Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

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.

Mitophagy-Related Gene Signature for Prediction Prognosis, Immune Scenery, Mutation, and Chemotherapy Response in Pancreatic Cancer

“…Moreover, patients in the high-risk groups had worse outcome in the training and validation cohorts. The 3-years survival AUC value was 0.785 in the TCGA cohort, 0.818 in the ICGC cohort, 0.821 in the GSE28735, and 0.822 in the GSE62452, validating the model's excellent accuracy compared to other prognostic models reported in the literature (AUC = 0.698, (Ma et al, 2021); AUC = 0.657 (Yuan et al, 2021)). After univariate and multivariate Cox regression analyses were conducted, the risk score was identified as an independent prognosis factor.…”

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

“…These genes were deemed as potential therapeutic targets for personalized treatment in tumor patients. Recently, along with the sequencing technology was rapidly developed, high-throughput genomics has been used for the exploration of tumor generation and progression-related genes ( 43 , 44 ). Moreover, the deep investigation of the molecular mechanisms of tumorigenesis and development can be implemented by high-throughput genomics.…”

Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation

“…The prognostic significance of differentially expressed ECMGs was examined utilizing univariate Cox regression analysis as well as Kaplan-Meier survival analysis. Gene Expression Profiling Interactive Analysis (GEPIA) platform was applied to evaluate the significant association of the above genes with tumor stage ( 15 , 16 ). Generally speaking, the differentially expressed genes considerably linked to tumor prognosis and stage were often regarded as the hub genes of the onset and progression of tumors.…”