Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis

CKS2 and RMI2 are two prognostic biomarkers of lung adenocarcinoma

Background

Lung adenocarcinoma (ACA) is the most common subtype of non-small-cell lung cancer. About 70%–80% patients are diagnosed at an advanced stage; therefore, the survival rate is poor. It is urgent to discover accurate markers that can differentiate the late stages of lung ACA from the early stages. With the development of biochips, researchers are able to efficiently screen large amounts of biological analytes for multiple purposes.

Methods

Our team downloaded GSE75037 and GSE32863 from the Gene Expression Omnibus (GEO) database. Next, we utilized GEO’s online tool, GEO2R, to analyze the differentially expressed genes (DEGs) between stage I and stage II–IV lung ACA. The using the Cytoscape software was used to analyze the DEGs and the protein-protein interaction (PPI) network was further constructed. The function of the DEGs were further analyzed by cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) online tools. We validated these results in 72 pairs human samples.

Results

We identified 109 co-DEGs, most of which were involved in either proliferation, S phase of mitotic cell cycle, regulation of exit from mitosis, DNA replication initiation, DNA replication, and chromosome segregation. Utilizing cBioPortal and University of California Santa Cruz databases, we further confirmed 35 hub genes. Two of these genes, encoding CDC28 protein kinase regulatory subunit 2 (CKS2) and RecQ-mediated genome instability 2 (RMI2), were upregulated in lung ACA compared with adjacent normal tissues. The Kaplan–Meier curves revealed upregulation of CKS2 and RMI2 are associated with worse survival. Using CMap analysis, we discovered 10 small molecular compounds that reversed the altered DEGs, the top five are phenoxybenzamine, adiphenine, resveratrol, and trifluoperazine. We also evaluated 72 pairs resected samples, results revealed that upregulation of CKS2 and RMI2 in lung ACA were associated with larger tumor size. Our results allow the deeper recognizing of the mechanisms of the progression of lung ACA, and may indicate potential therapeutic strategies for the therapy of lung ACA.

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions.One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan-Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions.Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways.This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.

Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

Distinct mRNA and long non-coding RNA expression profiles of decidual natural killer cells in patients with early missed abortion

Early non-chromosome-related missed abortion (MA) is commonly associated with an altered immunological environment during pregnancy. Human decidual natural killer (dNK) cells, the most abundant lymphocyte population within the first-trimester maternal-fetal interface, are vital maternal regulators of immune tolerance mediating successful embryo implantation and placentation. Previous studies have shown that dNK cells may play a role in MA. However, the gene expression status and specific altered manifestations of dNK cells in patients with early MA remain largely unknown. Here, we show that MA dNK cells have distinct mRNA and lncRNA expression profiles through RNA sequencing, with a total of 276 mRNAs and 67 lncRNAs being differentially expressed compared with controls. Protein-protein interaction analysis of differentially expressed mRNAs was performed to identify hub genes and key modules. An lncRNA-mRNA regulatory network characterized by the small-world property was constructed to reveal the regulation of mRNA transcription by differential hub lncRNAs. Functional annotation of differentially expressed mRNAs and lncRNAs was performed to disclose their potential roles in MA pathogenesis. Our data highlight several enriched biological processes (immune response, inflammatory response, cell adhesion, and extracellular matrix [ECM] organization) and signaling pathways (cytokine-cytokine receptor interaction, ECM-receptor interaction, Toll-like receptor signaling pathway, and phosphatidylinositol signaling system) that may influence MA. This study is the first to demonstrate the involvement of altered mRNA and lncRNA expression profiles in the dNK cell pathogenesis of early MA, facilitating a better understanding of the underlying molecular mechanisms and the development of novel MA therapeutic strategies targeting key mRNAs and lncRNAs.

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein-protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

7-Hydroxycoumarin Attenuates Colistin-Induced Kidney Injury in Mice Through the Decreased Level of Histone Deacetylase 1 and the Activation of Nrf2 Signaling Pathway

Colistin has been considered as the last line of defense against Gram-negative bacterial infections, however, the potential nephrotoxicity limited its clinical use. 7-Hydroxycoumarin (7-HC) possesses many beneficial pharmacological activities. This study aimed to investigate the nephroprotective effects of 7-HC against colistin-induced kidney injury. In vivo experiments showed that 7-HC alleviated kidney injury induced by colistin, as indicated by lower levels of serum neutrophil gelatinase-associated lipocalin, blood urea nitrogen and creatinine levels. Both in vivo and in vitro results demonstrated that 7-HC alleviated oxidative stress and apoptosis induced by colistin, as shown by decreased malondialdehyde levels, decreased caspase-3 and caspase-9 activities, and increased superoxide dismutase and catalase activities. We also found that colistin significantly induced histone deacetylase (HDAC) 1 expression that deacetylated histone 3 at Lys27 acetylation (H3K27AC) of Nrf2 promoter region and hence inhibiting Nrf2 signaling. 7-HC treatment restored histone acetylation at the Nrf2 promoter region and hence promoted Nrf2 expression. These results suggested that 7-HC alleviates colistin-induced renal injury and this effect was achieved by enhancement of renal antioxidant capacity with the decreased level of HDAC1 and the activation of Nrf2 signaling pathway.

