scholarly journals Integrated Bioinformatics Analysis for Identification of the Hub Genes Linked with Prognosis of Ovarian Cancer Patients

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Xiaofeng Li ◽  
Qiu Wang ◽  
Zhicheng Wu ◽  
Jiantong Zheng ◽  
Ling Ji

Background. One of the most usual gynecological state of tumor is ovarian cancer and is a major reason of gynecological tumor-related global mortality rate. There have been multiple risk elements related to ovarian cancer like the background of past cases associated with breast cancer or ovarian cancer, or excessive body weight issues, case history of smoking, and untimely menstruation or menopause. Because of unclear expressions, more than 70% of the ovarian cancer patient cases are determined during the early stage. Material and Methods. GSE38666, GSE40595, and GSE66957 were the three microarray datasets which were analyzed using GEO2R for screening the differentially expressed genes. GO, Kyoto Encyclopedia of Genes, and protein expression studies were performed for analysis of hub genes. Then, survival analysis was performed for all the hub genes. Results. From the dataset, a total of 199 differentially expressed genes (DEGs) were identified. Through the KEGG pathway study, it was noted that the DEGs are mainly linked with the AGE-RAGE signaling pathway, central carbon metabolism, and human papillomavirus infection. The survival analysis showed 4 highly expressed hub genes COL4A1, SDC1, CDKN2A, and TOP2A which correlated with overall survival in ovarian cancer patients. Moreover, the expression of the 4 hub genes was validated by the GEPIA database and the Human Protein Atlas. Conclusion. The results have shown that all 4 hub genes were found to be upregulated in ovarian cancer tissues which predict poor prognosis in patients with ovarian cancer.

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.


2021 ◽  
Author(s):  
Feifei Liu ◽  
Yu Wang ◽  
Wenxue Li ◽  
Diancheng Li ◽  
Yuwei Xin ◽  
...  

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.


2021 ◽  
Author(s):  
Muhammad Jamal ◽  
Abdul Saboor Khan ◽  
Hina Iqbal Bangash ◽  
Tian Xie ◽  
Tianbao Song ◽  
...  

Abstract Background Lung cancer (LUCA) is the leading cause of cancer-related morbidities and mortalities globally. Despite the recent advancements in lung cancer research, understanding of the molecular mechanism underlying LUCA tumorigenesis and prognosis remains suboptimal. This study aims to identify the candidate biomarkers and therapeutic genes in lung cancer. Methods In this study, gene expression profiles of GSE30219, GSE33532, GSE32863 and GSE43458 were downloaded from GEO. The differentially expressed genes (DEGs) in LUAD tissue and normal lung tissue with a p-value < 0.05 and a |log fold change (FC)| >1.0 were identified by GEO2R. For functional enrichment analysis of these DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed with KOBAS and DAVID tools. Next, the candidate hub genes were filtered out with Cytoscape using CytoHubba plugin. These hub genes were validated by (the Cancer Genome Atlas) TCGA-based gene expression analysis, protein-protein network interaction (PPI) analysis, survival analysis. Moreover, the expression of these genes in cancer and normal tissue was assessed in the Human Protein Atlas (HPA) database. In addition, miRNA network of the hub genes was constructed. Finally, DGIdb database was used to check the drug-targeting potentials of the hub genes. Results a total of 332 overlapping differentially expressed genes (DEGs) including 73 upregulated and 259 downregulated, respectively were identified. GO analysis revealed that the DEGs were principally regulating various cancer-associated functions and pathways. The module analysis revealed 55 hub genes in 4 modules. The survival analysis through Kaplan-Meier (KM) plotter indicated that the altered expression of these genes resulted in the poor overall survival (OS) of LUCA patients. Moreover, these genes show a differential expression on both protein and mRNA level in cancer patient compared to the normal. In addition, in addition, 6 potential microRNAs (miRNAs) interacting with hub genes were identified. Finally, a list of 117 therapeutic small molecules was tabulated that could facilitate LUCA treatment. Conclusions the findings of this study may help in the development of novel and reliable biomarkers for diagnosis, prognosis and therapeutic intervention for LUAD.


2021 ◽  
Vol 49 (10) ◽  
pp. 030006052110499
Author(s):  
Junhua Luo ◽  
Jinming Xu ◽  
Longhua Ou ◽  
Yingchen Zhou ◽  
Haichao Yun ◽  
...  

Objective To explore the hypermethylated long non-coding (lnc)RNAs involved in bladder carcinogenesis and prognosis. Methods Reduced representation bisulfite sequencing and RNA sequencing were performed on five paired tumor and adjacent normal tissue samples from bladder cancer patients. The differentially methylated regions around transcription start sites and differentially expressed genes, including lncRNAs, were analyzed. Correlations between DNA methylation modifications and the expression of lncRNAs were examined. Survival analysis was surveyed on the GEPIA web server. Results We identified 19,560 hypomethylated and 68,781 hypermethylated differentially methylated regions around transcription start sites in bladder cancer tissues. In total, 2321 differentially expressed genes were found in bladder tumors, among which, 367 were upregulated and 1954 were downregulated. There were 141 downregulated genes involving eight lncRNAs that were consistently hypermethylated, while 24 upregulated genes were consistently hypomethylated. Survival analysis demonstrated that hypermethylation of lncRNAs LINC00683 and MSC-AS1 were associated with poor overall survival in bladder cancer patients. Conclusion Some lncRNAs are controlled by DNA methylation in bladder cancer and they might be important factors in bladder carcinogenesis. Hypermethylated lncRNAs including LINC00683 and MSC-AS1 have the potential to be prognostic biomarkers for bladder cancer.


