G2/M checkpoint plays a vital role at the early stage of HCC by analysis of key pathways and genes

Prognostic significance of LRPPRC and its association with immune infiltration in liver hepatocellular carcinoma

BACKGROUND

Leucine rich pentatricopeptide repeat containing (LRPPRC) protein is a multifunctional protein involved in cell cycle progression and tumor development. However, its prognostic significance and association with immune infiltration in Liver hepatocellular carcinoma (LIHC) remain unclear.

METHODS

We utilized transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases of LIHC patients to investigate the potential pro-cancer role of LRPPRC, including differential expression of LRPPRC in LIHC, prognostic value, clinicopathological features, immune cell infiltration relevance and function enrichment analysis.

RESULTS

Our findings suggest that LRPPRC is upregulated in LIHC and exhibits correlations with survival, clinical stage, and tumor grade in LIHC patients. Additionally, immune infiltration analysis revealed significant negative correlations between LRPPRC expression and multiple tumor-infiltrating immune cells, including CTLs, DCs, pDCs, B cells, Th17 cells, neutrophils, T cells, Mast cells, Th1 cells, Tregs, and NK cells, whereas a significant positive correlation was observed with infiltration of Th2 cells, T helper cells and Tcms. Furthermore, functional enrichment analysis indicated that LRPPRC may be involved in G2m checkpoint, mitotic spindle, E2f targets, Wnt Beta catenin signaling, spermatogenesis and other processes.

Maternal embryonic leucine zipper kinase in tumor cell and tumor microenvironment: Emerging player and promising therapeutic opportunities

Maternal embryonic leucine zipper kinase (MELK) is a member of the AMPK (AMP-activated protein kinase) protein family, which is widely and highly expressed in multiple cancer types. Through direct and indirect interactions with other proteins, it mediates various cascades of signal transduction processes and plays an important role in regulating tumor cell survival, growth, invasion and migration and other biological functions. Interestingly, MELK also plays an important role in the regulation of the tumor microenvironment, which can not only predict the responsiveness of immunotherapy, but also affect the function of immune cells to regulate tumor progression. In addition, more and more small molecule inhibitors have been developed for the target of MELK, which exert important anti-tumor effects and have achieved excellent results in a number of clinical trials. In this review, we outline the structural features, molecular biological functions, potential regulatory mechanisms and important roles of MELK in tumors and tumor microenvironment, as well as substances targeting MELK. Although many molecular mechanisms of MELK in the process of tumor regulation are still unknown, it is worth affirming that MELK is a potential tumor molecular therapeutic target, and its unique superiority and important role provide clues and confidence for subsequent basic research and scientific transformation.

DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens

With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.

Semibulk RNA-seq analysis as a convenient method for measuring gene expression statuses in a local cellular environment

When biologically interpretation of the data obtained from the single-cell RNA sequencing (scRNA-seq) analysis is attempted, additional information on the location of the single cells, behavior of the surrounding cells, and the microenvironment they generate, would be very important. We developed an inexpensive, high throughput application while preserving spatial organization, named “semibulk RNA-seq” (sbRNA-seq). We utilized a microfluidic device specifically designed for the experiments to encapsulate both a barcoded bead and a cell aggregate (a semibulk) into a single droplet. Using sbRNA-seq, we firstly analyzed mouse kidney specimens. In the mouse model, we could associate the pathological information with the gene expression information. We validated the results using spatial transcriptome analysis and found them highly consistent. When we applied the sbRNA-seq analysis to the human breast cancer specimens, we identified spatial interactions between a particular population of immune cells and that of cancer-associated fibroblast cells, which were not precisely represented solely by the single-cell analysis. Semibulk analysis may provide a convenient and versatile method, compared to a standard spatial transcriptome sequencing platform, to associate spatial information with transcriptome information.

