Integrative Analysis of Ovarian Serious Adenocarcinoma to Understand Disease Network Biology

• Published in: Bioinformatics and Biomedical Engineering Vol. 13347; pp. 3 - 17
• Main Authors: Qazi, Sahar, Raza, Khalid
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Differentially expressed genes (DEGs); Disease-gene associations (DGA); Kaplan Meier analysis; Ovarian serous adenocarcinoma; Principal component analysis (PCA)
• ISBN: 3031078012; 9783031078019
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-07802-6_1

Ovarian cancer (OC) is the third leading gynecological malignancy in females that is silent and leads to significant deaths annually. As per GLOBOCAN 2020 statistics, Asia recorded a total of 100,854 ovarian cancer incidence cases with China and India leading the cases with 34.2% and 24.8% respectively.This paper aims to identify the genes that regulate ovarian cancer network biology by integrating high-grade serous ovarian adenocarcinoma data from TCGA and GEO databases. The data has been used to detect differentially expressed genes (DEGs), and further to assess the potential of the seed genes for disease-gene associations (DGA), principal component analysis (PCA), and Kaplan Meier (KM) survival estimations to give insights about these genes function in ovarian cancer pathway.We conclude that genes – CLDN3, CLDN4, NFKB1, GSN, MUC16, NANOG, FKBP10,and CD274 are highly significant and influential in dominating ovarian serous adenocarcinoma in females and must be further deployed to construct a specific ovarian cancer network depict functional attributes of each of these genes.