Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.

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.

RNA sequencing analysis of hepatocellular carcinoma identified oxidative phosphorylation as a major pathologic feature

Dysregulation of expression of functional genes and pathways plays critical roles in the etiology and progression of hepatocellular carcinoma (HCC). Next generation-based RNA sequencing (RNA-seq) offers unparalleled power to comprehensively characterize HCC at the whole transcriptome level. In this study, 17 fresh-frozen HCC samples with paired non-neoplastic liver tissue from Caucasian patients undergoing liver resection or transplantation were used for RNA-seq analysis. Pairwise differential expression analysis of the RNA-seq data was performed to identify genes, pathways, and functional terms differentially regulated in HCC versus normal tissues. At a false discovery rate (FDR) of 0.10, 13% (n = 4335) of transcripts were up-regulated and 19% (n = 6454) of transcripts were down-regulated in HCC versus non-neoplastic tissue. Eighty-five Kyoto Encyclopedia of Genes and Genomes pathways were differentially regulated (FDR, <0.10), with almost all pathways (n = 83) being up-regulated in HCC versus non-neoplastic tissue. Among the top up-regulated pathways was oxidative phosphorylation (hsa00190; FDR, 1.12E-15), which was confirmed by Database for Annotation, Visualization, and Integrated Discovery (DAVID) gene set enrichment analysis. Consistent with potential oxidative stress due to activated oxidative phosphorylation, DNA damage-related signals (e.g., the up-regulated hsa03420 nucleotide excision repair [FDR, 1.14E-04] and hsa03410 base excision repair [FDR, 2.71E-04] pathways) were observed. Among down-regulated genes (FDR, <0.10), functional terms related to cellular structures (e.g., cell membrane [FDR, 3.05E-21] and cell junction [FDR, 2.41E-07], were highly enriched, suggesting compromised formation of cellular structure in HCC at the transcriptome level. Interestingly, the olfactory transduction (hsa04740; FDR, 1.53E-07) pathway was observed to be down-regulated in HCC versus non-neoplastic tissue, suggesting impaired liver chemosensory functions in HCC. Our findings suggest oxidative phosphorylation and the associated DNA damage may be the major driving pathologic feature in HCC.

Inflammation-Related Gene Signature for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma

Purpose

The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.

Patients and Methods

IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.

Results

In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.

Conclusion

Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.

Identification of vital prognostic genes related to tumor microenvironment in pheochromocytoma and paraganglioma based on weighted gene co-expression network analysis

Pheochromocytoma and paraganglioma (PCPG) is a rare neuroendocrine tumor. This study aims to identify vital prognostic genes which were associated with PCPG tumor microenvironment (TME). We downloaded transcriptome data of PCPG from TCGA database and calculated the immune scores and stromal scores by using the ESTIMATE algorithm. DEGs related to TMB were then identified. We conducted WGCNA to further extract the TME-related modules. GO, KEGG pathway analysis, and PPI network were performed. Survival analysis was conducted to identify the hub genes associated with the prognosis of PCPG. A total of 150 PCPG samples were included in this study. We obtained 1507 and 2067 DEGs based on immune scores and stromal scores, respectively. WGCNA analysis identified the red module and brown module were correlated with immune sores while the turquoise module and red module were significantly associated with stromal scores. Functional enrichments analysis revealed that 307 TME-related genes were correlated with the inflammation or immune response. Survival analysis showed that three TME-relate genes (ADGRE1, CCL18, and LILRA6) were associated with PCPG prognosis. These three hub genes including ADGRE1, CCL18, and LILRA6 might be involved in the progression of PCPG and could serve as potential biomarkers and novel therapeutic targets.

Role of CYP4F2 as a novel biomarker regulating malignant phenotypes of liver cancer cells via the Nrf2 signaling axis

Hepatocellular carcinoma (HCC) is one of the most prevalent types of cancer worldwide. The present study attempted to identify a prognostic biomarker for HCC. RNA sequencing data from the GSE63863 dataset were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified based on a protein-protein interaction (PPI) network, and prognostic evaluation was subsequently conducted. Following lentiviral transfection, the migratory, proliferative and apoptotic abilities of cells were evaluated using wound healing, Cell Counting Kit-8, Transwell migration and apoptosis assays. A total of 192 DEGs were identified from 11 pairs of HCC and matched non-tumor samples. The PPI network revealed the top three modules, and eight genes were identified from these modules. The expression levels of cytochrome P450 family 4 subfamily F member 2 (CYP4F2) were downregulated in 50 HCC samples from The Cancer Genome Atlas and in the HCC Hep3B cell line. Low CYP4F2 expression was associated with a lower overall survival time. Functional studies revealed that CYP4F2 overexpression inhibited HCC cell proliferation and migration, and induced apoptosis. Furthermore, CYP4F2 overexpression repressed the expression of genes in the nuclear factor, erythroid 2 like 2 (Nrf2) signaling pathway, including Nrf2, heme oxygenase-1 and ferritin heavy chain 1, while increasing NAD(P)H quinone dehydrogenase 1 expression, suggesting that CYP4F2 overexpression reversed the antioxidant response of liver cancer cells. Overall, the present findings indicated that CYP4F2 may be a potential prognostic biomarker for predicting tumorigenesis and long-term survival rates in patients with HCC.

Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC.

Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves.

Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value.

Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.

DNA and RNA sequencing identified a novel oncogene VPS35 in liver hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) is the second leading cause of cancer mortality worldwide. Although cancer driver genes identified so far have been considered to be saturated or nearly saturated, challenges remain in discovering novel genes underlying carcinogenesis due to significant tumor heterogeneity. Here, in a small cohort of hepatitis B virus (HBV)-associated LIHC, we investigated the transcriptional patterns of tumor-mutated alleles using both whole-exome and RNA sequencing data. A graph clustering of the transcribed tumor-mutated alleles characterized overlapped functional clusters, and thus prioritized potentially novel oncogenes. We validated the function of the potentially novel oncogenes in vitro and in vivo. We showed that a component of the retromer complex-the vacuolar protein sorting-associated protein 35 (VPS35)-promoted the proliferation of hepatoma cell through the PI3K/AKT signaling pathway. In VPS35-knockout hepatoma cells, a significantly reduced distribution of membrane fibroblast growth factor receptor 3 (FGFR3) demonstrated the effects of VPS35 on sorting and trafficking of transmembrane receptor. This study provides insight into the roles of the retromer complex on carcinogenesis and has important implications for the development of personalized therapeutic strategies for LIHC.

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma

Background

Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.

Methods

A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease.

Results

A brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149–2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119–2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155–2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis.

Conclusion

A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

Transcriptomic Analysis of mRNA-lncRNA-miRNA Interactions in Hepatocellular Carcinoma

Fully elucidating the molecular mechanisms of non-coding RNAs (ncRNAs), including micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs), underlying hepatocarcinogenesis is challenging. We characterized the expression profiles of ncRNAs and constructed a regulatory mRNA-lncRNA-miRNA (MLMI) network based on transcriptome sequencing (RNA-seq) of hepatocellular carcinoma (HCC, n = 9) patients. Of the identified miRNAs (n = 203) and lncRNAs (n = 1,090), we found 16 significantly differentially expressed (DE) miRNAs and three DE lncRNAs. The DE RNAs were highly enriched in 21 functional pathways implicated in HCC (p < 0.05), including p53, MAPK, and NAFLD signaling. Potential pairwise interactions between DE ncRNAs and mRNAs were fully characterized using in silico prediction and experimentally-validated evidence. We for the first time constructed a MLMI network of reciprocal interactions for 16 miRNAs, three lncRNAs, and 253 mRNAs in HCC. The predominant role of MEG3 in the MLMI network was validated by its overexpression in vitro that the expression levels of a proportion of MEG3-targeted miRNAs and mRNAs was changed significantly. Our results suggested that the comprehensive MLMI network synergistically modulated carcinogenesis, and the crosstalk of the network provides a new avenue to accurately describe the molecular mechanisms of hepatocarcinogenesis.

A novel long noncoding RNA HHIP-AS1 suppresses hepatocellular carcinoma progression through stabilizing HHIP mRNA

Aberrant expression of long non-coding RNAs (lncRNAs) has been observed in hepatocellular carcinoma (HCC) and confirmed to participate in the initiation and progression of HCC. In the present study, we identified a novel functional lncRNA, hedgehog-interacting protein antisense RNA 1 (HHIP-AS1). The expression levels of HHIP-AS1 were significantly decreased in HCC tissues. Downregulation of HHIP-AS1 expression correlated with larger tumor size, metastasis, and advanced TNM stage, and also predicted worse overall survival rate of HCC patients. Through performing overexpression and knockdown experiments, the biological function of HHIP-AS1 was identified to suppress HCC cell proliferation, migration and invasion, while promote apoptosis. Further investigation showed that HHIP-AS1 interacted with and positively regulated the stability of HHIP mRNA in a HuR-dependent manner. HHIP-AS1 exerted its suppressive effects through HHIP. Taken together, our findings demonstrate that HHIP-AS1 represses HCC progression by promoting HHIP expression, and indicate that the use of HHIP-AS1 may offer a promising treatment for HCC patients.

CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis

Background

Salt sensitivity of blood pressure (SSBP) is an independent risk factor for cardiovascular disease. The pathogenic mechanisms of SSBP are still uncertain. This study aimed to construct the co-regulatory network of SSBP and data mining strategy based on the competitive endogenous RNA (ceRNA) theory.

Methods

LncRNA and mRNA microarray was performed to screen for candidate RNAs. Four criteria were used to select the potential differently expressed RNAs. The weighted correlation network analysis (WGCNA) package of R software and target miRNA and mRNA prediction online databases were used to construct the ceRNA co-regulatory network and discover the pathways related to SSBP. Gene ontology enrichment, gene set enrichment analysis (GSEA) and KEGG pathway analysis were performed to explore the functions of hub genes in networks.

