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 Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3392-3403 ◽  
Author(s):  
Jue Wang ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Proportional Hazards Model ◽  
Latent Trait ◽  
Joint Modeling ◽  
Fine Motor ◽  
Terminal Event ◽  
Longitudinal Outcomes

Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait linear mixed model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.

scholarly journals UBE2D1 RNA Expression Was an Independent Unfavorable Prognostic Indicator in Lung Adenocarcinoma, but Not in Lung Squamous Cell Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Liyan Hou ◽  
Yingbo Li ◽  
Ying Wang ◽  
Dongqiang Xu ◽  
Hailing Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
Lung Adenocarcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Survival Data ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Indicator ◽  
The Cancer Genome Atlas ◽  
Rna Expression

In this study, we investigated the potential prognostic value of ubiquitin-conjugating enzyme E2D1 (UBE2D1) RNA expression in different histological subtypes of non-small-cell lung cancer (NSCLC). A retrospective study was performed by using molecular, clinicopathological, and survival data in the Cancer Genome Atlas (TCGA)—Lung Cancer. Results showed that both lung adenocarcinoma (LUAD) (N=514) and lung squamous cell carcinoma (LUSC) (N=502) tissues had significantly elevated UBE2D1 RNA expression compared to the normal tissues (p<0.001 and p=0.036, respectively). UBE2D1 RNA expression was significantly higher in LUAD than in LUSC tissues. Increased UBE2D1 RNA expression was independently associated with shorter OS (HR: 1.359, 95% CI: 1.031–1.791, p=0.029) and RFS (HR: 1.842, 95% CI: 1.353–2.508, p<0.001) in LUAD patients, but not in LUSC patients. DNA amplification was common in LUAD patients (88/551, 16.0%) and was associated with significantly upregulated UBE2D1 RNA expression. Based on these findings, we infer that UBE2D1 RNA expression might only serve as an independent prognostic indicator of unfavorable OS and RFS in LUAD, but not in LUSC.

scholarly journals Increased GOLM1 Expression Independently Predicts Unfavorable Overall Survival and Recurrence-Free Survival in Lung Adenocarcinoma

2018 ◽  
Vol 25 (1) ◽  
pp. 107327481877800 ◽  
Author(s):  
Xi Liu ◽  
Lei Chen ◽  
Tao Zhang
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Survival Data ◽  
Methylation Status ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Indicator ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Recurrence Free Survival ◽  
Free Survival

Golgi membrane protein 1 (GOLM1) is a transmembrane glycoprotein of the Golgi cisternae, which is implicated in carcinogenesis of multiple types of cancer. In this study, using data from the Gene Expression Omnibus and The Cancer Genome Atlas, we compared the expression of GOLM1 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and studied its prognostic value in terms of overall survival (OS) and recurrence-free survival (RFS) in these 2 subtypes of non-small cell lung cancer (NSCLC). Results showed that GOLM1 was significantly upregulated in both LUAD and LUSC tissues compared to the normal controls. However, GOLM1 expression was higher in LUAD tissues than in LUSC tissues. More importantly, using over 10 years’ survival data from 502 patients with LUAD and 494 patients with LUSC, we found that high GOLM1 expression was associated with unfavorable OS and RFS in patients with LUAD, but not in patients with LUSC. The following univariate and multivariate analyses confirmed that increased GOLM1 expression was an independent prognostic indicator of poor OS (hazard ratio [HR]: 1.30, 95% confidence interval [CI]: 1.11-1.54, P = .002) and RFS (HR: 1.37, 95% CI: 1.14-1.64, P = .001) in patients with LUAD. Of 511 cases with LUAD, 248 (48.5%) had heterozygous loss (−1), while 28 (5.5%) of 511 cases with LUAD had low-level copy gain (+1). In addition, we also found that the methylation status of 1 CpG site (chr9: 88,694,942-88,694,944) showed a weak negative correlation with GOLM1 expression (Pearson r = −0.25). Based on these findings, we infer that GOLM1 might serve as a valuable prognostic biomarker in LUAD, but not in LUSC. In addition, DNA copy number alterations and methylation might be 2 important mechanisms of dysregulated GOLM1 in LUAD.

