Abstract
Background: Pancreatic adenocarcinoma (PAAD) has a considerably bad prognosis, and its pathophysiologic mechanism remains unclear. Thus, we aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms.Methods: WGCNA identified the modules most closely related to the PAAD stage and grade based on the Gene Expression Omnibus. The module genes significantly associated with PAAD progression and prognosis were considered as the real hub genes. Eligible genes in the most significant module were selected for construction and validation of a multigene prognostic signature based on the LASSO-Cox regression analysis in The Cancer Genome Atlas and the International Cancer Genome Consortium databases, respectively.Results: The brown module identified by WGCNA was most closely associated with the clinical characteristics of PAAD. Scaffold attachment factor B (SAFB) was significantly associated with PAAD progression and prognosis, and was identified as the real hub gene of PAAD. Moreover, both transcriptional and translational levels of SAFB were significantly lower in PAAD tissues compared with normal pancreatic tissues. In addition, a novel multigene-independent prognostic signature consisting of SAFB, SP1, and SERTAD3 was identified and verified. The predictive accuracy of our signature was superior to that of previous studies, especially for predicting 3- and 5-year survival probabilities. Furthermore, a prognostic nomogram based on independent prognostic variables was developed and validated using calibration curves. The predictive ability of this nomogram was also superior to the well-established AJCC stage and histological grade. The potential mechanisms of different prognoses between the high- and low-risk subgroups were also investigated using functional enrichment analysis, GSEA, ssGSEA, immune checkpoint analysis, and mutation profile analysis.Conclusion: SAFB was identified as the real hub gene of PAAD. A novel multigene-independent prognostic signature was successfully identified and validated to better predict PAAD prognosis. An accurate nomogram was also developed and verified to aid in the accurate treatment of tumors, as well as in early intervention.
Highlights
Pancreatic adenocarcinoma (PAAD) has a high incidence rate and primarily originates from the pancreatic exocrine cells (Pothuraju et al, 2018)
Functional enrichment analysis of the genes in the brown module revealed that these genes were involved in the mRNA catabolic process, the regulation of the mRNA metabolic process, nuclear export, the negative regulation of the ubiquitin-dependent protein catabolic process, and the regulation of RNA stability in terms of biological process (BP) categories (Figure 2E)
The molecular function (MF) results showed that these genes were mainly involved in DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, double- and single-stranded RNA binding, and histone binding (Figure 2E)
Summary
Pancreatic adenocarcinoma (PAAD) has a high incidence rate and primarily originates from the pancreatic exocrine cells (Pothuraju et al, 2018). Most researchers have failed to focus on the considerable interconnection between genes when constructing prognostic signatures, and the weighted gene co-expression network analysis (WGCNA) was developed. WGCNA provides a new approach in performing higher-resolution analysis, which can more accurately predict hub genes in a disease, providing a novel field of vision for the exploration of disease pathophysiology and the construction of disease prognostic signatures (Panahi and Hejazi, 2021). We aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
