Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

Identification and validation of IRF6 related to ovarian cancer and biological function and prognostic value

BACKGROUND

Ovarian cancer (OC) is a severe gynecological malignancy with significant diagnostic and therapeutic challenges. The discovery of reliable cancer biomarkers can be used to adjust diagnosis and improve patient care. However, serous OC lacks effective biomarkers. We aimed to identify novel biomarkers for OC and their pathogenic causes.

METHODS

The present study used the differentially expressed genes (DEGs) obtained from the "Limma" package and WGCNA modules for intersection analysis to obtain DEGs in OC. Three hub genes were identified-claudin 3 (CLDN3), interferon regulatory factor 6 (IRF6), and prostasin (PRSS8)-by searching for hub genes through the PPI network and verifying them in GSE14407, GSE18520, GSE66957, and TCGA + GTEx databases. The correlation between IRF6 and the prognosis of OC patients was further confirmed in Kaplan-Miller Plotter. RT-qPCR and IHC confirmed the RNA and protein levels of IRF6 in the OC samples. The effect of IRF6 on OC was explored using transwell invasion and scratch wound assays. Finally, we constructed a ceRNA network of hub genes and used bioinformatics tools to predict drug sensitivity.

RESULTS

The joint analysis results of TCGA, GTEx, and GEO databases indicated that IRF6 RNA and protein levels were significantly upregulated in serous OC and were associated with OS and PFS. Cell function experiments revealed that IRF6 knockdown inhibited SKOV3 cell proliferation, migration and invasion.

CONCLUSION

IRF6 is closely correlated with OC development and progression and could be considered a novel biomarker and therapeutic target for OC patients.

Knockdown of the UL-16 binding protein 1 promotes osteoblast differentiation of human mesenchymal stem cells by activating the SMAD2/3 pathway

Osteoporosis is caused by the imbalance of osteoblasts and osteoclasts. The regulatory mechanisms of differentially expressed genes (DEGs) in pathogenesis of osteoporosis are of significant and needed to be further investigated. GSE100609 dataset downloaded from Gene Expression Omnibus (GEO) database was used to identified DEGs in osteoporosis patients. KEGG analysis was conducted to demonstrate signaling pathways related to enriched genes. Osteoporosis patients and the human mesenchymal stem cells (hMSCs) were obtained for in vivo and in vitro resaerch. Lentivirus construction and viral infection was used to knockdown genes. mRNA expression and protein expression were detected via qRT-PCR and western blot assay separately. Alkaline phosphatase (ALP) activity detection, alizarin Red S (ARS) staining, and expression of bone morphogenetic protein 2 (BMP2), osteocalcin (OCN) and Osterix were evaluated to determine osteoblast differentiation capacity. UL-16 binding protein 1 (ULBP1) gene was upregulated in osteoporosis and downregulated in differentiated hMSCs. Knockdown of ULBP1 increased ALP activity, mineralization ability evaluated by ARS staining, expression of BMP2, OCN and Osterix in differentiated hMSCs. Furthermore, rescue experiment demonstrated that suppressed ULBP1 boosted osteoblast differentiation by activating TNF-β signaling pathway. Knockdown of ULBP1 gene could promoted osteoblast differentiation by activating TNF-β signaling pathway in differentiated hMSCs. ULBP1 may be a the Achilles' heel of osteoporosis, and suppression of ULBP1 could be a promising treatment for osteoporosis.

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Up-regulated and hypomethylated genes are causative factors and diagnostic markers of osteoporosis

BACKGROUND

Due to the lack of sensitive diagnostic biomarkers for osteoporosis (OP), there is an urgent need to identify and uncover biomarkers associated with the disease in order to facilitate early clinical diagnosis and effective intervention strategies.

METHODS

GEO2R was employed to conduct a screening of differentially expressed genes (DEGs) within the transcriptome sequencing data obtained from blood samples of OP patients within the GSE163849 dataset. Subsequently, we conducted expression confirmation of the identified DEGs using an additional dataset, GSE35959. To further explore Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, MicroRNA (miRNA) interactions, and drug predictions, we employed the DAVID, miRTarBase, and DrugBank databases. For validation purposes, clinical OP samples paired with normal controls were collected from the Pakistani population. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to assess the expression levels of DEGs and miRNA, while targeted bisulfite sequencing (bisulfite-seq) analysis was used to investigate methylation patterns. DNA and RNA from clinical OP and normal control samples were extracted using appropriate methods.

RESULTS

Out of total identified 269 DEGs, EGFR (epidermal growth factor receptor), HMOX1 (heme oxygenase-1), PGR (progesterone receptor), CXCL10 (C-X-C motif chemokine ligand 10), CCL5 (C-C motif chemokine ligand 5), and IL12B (interleukin 12B) were prioritized as top DEGs in OP patients. Expression validation of these genes on additional Gene Expression Omnibus (GEO) dataset and Pakistani OP patients revealed consistent significant up-regulation of these genes in OP patients. Receiver operating characteristic (ROC) analysis demonstrated that these DEGs displayed considerable diagnostic accuracy for detecting OP. Targeted bisulfite-seq analysis further revealed that EGFR, HMOX1, PGR, CXCL10, CCL5, and IL12B were hypomethylated in OP patients. Moreover, has-miR-27a-5p, a common expression regulator of the EGFR, HMOX1, PGR, CXCL10, CCL5, and IL12B was also significantly down-regulated in OP patients.

CONCLUSION

The DEGs that have been identified hold significant potential for the future development of diagnostic and treatment approaches for OP in preclinical and clinical applications.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

EBR and JA regulate aroma substance biosynthesis in 'Ruidu Hongyu' grapevine berries by transcriptome and metabolite combined analysis

Volatile compounds including terpenes, aldehyde, phenol, and alcohol are significantly contributed floral and fruity aromas to the Muscat variety. 'Ruidu Hongyu' grapevine is one of the newly developed grape varieties, and cultivation of this variety has been extended across China due to unique quality traits and taste. In this study, HS-SPME/GC-MS and transcriptome sequencing analysis were performed to evaluate the impact of exogenous 2,4-epibrassinolide (EBR), jasmonic acid (JA), and their signaling inhibitors brassinazole (Brz)/sodium diethyldithiocarbamate (DIECA) on the biosynthesis of aroma substances in 'Ruidu Hongyu' grapevine. According to the results, exogenous BR and JA promoted the accumulation of various aroma substances, including hexenal, 2-hexenal, nerol oxide, vanillin, hotrienol, terpineol, neral, nerol, geraniol, and geranic acid. After EBR and JA treatments, most of the genes responsible for terpene, aldehyde, and alcohol biosynthesis expressed at a higher level than the CK group. Relatively, EBR treatment could not only promote endogenous BR biosynthesis and metabolism but also elevate BR signaling transduction. JA treatment contributed to endogenous JA and MeJA accumulation, as well. Through transcriptome sequencing, a total of 3043, 903, 1470, and 607 DEGs were identified in JA vs. JD, JA vs. CK, BR vs. CK, and BR vs. Brz, respectively. There were more DEGs under both EBR and JA treatments at late fruit ripening stages. The findings of this study increase our understanding regarding aroma substances biosynthesis and endogenous BR/JA metabolism in response to exogenous EBR and JA signals.

Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis

Background

Despite the high prevalence of lung cancer, with a five-year survival rate of only 23%, the underlying molecular mechanisms of non-small cell lung cancer (NSCLC) remain unknown. There is a great need to identify reliable candidate biomarker genes for early diagnosis and targeted therapeutic strategies to prevent cancer progression.

Methods

In this study, four datasets obtained from the Gene Expression Omnibus were evaluated for NSCLC- associated differentially expressed genes (DEGs) using bioinformatics analysis. About 10 common significant DEGs were shortlisted based on their p-value and FDR (DOCK4, ID2, SASH1, NPR1, GJA4, TBX2, CD24, HBEGF, GATA3, and DDR1). The expression of significant genes was validated using experimental data obtained from TCGA and the Human Protein Atlas database. The human proteomic data for post- translational modifications was used to interpret the mutations in these genes.

Results

Validation of DEGs revealed a significant difference in the expression of hub genes in normal and tumor tissues. Mutation analysis revealed 22.69%, 48.95%, and 47.21% sequence predicted disordered regions of DOCK4, GJA4, and HBEGF, respectively. The gene-gene and drug-gene network analysis revealed important interactions between genes and chemicals suggesting they could act as probable drug targets. The system-level network showed important interactions between these genes, and the drug interaction network showed that these genes are affected by several types of chemicals that could serve as potential drug targets.

Conclusions

The study demonstrates the importance of systemic genetics in identifying potential drug- targeted therapies for NSCLC. The integrative system- level approach should contribute to a better understanding of disease etiology and may accelerate drug discovery for many cancer types.

LCK, FOXC1 and hsa-miR-146a-5p as potential immune effector molecules associated with rheumatoid arthritis

Rheumatoid arthritis (RA) is a type of systemic immune disease characterized by chronic inflammatory disease of the joints. However, the aetiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify potential immune effector molecules involved in RA. The three microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen 115 overlapping differentially expressed genes (DEGs). Subsequently, we constructed a protein-protein interaction (PPI) network encoded by these DEGs and identified 10 genes closely associated with RA - LCK, GZMA, GZMB, CD2, LAG3, IL-15, TNFRSF4, CD247, CCR5 and CCR7. Furthermore, in the miRNA-hub gene networks, we screened out hsa-miR-146a-5p, which is the miRNA controlling the largest number of hub genes. Finally, we found some transcription factors that closely interact with hub genes, such as FOXC1, GATA2, YY1, RUNX2, SREBF1, CEBPB and NFIC. This study successfully predicted that LCK, FOXC1 and hsa-miR-146a-5p can be used as potential immune effector molecules of RA. Our study may have potential implications for future prediction of disease progression in patients with symptomatic RA, and has important significance for the pathogenesis and targeted therapy of RA.

Exosomes: A potential tool for immunotherapy of ovarian cancer

Ovarian cancer is a malignant tumor of the female reproductive system, with a very poor prognosis and high mortality rates. Chemotherapy and radiotherapy are the most common treatments for ovarian cancer, with unsatisfactory results. Exosomes are a subpopulation of extracellular vesicles, which have a diameter of approximately 30-100 nm and are secreted by many different types of cells in various body fluids. Exosomes are highly stable and are effective carriers of immunotherapeutic drugs. Recent studies have shown that exosomes are involved in various cellular responses in the tumor microenvironment, influencing the development and therapeutic efficacy of ovarian cancer, and exhibiting dual roles in inhibiting and promoting tumor development. Exosomes also contain a variety of genes related to ovarian cancer immunotherapy that could be potential biomarkers for ovarian cancer diagnosis and prognosis. Undoubtedly, exosomes have great therapeutic potential in the field of ovarian cancer immunotherapy. However, translation of this idea to the clinic has not occurred. Therefore, it is important to understand how exosomes could be used in ovarian cancer immunotherapy to regulate tumor progression. In this review, we summarize the biomarkers of exosomes in different body fluids related to immunotherapy in ovarian cancer and the potential mechanisms by which exosomes influence immunotherapeutic response. We also discuss the prospects for clinical application of exosome-based immunotherapy in ovarian cancer.

MUM1L1 as a Tumor Suppressor and Potential Biomarker in Ovarian Cancer: Evidence from Bioinformatics Analysis and Basic Experiments

BACKGROUND

Ovarian cancer (OC) is the most prevalent gynecologic malignancy, with high mortality rates. However, its pathogenesis remains unclear. The current study aimed to explore potential biomarkers and suppressor genes for diagnosing and treating OC.

METHODS

Biochemical and bioinformatics approaches were used to detect differentially expressed genes (DEGs) in ovarian tissues via integration analysis. Kaplan-Meier plot analysis was performed to assess progression-free survival and overall survival according to DEGs. Then, we constructed a protein-protein interaction (PPI) network based on data from the STRING database to identify the related target genes of DEGs. Finally, DEGs regulating the proliferation, migration, and invasion of SKOV3 cell lines were validated via in vitro experiments.

RESULTS

Four DEGs (MUM1L1, KLHDC8A, CRYGD, and GREB1) with enriched expression in ovarian tissues were explicitly expressed in the ovary based on an analysis of all human proteins. MUM1L1 had high specificity, and its expression was higher in normal ovarian tissues than in OC tissues. Kaplan-Meier plot analysis showed that a high MUM1L1 expression was associated with longer progression-free survival and overall survival in OC. Based on the PPI analysis results, CBLN4, CBLN1, PTH2R, TMEM255B, and COL23A1 were associated with MUM1L1. In vitro studies revealed that MUM1L1 overexpression decreased the proliferation, migration, and invasion ability of SKOV3 cell lines. Meanwhile, MUM1L1 knockdown had contrasting results.

CONCLUSION

MUM1L1 is a tumor suppressor gene and is a potential biomarker for diagnosing and treating OC.

BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration

The discovery of cancer drivers and drug targets are often limited to the biological systems - from cancer model systems to patients. While multiomic patient databases have sparse drug response data, cancer model systems databases, despite covering a broad range of pharmacogenomic platforms, provide lower lineage-specific sample sizes, resulting in reduced statistical power to detect both functional driver genes and their associations with drug sensitivity profiles. Hence, integrating evidence across model systems, taking into account the pros and cons of each system, in addition to multiomic integration, can more efficiently deconvolve cellular mechanisms of cancer as well as learn therapeutic associations. To this end, we propose BaySyn - a hierarchical Bayesian evidence synthesis framework for multi-system multiomic integration. BaySyn detects functionally relevant driver genes based on their associations with upstream regulators using additive Gaussian process models and uses this evidence to calibrate Bayesian variable selection models in the (drug) outcome layer. We apply BaySyn to multiomic cancer cell line and patient datasets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas, respectively, across pan-gynecological cancers. Our mechanistic models implicate several relevant functional genes across cancers such as PTPN6 and ERBB2 in the KEGG adherens junction gene set. Furthermore, our outcome model is able to make higher number of discoveries in drug response models than its uncalibrated counterparts under the same thresholds of Type I error control, including detection of known lineage-specific biomarker associations such as BCL11A in breast and FGFRL1 in ovarian cancers. All our results and implementation codes are freely available via an interactive R Shiny dashboard at tinyurl.com/BaySynApp. The supplementary materials are available online at tinyurl.com/BaySynSup.

Identification and exploration of novel M2 macrophage-related biomarkers in the development of acute myocardial infarction

Background

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, is a major cause of morbidity and mortality worldwide. Macrophages play a critical role in ventricular remodeling after AMI. The regulatory mechanisms of the AMI progression remain unclear. This study aimed to identify hub regulators of macrophage-related modules and provide translational experiments with potential therapeutic targets.

Materials and methods

The GSE59867 dataset was downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis. The expression patterns of 22 types of immune cells were determined using CIBERSORT. GEO2R was used to identify differentially expressed genes (DEGs) through the limma package. Then, DEGs were clustered into different modules, and relationships between modules and macrophage types were analyzed using weighted gene correlation network analysis (WGCNA). Further functional enrichment analysis was performed using significantly associated modules. The module most significantly associated with M2 macrophages (Mϕ2) was chosen for subsequent analysis. Co-expressed DEGs of AMI were identified in the GSE123342 and GSE97320 datasets and module candidate hub genes. Additionally, hub gene identification was performed in GSE62646 dataset and clinical samples.

Results

A total of 8,760 DEGs were identified and clustered into ten modules using WGCNA analysis. The blue and turquoise modules were significantly related to Mϕ2, and 482 hub genes were discerned from two hub modules that conformed to module membership values > 0.8 and gene significance values > 0.25. Subsequent analysis using a Venn diagram assessed 631 DEGs in GSE123342, 1457 DEGs in GSE97320, and module candidate hub genes for their relationship with Mϕ2 in the progression of AMI. Finally, four hub genes (CSF2RB, colony stimulating factor 2 receptor subunit beta; SIGLEC9, sialic acid-binding immunoglobulin-like lectin 9; LRRC25, leucine-rich repeat containing 25; and CSF3R, colony-stimulating factor-3 receptor) were validated to be differentially expressed and to have high diagnostic value in both GSE62646 and clinical samples.

Conclusion

Using comprehensive bioinformatics analysis, we identified four novel genes that may play crucial roles in the pathophysiological mechanism of AMI. This study provides novel insights into the impact of macrophages on the progression of AMI and directions for Mϕ2-targeted molecular therapies for AMI.

Lactotransferrin promotes intervertebral disc degeneration by regulating Fas and inhibiting human nucleus pulposus cell apoptosis

Background: In recent years, intervertebral disc (IVD) degeneration (IDD) has increased in age. There is still a lack of effective treatment in clinics, which cannot improve the condition of IDD at the level of etiology.

Objective: To explore IDD pathogenesis at the cellular and gene levels and investigate lactotransferrin (LTF) expression in IDD patients and its possible mechanism.

Methods: We downloaded the IDD data set from the Gene Expression Omnibus (GEO) database, screened the differentially expressed genes (DEGs) and hub genes and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to construct a protein–protein interaction (PPI) network. Subsequently, we verified LTF's regulatory mechanism through cell experiments. IL-1β was used to intervene in nucleus pulposus cells (NPCs) to construct the IDD cell model, and LTF and Fas expression was detected by qRT–PCR. LTF inhibitor, Fas inhibitor, LTF mimic, and Fas mimic were used to intervene in each group. Western blotting was used to detect Fas, Caspase-3, Bax, and Bcl-2 expression.

Results: A total of 131 DEGs and 10 hub genes were screened. LTF mRNA in the IDD model was significantly higher than that in the control group, while Fas' mRNA was significantly lower. When LTF was upregulated or downregulated in NPCs, apoptosis marker expression showed the opposite trend. The rescue test showed that LTF and Fas' overexpression greatly enhanced NPC apoptosis.

Conclusion: LTF promotes IDD progression by regulating Fas in NPCs, and it may be an effective gene therapy target.

Genome-Wide Identification and Expression Profiles of 13 Key Structural Gene Families Involved in the Biosynthesis of Rice Flavonoid Scaffolds

Flavonoids are a class of key polyphenolic secondary metabolites with broad functions in plants, including stress defense, growth, development and reproduction. Oryza sativa L. (rice) is a well-known model plant for monocots, with a wide range of flavonoids, but the key flavonoid biosynthesis-related genes and their molecular features in rice have not been comprehensively and systematically characterized. Here, we identified 85 key structural gene candidates associated with flavonoid biosynthesis in the rice genome. They belong to 13 families potentially encoding chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), flavonol synthase (FLS), leucoanthocyanidin dioxygenase (LDOX), anthocyanidin synthase (ANS), flavone synthase II (FNSII), flavanone 2-hydroxylase (F2H), flavonoid 3′-hydroxylase (F3′H), flavonoid 3′,5′-hydroxylase (F3′5′H), dihydroflavonol 4-reductase (DFR), anthocyanidin reductase (ANR) and leucoanthocyanidin reductase (LAR). Through structural features, motif analyses and phylogenetic relationships, these gene families were further grouped into five distinct lineages and were examined for conservation and divergence. Subsequently, 22 duplication events were identified out of a total of 85 genes, among which seven pairs were derived from segmental duplication events and 15 pairs were from tandem duplications, demonstrating that segmental and tandem duplication events play important roles in the expansion of key flavonoid biosynthesis-related genes in rice. Furthermore, these 85 genes showed spatial and temporal regulation in a tissue-specific manner and differentially responded to abiotic stress (including six hormones and cold and salt treatments). RNA-Seq, microarray analysis and qRT-PCR indicated that these genes might be involved in abiotic stress response, plant growth and development. Our results provide a valuable basis for further functional analysis of the genes involved in the flavonoid biosynthesis pathway in rice.