Survival in metastatic castration resistant prostate cancer (mCRPC) trial participants.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15136-e15136
Author(s):  
Carmel Jo Pezaro ◽  
Aurelius Gabriel Omlin ◽  
Deborah Mukherji ◽  
Diletta Bianchini ◽  
Shahneen Kaur Sandhu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Data ◽  
Cox Model ◽  
Performance Status ◽  
Specific Antigen ◽  
Trial Participation ◽  
Castration Resistant Prostate Cancer ◽  
Castration Resistant ◽  
Kaplan Meier ◽  
Base Management

e15136 Background: Median overall survival (mOS) in patients (pts) with metastatic prostate cancer progressing despite castrate levels of testosterone (mCRPC) was 13-16 months (m) in the pre-docetaxel era. These data, obtained from clinical trials, were used to construct currently available prognostic nomograms. We hypothesise that these models no longer reflect survival. Pts and physicians urgently require updated prognostic data on which to base management decisions. Methods: Pts with mCRPC treated on phase I-III trials at our institution were identified and data retrospectively collected. Predicted survival by Halabi and Smaletz nomograms were compared to calculated survival using Kaplan-Meier analysis. Cox model multivariate (MV) analysis used variables at referral, including performance status (PS), Gleason (GS), prostate specific antigen (PSA), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), hemoglobin (Hb), visceral disease and albumin. Results: 423 pts with CRPC treated between 2003 and 2011 were included. At diagnosis median age was 62 years (y; 41.8 – 82.7); 226 (53.4%) had metastatic disease. Median interval from diagnosis to CRPC was 2.7y (0.2 – 21.7). At referral 248 pts (58.6%) were chemotherapy-naïve. Halabi and Smaletz models predicted mOS in chemo-naïve pts of 21m and 18m respectively, however the observed mOS was 32m (95%CI 28 – 38). Survival from CRPC was 43m (CI 37 – 46) and 39m (CI 34 - 44) in pre- and post-chemo pts, respectively. Conclusions: Despite aggressive disease characteristics, our pts lived significantly longer than predicted by current nomograms. MV analysis confirmed the importance of several previously identified prognostic factors. Survival data from this large cohort of CRPC pts should encourage men considering clinical trial participation. Previously developed nomograms no longer accurately predict survival.

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 Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xinyu Chong ◽  
Rui Peng ◽  
Yan Sun ◽  
Luyu Zhang ◽  
Zheng Zhang
Keyword(s):  
Gastric Cancer ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Key Genes ◽  
Secreted Phosphoprotein 1 ◽  
Centrality Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

scholarly journals Systematic Elucidation of the Mechanism of Quercetin against Gastric Cancer via Network Pharmacology Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Liangjun Yang ◽  
Zhipeng Hu ◽  
Jiajie Zhu ◽  
Qiting Liang ◽  
Hengli Zhou ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Molecular Docking ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Online Tool ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Novel Approach ◽  
Therapeutic Mechanisms ◽  
Kaplan Meier Plotter

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan–Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion, this study provided a novel approach to reveal the therapeutic mechanisms of quercetin on GC, which will ease the future clinical application of quercetin in the treatment of GC.

scholarly journals Significant Improvement of Survival By T2* Cardiovascular Magnetic Resonance in Thalassemia Major

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4567-4567
Author(s):  
Alessia Pepe ◽  
Antonella Meloni ◽  
Giovanni Carlo Del Vecchio ◽  
Maria Antonietta Romeo ◽  
Maria Rita Gamberini ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Survival Data ◽  
Cox Model ◽  
Thalassemia Major ◽  
Rank Test ◽  
Cardiac Siderosis ◽  
Kaplan Meier ◽  
Significant Difference

Abstract Background: In 2004 seven Italian centers reported survival data for patients with thalassemia major (TM) and showed that heart disease due to iron overload was the most common cause of death (Borgna et al Haematologica 2004). In the same years the accurate and noninvasive assessment of cardiac siderosis was made possible in Italy by the introduction of the T2* cardiovascular magnetic resonance (CMR). Purpose: We aimed to evaluate if the deployment of T2* CMR had an impact on the mortality rate. Methods: Four centers contributed to the present study, updating the data of the enrolled patients until August 31, 2010. For the patients who died, the date of the death represented the end of the study. 577 patients (264 females and 313 males) were included. Results: One-hundred and fifty-nine (27.6%) patients died, 124 of whom (77.9%) died before the year 2000. The Table shows the comparison between dead patients and survivors. Dead patients were significantly younger and they were more frequently males. Dead patients started chelation therapy significantly later. HIV, arrhythmias and heart failure were significantly more frequent in dead patients. According to the Cox model, the following variables were identified as significant univariate prognosticators for the death: male sex (HR=1.87, 95%CI=1.34-2.60, P<0.0001), HIV (HR=2.55, 95%CI=1.25-5.20, P=0.010) and heart failure (HR=8.86, 95%CI=6.37-12.31, P<0.0001). MRI was not performed in 406 patients (70.4%) and no patient had been scanned before his/her death. Among the survivors, MRI was not performed in the 59% of the cases (P<0.0001). The absence of an MRI scan was a significant univariate prognosticator for death (HR=43.25, 95%CI=11.32-165.33, P<0.0001). The study was restricted to the patients dead after 2004 (19/159=12%) or followed until August 2010 (N=357). In this subgroup of 376 patients, MRI was not performed in the 52.4% of the survivors and in all dead patients (P<0.0001). The absence of a MRI exam was reconfirmed as a strong predictive factor for death (HR=49.37, 95%CI=1.08-2263.24, P=0.046). The Kaplan-Meier curve is showed in Figure 1. The log-rank test revealed a significant difference in the curves (P<0.0001). Conclusions: Our data suggests that the use of T2* CMR, that enables individually tailored chelation regimes reducing the likelihood of developing decompensated cardiac failure, allowed the reduction of cardiac mortality in chronically transfused TM patients. Table 1. Table 1. Figure 1. Figure 1. Disclosures Pepe: Novartis: Speakers Bureau; Chiesi: Speakers Bureau; ApoPharma Inc: Speakers Bureau.