2021 ◽  
Author(s):  
Lele Cao¹ ◽  
Ye Meng¹ ◽  
Rui Huang ◽  
Xiaoxue Wang ◽  
Hui Qin

Abstract Background Multiple myeloma is a hematologic disorder of abnormal plasma cell proliferation. Although there are some agents with different mechanisms in the clinic, the treatment of multiple myeloma is still challenging for the reason that its recurrence and progression are common. Therefore, it is critical to determine novel biomarkers to improve the prognosis of patients. Methods Firstly, raw data of GSE82307 was collected from the Gene Expression Omnibus database. Secondly, the top 50% of most variant genes were employed to construct a gene co-expression network in the R.WGCNA algorithm, and module significance and module membership were utilized to identify hub modules and hub genes respectively. The gene ontology enrichment and Kyoto encyclopedia of genes and genomes pathway analysis were carried out to assess biological characteristics. Then a protein-protein interaction network was conducted based on the STRING website and Cytoscape software. Next, differentially expressed genes were analyzed using the limma R package. Finally, survival analysis was performed by Kaplan–Meier plotter to evaluate prognosis. Results 10826 genes were used to construct a co-expression network. In this network, the blue module was identified as a hub module in which 68 genes were identified as hub genes. Furthermore, 46 differentially expressed genes were screened in samples of GSE82307. Integrating hub genes and differentially expressed genes, we determined 14 key genes. Finally, survival analysis revealed that ten genes CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 were related to the relapse and prognosis of multiple myeloma. Conclusion Our study suggested that CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 may be potential biomarkers for predicting the relapse and prognosis of multiple myeloma.


PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251962
Author(s):  
Rong Fan ◽  
Lijin Dong ◽  
Ping Li ◽  
Xiaoming Wang ◽  
Xuewei Chen

Background With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC. Methods GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry. Results We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip. Conclusions In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.


Author(s):  
Kai Meng ◽  
Jinghe Cao ◽  
Yehao Dong ◽  
Mengchen Zhang ◽  
Chunfeng Ji ◽  
...  

Wilms tumor gene (WT1) is used as a marker for the diagnosis and prognosis of ovarian cancer. However, the molecular mechanisms involving WT1 in ovarian cancer require further study. Herein, we used bioinformatics and other methods to identify important pathways and hub genes in ovarian cancer affected by WT1. The results showed that WT1 is highly expressed in ovarian cancer and is closely related to the overall survival and progression-free survival (PFS) of ovarian cancer. In ovarian cancer cell line SKOV3, WT1 downregulation increased the mRNA expression of 638 genes and decreased the mRNA expression of 512 genes, which were enriched in the FoxO, AMPK, and the Hippo signaling pathways. The STRING online tool and Cytoscape software were used to construct a Protein-protein interaction (PPI) network and for Module analysis, and 18 differentially expressed genes (DEGs) were selected. Kaplan-Meier plotter analysis revealed that 16 of 18 genes were related to prognosis. Analysis of GEPIA datasets indicated that 7 of 16 genes were differentially expressed in ovarian cancer tissues and in normal tissues. The expression of IGFBP1 and FBN1 genes increased significantly after WT1 interference, while the expression of the SERPINA1 gene decreased significantly. The correlation between WT1 expression and that of these three genes was consistent with that of ovarian cancer tissues and normal tissues. According to the GeneMANIA online website analysis, there were complex interactions between WT1, IGFBP1, FBN1, SERPINA1, and 20 other genes. In conclusion, we have identified important signaling pathways involving WT1 that affect ovarian cancer, and distinguished three differentially expressed genes regulated by WT1 associated with the prognosis of ovarian cancer. Our findings provide evidence outlining mechanisms involving WT1 gene expression in ovarian cancer and provides a rational for novel treatment of ovarian cancer.


2021 ◽  
Vol 11 (5) ◽  
pp. 363
Author(s):  
Arafat Rahman Oany ◽  
Mamun Mia ◽  
Tahmina Pervin ◽  
Salem Ali Alyami ◽  
Mohammad Ali Moni

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern. In the present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4643 differentially expressed genes. The up-regulatory genes mainly concentrate on immune-inflammatory responses, and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment, and we mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC and are verified by expression profile analysis. From our study, we have found that genes LILRB2 and CYBB play crucial roles in CC, as reported here for the first time. Furthermore, the survivability of the hub genes was also assessed, and among them, finally, CXCR4 has been identified as one of the most potential differentially expressed genes that might play a vital role in the survival of CC patients. Thus, CXCR4 could be used as a prognostic and/or diagnostic biomarker and a drug target for CC.


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