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

The molecular mechanisms of adrenocortical carcinoma (ACC) carcinogenesis and progression remain unclear. In the present study, three microarray datasets from the Gene Expression Omnibus database were screened, which identified a total of 96 differentially expressed genes (DEGs). A protein-protein interaction network (PPI) was established for these DEGs and module analysis was performed using STRING and Cytoscape. A total of eight hub genes were identified from the most significant module; namely, calponin 1 (CNN1), myosin light chain kinase (MYLK), cysteine and glycine rich protein 1 (CSRP1), myosin heavy chain 11 (MYH11), fibulin extracellular matrix protein 2 (EFEMP2), fibulin 1 (FBLN1), microfibril associated protein 4 (MFAP4) and fibulin 5 (FBLN5). The biological functions of these hub genes were analyzed using the DAVID online tool. Changes in the expression of hub genes did not affect overall survival; however, downregulated EFEMP2 decreased disease-free survival. CSRP1 and MFAP4 expression levels were associated with adverse clinicopathological features. In conclusion, although all eight hub genes were downregulated in ACC, they appeared to have important functions in ACC carcinogenesis and progression. Identification of these genes complements the genetic expression profile of ACC and provides insight for the diagnosis, treatment and prognosis of ACC.

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs=0.635), CDK1 (P<0.0001, rs=0.511), centromere protein F (CENPF) (P<0.0001, rs=0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs=0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs=0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis

Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogenesis and the molecular mechanisms of sepsis is still not well understood. The aim of the present study was to identify the candidate key genes in the progression of sepsis.Microarray datasets GSE28750, GSE64457, and GSE95233 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Furthermore, to verify the results of the bioinformatics analyses, the expression levels of selected DEGs were quantified by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in libobolysaccharide (LPS)-induced Human Umbilical Vein Endothelial Cells (HUVECs) to support the result of bioinformatics analysis.Thirteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in apoptotic process, inflammatory response, innate immune response. Hub genes with high degrees, including MAPK14, SLC2A3, STOM, and MMP8, were demonstrated to have an association with sepsis. Furthermore, RT-PCR results showed that SLC2A3 and MAPK14 were significantly upregulated in the HUVECs induced by LPS compared with controls.In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms of sepsis, and provide candidate targets for diagnosis and treatment of sepsis.

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, which is a major cause of cancer morbidity and mortality worldwide. Thus, the aim of the present study was to identify the hub genes and underlying pathways of HCC via bioinformatics analyses. The present study screened three datasets, including GSE112790, GSE84402 and GSE74656 from the Gene Expression Omnibus (GEO) database, and downloaded the RNA-sequencing of HCC from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) in both the GEO and TCGA datasets were filtered, and the screened DEGs were subsequently analyzed for functional enrichment pathways. A protein-protein interaction (PPI) network was constructed, and hub genes were further screened to create the Kaplan-Meier curve using cBioPortal. The expression levels of hub genes were then validated in different datasets using the Oncomine database. In addition, associations between expression and tumor grade, hepatitis virus infection status, satellites and vascular invasion were assessed. A total of 126 DEGs were identified, containing 70 upregulated genes and 56 downregulated genes from the GEO and TCGA databases. By constructing the PPI network, the present study identified hub genes, including cyclin B1 (CCNB1), cell-division cycle protein 20 (CDC20), cyclin-dependent kinase 1, BUB1 mitotic checkpoint serine/threonine kinase β (BUB1B), cyclin A2, nucleolar and spindle associated protein 1, ubiquitin-conjugating enzyme E2 C (UBE2C) and ZW10 interactor. Furthermore, upregulated CCNB1, CDC20, BUB1B and UBE2C expression levels indicated worse disease-free and overall survival. Moreover, a meta-analysis of tumor and healthy tissues in the Oncomine database demonstrated that BUB1B and UBE2C were highly expressed in HCC. The present study also analyzed the data of HCC in TCGA database using univariate and multivariate Cox analyses, and demonstrated that BUB1B and UBE2C may be used as independent prognostic factors. In conclusion, the present study identified several genes and the signaling pathways that were associated with tumorigenesis using bioinformatics analyses, which could be potential targets for the diagnosis and treatment of HCC.

Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

Background

Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms.

Methods

To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot.

Conclusions

Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

Identification of key genes for esophageal squamous cell carcinoma via integrated bioinformatics analysis and experimental confirmation

Background

Esophageal squamous cell carcinoma (ESCC) as the main subtype of esophageal cancer (EC) is a leading cause of cancer-related death worldwide. Despite advances in early diagnosis and clinical management, the long-term survival of ESCC patients remains disappointing, due to a lack of full understanding of the molecular mechanisms.

Methods

In order to identify the differentially expressed genes (DEGs) in ESCC, the microarray datasets GSE20347 and GSE26886 from Gene Expression Omnibus (GEO) database were analyzed. The enrichment analyses of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Set Enrichment Analysis (GSEA) were performed for the DEGs. The protein-protein interaction (PPI) network of these DEGs was constructed using the Cytoscape software based on the STRING database to select as hub genes for weighted co-expression network analysis (WGCNA) with ESCC samples from TCGA database.

