GNG7 and ADCY1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma through bioinformatic-based analyses
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ABSTRACT:
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8516928 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Here we show that shotgun and targeted protein sequencing can be used to identify potential prognostic biomarkers in formalin-fixed paraffin-embedded specimens from 9 patients with PDAC with "short" survival (<12 months) and 10 patients with "long" survival (>45 months) undergoing surgical resection. A total of 24 and 147 proteins were significantly upregulated [fold change ?2 or ?0.5 and P<0.05; or different detection frequencies (?5 samples)] in patients with "short" survival (including GLUT1) and "long" survival (including C9orf64, FAM96A, CDH1 and CDH17), respectively. STRING analysis of these proteins indicated a tight protein-protein interaction network centered on TP53. Ingenuity pathway analysis linked proteins representing "activated stroma factors" and "basal tumor factors" to poor prognosis of PDAC. It also highlighted TCF1 and CTNNB1 as possible upstream regulators. Further parallel reaction monitoring verified that seven proteins were upregulated in patients with "short" survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with "long" survival, including EPCAM, LGALS4, VIL1, CLCA1 and TPPP3. Thus, we verified 25 protein biomarker candidates for PDAC prognosis at the tissue level. Furthermore, an activated stroma status and protein-protein interactions with TP53 might be linked to poor prognosis of PDAC.
| S-EPMC5839402 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD), which accounts for 60% of non-small-cell lung cancers, is poorly diagnosed and has a low average 5-year survival rate (approximately 20%). It remains the leading cause of cancer-related deaths worldwide. Studies on long noncoding RNAs (lncRNAs) in LUAD-related competing endogenous RNA (ceRNA) networks are limited. We aimed to identify novel prognostic biomarkers for LUAD using bioinformatic tools and data analysis. We systemically integrated differentially expressed genes and clinically significant modules using weighted correlation network analysis. We performed a functional analysis of the collected candidate genes and explored three LUAD-related genes (VWF, PECAM1, and COL1A1) associated with the overall survival rates of patients with LUAD. Based on Cox proportional hazards analysis of candidate mRNAs and lncRNAs together with differentially expressed microRNAs, we constructed ceRNA networks, obtained 12 lncRNAs in the ceRNA networks, and revealed seven novel lncRNAs AC021016.2, AC079630.1, AC116407.1, AC125807.2, AF131215.5, LINC01936, and RHOXF1-AS1. These lncRNAs were found to be associated with overall survival rates and are suitable for the prediction of prognosis by Kaplan-Meier survival and receiver operating characteristic curve analyses. In particular, three lncRNAs-AF131215.5, AC125807.2, and LINC01936-showed an independent prognostic value of overall survival for patients with LUAD. We evaluated the diagnostic capabilities of seven lncRNAs for patients with LUAD using principal component analysis and the Gene Set Variation Analysis index. lncRNAs and crucial genes could be effectively used for distinguishing LUAD tumors from normal tissues in the Gene Expression Omnibus profile. In particular, AC021016.2 showed a significant prognostic value in the validation dataset. Our findings reveal the significance of exploring lncRNAs in cancer-related ceRNAs using bioinformatic strategies.
| S-EPMC7822677 | biostudies-literature
Project description:The present study aimed to screen the key genes in pancreatic cancer and to explore the pathogenesis of pancreatic cancer. A total of three expression profiling datasets (GSE28735, GSE16515 and GSE15471) associated with pancreatic cancer were retrieved from the public gene chip database. The differentially expressed genes (DEGs) were screened by GEO2R and subjected to Gene Ontology (GO) and signaling pathway enrichment analysis. Furthermore, a protein interaction network was constructed. The GEPIA online database was used to screen for genes that affect the prognosis of pancreatic cancer. Finally, cell functional experiments were performed on the selected key genes. A total of 72 DEGs were identified, including 52 upregulated and 20 downregulated genes. Enrichment analysis revealed roles of the DEGs in endodermal cell differentiation, cell adhesion, extracellular matrix-receptor interaction and PI3K-Akt signaling pathway. In total, 10 key nodal genes were identified, including integrin subunit α 2 (ITGA2), ITGB6 and collagen α 1 chain 1. Through survival analysis, two genes with an impact on the prognosis of pancreatic cancer were identified, namely ITGA2 and ITGB6. Silencing of ITGB6 in a pancreatic cancer cell line significantly suppressed cell proliferation and induced cell cycle arrest at G2/M phase. The identified key genes and signaling pathways may help to deepen the understanding of the molecular mechanisms involved in pancreatic cancer and provide a theoretical basis to develop novel therapies.
