scholarly journals NMU Is a Poor Prognostic Biomarker in Patients with Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Tang ◽  
Chunsheng Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Mutations ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Specific Gene ◽  
Aberrant Expression ◽  
Protein Protein Interaction ◽  
Web Resource ◽  
Kaplan Meier

Lung adenocarcinoma (LUAD) is the most prevalent histologic type of lung cancer, associated with a high incidence rate and substantial mortality rate worldwide. Accumulating evidence shows that the aberrant expression of neuromedin U (NMU) contributes to the initiation and progression of cancer. Herein, we explored whether NMU could be adopted as a new diagnostic and therapeutic marker in LUAD. The UALCAN and GEPIA web resources were employed to assess data on the NMU expression in LUAD. The STRING web resource was used to develop the PPI (protein-protein interaction) network of NMU, whereas Cytoscape was applied for module analysis. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of NMU and the interacting proteins were examined using the WebGestalt tool. Survival analysis was performed with the Kaplan-Meier plotter tool. Results revealed that the NMU expression in LUAD was significantly higher than in the nonmalignant tissues. Moreover, higher NMU levels were dramatically related to shorter overall survival, first progression survival, and postprogression survival. The specific gene mutations G45V, R143T, and F152L of NMU occurred in LUAD samples and were associated with a worse prognosis in patients. KEGG and western blot analyses demonstrated an association of NMU with the cell cycle and the cAMP signaling cascade. Bioinformatic analysis and the in vitro experiments implicated NMU as a promising prognostic signature and treatment target for LUAD.

scholarly journals Bioinformatic analysis and identification of potential prognostic microRNAs and mRNAs in thyroid cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4674 ◽  
Author(s):  
Jianing Tang ◽  
Deguang Kong ◽  
Qiuxia Cui ◽  
Kun Wang ◽  
Dan Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Thyroid Cancer ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Rank Test ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Independent Risk Factors ◽  
Cancer Tissues

Thyroid cancer is one of the most common endocrine malignancies. Multiple evidences revealed that a large number of microRNAs and mRNAs were abnormally expressed in thyroid cancer tissues. These microRNAs and mRNAs play important roles in tumorigenesis. In the present study, we identified 72 microRNAs and 1,766 mRNAs differentially expressed between thyroid cancer tissues and normal thyroid tissues and evaluated their prognostic values using Kaplan-Meier survival curves by log-rank test. Seven microRNAs (miR-146b, miR-184, miR-767, miR-6730, miR-6860, miR-196a-2 and miR-509-3) were associated with the overall survival. Among them, three microRNAs were linked with six differentially expressed mRNAs (miR-767 was predicted to target COL10A1, PLAG1 and PPP1R1C; miR-146b was predicted to target MMP16; miR-196a-2 was predicted to target SYT9). To identify the key genes in the protein-protein interaction network , we screened out the top 10 hub genes (NPY, NMU, KNG1, LPAR5, CCR3, SST, PPY, GABBR2, ADCY8 and SAA1) with higher degrees. Only LPAR5 was associated with the overall survival. Multivariate analysis demonstrated that miR-184, miR-146b, miR-509-3 and LPAR5 were an independent risk factors for prognosis. Our results of the present study identified a series of prognostic microRNAs and mRNAs that have the potential to be the targets for treatment of thyroid cancer.

scholarly journals Evaluation of the TRIP13 level in breast cancer and insights into potential molecular pathways

2020 ◽  
Author(s):  
Jin Lan ◽  
Jingzhan Huang ◽  
Yuan Gao ◽  
Jingbo Sun ◽  
Longshan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Therapeutic Target ◽  
Cancer Metastasis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Breast Cancer Metastasis ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Kaplan Meier