scholarly journals Identification of key miRNA and hub genes in lung adenocarcinoma by bioinformatics and functional analysis

2020 ◽  
Author(s):  
Jiayao Zhu ◽  
Yan Zhang ◽  
Jingjing Lu ◽  
Le Wang ◽  
Xiaoren Zhu ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Target Gene ◽  
Target Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Differentially Expressed Mirnas

Abstract Background: lung adenocarcinoma is the main subtype of lung cancer and the most fatal malignant disease in the world. However, the pathogenesis of lung adenocarcinoma has not been fully elucidated.Methods: Three LUAD-associated datesets (GSE118370, GSE43767 and GSE74190) were downloaded from the Gene Expression Omnibus (GEO) datebase and the differentially expressed miRNAs (DEMs) and genes (DEGs) were screened by GEO2R. The prediction of target gene of differentially expressed miRNA were used miRWALK. Metascape was used to enrich the overlapped genes of DEGs and target genes. Then, the protein-protein interaction(PPI) and DEMs-DEGs regulatory network were created via String datebase and Cytoscape. Finally, overall survival analysis was established via the Kaplan–Meier curve and look for the possible prognostic biomarkers.Result: In this study, 433 differential genes were identified. There were 267 genes overlapped with the target gene of Dems, and eight hub genes (CDH1, CDH5, CAV1, MMP9, PECAM1, CD24, ENG, MME) were screened out. There were 85 different miRNAs in total, among which 16 miRNA target genes intersect with DEGs, 12 miRNAs with the highest interaction were screened out, and survival analysis of miRNA and hub genes was carried out.Conclusion: we found that miRNA-940, miRNA-125a-3p, miRNA-140-3p, miRNA-542-5p, CDH1, CDH5, CAV1, MMP9, PECAM1 may be related to the development of LUAD.

scholarly journals Influences of Tumor Size and Pleural Layer Invasion on the Prognosis of Patients with Stage IB Lung Adenocarcinoma

2021 ◽  
Author(s):  
Qihai Sui ◽  
Jiaqi Liang ◽  
Zhengyang Hu ◽  
Xinming Xu ◽  
Zhencong Chen ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Tumor Size ◽  
Validation Cohort ◽  
Cox Model ◽  
Histological Grade ◽  
Clinical Factors ◽  
Stage Ib ◽  
Kaplan Meier ◽  
Independent Risk Factors ◽  
University Department

Abstract Backgroud: Lung adenocarcinoma (ADC) at stage IB has its own prognostic characteristics. This study aimed to investigate the clinical factors that may affect the prognosis of patients with stage IB ADC.Methods: The data of ADC cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016) and patients in Zhongshan Hospital Affliated to Fudan University (Department of Thoracic Surgery, 2015-2016). Kaplan-Meier method was used to obtain the overall survival (OS). Factors that significantly related to the prognosis were evaluated by univariate and multivariate analysis (UVA, MVA) using the Cox model. A nomogram was developed and validated to predict the 3-year OSs of those patients.Results: 7605 patients with stage IB ADC were included ultimately and were divided into 2 groups, a training cohort (n = 5,324) and a test cohort (n = 2,281). Besides, there was a validation cohort (n = 272) for the verification of the nomogram model. Those with significantly older age, male, the white race, lower grades of tumor differentiation, larger tumor size (31-40mm) without pleural layer (PL) invasion as well as receiving sublobectomy suffered from poorer survival (P < 0.001), which were identified as independent factors for stage IB ADC (P < 0.001), and according to which, a nomogram model was created.Conclusion: Age, sex, race, histological grade, surgery to the primary site, and tumor size combined with PL invasion were independent risk factors for stage IB ADC, based on which a nomogram was constructed to predict the prognosis.

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