| S-EPMC8515505 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism.ObjectiveThis present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of PDAC and potential targets of treatment.MethodBased on the overlap genes obtained from 3 GEO datasets, the hub genes were identified using STRING and Cytoscape plugin MCODE. The enrichment and function analysis were applied using DAVID. The protein-protein interaction network was performed using cBioPortal and UCSC Xena. The Oncomine was finally used to determine the candidate gene by analyzing their expression between pancreas sample and PDAC sample.Results25 hub genes were selected from a total of 1006 DEGs from 3 GEO datasets, consisting of 14 upregulated genes and 11 downregulated genes. The overall decline of hub gene expression enriched in G1 phase of cell cycle in other subtypes of pancreatic cancer. Oncomine database was ultimately performed to determine the 8 candidate genes, including CXCL5, CCL20, NMU, F2R, ANXA1, EDNRA, LPAR6, and GNA15.ConclusionsConclusively, 8 candidate genes would become the potential PDAC combined biomarkers for diagnosis and therapeutic strategies.
| S-EPMC6717170 | biostudies-literature
Project description:Frequent disease relapse and a lack of effective therapies result in a very poor outcome in pancreatic ductal adenocarcinoma (PDAC) patients. Thus, identification of prognostic biomarkers and possible therapeutic targets is essential. Besides their function in cell-cell adhesion, desmogleins may play a role in tumour progression and invasion that has not been investigated in PDAC to date. This study evaluated desmoglein expression as a biomarker in PDAC.Using immunohistochemistry, we examined desmoglein 1 (DSG1), desmoglein 2 (DSG2) and desmoglein 3 (DSG3) expression in the tumour tissue of 165 resected PDAC cases. Expression levels were correlated to the patients' clinicopathological parameters and postoperative survival times. We confirmed these results in two independent gene expression data sets.A total of 36% of the tumours showed high DSG3 expression that correlated significantly with shorter patient survival (P=0.011) and poor tumour differentiation (P<0.001), whereas no such association was detected for DSG1 or DSG2. In RNA-Seq data and in microarray expression data, high DSG3 expression correlated significantly with poor survival (P=0.000356 and P=0.00499).We identify DSG3 as a negative prognostic biomarker in resected PDAC, as high DSG3 expression is associated with poor overall survival and poor tumour-specific survival. These findings suggest DSG3 and its downstream signalling pathways as possible therapeutic targets in DSG3-expressing PDAC.
| S-EPMC4815888 | biostudies-other
Project description:Background: Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC. Methods: A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis. Results: Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five "real" miRNAs were able to stratify PDAC patients into low-risk and high-risk groups. Conclusions: The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.
| S-EPMC6218787 | biostudies-literature
Project description:Due to the difficulties in early diagnosis of pancreatic adenocarcinoma (PAAD), many patients fail to receive optimal therapeutic regimens. The Secretory-Carrier-Membrane-Proteins (SCAMPs) are known to be dysregulated in a range of human diseases due to their characterized roles in mammalian cell exocytosis inferred from their functions as integral membrane proteins. However, the expression and prognostic value of SCAMPs in PAAD is poorly characterized. We compared cancer vs. healthy tissue and found that the expression of SCAMPs1-4 was upregulated in PAAD compared to normal tissue. In contrast, SCAMP5 expression was downregulated in PAAD. Moreover, the expression of SCAMPs1-4 was enhanced in PAAD cell lines according to Cancer Cell Line public database. Furthermore, the HPA, GEPIA databases and immunohistochemical analysis from 238 patients suggested that the loss of SCAMP1 led to improved overall survival (OS), whilst lower SCAMP5 levels led to a poorer OS. The univariate and multivariate analysis showed that SCAMP1 and SCAMP5 expression were independent prognostic factors of PAAD. In addition, the cBioPortal for Cancer Genomics, LinkedOmics datasets, and the GEPIA were used to identify the co-expression genes of SCAMP1,5 and the correlation between SCAMPs members. We conclude that SCAMPs 1 and 5 significantly represent promising diagnosis and prognostic biomarkers.