Results

A total of 746 DEGs were commonly shared in the two datasets including 286 upregulated genes and 460 downregulated genes in ESCC. The DEGs were enriched in biological processes such as extracellular matrix organization, proliferation and keratinocyte differentiation, and were enriched in biological pathways such as ECM-receptor interaction and cell cycle. GSEA analysis also indicated the enrichment of upregulated DEGs in cell cycle. The 40 DEGs were selected as hub genes. The MEblack module was found to be enriched in the cell cycle, Spliceosome, DNA replication and Oocyte meiosis. Among the hub genes correlated with MEblack module, GSEA analysis indicated that DEGs of TCGA samples with DLGAP5 upregulation was enriched in cell cycle. Moreover, the highly endogenous expression of DLGAP5 was confirmed in ESCC cells. DLGAP5 knockdown significantly inhibited the proliferation of ESCC cells.

Conclusions

DEGs and hub genes such as DLGAP5 from independent datasets in the current study will provide clues to elucidate the molecular mechanisms involved in development and progression of ESCC.

LINC02499, a novel liver-specific long non-coding RNA with potential diagnostic and prognostic value, inhibits hepatocellular carcinoma cell proliferation, migration, and invasion

AIM

Liver-specific non-coding RNAs have been reported to play crucial roles in hepatocellular carcinoma (HCC). We investigated the possible biological performance of a novel liver-specific long non-coding RNA, LINC02499, in HCC.

METHODS

The association between LINC02499 expression and HCC was evaluated based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and then confirmed in a HCC cohort by quantitative real-time polymerase chain reaction. The effects of LINC02499 on HCC cells were verified by gain- and loss-of-function assays. Pathway enrichment analyses were used to explore the potential mechanism of LINC02499 in HCC.

RESULTS

LINC02499 expression was remarkably decreased in HCC tissues compared to adjacent non-tumor tissues based on TCGA (P < 0.001) and GEO databases (P < 0.001) and our HCC cohort (P < 0.001). Decreased LINC02499 was also significantly associated with poorer overall survival in both the TCGA database (P = 0.009) and our HCC cohort (P = 0.002). Furthermore, the receiver operating characteristic analysis indicated that LINC02499 showed a good performance in HCC diagnosis (area under the curve = 0.879, P < 0.001), and both sensitivity and specificity were 83.8%. In addition, up- and downregulated LINC02499 significantly impacted proliferation, migration, and invasion abilities of HCC cells in vitro. Pathway enrichment analyses revealed that the potential target genes of LINC02499 were involved in "Complement and coagulation cascades" and "Butanoate metabolism" pathways.

CONCLUSION

LINC02499 could be a potential novel diagnostic and prognostic biomarker for HCC patients, and it could exert a tumor suppressor role in the progression of HCC.

Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

Promising diagnostic and prognostic value of six genes in human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Ample data have been reported to unravel the carcinogenesis over the past decades. Although pinpointing the cause of the HCC is challenging, this in and of itself may not be an insuperable problem. Indeed, the emergence of novel molecular targets has given rise to targeted therapy for HCC. Compared to traditional treatments, drugs with molecularly targeted agents are considered an optimal way to treat HCC. However, targeted approaches are currently limited among HCC patients. In our work, we explored more potential genes for targeted treatment of HCC. Initially, differentially expressed genes (DEGs) were identified in gene expression profiling interactive analysis (GEPIA) and NetworkAnalyst. Subsequently, 10 key genes were selected through enrichment analysis and PPI network construction. Based on the GEPIA and Oncomine databases, six upregulated genes were selected. High protein expression of these six genes were confirmed through the Human Protein Atlas database. In addition, these six genes were associated with unfavorable overall survival and progression-free survival based on Kaplan-Meier plotter bioinformatics. Moreover, gene expression was closely related to the tumor stages and pathological grades, as determined with UALCAN. More importantly, PTTG1, UBE2C, and ZWINT were identified as potential targets of anti-cancer drugs using cBioPortal. qPCR and western blot assays were used to show the high expression levels of the latter three genes in HCC cell lines. Collectively, these findings are expected to provide a theoretical basis for and give novel insights into clinical research of HCC.

Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis.

Methods

Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk.

Results

Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes.

Conclusions

This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

Identification of core genes associated with prostate cancer progression and outcome via bioinformatics analysis in multiple databases

Abstract

The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear.

Methods

Variation analysis of GSE38241, GSE69223, GSE46602 and GSE104749 were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified in GSE21032 and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer.

Result

Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database and GSE21032.

Conclusion

These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

Lung adenocarcinoma is one of the most common malignant tumors worldwide. Although efforts have been made to clarify its pathology, the underlying molecular mechanisms of lung adenocarcinoma are still not clear. The microarray datasets GSE75037, GSE63459 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify biomarkers for effective lung adenocarcinoma diagnosis and therapy. The differentially expressed genes (DEGs) were identified by GEO2R, and function enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The STRING database and Cytoscape software were used to construct and analyze the protein-protein interaction network (PPI). We identified 376 DEGs, consisting of 83 upregulated genes and 293 downregulated genes. Functional and pathway enrichment showed that the DEGs were mainly focused on regulation of cell proliferation, the transforming growth factor β receptor signaling pathway, cell adhesion, biological adhesion, and responses to hormone stimulus. Sixteen hub genes were identified and biological process analysis showed that these 16 hub genes were mainly involved in the M phase, cell cycle phases, the mitotic cell cycle, and nuclear division. We further confirmed the two genes with the highest node degree, DNA topoisomerase IIα (TOP2A) and aurora kinase A (AURKA), in lung adenocarcinoma cell lines and human samples. Both these genes were upregulated and associated with larger tumor size. Upregulation of AURKA in particular, was associated with lymphatic metastasis. In summary, identification of the DEGs and hub genes in our research enables us to elaborate the molecular mechanisms underlying the genesis and progression of lung adenocarcinoma and identify potential targets for the diagnosis and treatment of lung adenocarcinoma.

