Exploring prognostic genes in ovarian cancer stage-related coexpression network modules

Abstract

Identification of meaningful cluster modules of differential genes or representative biomarkers related to the stages of ovarian cancer (OC) is pivotal, which may help to detect mechanisms of OC progression and evaluate OC patients’ prognosis.We downloaded gene expression data and the corresponding clinical information of OC patients from The Cancer Genome Atlas (TCGA) database, which included 379 ovarian cancer patients. Differentially expressed genes (DEGs) of OC patients between stages were picked out using R. There were 731 differential genes between ovarian cancer stage II and stage III (DEGs II-III) and 563 differential genes between ovarian cancer stage III and stage IV (DEGs III-IV), then we performed GO analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, CytoHubba was used to detect the top 20 hub genes in DEGs II-III and DEGs III-IV, followed Cytoscape with search tool for the retrieval of interacting genes (STRING) and MCODE plug-in was utilized to construct protein-protein interaction (PPI) modules of these genes. Three important coexpression modules of DEGs II-III and 3 more meaningful modules of DEGs III-IV were detected from PPI network using molecular complex detection (MCODE) tool. In addition, 5 hub genes in these stage-related DEGs modules with worse overall survival were selected, including COL3A1, COL1A1, COL1A2, KRAS, NRAS. This bioinformatics analysis demonstrated that stage-related prognostic DEGs, such as COL3A1, COL1A1, COL1A2, KRAS, and NRAS might play an unfavorable role in the development as well as metastasis of ovarian cancer. Furthermore, they need to be experimentally verified as a new biomarker to predict OC patient prognosis.

Prognostic signatures associated with high infiltration of Tregs in bone metastatic prostate cancer

“…Studies indicated that COL3A1 is highly correlated with tumor progression and metastasis [ 20 , 21 ]. COL3A1 has been identified as a representative biomarker that is overregulated in the focal adhesion pathway, and plays an unfavorable role in the development and metastasis of ovarian and bladder cancer [ 22 , 23 ]. A previous study demonstrated that the mRNA and protein levels of COL3A1 are not only present in colorectal cancer (CRC) cells, but also increased in the plasma of CRC patients, which is associated with clinicopathologic factors and poor survival [ 24 ].…”

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform

“…For the data set of OV, 67 genes were identified by univariate Cox regression to be significantly associated with the cancer (Table 3) and seven of them, namely COL1A1, COL3A1, RPL10, ARHGAP5, LATS1, TSHR and SLC34A2, were found in the CGC data set. Genes COL1A1 and COL3A1 are demonstrated that played an unfavorable role in the development of ovarian cancer, and could be considered as the prognostic genes of OV [45]. Moreover, COL1A1 has also been found to be positively related with the degree of invasion, metastasis, and advanced stages of gastric cancer [45,46].…”

“…Genes COL1A1 and COL3A1 are demonstrated that played an unfavorable role in the development of ovarian cancer, and could be considered as the prognostic genes of OV [45]. Moreover, COL1A1 has also been found to be positively related with the degree of invasion, metastasis, and advanced stages of gastric cancer [45,46]. COL3A1 can also be considered to be a potential biomarker for colon cancer [47], breast cancer [48] and brain tumor [49].…”

Prediction of novel target genes and pathways involved in tall cell variant papillary thyroid carcinoma

“…[17] Cytohubba is a tool that provides 11 topological analysis and 6 centralities based on shortest paths to detect the hub genes (key genes). [18]…”