Low adipocyte hepatocellular carcinoma is associated with aggressive cancer biology and with worse survival

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, and non-alcoholic fatty liver disease is strongly associated with its development. To explore the role of adipocytes in HCC, we investigated intratumoral adipocytes, also known as cancer-associated adipocytes (CAA). Based on our prior breast cancer findings, we hypothesized that low intratumoral adipocytes would be associated with aggressive cancer biology, worse tumor microenvironment (TME), and clinical outcomes. The Cancer Genome Atlas (TCGA) was used and validated by the Gene Expression Omnibus (GEO) cohort. xCell algorithm was used to quantify intratumoral adipocytes and top 90% were defined as adipocyte high (AH) and bottom 10% as adipocyte low (AL). We found that AL-HCC was significantly associated with worse disease-free survival (DFS), disease-specific survival (DSS), and overall survival (OS). AL-HCC were higher-grade, had high MKI67 expression, enriched cell proliferation-related gene sets, and had increased altered fraction, aneuploidy, and homologous recombination defects. Also, anti-cancer immune cells, CD8, Th1, and M1 cells, as well as pro-cancer Th2 cells were increased in AL-HCC. Micro-RNAs miR-122 (associated with cholesterol metabolism) and miR-885 (associated with liver pathologies) were significantly increased in the AL TME. In conclusion, we found that AL-HCC has worse patient outcomes and is biologically more aggressive with enhanced cell proliferation. Our findings take initial steps to clarify the role of adipocytes in HCC.

HSPB11 is a Prognostic Biomarker Associated with Immune Infiltrates in Hepatocellular Carcinoma

Purpose

Patients and Methods

Results

Conclusion

Autophagy-Related Gene Pairs Signature for the Prognosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786—0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.

"Complimenting the Complement": Mechanistic Insights and Opportunities for Therapeutics in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and a leading cause of death in the US and worldwide. HCC remains a global health problem and is highly aggressive with unfavorable prognosis. Even with surgical interventions and newer medical treatment regimens, patients with HCC have poor survival rates. These limited therapeutic strategies and mechanistic understandings of HCC immunopathogenesis urgently warrant non-palliative treatment measures. Irrespective of the multitude etiologies, the liver microenvironment in HCC is intricately associated with chronic necroinflammation, progressive fibrosis, and cirrhosis as precedent events along with dysregulated innate and adaptive immune responses. Central to these immunological networks is the complement cascade (CC), a fundamental defense system inherent to the liver which tightly regulates humoral and cellular responses to noxious stimuli. Importantly, the liver is the primary source for biosynthesis of >80% of complement components and expresses a variety of complement receptors. Recent studies implicate the complement system in liver inflammation, abnormal regenerative responses, fibrosis, carcinogenesis, and development of HCC. Although complement activation differentially promotes immunosuppressive, stimulant, and angiogenic microenvironments conducive to HCC development, it remains under-investigated. Here, we review derangement of specific complement proteins in HCC in the context of altered complement regulatory factors, immune-activating components, and their implications in disease pathogenesis. We also summarize how complement molecules regulate cancer stem cells (CSCs), interact with complement-coagulation cascades, and provide therapeutic opportunities for targeted intervention in HCC.

Identification of SLED1 as a Potential Predictive Biomarker and Therapeutic Target of Post-Infarct Heart Failure by Bioinformatics Analyses

The aim of this study was to explore potential predictive biomarkers and therapeutic targets of post-infarct heart failure (HF) using bioinformatics analyses.CEL raw data of GSE59867 and GSE62646 were downloaded from the GEO database. Differentially expressed genes (DEGs) between patients with ST-segment elevation myocardial infarction (STEMI) and those with stable coronary artery disease (CAD) at admission and DEGs between admission and 6 months after myocardial infarction (MI) in patients with STEMI were analyzed. A gene ontology (GO) analysis and a gene set enrichment analysis (GSEA) were performed, and a protein-protein interaction network was constructed. Critical genes were further analyzed.In total, 147 DEGs were screened between STEMI and CAD at admission, and 62 DEGs were identified in patients with STEMI between admission and 6 months after MI. The results of GO and GSEA indicate that neutrophils, neutrophil-related immunity responses, and monocytes/macrophages play important roles in MI pathogenesis. SLED1 expression was higher in patients with HF than in those without HF at admission and 1 month after MI. GSEA indicates that mTORC1 activation, E2F targets, G2M checkpoint, and MYC targets v1 inhibition may play key roles in the development of post-infarct HF. Furthermore, SLED1 may be involved in the development of post-infarct HF by activating mTORC1 and inhibiting E2F targets, G2M checkpoint, and MYC targets v1.SLED1 may be a novel biomarker of post-infarct HF and may serve as a potential therapeutic target in this disease.

