Gene expression profile analysis of colorectal cancer to investigate potential mechanisms using bioinformatics

Prediction of Interactomic HUB Genes in Periodontitis With Acute Myocardial Infarction

BACKGROUND

Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at risk for AMI, has been one important research topic. According to recent research, AMI and PD interact via the systemic production of certain proinflammatory and anti-inflammatory cytokines, small signal molecules, and enzymes that control the onset and development of both disorders' chronic inflammatory reactions. This study uses machine learning to identify the interactome hub biomarker genes in acute myocardial infarction and periodontitis.

METHODS

GSE208194 and GSE222883 were chosen for our research after a thorough search using keywords related to the study's goal from the gene expression omnibus (GEO) datasets. DEGs were identified from the GEOR tool, and the hub gene was identified using Cytoscape-cytohubba. Using expression values, Random Forest, Adaptive Boosting, and Naive Bayes, widgets-generated transcriptomics data, were labelled, and divided into 80/20 training and testing data with cross-validation. ROC curve, confusion matrix, and AUC were determined. In addition, Functional Enrichment Analysis of Differentially Expressed Gene analysis was performed.

RESULTS

Random Forest, AdaBoost, and Naive Bayes models with 99%, 100%, and 75% AUC, respectively. Compared to RF, AdaBoost, and NB classification models, AdaBoost had the highest AUC. Categorization algorithms may be better predictors than important biomarkers.

CONCLUSIONS

Machine learning model predicts hub and non-hub genes from genomic datasets with periodontitis and acute myocardial infarction.

High expression of PPP1CC promotes NHEJ-mediated DNA repair leading to radioresistance and poor prognosis in nasopharyngeal carcinoma

Protein phosphatase 1 catalytic subunit gamma (PPP1CC) promotes DNA repair and tumor development and progression, however, its underlying mechanisms remain unclear. This study investigated the molecular mechanism of PPP1CC's involvement in DNA repair and the potential clinical implications. High expression of PPP1CC was significantly correlated with radioresistance and poor prognosis in human nasopharyngeal carcinoma (NPC) patients. The mechanistic study revealed that PPP1CC bound to Ku70/Ku80 heterodimers and activated DNA-PKcs by promoting DNA-PK holoenzyme formation, which enhanced nonhomologous end junction (NHEJ) -mediated DNA repair and led to radioresistance. Importantly, BRCA1-BRCA2-containing complex subunit 3 (BRCC3) interacted with PPP1CC to enhance its stability by removing the K48-linked polyubiquitin chain at Lys234 to prevent PPP1CC degradation. Therefore, BRCC3 helped the overexpressed PPP1CC to maintain its high protein level, thereby sustaining the elevation of DNA repair capacity and radioresistance. Our study identified the molecular mechanism by which PPP1CC promotes NHEJ-mediated DNA repair and radioresistance, suggesting that the BRCC3-PPP1CC-Ku70 axis is a potential therapeutic target to improve the efficacy of radiotherapy.

Oral vitamin D supplementation induces transcriptomic changes in rectal mucosa that are linked to anti-tumour effects

BACKGROUND

The risk for several common cancers is influenced by the transcriptomic landscape of the respective tissue-of-origin. Vitamin D influences in vitro gene expression and cancer cell growth. We sought to determine whether oral vitamin D induces beneficial gene expression effects in human rectal epithelium and identify biomarkers of response.

METHODS

Blood and rectal mucosa was sampled from 191 human subjects and mucosa gene expression (HT12) correlated with plasma vitamin D (25-OHD) to identify differentially expressed genes. Fifty subjects were then administered 3200IU/day oral vitamin D3 and matched blood/mucosa resampled after 12 weeks. Transcriptomic changes (HT12/RNAseq) after supplementation were tested against the prioritised genes for gene-set and GO-process enrichment. To identify blood biomarkers of mucosal response, we derived receiver-operator curves and C-statistic (AUC) and tested biomarker reproducibility in an independent Supplementation Trial (BEST-D).

