High neuronatin (NNAT) expression is associated with poor outcome in breast cancer

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.

Termination codon readthrough of NNAT mRNA regulates calcium-mediated neuronal differentiation

Termination codon readthrough (TCR) is a process in which ribosomes continue to translate an mRNA beyond a stop codon generating a C-terminally extended protein isoform. Here, we demonstrate TCR in mammalian NNAT mRNA, which encodes NNAT, a proteolipid important for neuronal differentiation. This is a programmed event driven by cis-acting RNA sequences present immediately upstream and downstream of the canonical stop codon and is negatively regulated by NONO, an RNA-binding protein known to promote neuronal differentiation. Unlike the canonical isoform NNAT, we determined that the TCR product (NNATx) does not show detectable interaction with the sarco/endoplasmic reticulum Ca2+-ATPase isoform 2 Ca2+ pump, cannot increase cytoplasmic Ca2+ levels, and therefore does not enhance neuronal differentiation in Neuro-2a cells. Additionally, an antisense oligonucleotide that targets a region downstream of the canonical stop codon reduced TCR of NNAT and enhanced the differentiation of Neuro-2a cells to cholinergic neurons. Furthermore, NNATx-deficient Neuro-2a cells, generated using CRISPR-Cas9, showed increased cytoplasmic Ca2+ levels and enhanced neuronal differentiation. Overall, these results demonstrate regulation of neuronal differentiation by TCR of NNAT. Importantly, this process can be modulated using a synthetic antisense oligonucleotide.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet .

In Silico Identification of Key Genes and Immune Infiltration Characteristics in Epicardial Adipose Tissue from Patients with Coronary Artery Disease

Background

The present study is aimed at identifying the differentially expressed genes (DEGs) and relevant biological processes and pathways associated with epicardial adipose tissue (EAT) from patients with coronary artery disease (CAD). We also explored potential biomarkers using two machine-learning algorithms and calculated the immune cell infiltration in EAT.

Materials and Methods

Three datasets (GSE120774, GSE64554, and GSE24425) were obtained from the Gene Expression Omnibus (GEO) database. The GSE120774 dataset was used to evaluate DEGs between EAT of CAD patients and the control group. Functional enrichment analyses were conducted to study associated biological functions and mechanisms using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). After this, the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were performed to identify the feature genes related to CAD. The expression level of the feature genes was validated in GSE64554 and GSE24425. Finally, we calculated the immune cell infiltration and evaluated the correlation between the feature genes and immune cells using CIBERSORT.

Results

We identified a total of 130 upregulated and 107 downregulated genes in GSE120774. Functional enrichment analysis revealed that DEGs are associated with several pathways, including the calcium signaling pathway, complement and coagulation cascades, ferroptosis, fluid shear stress and atherosclerosis, lipid and atherosclerosis, and regulation of lipolysis in adipocytes. TCF21, CDH19, XG, and NNAT were identified as feature genes and validated in the GSE64554 and GSE24425 datasets. Immune cell infiltration analysis showed plasma cells are significantly more numerous in EAT than in the control group (p = 0.001), whereas macrophage M0 (p = 0.024) and resting mast cells (p = 0.036) were significantly less numerous. TCF21, CDH19, XG, and NNAT were correlated with immune cells, including plasma cells, M0 macrophages, and resting mast cells.

Conclusion

TCF21, CDH19, XG, and NNAT might serve as feature genes for CAD, providing new insights for future research on the pathogenesis of cardiovascular diseases.