Advances in the early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers globally. In contrast to the declining death rates observed for all other common cancers such as breast, lung, and prostate cancers, the death rates for HCC continue to increase by ~2–3% per year because HCC is frequently diagnosed late and there is no curative therapy for an advanced HCC. The early diagnosis of HCC is truly a big challenge. Over the past years, the early diagnosis of HCC has relied on surveillance with ultrasonography (US) and serological assessments of alpha-fetoprotein (AFP). However, the specificity and sensitivity of US/AFP is not satisfactory enough to detect early onset HCC. Recent technological advancements offer hope for early HCC diagnosis. Herein, we review the progress made in HCC diagnostics, with a focus on emerging imaging techniques and biomarkers for early disease diagnosis.

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), to construct protein‒protein interaction networks (PPIs) and perform functional enrichment analysis and survival analyses. In total, we found 418 DEGs. We found 14 hub genes, and gene ontology (GO) analysis revealed DEG enrichment in networks and pathways related to cell cycle and proliferation, but also in cell movement, receptor signaling, and viral carcinogenesis. Compared with noncancerous tissues, TPM1, CRYAB, and CASQ2 were significantly downregulated in BCa, and the other hub genes were significant upregulated. Furthermore, MAD2L1 and CASQ2 potentially play a pivotal role in lymph nodal metastasis. CRYAB and CASQ2 were both significantly correlated with overall survival (OS) and disease-free survival (DFS). The present study highlights an up to now unrecognized possible role of CASQ2 in cancer (BCa). Furthermore, CRYAB has never been described in BCa, but our study suggests that it may also be a candidate biomarker in BCa.

Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

Genome-wide screening and in silico gene knockout to predict potential candidates for drug designing against Candida albicans

C. albicans infections are increasingly becoming a threat to public health with emergence of drug resistant strains. It emphasizes the need to look for alternate drug targets through genome-wide screening. In the present study, whole proteome of C. albicans SC5314 was analyzed in single click target mining workflow of TiDv2. A protein-protein interaction network (PPI) for the resulting putative targets was generated based on String database. Ninety four proteins with higher connectivity (degree ≥ 10) in the network are noted as hub genes. Among them, 24 are observed to be known targets while 70 are novel ones. Further, chokepoint analysis revealed FAS2, FOL1 and ERG5 as chokepoint enzymes in their respective pathways. Subsequently, the chokepoints were selected as prior interest for in silico gene knockout via MATLAB and COBRA Toolbox. In silico gene knockout pointed that FAS2 inhibition reduced the growth rate of pathogen from 0.2879 mmol.gDW^-1.h^-1 to zero. Furthermore, enzyme inhibition assay of FAS2 with cerulenin strengthen the computational outcome with MIC 1.25 μg/mL. Hence, the study establishes FAS2 as a promising target to design therapeutics against C. albicans.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis.

MATERIALS AND METHODS

To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).

RESULTS

In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine-cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway.

CONCLUSION

Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.

A twenty gene-based gene set variation score reflects the pathological progression from cirrhosis to hepatocellular carcinoma

The molecular mechanism of the pathological progression from cirrhosis to hepatocellular carcinoma (HCC) remains elusive. In the present study, tissue samples from normal liver, cirrhosis and HCC were subjected to differentially gene expression analysis, weighted gene correlation network analysis to identify the twenty hub genes (TOP2A, CDC20, PTTG1, CDCA5, CCNB2, PRC1, KIF20A, SF3B4, HSP90AB1, FOXD2, PLOD3, CCT3, SETDB1, VPS45, SPDL1, RACGAP1, MED24, KIAA0101, ZNF282, and USP21) in the pathological progression from cirrhosis to HCC. Each sample was calculated a hub gene set variation analysis (HGSVA) score using Gene Set Variation Analysis, The HGSVA score significantly increased with progression from cirrhosis to HCC, and this result was validated in two independent data sets. Moreover, this score may be used as a blood-based marker for HCC and is an independent prognostic factor of recurrence-free survival (RFS) and overall survival (OS). High expression of the hub genes may be driven by hypomethylation. The twenty gene-based gene set variation score may reflect the pathological progression from cirrhosis to HCC and is an independent prognostic factor for both OS and RFS.

Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive.

Objective

This study aims to mine hub genes associated with HCC using multiple databases.

Methods

Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients' information from TCGA database by survminer R package.

Results

From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients.

