Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis

Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach

BACKGROUND

Rice is a critical global food source, but it faces challenges due to nutritional deficiencies and the pressures of a growing population. Understanding the molecular mechanisms and protein functions in rice seed development is essential to improve yield and grain quality. However, there is still a significant knowledge gap regarding the key proteins and their interactions that govern rice seed development. Protein-protein interaction (PPI) analysis is a powerful tool for studying developmental processes like seed development, though its potential in rice research is yet to be fully realized. With the aim of unraveling the protein interaction landscape associated with rice seed development, this systems biology study conducted a PPI network-based analysis. Using a list of known seed development proteins from the Gene Ontology (GO) knowledgebase and literature, novel candidate proteins for seed development were predicted using an ensemble of network-based algorithms, including Majority Voting, Hishigaki Algorithm, Functional Flow, and Random Walk with Restart, which were selected based on their popularity and usability. The predictions were validated using enrichment analysis and cross-checked with independent transcriptomic analysis results. The rice seed development sub-network was further analyzed for community and hub detection.

RESULTS

The study predicted 196 new proteins linked to rice seed development and identified 14 sub-modules within the network, each representing different developmental pathways, such as endosperm development and seed growth regulation. Of these, 17 proteins were identified as intra-modular hubs and 6 as inter-modular hubs. Notably, the protein SDH1 emerged as a dual hub, acting as both an intra-modular and inter-modular hub, highlighting its importance in seed development PPI network stability.

CONCLUSIONS

These findings, including the identified hub proteins and sub-modules, provide a better understanding of the PPI interaction landscape governing seed development in rice. This information is useful for achieving a systems biology understanding of seed development. This study implements an ensemble of algorithms for the analysis and showcases how systems biology techniques can be applied in developmental biology.

Identification of molecular and cellular infection response biomarkers associated with anthrax infection through comparative analysis of gene expression data

Bacillus anthracis, a gram-positive bacillus capable of forming spores, causes anthrax in mammals, including humans, and is recognized as a potential biological weapon agent. The diagnosis of anthrax is challenging due to variable symptoms resulting from exposure and infection severity. Despite the availability of a licensed vaccines, their limited long-term efficacy underscores the inadequacy of current human anthrax vaccines, highlighting the urgent need for next-generation alternatives. Our study aimed to identify molecular biomarkers and essential biological pathways for the early detection and accurate diagnosis of human anthrax infection. Using a comparative analysis of Bacillus anthracis gene expression data from the Gene Expression Omnibus (GEO) database, this cost-effective approach enables the identification of shared differentially expressed genes (DEGs) across separate microarray datasets without additional hybridization. Three microarray datasets (GSE34407, GSE14390, and GSE12131) of B. anthracis-infected human cell lines were analyzed via the GEO2R tool to identify shared DEGs. We identified 241 common DEGs (70 upregulated and 171 downregulated) from cell lines treated similarly to lethal toxins. Additionally, 10 common DEGs (5 upregulated and 5 downregulated) were identified across different treatments (lethal toxins and spores) and cell lines. Network meta-analysis identified JUN and GATAD2A as the top hub genes for overexpression, and NEDD4L and GULP1 for underexpression. Furthermore, prognostic analysis and SNP detection of the two identified upregulated hub genes were carried out in conjunction with machine learning classification models, with SVM yielding the best classification accuracy of 87.5 %. Our comparative analysis of Bacillus anthracis infection revealed striking similarities in gene expression 241 profiles across diverse datasets, despite variations in treatments and cell lines. These findings underscore how anthrax infection activates shared genes across different cell types, emphasizing this approach in the discovery of novel gene markers. These markers offer insights into pathogenesis and may lead to more effective therapeutic strategies. By identifying these genetic indicators, we can advance the development of precise immunotherapies, potentially enhancing vaccine efficacy and treatment outcomes.

DTL promotes the growth and migration of melanoma cells through the ERK/E2F1/BUB1 axis

Melanoma is the most dangerous form of skin cancer. Hence, a better understanding of molecular mechanisms in melanoma pathogenesis is urgently needed, which provides a new insight into the therapy of melanoma. DTL gene is screened out in melanoma pathogenesis by integrated bioinformatics analysis, and its expression is validated in the tissue and cell samples of melanoma. Forced DTL expression facilitates the proliferation, invasion, migration and EMT of melanoma cells, while DTL knockdown suppresses the biological behavior of melanoma cells. In addition, DTL promotes the malignancy of melanoma in vivo. Mechanistically, BUB1 is the crucial downstream target of DTL. Reduced DTL expression suppresses BUB1 expression, while enhanced DTL expression induces BUB1 upregulation. Rescue experiments showed that growing and migrating of melanoma cells induced by DTL are partially impaired by BUB1 inhibition. In addition, the expression of phosphorylated ERK (p-ERK) and the downstream transcription factor E2F1 are reduced when DTL expression is blocked. Meanwhile, BUB1 levels are decreased when the expression of p-ERK or E2F1 is repressed. Notably, the growth and migration of melanoma cells by inhibition of ERK and knockdown of E2F1 was rescued by overexpressing BUB1. DTL gene may be a prognosis marker and represent a unique potential target for melanoma patients. DTL supports the biologically malignant activity of melanoma cells via the ERK/E2F1/BUB1 axis.

Unveiling novel prognostic biomarkers and therapeutic targets for HBV-associated hepatocellular carcinoma through integrated bioinformatic analysis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, with limited treatment options. The goal of this study was to use integrated bioinformatic analysis to find possible biomarkers for prognosis and therapeutic targets for hepatitis B (HBV)-associated HCC. Three microarray datasets (GSE84402, GSE121248, and E-GEOD-19665) from patients with HBV-associated HCC were combined and analyzed. We identified differentially expressed genes (DEGs) and performed pathway enrichment analysis. We constructed protein-protein interaction networks to identify hub genes. We identified a total of 374 DEGs, which included 90 up-regulated and 284 down-regulated genes. Pathway enrichment analysis revealed associations with cell cycle, oocyte meiosis, and the p53 signaling pathway for up-regulated DEGs. Twenty hub genes were identified, and 9 of them (ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1) were validated using the Cancer Genome Atlas data and Kaplan-Meier survival analysis. These genes were significantly associated with a poor prognosis in HCC patients. Our research shows that ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1 may be useful for predicting how HBV-associated HCC will progress and for finding new ways to treat it. In addition to these further studies are needed to elucidate the functions of the remaining 11 identified hub genes (RRM2, NUSAP1, PBK, CCNB1, CCNB2, BUB1B, NEK2, CENPF, ASPM, TOP2A, and BUB1) in HCC development and progression.

Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment

Background

The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

Objective

This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

Methodology

Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

Results

The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

Conclusion

The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Mitotic Functions and Characters of KIF11 in Cancers

Mitosis mediates the accurate separation of daughter cells, and abnormalities are closely related to cancer progression. KIF11, a member of the kinesin family, plays a vital role in the formation and maintenance of the mitotic spindle. Recently, an increasing quantity of data have demonstrated the upregulated expression of KIF11 in various cancers, promoting the emergence and progression of cancers. This suggests the great potential of KIF11 as a prognostic biomarker and therapeutic target. However, the molecular mechanisms of KIF11 in cancers have not been systematically summarized. Therefore, we first discuss the functions of the protein encoded by KIF11 during mitosis and connect the abnormal expression of KIF11 with its clinical significance. Then, we elucidate the mechanism of KIF11 to promote various hallmarks of cancers. Finally, we provide an overview of KIF11 inhibitors and outline areas for future work.

