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 Network Models of Prostate Cancer Immune Microenvironments Identify ROMO1 as Heterogeneity and Prognostic Marker

2021 ◽  
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 ◽  
Diagnosis And Prognosis ◽  
External Data ◽  
Key Genes

Abstract Background: Prostate 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.Methods: First, samples of PCa were retrieved from UCSC Xena data, 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 (DFS) 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.Results: In this study, we used tumor subtype analysis and an energetic co-expression network algorithm of WGCNA and MEGENA to identify a signal dominated by the ROMO1 tag to predict PCa prognosis. Cox regression analysis of this signal 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.Conclusions: In conclusion, our study suggests ROMO1 as a portal key prognostic gene for the diagnosis and prognosis of PCa. Moveover, we identify nutlin-3, fluorouracil, and other drugs as possible key drugs for diagnosis and prognosis, which provide new insights into the diagnosis and treatment of prostate cancer.

scholarly journals Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xin Rui ◽  
Siliang Shao ◽  
Li Wang ◽  
Jiangyong Leng
Keyword(s):  
Prostate Cancer ◽  
Immune Cells ◽  
Prediction Models ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Recurrence ◽  
Th2 Cells ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Prostate Cancer Recurrence

Abstract Background Some historic breakthroughs have been made in immunotherapy of advanced cancer. However, there is still little research on immunotherapy in prostate cancer. We explored the relationship between immune cell infiltration and prostate cancer recurrence and tried to provide new ideas for the treatment of prostate cancer. Methods Prostate cancer RNA-seq data and clinical information were downloaded from the TCGA database and GEO database. The infiltration of 24 immune cells in tissues was quantified by ssGSEA. Univariate Cox regression analysis was used to screen for immune cell types associated with tumor recurrence, weighted gene co-expression network analysis (WGCNA) and LASSO were used to identify hub genes which regulate prognosis in patients through immune infiltration. Then, the nomogram was constructed based on the hub gene to predict the recurrence of prostate cancer, and the decision curve analysis (DCA) was used to compare the accuracy with the PSA and Gleason prediction models. Result Analysis showed that Th2 cells and Tcm related to prostate cancer recurrence after radical prostatectomy, and they are independent protective factors for recurrence. Through WGCNA and Lasso, we identified that NDUFA13, UQCR11, and USP34 involved in the infiltration of Th2 cells and Tcm in tumor tissues, and the expression of genes is related to the recurrence of patients. Based on the above findings, we constructed a clinical prediction model and mapped a nomogram, which has better sensitivity and specificity for prostate cancer recurrence prediction, and performed better in comparison with PSA and Gleason’s predictions. Conclusion The immune cells Th2 cells and Tcm are associated with recurrence of PCa. Moreover, the genes NDUFA13, UQCR11, and USP34 may affect the recurrence of PCa by affecting the infiltration of Th2 cells and Tcm. Moreover, nomogram can make prediction effectively.

scholarly journals Comprehensive Analysis of the Tumor Microenvironment and Ferroptosis-Related Genes Predict Prognosis with Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-xue Li ◽  
Li Xiong ◽  
Yu Wen ◽  
Zi-jian Zhang
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
Drug Resistant ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Immune Infiltration ◽  
Tcga Dataset

The early diagnosis of ovarian cancer (OC) is critical to improve the prognosis and prevent recurrence of patients. Nevertheless, there is still a lack of factors which can accurately predict it. In this study, we focused on the interaction of immune infiltration and ferroptosis and selected the ESTIMATE algorithm and 15 ferroptosis-related genes (FRGs) to construct a novel E-FRG scoring model for predicting overall survival of OC patients. The gene expression and corresponding clinical characteristics were obtained from the TCGA dataset (n = 375), GSE18520 (n = 53), and GSE32062 (n = 260). A total of 15 FRGs derived from FerrDb with the immune score and stromal score were identified in the prognostic model by using least absolute shrinkage and selection operator (LASSO)–penalized COX regression analysis. The Kaplan–Meier survival analysis and time-dependent ROC curves performed a powerful prognostic ability of the E-FRG model via multi-validation. Gene Set Enrichment Analysis and Gene Set Variation Analysis elucidate multiple potential pathways between the high and low E-FRG score group. Finally, the proteins of different genes in the model were verified in drug-resistant and non–drug-resistant tumor tissues. The results of this research provide new prospects in the role of immune infiltration and ferroptosis as a helpful tool to predict the outcome of OC patients.