Cold atmospheric plasma increases IBRV titer in MDBK cells by orchestrating the host cell network

Enhancing virus multiplication could assist in the rapid production of vaccines against viral diseases. Cold atmospheric plasma (CAP), a physical approach relying on reactive oxygen species to achieve the desirable cellular outcome, was shown to be effective in enhancing virus propagation, where bovine rhinotrachieitis virus and Madin-Darby Bovine Kidney cells were used as the modeling virus and cell line, respectively. CAP was shown to create synergies with virus infection in arresting host cells at the G2/M stage, decreasing cell membrane potential, increasing intracellular calcium level, and inducing selective autophagy. In addition, CAP was demonstrated to suppress virus-triggered immunogenic signaling as evaluated by IRF7 expression. We presented evidences on CAP-triggered maximization of host resources toward virus multiplication that is advantageous for viral vaccine production, and opened a novel regime for applying CAP in the sector of medical care and health.

Graphene oxide nanofilm and chicken embryo extract decrease the invasiveness of HepG2 liver cancer cells

Background

The extracellular matrix (ECM) is a mosaic of various structural and functional proteins that cooperate with the cell, regulate adhesion, and consequently manage its further fate. Liver destruction is accompanied by a disruption of the physicochemical properties of the ECM which deregulates the cell–ECM interaction and can lead to uncontrolled proliferation and neoplastic transformation of cells. Therefore, it can be assumed that ECM modification and restoration of its characteristics for healthy tissue may counteract uncontrolled cell proliferation. The purpose of the presented research model was to optimise the physical characteristics of ECM by introducing a graphene oxide plane/nanofilm (nfGO) and enriching the cell environment with potentially missing proteins by adding a functional protein cocktail (chicken embryo liver extract) and determine the impact of these factors on cell–ECM cooperation and its consequences on adhesion, proliferation, and cell phase, which are factors of the invasiveness of cancer cells.

Results

Experiments were performed with non-cancer HS-5 cells and liver cancer cells HepG2 and C3A. The cells were divided into four groups: (1) control, (2) cultured on nfGO, (3) cultured with the addition of chicken embryo liver extract (CELE) and (4) cultured on the nfGO with the addition of CELE. CELE contained 1735 proteins; the top 57 of these proteins have been presented. The use of nfGO as well as CELE and nfGO + CELE reduced the proliferation of HepG2 cancer cells to the greatest extent; this is in contrast to non-cancer cells and also to C3A cancer cells. Furthermore, the combined use of the CELE protein cocktail and GO substrate effectively resulted in a decrease in the population of HepG2 cells in the G0/G1 phase and an increase of the population in G2/M. Molecular analysis of HepG2 cancer cells also showed an increase in the expression of genes responsible for adhesion such as focal adhesion kinase (fak), e-cadherin, and n-cadherin and a decrease in β-catenin, which is considered a proto-oncogene.

Conclusions

Studies have shown that both the GO surface structure on which the cells are grown as well as the presence of a multi-component natural cocktail of regulatory proteins, can modify the expression of integrins, increase adhesion and, as a consequence, proliferation and the cell cycle—entering the resting phase. For the first time, it has been documented that hepatic cancer cells of the HepG2 line under the influence of stimuli derived from mimic ECM (graphene oxide) in interaction with a unique protein complex derived from chicken liver embryo decreased of the invasiveness of cancer cells.

Integrated analysis of miRNAs and DNA methylation identifies miR-132-3p as a tumor suppressor in lung adenocarcinoma

BACKGROUND

Aberrant miRNA expression and DNA methylation are two major epigenetic events in lung adenocarcinoma (LUAD). We conducted a combined analysis of the molecular changes in LUAD.

METHODS

We analyzed differentially expressed miRNAs and methylated CpG loci in 489 LUAD tissues versus 49 normal lung tissues of the Cancer Genome Atlas (TCGA). The results were validated in cell lines and xenograft mouse models and additional pairs of 36 LUAD and 36 normal lung tissues.

