Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort

Prognostic value and potential molecular mechanism of ITGB superfamily members in hepatocellular carcinoma

We analyzed the prognostic value and potential molecular mechanisms of the members of integrin β (ITGB)superfamily in hepatocellular carcinoma (HCC) using data from The Cancer Genome Atlas (TCGA), cBioPortal, Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) HPA, Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER and Gene set enrichment analysis (GSEA) databases. ITGB4/5 mRNA was upregulated in HCC tissues in contrast to the normal liver tissues, whereas ITGB2/3/8 levels were lower in the former. ITGB4 was the most frequently mutated ITGB gene in HCC. Receiver operating characteristic curve (ROC) analysis showed that the expression levels of ITGB2/3/4/5/7/8 had significant diagnostic value in distinguishing HCC tissues from healthy liver tissues, ITGB8 had the highest diagnostic efficacy. The ITGB1/3/6/8 were also upregulated in the HCC tissues in contrast to healthy liver tissues. The expression of ITGB8 was verified by immunohistochemistry (IHC). Furthermore, ITGB6 and ITGB7 expression levels were strongly associated with the overall survival (OS) of HCC patients. The ITGB superfamily members exhibited homology and interactions in protein structure. In addition, ITGB6 together with ITGB7 were negatively related to the infiltration of multiple immune cell populations. GSEA results showed that ITGB6 was enriched in HCC migration and recurrence, whereas ITGB7 was significantly enriched in HIPPO, TOLL and JAK-STAT signaling pathways. In conclusion, ITGB6 and ITGB7 genes are possible to be prognostic biomarkers for HCC.

MicroRNA-125a-3p, -4530, and -92a as a Potential Circulating MicroRNA Panel for Noninvasive Pancreatic Cancer Diagnosis

MicroRNA (miRNA) expression dysregulations in pancreatic ductal adenocarcinoma (PDAC) have been studied widely for their diagnostic and prognostic utility. By the use of bioinformatics-based methods, in our previous study, we identified some potential miRNA panels for diagnosis of pancreatic cancer patients from noncancerous controls (the screening stage). In this report, we used 142 plasma samples from people with and without pancreatic cancer (PC) to conduct RT-qPCR differential expression analysis to assess the strength of the first previously proposed diagnostic panel (consisting of miR-125a-3p, miR-4530, and miR-92a-2-5p). As the result, we identified significant upregulation for all the three considered miRNAs in the serum of PC patients. After that, a three-miRNA panel in serum was developed. The area under the receiver operating characteristic curves (AUC) for the panel were 0.850, 0.910, and 0.86, respectively, indicating that it had a higher diagnostic value than individual miRNAs. Therefore, we detected a promising three-miRNA panel in the plasma for noninvasive PC diagnosis (miR-125a-3p, miR-4530, and miR-92a-2-5p).

Integrin β1 in Pancreatic Cancer: Expressions, Functions, and Clinical Implications

Simple Summary

Pancreatic cancer (PC) is a highly aggressive malignant tumor with an extremely poor prognosis. Early diagnosis and treatment are key to improving the survival rate of PC patients. Emerging studies show that integrins might contribute to the pathogenesis of PC. This review presents the various signaling pathways that are mediated by integrins in PC and emphasizes the multiple functions of integrin β1 in malignant behaviors of PC. It also discusses the clinical significance of integrin β1 as well as integrin β1-based therapy in PC patients.

Abstract

Pancreatic cancer (PC) is characterized by rapid progression and a high mortality rate. The current treatment is still based on surgical treatment, supplemented by radiotherapy and chemotherapy, and new methods of combining immune and molecular biological treatments are being explored. Despite this, the survival rate of PC patients is still very disappointing. Therefore, clarifying the molecular mechanism of PC pathogenesis and developing precisely targeted drugs are key to improving PC prognosis. As the most common β subunit of the integrin family, integrin β1 has been proved to be closely related to the vascular invasion, distant metastasis, and survival of PC patients, and treatment targeting integrin β1 in PC has gained initial success in animal models. In this review, we summarize the various signaling pathways by which integrins are involved in PC, focusing on the roles of integrin β1 in the malignant behaviors of PC. Additionally, recent studies regarding the feasibility of integrin β1 as a diagnostic and prognostic biomarker in PC are also discussed. Finally, we present the progress of several integrin β1-based clinical trials to highlight the potential of integrin β1 as a target for personalized therapy in PC.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

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

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.