Oncogenomic portals for the visualization and analysis of genome-wide cancer data

The Digital World of Cytogenetic and Cytogenomic Web Resources

The dynamic growth of technological capabilities at the cellular and molecular level has led to a rapid increase in the amount of data on the genes and genomes of organisms. In order to store, access, compare, validate, classify, and understand the massive data generated by different researchers, and to promote effective communication among research communities, various genome and cytogenetic online databases have been established. These data platforms/resources are essential not only for computational analyses and theoretical syntheses but also for helping researchers select future research topics and prioritize molecular targets. Furthermore, they are valuable for identifying shared recurrent genomic patterns related to human diseases and for avoiding unnecessary duplications among different researchers. The website interface, menu, graphics, animations, text layout, and data from databases are displayed by a front end on the screen of a monitor or smartphone. A database front-end refers to the user interface or application that enables accessing tabular, structured, or raw data stored in the database. The Internet makes it possible to reach a greater number of users around the world and gives them quick access to information stored in databases. The number of ways of presenting this data by front-ends increases as well. This requires unifying the ways of operating and presenting information by front-ends and ensuring contextual switching between front-ends of different databases. This chapter aims to present selected cytogenetic and cytogenomic Internet resources in terms of obtaining the needed information and to indicate how to increase the efficiency of access to stored information. Through a brief introduction of these databases and by providing examples of their usage in cytogenetic analyses, we aim to bridge the gap between cytogenetics and molecular genomics by encouraging their utilization.

Integrated analysis of FHIT gene alterations in cancer

The Fragile Histidine Triad Diadenosine Triphosphatase (FHIT) gene is located in the Common Fragile Site FRA3B and encodes an enzyme that hydrolyzes the dinucleotide Ap3A. Although FHIT loss is one of the most frequent copy number alterations in cancer, its relevance for cancer initiation and progression remains unclear. FHIT is frequently lost in cancers from the digestive tract, which is compatible with being a cancer driver event in these tissues. However, FHIT loss could also be a passenger event due to the inherent fragility of the FRA3B locus. Moreover, the physiological relevance of FHIT enzymatic activity and the levels of Ap3A is largely unclear. We have conducted here a systematic pan-cancer analysis of FHIT status in connection with other mutations and phenotypic alterations, and we have critically discussed our findings in connection with the literature to provide an overall view of FHIT implications in cancer.

Prognostic value of CASC15 and LINC01600 as competitive endogenous RNAs in lung adenocarcinoma: An observational study

Long noncoding RNAs (lncRNAs) can directly or indirectly regulate gene expression through interacting with microRNAs (miRNAs). Competitive endogenous RNAs render the roles of lncRNAs more complicated in the process of tumor occurrence and progression. However, the prognostic value of lncRNAs as potential biomarkers and their functional roles as competitive endogenous RNAs have not been clearly described for lung adenocarcinoma (LUAD). In the present study, the aberrant expression profiles of lncRNAs and miRNAs were analyzed at cBioPortal by interrogating LUAD dataset from The Cancer Genome Atlas (TCGA) database with 517 tissue samples. A total of 92 lncRNAs and 125 miRNAs with highly genetic alterations were identified. Further bioinformatics analysis was performed to construct a LUAD-related lncRNA-miRNA-mRNA ceRNA network, which included 24 highly altered lncRNAs, 21 miRNAs and 142 mRNAs. Some key lncRNAs in this network were subsequently identified as LUAD prognosis-related, and of those, CASC15 and LINC01600 both performed the potential prognostic characteristics with LUAD regarding OS and recurrence. Comprehensive analysis indicated that the expression of LINC01600 was significantly associated with KRAS mutation and lymph node metastasis, and CASC15 and LINC01600 were significantly tended towards co-occurrence, which may be due to the similarity of genes co-expressed by these 2 lncRNAs. Our findings provided novel insight into better understanding of ceRNA regulatory mechanisms in the pathogenesis of LUAD and facilitated the identification of potential biomarkers for prognosis.

Pan-cancer evidence of prognosis, immune infiltration, and immunotherapy efficacy for annexin family using multi-omics data

