1 were associated with HCC onset, progression, and prognosis, and exhibited higher expression in HCC compared with normal livers in the HPA database.

Conclusion

Our study revealed a highly connected module associated with liver carcinogenesis on a cirrhotic background, which may provide deeper understanding of the genetic alterations involved in the transition from cirrhosis to HCC, and offer valuable variables for screening and surveillance of HCC in high-risk patients with cirrhosis.

Genome‑wide profiling of lncRNA and mRNA expression in CRSwNP

Chronic rhinosinusitis with nasal polyps (CRSwNP) is one of the most prevalent chronic diseases. In patients with CRSwNP, the present study performed comprehensive bioinformatics analyses to characterize the transcriptome profiles of mRNAs and long non‑coding RNAs (lncRNAs). A total of 265 differentially expressed lncRNAs and 994 mRNAs were identified. The majority of up‑ and downregulated differentially expressed genes were significantly enriched in the biological process of 'signal transduction'. The most significantly enriched molecular function was 'protein binding' and the most significantly enriched cellular component was 'membrane'. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis led to identification of several significantly enriched pathways [false discovery rate (FDR)<0.05], including 'cytokine‑cytokine receptor interaction' (FDR=3.94x1016) and 'cell adhesion molecules' (CAMs) (FDR=1.28x10‑5). Key differentially expressed lncRNAs were identified, including lncRNA XLOC_010280, which regulates chemokine (C‑C motif) ligand 18 (CCL18) and inflammation, and RP11‑798M19.6, which regulates polypeptide N‑acetylgalactosaminyltransferase 7 (GALNT7) and cell proliferation. Based on the results of reverse transcription‑quantitative polymerase chain reaction, except for CCL8, neural precursor cell expressed developmentally downregulated gene 4‑like and GALNT7, the expression of 3 other selected genes was consistent with the results of integrated analysis. The results of the present study provide a foundation for future investigations into mRNAs and lncRNAs as diagnostic and therapeutic targets in CRSwNP.

Cytochrome P450 family members are associated with fast-growing hepatocellular carcinoma and patient survival: An integrated analysis of gene expression profiles

BACKGROUND/AIMS

The biological heterogeneity of hepatocellular carcinoma (HCC) makes prognosis difficult. Although many molecular tools have been developed to assist in stratification and prediction of patients by using microarray analysis, the classification and prediction are still improvable because the high-through microarray contains a large amount of information. Meanwhile, gene expression patterns and their prognostic value for HCC have not been systematically investigated. In order to explore new molecular diagnostic and prognostic biomarkers, the gene expression profiles between HCCs and adjacent nontumor tissues were systematically analyzed in the present study.

MATERIALS AND METHODS

In this study, gene expression profiles were obtained by repurposing five Gene Expression Omnibus databases. Differentially expressed genes were identified by using robust rank aggregation method. Three datasets (GSE14520, GSE36376, and GSE54236) were used to validate the associations between cytochrome P450 (CYP) family genes and HCC. GSE14520 was used as the training set. GSE36376 and GSE54236 were considered as the testing sets.

RESULTS

From the training set, a four-CYP gene signature was constructed to discriminate between HCC and nontumor tissues with an area under curve (AUC) of 0.991. Accuracy of this four-gene signature was validated in two testing sets (AUCs for them were 0.973 and 0.852, respectively). Moreover, this gene signature had a good performance to make a distinction between fast-growing HCC and slow-growing HCC (AUC = 0.898), especially for its high sensitivity of 95%. At last, CYP2C8 was identified as an independent risk factor of recurrence-free survival (hazard ratio [HR] =0.865, 95% confidence interval [CI], 0.754-0.992, P = 0.038) and overall survival (HR = 0.849; 95% CI, 0.716-0.995, P = 0.033).

CONCLUSIONS

In summary, our results confirmed for the first time that a four-CYP gene (CYP1A2, CYP2E1, CYP2A7, and PTGIS) signature is associated with fast-growing HCC, and CYP2C8 is associated with patient survival. Our findings could help to identify HCC patients at high risk of rapid growth and recurrence.

