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今天給大家還原一篇GEO數(shù)據(jù)挖掘的范文。

論文題目：

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

摘要：

Background: The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.

Results: The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply

analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO

datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.

Conclusion: This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention

of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

具體操作步驟：

一、在GEO檢索框中輸入“ovarian cancer geo accession”搜索數(shù)據(jù)

二、下載自己研究方向的數(shù)據(jù)（GDS3592，GSE54388，GSE66957）

三、分別對三套數(shù)據(jù)進行矯正，差異表達(dá)分析

四、將這三套數(shù)據(jù)進行合并

五、GO分析

六、KEGG分析

七、蛋白互作網(wǎng)絡(luò)分析

希望本文對大家有所幫助。完成這些分析，需要的時間往往比meta分析少，我們能在2個小時內(nèi)完成上面所有的分析，有些牛逼的人可能30分鐘就搞定了。如果想做這些分析，畫這些圖，但是自己又不會分析，可以找我們，我們的價格絕對比外面的公司便宜。