The annexin superfamily (ANXA) is made up of 12 calcium (Ca2+) and phospholipid binding protein members that have a high structural homology and play a key function in cancer cells. However, little research has been done on the annexin family's function in pan-cancer. We examined the ANXA family's expression in various tumors through public databases using bioinformatics analysis, assessed the differences in ANXA expression between tumor and normal tissues in pan-cancer, and then investigated the relationship between ANXA expression and patient survival, prognosis, and clinicopathologic traits. Additionally, we investigated the relationships among TCGA cancers' mutations, tumor mutation burden (TMB), microsatellite instability (MSI), immunological subtypes, immune infiltration, tumor microenvironment, immune checkpoint genes, chemotherapeutics sensitivity, and ANXAs expression. cBioPortal was also used to uncover pan-cancer genomic anomalies in the ANXA family, study relationships between pan-cancer ANXA mRNA expression and copy number or somatic mutations, and assess the prognostic values of these variations. Moreover, we investigated the relationship between ANXAs expression and effectiveness of immunotherapy in multiple cohorts, including one melanoma (GSE78220), one renal cell carcinoma (GSE67501), and three bladder cancer cohorts (GSE111636, IMvigor210 and our own sequencing dataset (TRUCE-01)), and further analyzed the changes of ANXAs expression before and after treatment (tislelizumab combined with nab-paclitaxel) of bladder cancer. Then, we explored the biological function and potential signaling pathway of ANXAs using gene set enrichment analysis (GSEA), and first conducted immune infiltration analysis with ANXAs family genes expression, copy number, or somatic mutations of bladder cancer by TIMER 2.0. Most cancer types and surrounding normal tissues expressed ANXA differently. ANXA expression was linked to patient survival, prognosis, clinicopathologic features, mutations, TMB, MSI, immunological subtypes, tumor microenvironment, immune cell infiltration, and immune checkpoint gene expression in 33 TCGA cancers, with ANXA family members varied. The anticancer drug sensitivity analysis showed that ANXAs family members were significantly related to a variety of drug sensitivities. In addition, we also discovered that the expression level of ANXA1/2/3/4/5/7/9/10 was positively or negatively correlated with objective responses to anti-PD-1/PD-L1 across multiple immunotherapy cohorts. The immune infiltration analysis of bladder cancer further showed the significant relationships between ANXAs copy number variations or mutation status, and infiltration level of different immune cells. Overall, our analyses confirm the importance of ANXAs expression or genomic alterations in prognosis and immunological features of various cancer and identified ANXA-associated genes that may serve as potential therapeutic targets.

Designing for Dissemination and Sustainability to Promote Equitable Impacts on Health

Designing for dissemination and sustainability (D4DS) refers to principles and methods for enhancing the fit between a health program, policy, or practice and the context in which it is intended to be adopted. In this article we first summarize the historical context of D4DS and justify the need to shift traditional health research and dissemination practices. We present a diverse literature according to a D4DS organizing schema and describe a variety of dissemination products, design processes and outcomes, and approaches to messaging, packaging, and distribution. D4DS design processes include stakeholder engagement, participatory codesign, and context and situation analysis, and leverage methods and frameworks from dissemination and implementation science, marketing and business, communications and visualarts, and systems science. Finally, we present eight recommendations to adopt a D4DS paradigm, reflecting shifts in ways of thinking, skills and approaches, and infrastructure and systems for training and evaluation.

Integrated bioinformatics analysis reveals CDK1 and PLK1 as potential therapeutic targets of lung adenocarcinoma

ABSTRACT

This study is to identify potential biomarkers and therapeutic targets for lung adenocarcinoma (LUAD).GSE6044 and GSE118370 raw data from the Gene Expression Omnibus database were normalized with Robust Multichip Average. After merging these two datasets, the combat function of sva packages was used to eliminate batch effects. Then, limma packages were used to filtrate differentially expressed genes. We constructed protein-protein interaction relationships using STRING database and hub genes were identified based on connectivity degrees. The cBioportal database was used to explore the alterations of the hub genes. The promoter methylation of cyclin dependent kinase 1 (CDK1) and polo-like Kinase 1 (PLK1) and their association with tumor immune infiltration in patients with LUAD were investigated using DiseaseMeth version 2.0 and TIMER databases. The Cancer Genome Atlas-LUAD dataset was used to perform gene set enrichment analysis.We identified 10 hub genes, which were upregulated in LUAD, among which 8 were successfully verified in the Cancer Genome Atlas and Oncomine databases. Kaplan-Meier analysis indicated that the expressions of CDK1 and PLK1 in LUAD patients were associated with overall survival and disease-free survival. The methylation levels in the promoter regions of these 2 genes in LUAD patients were lower than those in normal lung tissues. Their expressions in LUAD were associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as B cells, CD4+ T cells, and macrophages. Moreover, cell cycle, DNA replication, homologous recombination, mismatch repair, P53 signaling pathway, and small cell lung cancer signaling were significantly enriched in CDK1 and PLK1 high expression phenotype.CDK1 and PLK1 may be used as potential biomarkers and therapeutic targets for LUAD.

Expression and Gene Regulation Network of Adenosine Receptor A2B in Lung Adenocarcinoma: A Potential Diagnostic and Prognostic Biomarker