Identification of pathogenic genes and upstream regulators in allergic rhinitis

BACKGROUND

Allergic rhinitis (AR) is the main cause of irreversible blindness in older individuals. Our study aims to identify the key genes and upstream regulators in AR.

METHODS

To screen pathogenic genes of AR, an integrated analysis was performed by using the microarray datasets in AR derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We constructed the AR-specific transcriptional regulatory network to find the crucial transcriptional factors (TFs) which target the DEGs in AR. Electronic validation was performed to verify the DEGs obtained by integrated analysis.

RESULTS

From two GEO datasets obtained, we identified 793 DEGs (460 up-regulated and 333 down-regulated genes) between AR and normal control (NC). After GO and KEGG analysis, chronic inflammatory response and MAPK signaling pathway were significantly enriched pathways for DEGs. The expression of 6 genes (CLC, CST1, CRTAM, ILK, STAT1, and POSTN) was detected. The 6 genes in GEO: GSE51392 dataset played the same pattern with that in our integrated analysis.

CONCLUSIONS

The dysregulation of 3 genes (CST1, CLC and STAT1) may be involved in the pathogenesis of AR. AP-1 was associated with AR by regulating CST1 and CLC. Our finding can contribute to developing new potential biomarkers, revealing the underlying pathogenesis, and further raising new therapeutic targets for AR.

Clinical and prognostic value of chaperonin containing T-complex 1 subunit 3 in hepatocellular carcinoma: A Study based on microarray and RNA-sequencing with 4272 cases

Liver cancer is one of the few tumors with a steadily increasing morbidity and mortality; hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. We combined the expression profiles of Chaperonin Containing T-complex 1 Subunit 3 (CCT3) in HCC tissues based on microarray and RNA-sequencing data. The CCT3 expression levels were extracted and examined based on 421 samples from The Cancer Genome Atlas (TCGA) (HCC, n = 371; non-HCC, n = 50) and 3851 samples from 31 microarray or RNA-sequencing datasets (HCC, n = 1975; non-tumor = 1876). We used a variety of meta-analytic methods, including SMD forest maps, sensitivity analysis, subgroup analysis and sROC curves, to confirm the final results. Meanwhile, database-derived immunohistochemistry data was used for validation. We also further explained the potential mechanism of CCT3 in HCC through signal pathway analyses and PPI network construction with the CCT3 co-expressed genes. The mRNA and protein expression of CCT3 in HCC tissues were higher than in non-HCC tissues. The expression of CCT3 differed between groups when grouped according to clinicopathological parameters, such as race, family history, and histological grade. The results of standardised mean difference (SMD) forest map and summary receiver operating characteristic (sROC) curve revealed that CCT3 was highly expressed in HCC tissues and had a high ability to distinguish between cancer tissues and non-cancer tissues. The main form of CCT3 gene alteration in HCC was mRNA up-regulation and amplification (23%), and the most common mutation type was missense. The mRNA expression of CCT3 in HCC was negatively correlated with DNA methylation. According to the Kyoto Encyclopedia of Genes and Genomes pathway analysis, CCT3 can influence HCC occurrence and development through cell cycle and DNA replication pathways. In summary, this study carries out the staging and prognostic analysis of HCC. It suggests that CCT3 might play an important part in the tumorigenesis and progression of HCC and may have a certain prognostic value in HCC. Moreover, CCT3 might represent a promising biomarker for HCC.

CYP3A4 Gene Is a Novel Biomarker for Predicting a Poor Prognosis in Hepatocellular Carcinoma

BACKGROUND/AIM

Project HOPE (High-tech Omics-based Patient Evaluation) began in 2014 using integrated gene expression profiling (GEP) of cancer tissues as well as diathesis of each patient who underwent operation at our Institution. The aim of this study was to identify novel genes displaying altered gene expression related to the survival and early recurrence after hepatectomy for hepatocellular carcinoma (HCC) using the results of integrated GEP analysis.