Results

There were 274 lncRNAs and 36 mRNAs that differently expressed between salt-sensitive and salt-resistant groups (P < 0.05). Using WGCNA analysis, two modules were identified (blue and turquoise). The blue module had a positive relationship with salt-sensitivity (R = 0.7, P < 0.01), high-density lipoprotein (HDL) (R = 0.53, P = 0.02), and total cholesterol (TC) (R = 0.55, P = 0.01). The turquoise module was positively related with triglyceride (TG) (R = 0.8, P < 0.01) and low-density lipoprotein (LDL) (R = 0.54, P = 0.01). Furthermore, 84 ceRNA loops were identified and one loop may be of great importance for involving in pathogenesis of SSBP. KEGG analysis showed that differently expressed mRNAs were mostly enriched in the SSBP-related pathways. However, the enrichment results of GSEA were mainly focused on basic physical metabolic processes.

Conclusion

The microarray data mining process based on WGCNA co-expression analysis had identified 84 ceRNA loops that closely related with known SSBP pathogenesis. The results of our study provide implications for further understanding of the pathogenesis of SSBP and facilitate the precise diagnosis and therapeutics.

Association between the expression of carbonic anhydrase II and clinicopathological features of hepatocellular carcinoma

The present study aimed to examine the molecular marker associated with the therapy and prognosis of hepatocellular carcinoma (HCC), and further investigate the association between its expression and the clinicopathological features of HCC. To select the core genes closely associated with HCC, differentially expressed genes (DEGs) were analyzed and screened from Gene Expression Omnibus datasets (GSE 36376) using a bioinformatics approach. Tumor and adjacent tissues were collected form 112 patients of HCC who were treated by radical resection. The expression levels of carbonic anhydrase II (CA2) in the tumor and adjacent tissues were determined using reverse transcription-quantitative polymerase chain reaction analysis and immunohistochemistry. The χ² test was applied for observing the association between the expression of CA2 and clinicopathological features of patients with HCC. The effects of the expression of CA2 on the patients' overall survival (OS) and disease-free survival (DFS) were examined via Kaplan-Meier analysis. A total of 83 DEGs were screened and analyzed using gene network analysis, among which CA2 had direct interactions with more than one disease gene of HCC. The results of immunohistochemistry showed that CA2 was expressed at a lower level in the tumor tissues compared with the adjacent tissues (t=3.012, P=0.010). Single factor analysis revealed that the mRNA expression of CA2 was able to predict the recurrence of HCC, and was significantly associated with α-fetoprotein (AFP), microvascular invasion, tumor-node-metastasis (TNM) staging, and recurrence (P<0.05). The expression levels of AFP, CA2 and TNM staging were confirmed to be independent prognostic factors of HCC (P<0.05). Kaplan-Meier analysis demonstrated that the group with a high expression of CA2 showed increased DFS and OS, compared with the low expression group (P<0.05). These findings indicated that elevated CA2 increased DFS and OS of HCC, which suggested that CA2 may be a potential target for HCC therapy.

Integrated bioinformatics analysis to identify 15 hub genes in breast cancer

The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the ‘limma’ package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.

Profiling, clinicopathological correlation and functional validation of specific long non-coding RNAs for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignancies worldwide. Studies seeking to advance the overall understanding of lncRNA profiling in HCC remain rare.

Methods

The transcriptomic profiling of 12 HCC tissues and paired adjacent normal tissues was determined using high-throughput RNA sequencing. Fifty differentially expressed mRNAs (DEGs) and lncRNAs (DELs) were validated in 21 paired HCC tissues via quantitative real-time PCR. The correlation between the expression of DELs and various clinicopathological characteristics was analyzed using Student’s t-test or linear regression. Co-expression networks between DEGs and DELs were constructed through Pearson correlation co-efficient and enrichment analysis. Validation of DELs’ functions including proliferation and migration was performed via loss-of-function RNAi assays.

Results

In this study, we identified 439 DEGs and 214 DELs, respectively, in HCC. Furthermore, we revealed that multiple DELs, including NONHSAT003823, NONHSAT056213, NONHSAT015386 and especially NONHSAT122051, were remarkably correlated with tumor cell differentiation, portal vein tumor thrombosis, and serum or tissue alpha fetoprotein levels. In addition, the co-expression network analysis between DEGs and DELs showed that DELs were involved with metabolic, cell cycle, chemical carcinogenesis, and complement and coagulation cascade-related pathways. The silencing of the endogenous level of NONHSAT122051 or NONHSAT003826 could significantly attenuate the mobility of both SK-HEP-1 and SMMC-7721 HCC cells.

Conclusion

These findings not only add knowledge to the understanding of genome-wide transcriptional evaluation of HCC but also provide promising targets for the future diagnosis and treatment of HCC.