Identification of hub genes and analysis of prognostic values in pancreatic ductal adenocarcinoma by integrated bioinformatics methods

Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods.

METHODS

Based on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis.

RESULTS

Platelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC.

CONCLUSIONS

In this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.

Systematic Analysis of the Prognostic Significance and Roles of the Integrin Alpha Family in Non-Small Cell Lung Cancers

INTRODUCTION

Lung cancer is one of the most common cancer malignancies and the principal cause of cancer-associated deaths worldwide. Non-small cell lung cancers (NSCLCs) account for more than 80% of all lung cancer cases. Recent studies showed that the genes of the integrin alpha (α) (ITGA) subfamily play a fundamental role in various cancers. However, little is known about the expression and roles of distinct ITGA proteins in NSCLCs.

METHODS

Gene Expression Profiling Interactive Analysis and UALCAN (University of ALabama at Birmingham CANcer) web resources and The Cancer Genome Atlas (TCGA), ONCOMINE, cBioPortal, GeneMANIA, and Tumor Immune Estimation Resource databases were used to evaluate differential expression, correlations between the expression levels of individual genes, the prognostic value of overall survival (OS) and stage, genetic alterations, protein-protein interactions, and the immune cell infiltration of ITGAs in NSCLCs. We used R (v. 4.0.3) software to conduct gene correlation, gene enrichment, and clinical correlation of RNA sequencing data of 1016 NSCLCs from TCGA. To evaluate the expression of ITGA5/8/9/L at the expression and protein levels, qRT-PCR, immunohistochemistry (IHC), and hematoxylin and eosin (H&E) were performed, respectively.

RESULTS

Upregulated levels of ITGA11 messenger RNA and downregulated levels of ITGA1/3/5/7/8/9/L/M/X were observed in the NSCLC tissues. Lower expression of ITGA5/6/8/9/10/D/L was discovered to be expressively associated with advanced tumor stage or poor patient prognosis in patients with NSCLC. A high mutation rate (44%) of the ITGA family was observed in the NSCLCs. Gene Ontology functional enrichment analyses results revealed that the differentially expressed ITGAs could be involved in roles related to extracellular matrix (ECM) organization, collagen-containing ECM cellular components, and ECM structural constituent molecular functions. The results of the Kyoto Encyclopedia of Genes and Genomes analysis revealed that ITGAs may be involved in focal adhesion, ECM-receptor interaction, and amoebiasis; the expression of ITGAs was significantly correlated with the infiltration of diverse immune cells in NSCLCs. ITGA5/8/9/L was also highly correlated with PD-L1 expression. The validation results for marker gene expression in NSCLC tissues by qRT-PCR, IHC, and H&E staining indicated that the expression of ITGA5/8/9/L decreased compared with that in normal tissues.

CONCLUSION

As potential prognostic biomarkers in NSCLCs, ITGA5/8/9/L may fulfill important roles in regulating tumor progression and immune cell infiltration.

ASPM Is a Prognostic Biomarker and Correlates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma and Liver Hepatocellular Carcinoma

Background

Abnormal spindle microtubule assembly (ASPM) is a centrosomal protein and that is related to a poor clinical prognosis and recurrence. However, the relationship between ASPM expression, tumor immunity, and the prognosis of different cancers remains unclear.

Methods

ASPM expression and its influence on tumor prognosis were analyzed using the Tumor Immune Estimation Resource (TIMER), UALCAN, OncoLnc, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The relationship between ASPM expression and tumor immunity was analyzed using the TIMER and GEPIA databases, and the results were further verified using qPCR, western blot, and multiplex quantitative immuno fluorescence.

Results

The results showed that ASPM expression was significantly higher in most cancer tissues than in corresponding normal tissues, including kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), and breast invasive carcinoma (BRCA). ASPM expression was significantly higher in late-stage cancers than in early-stages cancers (e.g., KIRC, KIRP, LIHC, LUAD, and BRCA; p < 0.05), demonstrating a possible role of ASPM in cancer progression and invasion. Moreover, our data showed that high ASPM expression was associated with poor overall survival, and disease-specific survival in KIRC and LIHC (p < 0.05). Besides, Cox hazard regression analysis results showed that ASPM may be an independent prognostic factor for KIRC and LIHC. ASPM expression showed a strong correlation with tumor-infiltrating B cells, CD8⁺ T cells, and M2 macrophages in KIRC and LIHC.

Conclusions

These findings demonstrate that the high expression of ASPM indicates poor prognosis as well as increased levels of immune cell infiltration in KIRC and LIHC. ASPM expression may serve as a novel prognostic biomarker for both the clinical outcome and immune cell infiltration in KIRC and LIHC.

Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of 10%. The occurrence of metastasis, among other hallmarks, is the main contributor to its poor prognosis. Consequently, the elucidation of metastatic genes involved in the aggressive nature of the disease and its poor prognosis will result in the development of new treatment modalities for improved management of PC. There is a deep interest in understanding underlying disease pathology, identifying key prognostic genes, and genes associated with metastasis. Computational approaches, which have become increasingly relevant over the last decade, are commonly used to explore such interests. This review aims to address global studies that have employed global approaches to identify prognostic and metastatic genes, while highlighting their methods and limitations. A panel of 48 prognostic genes were identified across these studies, but only five, including ANLN, ARNTL2, PLAU, TOP2A, and VCAN, were validated in multiple studies and associated with metastasis. Their association with metastasis has been further explored here, and the implications of these genes in the metastatic cascade have been interpreted.

GSK2126458 has the potential to inhibit the proliferation of pancreatic cancer uncovered by bioinformatics analysis and pharmacological experiments

BACKGROUND

Pancreatic cancer is one of the most serious digestive malignancies. At present, there is an extreme lack of effective strategies in clinical treatment. The purpose of this study is to identify key genes and pathways in the development of pancreatic cancer and provide targets for the treatment of pancreatic cancer.

METHODS

GSE15471 and GSE62165 were used to screen differentially expressed genes by GEO2R tool. Hub genes prognostic potential assessed using the GEPIA and Kaplan-Meier plotter databases. The drug susceptibility data of pan-cancer cell lines is provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC). Finally, the effects of PI3K-Akt signaling pathway inhibitors on cell viability of pancreatic cancer cells were detected by cell proliferation and invasion assays.

RESULTS

A total of 609 differentially expressed genes were screened and enriched in the focal adhesion, phagosome and PI3K-Akt signaling pathway. Of the 15 hub genes we found, four were primarily associated with the PI3K-Akt signaling pathway, including COL3A1, EGF, FN1 and ITGA2. GDSC analysis showed that mTOR inhibitors are very sensitive to pancreatic cancer cells with mutations in EWSR1.FLI1 and RNF43. Cell proliferation and invasion results showed that mTOR inhibitors (GSK2126458) can inhibit the proliferation of pancreatic cancer cells.

CONCLUSIONS

This study suggested that the PI3K-Akt signaling pathway may be a key pathway for pancreatic cancer, our study uncovered the potential therapeutic potential of GSK2126458, a specific mTOR inhibitor, for pancreatic cancer.

An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer

Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.

Identification of 9-Gene Epithelial-Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts

BACKGROUND

The prognosis of patients with osteosarcoma is still poor due to the lack of effective prognostic markers. The EMT (epithelial-mesenchymal transition) serves as a promoter in the progression of osteosarcoma. This study systematically analyzed EMT-related genes to explore new markers for predicting the prognosis of osteosarcoma.

METHODS

RNA-Seq data and clinical information were obtained from the GEO database; GSVA and GSEA analysis were used to enrich pathways related to osteosarcoma progression; LASSO method analysis was used to construct the prognosis risk signature. The "Nomogram" package generated the risk prediction nomogram, and its clinical applicability was evaluated by decision curve analysis (DCA).

RESULTS

GSVA and GSEA analysis showed that the EMT signaling pathway was closely related to osteosarcoma progression. A 9-genes signature (LAMA3, LGALS1, SGCG, VEGFA, WNT5A, MATN3, ANPEP, FUCA1, and FLNA) was constructed. The overall survival (OS) of the high-risk scores group was significantly lower than the low-risk scores group. The 9-gene signature demonstrated good predictive accuracy. Cox regression analysis showed that the 9-gene signature provided independent prognostic factors for osteosarcoma patients. In addition, the predictive nomogram model could effectively predict the prognosis of osteosarcoma patients.

CONCLUSION

This study constructed a 9-gene signature as a new prognostic marker to predict osteosarcoma patients' survival.

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Background

Intratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.

Methods

We compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.

Results

A total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.

Conclusion

Our study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.

VUp-Regulation of VCAN Promotes the Proliferation, Invasion and Migration and Serves as a Biomarker in Gastric Cancer

Background

Versican (VCAN), a significant protein of extracellular matrix (ECM), is capable of accumulating in tumor stroma and critically impacts malignant transforming process and tumor progressing process. Promoted VCAN expression was identified in numerous malignant tumors and showed relationships to cancer relapse and ineffective breast, prostate, and many other cancer types of patients. Nevertheless, the molecular capability and prognosis importance exhibited by VCAN are infrequently presented in gastric cancer (GC).

Methods

According to 5 GC tissues and corresponding general tissues, mRNA expression profiles were taken here. VCAN expression in tissues was confirmed by quantitative reverse transcription polymerase reaction (qRT-PCR). The effect generated by VCAN expression on cell proliferating, invading and migrating processes was assessed in vitro with knockdown and overexpression strategies. Moreover, the relationships between immune response and VCAN expression in GC were assessed with the use of the software online.