Bioinformatic identification of differentially expressed genes associated with hepatocellular carcinoma prognosis

Hepatocellular carcinoma (HCC) is still a significant global health problem. The development of bioinformatics may provide the opportunities to identify novel therapeutic targets. This study bioinformatically identified the differentially expressed genes (DEGs) in HCC and associated them with HCC prognosis using data from published databases. The DEGs downloaded from the Gene Expression Omnibus (GEO) website were visualized using the Venn diagram software, and then subjected to the GO and KEGG analyses, while the protein-protein interaction network was analyzed using Cytoscape software with the Search Tool for the search tool for the retrieval of interacting genes and the molecular complex detection plug-in. Kaplan-Meier curves and the log rank test were used to associate the core PPI network genes with the prognosis. There were 57 upregulated and 143 downregulated genes in HCC samples. The GO and pathway analyses revealed that these DEGs are involved in the biological processes (BPs), molecular functions (MFs), and cell components (CCs). The PPI network covered 50 upregulated and 108 downregulated genes, and the core modules of this PPI network contained 34 upregulated genes. A total of 28 of these upregulated genes were associated with a poor HCC prognosis, 27 of which were highly expressed in HCC tissues. This study identified 28 DEGs to be associated with a poor HCC prognosis. Future studies will investigate their possible applications as prognostic biomarkers and potential therapeutic targets for HCC.

Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated Transcriptomic Analysis

Background

Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with nonmelanoma skin cancers (NMSC). However, the unclear pathogenesis of cSCC limits the application of molecular targeted therapy.

Methods

Three microarray datasets (GSE2503, GSE45164, and GSE66359) were downloaded from the Gene Expression Omnibus (GEO). After identifying the differentially expressed genes (DEGs) in tumor and nontumor tissues, five kinds of analyses, namely, functional annotation, protein-protein interaction (PPI) network, hub gene selection, TF-miRNA-mRNA regulatory network analysis, and ferroptosis mechanism, were performed.

Results

A total of 146 DEGs were identified with significant differences, including 113 upregulated genes and 33 downregulated genes. The enriched functions and pathways of the DEGs included microtubule-based movement, ATP binding, cell cycle, P53 signaling pathway, oocyte meiosis, and PLK1 signaling events. Nine hub genes were identified (CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, and ASPM). Finally, RRM2, AURKA, and SAT1 were identified as significant ferroptosis-related genes in cSCC. The differential expression of these genes has been verified in two other independent datasets.

Conclusions

By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC and are expected to become future biomarkers or therapeutic targets.

Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias

The KMT2A (MLL) gene rearrangements (KMT2A-r) are associated with a diverse spectrum of acute leukemias. Although most KMT2A-r are restricted to nine partner genes, we have recently revealed that KMT2A-USP2 fusions are often missed during FISH screening of these genetic alterations. Therefore, complementary methods are important for appropriate detection of any KMT2A-r. Here we use a machine learning model to unravel the most appropriate markers for prediction of KMT2A-r in various types of acute leukemia. A Random Forest and LightGBM classifier was trained to predict KMT2A-r in patients with acute leukemia. Our results revealed a set of 20 genes capable of accurately estimating KMT2A-r. The SKIDA1 (AUC: 0.839; CI: 0.799-0.879) and LAMP5 (AUC: 0.746; CI: 0.685-0.806) overexpression were the better markers associated with KMT2A-r compared to CSPG4 (also named NG2; AUC: 0.722; CI: 0.659-0.784), regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations. Thus, we provide novel and accurate options for the diagnostic screening and therapy of KMT2A-r leukemia, regardless of leukemia subtype.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Network based analysis identifies TP53m-BRCA1/2wt-homologous recombination proficient (HRP) population with enhanced susceptibility to Vigil immunotherapy

Thus far immunotherapy has had limited impact on ovarian cancer. Vigil (a novel DNA-based multifunctional immune-therapeutic) has shown clinical benefit to prolong relapse-free survival (RFS) and overall survival (OS) in the BRCA wild type and HRP populations. We further analyzed molecular signals related to sensitivity of Vigil treatment. Tissue from patients enrolled in the randomized double-blind trial of Vigil vs. placebo as maintenance in frontline management of advanced resectable ovarian cancer underwent DNA polymorphism analysis. Data was generated from a 981 gene panel to determine the tumor mutation burden and classify variants using Ingenuity Variant Analysis software (Qiagen) or NIH ClinVar. Only variants classified as pathogenic or likely pathogenic were included. STRING application (version 1.5.1) was used to create a protein-protein interaction network. Topological distance and probability of co-mutation were used to calculated the C-score and cumulative C-score (cumC-score). Kaplan-Meier analysis was used to determine the relationship between gene pairs with a high cumC-score and clinical parameters. Improved relapse free survival in Vigil treated patients was found for the TP53m-BRCAwt-HRP group compared to placebo (21.1 months versus 5.6 months p = 0.0013). Analysis of tumor mutation burden did not reveal statistical benefit in patients receiving Vigil versus placebo. Results suggest a subset of ovarian cancer patients with enhanced susceptibility to Vigil immunotherapy. The hypothesis-generating data presented invites a validation study of Vigil in target identified populations, and supports clinical consideration of STRING-generated network application to biomarker characterization with other cancer patients targeted with Vigil.

Integrated Analysis of FAM57A Expression and Its Potential Roles in Hepatocellular Carcinoma

Background

Family with sequence similarity 57 member A (FAM57A) is a membrane associated gene contributing to lung carcinogenesis. In hepatocellular carcinoma (HCC) and other cancers, whether FAM57A exerts similar roles has been rarely reported. Herein, the biological functions and clinical significance of FAM57A in HCC were explored.

Methods

Initially the differential expression of FAM57A between nontumor and HCC tissues was validated using a number of publicly accessible databases and immunohistochemistry (IHC). Then, the Kruskal–Wallis rank sum test or the Wilcoxon rank sum test as well as logistic regression were employed to analyze the association of FAM57A expression with clinical characteristics of HCC. The Cox regression and Kaplan–Meier analyses were conducted to assess the prognostic significance. Besides, with the coexpression analysis, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the molecular pathomechanisms that were mediated by FAM57A in HCC were elucidated. Furthermore, the correlations between FAM57A expression and tumor-infiltrating immune cells (TIICs) or immune checkpoint genes were analyzed. Finally, in vitro cell functional assay was carried out to preliminarily verify the role of FAM57A in HCC.