Adenosinereceptor A2B (ADORA2B) encodes a protein belonging to the G protein–coupled receptor superfamily. Abnormal expression of ADORA2B may play a pathophysiological role in some human cancers. We investigated whether ADORA2B is a potential diagnostic and prognostic biomarker for lung adenocarcinoma (LUAD). The expression, various mutations, copy number variations, mRNA expression levels, and related network signaling pathways of ADORA2B were analyzed using bioinformatics-related websites, including Oncomine, UALCAN, cBioPortal, GeneMANIA, LinkedOmics, KM Plotter, and TIMER. We found that ADORA2B was overexpressed and amplified in LUAD, and a high ADORA2B expression predicted a poor prognosis for LUAD patients. Pathway analyses of ADORA2B in LUAD revealed ADORA2B-correlated signaling pathways, and the expression level of ADORA2B was associated with immune cell infiltration. Furthermore, ADORA2B mRNA and protein levels were significantly higher in human LUAD cell lines (A549 cells and NCl-H1299 cells) than in normal human bronchial epithelial (HBE) cells, and the transcript levels of genes positively or negatively correlated with ADORA2B were consistent and statistically significant. siRNA transfection experiments and functional experiments further confirmed these results. In vitro results were also consistent with those of bioinformatics analysis. Our findings provide a foundation for studying the role of ADORA2B in tumorigenesis and support the development of new drug targets for LUAD.

In silico assessment of EpCAM transcriptional expression and determination of the prognostic biomarker for human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)

Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein which is involved in cell signaling, proliferation, maturation, and movement, all of which are crucial for the proper development of cells and tissues. Cleavage of the EpCAM protein leads to the up-regulation of c-myc, e-fabp, and cyclins A and E which promote tumorigenesis. EpCAM can act as potential diagnostic and prognostic biomarker for different types of cancers as it is also found to be expressed in epithelia and epithelial-derived neoplasms. Hence, we aimed to analyze the EpCAM gene expression and any associated feedback in the patients of two major types of lung cancer (LC) i.e., lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), based on the publicly available online databases. In this study, server-based gene expression analysis represents the up-regulation of EpCAM in both LUAD and LUSC subtypes as compared to the corresponding normal tissues. Besides, the histological sections revealed the over-expression of EpCAM protein in cancerous tissues by depicting strong staining signals. Furthermore, mutation analysis suggested missense as the predominant type of mutation both in LUAD and LUSC in the EpCAM gene. A significant correlation (P-value < 0.05) between the higher EpCAM expression and lower patient survival was also found in this study. Finally, the co-expressed genes were identified with their ontological features and signaling pathways associated in LC development. The overall study suggests EpCAM to be a significant biomarker for human LC prognosis.

Pan-Cancer Prognostic Role and Targeting Potential of the Estrogen-Progesterone Axis

Introduction

Estrogen receptors (ESRs) and progesterone receptors (PGRs) are associated with the development and progression of various tumors. The feasibility of ESRs and PGRs as prognostic markers and therapeutic targets for multiple cancers was evaluated via pan-cancer analysis.

Methods

The pan-cancer mRNA expression levels, genetic variations, and prognostic values of ESR1, ESR2, and PGR were analyzed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and cBioPortal. The expression levels of ERa, ERb, and PGR proteins were detected by immunohistochemical staining using paraffin-embedded tissue specimens of ovarian serous cystadenocarcinoma (OV) and uterine endometrioid adenocarcinoma (UTEA). Correlation between immunomodulators and immune cells was determined based on the Tumor and Immune System Interaction Database (TISIDB).

Results

ESR1, ESR2, and PGR mRNAs were found to be differentially expressed in different cancer types, and were associated with tumor progression and clinical prognosis. ERa, ERb, and PGR proteins were further determined to be significantly differentially expressed in OV and UTEA via immunohistochemical staining. The expression of ERa protein was positively correlated with a high tumor stage, whereas the expression of PGR protein was conversely associated with a high tumor stage in patients with OV. In patients with UTEA, the expression levels of both ERa and PGR proteins were conversely associated with tumor grade and stage. In addition, the expression levels of ESR1, ESR2, and PGR mRNAs were significantly associated with the expression of immunomodulators and immune cells.

Conclusion

ESR1, ESR2, and PGR are potential prognostic markers and therapeutic targets, as well as important factors for the prediction, evaluation, and individualized treatment in several cancer types.

Potential functions of hsa-miR-155-5p and core genes in chronic myeloid leukemia and emerging role in human cancer: A joint bioinformatics analysis

Considering the critical roles of hsa-miR-155-5p participated in hematopoietic system, this study aims to clarify the possible pathogenesis of chronic myeloid leukemia (CML) induced by hsa-miR-155-5p.Three different strategies were employed, namely a network-based pipeline, a survival analysis and genetic screening method, and a simulation modeling approach, to assess the oncogenic role of hsa-miR-155-5p in CML. We identified new potential roles of hsa-miR-155-5p in CML, involving the BCR/ABL-mediated leukemogenesis through MAPK signaling. Several promising targets including E2F2, KRAS and FLI1 were screened as candidate diagnostic marker genes. The survival analysis revealed that mRNA expression of E2F2, KRAS and FLI1 was negatively correlated with hsa-miR-155-5p and these targets were significantly associated with poor overall survival. Furthermore, an overlap between CML-related genes and hsa-miR-155-5p target genes was revealed using competing endogenous RNA (ceRNA) networks analysis. Taken together, our results reveal the dynamic regulatory aspect of hsa-miR-155-5p as potential player in CML pathogenesis.

