Abstract:Apolipoprotein A2-ATQ/AT (apoA2-ATQ/AT) has been identified as a minimally invasive biomarker for detecting pancreatic cancer (PC) and high-risk (HR) individuals for PC. To establish an efficient enrichment strategy for HR, we carried out a plasma apoA2-ATQ/AT level-based prospective screening study among the general population. The subjects for the screening study were recruited at six medical check-up facilities in Japan between October 2015 and January 2017. We evaluated the positive predictive value (PPV) … Show more
“…Then, GO and REACTOME pathway analyses were used to investigate the interactions of these DEGs. Increasing evidence shows that LAPTM4B [52], CEACAM6 [53], SERPINE2 [54] and VNN1 [55], SPHK1 [56], HRG (histidine rich glycoprotein) [57], VEGFC (vascular endothelial growth factor C) [58], ANXA3 [59], APOA2 [60], LCN2 [61], TIMP1 [62], CD63 [63], CD151 [64], MAL2 [65], ARNTL2 [66], PKD2 [67], E2F1 [68], MMP1 [69], CCR7 [70], NOTCH2 [71], BTLA (B and T lymphocyte associated) [72], TFRC (transferrin receptor) [73], CD4 [74], ATM (ATM serine/threonine kinase) [75], LEF1 [76], CSF1R [77], CTSB (cathepsin B) [78], DUSP2 [79] and NR4A1 [80] are closely associated with progression of PDAC. PTGER3 [81] and MAGI2 [82] are linked with angiogenesis, chemoresistance, cell proliferation and migration in ovary cancer, but these genes might be liable for growth PDAC.…”
The current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Limma package of R was used to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) networks of the DEGs were constructed. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Furthermore, consistent hub genes were further validated. Molecular docking experiment was conducted. A total of 463 DEGs (232 up regulated and 231 down regulated genes) were identified between very early PDAC and normal control samples. The results of Functional enrichment analysis revealed that the DEGs were significantly enriched in vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis and adaptive immune system. The PPI network and gene regulatory network of up regulated genes and down regulated genes were established, and hub genes were identified. The expression of hub genes (CCNB1, FHL2, HLA-DPA1 and TUBB1) were also validated to be differentially expressed among PDAC and normal control samples. Molecular docking experiment predicted the novel inhibitory molecules for CCNB1 and FHL2. The identification of hub genes in PDAC enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.
“…Then, GO and REACTOME pathway analyses were used to investigate the interactions of these DEGs. Increasing evidence shows that LAPTM4B [52], CEACAM6 [53], SERPINE2 [54] and VNN1 [55], SPHK1 [56], HRG (histidine rich glycoprotein) [57], VEGFC (vascular endothelial growth factor C) [58], ANXA3 [59], APOA2 [60], LCN2 [61], TIMP1 [62], CD63 [63], CD151 [64], MAL2 [65], ARNTL2 [66], PKD2 [67], E2F1 [68], MMP1 [69], CCR7 [70], NOTCH2 [71], BTLA (B and T lymphocyte associated) [72], TFRC (transferrin receptor) [73], CD4 [74], ATM (ATM serine/threonine kinase) [75], LEF1 [76], CSF1R [77], CTSB (cathepsin B) [78], DUSP2 [79] and NR4A1 [80] are closely associated with progression of PDAC. PTGER3 [81] and MAGI2 [82] are linked with angiogenesis, chemoresistance, cell proliferation and migration in ovary cancer, but these genes might be liable for growth PDAC.…”
The current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Limma package of R was used to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) networks of the DEGs were constructed. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Furthermore, consistent hub genes were further validated. Molecular docking experiment was conducted. A total of 463 DEGs (232 up regulated and 231 down regulated genes) were identified between very early PDAC and normal control samples. The results of Functional enrichment analysis revealed that the DEGs were significantly enriched in vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis and adaptive immune system. The PPI network and gene regulatory network of up regulated genes and down regulated genes were established, and hub genes were identified. The expression of hub genes (CCNB1, FHL2, HLA-DPA1 and TUBB1) were also validated to be differentially expressed among PDAC and normal control samples. Molecular docking experiment predicted the novel inhibitory molecules for CCNB1 and FHL2. The identification of hub genes in PDAC enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.