Results

FAM57A expression was demonstrated to be higher in HCC samples than in nontumor samples (all p-values <0.05), statistically correlated with clinicopathological characteristics (clinical stage, T stage, pathological grade), and inversely correlated to HCC patient survival. Univariate and multivariate Cox regression analyses showed that FAM57A expression could independently predict prognosis in HCC patients. Functional enrichment analyses further indicated that FAM57A was involved in multiple tumor-related pathways. FAM57A expression was positively correlated with TIICs, gene markers of TIICs, as well as immune checkpoint genes. Also, high expression of FAM57A predicted a poor prognosis for HCC based on immune cell subgroups. Functional assay of FAM57A knockdown significantly inhibited cell proliferation and induced cell apoptosis in HCC cells.

Conclusions

Our results indicated that FAM57A could be used as a biomarker to predict the prognosis and immunotherapy response for HCC patients and might function as an oncogene to promote HCC progression.

ZWINT is a Promising Therapeutic Biomarker Associated with the Immune Microenvironment of Hepatocellular Carcinoma

Background

The prognosis of patients with advanced hepatocellular carcinoma (HCC) is still poor, effective therapeutic targets are needed. ZW10 interacting kinetochore protein (Zwint) is an essential component of the mitotic spindle checkpoint and is upregulated in cancers. Disappointing, the role of ZWINT in HCC has not been fully illuminated.

Methods

Multiple tools, including TIMER2.0, Oncomine, GEPIA2, UALCAN, LinkedOmics, Kaplan-Meier Plotter, cBioPortal, and MethSurv, etc. were applied to comprehensively analyze the expression, genetic alternations, clinicopathological relevance, prognostic value, and DNA methylation of ZWINT, along with its correlations with immune infiltration in HCC. Besides, gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) analysis were performed for the correlated genes of ZWINT, closely interconnected clusters and hub proteins in the PPI network were discovered to learn the underlying biological mechanisms.

Results

We found ZWINT was significantly upregulated in diverse cancers including HCC, compared with the corresponding normal controls. ZWINT upregulation was significantly associated with unfavorable clinicopathological features and survivals of HCC patients. Genetic alternations of ZWINT frequently occurred, which were linked to worse outcomes of HCC patients. The results of GSEA displayed ZWINT and its correlated genes might be components of condensed chromosomes and spindles, which participated in biological processes and signaling pathways involving DNA replication, cytokinesis, and cell cycle checkpoint, etc. Three highly interconnected clusters and 10 hub proteins were identified from the PPI network constructed with the correlated genes of ZWINT. Moreover, ZWINT expression was found positively correlated with infiltration levels of various immune cells, especially myeloid-derived suppressor cells.

Conclusion

This study demonstrated ZWINT might be a promising unfavorable prognostic biomarker and a therapeutic target of HCC, which could regulate HCC progression through cell division and immunosuppression.

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

ASPM combined with KIF11 promotes the malignant progression of hepatocellular carcinoma via the Wnt/β-catenin signaling pathway

To investigate the molecular mechanism of assembly factor for spindle microtubules (ASPM) in the regulation of the malignant progression of hepatocellular carcinoma (HCC), bioinformatics analysis was utilized to analyze the role of ASPM in the malignant progression of HCC and its potential interaction with the kinesin family member 11 (KIF11) gene. The expression levels of ASPM and KIF11 were detected by reverse transcription-quantitative PCR and western blotting. Following knockdown of ASPM expression, Cell Counting Kit-8/colony formation assays were performed to detect cell viability and proliferation. Wound healing and Transwell assays were employed to detect cell migration and invasion. Additionally, a co-immunoprecipitation (CO-IP) assay was used to detect whether there was an interaction between ASPM and KIF11. KIF11 overexpression was performed to verify if ASPM exerted its effects via KIF11. ASPM was highly expressed in HCC tissues and cells, and was closely associated with a poor prognosis of patients with HCC. Interference with ASPM expression markedly inhibited the viability, proliferation, invasion and migration of HCC cells. Using a CO-IP assay, it was revealed that there was an interaction between ASPM and KIF11. Rescue experiments subsequently revealed the regulatory effects of ASPM on the activity, proliferation, invasion and migration of HCC cells via KIF11. Finally, western blot analysis demonstrated that ASPM in combination with KIF11 promoted the malignant progression of HCC by regulating the activity of the Wnt/β-catenin signaling pathway. Therefore, the present study demonstrated that ASPM may interact with KIF11 in HCC cells to promote the malignant progression of HCC via the Wnt/β-catenin signaling pathway.

Zwint facilitates melanoma progression by promoting c-Myc expression

ZW10 interactor (Zwint) is upregulated in various types of tumors and exerts a carcinogenic effect. However, little is known about the expression profile, function and molecular mechanisms of action of Zwint in melanoma. Therefore, the aim of the present study was to investigate the expression levels of Zwint in melanoma cell lines and tissues. It was revealed that Zwint was highly expressed in melanoma samples. Functional experiments indicated that Zwint knockdown suppressed the proliferation and migration of A375 melanoma cells. Further mechanistic studies demonstrated that Zwint knockdown decreased the protein expression levels of c-Myc, MMP-2, Slug, mTOR, phosphorylated (p)-mTOR, p-p38 and fibronectin, while it increased the protein expression levels of E-cadherin and MMP-9. Among these genes, c-Myc, MMP-2 and Slug were overexpressed to investigate their effects on cell proliferation following Zwint knockdown. The results demonstrated that overexpression of c-Myc, but not MMP-2 or Slug, rescued the effects of Zwint knockdown on melanoma cell proliferation and migration. Taken together, the results of the present study suggested that Zwint may act as an oncogene in melanoma by regulating c-Myc expression.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Construction of a novel prognostic-predicting model correlated to ovarian cancer

Background: Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS).

Results: We identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein–protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival.

Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.

Identification of the Hub Genes Associated with the Prognosis of Ovarian Cancer Patients via Integrated Bioinformatics Analysis and Experimental Validation

Background

This study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.

Methods

Four microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein–protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.

Results

A total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.

Conclusion

Our results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.

Specific Antiproliferative Properties of Proteinaceous Toxin Secretions from the Marine Annelid Eulalia sp. onto Ovarian Cancer Cells

As Yondelis joins the ranks of approved anti-cancer drugs, the benefit from exploring the oceans' biodiversity becomes clear. From marine toxins, relevant bioproducts can be obtained due to their potential to interfere with specific pathways. We explored the cytotoxicity of toxin-bearing secretions of the polychaete Eulalia onto a battery of normal and cancer human cell lines and discovered that the cocktail of proteins is more toxic towards an ovarian cancer cell line (A2780). The secretions' main proteins were identified by proteomics and transcriptomics: 14-3-3 protein, Hsp70, Rab3, Arylsulfatase B and serine protease, the latter two being known toxins. This mixture of toxins induces cell-cycle arrest at G2/M phase after 3h exposure in A2780 cells and extrinsic programmed cell death. These findings indicate that partial re-activation of the G2/M checkpoint, which is inactivated in many cancer cells, can be partly reversed by the toxic mixture. Protein-protein interaction networks partake in two cytotoxic effects: cell-cycle arrest with a link to RAB3C and RAF1; and lytic activity of arylsulfatases. The discovery of both mechanisms indicates that venomous mixtures may affect proliferating cells in a specific manner, highlighting the cocktails' potential in the fine-tuning of anti-cancer therapeutics targeting cell cycle and protein homeostasis.

Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

Background: The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy.

Materials and methods: We identified differential genes (DEGs) that were up- and down-regulated in the three datasets (GSE26942, GSE13911, and GSE118916) and created protein–protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment.

Results: Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan–Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy.

Conclusion: ECM–receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC patients.

An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion.

Identification of driver genes and key pathways of non-functional pituitary adenomas predicts the therapeutic effect of STO-609

Objective

Our study is to identify DEGs (Differentially Expressed Genes), comprehensively investigate hub genes, annotate enrichment functions and key pathways of Non-functional pituitary adenomas (NFPAs), and also to verify STO-609 therapeutic effect.

Methods

The gene expression level of NFPA and normal tissues were compared to identify the DEGs (Differential expressed genes) based on gene expression profiles (GSE2175, GSE26966 and GSE51618). Enrichment functions, pathways and key genes were identified by carrying out GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis and PPI (Protein-Protein Interation) network analysis. Moreover, experiments in vitro were conducted to verify the anti-NFPAs effects of STO-609.

Results

169 over-expression genes and 182 low expression genes were identified among 3 datasets. Dopaminergic synapse and vibrio cholerae infection pathways have distinctly changed in NFPA tissues. The Ca²⁺/CaM pathway played important roles in NFPA. Four hub proteins encoded by genes CALM1, PRDM10, RIPK4 and MAD2L1 were recognized as hub proteins. In vitro, assays showed that STO-609 induced apoptosis of NFPA cells to inhibit the hypophysoma cellular viability, diffusion and migration.

Conclusion

Four hub proteins, encoded by gene CALM1, PRDM10, RIPK4 and MAD2L1, played important roles in NFPA development. The Ca²⁺/CaM signaling pathway had significant alternations during NFPA forming process, the STO-609, a selective CaM-KK inhibitor, inhibited NFPA cellular viability, proliferation and migration. Meanwhile, NFPA was closely related to parkinson’s disease (PD) in many aspects.

Identification of Potential Hub Genes and Therapeutic Drugs in Malignant Pleural Mesothelioma by Integrated Bioinformatics Analysis

INTRODUCTION

Malignant pleural mesothelioma (MPM) is closely linked to asbestos exposure and is an extremely aggressive tumor with poor prognosis.

OBJECTIVE

Our study aimed to elucidate hub genes and potential drugs in MPM by integrated bioinformatics analysis.

METHODS

GSE42977 was download from the Gene Expression Omnibus (GEO) database; the differentially expressed genes (DEGs) with adj.p value <0.05 and |logFC| ≥2 were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by DAVID database. The STRING database was used to construct a protein-protein interaction network, and modules analysis and hub genes acquisition were performed by Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to assess the impact of hub genes on the prognosis of MPM patients. The Drug-Gene Interaction database (DGIdb) was used to select the related drugs.

RESULTS

A total of 169 upregulated and 70 downregulated DEGs were identified. These DEGs are enriched in the pathway of extracellular matrix-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and PPAR signaling pathway. Finally, 10 hub genes (CDC20, CDK1, UBE2C, TOP2A, CCNB2, NUSAP1, KIF20A, AURKA, CEP55, and ASPM) were identified, which are considered to be closely related to the poor prognosis of MPM. In addition, 119 related drugs that may have a therapeutic effect on MPM were filtered out.

CONCLUSION

These discovered genes and small-molecule drugs provide some new ideas for further research on MPM.

Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer

BACKGROUND

Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive.

RESULTS

Two eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system development, vasculature development, oocyte maturation, and oocyte meiosis signalling pathways. A total of 160 pairs of differentially expressed miRNAs and mRNAs, including 22 differentially expressed miRNAs and 71 overlapping differentially expressed mRNAs, were validated in endometrial cancer samples using starBase v2.0 project. The prognosis analysis revealed that Cyclin E1 (CCNE1, one of the 82 hub genes, which correlated with hsa-miR-195 and hsa-miR-424) was significantly linked to a worse overall survival in endometrial cancer patients.

CONCLUSIONS

The hub genes and differentially expressed miRNAs identified in this study might be used as prognostic biomarkers for endometrial cancer and molecular targets for its treatment.

Identification and validation of core genes for serous ovarian adenocarcinoma via bioinformatics analysis

Ovarian cancer is a fatal gynaecological malignancy in women worldwide, and serous ovarian cancer (SOC) is considered the most common histological subtype of this malignancy. Thus, the present study aimed to identify the core genes for SOC via bioinformatics analysis. The GSE18520 and GSE14407 datasets were downloaded from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) and perform gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify the core genes, while The Cancer Genome Atlas (TCGA) database was used to screen for prognosis-associated DEGs. Furthermore, clinical samples were collected for further validation of kinesin family member 11 (KIF11) gene. In the GEO analysis, a total of 198 DEGs were identified, including 81 upregulated and 117 downregulated genes compared SOC to normal tissue. GSEA across the two datasets demonstrated that 16 gene sets, including those involved in the cell cycle and DNA replication, were notably associated with SOC. A PPI network of the DEGs was constructed with 130 nodes and 387 edges. Subsequently, 20 core genes involved in the same top-ranked module were filtered out by submodule analysis. Survival analysis identified three predictive genes for SOC prognosis, including KIF11, CLDN3 and FGF13. KIF11 was identified as a core and predictive gene and thus was further validated using clinical samples. The results demonstrated that KIF11 was upregulated in tumour tissues compared with adjacent normal tissues and was associated with aggressive factors, including tumour grade, TNM stage and lymph node invasion. In conclusions, the present study identified the core genes and gene sets for SOC, thus extending the understanding of SOC occurrence and progression. Furthermore, KIF11 was identified as a promising tumour-promoting gene and a potential target for the diagnosis and treatment of SOC.

Identification of APEX2 as an oncogene in liver cancer

BACKGROUND

DNA damage is one of the critical contributors to the occurrence and development of some cancers. APEX1 and APEX2 are the most important molecules in the DNA damage, and APEX1 has been identified as a diagnostic and prognostic biomarker in liver hepatocellular carcinoma (LIHC). However, the expression of APEX2 and its functional mechanisms in LIHC are still unclear.

AIM

To examine the expression of APEX2 and the potential mechanism network in LIHC.

METHODS

We conducted a pan-cancer analysis of the expression of APEX1 and APEX2 using the interactive TIMER tool. GEO datasets, including GSE14520, GSE22058, and GSE64041, were used to compare the APEX2 expression level in tumor tissues and adjacent non-tumor tissues. Then, we calculated the 5-year survival rate according to the web-based Kaplan-Meier analysis. We included the TCGA liver cancer database in GSEA analysis based on the high and low APEX2 expression, showing the potential mechanisms of APEX2 in LIHC. After that, we conducted Pearson correlation analysis using GEPIA2. Next, we performed quantitative polymerase chain reaction (qPCR) assay to examine the APEX2 levels in normal liver cell line LO2 and several liver cancer cell lines, including HepG2, Huh7, SMMC7721, and HCCLM3. APEX2 in HCCLM3 cells was knocked down using small interfering RNA. The role of APEX2 in cell viability was confirmed using CCK-8. Dual-luciferase reporter assay was performed to examine the promoter activity of CCNB1 and MYC.