RESULTS

Six hundred twenty-nine genes were associated with 25-OHD level (P < 0.01), highlighting 453 GO-term processes (FDR<0.05). In the whole intervention cohort, vitamin D supplementation enriched the prioritised mucosal gene-set (upregulated gene-set P < 1.0E-07; downregulated gene-set P < 2.6E-05) and corresponding GO terms (P = 2.90E-02), highlighting gene expression patterns consistent with anti-tumour effects. However, only 9 individual participants (18%) showed a significant response (NM gene-set enrichment P < 0.001) to supplementation. Expression changes in HIPK2 and PPP1CC expression served as blood biomarkers of mucosal transcriptomic response (AUC=0.84 [95%CI 0.66-1.00]) and replicated in BEST-D trial subjects (HIPK2 AUC=0.83 [95%CI 0.77-0.89]; PPP1CC AUC=0.91 [95%CI 0.86-0.95]).

CONCLUSIONS

Higher plasma 25-OHD correlates with rectal mucosa gene expression patterns consistent with anti-tumour effects, and this beneficial signature is induced by short-term vitamin D supplementation. Heterogenous gene expression responses to vitamin D may limit the ability of randomised trials to identify beneficial effects of supplementation on CRC risk. However, in the current study blood expression changes in HIPK2 and PPP1CC identify those participants with significant anti-tumour transcriptomic responses to supplementation in the rectum. These data provide compelling rationale for a trial of vitamin D and CRC prevention using easily assayed blood gene expression signatures as intermediate biomarkers of response.

The Association between Periodontitis and Human Colorectal Cancer: Genetic and Pathogenic Linkage

Periodontitis has been associated with an increased risk of and mortality associated with human colorectal cancer (CRC). Current evidence attributes such an association to the direct and indirect effects of virulence factors belonging to periodontal pathogens, to inflammatory mediators and to genetic factors. The aims of the study were to assess the existence of a genetic linkage between periodontitis and human CRC, to identify genes considered predominant in such a linkage, thus named leader genes, and to determine pathogenic mechanisms related to the products of leader genes. Genes linking periodontitis and CRC were identified and classified in order of predominance, through an experimental investigation, performed via computer simulation, employing the leader gene approach. Pathogenic mechanisms relating to leader genes were determined through cross-search databases. Of the 83 genes linking periodontitis and CRC, 12 were classified as leader genes and were pathogenically implicated in cell cycle regulation and in the immune-inflammatory response. The current results, obtained via computer simulation and requiring further validation, support the existence of a genetic linkage between periodontitis and CRC. Cell cycle dysregulation and the alteration of the immuno-inflammatory response constitute the pathogenic mechanisms related to the products of leader genes.

Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence

This study aimed to identify differentially expressed genes (DEGs) related to the colorectal normal mucosa-adenoma-carcinoma sequence using bioinformatics analysis. Raw data files were downloaded from Gene Expression Omnibus (GEO) and underwent quality assessment and preprocessing. DEGs were analyzed by the limma package in R software (R version 3.3.2). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the DAVID online tool. In a comparison of colorectal adenoma (n = 20) and colorectal cancer (CRC) stage I (n = 31), II (n = 38), III (n = 45), and IV (n = 62) with normal colorectal mucosa (n = 19), we identified 336 common DEGs. Among them, seven DEGs were associated with patient prognosis. Five (HEPACAM2, ITLN1, LGALS2, MUC12, and NXPE1) of the seven genes presented a sequentially descending trend in expression with tumor progression. In contrast, TIMP1 showed a sequentially ascending trend. GCG was constantly downregulated compared with the gene expression level in normal mucosa. The significantly enriched GO terms included extracellular region, extracellular space, protein binding, and carbohydrate binding. The KEGG categories included HIF-1 signaling pathway, insulin secretion, and glucagon signaling pathway. We discovered seven DEGs in the normal colorectal mucosa-adenoma-carcinoma sequence that was associated with CRC patient prognosis. Monitoring changes in these gene expression levels may be a strategy to assess disease progression, evaluate treatment efficacy, and predict prognosis.

Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics

Background

Progesterone plays an essential role in mammalian ovulation. Although much is known about this process, the gene networks involved in ovulation have yet to be established. When analyze the mechanisms of ovulation, we often need to determine key genes or pathways to investigate the reproduction features. However, traditional experimental methods have a number of limitations.