Conclusion

TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients' outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan–Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

Identification of candidate genes for the diagnosis and treatment of cholangiocarcinoma using a bioinformatics approach

Cholangiocarcinoma (CCA) is a biliary epithelial tumor with poor prognosis. As the key genes and signaling pathways underlying the disease have not been fully elucidated, the aim of the present study was to improve the understanding of the molecular mechanisms associated with CCA. The microarray datasets GSE26566 and GSE89749 were downloaded from the Gene Expression Omnibus and differentially expressed genes (DEGs) between CCA and normal bile duct samples were identified. Gene and pathway enrichment analyses were performed, and a protein-protein interaction network was constructed and analyzed. A total of 159 DEGs and 10 hub genes were identified. The functions and pathways of the DEGs were mainly enriched in ‘heparin binding’, ‘serine-type endopeptidase activity’, ‘calcium ion binding’, ‘pancreatic secretion’, ‘fat digestion and absorption’ and ‘protein digestion and absorption’. Survival analysis revealed that the upregulated expression of carboxypeptidase B1 and Kruppel like factor 4 was significantly associated with lower overall survival rate. In summary, the present study identified DEGs and hub genes associated with CCA, which may serve as potential diagnostic and therapeutic targets for the disease.

Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The presently available understanding of the pathogenesis of ACC is incomplete and the treatment options for patients with ACC are limited. Gene marker identification is required for accurate and timely diagnosis of the disease. In order to identify novel candidate genes associated with the occurrence and progression of ACC, the microarray datasets, GSE12368 and GSE19750, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. A protein-protein interaction network (PPI) was constructed to identify significantly altered modules, and module analysis was performed using Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 228 DEGs were screened, consisting of 29 up and 199 downregulated genes. The enriched functions and pathways of the DEGs primarily included 'cell division', 'regulation of transcription involved in G1/S transition of mitotic cell cycle', 'G1/S transition of mitotic cell cycle', 'p53 signaling pathway' and 'oocyte meiosis'. A total of 14 hub genes were identified, and biological process analysis revealed that these genes were significantly enriched in cell division and mitotic cell cycle. Furthermore, survival analysis revealed that AURKA, TYMS, GINS1, RACGAP1, RRM2, EZH2, ZWINT, CDK1, CCNB1, NCAPG and TPX2 may be involved in the tumorigenesis, progression or prognosis of ACC. In conclusion, the 14 hub genes identified in the present study may aid researchers in elucidating the molecular mechanisms associated with the tumorigenesis and progression of ACC, and may be powerful and promising candidate biomarkers for the diagnosis and treatment of ACC.

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

TPM2 as a potential predictive biomarker for atherosclerosis

Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

Intersectin 1 (ITSN1) identified by comprehensive bioinformatic analysis and experimental validation as a key candidate biological target in breast cancer

Background

As one of the most common cancers, breast carcinoma is the most common disease in women. Intersectin 1 (ITSN1) contributes to the actin cytoskeleton reconstruction in breast cancer.

Purpose

The objective of this study to explore the functions of ITSN1 in breast carcinoma.

Methods

We downloaded microarray datasets GSE8087, GSE50697, and GSE98238 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were used to construct a protein–protein interaction (PPI) network using STRING database, and the modules from PPI network were verified by Cytoscape software. Gene ontology terms and Kyoto Encyclopedia of Gene and Genome pathway were used to analyze the biological functions using the DAVID database. ONCOMINE, GEPIA, UALCAN, and Human Protein Atlas databases were used to investigate the characteristics of ITSN1 for the prognosis of breast carcinoma. Cell counting kit-8, flow cytometry, and colony formation assays were used to detect cell viability, cell apoptosis, and cell proliferation. RT-PCR and Western blot assays were used to detect ITSN1, Ki67, and cleaved caspase-3 expressions.

Results

Low expressions of ITSN1 were significantly associated with clinical cancer stages. RT-PCR and Western blot analysis showed low expression of ITSN1 in breast cancer tissues and cell lines. ITSN1 inhibition could promote cell proliferation and inhibit cell apoptosis, while ITSN1 overexpression could inhibit cell proliferation and increase cell apoptosis by regulating the levels of expression of Ki67 and cleaved-caspase-3.

Conclusion

The results indicated that ITSN1 could be a prognostic biomarker for survivals of breast cancer patients.

Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis

Evidence suggests that hepatitis C virus (HCV) infection is among the main causes of hepatocellular carcinoma (HCC). In addition, HCV-induced HCC (HCV-HCC) exhibits adverse clinical outcomes and limited therapeutic treatments are available for this condition. To investigate key biomarkers in the occurrence and development of HCV-HCC, microarray datasets GSE62232, GSE69715 and GSE107170 were downloaded from the Gene Expression Omnibus database for analysis. The differentially expressed genes between HCV-HCC and normal tissue were identified using the GEO2R online tool. The function enrichment analyses including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed using the Database for Annotation, Visualization and Integrated Discovery online tool. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. A total of 368 DEGs were identified, and the top 10 hub genes with a high degree of connectivity were selected for further analysis. Subsequently, overall survival and disease-free survival analysis revealed that there was a significant association between altered expression of HMMR, CCNB1 and KIF20A, and poor clinical outcome. In summary, these results indicate that HMMR, CCNB1 and KIF20A are potential targets for diagnosis and therapy of HCV-HCC.

Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis

Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein-protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan-Meier plotter was used to analyze overall survival (OS). Additionally, one co-expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC.

Screening and identification of potential target genes in head and neck cancer using bioinformatics analysis

Head and neck cancer (HNC) is the sixth most common cancer worldwide. Recent studies on the pathogenesis of HNC have identified some biochemical associations of this disease, but the molecular mechanisms are not clear. To explore the genetic alterations in head and neck tumors, to identify new high-specificity and high-sensitivity tumor markers, and to investigate potentially effective therapeutic targets, in silico methods were used to study HNC. The GSE58911 microarray dataset was downloaded from the Gene Expression Omnibus online database to identify potential target genes in the carcinogenesis and progression of HNC. Differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. In addition, a protein-protein interaction network was also constructed, and gene analysis was undertaken using Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 648 differentially expressed genes were identified. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology functional enrichment analysis of DEGs included muscle system process, extracellular matrix organization, actin binding, structural molecule activity, structural constituent of muscle, extracellular region part, ECM-receptor interaction, amoebiasis, focal adhesion, drug metabolism-cytochrome P450, and chemical carcinogenesis. There were 26 hub genes identified and biological process analysis revealed that these genes were mainly enriched in extracellular matrix organization, serine-type endopeptidase activity, extracellular matrix, and complement and coagulation cascades. Survival analysis revealed that interleukin (IL)-8 (C-X-C motif chemokine ligand 8), IL1B, and serpin family A member 1 may be involved in the carcinogenesis of HNC. In summary, the DEGs and hub genes identified in the present study may increase understanding of the molecular mechanisms of development of HNC and provide potential target genes for clinical diagnosis and targeted therapy.

Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma

Objective: The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients.

Methods: All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were investigated by DESeq and edgeR algorithm. Survival analysis was used to confirm underlying prognostic indicators.

Results: In the present study, we constructed a ceRNA network based on 16 differentially expressed genes (DEGs), 7 differentially expressed microRNAs and 34 differentially expressed long non-coding RNAs (DELs). Among these dysregulated RNAs, three DELs (AP002478.1, HTR2A-AS1, and ERVMER61-1) and six DEGs (enhancer of zeste homolog 2 [EZH2], kinesin family member 23 [KIF23], chromobox 2 [CBX2], centrosomal protein 55 [CEP55], cell division cycle 25A [CDC25A], and claspin [CLSPN]) were used for construct a prognostic signature for HCC overall survival (OS), and performed well in HCC OS (adjusted P<0.0001, adjusted hazard ratio = 2.761, 95% confidence interval = 1.838-4.147). Comprehensive survival analysis demonstrated that this prognostic signature may be act as an independent prognostic indicator of HCC OS. Functional assessment of these dysregulated DEGs in the ceRNA network and gene set enrichment of this prognostic signature suggest that both were enriched in the biological processes and pathways of the cell cycle, cell division and cell proliferation.

Conclusions: Our current study constructed a ceRNA network for HCC, and developed a prognostic signature that may act as an independent indicator for HCC OS.

Anticancer Effects of Emodin on HepG2 Cell: Evidence from Bioinformatic Analysis

Hepatocellular carcinoma (HCC) is a primary cause of cancer-related death in the world. Despite the fact that there are many methods to treat HCC, the 5-year survival rate of HCC is still at a low level. Emodin can inhibit the growth of HCC cells in vitro and in vivo. However, the gene regulation of emodin in HCC has not been well studied. In our research, RNA sequencing technology was used to identify the differentially expressed genes (DEGs) in HepG2 cells induced by emodin. A total of 859 DEGs were identified, including 712 downregulated genes and 147 upregulated genes in HepG2 cells treated with emodin. We used DAVID for function and pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING, and Cytoscape was used for module analysis. The enriched functions and pathways of the DEGs include positive regulation of apoptotic process, structural molecule activity and lipopolysaccharide binding, protein digestion and absorption, ECM-receptor interaction, complement and coagulation cascades, and MAPK signaling pathway. 25 hub genes were identified and pathway analysis revealed that these genes were mainly enriched in neuropeptide signaling pathway, inflammatory response, and positive regulation of cytosolic calcium ion concentration. Survival analysis showed that LPAR6, C5, SSTR5, GPR68, and P2RY4 may be involved in the molecular mechanisms of emodin therapy for HCC. A quantitative real-time PCR (qRT-PCR) assay showed that the mRNA levels of LPAR6, C5, SSTR5, GPR68, and P2RY4 were significantly decreased in HepG2 cells treated with emodin. In conclusion, the identified DEGs and hub genes in the present study provide new clues for further researches on the molecular mechanisms of emodin.

Identification of biomarkers in macrophages of atherosclerosis by microarray analysis

BACKGROUND

Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages.

METHODS

Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes.

RESULTS

A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1.

CONCLUSION

Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.

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.

