泛癌基因鉴定：揭示胰腺癌发展机制的关键候选基因

需积分: 5 4 浏览量更新于2024-09-05 收藏 1.05MB PDF 举报

Identification of key candidate genes for pancreatic cancer MECP2 是人类神经发育过程中受到 RNA 结合蛋白和 miRNA 共同调节的后转录调节基因。研究人员通过整合三个 Gene Expression Omnibus (GEO) 数据集，鉴定了 226 个差异表达基因 (DEGs)，其中包括 179 个上调基因和 47 个下调基因。进一步的功能富集分析和通路富集分析结果表明，这些 DEGs 参与了细胞外基质 (ECM) 过程。在这项研究中，研究人员使用 protein-protein 相互作用网络来分析 DEGs 之间的相互作用关系，结果表明 163 个 DEGs 形成了一个复杂的网络，包含 438 条边。通过对网络的进一步分析，研究人员鉴定出了 24 个 hub 基因，这些基因参与了多种生物过程，包括细胞外基质的重组、细胞迁移和细胞生长。这项研究的结果为 pancreatic cancer 的发病机理提供了新的见解，并且鉴定出了多个候选基因，这些基因可能参与 pancreatic cancer 的发生和发展。这些结果将有助于开发新的治疗策略和药物目标，提高 pancreatic cancer 的治疗效果。通过这项研究，我们可以了解到，MECP2 基因在人类神经发育过程中扮演着重要的调节角色，而 RNA 结合蛋白和 miRNA 则共同调节 MECP2 的表达。同时，这项研究也表明， pancreatic cancer 的发病机理非常复杂，涉及到多种生物过程和基因调节网络。因此，这项研究结果对我们的了解 pancreatic cancer 的发病机理和治疗策略产生了重要影响，并为今后的研究和治疗提供了新的方向和思路。知识点： 1. MECP2 基因在人类神经发育过程中受到 RNA 结合蛋白和 miRNA 共同调节的后转录调节。 2. pancreatic cancer 的发病机理非常复杂，涉及到多种生物过程和基因调节网络。 3. Gene Expression Omnibus (GEO) 数据集可以用于鉴定差异表达基因和分析基因调节网络。 4. protein-protein 相互作用网络可以用于分析基因之间的相互作用关系。 5. hub 基因在基因调节网络中扮演着重要的调节角色。 6. pancreatic cancer 的治疗策略可以通过鉴定候选基因和开发新的药物目标来提高疗效。

EXPERIMENTAL AND THERAPEUTIC MEDICINE 18: 451-458, 2019

Abstract. Although pancreatic cancer has the highest mortality

rate among all neoplasms worldwide, its exact mechanism

remains poorly understood. In the present study, three Gene

Expression Omnibus (GEO) datasets were integrated to

elucidate the potential genes and pathways that contribute

to the development of pancreatic cancer. Initially, a total of

226 differentially expressed genes (DEGs) were identified

in the three GEO datasets, containing 179 upregulated and

47 downregulated DEGs. Furthermore, function and pathway

enrichment analyses were performed to explore the function

and pathway of these genes, and the results indicated that the

DEGs participated in extracellular matrix (ECM) processes. In

addition, a protein-protein interaction network was constructed

and 163 genes of the 229 DEGs were ltered into the network,

resulting in a network complex of 163 nodes and 438 edges.

Finally, 24 hub genes were identied in the network, and the

top 2 most signicant modules were selected for function and

pathway analysis. The hub genes were involved in several

processes, including activation of matrix, degradation of ECM

and ECM organization. Taken collectively, the data demon-

strated potential key genes and pathways in pancreatic cancer,

which may provide novel insights to the mechanism of pancre-

atic cancer. In addition, these hub genes and pathways may be

considered as targets for the treatment of pancreatic cancer.

Introduction

Pancreatic cancer is one of the most prevalent and lethal malig-

nancies worldwide (1). Although substantial progress has been

made in adjuvant and neo-adjuvant chemotherapies during

previous decades, pancreatectomy remains the most effective

treatment, notably for early stage pancreatic cancer cases.