Abstract Background: TRIP13 is a member belonging to a large AAA+ ATPases protein super family. Emerging evidences had shown that TRIP13 may serve as an oncogene However, the function of TRIP13 in BC has not yet been elucidated. Methods: By utilizing the multiple database across BC, we presented the expression of TRIP13 in BC tissue and normal control. We then verified the expression of TRIP13 in patients with BC by immunohistochemical (IHC) staining. Kaplan-Meier plots were used to perform the survival analysis. Further, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and PPI (protein-protein interaction) network were performed to explore the biological function and potential regular pathway of TRIP13 in BC. Results: The multiple database and immunohistochemical (IHC) showed that higher TRIP13 expression in BC tissue compared to normal tissue. TRIP13 was highly exprssed in lung metastasis lesion compared with primary tumor in our BALB/C mice 4T1 BC models. Kaplan-Meier plots also revealed that high TRIP13 expression correlated to poor survival in patients with BC. Moreover, GSEA analysis revealed that TRIP13 was primarily enriched in the processes of cell division and proliferation. Finally 10 hub genes with a high score of connectivity were filtered from the PPI network, including MAD2L1, CDC20, CDC5L, CDK1, CCNA2, BUB1B, RAD51, SPO11, KIF11 and AURKB. Conclusion: High TRIP13 expression predicted poor prognosis and contributed tumor growth and metastasis in the BC. Thus, ARL3 may be a prognostic marker and therapeutic target for glioma. TRIP13 may be a favorable biomarker and effective therapeutic target for BC. Keywords: TRIP13; breast cancer; metastasis; bioinformatic analysis, prognosis

Transmembrane p24 Trafficking Protein 2 Regulates Inflammation Through the TLR4/NF-κB Signaling Pathway in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Longhua Feng ◽  
Pengjiang Cheng ◽  
Zhengyun Feng ◽  
Xiaoyu Zhang
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Inflammatory Factors ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
New Strategy

Abstract Background: To investigate the role of transmembrane p24 trafficking protein 2 (TMED2) in lung adenocarcinoma (LUAD) and determine whether TMED2 knockdown could inhibit LUAD in vitro and in vivo.Methods: TIMER2.0, Kaplan-Meier plotter, gene set enrichment analysis (GSEA), Target Gene, and pan-cancer systems were used to predict the potential function of TMED2. Western blotting and immunohistochemistry were performed to analyze TMED2 expression in different tissues or cell lines. The proliferation, development, and apoptosis of LUAD were observed using a lentivirus-mediated TMED2 knockdown. Bioinformatics and western blot analysis of TMED2 against inflammation via the TLR4/NF-κB signaling pathway were conducted. Results: TMED2 expression in LUAD tumor tissues was higher than that in normal tissues and positively correlated with poor survival in lung cancer and negatively correlated with apoptosis in LUAD. The expression of TMED2 was higher in tumors or HCC827 cells. TMED2 knockdown inhibited LUAD development in vitro and in vivo and increased the levels of inflammatory factors via the TLR4/NF-κB signaling pathway. TMED2 was correlated with TME, immune score, TME-associated immune cells, their target markers, and some mechanisms and pathways, as determined using the TIMER2.0, GO, and KEGG assays.Conclusions: TMED2 may regulate inflammation in LUAD through the TLR4/NF-κB signaling pathway, and enhance the proliferation, development, and prognosis of LUAD by regulating inflammation, which provide a new strategy for treating LUAD by regulating inflammation.

scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

Molecular landscape of gastric cancer (GC) harboring mutations of histone methyltransferases.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 418-418
Author(s):  
Jingyuan Wang ◽  
Joanne Xiu ◽  
Yasmine Baca ◽  
Richard M. Goldberg ◽  
Philip Agop Philip ◽  
...  
Keyword(s):  
Dna Damage ◽  
Cancer Progression ◽  
Dna Damage Repair ◽  
Treatment Strategies ◽  
Gene Mutations ◽  
Mutation Rates ◽  
Aberrant Expression ◽  
Damage Repair