Results

Data, in this study, were acquired from GSE41836 and GSE54584 which provided different samples. They were analyzed with the GEO2R and 546 differentially expressed genes were obtained from two data sets using bioinformatics (absolute log₂ FC > 1, P < 0.05). This study identified four genes (PGR, RELN, PDE10A and PLA2G4A) by protein-protein interaction networks and pathway analysis, and their functional enrichments were associated with ovulation. Then, the top 25 statistical pathway enrichments related to hCG treatment were analyzed. Furthermore, gene network analysis identified certain interconnected genes and pathways involved in progestogenic mechanisms, including progesterone-mediated oocyte maturation, the MAPK signaling pathway, the GnRH signaling pathway and focal adhesion, etc. Moreover, we explored the four target gene pathways. q-PCR analysis following hCG and RU486 treatments confirmed the certain novel progestogenic-associated genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A).

Conclusions

The results suggested four key genes, nine predicted genes and eight pathways to be involved in progestogenic networks. These networks provide important regulatory genes and signaling pathways which are involved in ovulation. This study provides a fundamental basis for subsequent functional studies to investigate the regulation of mammalian ovulation.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0577-7) contains supplementary material, which is available to authorized users.

Gene expression profile analysis of the progression of carotid atherosclerotic plaques

The present study aimed to examine the universal gene expression signature and the underlying molecular mechanisms involved in the progression of carotid atherosclerotic plaques. The gene expression dataset, GSE28829, containing 13 early and 16 advanced carotid atherosclerotic plaques was selected for analysis. The differentially expressed genes (DEGs) were identified and analyzed using bioinformatics analyses, including cluster analysis, Gene Ontology (GO) and pathway enrichment analyses. Finally, a protein‑protein interaction (PPI) was constructed and analyzed. A total of 515 downregulated and 243 downregulated DEGs were identified. The cluster analysis revealed two separate two groups. In addition, the GO terms enriched by the upregulated DEGs were associated with immune response, and the downregulated DEGs were associated with cell adhesion. The upregulated DEGs were enriched in pathways associated with signaling in the immune system, and the downregulated DEGs were enriched in pathways associated with muscle contraction. In the PPI network analysis, ITGAM and ACTN2 had the highest decrees of connectivity in the upregulated and downregulated DEGs, respectively. These findings suggested that deregulation of the immune system and smooth muscle cell cytoskeleton accelerates the progression of carotid atherosclerotic plaques. The DEGs identified may offer potential in the prevention and treatment of atherosclerosis in the carotid artery.

Gene expression profile analysis of the bone microenvironment in patients with spinal metastases

The present study aimed to identify the underlying molecular mechanisms associated with spinal metastases. Gene expression profiles in cancellous bone samples from the spines of five patients with spinal metastases, with different primary cancers, and three normal control patients were measured using microarray analysis and subsequently compared. The differentially expressed genes (DEGs) identified were filtered using bioinformatics analyses followed by cluster analysis, gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein-protein interaction network was constructed and analyzed. A total of 152 upregulated and 388 downregulated DEGs were identified. The cluster analysis demonstrated a marked difference between the gene expression profiles of samples from patients with spinal metastases and those from normal patients. The GO terms enriched in the upregulated DEGs were associated with cell death, and those enriched in the downregulated DEGs were associated with the cell cycle. The upregulated DEGs were enriched in signaling pathways associated with tight junctions, and the downregulated DEGs were enriched in signaling pathways associated with porphyrin metabolism. In the PPI network constructed, transcription factor AP-1 and proliferating cell nuclear antigen had the highest connectivity degrees with the upregulated and downregulated DEGs, respectively. The gene expression profile data from the present study provides new insights into the underlying molecular mechanisms of spinal metastases, and will aid in the development of novel anticancer treatments.

Identification of miRNA-mRNA Network Associated with Acute Myeloid Leukemia Survival

Background

Acute myeloid leukemia (AML) is a common hematologic malignancy of adults. The pathophysiological mechanism of AML is not well understood. The purpose of this study was to examine the crucial miRNAs and mRNAs associated with AML survival.

Material/Methods

The full clinical dataset of miRNA and mRNA expression profiling of AML patients was downloaded from The Cancer Genome Atlas database. Univariate Cox regression analysis was performed to obtain those miRNAs and mRNAs associated with AML survival. A miRNA-mRNA interaction network was constructed. The underlying functions of mRNAs were predicted through Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment. The expression levels of miRNAs and mRNAs were detected by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

Fourteen miRNAs and 830 mRNAs associated with AML survival were identified. Of the 14 miRNAs, hsa-mir-425, hsa-mir-1201, and hsa-mir-1978 were identified as risk factors and the other 11 miRNAs were identified as protective factors of AML survival. For target-genes of miRNAs, GTSF1, RTN4R, and CD44 were the top risk factor target-genes associated with AML survival. An interaction network was constructed that including 607 miRNA-target gene pairs associated with AML survival. Target-genes associated with AML survival were significantly enriched in several pathways including pancreatic secretion, calcium signaling pathway, natural killer cell mediated cytotoxicity, and Alzheimer’s disease. The qRT-PCR results were consistent with our bioinformatics analyses.

