A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors

Abstract

Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

Hypoxia-induced inhibin promotes tumor growth and vascular permeability in ovarian cancers

“…While inhibin levels (inhibin A and B) cycle across the lifespan of healthy females and dramatically decrease at the onset of menopause 13 , elevated inhibinα levels are found in ovarian, gastric, hepatocellular, and prostate cancers 14 – 17 . Total inhibin protein levels comprising free inhibinα, inhibin A and inhibin B are also an established diagnostic marker alone and/or in combination with CA125, for ovarian cancers 18 and have been proposed as a potential tumor specific target for therapy 8 , 15 , 17 , 19 , 20 . Inhibinα levels are also predictive of survival in multiple cancer types with gene signatures that correlate with INHA expression, providing a highly accurate prognostic model for predicting patient outcomes 20 .…”

“…Total inhibin protein levels comprising free inhibinα, inhibin A and inhibin B are also an established diagnostic marker alone and/or in combination with CA125, for ovarian cancers 18 and have been proposed as a potential tumor specific target for therapy 8 , 15 , 17 , 19 , 20 . Inhibinα levels are also predictive of survival in multiple cancer types with gene signatures that correlate with INHA expression, providing a highly accurate prognostic model for predicting patient outcomes 20 . However, the mechanism of inhibin expression in cancers have not been delineated.…”

Correlation of INHBA Overexpression with Pathological Features, Antitumor Immune Response and Clinical Prognosis in Cervical Cancer

“…In breast cancer cells, downregulation of INHBA can delay the growth of primary tumors, inhibit the migration of tumor cells, and reduce the possibility of lung metastasis [ 7 ]. At present, it has been found that there is differential expression of INHBA in cervical cancer tissues [ 8 ], but whether it is related to the response of cervical cancer immune cells is unclear.…”

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

“…Bioinformatic methods, including analysis of GBM microarray and high-throughput sequencing, are effective strategies for the exploration of therapeutic targets in GBM [6][7][8]. Weighted gene coexpression network analysis (WGCNA) is a potent tool for uncovering complex mechanisms and multigene analysis of high-throughput data, particularly for exploring the relationship between genes and traits of samples [9][10][11].…”