The clinicopathological significance of decreased miR-125b-5p in hepatocellular carcinoma: evidence based on RT-qPCR, microRNA-microarray, and microRNA-sequencing

The aim of the study was to comprehensively evaluate the clinical value of miR-125b-5p in hepatocellular carcinoma (HCC) and its potential molecular mechanisms. MiR-125b-5p expression was remarkably lower as examined by real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) in 95 paired HCC and nonmalignant liver tissues in house (P<0.001), which was in accord with the results from miRNA-sequencing data with 371 cases of HCC. miRNA-chips from Gene Expression Omnibus (GEO) and ArrayExpress were screened. Among the seven included miRNA-chips, the relative expression of miR-125b-5p expression levels showed decreasing trends in HCC tissue samples compared with non-cancerous liver tissue samples. Altogether, A total of 655 cases of HCC tissues and 334 non-HCC liver tissues were included in the final meta-analysis. We observed that the expression of miR-125b-5p indeed decreased markedly in HCC tissues compared with the non-HCC tissues (SMD: -1.414, 95% CI: -1.894 to -0.935, P<0.001). The area under the SROC curve of lower expression of miR-125b-5p was 0.91 (95% CI: 0.89 to 0.94). A Kaplan-Meier survival analysis indicated that the lower expression or the absence of miR-125b-5p may be a risk factor for the poor outcome of HCC patients. Furthermore, the potential target genes of miR-125b-5p from 11 miRNA target prediction databases were intersected with 1,486 differentially expressed genes (DEGs) as calculated by RNA-sequencing data. Finally, a total of 330 GEGs were collected and enriched in the pathways of lysosome, focal adhesion, and pathways in cancer. In conclusion, this study utilizes a variety of research methods to confirm the lower level of miR-125b-5p in HCC tissues. This lower expression level of miR-125b-5p is closely related to increased disease progression in HCC patients.

Identification of Core Gene Biomarkers in Patients with Diabetic Cardiomyopathy

Diabetic cardiomyopathy (DCM) is a disorder of the myocardium in diabetic patients, which is one of the critical complications of diabetes giving rise to an increased mortality. However, the underlying mechanisms of DCM remain incompletely understood presently. This study was designed to screen the potential molecules and pathways implicated with DCM. GSE26887 involving 5 control individuals and 7 DCM patients was selected from the GEO database to identify the differentially expressed genes (DEGs). DAVID was applied to perform gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was also constructed to visualize the interactions among these DEGs. To further validate significant genes and pathways, quantitative real-time PCR (qPCR) and Western blot were performed. A total of 236 DEGs were captured, including 134 upregulated and 102 downregulated genes. GO, KEGG, and the PPI network disclosed that inflammation, immune disorders, metabolic disturbance, and mitochondrial dysfunction were significantly enriched in the development of DCM. Notably, IL6 was an upregulated hub gene with the highest connectivity degree, suggesting that it may interact with a great many molecules and pathways. Meanwhile, SOCS3 was also one of the top 15 hub genes in the PPI network. Herein, we detected the protein level of STAT3 and SOCS3 in a mouse model with DCM. Western blot results showed that the protein level of SOCS3 was significantly lower while phosphorylated-STAT3 (P-STAT3) was activated in mice with DCM. In vitro results also uncovered the similar alterations of SOCS3 and P-STAT3 in cardiomyocytes and cardiac fibroblasts induced by high glucose (HG). However, overexpression of SOCS3 could significantly reverse HG-induced cardiomyocyte hypertrophy and collagen synthesis of cardiac fibroblasts. Taken together, our analysis unveiled potential biomarkers and molecular mechanisms in DCM, which could be helpful to the diagnosis and treatment of DCM.

Aneuploidy: Cancer strength or vulnerability?

Aneuploidy is a very rare and tissue‐specific event in normal conditions, occurring in a low number of brain and liver cells. Its frequency increases in age‐related disorders and is one of the hallmarks of cancer. Aneuploidy has been associated with defects in the spindle assembly checkpoint (SAC). However, the relationship between chromosome number alterations, SAC genes and tumor susceptibility remains unclear. Here, we provide a comprehensive review of SAC gene alterations at genomic and transcriptional level across human cancers and discuss the oncogenic and tumor suppressor functions of aneuploidy. SAC genes are rarely mutated but frequently overexpressed, with a negative prognostic impact on different tumor types. Both increased and decreased SAC gene expression show oncogenic potential in mice. SAC gene upregulation may drive aneuploidization and tumorigenesis through mitotic delay, coupled with additional oncogenic functions outside mitosis. The genomic background and environmental conditions influence the fate of aneuploid cells. Aneuploidy reduces cellular fitness. It induces growth and contact inhibition, mitotic and proteotoxic stress, cell senescence and production of reactive oxygen species. However, aneuploidy confers an evolutionary flexibility by favoring genome and chromosome instability (CIN), cellular adaptation, stem cell‐like properties and immune escape. These properties represent the driving force of aneuploid cancers, especially under conditions of stress and pharmacological pressure, and are currently under investigation as potential therapeutic targets. Indeed, promising results have been obtained from synthetic lethal combinations exploiting CIN, mitotic defects, and aneuploidy‐tolerating mechanisms as cancer vulnerability.

SPAG5 interacts with CEP55 and exerts oncogenic activities via PI3K/AKT pathway in hepatocellular carcinoma

Background

Deregulation of microtubules and centrosome integrity is response for the initiation and progression of human cancers. Sperm-associated antigen 5 (SPAG5) is essential for the spindle apparatus organization and chromosome segregation, but its role in hepatocellular carcinoma (HCC) remains undefined.

