Journal of Data Mining in Genomics & Proteomics

Expression of NUP62 in the Development of Ovarian Cancer

Chen Yong¹, Chen Hongyu², Huang Shirong³ and Fan Xiangqun^{4* 1}Department of Laboratory Medicine, Fujian Medical University, Fu’an City, China
²Department of Laboratory Medicine, Zhongwei Tech Innovation Medical Research Institute Co., Ltd, Beijing, China
³Department of Laboratory Medicine, Fujian Medical University, Fuzhou, China
⁴Department of Pathology, Fujian Medical University, Fuzhou, China
^*Correspondence: Fan Xiangqun, Department of Pathology, Fujian Provincial Maternity and Children’s Hospital, Fujian Medical University, Fuzhou, China, Email:

Received: 19-Mar-2021 Published: 09-Apr-2021, DOI: 10.35248/2153-0602.21.12.240

Introduction

Ovarian cancer is one of the most common malignant tumors because its morbidity and mortality are the highest in the world [1-3]. Adenocarcinoma is the most common tissue type of ovarian cancer. It has a high degree of malignancy and rapid development. Most of them are in the late stage when they are diagnosed. The median survival time is about one year, and the 5-year survival rate is less than 16%. The prognosis is poor, and the incidence is still increasing year by year. At present, the diagnosis of ovarian cancer mainly depends on pathological and cytological examination, which requires biopsy to obtain tissues, and is not easy to obtain repeatedly. However, relying on symptoms, signs and imaging diagnosis has greater limitations. Therefore, the use of tumor markers to assist ovarian cancer screening and clinical diagnosis has been paid more and more attention and recognition.

In this study, we first screened the differentially expressed genes at the population level of patients with ovarian cancer, and then combined multiple gene expression profile data sets of ovarian cancer relative to the normal matched tissues beside the cancer to identify the genes with a lower confidence limit of differential coverage of more than 60%. The differentially expressed gene NUP62 was screened out and its related action network was found by directed regulatory network. For further verification, we detected the expression level of NUP62 and related miRNA in ovarian cancer samples.

Literature Review

We selected the universal different expression gene, which have not yet been studied and with more than 60% coverage based on the 69 pairs of matched ovarian cancer and normal expression profiles samples with the directed network, and then analyzing the multidimensional data of NUP62 and miRNA-495

Data and preprocessing

Gene Expression Omnibus was used in this study. The methylation profiles of the matched ovarian cancer and its adjacent normal tissue promoters are all standardized series matrix files downloaded directly from gene expression omnibus.

Recognition of differentially expressed genes with high coverage

Firstly, the difference of expression value of each gene in ovarian cancer and its matched normal tissues beside the cancer was calculated, and the difference of expression value of all genes in each sample was ranked from small to large, and the rank divided by the total number of genes was taken as the difference score of expression value in this pair of samples.

Histological examination

In order to verify the clinical practicability of the difference of bioinformatics screening, we take the patients' tissue samples for left immunohistochemical staining and serological test.

Screening differentially expressed genes with high coverage in ovarian cancer

We control (False Discovery Rate) FDR <0.01. In all ovarian cancer samples, the frequency of these differentially expressed genes ranges from 60.25% to 75.42%, among which 523 differentially up-regulated genes and 89 differentially down-regulated genes cover more than 60% (Table 1).

Table 1: High coverage differential gene.

The relationship between high coverage differentially expressed genes and directed regulatory networks in ovarian cancer

The database of human network signal regulation includes activation, inhibition and physical interaction. In order to further refine the study, we filtered out some high coverage differentially expressed genes that were widely studied in cancer, and got four high coverage differentially expressed genes that were less studied or not studied in ovarian cancer (Table 2).

Table 2: Regulatory genes of high coverage differential genes.

Then, these four high coverage differentially expressed genes, together with the selected group differentially expressed genes, were mapped into a directed regulatory network to ensure that each node is related to ovarian cancer, and a one-step and two-step regulatory neighborhood sub network of high coverage differentially expressed genes is obtained. Among them, NUP62 can directly activate the regulated differential expression gene, which can be down regulated and significantly inhibit cell proliferation, cell cycle arrest in G1/S phase and induce cell apoptosis. In ovarian cancer, NUP62 has a positive correlation with invasion depth, lymph node metastasis, distant metastasis, tumor differentiation and TMN stage (Figure 1).

Figure 1: Regulatory network of differential genes

Multi-dimensional analysis of NUP62

In order to explore the reasons for the changes in the expression of NUP62, we calculated whether the miRNA of NUP62 regulated gene as the target gene changed (Table 3), and found that the hsa-mir-495 with NUP62 as the target gene had a copy number deletion. At the same time, the miRNAs targeting MAPK14 and CSNK2A1, the upstream regulatory genes of NUP62, also changed at the copy number level.

Table 3: Changes of miRNA copy number related to differential expression of NUP62.

Discussion

The relatively normal differentially expressed genes in ovarian cancer indicated important information about the target of cancer occurrence, development and treatment [4]. Although the heterogeneity of ovarian cancer is very high, there will be a part of gene expression patterns in almost all ovarian cancer occurrence and development process, and this part of universally different expression genes may have potential significance for the occurrence, development and diagnosis and treatment of ovarian cancer. The relationship between NUP62 and ovarian cancer has not been studied. The high coverage genes we identified are still high coverage genes in different stages and subtypes. Based on the analysis of NUP62 gene with high coverage and differential expression, a biological molecular model was established, which provided a basis for further experimental verification. However, because the expression patterns of some genes are different in different stages or subtypes of ovarian cancer, the calculated coverage rate in all ovarian cancer samples does not represent the frequency of gene differential expression in a certain stage or subtype, so it is necessary to further classify ovarian cancer by stages and carry out corresponding analysis.

In view of the high heterogeneity of ovarian cancer patients and the complexity of cancer molecules, individualized diagnosis and treatment has also been promoted. In the process of occurrence and development of ovarian cancer, genes that are generally differentially expressed when compared with their own normal state suggest important information such as carcinogenesis mechanism and treatment target [5] which can be used as molecular markers. However, the coverage of differentially expressed genes in ovarian cancer is very small. In most studies, the single sample data set used for screening differentially expressed genes is small in sample size and poor in statistical efficiency due to the problems of laboratory funds, equipment and sample sources [6]. Moreover, due to the biological variation between individuals, the difference in the relative average reference value of gene expression does not mean the difference in the relative normal self [7].

Conclusion

Therefore, if we can integrate multiple data sets of matched samples of ovarian cancer relative to the adjacent cancer, and screen out the differential expression genes that do not depend on ovarian cancer subtypes and have high coverage, that is, the common molecular changes of ovarian cancer patients, it may provide new research ideas for exploring the mechanism of ovarian cancer occurrence and development, and have potential value for optimizing the diagnosis and treatment scheme of ovarian cancer.

Funding

This work was supported by the National Natural Science Foundation of Fujian Province (2017J01168). Fujian Provincial Key Laboratory sources played key supportive role for sample collection, molecular analysis of patient samples, bioinformatics analysis.

Conflict of Interests

The authors have no competing interests to declare.

Availability of Data and Materials

All data and materials generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics Approval

The authors sincerely thank the participants for their help and willingness to take part in this study.

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