RESULTS

A total of 125 differentially expressed miRNAs and 145 differentially methylated CpG loci were identified in the LUAD versus normal lung tissues of TCGA data. Expression of the 22 miRNAs was inversely correlated with the 47 differentially methylated sites located in the miRNAs. Molecular and cellular function analysis showed that the abnormally methylated miRNAs were mainly involved in cell-to-cell signaling and interaction in airway cells. The DNA methylation status and altered expressions of miRNAs and their target genes were confirmed in 36 pairs of lung tumor and noncancerous lung tissues. Furthermore, aberrant miRNA expressions or DNA methylations alone could be involved in tumorigenesis of LUAD via different pathways. In addition, elevated miR-132-3p expression, reduced expression of its targeted gene (ZEB2), and decreased cell proliferation was observed in lung cancer cells treated with DNA methyltransferase inhibitor. Moreover, in vitro and in vivo analyses showed that miR-132-3p-3p downregulation via DNA methylation promoted tumorigenicity of lung cancer by directly regulating ZEB2.

CONCLUSIONS

The interaction between two epigenetic aberrations could have important functions in LUAD. miR-132-3p might act as a tumor suppressor in the tumorigenicity of LUAD.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Systemically investigating relationship between aberrant miRNA expression and DNA methylation in lung cancer could improve understanding of lung tumorigenesis and develop diagnostic and therapeutic targets.

WHAT THIS STUDY ADDS

Three forms of relationships between the two epigenetic changes are defined. miR-132-3p is further identified as a tumor suppressor in lung cancer.

Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.

CDC25B is associated with the risk of hepatocellular carcinoma, but not related to persistent infection of hepatitis B virus in a Chinese population

The cell division cycle 25 (CDC25) gene members, including CDC25A, CDC25B and CDC25C, are reported to be associated with several human cancers. Here, we aim to investigate the association of functional polymorphisms of CDC25 gene family with the risk of hepatocellular carcinoma (HCC) and persistent infection of Hepatitis B virus (HBV) in a Chinese HBV-related population. First, we used bioinformatics tools to systematically screen functional polymorphisms within CDC25 gene family. Second, we evaluated the effects of candidate polymorphisms by recruiting 790 HCC cases, 709 persistent HBV carriers (PHC), and 741 subjects with HBV natural clearance (SHNC). MassARRAY platform was used for genotyping. At last, we conducted functional prediction and assay to further explore the pathogenic mechanism of the identified polymorphism. Our results demonstrated that CDC25B rs2295348 played a protective role in HCC risk in a HBV-related Chinese population (adjusted odds ratio [OR] = 0.77, 95% confidence interval [CI] 0.65-0.93, P = 0.006). It showed a more significantly reduced HCC risk in the SHNC population (adjusted OR = 0.73, 95% CI 0.59-0.89, P = 0.002). However, we did not observe the association between CDC25B rs2295348 and the risk of persistent HBV infection. Further functional prediction and assay demonstrated that the mutant A allele of CDC25B rs2295348 might significantly decrease gene expression to modify the HCC risk. Our results suggest that CDC25B rs2295348 may confer a protective effect on HCC risk in a HBV-related Chinese population, but do not influence the susceptibility to persistent HBV infection.

Carbohydrate response element binding protein (ChREBP) correlates with colon cancer progression and contributes to cell proliferation

Cancers are characterized by reprogrammed glucose metabolisms to fuel cell growth and proliferation. Carbohydrate response element binding protein (ChREBP) is a glucose-mediated transcription factor that strongly regulates glycolytic and lipogenic pathways. It has been shown to associate with metabolic diseases, such as obesity, diabetes and non-alcoholic fatty liver diseases. However, how it associates with cancers has not been well understood. In this study, ChREBP expression was assessed by immunohistochemistry in colon tissue arrays containing normal colon tissue and cancer tissue at different clinical stages. Tissue mRNA levels of ChREBP were also measured in a cohort of colon cancer patients. We found that ChREBP mRNA and protein expression were significantly increased in colon cancer tissue compared to healthy colon (p < 0.001), and their expression was positively correlated to colon malignancy (for mRNA, p = 0.002; for protein p < 0.001). Expression of lipogenic genes (ELOVL6 and SCD1) in colon cancer was also positively associated with colon malignancy (for both genes, p < 0.001). In vitro, ChREBP knockdown with siRNA transfection inhibited cell proliferation and induced cell cycle arrest without changes in apoptosis in colon cancer cell lines (HT29, DLD1 and SW480). Glycolytic and lipogenic pathways were inhibited but the p53 pathway was activated after ChREBP knockdown. Taken together, ChREBP expression is associated with colon malignancy and it might contribute to cell proliferation via promoting anabolic pathways and inhibiting p53. In addition, ChREBP might represent a novel clinical useful biomarker to evaluate the malignancy of colon cancer.

