Analysis of molecular pathways in pancreatic ductal adenocarcinomas with a bioinformatics approach

Alleviation of endothelial dysfunction of Pheretima guillemi (Michaelsen)-derived protein DPf3 in ponatinib-induced thrombotic zebrafish and mechanisms explored through ox-LDL-induced HUVECs and TMT-based proteomics

ETHNOPHARMACOLOGICAL RELEVANCE

Thrombus generation is one of the leading causes of death in human, and vascular endothelial dysfunction is a major contributor to thrombosis. Pheretima guillemi (Michaelsen), a traditional medicinal animal known as "Dilong", has been utilized to cure thrombotic disorders for many years. DPf3, a group of functional proteins extracted from P. guillemi, has been characterized and identified to possess antithrombotic bioactivity via in vitro and ex vivo experiments.

AIM OF THE STUDY

This study is aimed to investigate the vascular-protection activity and related mechanism of antithrombotic protein DPf3 purified from Pheretima guillelmi systematically.

MATERIALS AND METHODS

The antithrombotic activity and vascular endothelium protection effect of DPf3 was explored in vivo using ponatinib-induced vascular endothelial injury zebrafish thrombus model. Then, (hi) ox-LDL-induced HUVECs was applied to investigate the protection mechanism of DPf3 against the injury of vascular endothelium. In addition, TMT-based proteomics analysis was used to study the biomarkers, biological processes and signal pathways involved in the antithrombotic and vascular protective effects of DPf3 holistically.

RESULTS

DPf3 exerted robust in vivo antithrombosis and vascular endothelial protection ability. DPf3 was identified to prevent HUVECs from damage by reducing ROS production, and to reduce monocyte adhesion by decreasing the protein content of adhesion factor VCAM 1. DPf3 was also observed to weaken the migration ability of injured cells and inhibit abnormal angiogenesis. The mechanism of DPf3's antithrombotic and vascular protective activity was mainly related to the regulation of lipid metabolism, energy metabolism, complement and coagulation system, ECM receptor interaction, MAPK signal pathway, etc. CONCLUSIONS: This study demonstrates that DPf3 has strong antithrombotic and endothelial protective effects. The endothelial protective ability and related mechanisms of DPf3 provide a scientific reference for the traditional use of earthworms in the treatment of thrombosis.

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.

METHOD AND RESULTS

In this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells CONCLUSION: This research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM-receptor interaction, and focal adhesion signaling. The protein-protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan-Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

Current Insights on the Diverse Structures and Functions in Bacterial Collagen-like Proteins

The dearth of knowledge on the diverse structures and functions in bacterial collagen-like proteins is in stark contrast to the deep grasp of structures and functions in mammalian collagen, the ubiquitous triple-helical scleroprotein that plays a central role in tissue architecture, extracellular matrix organization, and signal transduction. To fill and highlight existing gaps due to the general paucity of data on bacterial CLPs, we comprehensively reviewed the latest insight into their functional and structural diversity from multiple perspectives of biology, computational simulations, and materials engineering. The origins and discovery of bacterial CLPs were explored. Their genetic distribution and molecular architecture were analyzed, and their structural and functional diversity in various bacterial genera was examined. The principal roles of computational techniques in understanding bacterial CLPs' structural stability, mechanical properties, and biological functions were also considered. This review serves to drive further interest and development of bacterial CLPs, not only for addressing fundamental biological problems in collagen but also for engineering novel biomaterials. Hence, both biology and materials communities will greatly benefit from intensified research into the diverse structures and functions in bacterial collagen-like proteins.

Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC.

Methods

The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein-protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website.

Results

A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05).

Conclusions

The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis

Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein-protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan-Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA-DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.

Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Pancreatic cancer is regarded as the most fatal and aggressive malignancy cancer due to its low 5-year survival rate and poor prognosis. The approaches of early diagnosis and treatment are limited, which makes it urgent to identify the complex mechanism of pancreatic oncogenesis. In this study, we used RNA-seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in paired tumor and normal pancreatic samples from ten patients. More than 1000 differentially expressed genes were identified, nearly half of which were also found to be differentially expressed in the majority of examined patients. Functional enrichment analysis revealed that these genes were significantly enriched in multicellular organismal and metabolic process, secretion, mineral transport, and intercellular communication. In addition, only 24 differentially expressed miRNAs were found, all of which have been reported to be associated with pancreatic cancer. Furthermore, an integrated miRNA-mRNA interaction network was generated using multiple resources. Based on the calculation of disease correlation scores developed here, several genes present in the largest connected subnetwork, such as albumin, ATPase H⁺ /K⁺ exchanging alpha polypeptide and carcinoembryonic antigen-related cell adhesion molecule 1, were considered as novel genes that play important roles in the development of pancreatic cancer. Overall, our data provide new insights into further understanding of key molecular mechanisms underlying pancreatic tumorigenesis.

Integrated SWATH-based and targeted-based proteomics provide insights into the retinal emmetropization process in guinea pig

Myopia is generally regarded as a failure of normal emmetropization process, however, its underlying molecular mechanisms are unclear. To investigate the retinal protein profile changes during emmetropization, we studied differential protein expressions of ocular growth in young guinea pigs at 3 and 21 days old respectively, when significant axial elongation was detected (P < 0.001, n = 10). Independent pooled retinal samples of both eyes were subjected to SWATH mass spectrometry (MS) followed by bioinformatics analysis using cloud-based platforms. A comprehensive retina SWATH ion-library consisting of 3138 (22,871) unique proteins (peptides) at 1% FDR was constructed. 40 proteins were found to be significantly up-regulated and 8 proteins down-regulated during emmetropization (≥log2 of 0.43 with ≥2 peptides matched per protein; P < 0.05). Using pathway analysis, the most significant pathway identifiable was 'phototransduction' (P = 1.412e-4). Expression patterns of 7 proteins identified in this pathway were further validated and confirmed (P < 0.05) with high-resolution Multiple Reaction Monitoring (MRM-HR) MS. Combining discovery and targeted proteomics approaches, this study for the first time comprehensively profiled protein changes in the guinea pig retina during normal emmetropization-associated eye growth. The findings of this study are also relevant to the myopia development, which is the result of failed emmetropization.

SIGNIFICANCE

Myopia is considered as a failure of emmetropization. However, the underlying biochemical mechanism of emmetropization, a visually guided process in which eye grows towards the optimal optical state of clear vision during early development, is not well understood. Retina is known as the key tissue to regulate this active eye growth. we studied eye growth of young guinea pigs and harvested their retinal tissues. A comprehensive SWATH ion library with identification of a total 3138 unique proteins were established, in which 48 proteins exhibited significant differential expressions between 3 and 21 days old. After MRM-HR confirmation, 'phototransduction' were found as the most active pathway during emmetropic eye growth. This study is the first in discovering key retinal protein players and pathways which are presumably orchestrated by biological mechanism(s) underlying emmetropization.

Identification of key genes and long non‑coding RNAs in celecoxib‑treated lung squamous cell carcinoma cell line by RNA‑sequencing