Spontaneous and ART-induced large offspring syndrome: similarities and differences in DNA methylome

Large/abnormal offspring syndrome (LOS/AOS) is a congenital overgrowth syndrome reported in ruminants produced by assisted reproduction (ART-LOS) which exhibit global disruption of the epigenome and transcriptome. LOS/AOS shares phenotypes and epigenotypes with the human congenital overgrowth condition Beckwith-Wiedemann syndrome. We have reported that LOS occurs spontaneously (SLOS); however, to date, no study has been conducted to determine if SLOS has the same methylome epimutations as ART-LOS. In this study, we performed whole-genome bisulphite sequencing to examine global DNA methylation in bovine SLOS and ART-LOS tissues. We observed unique patterns of global distribution of differentially methylated regions (DMRs) over different genomic contexts, such as promoters, CpG Islands, shores and shelves, as well as at repetitive sequences. In addition, we included data from two previous LOS studies to identify shared vulnerable genomic loci in LOS. Overall, we identified 320 genomic loci in LOS that have alterations in DNA methylation when compared to controls. Specifically, there are 25 highly vulnerable loci that could potentially serve as molecular markers for the diagnosis of LOS, including at the promoters of DMRT2 and TBX18, at the imprinted gene bodies of IGF2R, PRDM8, and BLCAP/NNAT, and at multiple CpG Islands. We also observed tissue-specific DNA methylation patterns between muscle and blood, and conservation of ART-induced DNA methylation changes between muscle and blood. We conclude that as ART-LOS, SLOS is an epigenetic condition. In addition, SLOS and ART-LOS share similarities in methylome epimutations.

Integrated genomics point to immune vulnerabilities in pleural mesothelioma

Pleural mesothelioma is an aggressive malignancy with limited effective therapies. In order to identify therapeutic targets, we integrated SNP genotyping, sequencing and transcriptomics from tumours and low-passage patient-derived cells. Previously unrecognised deletions of SUFU locus (10q24.32), observed in 21% of 118 tumours, resulted in disordered expression of transcripts from Hedgehog pathways and the T-cell synapse including VISTA. Co-deletion of Interferon Type I genes and CDKN2A was present in half of tumours and was a predictor of poor survival. We also found previously unrecognised deletions in RB1 in 26% of cases and show sub-micromolar responses to downstream PLK1, CHEK1 and Aurora Kinase inhibitors in primary mesothelioma cells. Defects in Hippo pathways that included RASSF7 amplification and NF2 or LATS1/2 mutations were present in 50% of tumours and were accompanied by micromolar responses to the YAP1 inhibitor Verteporfin. Our results suggest new therapeutic avenues in mesothelioma and indicate targets and biomarkers for immunotherapy.

The predictive potential of Neuronatin for neoadjuvant chemotherapy of breast cancer

BACKGROUND

Neuronatin (NNAT) determined by immunohistochemistry is a negative prognostic biomarker for breast cancer, independent of the major clinicopathological markers.

OBJECTIVE

Here, we investigated whether NNAT is also a predictive biomarker for pathological remission after neoadjuvant chemotherapy.

METHODS

One hundred and four breast cancer patients, treated with systemic neoadjuvant chemotherapy were included in this retrospective study. NNAT was detected in formaldehyde fixed, paraffin embedded primary cancer tissue by immunohistochemistry and an immuno-reactive score (IRS) determined. Pathological remission was scored according to Sinn and by evaluation of cytopathic effects. NNAT-IRS was correlated with clinicopathological parameters as well as relapse free and overall survival and for pathological remission after neoadjuvant therapy.

RESULTS

NNAT IRS was an independent prognostic marker for relapse free and overall survival and the time from diagnosis to the "tumor-free" state. NNAT IRS was associated with Luminal-A tumors and correlated slightly negative with age and lymph-node metastasis. There was no significant correlation of NNAT-IRS with Sinn's remission score, but with cytopathic effects of chemotherapy.

CONCLUSIONS

We confirmed the prognostic impact of NNAT-IRS in an independent cohort of neoadjuvantly treated patients. Additionally, a correlation with a score for pathological remission under systemic neoadjuvant chemotherapy for breast cancer was found.

Epigenome-wide scan identifies differentially methylated regions for lung cancer using pre-diagnostic peripheral blood

BACKGROUND

DNA methylation markers have been associated with lung cancer risk and may identify aetiologically relevant genomic regions, or alternatively, be markers of disease risk factors or biological processes associated with disease development.