The oncogene AAMDC links PI3K-AKT-mTOR signaling with metabolic reprograming in estrogen receptor-positive breast cancer

Adipogenesis associated Mth938 domain containing (AAMDC) represents an uncharacterized oncogene amplified in aggressive estrogen receptor-positive breast cancers. We uncover that AAMDC regulates the expression of several metabolic enzymes involved in the one-carbon folate and methionine cycles, and lipid metabolism. We show that AAMDC controls PI3K-AKT-mTOR signaling, regulating the translation of ATF4 and MYC and modulating the transcriptional activity of AAMDC-dependent promoters. High AAMDC expression is associated with sensitization to dactolisib and everolimus, and these PI3K-mTOR inhibitors exhibit synergistic interactions with anti-estrogens in IntClust2 models. Ectopic AAMDC expression is sufficient to activate AKT signaling, resulting in estrogen-independent tumor growth. Thus, AAMDC-overexpressing tumors may be sensitive to PI3K-mTORC1 blockers in combination with anti-estrogens. Lastly, we provide evidence that AAMDC can interact with the RabGTPase-activating protein RabGAP1L, and that AAMDC, RabGAP1L, and Rab7a colocalize in endolysosomes. The discovery of the RabGAP1L-AAMDC assembly platform provides insights for the design of selective blockers to target malignancies having the AAMDC amplification.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Integration of Online Omics-Data Resources for Cancer Research

The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics - such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single- and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. We believe this systematic review will encourage scientists and clinicians worldwide to utilize the online resources to explore and integrate the available omics datasets that may provide a window of opportunity to generate new biological insights and contribute to the advancement of the field of cancer research.

Computational assessment of MCM2 transcriptional expression and identification of the prognostic biomarker for human breast cancer

Minichromosome maintenance protein 2 (MCM2) is a highly conserved protein from the MCM protein family that plays an important role in eukaryotic DNA replication as well as in cell cycle progression. In addition, it maintains the ploidy level consistency in eukaryotic cells, hence, mutations or alteration of this protein could result in the disintegration of the fine-tuned molecular machinery that can lead to uncontrolled cell proliferation. Moreover, MCM2 has been found to be an important marker for progression and prognosis in different cancers. Therefore, we aimed to analyze the MCM2 expression and the associated outcome in breast cancer (BC) patients based on the publicly available online databases. In this study, server-based gene expression analyses indicate the upregulation of MCM2 (p < 10^-6; fold change>2.0) in various BC subtypes as compared to the respective normal tissues. Besides, the evaluation of histological sections from healthy and cancer tissues showed strong staining signals indicating higher expression of MCM2 protein. The overexpression of MCM2 was significantly correlated to promoter methylation and was related to patients' clinical features. Further, mutation analysis suggested missense as the predominant type of mutation (71.4%) with 18 copy-number alterations and 0.2% mutation frequency in the MCM2 gene. This study revealed a significant correlation (Cox p ≤ 0.05) between the higher MCM2 expression and lower patient survival. Finally, we identified the co-expressed genes with gene ontological features and signaling pathways associated in BC development. We believe that this study will provide a basis for MCM2 to be a significant biomarker for human BC prognosis.

PROM1 and PROM2 expression differentially modulates clinical prognosis of cancer: a multiomics analysis

Prominin 1 (PROM1) is considered a biomarker for cancer stem cells, although its biological role is unclear. Prominin 2 (PROM2) has also been associated with certain cancers. However, the prognostic value of PROM1 and PROM2 in cancer is controversial. Here, we performed a systematic data analysis to examine whether prominins can function as prognostic markers in human cancers. The expression of prominins was assessed and their prognostic value in human cancers was determined using univariate and multivariate survival analyses, via various online platforms. We selected a group of prominent functional protein partners of prominins by protein-protein interaction analysis. Subsequently, we investigated the relationship between mutations and copy number alterations in prominin genes and various types of cancers. Furthermore, we identified genes that correlated with PROM1 and PROM2 in certain cancers, based on their levels of expression. Gene ontology and pathway analyses were performed to assess the effect of these correlated genes on various cancers. We observed that PROM1 was frequently overexpressed in esophageal, liver, and ovarian cancers and its expression was negatively associated with prognosis, whereas PROM2 overexpression was associated with poor overall survival in lung and ovarian cancers. Based on the varying characteristics of prominins, we conclude that PROM1 and PROM2 expression differentially modulates the clinical outcomes of cancers.

