scholarly journals A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teppei Iwata ◽  
Anna S. Sedukhina ◽  
Manabu Kubota ◽  
Shigeko Oonuma ◽  
Ichiro Maeda ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Survival ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Poor Clinical Outcome ◽  
Bioinformatics Analyses ◽  
Tcga Dataset ◽  
Definition Of ◽  
Improve Prostate Cancer

AbstractA subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. When conducting bioinformatics analyses, the definition of factors such as “overexpression” and “shorter survival” is vital, as poor definition may lead to mis-estimations. To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. Next, genes associated with shorter survival were entered into pathway analysis to identify pathways that were altered in a shorter survival cohort. We identified pathways involving upregulation of GRB2. Overexpression of GRB2 was linked to shorter survival in the TCGA dataset, a finding validated by histological examination of biopsy samples taken from the patients for diagnostic purposes. Thus, GRB2 is a novel biomarker that predicts shorter survival of patients with aggressive prostate cancer (GS ≧8).

Early PSA progression in abiraterone/enzalutamide-treated patients with metastatic castration-resistant prostate cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16530-e16530
Author(s):  
Fernando López-Campos ◽  
Alfonso Gomez-Iturriaga ◽  
Casilda Llacer Perez ◽  
Ivan Henriquez ◽  
Paula Peleteiro ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiographic Progression ◽  
Cox Regression ◽  
Castration Resistant Prostate Cancer ◽  
Cox Models ◽  
Predictive Values ◽  
Castration Resistant ◽  
Psa Progression ◽  
Definition Of

e16530 Background: Changes in PSA are widely used as a biomarker for the monitoring of treatment outcome in Metastatic Castration-Resistant Prostate Cancer (mCRPC) in the clinical real-world setting. Early PSA changes (before 12 weeks) are not considered in the definition of PSA Progression (PSAProg) due to the potential for spurious “flare” reactions. We aimed to evaluate the significance of an early PSA increase in Abiraterone/Enzalutamide (Abi/Enz)-treated mCRPC patients (pts). Methods: We retrospectively evaluated Abi/Enz-treated mCRPC pts from 11 hospitals between 2011-2018. Early PSAProg was defined as a 25% increase in PSA from baseline at 4 (PSAProg4) or 8 (PSAProg8) weeks after treatment initiation. PSA progression at 12 weeks (PSAProg12) was confirmed by a second reading. Uni- and multivariable (MV) Cox regression models were conducted to explore the association of PSAProg and overall (OS) and radiographic progression-free (rPFS) survival. Sensitivity (Se), specificity (Sp) and predictive values (PPV, NPV) for the association of early PSAProg with PSAProg12 were calculated. Results: We analyzed 581 mCRPC pts; median follow-up: 19.1 months. 96 (17.1%); 105 (21.6%) and 85 (16.9%) pts had PSAprog at 4, 8 and 12 wks. PSAProg4 and PSAProg8 were significantly associated with confirmed PSAProg12. 55.3% of pts with PSAProg4 and 66.7% of pts with PSAProg8 had a confirmed PSAProg12. Only 9% of pts with no PSA prog at 4 wks and 4.1% of pts with no PSAProg8 had a confirmed PSAProg12. PSAProg4 had Se: 56.6%, Sp: 90.5%, PPV: 55.2%, NPV: 91% for the detection of PSAProg12. PSAProg8 had Se: 81.9%, Sp: 91.2%, PPV: 66.7%, NPV: 95.9% for the detection of PSAProg12. PSAprog at 4, 8 and 12 wks was significantly associated with OS and rPFS in uni- and MV Cox models (Table). Conclusions: Early PSAProg after Abi/Enz is significantly associated with both confirmed PSA Prog at 12 wks and outcome, and may help identify pts not benefitting from Abi/Enz before clinical or radiographic progression. Prospective validation studies are needed. [Table: see text]

scholarly journals Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110521
Author(s):  
Licheng Wang ◽  
Yicong Yao ◽  
Chengdang Xu ◽  
Xinan Wang ◽  
Denglong Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
T Cells ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Mutational Burden ◽  
Clinicopathologic Feature ◽  
Tumor Mutational Burden

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

scholarly journals Identification of ARHGEF38, NETO2, GOLM1, and SAPCD2 Associated With Prostate Cancer Progression by Bioinformatic Analysis and Experimental Validation

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuolun Sun ◽  
Yunhua Mao ◽  
Xu Zhang ◽  
Shuo Lu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Progression ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Tcga Dataset