“…The comparison of background characteristics between apoA2-ATQ/AT-positive and -negative groups in the experimental screening study showed that age (years) [odds ratio (OR) 1.37, 95% CI 1.01–1.85], white blood cells (OR 1.32, 95% CI 1.11–1.56), total cholesterol (OR 0.94, 95% CI 0.92–0.97), low-density lipoprotein (OR 1.05, 95% CI 1.01–1.08), and pancreatic lesions detected by ultrasonography (%) (OR 3.04, 95% CI 1.01–9.14) remained significant on the multivariate logistic regression analyses (Table 1 ) [ 22 ]. Interestingly, the most strongly associated factor in the comparison of background characteristics was pancreatic lesions detected by ultrasonography.…”
Section: Experimental Screening For Pancreatic Cancer Using the Apoa2...mentioning
confidence: 99%
“… Figure 6. Flow diagram of the experimental screening of pancreatic cancer using the apoA2-i blood test in the Kobe Cancer Screening Network [ 22 ]. CECT, contrast-enhanced computed tomography; MRCP, magnetic resonance cholangiopancreatography; EUS, endoscopic ultrasonography; PDAC, pancreatic adenocarcinoma cancer; IPMN, intraductal papillary mucinous neoplasms; CP, chronic pancreatitis; NET, pancreatic neuroendocrine tumors; AIP, autoimmune pancreatitis.…”
Though pancreatic cancer is uncommon, with an age-adjusted annual incidence of 12.9 cases per 100,000 person-years, it is considered a refractory cancer due to the mortality of 11.0 per 100,000 person-years. To efficiently identify patients with potentially surgically-curable pancreatic cancer, high-risk individuals (HRIs) for pancreatic cancer should be identified by easily and minimally invasive methods from the general population. We have identified unique processing patterns in the C-terminal amino acids of apolipoprotein A2 homodimer in the blood of patients with pancreatic cancer and in HRIs, and we called them apoA2-isoforms (apoA2-i). We then established an enzyme-linked immunosorbent assay (ELISA) to measure circulating apoA2-i in the blood stream. The diagnostic accuracy of apoA2-i to distinguish pancreatic cancer HRIs was verified by several retrospective studies, blind testing with the National Cancer Institute (NCI) Early Detection Research Network (EDRN), a prospective study with prediagnostic samples organized by the European Prospective Investigation into Cancer and Nutrition (EPIC) study, and the prospective screening study of pancreatic cancer in Kobe. The apoA2-i blood test is a potential biomarker to identify HRIs and the curative stage of pancreatic cancer in the general population.
“…APOA2 was prospectively evaluated in 2019 where it was identified to be useful when utilized in combination with CA19-9 to improve detection of PC up to 18 mo prior to diagnosis and was suggested to be a useful first measure of PC detection prior to imaging[ 207 ]. This was built upon in 2020, where APOA2-ATQ/AT was implemented in a screening cohort in which an elevated level resulted in a PPV of 33.3% for the diagnosis of PC[ 208 ].…”
Pancreatic cancer (PC) is a leading cause of cancer related mortality on a global scale. The disease itself is associated with a dismal prognosis, partly due to its silent nature resulting in patients presenting with advanced disease at the time of diagnosis. To combat this, there has been an explosion in the last decade of potential candidate biomarkers in the research setting in the hope that a diagnostic biomarker may provide a glimmer of hope in what is otherwise quite a substantial clinical dilemma. Currently, serum carbohydrate antigen 19-9 is utilized in the diagnostic work-up of patients diagnosed with PC however this biomarker lacks the sensitivity and specificity associated with a gold-standard marker. In the search for a biomarker that is both sensitive and specific for the diagnosis of PC, there has been a paradigm shift towards a focus on liquid biopsy and the use of diagnostic panels which has subsequently proved to have efficacy in the diagnosis of PC. Currently, promising developments in the field of early detection on PC using diagnostic biomarkers include the detection of microRNA (miRNA) in serum and circulating tumour cells. Both these modalities, although in their infancy and yet to be widely accepted into routine clinical practice, possess merit in the early detection of PC. We reviewed over 300 biomarkers with the aim to provide an in-depth summary of the current state-of-play regarding diagnostic biomarkers in PC (serum, urinary, salivary, faecal, pancreatic juice and biliary fluid).
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