Functional characterization of MCAK/Kif2C cancer mutations using high-throughput microscopic analysis

The microtubule (MT)-depolymerizing activity of MCAK/Kif2C can be quantified by expressing the motor in cultured cells and measuring tubulin fluorescence levels after enough hours have passed to allow tubulin autoregulation to proceed. This method allows us to score the impact of point mutations within the motor domain. We found that, despite their distinctly different activities, many mutations that impact transport kinesins also impair MCAK/Kif2C's depolymerizing activity. We improved our workflow using CellProfiler to significantly speed up the imaging and analysis of transfected cells. This allowed us to rapidly interrogate a number of MCAK/Kif2C motor domain mutations documented in the cancer database cBioPortal. We found that a large proportion of these mutations adversely impact the motor. Using green fluorescent protein-FKBP-MCAK CRISPR cells we found that one deleterious hot-spot mutation increased chromosome instability in a wild-type (WT) background, suggesting that such mutants have the potential to promote tumor karyotype evolution. We also found that increasing WT MCAK/Kif2C protein levels over that of endogenous MCAK/Kif2C similarly increased chromosome instability. Thus, endogenous MCAK/Kif2C activity in normal cells is tuned to a mean level to achieve maximal suppression of chromosome instability.

mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations

Mitochondria participate in metabolism and signaling. They adapt to the requirements of various cell types. Publicly available expression data permit to study expression dynamics of genes with mitochondrial function (mito-genes) in various cell types, conditions and organisms. Yet, we lack an easy way of extracting these data for mito-genes. Here, we introduce the visual data mining platform mitoXplorer, which integrates expression and mutation data of mito-genes with a manually curated mitochondrial interactome containing ∼1200 genes grouped in 38 mitochondrial processes. User-friendly analysis and visualization tools allow to mine mitochondrial expression dynamics and mutations across various datasets from four model species including human. To test the predictive power of mitoXplorer, we quantify mito-gene expression dynamics in trisomy 21 cells, as mitochondrial defects are frequent in trisomy 21. We uncover remarkable differences in the regulation of the mitochondrial transcriptome and proteome in one of the trisomy 21 cell lines, caused by dysregulation of the mitochondrial ribosome and resulting in severe defects in oxidative phosphorylation. With the newly developed Fiji plugin mitoMorph, we identify mild changes in mitochondrial morphology in trisomy 21. Taken together, mitoXplorer (http://mitoxplorer.ibdm.univ-mrs.fr) is a user-friendly, web-based and freely accessible software, aiding experimental scientists to quantify mitochondrial expression dynamics.

Gene Expression Alterations and Molecular Analysis of CHEK1 in Solid Tumors

Alterations in the Checkpoint kinase (CHEK1) gene, its regulation, and the possible clinical outcomes in human solid tumors have not been previously examined. Therefore, the present study was carried out to evaluate the expression of CHEK1 in solid tumors as well as the mechanism by which it can be regulated through non-coding RNAs. The expression of CHEK1 was investigated using Oncomine analysis. cBioPortal, Kaplan-Meier Plotter, and PrognoScan were performed to identify the prognostic roles of this gene in solid tumors. The copy number alteration, mutation, interactive analysis, and visualization of the altered networks were performed by cBioPortal. The molecular binding analysis was carried out by Schrodinger suite, PATCHDOCK, and discovery studio visualizer. The study demonstrated that the CHEK1 gene was differentially expressed in four different cancers, and that reduced CHEK1 mRNA expression is an unfavorable prognostic factor for patients with gastric and colorectal cancer. The molecular docking results showed that the CHEK1 gene can be regulated by microRNAs (miR-195-5p) due to the number of stable hydrogen atoms observed within the distance of 2.0 Å and the favorable amino acids (Ala221, Ile353, Ile365, Ile756, Val797, Val70, Val154, Ile159, Val347, Tyr804, Phe811, Tyr815, and Phe156) identified in the binding pocket of the argonaute protein. Due to the possibility of CHEK1's involvement in solid tumors, it may potentially be a target for therapeutic intervention in cancer. Further studies into the interaction between CHEK1 and other co-expressed genes may give further insight into other modes of regulation of this gene in cancer patients.

A Multi-Omics Analysis of Bone Morphogenetic Protein 5 (BMP5) mRNA Expression and Clinical Prognostic Outcomes in Different Cancers Using Bioinformatics Approaches

Cumulative studies have provided controversial evidence for the prognostic values of bone morphogenetic protein 5 (BMP5) in different types of cancers such as colon, breast, lung, bladder, and ovarian cancer. To address the inconsistent correlation of BMP5 expression with patient survival and molecular function of BMP5 in relation to cancer progression, we performed a systematic study to determine whether BMP5 could be used as a prognostic marker in human cancers. BMP5 expression and prognostic values were assessed using different bioinformatics tools such as ONCOMINE, GENT, TCGA, GEPIA, UALCAN, PrognoScan, PROGgene V2 server, and Kaplan-Meier Plotter. In addition, we used cBioPortal database for the identification and analysis of BMP5 mutations, copy number alterations, altered expression, and protein-protein interaction (PPI). We found that BMP5 is frequently down-regulated in our queried cancer types. Use of prognostic analysis showed negative association of BMP5 down-regulation with four types of cancer except for ovarian cancer. The highest mutation was found in the R321*/Q amino acid of BMP5 corresponding to colorectal and breast cancer whereas the alteration frequency was higher in lung squamous carcinoma datasets (>4%). In PPI analysis, we found 31 protein partners of BMP5, among which 11 showed significant co-expression (p-value < 0.001, log odds ratio > 1). Pathway analysis of differentially co-expressed genes with BMP5 in breast, lung, colon, bladder and ovarian cancers revealed the BMP5-correlated pathways. Collectively, this data-driven study demonstrates the correlation of BMP5 expression with patient survival and identifies the involvement of BMP5 pathways that may serve as targets of a novel biomarker for various types of cancers in human.

