Combining differential expression and differential coexpression analysis identifies optimal gene and gene set in cervical cancer

Objective: The objective of this study is to investigate the optimal gene and functional-related gene set in cervical cancer through combing the differential expression (DE) and differential coexpression (DC) analysis. Materials and Methods: To achieve this, we first measured expression data of cervical cancer by incorporating DE and DC effects utilizing absolute t-value in t -statistic and Z -test, respectively. Then, we selected the optimal threshold pair to determine both high DE and high DC (HDE_HDC) partition on the basis of Chi-square maximization, and the best threshold pair divided all genes into four parts, including HDE_HDC, high DE and low DC (HDE_LDC), low DE and high DC (LDE_HDC), and low DE and low DC (LDE_LDC). Using the known functional gene sets, functional relevance of partition genes was explored to determine the best-associated gene set based on the functional information (FI) conception. Results: Under the optimal threshold pair of 3.629 and 1.108 for DE and DC, respectively genes were divided into four partitions: HDE_HDC (311 genes), HDE_LDC (2072 genes), LDE_HDC (seventy genes), and LDE_LDC (1623 genes). Meanwhile, the gene set epidermis development was the best-associated gene set with the largest △G* = 10.496. Among the genes of epidermis development, zinc finger protein 135 ( ZNF135) attained highest minimum FI gain of 41.226. Conclusion: The combination of DE and DC analysis showed higher mean FI relative to individual DE and DC analyses. We successfully exhibited the optimal gene set epidermis development and gene ZNF135 , which might be crucial for the prevention and treatment of cervical cancer.