scholarly journals Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9120
Author(s):  
Ying Wan ◽  
Xiaolian Zhang ◽  
Huilin Leng ◽  
Weihua Yin ◽  
Wenxing Zeng ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Candidate Genes ◽  
Interaction Network ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Genome Database ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Degree Of Malignancy ◽  
Geo Database

Background Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA. Methods The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database. Results In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes. Conclusion The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

scholarly journals Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110210
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Interaction Network ◽  
Maximal Clique ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Uterine Carcinosarcoma ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

2021 ◽  
Author(s):  
Q Shi ◽  
Z Meng ◽  
XX Tian ◽  
YF Wang ◽  
WH Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Prognostic Index ◽  
Interaction Network ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Cox Regression Analysis ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

scholarly journals Identification of hub driving genes and regulators of lung adenocarcinoma based on the gene Co-expression network

2020 ◽  
Vol 40 (4) ◽  
Author(s):  
Zihao Xu ◽  
Zilong Wu ◽  
Jiatang Xu ◽  
Jingtao Zhang ◽  
Bentong Yu
Keyword(s):  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Characteristic Curve ◽  
Interaction Network ◽  
Mapk Signaling ◽  
The Cancer Genome Atlas ◽  
Circular Rnas ◽  
Node Degree ◽  
Protein Protein Interaction ◽  
Cancer Tissues

Abstract Lung adenocarcinoma (LUAD) remains the leading cause of cancer-related deaths worldwide. Increasing evidence suggests that circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) can regulate target gene expression and participate in tumor genesis and progression. However, hub driving genes and regulators playing a potential role in LUAD progression have not been fully elucidated yet. Based on data from The Cancer Genome Atlas database, 2837 differentially expressed genes, 741 DE-regulators were screened by comparing cancer tissues with paracancerous tissues. Then, 651 hub driving genes were selected by the topological relation of the protein–protein interaction network. Also, the target genes of DE-regulators were identified. Moreover, a key gene set containing 65 genes was obtained from the hub driving genes and target genes intersection. Subsequently, 183 hub regulators were selected based on the analysis of node degree in the ceRNA network. Next, a comprehensive analysis of the subgroups and Wnt, mTOR, and MAPK signaling pathways was conducted to understand enrichment of the subgroups. Survival analysis and a receiver operating characteristic curve analysis were further used to screen for the key genes and regulators. Furthermore, we verified key molecules based on external database, LRRK2, PECAM1, EPAS1, LDB2, and HOXA11-AS showed good results. LRRK2 was further identified as promising biomarker associated with CNV alteration and various immune cells’ infiltration levels in LUAD. Overall, the present study provided a novel perspective and insight into hub driving genes and regulators in LUAD, suggesting that the identified signature could serve as an independent prognostic biomarker.

scholarly journals PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Linan Xing ◽  
Songyu Tian ◽  
Wanqi Mi ◽  
Yongjian Zhang ◽  
Yunyan Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Ovarian Cancer Cells ◽  
Tissue Cell ◽  
Platinum Resistance ◽  
Western Blot Assay ◽  
Protein Protein Interaction ◽  
Platinum Based Chemotherapy ◽  
Novel Target

Ovarian cancer is the most frequent cause of death among gynecologic malignancies. A total of 80% of patients who have completed platinum-based chemotherapy suffer from relapse and develop resistance within 2 years. In the present study, we obtained patients' complete platinum (cisplatin and carboplatin) medication information from The Cancer Genome Atlas database and then divided them into two categories: resistance and sensitivity. Difference analysis was performed to screen differentially expressed genes (DEgenes) related to platinum response. Subsequently, we annotated DEgenes into the protein–protein interaction network as seed nodes and analyzed them by random walk. Finally, second-ranking protease serine 1 gene (PRSS1) was selected as a candidate gene for verification analysis. PRSS1's expression pattern was continuously studied in Oncomine and cBio Cancer Genomic Portal databases, revealing the key roles of PRSS1 in ovarian cancer formation. Hereafter, we conducted in-depth explorations on PRSS1's platinum response to ovarian cancer through tissue and cytological experiments. Quantitative real-time polymerase chain reaction and Western blot assay results indicated that PRSS1 expression levels in platinum-resistant samples (tissue/cell) were significantly higher than in samples sensitive to platinum. By cell transfection assay, we observed that knockdown of PRSS1 reduced the resistance of ovarian cancer cells to cisplatin. Meanwhile, overexpression of PRSS1 increased the resistance to cisplatin. In conclusion, we identified a novel risk gene PRSS1 related to ovarian cancer platinum response and confirmed its key roles using multiple levels of low-throughput experiments, revealing a new treatment strategy based on a novel target factor for overcoming cisplatin resistance in ovarian cancer.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Evaluation of SYK Gene as a Prognostic Biomarker and Suggested Aromatic Potential Phytochemicals to Halt the Colorectal Cancer

2021 ◽  
Author(s):  
Partha Biswas ◽  
Dipta Dey ◽  
Atikur Rahman ◽  
Md. Aminul Islam ◽  
Tasmina Ferdous Susmi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Docking ◽  
Drug Repositioning ◽  
Treatment Strategies ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Gene Encoding ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Background: Colorectal cancer is considered the third most fetal among all type of cancer. Spleen tyrosine kinase (SYK) is a non-receptor type tyrosine-protein that plays crucial role in signaling mediated via immune receptor. We adopted an onco-informatics analysis to evaluate the SYK expression and prognostic value of SYK in colorectal cancer, and identification of potential phytochemicals which may inhibit overexpression of SYK protein as well as minimized colorectal cancer. Materials &amp; Methods: Differential expression of SYK gene was analyzed using the several transcriptomic databases including Oncomine, UALCAN, GENT2 and GEPIA2. The server, cBioPortal was used to analyze mutation and copy number alterations whereas GENT2, GEPIA, OncoLnc and PrognoScan were employed to examine the survival rate. A protein-protein interaction network of SYK and co-expressed genes of SYK was conducted via GeneMANIA. Considering SYK gene encoding protein as drug target, selected phytochemicals were assessed by molecular docking using PyRx 0.8 packages. YASARA molecular dynamics simulators were applied for the post validation of the molecular docking data. Results: We have observed significant overexpression of mRNA expression levels of SYK gene colorectal adenocarcinoma (COAD) samples compared with normal tissues. Significant methylation level and various genetic alterations are assembled in SYK gene which can lead to the development of colorectal cancer. As a result, lower level of SYK expression was related to the more chances of patients&rsquo; survival by which all the outcomes from the multiple bioinformatics platforms and web resources have demonstrated the significant evidences that the SYK kinsase can possess as a potential biomarker for the treatment of colorectal cancer. Here, aromatic phytochemicals namely, Kaempferol and Glabridin targeting SYK showed more stability compared to controls and may be useful for the treatment of colorectal cancer. Conclusion: Our study showed dysregulated expression of SYK in colorectal cancer and potentiality to act as a biomarker for the prognosis of CRC. Moreover, we have shown phytochemicals (Kaempferol and Glabridin) target SYK as potential treatment strategies and drug repositioning potentiality in colorectal cancer.

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