Association between the expression of secreted phosphoprotein - related genes and prognosis of human cancer

Background

While many studies have assessed the predictive value of secreted phosphoprotein (SPP) genes in cancer, the findings have been inconsistent. To resolve these inconsistencies, we systematically analyzed the available data to determine whether SPP1 and SPP2 are prognostic markers in the context of human cancer.

Methods

The expression of SPP1 and SPP2 was assessed by Oncomine analysis. The PrognoScan database was used to assess the prognostic value of SPP1 and SPP2, with cBioPortal used to assess copy number variations. The STRING database was used to generate a Protein - Protein Interaction (PPI) network for SPP genes.

Results

SPP1 was more likely to be over-expressed in breast, bladder, colorectal, head, neck, liver, lung, and esophageal cancers. SPP2 was expressed at lower levels in colorectal cancer, leukemia, liver cancer and pancreatic cancer. In addition, SPP1 and SPP2 mutations mainly occurred in cutaneous melanoma and endometrial cancer.

Conclusions

Our results suggest that SPP1 and SPP2 may be effective therapeutic or diagnostic targets in certain cancers. Further research is required to confirm these results and verify the value of SPP1 and SPP2 as clinical markers of cancer prognosis.

Bioinformatics analysis of the expression of HOXC13 and its role in the prognosis of breast cancer

The homeobox (HOX) genes, a class of transcription factors, are known to promote embryonic development and induce tumor formation. To date, the HOXA and HOXB gene families have been reported to be associated with breast cancer. However, the expression and exact role of homeobox C13 (HOXC13) in breast cancer has not yet been investigated. In the present study, the HOXC13 expression in human breast cancer was evaluated using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). Next, the Gene expression-based Outcome for Breast cancer online database, cBioportal, University of California Santa Cruz Xena browser and bc-GenExMinerv were used to explore the specific expression of HOXC13 in breast cancer. The methylation and mutation status of HOXC13 in breast cancer was then validated using the CCLE and cBioportal databases. Finally, the co-expression of HOX transcript antisense RNA (HOTAIR) and HOXC13 in breast cancer were analyzed and their impact on clinical prognosis determined. It was found that the expression of HOXC13 was high in breast cancer compared with other types of cancer, such as gastric cancer and colon cancer. Following co-expression analysis, a significant positive association was identified between HOTAIR and HOXC13. An association between HOTAIR and HOXC13, and lymph node and distant metastasis recurrence was also revealed during the development of breast cancer. Of note, survival analysis showed that high expression of HOTAIR and HOXC13 predicted poor prognosis. These findings revealed that HOXC13 plays an important role in the progression of breast cancer. However, the specific mechanism needs to be confirmed by subsequent experiments.

Increased expression of TET3 predicts unfavorable prognosis in patients with ovarian cancer-a bioinformatics integrative analysis

Ovarian carcinoma is a lethal gynecological malignancy. Women with ovarian cancer (OC) are highly recurrent and typically diagnosed at late stage. Ten-eleven translocation protein 3 (TET3) belongs to the family of ten-eleven translocations (TETs) which induce DNA demethylation and gene regulation in epigenetic level by converting 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Previous studies indicated that TET3 is overexpressed in ovarian cancer tissues. However, the clinic-pathological functions and prognostic values of TET3 remain unclear. Here we performed an integrative study to identify the role of TET3 by bioinformatics analysis. The TET3 expression in ovarian cancer was assessed with Oncomine database, and validated with TCGA and GTEx database. The correlation of TET3 gene alteration and clinic-pathological functions was addressed by integrative analysis of GEO datasets. Then we showed mainly TET3 gain and diploid but less deletion in ovarian cancer by copy number alteration (CNA) or mutation analysis with cBioPortal. Furthermore, by using Kaplan-Meier plotter (K-M plotter), we evaluated that high TET3 level was associated with poor survival in ovarian cancer patients, which was validated with analysis by PrognoScan database and gene differential analyses with TCGA and GTEx. This is the first study demonstrated that elevated expression of TET3 is associated with poor clinic-pathological functions, poor prognosis, wherein TET3, which presents epigenetic changes or methylation changes, might be served as a diagnostic marker or therapeutic target for ovarian cancer.