Results

There are 181 genes up-regulated and 530 genes down-regulated in GC. According to pathway study, the mentioned differently expressed mRNAs showed correlations with a number of vital physiological processes, cellular components, molecular functions and critical cancer signal pathways. VCAN was reported to be noticeably promoted in GC tissues and related to individual cancer age, race, and stages. VCAN was up-regulated in 16 GC tissues compared to adjacent non-tumorous tissue specimens via qRT-PCR. GC patients exhibiting higher VCAN expression had less post-progression survival (PPS), first progression (FP) and overall survival (OS). Experimental processes in vitro revealed VCAN knockdown hindered, proliferated, invaded, and migrated levels of GC cells, whereas overexpression of VCAN played the opposite effect. Immune factors may interact with VCAN mRNA in GC, and VCAN was found noticeably linked with regulatory T cells (Tregs).

Conclusion

According to the mentioned results, VCAN critically impacts GC progression. Accordingly, VCAN is likely to be a potentially feasible prognosis marking element and a prominent cancer drug for GC patients.

Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

Background

Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms.

Methods

To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot.

Conclusions

Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

DynaVenn: web-based computation of the most significant overlap between ordered sets

BACKGROUND

In many research disciplines, ordered lists are compared. One example is to compare a subset of all significant genes or proteins in a primary study to those in a replication study. Often, the top of the lists are compared using Venn diagrams, ore more precisely Euler diagrams (set diagrams showing logical relations between a finite collection of different sets). If different cohort sizes, different techniques or algorithms for evaluation were applied, a direct comparison of significant genes with a fixed threshold can however be misleading and approaches comparing lists would be more appropriate.

RESULTS

We developed DynaVenn, a web-based tool that incrementally creates all possible subsets from two or three ordered lists and computes for each combination a p-value for the overlap. Respectively, dynamic Venn diagrams are generated as graphical representations. Additionally an animation is generated showing how the most significant overlap is reached by backtracking. We demonstrate the improved performance of DynaVenn over an arbitrary cut-off approach on an Alzheimer's Disease biomarker set.

CONCLUSION

DynaVenn combines the calculation of the most significant overlap of different cohorts with an intuitive visualization of the results. It is freely available as a web service at http://www.ccb.uni-saarland.de/dynavenn.

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.

Diagnostic and prognostic values of integrin α subfamily mRNA expression in colon adenocarcinoma

The integrin α (ITGA) subfamily genes play a fundamental role in various cancers. However, the potential mechanism and application values of ITGA genes in colon adenocarcinoma (COAD) remain elusive. The present study investigated the significance of the expression of ITGA genes in COAD from the perspective of diagnosis and prognosis. A COAD RNA-sequencing dataset was obtained from The Cancer Genome Atlas. The present study investigated the biological function of the ITGA subfamily genes through bioinformatics analysis. Reverse transcription-quantitative polymerase chain reaction was applied to investigate the distribution of integrin α8 (ITGA8) expression in COAD tumors and adjacent normal tissues. Bioinformatics analysis indicated that ITGA genes were noticeably enriched in cell adhesion and the integrin-mediated signaling pathway, and co-expressed with each other. It was also revealed through observation that the majority of gene expression was significantly low in tumor tissues (P<0.05), and diagnostic receiver operating characteristic curves revealed that most of the genes could serve as significant diagnostic markers in COAD (P<0.05), especially ITGA8 which had a high diagnostic value with an area under curve (AUC) of 0.989 [95% confidence interval (CI) 0.980–0.997] in COAD (P<0.0001). In addition, ITGA8 expression was verified in clinical samples and it was revealed that it was higher in adjacent normal tissues (P=0.041) compared to COAD tissues, and the AUC was 0.704 (95% CI, 0.577–0.831; P<0.0085). Multivariate survival analysis indicated that integrin α (ITGA5) may be an independent prognostic indicator for COAD overall survival. Gene set enrichment analysis indicated that ITGA5 may participate in multiple biological processes and pathways. The present study revealed that ITGA genes were associated with the diagnosis and prognosis of COAD. The mRNA expression of ITGA8 may be a potential diagnosis biomarker and ITGA5 may serve as an independent prognosis indicator for COAD.

The role of the phoP transcriptional regulator on biofilm formation of avian pathogenic Escherichia coli

PhoP plays an important role as a transcriptional regulator in the two-component phoP/phoQ regulatory system, which is widely present in Gram-negative bacteria. In this study, we used DNA microarray-based technology to evaluate the role of phoP in biofilm formation in avian pathogenic Escherichia coli (APEC). Differences in gene transcription between APEC wild-type and phoP mutant strains were determined. Mutation of the phoP transcriptional regulator affects the expression profile of genes involved in processes such as flagellar assembly, ABC transporters, quorum sensing, and bacterial chemotaxis. Deletion of phoP in APEC reduced biofilm formation, as indicated by crystal violet staining and scanning electron microscopy (SEM). In addition, the phoP gene was found to be associated with changes in bacterial drug resistance and cell-membrane-related properties. This study shows that phoP plays an important regulatory role in APEC biofilm formation, and provides new insights into strategies for preventing and controlling APEC infection.