RESULTS

APEX1 and APEX2 are both highly expressed in the tumor tissues of BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, and STAD. APEX2 overexpression in LIHC was validated using GSE14520, GSE22058, and GSE64041 datasets. The survival analysis showed that LIHC patients with high expression of APEX2 had a lower overall survival rate, even in the AJCC T1 patients. High level of APEX2 could indicate a lower overall survival rate in patients with or without viral hepatitis. The GSEA analysis identified that kinetochore and spindle microtubules are the two main cellular components of APEX2 in GO Ontology. APEX2 was also positively associated with molecular function regulation of chromosome segregation and DNA replication. The results of KEGG analysis indicated that APEX2 expression was positively correlated with cell cycle pathway and pro-oncogenic MYC signaling. Pearson correlation analysis showed that APEX2 had a significant positive correlation with CCNB1 and MYC. APEX2 level was higher in liver cancer cell lines than in normal liver LO2 cells. Small interfering RNA could knock down the APEX2 expression in HCCLM3 cells. Knockdown of APEX2 resulted in a decrease in the viability of HCCLM3 cells as well as the expression and promoter activity of CCNB1 and MYC.

CONCLUSION

APEX2 is overexpressed in LIHC, and the higher APEX2 level is associated with a worse prognosis in overall survival. APEX2 is closely involved in the biological processes of chromosome segregation and DNA replication. APEX2 expression is positively correlated with the pro-oncogenic pathways. Knockdown of APEX2 could inhibit the cell viability and CCNB1 and MYC pathways, suggesting that APEX2 is an oncogene in LIHC, which could be a potential pharmaceutic target in the anti-tumor therapy.

Involvement of the NF-κB signaling pathway in proliferation and invasion inhibited by Zwint-1 deficiency in Pancreatic Cancer Cells

Pancreatic cancer (PC) is an intractable cancer that is difficult to diagnose early and has a 5-year survival rate of less than 8%. ZW10-interacting kinetochore protein (ZWINT) is a crucial gene that contributes to chromosome instability and is essential for spindle assembly and kinetochore-microtubule attachment during meiosis and mitosis. However, the mechanism through which Zwint-1 promotes PC progression is yet to be elucidated. Here, we report that Zwint-1 is highly expressed in clinical PC specimens (based on analysis of the Gene Expression Profiling Interactive Analysis database) and various PC cell lines. Importantly, Zwint-1-deficient PC cells showed reduced nuclear factor-kappa B (NF-κB) (Ser536) phosphorylation along with inhibited proliferation and colony formation due to downregulation of NF-κB-regulated genes such as CCND1, cIAP1/2, and XIAP. In addition, Zwint-1-deficient PC cells showed reduced invasion and migration abilities, and decreased expression levels of the metalloproteinases MMP2 and MMP9. Furthermore, Zwint-1 deficiency arrested the PC cell cycle at the G2/M phase because the chromosomes failed to segregate properly, and the apoptosis rate in these cells gradually increased, accompanied by increased caspase-3 activation and anti-poly (ADP ribose) polymerase cleavage. Apoptosis caused by Zwint-1 deficiency was demonstrated to occur through caspase-dependent pathways based on experiments involving treatment with a pan-caspase inhibitor (Z-VAD-Fmk). Thus, Zwint-1 contributes to cell growth, invasion, and survival through NF-κB signaling pathways, suggesting that it could serve as a PC biomarker and new therapeutic target.

Identification of crucial genes correlated with esophageal cancer by integrated high-throughput data analysis

BACKGROUND

Esophageal cancer (ESCA) is one of the most deadly malignancies in the world. Although the management and treatment of patients with ESCA have improved, the overall 5-year survival rate is still very poor.

METHODS

The study aimed to identify potential key genes associated with the pathogenesis and prognosis of ESCA. In the study, integrated bioinformatics methods were used to screen differentially expressed genes (DEGs) between ESCA and normal tissue in the data set of gene expression profiles. The hub gene in DEGs was further analyzed by protein-protein interaction (PPI) network and survival analysis to explore its relationship with the pathogenesis and poor prognosis of ESCA.

RESULTS

134 up-regulated genes and 183 down-regulated genes were obtained in ESCA compared with normal tissues. Moreover, the PPI network was established with 176 nodes and 800 interactions. Ten hub genes (AURKA, CDC20, BUB1, TOP2A, ASPM, DLGAP5, TPX2, CENPF, UBE2C, and NEK2) were filtered out based on the degree value. Functional enrichment analysis indicated that a variety of extracellular related items and ECM-receptor interaction pathway were all correlated with the ESCA.

CONCLUSIONS

The results of this study would provide some guidance for further study of diagnostic and prognostic biomarkers to promote ESCA treatment.

Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer

Background

Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC.

Methods

Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively.

Results

A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment.

Conclusions

In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.

ZW10 interacting kinetochore protein may serve as a prognostic biomarker for human breast cancer: An integrated bioinformatics analysis

ZW10 interacting kinetochore protein (ZWINT) is an essential component for the mitotic spindle checkpoint and has been reported to be upregulated in numerous types of human cancer. Nonetheless, its role in breast cancer (BC) remains unclear. Herein, it was demonstrated that the expression of ZWINT was significantly higher in BC than in normal breast tissues, on the basis of integrated analysis of bioinformatics studies, cancer database analyses and immunohistochemical detection. Elevated ZWINT levels were associated with a number of clinicopathological characteristics in patients with BC. These characteristics include: i) Positive human epidermal growth factor receptor 2 expression; ii) triple-negative BC; iii) younger age; iv) basal-like subtype; and v) greater Scarff-Bloom-Richardson grades. Additionally, prognostic analysis indicated that shorter relapse-free survival, overall survival and metastatic relapse-free survival may be associated with high ZWINT expression. A total of 16 pathways associated with high ZWINT expression, including Myc targets V1/2, DNA repair and mitotic spindle pathways, were identified using Gene Set Enrichment Analysis. In addition, a positive correlation between cyclin-dependent kinase 1 (CDK1) and ZWINT mRNA expression was identified by co-expression analysis. The present study suggested that ZWINT may serve as an effective prognostic biomarker for BC. In addition, ZWINT may be implicated in the CDK1-mediated initiation and progression of BC. However, further research is required to understand the role of ZWINT in BC.

Comprehensive analysis of mRNA-level and miRNA-level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.

The overexpression of ZWINT in integrated bioinformatics analysis forecasts poor prognosis in breast cancer

BACKGROUND

Zeste White 10 interactor (ZW10 interactor, ZWINT) is a centromeric complex required for a mitotic spindle checkpoint. According to previous studies, it was overexpressed in people with recurrent tumors. However, the expression of ZWINT in breast cancer has not been thoroughly studied. In addition, the correlations of ZWINT to prognosis in breast cancer remain unclear.

METHODS

In this study, the expression of ZWINT in different types of tumors was analyzed based on the Oncomine database, and the effect of ZWINT expression on clinical prognosis was evaluated by Kaplan-Meier plotter.

