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Non-functioning pituitary adenomas (NFPAs) are tumors with clinically challenging features since they have insidious progression. A complex network of gene interactions is thought to have roles in tumor formation and progression. Therefore, revealing the genetic network behind NFPA tumorigenesis is not only essential to attain further knowledge of tumor biology, but also plays a fundamental role in the development of efficacious treatment strategies. Differential co-expression network analysis is an outstanding approach for elucidation of groups of genes which show distinct co-expression patterns among phenotypes. In this study, we carried out a differential co-expression network analysis of NFPA-associated transcriptome dataset ( n  =  40 ) considering invasive ( n  =  22 ) and non-invasive ( n  =  18 ) phenotypes. Furthermore, we identified differentially co-expressed and co-regulated mRNA modules, which might be considered as potential systems biomarkers for NFPA prognosis and invasiveness. As a result, we have identified a novel 13-gene module, including  CEACAM6, CYP4B1, EIF2S2, HID1, IFFO1, MYO18A, PDCD2, SGIP1, SWSAP1 , and four unknown genes ( A_24_P127621, A_24_P255786, A_24_P683553 , and  A_24_P916979 ), which was able to categorize the patients into two groups as invasive and non-invasive NFPA with distinct prognosis. The prognostic core module genes were associated with progression and prognosis of brain and glandular based cancers as well. Furthermore, these module genes were also expressed in blood, salivary gland, and spinal cord tissues. These results may provide the evidence on featured gene module which might play a prominent role in NFPA prognosis and sub-typing as effective biomarkers and therapeutic targets in the future.

Co-expression Network Analysis Elucidated a Core Module in Association With Prognosis of Non-functioning Non-invasive Human Pituitary Adenoma