Prostate cancer (PCa) represents one of the most prevalent types of cancers and is a large health burden for men. The pathogenic mechanisms of PCa still need further investigation. The aim of this study was to construct an effective signature to predict the prognosis of PCa patients and identify the biofunctions of signature-related genes. First, we screened differentially expressed genes (DEGs) between PCa and normal control tissues in The Cancer Genome Atlas (TCGA) and GSE46602 datasets, and we performed weighted gene co-expression network analysis (WGCNA) to determine gene modules correlated with tumors. In total, 124 differentially co-expressed genes were retained. Additionally, five genes (ARHGEF38, NETO2, PRSS21, GOLM1, and SAPCD2) were identified to develop the prognostic signature based on TCGA dataset. The five-gene risk score was verified as an independent prognostic indicator through multivariate Cox regression analyses. The expression of the five genes involved in the signature was detected in the Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine databases. In addition, we utilized DiseaseMeth 2.0 and MEXPRESS for further analysis and found that abnormal methylation patterns may be a potential mechanism for these five DEGs in PCa. Finally, we observed that these genes, except PRSS21, were highly expressed in tumor samples and PCa cells. Functional experiments revealed that silencing ARHGEF38, NETO2, GOLM1, and SAPCD2 suppressed the proliferation, migration, and invasiveness of PCa cells. In summary, this prognostic signature had significant clinical significance for treatment planning and prognostic evaluation of patients with PCa. Thus, ARHGEF38, NETO2, GOLM1, and SAPCD2 may serve as oncogenes in PCa.

scholarly journals EMILIN2 Correlated With Its Methylation and Immune Infiltration Could Be an Independent Prognostic Biomarker in LGG

2021 ◽  
Author(s):  
Li-chong Wang ◽  
Zhe Zhang ◽  
Zi-long Tan ◽  
Qiao-li Lv ◽  
Shu-hui Chen ◽  
...  
Keyword(s):  
Immune Cells ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Low Grade ◽  
Central Nervous System Neoplasms ◽  
Immune Infiltration ◽  
Nervous System Neoplasms ◽  
Slow Growing

Abstract Low-grade gliomas (LGGs) are slow-growing brain cancer in central nervous system neoplasms. EMILIN2 is an extracellular matrix (ECM) protein which could influence the progress of some tumour which is unclear in LGG. In our study, the methylation, expression, prognosis and immune value of EMILIN2 were analysed in LGG through bioinformatics analysis. we first analysed the LGG data from TCGA and discovered that the EMILIN2 expression, negatively correlated to the EMILIN2 methylation could predict a poor prognosis and associated with different clinical parameters. Moreover, univariate and multivariate Cox regression were performed in CGGA showed that the EMILIN2 could be an independent prognostic biomarker in LGG. Finally, EMILIN2 expression showed a correlation with gene makers in some immune cells which identified the significance of EMILIN2 in immune infiltration. In conclusion, EMILIN2 could act as an independent prognostic biomarker which might be associated with the malignancy and development of gliomas and play a crucial role in glioma in immune infiltration.

scholarly journals Network models of prostate cancer immune microenvironments identify ROMO1 as heterogeneity and prognostic marker

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Lei Wang ◽  
Xudong Liu ◽  
Zhe Liu ◽  
Yafan Wang ◽  
Mengdi Fan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Network Models ◽  
Principal Component ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
External Data ◽  
Key Genes ◽  
Tcga Dataset

AbstractProstate cancer (PCa) is the fifth leading cause of death from cancer in men worldwide. Its treatment remains challenging due to the heterogeneity of the tumor, mainly because of the lack of effective and targeted prognostic markers at the system biology level. First, the data were retrieved from TCGA dataset, and valid samples were obtained by consistent clustering and principal component analysis; next, key genes were analyzed for prognosis of PCa using WGCNA, MEGENA, and LASSO Cox regression model analysis, while key genes were screened based on disease-free survival significance. Finally, TIMER data were selected to explore the relationship between genes and tumor immune infiltration, and GSCAlite was used to explore the small-molecule targeted drugs that act with them. Here, we used tumor subtype analysis and an energetic co-expression network algorithm of WGCNA and MEGENA to identify a signal dominated by the ROMO1 to predict PCa prognosis. Cox regression analysis of ROMO1 was an independent influence, and the prognostic value of this biomarker was validated in the training set, the validated data itself, and external data, respectively. This biomarker correlates with tumor immune infiltration and has a high degree of infiltration, poor prognosis, and strong correlation with CD8+T cells. Gene function annotation and other analyses also implied a potential molecular mechanism for ROMO1. In conclusion, we putative ROMO1 as a portal key prognostic gene for the diagnosis and prognosis of PCa, which provides new insights into the diagnosis and treatment of PCa.