MATERIALS AND METHODS

The present study included 92 patients. Genes with aberrant expression were selected by the difference of expression levels with ≥10-fold change between tumor and non-tumor tissues.

RESULTS

GEP analysis showed that down-regulation was frequently observed in the PRSS8 (64%), CYP3A4 (61%) and EPCAM (57%) genes. Multivariate analysis revealed tumor stage ≥II (p=0.008) and down-regulation of the CYP3A4 gene (p=0.036) as independent predictor for overall survival. Furthermore, multivariate analysis identified maximum tumor diameter ≥74mm (p=0.008), presence of intrahepatic-metastasis (p=0.020), and down-regulation of CYP3A4 gene (p=0.019) as independent predictors for early recurrence.

CONCLUSION

CYP3A4 was identified as a novel tumor suppressor gene related to a poor prognosis in HCC.

The down-regulation of the CYP2C19 gene is associated with aggressive tumor potential and the poorer recurrence-free survival of hepatocellular carcinoma

Project HOPE (High-tech Omics-based Patient Evaluation) began in 2014 using integrated gene expression profiling (GEP) of cancer tissues as well as diathesis of each patient who underwent an operation at our institution. The aim of this study was to clarify the association between the expression of cytochrome P450s (CYP) genes and recurrence of hepatocellular carcinoma (HCC). The present study included 92 patients. Genes with aberrant expression were selected based on a ≥10-fold difference in the expression between tumor and non-tumor tissues. The GEP analysis showed that the down-regulated genes in tumor tissue were CYP3A4 in 56 patients (61%), CYP2C8 in 44 patients (48%), CYP2C19 in 30 patients (33%), CYP2D6 in 11 patients (12%), CYP3A5 in 7 patients (8%) and CYP1B1 in 2 patients (2%). There was no patients with down-regulation of the CYP17A1 gene. A multivariate analysis revealed that the presence of microscopic portal invasion (hazard ratio [HR] 2.57, 95% confidence interval [CI] 1.30–5.05 P = 0.006), the presence of intrahepatic-metastasis (HR 3.09 95% CI 1.52–6.29 P = 0.002) and down-regulation of the CYP2C19 gene (HR 3.69 95% CI 1.83–7.46 P < 0.001) were independent predictors for the recurrence-free survival (RFS). The down-regulation of the CYP2C19 gene were correlated with the RFS in HCC.

SOCS1 regulates senescence and ferroptosis by modulating the expression of p53 target genes

The mechanism by which p53 suppresses tumorigenesis remains poorly understood. In the context of aberrant activation of the JAK/STAT5 pathway, SOCS1 is required for p53 activation and the regulation of cellular senescence. In order to identify p53 target genes acting during the senescence response to oncogenic STAT5A, we characterized the transcriptome of STAT5A-expressing cells after SOCS1 inhibition. We identified a set of SOCS1-dependent p53 target genes that include several secreted proteins and genes regulating oxidative metabolism and ferroptosis. Exogenous SOCS1 was sufficient to regulate the expression of p53 target genes and sensitized cells to ferroptosis. This effect correlated with the ability of SOCS1 to reduce the expression of the cystine transporter SLC7A11 and the levels of glutathione. SOCS1 and SOCS1-dependent p53 target genes were induced during the senescence response to oncogenic STAT5A, RasV12 or the tumor suppressor PML. However, while SOCS1 sensitized cells to ferroptosis neither RasV12 nor STAT5A mimicked the effect. Intriguingly, PML turned cells highly resistant to ferroptosis. The results indicate different susceptibilities to ferroptosis in senescent cells depending on the trigger and suggest the possibility of killing senescent cells by inhibiting pathways that mediate ferroptosis resistance.

DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates

Motivation

High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.

Results

We present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.

Availability and implementation

The software is available at https://github.com/hao-peng/DEIsoM

Supplementary information

Supplementary data are available at Bioinformatics online.