Conclusions

The miRNA hsa-mir-425 was identified as the top risk factor miRNA of AML survival and CD44 was identified as one of the top three risk factor target-genes associated with AML survival. Both hsa-mir-425 and CD44 may play key roles in progression and development of AML through calcium signaling pathway and natural killer cell mediated cytotoxicity.

Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis

Colorectal cancer (CRC) is one of the most common malignant diseases worldwide, but the involved signaling pathways and driven-genes are largely unclear. This study integrated four cohorts profile datasets to elucidate the potential key candidate genes and pathways in CRC. Expression profiles GSE28000, GSE21815, GSE44076 and GSE75970, including 319 CRC and 103 normal mucosa, were integrated and deeply analyzed. Differentially expressed genes (DEGs) were sorted and candidate genes and pathways enrichment were analyzed. DEGs-associated protein–protein interaction network (PPI) was performed. Firstly, 292 shared DEGs (165 up-regulated and 127 down-regulated) were identified from the four GSE datasets. Secondly, the DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Thirdly, 180 nodes/DEGs were identified from DEGs PPI network complex. Lastly, the most significant 2 modules were filtered from PPI, 31 central node genes were identified and most of the corresponding genes are involved in cell cycle process, chemokines and G protein-coupled receptor signaling pathways. Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in CRC, which could improve our understanding of the cause and underlying molecular events, and these candidate genes and pathways could be therapeutic targets for CRC.

Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology

With ongoing developments in technology, changes in DNA methylation levels have become prevalent to study cancer biology. Previous studies report that DNA methylation affects gene expression in a direct manner, most probably by blocking gene regulatory regions. In this study, we have studied the interplay between methylation and expression to improve our knowledge of cancer aetiology. For this purpose, we have investigated which genomic regions are of higher importance; hence, first exon, 5'UTR and 200 bp near the transcription start sites are proposed as being more crucial compared to other genomic regions. Furthermore, we have searched for a valid methylation level change threshold, and as a result, 25 % methylation change in previously determined genomic regions showed the highest inverse correlation with expression data. As a final step, we have examined the commonly affected genes and pathways by integrating methylation and expression information. Remarkably, the GPR115 gene and ErbB signalling pathway were found to be significantly altered for all cancer types in our analysis. Overall, combining methylation and expression information and identifying commonly affected genes and pathways in a variety of cancer types revealed new insights of cancer disease mechanisms. Moreover, compared to previous methylation-based studies, we have identified more important genomic regions and have defined a methylation change threshold level in order to obtain more reliable results. In addition to the novel analysis framework that involves the analysis of four different cancer types, our study exposes essential information regarding the contribution of methylation changes and its impact on cancer disease biology, which may facilitate the identification of new drug targets.

Diametrical diseases reflect evolutionary-genetic tradeoffs: Evidence from psychiatry, neurology, rheumatology, oncology and immunology

Tradeoffs centrally mediate the expression of human adaptations. We propose that tradeoffs also influence the prevalence and forms of human maladaptation manifest in disease. By this logic, increased risk for one set of diseases commonly engenders decreased risk for another, diametric, set of diseases. We describe evidence for such diametric sets of diseases from epidemiological, genetic and molecular studies in four clinical domains: (i) psychiatry (autism vs psychotic-affective conditions), (ii) rheumatology (osteoarthritis vs osteoporosis), (iii) oncology and neurology (cancer vs neurodegenerative disorders) and (iv) immunology (autoimmunity vs infectious disease). Diametric disorders are important to recognize because genotypes or environmental factors that increase risk for one set of disorders protect from opposite disorders, thereby providing novel and direct insights into disease causes, prevention and therapy. Ascertaining the mechanisms that underlie disease-related tradeoffs should also indicate means of circumventing or alleviating them, and thus reducing the incidence and impacts of human disease in a more general way.