Integrative module analysis of HCC gene expression landscapes

Despite hepatocellular carcinoma (HCC) being a common cancer globally, its initiation and progression are not well understood. The present study was designed to investigate the hub genes and biological processes of HCC, which change substantially during its progression. Three gene expression profiles of 480 patients with HCC were obtained from the Gene Expression Omnibus database. Subsequent to performing functional annotations and constructing protein-protein interaction (PPI) networks, 657 differentially expressed genes were identified, which were subsequently used to screen candidate hub genes. PPI networks were modularized using the weighted gene correlation network analysis algorithm, the topological overlapping matrix and the hierarchical cluster tree, which were utilized via STRING. Clinical data obtained from The Cancer Genome Atlas were then analyzed to validate the experiments performed using six hub genes. Additionally, a transcription factor and microRNA-mRNA network were constructed to determine the potential regulatory mechanisms of six hub genes. The results revealed that the oxidation-reduction process and cell cycle associated processes were markedly involved in HCC progression. Six highly expressed genes, including cyclin B2, cell division cycle 20, mitotic arrest deficient 2 like 1, minichromosome maintenance complex component 2, centromere protein F and BUB mitotic checkpoint serine/threonine kinase B, were confirmed as hub genes and validated via experiments associated with cell division. These hub genes are necessary for confirmatory experiments and may be used in clinical gene therapy as biomarkers or drug targets.

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

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

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

Application of RNA-sequencing to identify transcriptome modification by DCLK1 in colorectal cancer cells

Doublecortin like kinase 1 (DCLK1) is a cancer stem cell marker for the colorectal cancer (CRC). It plays critical roles in the oncogenesis, progression and metastasis of CRC. DCLK1 can be an intriguing therapeutic target for CRC treatment. However, the molecular mechanism of how DCLK1 functions is unclear currently. In our research, we aim to apply RNA-Sequencing (RNA Seq) technology, a high throughput massively Next Generation Sequencing approach, to monitor transcriptome changes due to DCLK1 over-expression in the CRC cells. In order to achieve our goal, RNA from quadruplicate samples from two clones of isogenic DCLK1 stable over-expression cells and the parental wild type HCT116 cells was sent for RNA Seq on the Illumina NextSeq500 platform. Differentially expressed (DE) genes were evaluated by t-test (P <0.05 and fold-change ±1.5 or greater) using two methods: (1) FWER; and (2) Benjamani and Hochberg FDR (false discovery rate) which corrects for multiple comparisons. Gene networks and functional analysis were evaluated using Ingenuity Pathways Analysis (IPA). We identified 1463 DE genes common for both DCLK1 overexpression clone A and clone B cells. IPA results indicated that 72 canonical pathways were significantly modified by DCLK1 over-expression (P<0.05), among which 9 out of the top 10 pathways are involved in the cell cycle regulation, indicating that DCLK1 might play its tumorigenesis role via activation of pathways facilitating cell proliferation, repression of pathways inhibiting cells proliferation and function against pathways facilitating cell apoptosis. Cell cycle analysis results confirmed the IPA findings, which demonstrated that DCLK1 over-expression cells had much less G0/G1 cells but much more S and G2/M cells (P<0.05). In conclusion, DCLK1 over-expression significantly modified transcriptome profile of CRC cancer cells. Control of the cell cycle regulation might be one of the critical mechanism for DCLK1 function. Our findings provide more direct evidence for the development of DCLK1 as a therapeutic target for CRC treatment, and will be of great benefit for the discovery of novel therapeutic target within the DCLK1 molecular network for the treatment of colorectal cancer patients.