Circular RNA_0000326 promotes bladder cancer progression via microRNA-338-3p/ETS Proto-Oncogene 1/phosphoinositide-3 kinase/Akt pathway

Circular RNAs (circRNAs) play a pivotal regulatory role in bladder cancer (BC) occurrence and progression. The expression level, role and mechanism of circ_0000326 in BC remain unknown. In the present study, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to evaluate the expressions of circ_0000326, microRNA-338-3p (miR-338-3p) and ETS Proto-Oncogene 1(ETS1) mRNA in BC tissues and cell lines. Cell counting kit-8 (CCK-8) assay, wound healing assay and flow cytometry were used to detect the impacts of circ_0000326 on BC cell growth, migration and apoptosis. Western blot was used to detect the expressions of ETS1, phospho-phosphoinositide-3 kinase (p-PI3K), phospho-AKT, PI3K and AKT protein. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the biological function of ETS1 in BC. Here, we found that circ_0000326 expression was significantly elevated in BC cell lines and tissues, and circ_0000326 could promote BC cell growth and migration, and inhibit apoptosis. Dual-luciferase reporter gene assay confirmed that circ_0000326 and ETS1 could bind directly to miR-338-3p. Furthermore, circ_0000326 sponged miR-338-3p and up-regulated ETS1 expression. ETS1 was associated with the activation of PI3K/AKT pathway. Moreover, circ_0000326 could activate PI3K/AKT pathway by miR-338-3p/ETS1 axis. Collectively, circ_0000326/miR-338-3p/ETS1/PI3K/AKT pathway is involved in regulating BC progression.

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma

Background: Most cholangiocarcinoma (CCA) patients are diagnosed at an advanced stage of disease, and the postoperational recurrence rates are high in those undergoing resection. The lack of satisfying biomarkers for early diagnoses and effective targeting of driver pathways is the leading reason for therapeutic failures. The goal of this study was to find a biomarker for making improved diagnoses with enhanced prognostic capabilities for CCA. Materials and Methods: Our study used bioinformatic analyses of microarray data from the Gene Expression Omnibus (GEO) database and investigated mitotic arrest deficient 2-like protein 1 (MAD2L1) expression in tumor and adjacent non-neoplastic biliary ducts through immunocytochemistry in 42 surgically removed primary CCAs from a single institute. In vitro and in vivo models were used to explore the function of MAD2L1. Results: In total, 297 high probability differentially expressed genes (DEGs) were obtained from overlapping the DEGs from the three individual data sets. Through enrichment assays and protein-protein interaction networks analyses, seven hub genes were identified. MAD2L1 was picked up as a novel biomarker based on hierarchical cluster analyses and Kaplan-Meier survival analyses. MAD2L1 was expressed in cancer tissues but not in the surrounding normal tissue, with 31 (73.81%) of 42 CCAs MAD2L1 positive by immunohistochemistry (IHC). MAD2L1 expression levels were significantly correlated with tumor size, pathological grade, and clinical stage. A Kaplan-Meier survival analysis demonstrated an inverse correlation with MAD2L1 expression. Real-time polymerase chain reaction and immunoblotting results further confirmed the results of IHC and bioinformatic analyses. In vitro and in vivo models demonstrated decreasing MAD2L1 could significantly suppress tumor growth, whereas increasing MAD2L1 could promote tumor growth. Conclusion: MAD2L1 could be used as a biomarker to predict prognosis and potential therapeutic target in CCA. Clinical Trial Registration Number: [2020]KY157-01.

Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.

PROTEOMIC ANALYSIS OF TAENIA SOLIUM CYST FLUID BY SHOTGUN LC-MS/MS

Taenia solium cysts were collected from pig skeletal muscle and analyzed via a shotgun proteomic approach to identify known proteins in the cyst fluid and to explore host-parasite interactions. Cyst fluid was aseptically collected and analyzed with shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gene alignment and annotation were performed using Blast2GO software followed by gene ontology analysis of the annotated proteins. The pathways were further analyzed with the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a protein-protein interaction (PPI) network map was generated using STRING software. A total of 158 known proteins were identified, most of which were low-molecular-mass proteins. These proteins were mainly involved in cellular and metabolic processes, and their molecular functions were predominantly related to catalytic activity and binding functions. The pathway enrichment analysis revealed that the known proteins were mainly enriched in the PI3K-Akt and glycolysis/gluconeogenesis signaling pathways. The nodes in the PPI network mainly consisted of enzymes involved in sugar metabolism. The cyst fluid proteins screened in this study may play important roles in the interaction between the cysticerci and the host. The shotgun LC-MS/MS, gene ontology, KEGG, and PPI network map data will be used to identify and analyze the cyst fluid proteome of cysticerci, which will provide a basis for further exploration of the invasion and activities of T. solium.

Distinctive Properties of Endothelial Cells from Tumor and Normal Tissue in Human Breast Cancer

Tumor microenvironments shape aggressiveness and are largely maintained by the conditions of angiogenesis formation. Thus, endothelial cells’ (ECs) biological reactions are crucial to understand and control the design of efficient therapies. In this work, we used models of ECs to represent a breast cancer tumor site as well as the same, healthy tissue. Cells characterization was performed at the transcriptome and protein expression levels, and the cells functional biological responses (angiogenesis and permeability) were assessed. We showed that the expression of proteins specific to ECs (ACE+, VWF+), their differentiation (CD31+, CD 133+, CD105+, CD34-), their adhesion properties (ICAM-1+, VCAM-1+, CD62-L+), and their barrier formation (ZO-1+) were all downregulated in tumor-derived ECs. NGS-based differential transcriptome analysis confirmed CD31-lowered expression and pointed to the increase of Ephrin-B2 and SNCAIP, indicative of dedifferentiation. Functional assays confirmed these differences; angiogenesis was impaired while permeability increased in tumor-derived ECs, as further validated by the distinctly enhanced VEGF production in response to hypoxia, reflecting the tumor conditions. This work showed that endothelial cells differed highly significantly, both phenotypically and functionally, in the tumor site as compared to the normal corresponding tissue, thus influencing the tumor microenvironment.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.

Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

Esophageal cancer is a common tumor of the digestive system with poor prognosis. This study was to gain a better understanding of the mechanisms involved in esophageal cancer and to identify new prognostic markers. We downloaded the esophageal cancer miRNA expression profile microarray data (GSE113740, GSE112264, GSE122497, GSE113486, and GSE106817) from the GEO database, extracted the esophageal cancer miRNA sequencing data from The Cancer Genome Atlas (TCGA) database, and then used a bioinformatics approach to select common differentially expressed miRNAs (DEMs). Differentially expressed genes (DEGs) were selected by predicting DEM target genes using the miRWalk database and intersecting with differential genes obtained from TCGA database for esophageal cancer. The STRING database was used to obtain protein-protein interaction (PPI) relationships to construct the DEM-DEG network. Furthermore, we selected core genes and core miRNAs associated with esophageal cancer prognosis by performing survival and univariate/multivariate COX analysis on DEMs and DEGs in the network and performed GSEA analysis on core genes alone, and finally the expression of the markers was verified by qPCR in esophageal cancer cell lines Eca109, SKGT-4 and normal esophageal epithelial cells HEEC. Nine DEMs were obtained, of which three were upregulated and six were downregulated, and 326 DEGs were obtained, of which 105 were upregulated and 221 were downregulated. Survival univariate/multivariate COX analysis revealed that five genes, ZBTB16, AQP4, ADCYAP1R1, PDGFD, and VIPR2, and two microRNAs, miR-99a-5p, and miR-508-5p, were related to esophageal cancer prognosis. GSEA analysis showed that the following genes may be involved in esophageal cancer prognosis: ZBTB16 may through the MTOR signaling pathway, AQP4 through the GNRH signaling pathway, ADCYAP1R1 through the PPAR signaling pathway, VIPR2 through the P53 signaling pathway and PDGFD through the PENTOSE-PHOSPHATE signaling pathway.

Identification and Exploration of Novel Macrophage M2-Related Biomarkers and Potential Therapeutic Agents in Endometriosis

Endometriosis (EM) is a chronic neuroinflammatory disorder that is associated with pain and infertility that affects ∼10% of reproductive-age women. The pathophysiology and etiology of EM remain poorly understood, and diagnostic delays are common. Exploration of the underlying molecular mechanism, as well as novel diagnostic biomarkers and therapeutic targets, is urgently needed. Inflammation is known to play a key role in the development of lesions, which are a defining feature of the disorder. In our research, the CIBERSORT and WGCNA algorithms were used to establish a weighted gene co-expression network and to identify macrophage-related hub genes using data downloaded from the GEO database (GSE11691, 7305). The analysis identified 1,157 differentially expressed genes (DEGs) in EM lesions, of which five were identified as being related to M2 macrophages and were validated as differentially expressed by qRT-PCR and immunohistochemistry (IHC). Of these putative novel biomarker genes, bridging integrator 2 (BIN2), chemokine receptor 5 (CCR5), and macrophage mannose receptor 1 (MRC1) were upregulated, while spleen tyrosine kinase (SYK) and metalloproteinase 12 (ADAM12) were downregulated in ectopic endometria vs. normal endometria. Meanwhile, 23 potentially therapeutic small molecules for EM were obtained from the cMAP database, among which topiramate, isoflupredone, adiphenine, dexverapamil, MS-275, and celastrol were the top six molecules with the highest absolute enrichment values. This is our first attempt to use the CIBERSORT and WGCNA algorithms for the identification of novel Mϕ2 macrophage-related biomarkers of EM. Our findings provide novel insights into the impact of immune cells on the etiology of EM; nevertheless, further investigation of these key genes and therapeutic drugs is needed to validate their effects on EM.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

Background: Despite the prominent development of medical technology in recent years, the prognosis of non-small cell lung cancer (NSCLC) is still not optimistic. It is crucial to identify more reliable diagnostic biomarkers for the early diagnosis and personalized therapy of NSCLC and clarify the molecular mechanisms underlying NSCLC progression.

Methods: In the present study, bioinformatics analysis was performed on three datasets obtained from the Gene Expression Omnibus to identify the NSCLC-associated differentially expressed genes (DEGs). Immunohistochemistry-based tissue microarray of human NSCLC was used to experimental validating the potential targets obtained from bioinformatics analysis.

Results: By using protein–protein interaction (PPI) network analysis, Kaplan–Meier plotter, and Gene Expression Profiling Interactive Analysis, we selected 40 core DEGs for further study. Then, a re-analysis of 40 selected genes via Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that nine key genes involved in the cell cycle and p53 signaling pathway participated in the development of NSCLC. Then, we checked the protein level of nine key genes by semi-quantitative of IHC and checked the distribution at a single-cell level. Finally, we validated dual-specificity protein kinase TTK as a biomarker for prognosis in a tissue microarray. High TTK expression associated with a higher histological stage, advanced TNM stage, high frequency of positive lymph nodes, and worse 5-year overall survival.

Conclusions: We found nine key genes were enriched in the cell cycle and p53 signaling pathway. TTK could be considered as a potential therapeutic target and for the prognosis biomarker of NSCLC. These findings will provide new insights for the development of individualized therapeutic targets for NSCLC.

Construction of a novel prognostic-predicting model correlated to ovarian cancer

Background: Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS).

Results: We identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein–protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival.

Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.

Similarity and difference of pathogenesis among lung cancer subtypes suggested by expression profile data

Lung cancer is difficult to diagnose, has a high mortality rate and a high recurrence rate. By grouping and analyzing the gene expression in lung cancer samples, we selected the differentially expressed genes (DEGs) in total lung cancers or each subgroup, and then searched for the similarities and differences among these. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed, in addition to predictable cell proliferation or immune-related pathways, 'hemostasis', 'coagulation' and 'viral myocarditis' were also enriched in common DEGs, while specific functions or pathways were enriched in different subgroups. This may have implications for the treatment of total lung cancer or different subtypes. Through bioinformatics analysis, hub genes were obtained from total lung cancer and each subgroup respectively. Survival analysis of common hub genes led us to find that ZWINT, A2M, POLR2H and KIF11 are associated with unclassified lung cancer survival. For the construction of miRNA regulatory network, miR-16-5p was related to all of these four genes, and its expression is significantly different between lung cancers and normal samples. Combined with the hub genes of each subtype, it may have the ability of early screening and typing.

A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data

The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations.

Knockdown of KIF15 promotes cell apoptosis by activating crosstalk of multiple pathways in ovarian cancer: bioinformatic and experimental analysis

BACKGROUND

Ovarian cancer (OC) is the most lethal malignancy of women. Unlimited proliferation is a fundamental feature of OC cells. The genes associated with cell proliferation may be histopathologic biomarkers and targets of anti-tumor therapeutic strategies. The present study aimed to identify proliferation-associated biomarkers with prognostic, diagnostic, and therapeutic value and reveal the underlying molecular mechanism of candidate genes involved in OC by a combination of bioinformatic and experimental methods.