scholarly journals A Comprehensive Bioinformatics Analysis of TIMP2 in Multiple Malignancies

2021 ◽  
Author(s):  
Dan-Dan Wang ◽  
Wen-Xiu Xu ◽  
Wen-Quan Chen ◽  
Su-Jin Yang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Immune Cells ◽  
Cancer Survival ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Transcriptional Level ◽  
Gene Markers ◽  
Online Tool ◽  
Multiple Malignancies

Abstract Background: Tissue inhibitor of metalloproteinase-2 (TIMP2), an endogenous inhibitor of matrix metalloproteinases, has been disclosed to participate in the development and carcinogenesis of multiple malignancies. However, the prognosis of TIMP2 in different cancers and its correlation with tumor microenvironment and immunity have not been clarified.Methods: In this study, we conducted a comprehensive bioinformatics analysis to evaluate the prognostic and therapeutic value of TIMP2 in cancer patients by utilizing a series of databases, including ONCOMINE, GEPIA, cBioPortal, GeneMANIA, Metascape, and Sangerbox online tool. The expression of TIMP2 in different cancers were analyzed by Oncomine, TCGA and GTEx databases and mutation status of TIMP2 in cancers was then verified using cBioportal database. The protein-protein interaction (PPI) network of the TIMP family was exhibited by GeneMANIA. The prognosis of TIMP2 in cancers was performed though GEPIA database and cox regression. Additionally, the correlations between TIMP2 expression and immunity (immune cells, gene markers of immune cells, TMB, MSI, and neoantigen) were explored using Sangerbox online tool.Results: The transcriptional level of TIMP2 in most cancerous tissues were significantly elevated. Survival analysis revealed that elevated expression of TIMP2 was associated with unfavorable survival outcome in multiple cancers. Enrichment analysis demonstrated the possible mechanisms of TIMPs and their associated genes mainly involved in pathways including extracellular matrix (ECM) regulators, degradation of ECM and ECM disassembly, and several other signaling pathways. Conclusions: Our findings systematically dissected that TIMP2 was a potential prognostic maker in various cancers and use the inhibitor of TIMP2 may be an effective strategy for cancer therapy to improve the poor cancer survival and prognostic accuracy, but concrete mechanisms need to be validated by subsequent experiments.

PCAT-1 Expression is Associated With The Prognosis in Prostate Cancer.

2020 ◽  
Author(s):  
Sheng Li ◽  
Xiaolan Ruan ◽  
Tongzu Liu
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Survival Curves ◽  
Chi Square ◽  
Cox Regression Analysis ◽  
Chi Square Test ◽  
Cox Analysis

Abstract Purpose: In the study, we aimed to estimate the prognostic significance of PCAT-1 in patients with prostate cancer (PCa).Methods: The expression of PCAT-1 in paired PCa tissues and normal controls was examined via quantitative real-time polymerase chain reaction (qRT-PCR). The influence of PCAT-1 level on clinical features was assessed using Chi-square test. The survival curves were plotted to estimate the prognosis of patients. And the Cox analysis was carried out to find promising predictive factors for patients.Results: The expression level of PCAT-1 in PCa tissues was significantly elevated compared with the adjacent normal control (P<0.0001). The increased expression of PCAT-1 was affected by high Gleason score (P=0.017), positive serum PSA (P=0.011) and advanced TNM stage (P=0.003). The Kaplan-Meier survival curves showed that the overall survival rate of patients with high PCAT-1 expression was significantly lower than those with low PCAT-1 expression (P<0.001). Both univariate analysis (P=0.000, HR=10.623, 95%CI=5.798-19.464) and multivariate Cox regression analysis (P=0.000, HR=10.996, 95%CI=5.896-20.507) revealed that PCAT-1 could act as a prognostic biomarker for PCa patients.Conclusion: Taken together, overexpression of PCAT-1 is involved in PCa progression and could be a potential prognostic biomarker for PCa patients.

scholarly journals Prostate Cancer Survival By Risk of Progression and Other Prognostic Factors in Mallorca, Spain

2021 ◽  
Author(s):  
Juan-José Montaño ◽  
Antoni Barceló ◽  
Paula Franch ◽  
Jaume Galceran ◽  
Alberto Ameijide ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Cancer Survival ◽  
Cox Regression ◽  
Strong Relationship ◽  
Low Risk ◽  
Medium Risk ◽  
Risk Of Progression ◽  
Very High