Methods

The expression of SPAG5 in HCC were examined in a large cohort of patients by RT-PCR, western blot and IHC. The clinical significance of SPAG5 was next determined by statistical analyses. The biological function of SPAG5 in HCC and the underlying mechanisms were investigated, using in vitro and in vivo models.

Results

Here, we demonstrated that SPAG5 exhibited pro-HCC activities via the activation of PI3K/AKT signaling pathway. SPAG5 expression was increased in HCC and correlated with poor outcomes in two independent cohorts containing 670 patients. High SPAG5 expression was associated with poor tumor differentiation, larger tumor size, advanced TNM stage, tumor vascular invasion and lymph node metastasis. In vitro and in vivo data showed that SPAG5 overexpression promoted tumor growth and metastasis, whereas SPAG5 knockdown led to the opposite phenotypes. SPAG5 interacted with centrosomal protein CEP55 to trigger the phosphorylation of AKT at Ser473. Inhibition of PI3K/AKT signaling markedly attenuated SPAG5-mediated cell growth. Furthermore, SPAG5 expression was suppressed by miR-363-3p which inhibited the activity of SPAG5 mRNA 3’UTR. Ectopic expression of SPAG5 partly abolished the miR-363-3p-caused cell cycle arrest and suppression of cell proliferation and migration.

Conclusions

Collectively, these findings indicate that SPAG5 serves a promising prognostic factor in HCC and functions as an oncogene via CEP55-mediated PI3K/AKT pathway. The newly identified miR-363-3p/SPAG5/CEP55 axis may represent a potential therapeutic target for the clinical intervention of HCC.

Electronic supplementary material

The online version of this article (10.1186/s12943-018-0872-3) contains supplementary material, which is available to authorized users.

Diagnostic utility of plasma lncRNA small nucleolar RNA host gene 1 in patients with hepatocellular carcinoma

Long non-coding RNAs (lncRNAs) offer great potential as cancer biomarkers. Owing to the limited sensitivity and specificity of α-fetoprotein (AFP) for the diagnosis of hepatocellular carcinoma (HCC), the present study used an lncRNA microarray to screen aberrantly expressed lncRNAs in HCC tissues. Subsequently, the expression profile of the target lncRNAs was investigated in plasma from patients with HCC or hepatitis B virus-positive chronic hepatitis and cirrhosis (HCH), as well as from healthy volunteers. A total of six aberrantly expressed lncRNAs were identified in HCC tissues and corresponding normal tissues, from which only small nucleolar RNA host gene 1 (SNHG1) expression in HCC tissues demonstrated a good correlation with those in plasma from HCC patients. Subsequent analysis revealed that high plasma SNHG1 expression levels were correlated with tumor size, TNM stage and AFP levels. Receiver operating characteristic analysis demonstrated that SNHG1 yields an excellent diagnostic ability to differentiate between patients with HCC and unaffected control patients, which was superior to that of AFP. The combination of SNHG1 with AFP may be able to distinguish HCC from HCH or healthy volunteers with the area under the curve values of 0.86 and 0.97, respectively. In summary, it was demonstrated that plasma SNHG1 has great potential as a sensitive and reliable biomarker for the diagnosis of HCC.

Aberrant KIF20A expression might independently predict poor overall survival and recurrence-free survival of hepatocellular carcinoma

Kinesin family member 20A (KIF20A) is an essential regulator of cytokinesis. In this study, by performing a retrospective study based on data from the Cancer Genome Atlas (TCGA)-Liver and Hepatocellular Carcinoma (LIHC) cohort, we tried to assess the independent prognostic value of KIF20A in terms of overall survival (OS) and recurrence-free survival (RFS). Results showed that normal liver tissues had very low KIF20A expression compared with normal tissues in other cohorts in TCGA. However, the primary HCC tissues (N = 371) had significantly elevated KIF20A expression than normal liver tissues (N = 50). Immunohistochemistry (IHC) data showed that normal hepatocytes had weak KIF20A staining. In comparison, some HCC tissues had medium and strong KIF20A expression, with nuclear-enhanced staining. By grouping patients with primary HCC (N = 365) into high and low KIF20A expression groups, we found that the high expression group had a substantially higher proportion of high-grade tumors (G3/G4) (34/65, 52.3% vs. 96/295, 32.5%, P = 0.0027), advanced tumors (stage III/IV) (28/61, 45.9% vs. 59/280, 21.1%, P < 0.0001) and death (44/67, 65.7% vs. 86/298, 28.9%, P < 0.0001) compared with the low expression group. Kaplan-Meier curves of OS and RFS indicated that high KIF20A expression was associated with worse survival outcomes. Subgroup analysis confirmed the associations in G1/G2, G3/G4 tumors and in early and advanced stages. Following univariate and multivariate analysis revealed that KIF20A expression was an independent prognostic indicator for poor OS (HR: 1.304, 95%CI: 1.157-1.469, P < 0.001) and RFS (HR: 1.144, 95%CI: 1.028-1.272, P < 0.001). Based on these findings, we infer that KIF20A was aberrantly expressed in HCC tissues and its expression might independently predict poor OS and RFS. © 2018 IUBMB Life, 70(4):328-335, 2018.