Despite this, a previous study demonstrated that only 20% of

patients present with localized and non-metastatic disease, and

are therefore suitable for initial resection (2). Due to its specic

tumor biology, pancreatic cancer is characterized with early

recurrence and metastasis and resistance to chemotherapy

and radiotherapy. The 5-year overall survival rate is <5% (3).

Therefore, an improved understanding of the underlying mech-

anism of pancreatic cancer is required for the development of

effective therapy and the improvement of patient survival.

Previously, the development of high throughout sequencing

has resulted in the production of numerous gene expression

proles of neoplasms that are freely available via the Gene

Expression Omnibus (GEO) database (4). Based on these data,

the different aspects of the mechanism of pancreatic neoplasm

development and the resistance to chemotherapy may be inves-

tigated. However, only a small part of these data has been used,

and the majority of them have only been deposited. Using a

bioinformatic analysis, these data may be re-analysed and

used to provide valuable information for subsequent investiga-

tion. During the re-analysis process, differentially expressed

genes (DEGs) are initially identified, and subsequently the

functions and pathways of the genes involved are investigated.

Several studies performed in pancreatic cancer have been

performed previously (5,6). Although the majority of these

studies only focused on the identication of the most signicant

genes, the tumor and normal tissues were not paired in those

analyses. Therefore, in the present study, three GEO datasets

were selected, which contained paired tumor tissues and

corresponding normal tissues, and the microarray data was

analysed. The analysis led to the identication of the DEGs, and

Gene Ontology (GO) and pathway enrichment analysis were

subsequently performed to explore the biological functions and

pathways of these genes. Furthermore, a protein-protein inter-

action (PPI) network was constructed and a module analysis

was performed to explore the hub genes in pancreatic cancer.

The present study may provide novel insights into the under-

standing of the mechanism of pancreatic cancer formation and

its corresponding hub genes, and the pathways involved may

serve as potential targets for the treatment of this cancer type.

Materials and methods

Data source. The microarray data for the investigation

of pancreatic cancer were downloaded from the GEO

Identication of key candidate genes for pancreatic

cancer by bioinformatics analysis

KUI LV, JIANYING YANG, JUNFENG SUN and JIANGUO GUAN

Department of Emergency Medicine, Anhui No. 2 Provincial People's Hospital,

Hefei, Anhui 230041, P.R. China

Received November 2, 2018; Accepted March 15, 2019

DOI: 10.3892/etm.2019.7619

Correspondence to: Dr Jianguo Guan, Department of Emergency

Medicine, Anhui No. 2 Provincial People's Hospital, 1868 Danshan

Road, Hefei, Anhui 230041, P.R. China

E-mail: guanjgah@163.com

Key words: pancreatic cancer, bioinformatics analysis, differentially

expressed gene, extracellular matrix

下载后可阅读完整内容，剩余7页未读，立即下载

fanzhang9

粉丝: 0
资源: 1

泛癌基因鉴定：揭示胰腺癌发展机制的关键候选基因

14.5(FromXcode_12.5_Release_Candidate_xip).zip

《6G总体愿景与潜在关键技术》白皮书 6G Vision and Candidate Technologies.pdf

Expression profiles of key candidate genes involved in steroidogenesis during follicular atresia in the pig ovary

candidate.......1

Identification of diurnal and circadian candidate genes in rice by using cDNA array

TF_Certificate_Candidate_Handbook.pdf

FuTURE论坛_6G GAP ANALYSIS AND CANDIDATE ENABLING TECHNOLOGIES.pdf

IAPP_Privacy_Certification_Candidate_Handbook_3.0.0.pdf

No qualifying bean of type [com.mzm.dao.UserDao] found for dependency: expected at least 1 bean which qualifies as autowire candidate for this dependency. Dependency annotations: {@org.springframework.beans.factory.annotation.Autowired(required=true)}

No qualifying bean of type found for dependency: expected at least 1 bean which qualifies as autowire candidate for this dependency.

最新资源