A TFAP2C Gene Signature Is Predictive of Outcome in HER2-Positive Breast Cancer

The AP-2γ transcription factor, encoded by the TFAP2C gene, regulates the expression of estrogen receptor-alpha (ERα) and other genes associated with hormone response in luminal breast cancer. Little is known about the role of AP-2γ in other breast cancer subtypes. A subset of HER2⁺ breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2γ. Herein, we sought to define AP-2γ gene targets in HER2⁺ breast cancer and identify genes accounting for physiologic effects of growth and invasiveness regulated by AP-2γ. Comparing HER2⁺ cell lines that demonstrated differential response to growth and invasiveness with knockdown of TFAP2C, we identified a set of 68 differentially expressed target genes. CDH5 and CDKN1A were among the genes differentially regulated by AP-2γ and that contributed to growth and invasiveness. Pathway analysis implicated the MAPK13/p38δ and retinoic acid regulatory nodes, which were confirmed to display divergent responses in different HER2⁺ cancer lines. To confirm the clinical relevance of the genes identified, the AP-2γ gene signature was found to be highly predictive of outcome in patients with HER2⁺ breast cancer. We conclude that AP-2γ regulates a set of genes in HER2⁺ breast cancer that drive cancer growth and invasiveness. The AP-2γ gene signature predicts outcome of patients with HER2⁺ breast cancer and pathway analysis predicts that subsets of patients will respond to drugs that target the MAPK or retinoic acid pathways. IMPLICATIONS: A set of genes regulated by AP-2γ in HER2⁺ breast cancer that drive proliferation and invasion were identified and provided a gene signature that is predictive of outcome in HER2⁺ breast cancer.

The potential use of big data in oncology

In this era of information technology, big data analysis is entering biomedical sciences. But what is big data, where do they come from and what can we do with it? In this commentary, the main sources of big data are explained, especially in (head and neck) oncology. It also touches upon the need to integrate various sources of clinical, pathological and quality-of-life data. It discusses some initiatives in linking of such datasets on a nation-wide scale in the Netherlands. Finally, it touches upon important issues regarding governance, FAIRness of data and the need to bring into place the necessary infrastructures needed to fully exploit the full potential of big data sets in head and neck cancer.

Meta-analysis of gene expression alterations and clinical significance of the HECT domain-containing ubiquitin ligase HUWE1 in cancer

E3 ubiquitin-protein ligase (HUWE1) has previously been identified as a HECT domain-containing ubiquitin ligase (E3) that is involved in several signaling pathways, transcriptional regulation, neural differentiation, DNA damage responses and apoptosis. However, the function of HUWE1 in the various types of cancer remains unclear. A previous study indicated that HUWE1 exhibited different roles depending on the cancer type due to the ubiquitination of various substrates. The objective of the present study was to determine whether HUWE1 can be employed as a prognostic indicator in human cancer. The expression of HUWE1 was examined using the Oncomine database, and gene alterations during carcinogenesis, copy number alterations and mutations of HUWE1 were then analyzed using cBioPortal, which is the International Cancer Genome Consortium and the Tumorscape database. Furthermore, the association between HUWE1 expression and patient survival was evaluated using Kaplan-Meier plotter and the PrognoScan databases. In addition, the present study attempted to establish the functional association between HUWE1 expression and cancer phenotypes, and the results revealed that HUWE1 may serve as a diagnostic marker or therapeutic target for certain types of cancer. HUWE1 may serve an oncogenic role in breast, brain and prostate cancer, while it may serve an anti-oncogenic role in colorectal cancer and certain lung cancers. The function of HUWE1 and its mechanisms require more in-depth and extensive investigation in future studies.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.

Integrative analysis of transcriptomics and clinical data uncovers the tumor-suppressive activity of MITF in prostate cancer

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

Systematic expression alteration analysis of master reprogramming factor OCT4 and its three pseudogenes in human cancer and their prognostic outcomes

OCT4 is a master transcription factor that regulates the pluripotency of pluripotent stem cells and cancer stem cells along with other factors, including SOX2, KLF4, and C-MYC. Three different transcripts, OCT4A, OCT4B, and OCT4B1, are known to be generated by alternative splicing and eight OCT4 pseudogenes have been found in the human genome. Among them, we examined OCT4 and three pseudogenes (POU5F1P1, POU5F1P3, and POU5F1P4) because of their high expression possibility in cancer. In addition, previous studies indicated that OCT4 expression is augmented in cervical cancer and associated with poor prognosis, whereas OCT4 is down-regulated and correlated with good clinical outcomes in breast cancer. Because of these conflicting reports, we systematically evaluated whether expression of OCT4 and its pseudogenes can serve as oncogenic markers in various human cancers using the Oncomine database. Moreover, copy number alterations and mutations in OCT4 gene and its pseudogenes were analyzed using cBioPortal and the relationship between expression of OCT4 and pseudogenes and survival probability of cancer patients were explored using Kaplan-Meier plotter, OncoLnc, PROGgeneV2, and PrognoScan databases. Multivariate survival analysis was further conducted to determine the risk of the expression of the occurrence of OCT4 and its pseudogenes on certain cancer types using data from the Kaplan-Meier plotter. Overall, an association between expression of OCT4 and pseudogenes and cancer prognosis were established, which may serve as a therapeutic target for various human cancers.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.