| S-EPMC7906166 | biostudies-literature
Project description:Background: Pancreatic ductal adenocarcinoma (PDAC) leads to the majority of cancer-related deaths due to its morbidity with similar mortality. Lack of effective prognostic biomarkers are the main reason for belated post-operative intervention of recurrence which causes high mortality. Numerous systematic reviews and meta-analyses have explored the prognostic value of biomarkers in PDAC so far. In this article, we performed an umbrella review analyzing these studies to provide an overview of associations between prognostic biomarkers and PDAC survival outcome and synthesized these results to guide better clinical practice. Methods: Systematic reviews and meta-analyses investigating the associations between PDAC survival outcomes and prognostic biomarkers were acquired via the PubMed and Embase databases from inception till February 1, 2020. Associations supported by nominally statistically significant results were classified into strong, highly suggestive, suggestive, and weak based on several critical factors such as the statistical significance of summary estimates, the number of events, the estimate of the largest study included, interstudy heterogeneity, small-study effects, 95% predictive interval (PI), excess significance bias, and the results of credibility ceiling sensitivity analyses. Results: We included 41 meta-analyses containing 63 associations between PDAC survival outcomes and prognostic biomarkers. Although, none was supported by strong evidence among these associations, an association between C-reactive protein to albumin ratio (CAR) and PDAC overall survival (OS) and an association between neutrophil-lymphocyte ratio (NLR) and PDAC OS were supported by highly suggestive evidence. Otherwise, the association between lactate dehydrogenase (LDH) and PDAC OS was supported by suggestive evidence. The remaining 60 associations were supported by weak or not suggestive evidence. Conclusion: Associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. And the association between LDH and PDAC OS was supported by suggestive evidence. Although the methodological quality of the included systematic reviews and meta-analyses which were evaluated by AMSTAR2.0 is generally poor, the identification of the relatively robust prognostic biomarkers of PDAC may guide better post-operative intervention and follow-up to prolong patients' survival.
| S-EPMC7527774 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a tumor with a poor prognosis, short overall survival and few chemotherapeutic choices. MicroRNAs (miRNAs) are non-coding, single-stranded RNAs of around 22 nucleotides involved in the pathogenic mechanisms of carcinogenesis and metastasis. They have been studied in many tumors in order to identify potential diagnostic, prognostic or therapeutic targets. In the current literature, many studies have analyzed the role of miRNAs in PDAC. In fact, the absence of appropriate biomarkers, the difficultly of early detection of this tumor, and the lack of effective chemotherapy in patients with unresectable disease have focused attention on miRNAs as new, interesting advance in this malignancy. In this review we analyzed the role of miRNAs in PDAC in order to understand the mechanisms of action and the difference between the onco-miRNA and the tumor suppressor miRNA. We also reviewed all the data related to the use of these molecules as predictive as well as prognostic biomarkers in the course of the disease. Finally, the possible therapeutic use of miRNAs or anti-miRNAs in PDAC is also discussed. In conclusion, although there is still no clinical application for these molecules in PDAC, it is our opinion that the preclinical evidence of the role of specific miRNAs in carcinogenesis, the possibility of using miRNAs as diagnostic or prognostic biomarkers, and their potential therapeutic role, warrant future studies in PDAC.
| S-EPMC4695121 | biostudies-literature
Project description:Ovarian cancer (OC) is characterized by late-stage presentation, chemoresistance, and poor survival. Evaluating the prognosis of OC patients via effective biomarkers is essential to manage OC progression and to improve survival; however, it has been barely established. Here, we intend to identify differentially expressed genes (DEGs) as potential prognostic biomarkers of OC via bioinformatic analyses. Initially, a total of thirteen DEGs were extracted from different public databases as candidates. The expression of KIF20A, one of the DEGs, was correlated with a worse outcome of OC patients. The functional correlation of the DEGs with mitosis and the prognostic value of KIF20A imply a high correlation between mitotic kinesins (KIFs) and OC development. Finally, we found that KIF20A, together with the other nine mitotic KIFs (4A, 11, 14, 15, 18A, 18B, 23, C1, and2C) were upregulated and activated in OC tissues. Among the ten, seven overexpressed mitotic KIFs (11, 14, 18B, 20A, 23, and C1) were correlated with unfavorable clinical prognosis. Moreover, KIF20A and KIF23 overexpression was associated with worse prognosis in OC patients treated with platinum/taxol chemotherapy, while OCs overexpressing mitotic KIFs (11, 15, 18B, and C1) were resistant to MAPK pathway inhibitors. In conclusion, worse outcomes of OC patients were correlated with overexpression of several mitotic KIFs, which may serve both as prognostic biomarkers and therapeutic targets for OC.
| S-EPMC8871464 | biostudies-literature