Development of a novel gene signature in patients without Helicobacter pylori infection gastric cancer

Gastric cancer (GC) is one of the most fatal common cancers in worldwide. Helicobacter pylori (H. pylori) infection is closely related to the development of GC, although the mechanism is still unclear. In our study, we aim to develop a robust messenger RNA (mRNA) signature associated with H. pylori (-) GC that can sensitively and efficiently predict the prognostic. The RNA-seq expression profile and corresponding clinical data of 598 gastric cancer samples and 63 normal samples obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Using gene set enrichment analysis H. pylori (+) GC and H. pylori (-) GC patients and normal samples to select certain genes for further analysis. Using univariate and multivariate Cox regression model to establish a gene signature for predicting the overall survival (OS). Finally, we identified G2/M related seven-mRNA signature (TGFB1, EGF, MKI67, ILF3, INCENP, TNPO2, and CHAF1A) closely related to the prognosis of patients with H. pylori (-) GC. The seven-mRNA signature was identified to act as an independent prognostic biomarker by stratified analysis and multivariate Cox regression analysis. It was also validated on two test groups from TCGA and GSE15460 and shown that patients with high-risk scores based on the expression of the seven mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < .0001). In this study, we developed a seven-mRNA signature related to G2/M checkpoint from H. pylori (-) GCs that as an independent biomarker potentially with a good performance in predicting OS and might be valuable for the clinical management for patients with GC.

Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant cancers with no effective targets and treatments. However, the molecular pathogenesis of HCC remains largely uncertain. The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. The identification of the crucial genes and pathways would contribute to the development of novel molecular targets and biomarker-driven treatments for HCC.

Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study

The liver is an amazing organ due to its powerful regenerative capacity. Although many studies on liver regeneration have been documented, the detailed mechanisms remain unclear. Two-third partial hepatectomy (PH) in rodents plays a crucial role in the study of liver regeneration. In this study, the time series data of gene expression during liver regeneration in mouse were analyzed using the gene set numbered GSE6998 in GEO. A variety of bioinformatics methods, including masigPro, Weighted Gene Co-expression Network Analysis (WGCNA), spatial analysis of functional enrichment (SAFE) and ingenuity canonical pathway analysis (IPA) were used to identify and compare the significantly changed pathways, potential upstream regulators and key genes during liver regeneration. Our study showed that liver regeneration in the mouse is a coordinated process, which cell-cycle-related progress are at the centre of the interaction network involved in liver regeneration. Several candidate upstream regulators including PPARA, NFE2L2, MAD1 and CNR1 and some key genes such as Cdk1, Plk1, Cdc20, Aurka, Racgap1, Cenpa, Rrm1, Rrm2 were identified. In conclusion, these findings could contribute to revealing the molecular mechanism of liver regeneration after PH, which could provide new ideas and treatment methods for regenerative medicine, oncological drug development and oncological treatment.

Dysfunction of Sister Chromatids Separation Promotes Progression of Hepatocellular Carcinoma According to Analysis of Gene Expression Profiling

Despite studying the various molecular mechanisms of hepatocellular carcinoma (HCC), effective drugs and biomarkers in HCC therapy are still scarce. The present study was designed to investigate dysregulated pathways, novel biomarkers and therapeutic targets for HCC. The gene expression dataset of GSE14520, which included 362 tumor and their paired non-tumor tissues of HCC, was extracted for processing by the Robust multi-array average (RMA) algorithm in the R environment. SAM methods were leveraged to identify differentially expressed genes (DEGs). Functional analysis of DEGs was performed using DAVID. The GeneMania and Cytohubba were used to construct the PPI network. To avoid individual bias, GSEA and survival analysis were employed to verify the results. The results of these analyses indicated that separation of sister chromatids was the most aberrant phase in the progression of HCC, and the most frequently involved genes, EZH2, GINS1, TPX2, CENPF, and BUB1B, require further study to be used as drug targets or biomarkers in diagnosis and treatment of HCC.

Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

Objective

Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.

Methods

The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.

Results

A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.

Conclusion

In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.