scholarly journals A Prognostic Nomogram Combining Gene Expression Profiles and TNM Stage Predicts Survival in Gastric Cancer

2021 ◽  
Author(s):  
Jian Huang ◽  
Dongcun Wang ◽  
Xiaoliang Wang ◽  
Xiaoxing Ye ◽  
Jiping Da
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
High Morbidity

Abstract BackgroundGastric carcinoma (GC) is a highly aggressive malignancy and is associated with high morbidity and mortality rates around the world, the current tumor-node-metastasis (TNM) staging system is inadequate to predict overall survival (OS) in GC patients. therefore, potential forecasting methods for prognosis are important to investigate.MethodsDifferentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas (TCGA). We then construct a risk score signature model by univariate Cox proportional hazards regression (CPHR) analysis, the Kaplan-Meier method（KM）and multivariate CPHR analysis. Using TNM stage, we developed a signature-based nomogram. Finally, we utilize an independent Gene Expression Omnibus dataset (GSE62254) validate the prognostic value of risk score signature model and nomogram.ResultsWe identified five OS-related mRNAs among 1113 mRNAs that were differentially expressed between GC and normal samples in the TCGA dataset. We then constructed a five-mRNA signature model, which efficiently distinguished high-risk from low-risk patient in both cohort, and even viable in the TNM stage-III, gender(male, female) and age(＜65-year-old, ≥65-year-old) subgroups (P<0.05). Utilizing TNM stage, we developed a signature-based nomogram, which performed better than use the TNM stage or five-mRNA signature alone for prognostic prediction in the TCGA and GSE62254 dataset.ConclusionsThese results suggest that both risk signature and nomogram were effective prognostic indicators for patients with GCs, and could potentially be used for individualized management of such patients.

scholarly journals The Prognostic Significance of PDE7B in Cytogenetically Normal Acute Myeloid Leukemia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ling Cao ◽  
Weilong Zhang ◽  
Xiaoni Liu ◽  
Ping Yang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Expression Profiles ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Acute Myeloid

AbstractAcute myeloid leukemia (AML) is a malignant hematological disease in which nearly half have normal cytogenetics. We have tried to find some significant molecular markers for this part of the cytogenetic normal AML, which hopes to provide a benefit for the diagnosis, molecular typing and prognosis prediction of AML patients. In the present study, we calculated and compared the gene expression profiles of cytogenetically normal acute myeloid leukemia (CN-AML) patients in database of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and dataset Vizome (a total of 632 CN-AML samples), and we have demonstrated a correlation between PDE7B gene and CN-AML. Then we proceeded to a survival analysis and prognostic risk analysis between the expression levels of PDE7B gene and CN-AML patients. The result showed that the event-free survival (EFS) and overall survival (OS) were significantly shorter in CN-AML patients with high PDE7B levels in each dataset. And we detected a significantly higher expression level of PDE7B in the leukemia stem cell (LSC) positive group. The Cox proportional hazards regression model showed that PDE7B is an independent risk predictor for CN-AML. All results indicate that PDE7B is an unfavorable prognostic factor for CN-AML.

scholarly journals SELE gene as a characteristic prognostic biomarker of colorectal cancer

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110043
Author(s):  
Na Li ◽  
Honghe Xiao ◽  
Jiangli Shen ◽  
Ximin Qiao ◽  
Fenjuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Expression Profiles ◽  
Immunohistochemical Analysis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective To investigate the expression and clinical value of the E-selectin gene ( SELE) in colorectal cancer (CRC). Methods Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan–Meier and Cox proportional hazards regression analyses. Results Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage. Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

scholarly journals Effect of differential hypoxia-related gene expression on glioblastoma

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110137
Author(s):  
Chao-Qun Lin ◽  
Lu-Kui Chen
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Prognostic Indicators ◽  
Related Gene ◽  
Scoring Model ◽  
Risk Scoring