RESULTS

In breast cancer, lung cancer, sarcoma, ovarian cancer, bladder cancer, liver cancer and cervical cancer, the expression of ZWINT was higher than that in normal tissues, but in gastric cancer, prostate cancer, myeloma, renal cancer and pancreatic cancer, the expression of ZWINT was lower. In addition, a meta-analysis of 22 cancer database studies found that the ZWINT gene was over-expressed in breast cancer tissues compared with normal tissues (P=4.05×10-6). Through the survival analysis of Kaplan-Meier plotter, it is found that the high expression of ZWINT is related to the worse overall survival (OS) [hazard ratio (HR) =1.73, 95% confidence interval (CI): 1.39-2.51, P=5.4×10-7], RFS (HR =1.68, 95% CI: 1.51-1.88, P<1×10-16) and distant metastasis-free survival (DMFS) (HR =1.55, 95% CI: 1.28-1.89, P=7.9×10-6) in all BC patients.

CONCLUSIONS

Our results strongly suggest that over expression of ZWINT is closely related to poor prognosis of breast cancer. ZWINT may be a prognostic biomarker for the treatment of BC.

TBX2 Identified as a Potential Predictor of Bone Metastasis in Lung Adenocarcinoma via Integrated Bioinformatics Analyses and Verification of Functional Assay

Objective: Bone metastasis from patients with advanced lung adenocarcinoma (LAC) is a very serious complication. To better understand the molecular mechanism, our current study sheds light on identification of hub genes mediating bone metastatic spread by combining bioinformatic analysis with functional verification. Methods: First, we downloaded a lung adenocarcinoma dataset (GSE76194) from Gene Expression Omnibus, analyzed differentially expressed genes (DEGs) through Limma package in R software and constructed a protein-protein interaction network. Based on that preliminary data, we further performed modular and topological analysis using Cystoscope to obtain biological connected genes. Through literature searching and performing mRNA expression analysis on the other independent public dataset (GSE10799), we finally focused on TBX2. Functional effects of TBX2 were performed in tumorigenicity assays including migration and invasion assays, cell proliferation assay, and cell cycle assay. In addition, mechanically, we found enriched pathways related to bone metastasis using Gene Set Enrichment Analysis (GSEA) and validated our results by western blot. Result: A total of 1132 significant genes were sorted initially. We selected common significant genes (log FC>2; p<0.01) from both the biological network data and microarray data. In total, 44 such genes were identified. we found TBX2, along with 10 other genes, to be reported with relevance to bone metastasis in other cancer types. Moreover, TBX2 showed significantly higher expression levels in patients that were found positive for metastasis to bone marrow compared to patients that did not exhibit this type of metastasis in the other separated cohort (GSE10799). Thus, we finally focused on TBX2. We found that TBX2 had detectable expression in LAC cell lines and silencing endogenous TBX2 expression in A549 and H1299 cell lines markedly suppressed migration and invasion, cell proliferation and arrested cell-cycle. Pathway enrichment analyses suggested that TBX2 drove LAC oncogenesis and metastasis through various pathways with epithelial mesenchymal transition (EMT) figuring prominently in the bone metastatic group, which was evidenced by western blot. Conclusion: Collectively, TBX2 plays as a potential predictor of bone metastasis from LAC, yielding a better promise view towards "driver" gene responsible for bone metastasis.

A new method for mining information of co-expression network based on multi-cancers integrated data

Background

Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.

Methods

In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.

Results

We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.

Conclusions

The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer

Objective

Non-small-cell lung cancer (NSCLC) accounts for >85% of lung cancers, and its incidence is increasing. We explored expression differences between NSCLC and normal cells and predicted potential target sites for detection and diagnosis of NSCLC.

Methods

Three microarray datasets from the Gene Expression Omnibus database were analyzed using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted. Then, the String database, Cytoscape, and MCODE plug-in were used to construct a protein–protein interaction (PPI) network and screen hub genes. Overall and disease-free survival of hub genes were analyzed using Kaplan-Meier curves, and the relationship between expression patterns of target genes and tumor grades were analyzed and validated. Gene set enrichment analysis and receiver operating characteristic curves were used to verify enrichment pathways and diagnostic performance of hub genes.

Results

In total, 293 differentially expressed genes were identified and mainly enriched in cell cycle, ECM–receptor interaction, and malaria. In the PPI network, 36 hub genes were identified, of which 6 were found to play significant roles in carcinogenesis of NSCLC: CDC20, ECT2, KIF20A, MKI67, TPX2, and TYMS.

Conclusion

The identified target genes can be used as biomarkers for the detection and diagnosis of NSCLC.

Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer

Purpose

Ovarian cancer is the leading cause of gynecologic cancer-related death worldwide. Early diagnosis of ovarian cancer can significantly improve patient prognosis. Hence, there is an urgent need to identify key diagnostic and prognostic biomarkers specific for ovarian cancer. Because high-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and accounts for the majority of deaths, we identified potential biomarkers for the early diagnosis and prognosis of HGSOC.

Methods

Six datasets (GSE14001, GSE18520, GSE26712, GSE27651, GSE40595, and GSE54388) were downloaded from the Gene Expression Omnibus database for analysis. Differentially expressed genes (DEGs) between HGSOC and normal ovarian surface epithelium samples were screened via integrated analysis. Hub genes were identified by analyzing protein-protein interaction (PPI) network data. The online Kaplan-Meier plotter was utilized to evaluate the prognostic roles of these hub genes. The expression of these hub genes was confirmed with Oncomine datasets and validated by quantitative real-time PCR and Western blotting.

Results

A total of 103 DEGs in patients with HGSOC-28 upregulated genes and 75 downregulated genes-were successfully screened. Enrichment analyses revealed that the upregulated genes were enriched in cell division and cell proliferation and that the downregulated genes mainly participated in the Wnt signaling pathway and various metabolic processes. Ten hub genes were associated with HGSOC pathogenesis. Seven overexpressed hub genes were partitioned into module 1 of the PPI network, which was enriched in the cell cycle and DNA replication pathways. Survival analysis revealed that MELK, CEP55 and KDR expression levels were significantly correlated with the overall survival of HGSOC patients (P < 0.05). The RNA and protein expression levels of these hub genes were validated experimentally.

Conclusion

Based on an integrated analysis, we propose the further investigation of MELK, CEP55 and KDR as promising diagnostic and prognostic biomarkers of HGSOC.

Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

BACKGROUND

Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated.

METHODS

We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed.

RESULTS

A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC.

CONCLUSIONS

Our study suggests that miR-182 is essential for the biological progression of OC.

Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma

Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein-protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan-Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Identification of driver genes and key pathways of prolactinoma predicts the therapeutic effect of genipin

The purpose of the present study was to identify the potential targets and markers for diagnosis, therapy and prognosis in patients with prolactinoma at the molecular level and to determine the therapeutic effects of genipin in prolactinoma. The gene expression profiles of GSE2175, GSE26966 and GSE36314 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified after comparing between gene expression profiles of the prolactinoma tissues and normal tissues. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein‑protein interaction (PPI) network analysis were conducted. In addition, in vitro, scratch assay, colony‑forming assay, Cell Counting Kit 8 (CCK8) assay and flow cytometry were performed to verify the functional effects of genipin. An aggregate of 12,695, 3,847 and 5,310 DEGs were identified from GSE2175, GSE26966 and GSE36314, respectively. The results of GO and KEGG analysis showed that the DEGs significant and important for prolactinoma were mostly involved with 'spindle pole' and 'oocyte meiosis'. A total of 20 genes were selected as hub genes with high degrees after PPI network analysis, including mitogen‑activated protein kinase 1 (MAPK1), MYC, early growth response 1 (EGR1), Bcl2 and calmodulin 1 (CALM1). CCK8 assay, colony‑forming assay and scratch assay were performed to verify the anti‑prolactinoma effect of genipin. The results of flow cytometry showed that apoptosis was increased by genipin. MAPK1, MYC, EGR1, Bcl2 and CALM1 were screened as main hub genes. Genipin upregulated the expression level of EGR1 and p21 (downstream mediator of EGR1) and EGR1, inhibited the proliferation and migration of prolactinoma cells. Genipin is a promising drug for treatment of patients with prolactinoma.

Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO)

BACKGROUND

Chronic periodontitis (CP) is a multifactorial inflammatory disease. For the diagnosis of CP, it is necessary to investigate molecular biomarkers and the biological pathway of CP. Although analysis of mRNA expression profiling with microarray is useful to elucidate pathological mechanisms of multifactorial diseases, it is expensive. Therefore, we utilized pooled microarray gene expression data on the basis of data sharing to reduce hybridization costs and compensate for insufficient mRNA sampling. The aim of the present study was to identify molecular biomarker candidates and biological pathways of CP using pooled datasets in the Gene Expression Omnibus (GEO) database.

METHODS

Three pooled transcriptomic datasets (GSE10334, GSE16134, and GSE23586) of gingival tissue with CP in the GEO database were analyzed for differentially expressed genes (DEGs) using GEO2R, functional analysis and biological pathways with the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction (PPI) network and hub gene with the Search Tool for the Retrieval of Interaction Genes database, and biomarker candidates for diagnosis and prognosis and upstream regulators of dominant biomarker candidates with the Ingenuity Pathway Analysis database.

RESULTS

We shared pooled microarray datasets in the GEO database. One hundred and twenty-three common DEGs were found in gingival tissue with CP, including 81 upregulated genes and 42 downregulated genes. Upregulated genes in Gene Ontology were significantly enriched in immune responses, and those in the Kyoto Encyclopedia of Genes and Genomes pathway were significantly enriched in the cytokine-cytokine receptor interaction pathway, cell adhesion molecules, and hematopoietic cell lineage. From the PPI network, the 12 nodes with the highest degree were screened as hub genes. Additionally, six biomarker candidates for CP diagnosis and prognosis were screened.

CONCLUSIONS

We identified several potential biomarkers for CP diagnosis and prognosis (e.g., CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN) and upstream regulators of biomarker candidates for CP diagnosis (TNF and TGF2). We also confirmed key genes of CP pathogenesis such as CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA. To our knowledge, this is the first report to reveal associations of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP.

ZWINT is the next potential target for lung cancer therapy

PURPOSE

We aimed to analyze the expression of ZWINT, NUSAP1, DLGAP5, and PRC1 in tumor tissues and adjacent tissues with public data.

METHODS

The expression patterns of four genes were detected in cancer tissues and adjacent tissues by qRT-PCR. The overall survival analysis was used to explore these genes in lung adenocarcinoma and squamous cell carcinoma patients. Knockdown assays were used to select the most suitable gene among these four genes. Cell function assays with the knockdown gene were conducted in A549 and NCL H226 cells. The role of the knockdown gene in lung cancer was dissected in a mice tumor model. Transcriptome sequencing analyses with the knockdown gene were analyzed.

RESULTS

Overexpression of these genes was significantly detected in cancer tissues (P < 0.01). Overall survival revealed that high expression of these genes is closely related with poor prognosis of lung adenocarcinoma patients (P < 0.05). Knockdown of ZWINT reduced proliferation in NCI H226 and A549 cells (P < 0.05). Knockdown also inhibited cell migration, invasion, apoptosis, and colony formation (P < 0.05). ZWINT knockdown reduced tumor volume (P < 0.05). Transcriptome sequencing of ZWINT knockdown-treated A549 and NCI H226 cells indicated that 100 and 426 differentially expressed genes were obtained, respectively. Gene ontology analysis suggested that binding, biological regulation, and multicellular organismal processes were the most enriched. KEGG analysis revealed that TNF, P53, and PI3K signal networks would be the most potential ZWINT-related pathways and were identified by Western blot analysis.

CONCLUSIONS

ZWINT may be a novel target for lung cancer therapy.

RCC2, a regulator of the RalA signaling pathway, is identified as a novel therapeutic target in cisplatin-resistant ovarian cancer

Currently, cisplatin (DDP) is the first-line chemotherapeutic agent used for treatment of ovarian cancer, but gradually acquired drug resistance minimizes its therapeutic outcomes. We aimed to identify crucial genes associated with DDP resistance in ovarian cancer and uncover potential mechanisms. Two sets of gene expression data were downloaded from Gene Expression Omnibus, and bioinformatics analysis was conducted. In our study, the differentially expressed genes between DDP-sensitive and DDP-resistant ovarian cancer were screened in GSE15709 and GSE51373 database, and chromosome condensation 2 regulator (RCC2) and nucleoporin 160 were identified as 2 genes that significantly up-regulated in DDP-resistant ovarian cancer cell lines compared with DDP-sensitive cell lines. Moreover, RCC2, Ral small GTPase (RalA), and Ral binding protein-1 (RalBP1) expression was found to be significantly higher in DDP-resistant ovarian cancer tissues than in DDP-sensitive tissues. RCC2 plays a positive role in cell proliferation, apoptosis, and migration in DDP-resistant ovarian cancer cell lines in vitro and in vivo. Furthermore, RCC2 could interact with RalA, thus promoting its downstream effector RalBP1. RalA knockdown could reverse the effects of RCC2 overexpression on DDP-resistant ovarian cancer cell proliferation, apoptosis, and migration. Similarly, RalA overexpression could alleviate the effects of RCC2 knockdown in DDP-resistant ovarian cancer cells. Taken together, RCC2 may function as an oncogene, regulating the RalA signaling pathway, and intervention of RCC2 expression might be a promising therapeutic strategy for DDP-resistant ovarian cancer.-Gong, S., Chen, Y., Meng, F., Zhang, Y., Wu, H., Li, C., Zhang, G. RCC2, a regulator of the RalA signaling pathway, is identified as a novel therapeutic target in cisplatin-resistant ovarian cancer.

Decreased Expression of ZWINT is Associated With Poor Prognosis in Patients With HCC After Surgery

Background:

ZW10 interactor was recently reported to correlate with human cancers. However, the prognostic value of ZW10 interactor in hepatocellular carcinoma was not reported.