METHODS

In a nested case-control study, we measured blood leukocyte DNA methylation levels in pre-diagnostic samples collected from 430 participants (208 cases; 222 controls) in the 1989 CLUE II cohort. We compared DNA methylation levels with case/control status to identify novel genomic regions, both single CpG sites and differentially methylated regions (DMRs), while controlling for known DNA methylation changes associated with smoking using a previously described pack-years-based smoking methylation score. Stratification analyses were conducted over time from blood draw to diagnosis, histology, and smoking status.

RESULTS

We identified 16 single CpG sites and 40 DMRs significantly associated with lung cancer risk (q < 0.05). The identified genomic regions were associated with genes including H19, HOXA3/HOXA4, RUNX3, BRICD5, PLXNB2, and RP13. For the single CpG sites, the strongest association was noted for cg09736286 in the DIABLO gene (OR [for 1 SD] = 2.99, 95% CI: 1.95-4.59, P-value = 4.81 × 10-7). We found that CpG sites in the HOXA3/HOXA4 region were hypermethylated in cases compared to controls.

CONCLUSION

The single CpG sites and DMRs that we identified represented significant measurable differences in lung cancer risk, providing potential biomarkers for lung cancer risk stratification. Future studies will need to examine whether these regions are causally related to lung cancer.

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Aim

Immune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).

Methods

Immune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.

Results

We selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.

Conclusion

The current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

Lidocaine prevents breast cancer growth by targeting neuronatin to inhibit nerve fibers formation

Lidocaine has been shown to inhibit the invasion and metastasis of breast cancer, but the mechanism still remains unclear. This study explored the relationship between lidocaine and circulating seeding of breast cancer cells from the perspective of nerve fiber formation. The cell lines MDA-MB-231 and 4T1 were subcutaneously inoculated in mice to simulate the tumor self-seeding by circulating cancer cells. Lidocaine was used to treat these mice and tumor growth was observed. Silver staining was performed to observe the distribution of nerve fibers in tumor-bearing tissues, and immunohistochemical analysis was performed to observe the expression levels of nerve-related proteins. The results showed that lidocaine treatment effectively inhibited tumor growth and nerve fiber formation, and down-regulated the expression levels of protein gene product 9.5, neurofilament, nerve growth factor (NGF), and neuronatin (Nnat). Overexpression NGF and Nnat both could reverse the therapeutic effects of lidocaine. These results suggest that the effect of lidocaine on inhibiting breast cancer invasion and metastasis may be achieved by targeting Nnat, regulating the production of NGFs in cancer cells, and subsequently inhibiting the formation of nerve fibers.

Glyoxalase 1 expression analysis by immunohistochemistry in breast cancer

Glyoxalase-1 (GLO-1) is the key enzyme in aldehyde defence in cancer cells. We here evaluated the prognostic impact and association with clinico-pathological parameters and relapse-free as well as overall survival in tumor samples from 187 breast cancer patients. The determined GLO1-immunoreactive score (GLO1-IRS) did not correlate with parameters such as grading, size, hormone receptors or ki67. However, an association of GLO1-IRS with the advanced glycation end product N^ε-(carboxymethyl)lysine (p = 0.07) and HER2 (p = 0.06), and a strong correlation with VEGF (p = 0.008) was found. In survival analysis, no significant impact of GLO-1 IRS could be deduced for all patients. However, GLO1-IRS correlated with treatment by radiotherapy (p = 0.008) and high GLO1-IRS predicted a shorter relapse free survival after radiotherapy (log-rank p = 0.067). METABRIC- and TCGA expression-data were analyzed for correlation of regulatory genes of the NF-κB-pathway (RELA, RELB, IRAK1), the oxidative-stress associated transcription factor nrf2 (NFE2L2), the receptor for AGEs (AGER, RAGE) as well as enzymes associated with aldehyde defense. Here, RELA, RELB and NFE2L2 correlated significantly with GLO1 expression, but there were conflicting results between the two data sources. In conclusion, GLO1 was highly expressed in cancer cells, correlated surprisingly weak with survival, but we could show a positive association with the AGE CML as well as VEGF. Gene expression data suggest a regulation of GLO-1 mRNA via both, inflammation (NF-kB) and oxidative stress (NFE2L2) in tumors.