Objective Glioblastoma (GB) is a refractory malignancy with a high rate of recurrence and treatment resistance. Hypoxia-related genes are promising prognostic indicators for GB, so we herein developed a reliable hypoxia-related gene risk scoring model to predict the prognosis of patients with GB. Method Gene expression profiles and corresponding clinicopathological features of patients with GB were obtained from the Cancer Genome Atlas (TCGA; n = 160) and Gene Expression Omnibus (GEO) GSE7696 (n = 80) databases. Univariate and multivariate Cox regression analyses of differentially expressed hypoxia-related genes were performed using R 3.5.1 software. Result Fourteen prognosis-related genes were identified and used to construct a risk signature. Patients with high-risk scores had significantly lower overall survival (OS) than those with low-risk scores. The median risk score was used as a critical value and for OS prediction in an independent external verification GSE7696 cohort. Risk score was not significantly affected by clinical-related factors. We also developed a prediction nomogram based on the TCGA training set to predict survival rates, and included six independent prognostic parameters in the TCGA prediction model. Conclusion We determined a reliable hypoxia-related gene risk scoring model for predicting the prognosis of patients with GB.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

Evidence for a field effect in early prostate cancer (PCa): Gene expression profiles in normal-appearing prostate tissue (NT) adjacent to tumor (T) as predictors of clinical outcome.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 5029-5029 ◽  
Author(s):  
Eric A. Klein ◽  
Sara Moscovita Falzarano ◽  
Nan Zhang ◽  
Dejan Knezevic ◽  
Tara Maddala ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Field Effect ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cox Proportional Hazards ◽  
Clinical Recurrence ◽  
Molecular Alterations ◽  
Cox Proportional Hazards Models

5029 Background: We previously identified genes whose expression predicts aggressive PCa (clinical recurrence (cR), prostate cancer death (PCD), adverse pathology) when assessed in histologically heterogeneous tumor foci and in biopsies (Klein ASCO 2012). These results enabled the definition of a multi-gene Genomic Prostate Score (GPS), which has been clinically validated (Cooperberg AUA 2013). There is interest regarding a possible field effect in PCa, i.e. molecular alterations throughout the gland that may influence PCa development. We conducted exploratory analyses to evaluate gene expression, including GPS, in adjacent normal-appearing tissue (NT) for prediction of cR and PCD. Methods: Cohort sampling was used to select 127 patients with and 374 without cR from 2,641 patients treated with RP for T1/T2 PCa. Expression of 732 genes was measured by qRT-PCR separately in T and NT (defined as > 3 mm from T) specimens. GPS (0-100 units) was determined using the genes and algorithm from the validation study. Analysis used Cox proportional hazards models and Storey’s false discovery rate (FDR) control. Results: 410 evaluable patients had paired T and NT. Of the 405 genes which were predictive of outcome in T (FDR < 20%), 289 (71%) showed similar but weaker effects in NT. 47 genes were associated with cR in NT (FDR < 20%), of which 34 also concordantly predicted cR in T (FDR < 20%). GPS assessed in NT significantly predicted time to cR (HR/20 units = 1.8; 95% CI: 1.3-2.4; p< 0.001) and PCD (HR/20 units = 1.9; 95% CI: 1.2-3.0; p = 0.005) but was less predictive than GPS in T (HR/20 units = 4.8 for cR; 95% CI: 3.7-6.2; p < 0.001 and HR/20 units = 6.9 for PCD; 95% CI: 4.4-10.7; p < 0.001). The strongest components of GPS in predicting cR and PCD in NT were stromal response and androgen signaling genes (p < 0.05); proliferation and cellular organization genes did not consistently provide a significant contribution in NT. Conclusions: These data indicate that gene expression profiles, including GPS, can predict outcome in NT, albeit more weakly than in tumor. These findings suggest that there is an underlying field effect associated with the development of aggressive PCa.

Genomic Complexity and Recurrent Aberration in Multiple Myeloma: Analysis of a Compendium of aCGH and Gene Expression Profiles.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1682-1682
Author(s):  
Wee-Joo Chng ◽  
Ian Lee ◽  
Victor Jimenez-zepeda ◽  
Esteban Braggio ◽  
Jonathan Keats ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Clinical Status ◽  
Gene Expression Profiles ◽  
Cox Proportional Hazards ◽  
Comparative Genomic ◽  
Initial Attempt ◽  
Genomic Complexity