RESULTS

KIF15 was upregulated in early-stage OC tissues and could predict poor prognosis of patients of Stage I and II. The knockdown of KIF15 significantly inhibited cell proliferation, tumor formation, and growth as well as promoting apoptosis of OC cells. A combination of experimental and bioinformatic analyses revealed KIF15 knockdown promoted cell apoptosis by activating crosstalk of multiple pathways in OC.

CONCLUSION

KIF15, an early-stage prognostic gene, was identified as a candidate histopathologic biomarker and therapeutic target of OC.

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.

METHODS

The bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.

RESULTS

We ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.

CONCLUSION

The present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

Analysis and Validation of Hub Genes in Blood Monocytes of Postmenopausal Osteoporosis Patients

Osteoporosis is a common systemic bone disease caused by the imbalance between osteogenic activity and osteoclastic activity. Aged women are at higher risk of osteoporosis, partly because of estrogen deficiency. However, the underlying mechanism of how estrogen deficiency affects osteoclast activity has not yet been well elucidated. In this study, GSE2208 and GSE56815 datasets were downloaded from GEO database with 25 PreH BMD women and 25 PostL BMD women in total. The RRA algorithm determined 38 downregulated DEGs and 30 upregulated DEGs. Through GO analysis, we found that downregulated DEGs were mainly enriched in myeloid cell differentiation, cytokine-related functions while upregulated DEGs enriched in immune-related biological processes; pathways like Notch signaling and MAPK activation were found in KEGG/Rectome pathway database; a PPI network which contains 66 nodes and 91 edges was constructed and three Modules were obtained by Mcode; Correlation analysis helped us to find highly correlated genes in each module. Moreover, three hub genes FOS, PTPN6, and CTSD were captured by Cytohubba. Finally, the hub genes were further confirmed in blood monocytes of ovariectomy (OVX) rats by real-time PCR assay. In conclusion, the integrative bioinformatics analysis and real-time PCR analysis were utilized to offer fresh light into the role of monocytes in premenopausal osteoporosis and identified FOS, PTPN6, and CTSD as potential biomarkers for postmenopausal osteoporosis.

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Cytokine Pathways Involved in COVID-19 After Rhinovirus Infection in Asthma Patients

Background

Rhinovirus (RV) is the most common pathogen involved in asthma, and COVID-19, caused by SARS-COV-2, may be more severe in asthma patients. Here, we applied integrated bioinformatics to identify potential key genes and cytokine pathways after RV infection in asthma, and analyzed changes in angiotensin-converting enzyme 2 (ACE2), the cellular receptor of SARS-COV-2.

Material/Methods

The gene expression profile dataset GSE149273 was downloaded from NCBI-GEO, which included 90 samples of non-infected, RVA, and RVC. Differentially expressed genes (DEGs) were identified using t tests in the limma R package, and subsequently investigated by GO, KEGG, and DO analysis. Moreover, the expression of ACE2 and the proportion of immune cells were further analyzed to determine the effects of RV on cytokines.

Results

A total of 555 DEGs of RVA and 421 of RVC were identified. There were 415 DEGs in RVA and RVC, of which 406 were upregulated and 9 were downregulated. The functional enrichment analysis showed that most DEGs were obviously enriched in cytokines, and were mainly enriched in “influenza” and “hepatitis C, chronic”. In addition, the expression of ACE2 increased significantly and the proportion of immune cytokines significantly changed after RV infection. Our results suggest that RV can activate the cytokine pathway associated with COVID-19 by increasing ACE2.

Conclusions

The DEGs and related cytokine pathways after asthma RV infection identified using integrated bioinformatics in this study elucidate the potential link between RV and COVID-19.

Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

Osteosarcoma is one of the most aggressive malignant bone tumors worldwide. Although great advancements have been made in its treatment owing to the advent of neoadjuvant chemotherapy, the problem of lung metastasis is a major obstacle in the improvement of survival outcomes. Thus, the aim of the present study is to screen novel and key biomarkers, which may act as potential prognostic markers and therapeutic targets in osteosarcoma. We utilized the robust rank aggregation (RRA) method to integrate three osteosarcoma microarray datasets downloaded from the Gene Expression Omnibus (GEO) database, and we identified the robust differentially expressed genes (DEGs) between primary and metastatic osteosarcoma tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of robust DEGs. The results of enrichment analysis showed that the robust DEGs were closely associated with osteosarcoma development and progression. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm, and we found that macrophages are the most principal infiltrating immune cells in osteosarcoma, especially macrophages M0 and M2. Then, the protein–protein interaction network and key modules were constructed by Cytoscape, and 10 hub genes were selected by plugin cytoHubba from the whole network. The survival analysis of hub genes was also carried out based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The integrated bioinformatics analysis was utilized to provide new insight into osteosarcoma development and metastasis and identified EGR1, CXCL10, MYC, and CXCR4 as potential biomarkers for prognosis of osteosarcoma.

Altered Organelle Calcium Transport in Ovarian Physiology and Cancer

Calcium levels have a huge impact on the physiology of the female reproductive system, in particular, of the ovaries. Cytosolic calcium levels are influenced by regulatory proteins (i.e., ion channels and pumps) localized in the plasmalemma and/or in the endomembranes of membrane-bound organelles. Imbalances between plasma membrane and organelle-based mechanisms for calcium regulation in different ovarian cell subtypes are contributing to ovarian pathologies, including ovarian cancer. In this review, we focused our attention on altered calcium transport and its role as a contributor to tumor progression in ovarian cancer. The most important proteins described as contributing to ovarian cancer progression are inositol trisphosphate receptors, ryanodine receptors, transient receptor potential channels, calcium ATPases, hormone receptors, G-protein-coupled receptors, and/or mitochondrial calcium uniporters. The involvement of mitochondrial and/or endoplasmic reticulum calcium imbalance in the development of resistance to chemotherapeutic drugs in ovarian cancer is also discussed, since Ca²⁺ channels and/or pumps are nowadays regarded as potential therapeutic targets and are even correlated with prognosis.

The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis

Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination and Fanconi anaemia pathways. Associations between these two functional genes were explored in the study, and we performed a random walk algorithm based on reconstructed gene‐gene network, including protein‐protein interaction and co‐expression relations. Following the random walk, all genes were ranked and GSEA analysis showed that the biological functions focused primarily on autophagy, histone modification and gluconeogenesis. Based on three types of seed nodes, the top two genes were utilized as examples. We selected a total of six candidate genes (FANCA, FANCG, POLD1, KDM1A, BLM and BRCA1) for subsequent verification. The validation results of the six candidate genes have significance in three independent ovarian cancer data sets with platinum‐resistant and platinum‐sensitive information. To explore the correlation between biomarkers and clinical prognostic factors, we performed differential analysis and multivariate clinical subgroup analysis for six candidate genes at both mRNA and protein levels. And each of the six candidate genes and their neighbouring genes with a mutation rate greater than 10% were also analysed by network construction and functional enrichment analysis. In the meanwhile, the survival analysis for platinum‐treated patients was performed in the current study. Finally, the RT‐qPCR assay was used to determine the performance of candidate genes in ovarian cancer platinum response. Taken together, this research demonstrated that comprehensive bioinformatics methods could help to understand the molecular mechanism of platinum response and provide new strategies for overcoming platinum resistance in ovarian cancer treatment.

Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.

Identification and Validation of Potential Biomarkers and Their Functions in Acute Kidney Injury

Acute kidney injury (AKI) is a global public health concern associated with high morbidity, mortality, and health-care costs, and the therapeutic measures are still limited. This study aims to investigate crucial genes correlated with AKI, and their potential functions, which might contribute to a better understanding of AKI pathogenesis. The high-throughput data GSE52004 and GSE98622 were downloaded from Gene Expression Omnibus; four group sets were extracted and integrated. Differentially expressed genes (DEGs) in the four group sets were identified by limma package in R software. The overlapping DEGs among four group sets were further analyzed by the VennDiagram package, and their potential functions were analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, the protein-protein interaction (PPI) network was constructed by STRING, and the functional modules of the PPI network were filtered by MCODE and ClusterOne in Cytoscape. Hub genes of overlapping DEGs were identified by Cyto-Hubba and cytoNCA. The expression of 35 key genes was validated by quantitative real-time PCR (qRT-PCR). Western blot and immunofluorescence were performed to validate an important gene Egr1. A total of 722 overlapping DEGs were differentially expressed in at least three group sets. These genes mainly enriched in cell proliferation and fibroblast proliferation. Additionally, 5 significant modules and 21 hub genes, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln were screened by analyzing the PPI networks. The 5 significant modules were mainly enriched in complement and coagulation cascades and Metabolic pathways, and the top 21 hub genes were mainly enriched in positive regulation of cell proliferation. Through validation, Krt20 were identified as the top 1 upregulated genes with a log2 (fold change) larger than 10 in all these 35 genes, and 21 genes were validated as significantly upregulated; Egr1 was validated as an upregulated gene in AKI in both RNA and protein level. In conclusion, by integrated analysis of different high-throughput data and validation by experiment, several crucial genes were identified in AKI, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln. These genes were very important in the process of AKI, which could be further utilized to explore novel diagnostic and therapeutic strategies.

Upregulated expression of MMP family genes is associated with poor survival in patients with esophageal squamous cell carcinoma via regulation of proliferation and epithelial‑mesenchymal transition

Matrix metalloproteinases (MMPs) are involved in the cleavage of several components of the extracellular matrix and serve important roles in tumor growth, metastasis and invasion. Previous studies have focused on the expression of one or several MMPs in esophageal squamous cell carcinoma (ESCC); however, in the present study, the transcriptomics of all 23 MMPs were systematically investigated with a focus on the prognostic value of the combination of MMPs. In this study, 8 overlapping differentially expressed genes of the MMP family were identified based on data obtained from Gene Expression Omnibus and The Cancer Genome Atlas. The prognostic value of these MMPs were investigated; the receiver operating characteristic curves, survival curves and nomograms showed that the combination of 6 selected MMPs possessed a good predictive ability, which was more accurate than the prediction model based on Tumor‑Node‑Metastasis stage. Gene set enrichment analysis and gene co‑expression analysis were performed to investigate the potential mechanism of action of MMPs in ESCC. The MMP family was associated with several signaling pathways, such as epithelial‑mesenchymal transition (EMT), Notch, TGF‑β, mTOR and P53. Cell Counting Kit‑8, colony formation, wound healing assays and western blotting were used to determine the effect of BB‑94, a pan‑MMP inhibitor, on proliferation and migration of ESCC cells. BB‑94 treatment decreased ESCC cell growth, migration and EMT. Therefore, MMPs may serve both as diagnostic and prognostic biomarkers of ESCC, and MMP inhibition may be a promising preventive and therapeutic strategy for patients with ESCC.

Understanding the Genetic Domestication History of the Jianchang Duck by Genotyping and Sequencing of Genomic Genes Under Selection

The Jianchang duck is mainly distributed in Southwest China, and has the characteristics of fast growth rate and strong abilities in lipid deposition in the liver. In order to investigate the effects of domestication process on formation of the unique characteristics of Jianchang duck, the whole genome of sixteen individuals and three pooling of Jianchang duck were re-sequenced, and genome data of 70 mallards and 83 domestic ducks from thirteen different places in China were obtained from NCBI. The population stratification and evolution analysis showed gene exchanges existed between the Jianchang and other domestic duck populations, as well as Jianchang ducks and mallards. Genomic comparison between mallards and Jianchang ducks showed genes, including CNTN1, CHRNA9, and SHANK2, which is involved in brain and nerve development, experienced strong positive selection in the process of Jianchang duck domestication. The genomic comparison between Jianchang and domestic duck populations showed that HSD17B12 and ESM1, which affect lipid metabolism, experienced strong positive selection during the domestication process. FST analysis among populations of Jianchang duck with different plumage colors indicated that MITF was related to the phenotype of a white feather, while MC1R was related to the phenotype of hemp feather. Our results provided a base for the domestication process of Jianchang duck and the genomic genes for unique traits.

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is one of the most fatal gynecological malignancies in the world and confers a poor 5-year survival rate. The present study was designed to discover novel prognostic markers for patients with OC in order to estimate disease metastasis or recurrence. Based on the large cohorts of transcriptome data from multicenter sources, a comprehensive analysis was performed to explore potential prognostic markers. A total of 269 differentially expressed genes were identified, of which 32 were upregulated and 237 downregulated in OC tissues compared with the corresponding expression in normal tissues. Kaplan-Meier analysis, log-rank test and nomogram analysis were employed to demonstrate that low expression levels of claudin 10 (CLDN10) were associated with a less favorable disease prognosis. The most promising prognostic marker for OC was subsequently selected. Additionally, the prognostic nomogram was constructed in order to assess the 5-year survival rate using CLDN10 expression as a prognostic marker for OC. Furthermore, gene set enrichment analysis and analysis of the tumor-associated competing endogenous RNA network were performed to elucidate the potential biological processes associated with CLDN10 expression. The current results indicated that CLDN10 may influence OC progression via transforming growth factor-β (TGF-β)- or WNT/β-catenin-induced epithelial-to-mesenchymal transition (EMT). The associations among CLDN10, microRNA-486-5p, TGF-β, WNT/β-catenin and EMT should be further investigated in future studies.

mRNA level of ROCK1, RHOA, and LIMK2 as genes associated with apoptosis in evaluation of effectiveness of adalimumab treatment

BACKGROUND

Psoriasis is a multifactorial autoimmune disease, which underlies the abnormalities of the apoptotic process. In cases of psoriasis and psoriatic arthritis, biological treatment is used. This study aimed to determine any changes in the expression of the genes associated with apoptosis in patients with psoriatic arthritis treated with adalimumab and to assess any phenotypic modifications based on changes in dermatological indexes.