Systematic analysis of gene expression alterations and clinical outcomes of adenylate cyclase-associated protein in cancer

Adenylate Cyclase-associated protein (CAP) is an evolutionarily conserved protein that regulates actin dynamics. Our previous study indicates that CAP1 is overexpressed in NSCLC tissues and correlated with poor clinical outcomes, but CAP1 in HeLa cells actually inhibited migration and invasion, the role of CAP was discrepancy in different cancer types. The present study aims to determine whether CAP can serve as a prognostic marker in human cancers. The CAP expression was assessed using Oncomine database to determine the gene alteration during carcinogenesis, the copy number alteration, or mutations of CAP using cBioPortal, International Cancer Genome Consortium, and Tumorscape database investigated, and the association between CAP expression and the survival of cancer patient using Kaplan-Meier plotter and PrognoScan database evaluated. Therefore, the functional correlation between CAP expression and cancer phenotypes can be established; wherein CAP might serve as a diagnostic marker or therapeutic target for certain types of cancers.

Advances in Text Mining and Visualization for Precision Medicine

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care.

Systematic analysis of gene expression alterations and clinical outcomes of STAT3 in cancer

Accumulated studies have provided controversial evidences of prognostic value for signal transducer and activator of transcription proteins 3 (STAT3) in cancers. To address this inconsistency, we performed a systematic analysis to determine whether STAT3 can serve as a prognostic marker in human cancers. STAT3 expression was assessed using Oncomine analysis. cBioPortal, Kaplan-Meier Plotter, and Prognoscan were performed to identify the prognostic roles of STAT3 in human cancers. The copy number alteration, mutation, interactive analysis, and visualize the altered networks were performed by cBioPortal. We found that STAT3 was more frequently overexpressed in lung, ovarian, gastric, blood and brain cancers than their normal tissues and its expression might be negatively related with the prognosis. In addition, STAT3 mutation mainly occurred in uterine cancer and existed in a hotspot in SH2 domain. Those findings suggest that STAT3 might serve as a diagnostic and therapeutic target for certain types of cancer, such as lung, ovarian, gastric, blood and brain cancers. However, future research is required to validate our findings and thus promote the clinical utility of STAT3 in those cancers prognosis evaluation.

The use of databases, data mining and immunoinformatics in vaccinology: where are we?

INTRODUCTION

Vaccinology has evolved from a sub-discipline focussed on simplistic vaccine development based on antibody-mediated protection to a separate discipline involving epidemiology, host and pathogen biology, immunology, genomics, proteomics, structure biology, protein engineering, chemical biology, and delivery systems. Data mining in combination with bioinformatics has provided a scaffold linking all these disciplines to the design of vaccines and vaccine adjuvants. Areas covered: This review provides background knowledge on immunological aspects which have been exploited with informatics for the in silico analysis of immune responses and the design of vaccine antigens. Furthermore, the article presents various databases and bioinformatics tools, and discusses B and T cell epitope predictions, antigen design, adjuvant research and systems immunology, highlighting some important examples, and challenges for the future. Expert opinion: Informatics and data mining have not only reduced the time required for experimental immunology, but also contributed to the identification and design of novel vaccine candidates and the determination of biomarkers and pathways of vaccine response. However, more experimental data is required for benchmarking immunoinformatic tools. Nevertheless, developments in immunoinformatics and reverse vaccinology, which are nascent fields, are likely to hasten vaccine discovery, although the path to regulatory approval is likely to remain a necessary impediment.

The depletion of PinX1 involved in the tumorigenesis of non-small cell lung cancer promotes cell proliferation via p15/cyclin D1 pathway

Background

The telomerase/telomere interacting protein PinX1 has been suggested as a tumor suppressor. However, the clinical and biological significance of PinX1 in human non-small cell lung cancer (NSCLC) is unclear.

Methods

PinX1 gene/expression pattern and its association with NSCLC patient survival were analyzed in cBioportal Web resource and two cohorts of NSCLC samples. A series of in vivo and in vitro assays were performed to elucidate the function of PinX1 on NSCLC cells proliferation and underlying mechanisms.

Results

More frequency of gene PinX1 homozygous deletion and heterozygote deficiency was first retrieved from cBioportal Web resource. Low expression of PinX1 correlated with smoking condition, histological type, T stage, N stage, M stage and TNM stage, and was an independent predictor for overall survival in a learning cohort (n = 93) and a validation cohort (n = 51) of NSCLC patients. Furthermore, knockdown of PinX1 dramatically accelerated NSCLC cell proliferation and G1/S transition, whereas ectopic overexpression of PinX1 substantially inhibited cell viability and cell cycle transition in vitro and in vivo. p15/cyclin D1 pathway and BMP5 might contribute to PinX1-associated cell proliferation and cell cycle transition.

Conclusion

The cost-effective expression of PinX1 could constitute a novel molecular predictor/marker for NSCLC management.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0637-4) contains supplementary material, which is available to authorized users.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.