Celecoxib is an inhibitor of cyclooxygenase-2, a gene that is often aberrantly expressed in the lung squamous cell carcinoma (LSQCC). The present study aims to provide novel insight into chemoprevention by celecoxib treatment. The human LSQCC cell line SK‑MES‑1 was treated with or without celecoxib and RNA‑sequencing (RNA‑seq) was performed on the Illumina HiSeq 2000 platform. Expression levels of genes or long non‑coding RNAs (lncRNAs) were calculated by Cufflinks software. Subsequently, differentially expressed genes (DEGs) and differentially expressed lncRNAs (DE‑LNRs) between the two groups were selected using the limma package and LNCipedia 3.0, respectively; followed by co‑expression analysis based on their expression correlation coefficient (CC). Enrichment analysis for the DEGs and co‑expressed DE‑LNRs were performed. Protein‑protein interaction (PPI) network analysis for DEGs was performed using STRING database. A set of 317 DEGs and 25 DE‑LNRs were identified between celecoxib‑treated and non‑treated cell lines. A total of 12 pathways were enriched by the DEGs, including 'protein processing in endoplasmic reticulum' for activating transcription factor 4 (ATF4), 'mammalian target of rapamycin (mTOR) signaling pathway' for vascular endothelial growth factor A (VEGFA) and 'ECM‑receptor interaction' for fibronectin 1 (FN1). Genes such as VEGFA, ATF4 and FN1 were highlighted in the PPI network. VEGFA was linked with lnc‑AP000769.1‑2:10 (CC= ‑0.99227), whereas ATF4 and FN1 were closely correlated with lnc‑HFE2‑2:1 (CC=0.996159 and ‑0.98714, respectively). lncRNAs were also enriched in pathways such as 'mTOR signaling pathway' for lnc‑HFE2‑2:1. Several important molecules were identified in celecoxib‑treated LSQCC cell lines, such as VEGFA, ATF4, FN1, lnc‑AP000769.1‑2:10 and lnc‑HFE2‑2:1, which may enhance the anti‑cancer effects of celecoxib on LSQCC.

Expression and influence of Notch signaling in oral squamous cell carcinoma

Notch signaling dysregulation plays an important role in altering cancer cell behaviors; however, its role in oral squamous cell carcinoma (OSCC) remains controversial. This study aimed to investigate the role of Notch signaling related genes in human OSCC using a meta-analysis of Gene Expression Omnibus database (GEO-publicly available gene expression microarray data) and to examine the role of Notch signaling in OSCC behaviors. The meta-analysis included 13 GEO datasets and was performed by combining effect sizes in a random effect model. The results demonstrated that in OSCC dysregulated genes participated in the metabolic process and protein binding as determined by gene ontology analysis. Enriched pathway analysis demonstrated the majority of the dysregulated genes were involved in pathway categories as follow; pathway in cancers, small cell lung cancer, extracellular matrix-receptor interaction, focal adhesion, and cell cycle progression. Interestingly, the enriched pathway analysis also demonstrated that OSCC samples exhibited an upregulation of genes in Notch signaling pathway, namely JAG1, JAG2, ADAM17, NCSTN, PSEN1, NCOR2, NUMB, DVL3, HDAC1, and HDAC2. Furthermore, Notch signaling inhibition by a γ-secretase inhibitor significantly decreased OSCC cell proliferation in vitro, corresponding with a decrease in C-FOS mRNA expression. The study demonstrated that Notch signaling is dysregulated in human OSCC and plays a role in cell proliferation. (J Oral Sci 58, 283-294, 2016).

In silico docking of methyl isocyanate (MIC) and its hydrolytic product (1, 3-dimethylurea) shows significant interaction with DNA Methyltransferase 1 suggests cancer risk in Bhopal-Gas- Tragedy survivors

DNA methyltransferase 1 (DNMT1) is a relatively large protein family responsible for maintenance of normal methylation, cell growth and survival in mammals. Toxic industrial chemical exposure associated methylation misregulation has been shown to have epigenetic influence. Such misregulation could effectively contribute to cancer development and progression. Methyl isocyanate (MIC) is a noxious industrial chemical used extensively in the production of carbamate pesticides. We here applied an in silico molecular docking approach to study the interaction of MIC with diverse domains of DNMT1, to predict cancer risk in the Bhopal population exposed to MIC during 1984. For the first time, we investigated the interaction of MIC and its hydrolytic product (1,3-dimethylurea) with DNMT1 interacting (such as DMAP1, RFTS, and CXXC) and catalytic (SAM, SAH, and Sinefungin) domains using computer simulations. The results of the present study showed a potential interaction of MIC and 1,3-dimethylurea with these domains. Obviously, strong binding of MIC with DNMT1 interrupting normal methylation will lead to epigenetic alterations in the exposed humans. We suggest therefore that the MIC- exposed individuals surviving after 1984 disaster have excess risk of cancer, which can be attributed to alterations in their epigenome. Our findings will help in better understanding the underlying epigenetic mechanisms in humans exposed to MIC.