Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Irigoyen, A.; Jiménez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gállego, Javier; Guzman, Francisco M. Ortuño; Ponce, Carmen Guillén; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, José Luis Paniza

doi:10.1371/journal.pone.0194844

Cited by 29 publications

(28 citation statements)

References 66 publications

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“…Here, we not only pointed five genes independently differentially expressed among datasets but also created an automatic tool to classify the samples and give the probability of being normal or PDAC. In contrast with the list of five differentially genes reported by Irigoyen et al 2018 [77], the CG list reported here did not include any of these genes.…”

Section: Discussionmentioning

confidence: 66%

“…The datasets used in both works are different, with this in mind, sample preparation or microarray technologies (Affymetrix and Illumina) could be possible explanations to different gene lists. Furthermore, the use of ten datasets here in contrast with two datasets by Irigoyen et al 2018 [77] could also produce different results. Another explanation for these differences in the gene list presented here could be due to PDAC subtypes already studied in gene expression and clinical level [10].…”

Section: Discussionmentioning

confidence: 77%

“…The development of automatic classifiers based on artificial intelligence can aid the PDAC diagnosis. Five possible PDAC biomarkers were already pointed (FAIM3, IRANK3, DENND2D, PLBD1, AGPAT) based on gene expression, achieving a combined sensitivity of 100% and specificity of 94% [77]; however, no automatic classification was produced. These five genes were pointed as potential biomarkers in PDAC diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

2020

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Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods: We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. Results: This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. Conclusion: The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

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Section: Discussionmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

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PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

2020

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“…Since the gene expression datasets of PBMCs were related to different platforms, we merged the data through cross-platform normalization algorithms [23] to (1) increase sample sizes and improve genes signature selection [23,[31][32][33][34]; (2) appraise the heterogeneity of the overall estimate, and 3decrease the effects of individual study-specific biases [23,34]. Finally, we integrated all datasets related to the same platform and built three major datasets from Hitachisoft, Affymetrix and Illumina.…”

Section: Cross-platform Normalizationmentioning

confidence: 99%

An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

Maleknia

Salehi

Sharifi‐Zarchi

et al. 2020

Preprint

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Background: To perceive a comprehensive illustration of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, draw a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to distinguish key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. Methods: In the present study, six gene expression microarray datasets included SLE patients (n=220) and healthy controls (n=135) were collected. We integrated the datasets by cross-platform normalization. Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from SLE, TCR and BCR signaling pathways and the parameters of BNs were estimated using integrated datasets. Finally, a meta-analysis was performed to distinguish the signaling pathways alterations in the SLE pathogenesis. Results: We identified the most dysregulated genes involved in the clearance mechanism (most inhibition in MACROH2A2 and H4C9, most activation in SNRPD3, MACROH2A1 and RO60). Augmented autoantigens presentation by MHCII (HLA-DQA1, HLA-DOB and HLA-DPB1) and CD80 and CD86 to CD28 biological functions were also observed. The increased expression of FCGR1A, C8G, C7 and C9 genes and decreasing biological functions in edges from C1R and C1S to C2 and from C1R to C4B, all involved in end-organ damage, were detected. On the other hand, many of subnetworks alterations of TCR and BCR reported in previous research (PI3K/AKT, MAPK, AP-1 transcription factor). Conclusions: Overall, the BNrich as a hybridized network construction method, which integrates intergenic relations inferred from signaling pathways and gene expression profiling, can be employed to study complex diseases. Here we applied BNrich and highlighted significant genes and intergenic relationships and systemic alterations in SLE, TCR and BCR signaling pathways.

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“…Blood and saliva GEO data veri cation: We investigated a validation by comparing the key genes mRNA standardized values in ve independent GEO datasets: four of which were from blood samples (GSE74629 [16], GSE49641 [17], GSE49515 [18] and GSE15932), and the last one was from saliva samples (GSE14245 [19]).…”

Section: Data Validationmentioning

confidence: 99%

An Integrative Data Mining for the Identification and Validation of Oncogenic Biomarkers in Pancreatic Carcinoma

Jin

Ruan

Shangguan

et al. 2020

Preprint

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Background: Pancreatic carcinoma (PC) is a severe disease associated with high mortality. Although strategies for cancer therapy made great progress, outcomes of pancreatic ductal adenocarcinoma patients remain extremely poor. Therefore, it is urgent to find novel biomarkers and therapeutic targets to improve outcomes of patients. Methods: To identify general applicable targets for early diagnosis and therapy, we selected related microarray data which including three mRNA microarray datasets GSE62165, GSE15471, GSE32676 and two miRNA datasets GSE24279, GSE32678, and combinative analysis was performed by GEO2R. Functional and pathway enrichment analysis were performed using the DAVID database. MiRTarBase, miRWalk, Diana Tools and TBtools were used to get keys. TCGA database, HPA database and western blot experiments were used to verify diagnostic and prognostic value of key genes.Results: By integrating mRNA and miRNA expression profiles, we identified 114 differentially expressed genes (DEGs) and 114 differentially expressed miRNAs (DEMs), respectively. Furthermore, three overlapping key genes, RUNX2, LAMC2 and FBXO32, were found by compared with DEMs target genes and DEGs. In detail, deregulation of 8 key miRNAs were closely related to poor outcomes and participated in crucial genes regulation which may contribute to build a miRNA biomarker panel for prognosis. Moreover, we confirmed that RUNX2 showed a potential property for distinguishing PC and normal people. We also demonstrated that aberrant over-expression of LAMC2 was associated with poor prognosis of PC patients as well as human tumor status and subtypes. The protein levels of RUNX2 and LAMC2 in PC patients were further verified by IHC from Human Protein Atlas and western blot experiments. Conclusions: In summary, our current study identified that RUNX2 and LAMC2 may be promising targets for early diagnosis and therapy of PC patients.

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Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Cited by 29 publications

References 66 publications

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

An Integrative Data Mining for the Identification and Validation of Oncogenic Biomarkers in Pancreatic Carcinoma

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