Genetic and microenvironmental differences in non-smoking lung adenocarcinoma patients compared with smoking patients

Background

Non-smoking-related lung adenocarcinoma (LUAD) has its own characteristics. Genetic and microenvironmental differences in smoking and non-smoking LUAD patients were analyzed to elucidate the oncogenesis of non-smoking-related LUAD, which will improve our understanding of the underlying molecular mechanism and be of clinical use in the future.

Methods

The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) databases were used for clinical and genomic information. Various bioinformatics tools were used to analyze differences in somatic mutations, RNA and microRNA (miRNA) expression, immune infiltration, and stemness indices. GO, KEGG, and GSVA analyses were performed with R. A merged protein-protein interaction (PPI) network was constructed and analyzed. A miRNA-differentially expressed gene network was constructed with miRNet. qRT-PCR was used for validation of 4 most significantly differently expressed genes and 2 miRNAs in tumor samples obtained from 20 pairs of non-smoking and smoking patients.

Results

Five hundred and one patients with LUAD were obtained, including 210 in the non-smoking group and 292 in the smoking group. A total of 174 significantly altered somatic mutations were detected, including mutations in tumor protein p53 and epidermal growth factor receptor, which were downregulated in non-smoking-related LUAD. At the RNA level, 231 significantly differentially expressed genes were obtained; 124 were upregulated and 107 downregulated in the non-smoking group. GSVA analysis revealed 42 significant pathways. Other functional and enrichment analyses of somatic mutations and RNA expression levels revealed that these genes were significantly enriched in receptor activity regulation and receptor binding. Differences in microenvironments including immune infiltration (e.g., CD8⁺ T cells and resting mast cells) and stemness indices were also found between groups. A 79-pair interaction was found between differentially expressed genes and miRNAs, of which miR-335-5p and miR-34a-5p were located in the center. Twenty-one genes, including vitronectin, neurotensin, and neuronatin, were differentially expressed in both non-smoking LUAD patients and DMSO-treated A549 cells. And the different expression of neurotensin, neuronatin, trefoil factor family2, regenerating family member 4, miR-377-5p, miR-34a were verified with the same tendency in our own samples.

Conclusions

Non-smoking LUAD patients, compared to smokers, have different characteristics in terms of somatic mutation, gene, and miRNA expression and the microenvironment, indicating a diverse mechanism of oncogenesis.

Dynamic Changes in the Localization of Neuronatin-Positive Cells during Neurogenesis in the Embryonic Rat Brain

Neuronatin (NNAT) was first identified as a gene selectively and abundantly expressed in the cytoplasm of the newborn mouse brain, and involved in neonatal neurogenesis. However, the particular roles of NNAT in the developing prenatal brain have not been identified, especially in mid to late stages. In this study, we performed immunohistochemical analyses of NNAT and SOX2 proteins, a nuclear transcription factor and neural stem/progenitor marker, in the rat brain on embryonic days 13.5, E16.5, and E20.5. NNAT signals were broadly observed across the developing brain on E13.5 and gradually more localized in later stages, eventually concentrated in the alar and basal parts of the terminal hypothalamus, the alar plate of prosomere 2 of the thalamus, and the choroid plexus in the lateral and fourth ventricles on E20.5. In particular, the mammillary body in the basal part of the terminal hypothalamus, a region with a high number of SOX2-positive cells, evidenced intense NNAT signals on E20.5. The intracellular localization of NNAT showed diverse profiles, suggesting that NNAT was involved in various cellular functions, such as cell differentiation and functional maintenance, during prenatal neurogenesis in the rat brain. Thus, the present observations suggested diverse and active roles of the NNAT protein in neurogenesis. Determining the function of this molecule may assist in the elucidation of the mechanisms involved in brain development.