Abstract Multiple Myeloma (MM) is the second most prevalent haematological disorder in the US with great recent progress propelled by various technological and therapeutic advances. Nevertheless, the complexity of MM genomes remains inadequately characterized. We have combined high-resolution (44K Agilent) array comparative genomic hybridization (aCGH) and gene expression data with clinical outcomes in an initial attempt at indexing genomic complexity in MM and its clinical implication in 100 MM patients. We enumerated the Number of Aberrations per Tumor (NAPT) as defined by the ADM-1 algorithm available within CGH Analytics. There appear to be no difference in degree of genetic complexity between hyperdiploid and non-hyperdiploid myeloma. Generally, t(11;14) have low NAPT whereas D2 tumors have high NAPT. Amongst the high-risk genetic subtypes, tumors with maf translocations have relatively low NAPT whereas t(4;14) have high NAPT. Whilst a Multivariate Cox Proportional Hazards regression of survival outcome showed little statistical support for popular indicators such as ploidy and TC classification, results for NAPT were considerably more encouraging (p~ 0.07, median survival of 40 months in low complexity vs 18 months in high). Log-rank tests further supported prognosis based on NAPT independent of clinical status and ploidy. To characterize complexity, we clustered aCGH profiles by clinical status, ploidy and TC classification. Clear patterns of aggregation and recurrent aberration were observed suggesting that they were possibly instrument to aberrant gene expression. Indeed, expression of a large proportion of genes approximately 2Mb of the recurrent breakpoints varied significantly between patients carrying aberrations vis-a-viz others that did not. Comparison against known gene sets showed an enrichment for cell-cycle and apoptosis genes. We are presently characterizing several such breakpoint-associated genes for their functional significance.

scholarly journals Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254368
Author(s):  
Gang Liu ◽  
Jian-ying Ma ◽  
Gang Hu ◽  
Huan Jin
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Critical Role ◽  
Clinicopathological Features ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

scholarly journals Construction and Validation of an Immune Cell Signature Score to Evaluate Prognosis and Therapeutic Efficacy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Linfeng Xu ◽  
Xingxing Jian ◽  
Zhenhao Liu ◽  
Jingjing Zhao ◽  
Siwen Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Marker Genes ◽  
High Morbidity ◽  
Therapeutic Benefits

Background: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with high morbidity and mortality worldwide. Tumor immune microenvironment (TIME) plays a pivotal role in the outcome and treatment of HCC. However, the effect of immune cell signatures (ICSs) representing the characteristics of TIME on the prognosis and therapeutic benefit of HCC patients remains to be further studied.Materials and methods: In total, the gene expression profiles of 1,447 HCC patients from several databases, i.e., The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, and Gene Expression Omnibus, were obtained and applied. Based on a comprehensive collection of marker genes, 182 ICSs were evaluated by single sample gene set enrichment analysis. Then, by performing univariate and multivariate Cox analysis and random forest modeling, four significant signatures were selected to fit an immune cell signature score (ICSscore).Results: In this study, an ICSscore-based prognostic model was constructed to stratify HCC patients into high-risk and low-risk groups in the TCGA-LIHC cohort, which was successfully validated in two independent cohorts. Moreover, the ICSscore values were found to positively correlate with the current American Joint Committee on Cancer staging system, indicating that ICSscore could act as a comparable biomarker for HCC risk stratification. In addition, when setting the four ICSs and ICSscores as features, the classifiers can significantly distinguish treatment-responding and non-responding samples in HCC. Also, in melanoma and breast cancer, the unified ICSscore could verify samples with therapeutic benefits.Conclusion: Overall, we simplified the tedious ICS to develop the ICSscore, which can be applied successfully for prognostic stratification and therapeutic evaluation in HCC. This study provides an insight into the therapeutic predictive efficacy of prognostic ICS, and a novel ICSscore was constructed to allow future expanded application.

scholarly journals Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Medha Suman ◽  
Pierre-Antoine Dugué ◽  
Ee Ming Wong ◽  
JiHoon Eric Joo ◽  
John L. Hopper ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Lobular Breast Cancer ◽  
Genome Wide ◽  
Invasive Lobular Breast Cancer

Abstract Background Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC. Methods Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data. Results We identified 2771 VMRs (P < 10−8) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC. Conclusions Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.

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