METHODS

The study included 20 patients with psoriatic arthritis treated biologically and 20 healthy volunteers. The research material consisted of peripheral blood mononuclear cells (PBMCs) from which the total RNA was isolated. Changes in the gene expression were determined using oligonucleotide microarrays and RT-qPCR. The clinical condition was assessed based on selected indicators: PASI, BSA [%], DAS28, and DLQI, which were determined every 3 months.

RESULTS

There were changes in the expression of genes associated with apoptosis. Significant differences were found for ROCK1, RhoA, and LIMK2 expression profiles in PBMCs. At the initial stage of treatment, a decrease in the PASI and BSA rates was observed. At the later stages, the values of these indicators increased once again. There were correlations between the changes in these genes' expression and the dermatological markers.

CONCLUSION

Adalimumab influences the expression of genes related to apoptosis and the values of dermatological indicators of patients. Changes in the expression level of genes associated with apoptosis suggest that ROCK1, RhoA, and LIMK2 may be genes that can potentially be indicators of treatment effectiveness and lack of response to biological treatment.

Identification for Exploring Underlying Pathogenesis and Therapy Strategy of Oral Squamous Cell Carcinoma by Bioinformatics Analysis

BACKGROUND Oral squamous cell carcinoma (OSCC), one of the most common cavity-associated cancers, has a high incidence and worldwide mortality. However, the cause and underlying molecular mechanisms of OSCC remain unclear. MATERIAL AND METHODS Three microarray datasets (GSE23558, GSE34105, and GSE74530) from the Gene Expression Omnibus (GEO) database were downloaded and then integrated to gain differentially expressed genes (DEGs). We performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs in order to elucidate DEGs' biological roles. Protein-protein interaction (PPI) networks were established in order to identify hub genes. To validate the gene markers for OSCC, the data of TCGA OSCC were also assessed. RESULTS Together, 651 DEGs containing 288 upregulated genes and 363 downregulated genes were screened out, which could completely distinguish between OSCC and normal control tissues by principal component analysis (PCA). The GO analysis indicated the DEGs were enriched in chemokine activity in the biological process group. The molecular functions of DEGs included growth factor activity. The molecular functions included oxidoreductase activity. The main DEG-associated cellular components included extracellular exosome. The KEGG pathway analysis indicated the DEGs were mainly participated in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450 and glutathione metabolism signal pathway. The co-expression network identified core genes from the PPI network. Additionally, Kaplan-Meier survival analysis showed that CSF2 and EGF genes were significantly correlated with OSCC patients' overall survival. CONCLUSIONS Our study using an integrated bioinformatics analysis might provide valuable information for exploring potential new molecular biomarkers and therapeutic targets for OSCC.

Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis

Endometriosis is a common gynecological disease characterized by the presence and growth of endometrial tissue outside the uterus, including the pelvis and abdominal cavity. This condition causes various clinical symptoms, such as non-menstrual pelvic pain, dysmenorrhea and infertility, seriously affecting the health and quality of life of women. To date, the specific mechanism and the key molecules of endometriosis remain uncertain. The purpose of the present study was to elucidate the mechanisms involved in the development and persistence of the disease. A number of mRNA expression profile datasets (namely GSE11691, GSE23339, GSE25628 and GSE78851) were downloaded from the Gene Expression Omnibus (GEO) database. These gene expression profiles were normalized, and the differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis. A total of 103 DEGs were screened upon excluding the genes that exhibited inconsistency of expression (P<0.05). Furthermore, the Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and construction of protein-protein interaction networks of DEGs were performed using online software. The results revealed that the DEGs were closely associated with cell migration, adherens junction and hypoxia-inducible factor signaling. In addition, immunohistochemical assay results were found to be consistent with the bioinformatics results. The present study may help us understand underlying molecular mechanisms and the development of endometriosis, which has a great clinical significance for early diagnosis of the disease.

Analysis of Differential Expression of microRNAs and Their Target Genes in Prostate Cancer: A Bioinformatics Study on Microarray Gene Expression Data

Early diagnosis of prostate cancer (PCa) as the second most common cancer in men is not associated with precise and specific results. Thus, alternate methods with high specificity and sensitivity are needed for accurate and timely detection of PCa. MicroRNAs regulate the molecular pathways involved in cancer by targeting multiple genes. The aberrant expression of the microRNAs has been reported in different cancer types including PCa. In this bioinformatics study, we studied differential expression profiles of microRNAs and their target genes in four PCa gene expression omnibus (GEO) databases. PCa diagnostic biomarker candidates were investigated using bioinformatics tools for analysis of gene expression data, microRNA target prediction, pathway and GO annotation, as well as ROC curves. The results of this study revealed significant changes in the expression of 14 microRNAs and 40 relevant target genes, which ultimately composed four combination panels (miR- 375+96+663/ miR- 133b+143- 3p + 205/ C2ORF72 + ENTPD5 + GLYAT11/LAMB3 + NTNG2+TSLP) as candidate biomarkers capable to distinguish between PCa tumor samples and normal prostate tissue samples. These biomarkers may be suggested for a more accurate early diagnosis of PCa patients along with current diagnostic tests.

Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis

Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes were analyzed by integration with annotations from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by construction of a protein‐protein interaction network and weighted gene coexpression analysis. We identified 139 differentially expressed genes associated with the progression of COPD, among which 14 Hub genes were identified and found to be enriched in certain categories, including immune and inflammatory response, response to lipopolysaccharide and receptor for advanced glycation end products binding; in addition, these Hub genes are involved in multiple signaling pathways, particularly hematopoietic cell lineage and cytokine‐cytokine receptor interaction. The 14 Hub genes were positively or negatively associated with COPD by wgcna analysis. The genes CX3CR1,PTGS2,FPR1,FPR2, S100A12,EGR1,CD163, S100A8 and S100A9 were identified to mediate inflammation and injury of the lung, and play critical roles in the pathogenesis of COPD. These findings improve our understanding of the underlying molecular mechanisms of COPD.

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.

Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis

Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer.

Results: The mRNA and microRNA expression profile datasets (GSE7803, GSE9750, GSE63514, and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein–protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues.

Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. They might be of clinical significance for the diagnosis and prognosis of cervical cancer, and provide effective targets for the treatment of cervical cancer.