418 Background: Alteration of histone modifications participating in transcription and genomic instability, has been recognized as an important role in tumorigenesis. Aberrant expression of histone-lysine N-methyltransferase 2 ( KMT2) family, which methylate histone H3 on lysine 4, is significantly correlated with poor survival in GC. Understanding how gene mutations of KMT2 family interact to affect cancer progression could lead to new treatment strategies. Methods: A total of 1,245 GC were analyzed using next-generation sequencing (NGS) and immunohistochemistry (IHC; Caris Life Sciences, Phoenix, AZ). Tumor mutational burden (TMB) was calculated based on somatic nonsynonymous mutations, and MSI status was evaluated by a combination of IHC, fragment analysis and NGS. PD-L1 status was analyzed by IHC (SP142). Gene fusions were detected by Archer (N = 59) or whole-transcriptome sequencing (N = 129). Results: The overall mutation rate of genes in KMT2 family was 10.6% ( KMT2A: 1.7 %, KMT2C: 4.7%, KMT2D: 7.1%). Overall, the mutation rates were significantly higher in KMT2-mutated (MT) GC than KMT2-wild type (WT) GC, except for TP53 (43% vs 63%, p < .0001). Interestingly, among the genes with significant higher mutation rates in KMT2-MT GC, 28% (21/76) of them were related to DNA damage repair (including BRCA1/ 2, RAD50) and 33% (25/76) of them were related to chromatin remodeling (including ARID1A/ 2, SMARCA4). Overexpression of HER2, amplifications of KRAS, CDK6 and HER2 were significant lower, while PCM1 and BCL3 amplifications were significant higher in KMT2-MT, compared to KMT2-WT GC ( p < .05). Significantly higher prevalence of TMB-high ( > 17mut/MB) (49% vs 3%), MSI-H (53% vs 2%), and PD-L1 overexpression (20% vs 7%) were present in KMT2-MT GC, compared to KMT2-WT GC ( p < .001). The rates of fusions involving ARHGAP26 (19% vs 3%, p < .01)and RELA (29% vs 0%, p < .0001) were significantly higher in KMT2-MT than those in KMT2-WT GC. Conclusions: This is the largest study to investigate the distinct genomic landscape between KMT2-MT and WT GC. Our data indicates that KMT2-MT GC patients could potentially benefit from agents targeting DNA damage repair and immunotherapy, which warrants further in-vitro and in-vivo investigation.

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 Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziyuan Shen ◽  
Chenlu He ◽  
Haimiao Chen ◽  
Lishun Xiao ◽  
Yingliang Jin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Survival Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Function Analysis ◽  
Cox Model ◽  
Lung Squamous Cell Carcinoma ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kaplan Meier

Background. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM). Methods. Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes. Results. We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD. Conclusions. The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

scholarly journals Bioinformatics Approach to Identify Common Gene Signatures of Patients With Coronavirus 2019 and Lung Adenocarcinoma

2021 ◽  
Author(s):  
Xiao Liang ◽  
Yali Chen ◽  
Yuchao Fan
Keyword(s):  
Lung Adenocarcinoma ◽  
Severe Disease ◽  
Enrichment Analysis ◽  
Immune Checkpoints ◽  
Specific Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Tf Gene

Abstract Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman’s correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), cellular component (CC) as well as to pathways, specific organs, cells and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

scholarly journals Aberrant Expression of SCARA5 in Lung Adenocarcinoma and its Clinical Significance

2020 ◽  
Author(s):  
Genhua Yu ◽  
Xufeng Gong ◽  
Aixia Fu ◽  
Yan Hou ◽  
Jingping Xia ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
A549 Cell ◽  
Quantitative Polymerase Chain Reaction ◽  
Scavenger Receptor ◽  
Prognostic Significance ◽  
Area Under The Curve ◽  
Suppressor Gene ◽  
Aberrant Expression

Abstract BackgroundLung adenocarcinoma, the main subtype of non-small cell lung cancer, leading one of the most aggressive and fatal causes of malignant deaths around the world. Scavenger receptor class A member 5 (SCARA5), a newly discovered tumor suppressor gene, belonged to the SR family. The present study’s object was to explore the clinical impacts of SCARA5 in lung adenocarcinoma treatment. MethodsSCARA5 expressions in 120 paired lung adenocarcinoma patients’ tissues and cell lines were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). CCK-8, EdU, flow cytometry, and western blot assays further demonstrated the significance of SCARA5 on A549 cell growth. Then, the relationships between the expression level of SCARA5 and pathological factors were analyzed. Finally, the receiver operating characteristic (ROC) curve and overall survival analysis was carried out to verify the diagnostic and prognostic significance of SCARA5 in lung adenocarcinoma. ResultsSCARA5 was prominently decreased in lung adenocarcinoma cells and tissues compared with human bronchial epithelial cells and para-carcinoma non-tumor tissues. Meanwhile, SCARA5 expression was strongly correlated with smoking (P = 0.0011), TNM stage (P < 0.0001) and lymph node metastasis (P = 0.0005). Furthermore, SCARA5 may be a crucial biomarker for lung adenocarcinoma diagnosis with an area under the curve (AUC) of 0.9102 while SCARA5 could significantly reduce overall survival (OS; P = 0.0006). In vitro experiments, we found that SCARA5 overexpressing could significantly hinder A549 cell proliferation but facilitate apoptosis through the AKT signaling pathway. ConclusionsSCARA5 might be an important diagnostic and prognostic biomarker for patients with lung adenocarcinoma.

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