Abstract Objectives: 1) to find out the distribution of prostate cancer by risk of progression; 2) to determine the cause-specific survival by risk of progression in prostate cancer; 3) to identify the factors associated with the risk of dying from this cancer.Methods: Incident prostate cancer cases diagnosed between 2006 and 2011 were identified through the Mallorca Cancer Registry. Inclusion criteria: invasive cases with code C61.9 and any histology. Cases identified exclusively through death certificate were excluded. We collected: age; date and method of diagnosis; date of follow-up or death; T, N, M and stage according to the TNM 7th edition; Gleason score; PSA; histology according to the ICD-O 3rd edition 6 ; comorbidities and treatments. We calculated risk in 4 categories: low, medium, high and very high. End point of follow-up was 31 December 2014. Multiple imputation (MI) was performed to estimate cases with unknown risk of progression. Survival analysis was performed using the actuarial and Kaplan-Meier methods, as well as the Cox regression model.Results: We identified 2921 cases. After MI, 9.5% had low risk, 24.9% medium risk, 42.7% high risk and 22.9% very high risk. Five years after diagnosis, survival after MI was 89% globally, that being 100% for low risk cases, 96% for medium risk, 93% for high risk and 69% for very high risk. Cases with histology other than adenocarcinoma, with high and, especially, very high risk of progression, as well as with systemic, mixed and observation/unspecified treatments have worse prognosis. Treatment showed a strong relationship with age and life expectancy.Conclusions: Risk of progression and treatment were the main variables associated to survival in prostate cancer.

scholarly journals Geographically Weighted Cox Regression for Prostate Cancer Survival Data in Louisiana

2019 ◽  
Vol 52 (4) ◽  
pp. 570-587 ◽  
Author(s):  
Yishu Xue ◽  
Elizabeth D. Schifano ◽  
Guanyu Hu
Keyword(s):  
Prostate Cancer ◽  
Survival Data ◽  
Cancer Survival ◽  
Cox Regression ◽  
Cancer Survival Data

scholarly journals Weighted Gene Co-expression Network Analysis Identifies CALD1 as a Biomarker Related to M2 Macrophages Infiltration in Stage III and IV Mismatch Repair-Proficient Colorectal Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Hang Zheng ◽  
Yuge Bai ◽  
Jingui Wang ◽  
Shanwen Chen ◽  
Junling Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Mismatch Repair ◽  
Antigen Processing ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Gene Set Enrichment Analysis ◽  
Stage Iii ◽  
Receptor Interaction ◽  
M2 Macrophage ◽  
Bioinformatics Analyses

Immunotherapy has achieved efficacy for advanced colorectal cancer (CRC) patients with a mismatch-repair-deficient (dMMR) subtype. However, little immunotherapy efficacy was observed in patients with the mismatch repair-proficient (pMMR) subtype, and hence, identifying new immune therapeutic targets is imperative for those patients. In this study, transcriptome data of stage III/IV CRC patients were retrieved from the Gene Expression Omnibus database. The CIBERSORT algorithm was used to quantify immune cellular compositions, and the results revealed that M2 macrophage fractions were higher in pMMR patients as compared with those with the dMMR subtype; moreover, pMMR patients with higher M2 macrophage fractions experienced shorter overall survival (OS). Subsequently, weighted gene co-expression network analysis and protein–protein interaction network analysis identified six hub genes related to M2 macrophage infiltrations in pMMR CRC patients: CALD1, COL6A1, COL1A2, TIMP3, DCN, and SPARC. Univariate and multivariate Cox regression analyses then determined CALD1 as the independent prognostic biomarker for OS. CALD1 was upregulated specifically the in CMS4 CRC subtype, and single-sample Gene Set Enrichment Analysis (ssGSEA) revealed that CALD1 was significantly correlated with angiogenesis and TGF-β signaling gene sets enrichment scores in stage III/IV pMMR CRC samples. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm and correlation analysis revealed that CALD1 was significantly associated with multiple immune and stromal components in a tumor microenvironment. In addition, GSEA demonstrated that high expression of CALD1 was significantly correlated with antigen processing and presentation, chemokine signaling, leukocyte transendothelial migration, vascular smooth muscle contraction, cytokine–cytokine receptor interaction, cell adhesion molecules, focal adhesion, MAPK, and TGF-beta signaling pathways. Furthermore, the proliferation, invasion, and migration abilities of cancer cells were suppressed after reducing CALD1 expression in CRC cell lines. Taken together, multiple bioinformatics analyses and cell-level assays demonstrated that CALD1 could serve as a prognostic biomarker and a prospective therapeutic target for stage III/IV pMMR CRCs.

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