Methods:

The expression level of ZW10 interactor was evaluated by Western blot and immunohistochemistry using tissue microarray. In the present study, we used 5 pairs of hepatocellular carcinoma and peritumoral frozen tissues for Western blot, and 70 paired paraffin-embedded hepatocellular carcinoma and peritumoral tissues as expression pattern cohort (cohort 1), and 280 paraffin-embedded hepatocellular carcinoma tissues were used as prognostic cohort (cohort 2). The integral optic density representing the expression level of ZW10 interactor in each tissue sample, was calculated using Image-Pro Plus. The integral optic density was added to the X-tile software for calculating the outcome-based cut point. Kaplan-Meier and Cox regression were used to evaluate the prognostic values.

Results:

The expression level ZW10 interactor was decreased in hepatocellular carcinoma tissues in 85.7% (60/70) of the cases compared to the corresponding peritumoral tissues evaluated by immunohistochemistry. Similar result was obtained by Western blot analysis using frozen tissue. Expression of ZW10 interactor was closely correlated with age (P = .0001) and liver cirrhosis in cohort 1 and tumor node metastasis (P = .018), tumor size (P = .005), and vascular invasion (P = .022) in cohort 2 based on χ² analyses. Survival analyses indicated that patients with hepatocellular carcinoma having low ZW10 interactor expression had a shorter overall survival time and time to recurrence compared to cases with high ZW10 interactor expression in the prognostic cohort (P < .0001 for both overall survival and time to recurrence ). Univariate and multivariate Cox analyses indicated that ZW10 interactor was an independent prognostic factor for overall survival (P = .033).

Conclusions:

The present study clearly showed that ZW10 interactor was frequently decreased in hepatocellular carcinoma compared to nontumoral liver tissues, and ZW10 interactor could serve as a potential prognostic marker in patients with hepatocellular carcinoma after surgery.

Bioinformatics-based analysis reveals elevated MFSD12 as a key promoter of cell proliferation and a potential therapeutic target in melanoma

Although recent therapeutic advances based on our understanding of molecular phenomena have prolonged the survival of melanoma patients, the prognosis of melanoma remains dismal and further understanding of the underlying mechanism of melanoma progression is needed. In this study, differential expression analyses revealed that many genes, including AKT1 and CDK2, play important roles in melanoma. Functional analyses of differentially expressed genes (DEGs), obtained from the GEO (Gene Expression Omnibus) database, indicated that high proliferative and metastatic abilities are the main characteristics of melanoma and that the PI3K and MAPK pathways play essential roles in melanoma progression. Among these DEGs, major facilitator superfamily domain-containing 12 (MFSD12) was found to have significantly and specifically upregulated expression in melanoma, and elevated MFSD12 level promoted cell proliferation by promoting cell cycle progression. Mechanistically, MFSD12 upregulation was found to activate PI3K signaling, and a PI3K inhibitor reversed the increase in cell proliferation endowed by MFSD12 upregulation. Clinically, high MFSD12 expression was positively associated with shorter overall survival (OS) and disease-free survival (DFS) in melanoma patients, and MFSD12 was an independent prognostic factor for OS and DFS in melanoma patients. Therapeutically, in vivo assays further confirmed that MFSD12 interference inhibited tumor growth and lung metastasis in melanoma. In conclusion, elevated MFSD12 expression promotes melanoma cell proliferation, and MFSD12 is a valuable prognostic biomarker and promising therapeutic target in melanoma.

Exploration of the diagnostic value and molecular mechanism of miR‑1 in prostate cancer: A study based on meta‑analyses and bioinformatics

Prostate cancer (PCa) remains a principal issue to be addressed in male cancer-associated mortality. Therefore, the present study aimed to examine the clinical value and associated molecular mechanism of microRNA (miR)-1 in PCa. A meta-analysis was conducted to evaluate the diagnosis of miR-1 in PCa via Gene Expression Omnibus and ArrayExpress datasets, The Cancer Genome Atlas miR-1 expression data and published literature. It was identified that expression of miR-1 was significantly downregulated in PCa. Decreased miR-1 expression possessed moderate diagnostic value, with area under the curve, sensitivity, specificity and odds ratio values at 0.73, 0.77, 0.57 and 4.60, respectively. Using bioinformatics methods, it was revealed that a number of pathways, including the ‘androgen receptor signaling pathway’, ‘androgen receptor activity’, ‘transcription factor binding’ and ‘protein processing in the endoplasmic reticulum’, were important in PCa. A total of seven hub genes, including phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccin ocarboxamide synthase (PAICS), cadherin 1 (CDH1), SRC proto-oncogene, non-receptor tyrosine kinase, twist family bHLH transcription factor 1 (TWIST1), ZW10 interacting kinetochore protein (ZWINT), PCNA clamp associated factor (KIAA0101) and androgen receptor, among which, five (PAICS, CDH1, TWIST1, ZWINT and KIAA0101) were significantly upregulated and negatively correlated with miR-1, were identified as key miR-1 target genes in PCa. Additionally, it was investigated whether miR-1 and its hub genes were associated with clinical features, including age, tumor status, residual tumor, lymph node metastasis, pathological T stage and prostate specific antigen level. Collectively the results suggest that miR-1 may be involved in the progression of PCa, and consequently be a promising diagnostic marker. The ‘androgen receptor signaling pathway’, ‘androgen receptor activity’, ‘transcription factor binding’ and ‘protein processing in the endoplasmic reticulum’ may be crucial interactive pathways in PCa. Furthermore, PAICS, CDH1, TWIST1, ZWINT and KIAA0101 may serve as crucial miR-1 target genes in PCa.

Identification of key pathways and biomarkers in sorafenib-resistant hepatocellular carcinoma using bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most common malignant types of cancer, with a high mortality rate. Sorafenib is the sole approved oral clinical therapy against advanced HCC. However, individual patients exhibit varying responses to sorafenib and the development of sorafenib resistance has been a new challenge for its clinical efficacy. The current study identified gene biomarkers and key pathways in sorafenib-resistant HCC using bioinformatics analysis. Gene dataset GSE73571 was obtained from the Gene Expression Omnibus (GEO) database, including four sorafenib-acquired resistant and three sorafenib-sensitive HCC phenotypes. Differentially expressed genes (DEGs) were identified using the web tool GEO2R. Functional and pathway enrichment of DEGs were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. A total of 1,319 DEGs were selected, which included 593 upregulated and 726 downregulated genes. Functional and pathway enrichment analysis revealed DEGs enriched in negative regulation of endopeptidase activity, cholesterol homeostasis, DNA replication and repair, coagulation cascades, insulin resistance, RNA transport, cell cycle and others. Eight hub genes, including kininogen 1, vascular cell adhesion molecule 1, apolipoprotein C3, alpha 2-HS glycoprotein, erb-b2 receptor tyrosine kinase 2, secreted protein acidic and cysteine rich, vitronectin and vimentin were identified from the PPI network. In conclusion, the present study identified DEGs and key genes in sorafenib-resistant HCC, which further the knowledge of potential mechanisms in the development of sorafenib resistance and may provide potential targets for early diagnosis and new treatments for sorafenib-resistant HCC.

Analysis of protein-protein interaction network based on transcriptome profiling of ovine granulosa cells identifies candidate genes in cyclic recruitment of ovarian follicles

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.