G-Protein-Coupled Estrogen Receptor (GPER)-Specific Agonist G1 Induces ER Stress Leading to Cell Death in MCF-7 Cells

The G-protein-coupled estrogen receptor (GPER) mediates rapid non-genomic effects of estrogen. Although GPER is able to induce proliferation, it is down-regulated in breast, ovarian and colorectal cancer. During cancer progression, high expression levels of GPER are favorable for patients’ survival. The GPER-specific agonist G1 leads to an inhibition of cell proliferation and an elevated level of intracellular calcium (Ca²⁺). The purpose of this study is to elucidate the mechanism of G1-induced cell death by focusing on the connection between G1-induced Ca²⁺ depletion and endoplasmic reticulum (ER) stress in the estrogen receptor positive breast cancer cell line MCF-7. We found that G1-induced ER Ca²⁺ efflux led to the activation of the unfolded protein response (UPR), indicated by the phosphorylation of IRE1α and PERK and the cleavage of ATF6. The pro-survival UPR signaling was activated via up-regulation of the ER chaperon protein GRP78 and translational attenuation indicated by eIF2-α phosphorylation. However, the accompanying pro-death UPR signaling is profoundly activated and responsible for ER stress-induced cell death. Mechanistically, PERK-phosphorylation-induced JNK-phosphorylation and IRE1α-phosphorylation, which further triggered CAMKII-phosphorylation, are both implicated in G1-induced cell death. Our study indicates that loss of ER Ca²⁺ is responsible for G1-induced cell death via the pro-death UPR signaling.

Multi-view based integrative analysis of gene expression data for identifying biomarkers

The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multiple gene expression datasets integration, classification and identification of significant biomarkers. We design a novel integrative framework, MVIAm - Multi-View based Integrative Analysis of microarray data for identifying biomarkers. It applies multiple cross-platform normalization methods to aggregate multiple datasets into a multi-view dataset and utilizes a robust learning mechanism Multi-View Self-Paced Learning (MVSPL) for gene selection in cancer classification problems. We demonstrate the capabilities of MVIAm using simulated data and studies of breast cancer and lung cancer, it can be applied flexibly and is an effective tool for facing the four challenges of gene expression data analysis. Our proposed model makes microarray integrative analysis more systematic and expands its range of applications.

Proteomic Advances in Glial Tumors through Mass Spectrometry Approaches

Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.

The role of PEG3 in the occurrence and prognosis of colon cancer

Purpose

Imprinted genes are often identified as key players in the etiology and prognosis of many tumors; however, the role they play in colon cancer remains unclear. Along with the development of big data analysis came the discovery of a wealth of genetic prognostic factors, like microsatellite instability for colon cancer, which need to be taken into consideration when evaluating new biomarkers for the disease.

Methods

We systematically mined public databases to find recurrence free survival (RFS)-related imprinted genes for colon cancer patients on the mRNA level by univariate and multivariate survival analyses. We then investigated the association of methylation status and microRNA expression of the targeted imprinted genes with survival rate of colon cancer patients. Lastly, in a clinical study we used qRT-PCR and immunohistochemistry to quantify mRNA and protein expression of the imprinted genes that related to RFS in our bioinformatics screening, respectively, in 20 tumor tissues compared to paired adjacent tissues.

Results

The results show that paternally expressed gene 3 (PEG3) is the only imprinted gene related to colon cancer patient prognosis on the mRNA level in our datasets, and high mRNA expression of PEG3 is associated with a poor prognosis. Furthermore, the methylation beta value of cg13960339, as well as the expression of 4 microRNAs, negatively correlated with PEG3 mRNA level and were correlated with the prognosis of colon cancer patients. Moreover, the expression of PEG3 mRNA in colon cancer is significantly lower, but PEG3 protein expression is significantly higher compared to that in normal tissues.

Conclusion

PEG3 is likely associated with the progression and prognosis of colon cancer.