Prediction of PSA spikes by isotope and patient age following permanent prostate brachytherapy

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15535-15535
Author(s):  
C. Bostancic ◽  
G. S. Merrick ◽  
W. Butler ◽  
K. Wallner ◽  
Z. Allen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Specific Antigen ◽  
Disease Free Survival ◽  
Low Risk ◽  
Prostate Brachytherapy ◽  
Patient Age ◽  
Cox Regression Analysis ◽  
Patient Âgé

15535 Background: To evaluate prostate specific antigen (PSA) spikes (bounces) following permanent prostate brachytherapy in low-risk patients randomized to Pd-103 or I-125. Methods: The study population consisted of 164 prostate cancer patients who were part of a prospective randomized trial comparing Pd-103 with I-125 for low-risk disease. Sixty-one patients (37.2%) received short course cytoreductive androgen deprivation therapy (ADT). No patient received supplemental XRT. The median follow-up was 5.4 years. All patients were implanted at least 3 years prior to analysis. On average, 10.1 post-treatment PSA’s were obtained per patient. Biochemical disease-free survival was defined as a PSA = 0.40 ng/mL after nadir. A PSA spike was defined as a rise of = 0.2 ng/mL followed by a durable decline to pre- spike levels. Multiple clinical, treatment and dosimetric parameters were evaluated as predictors for a PSA spike. Results: Forty- four patients (26.9%) developed a PSA spike including 45.7% (21/46) of the hormone naïve I-125 patients and 14.0% (8/57) of the hormone naïve Pd-103 patients. In hormone naïve patients, the mean time between implant and spike was 22.6 months and 18.7 months for I-125 and Pd-103 patients, respectively. In patients receiving neoadjuvant ADT, the incidence of spikes was comparable between isotopes (28.1% for I- 125 and 20.7% for Pd-103). The incidence of spikes was substantially different in patients < 65 vs = 65 years of age (16.3% vs. 38.5%). In multivariate Cox regression analysis, patient age at implant (p < 0.001) and isotope (p = 0.002) were significant predictors for spike. Conclusions: In low-risk prostate cancer patients, PSA spikes are most common in patients implanted with I-125 and/or younger than 65 years of age. Differences in isotope-related spikes are most pronounced in hormone naïve patients. No significant financial relationships to disclose.

scholarly journals Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated With Metabolism to Predict the Prognosis of Patients With Prostate Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihao Zou ◽  
Ren Liu ◽  
Yingke Liang ◽  
Rui Zhou ◽  
Qishan Dai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Affinity Purification ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cox Regression Analysis

BackgroundProstate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.MethodsThe mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.ResultsWe identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).ConclusionThe five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

Prognostic role of aberrant mTOR activation in patients with stage II and III colorectal cancer

2020 ◽  
Vol 14 (12) ◽  
pp. 1127-1137
Author(s):  
Tong-Tong Zhang ◽  
Yi-Qing Zhu ◽  
Hong-Qing Cai ◽  
Jun-Wen Zheng ◽  
Jia-Jie Hao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Stage Ii ◽  
Poor Prognostic Factor ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Risk Predictor

Aim: This study aimed to develop an effective risk predictor for patients with stage II and III colorectal cancer (CRC). Materials & methods: The prognostic value of p-mTOR (Ser2448) levels was analyzed using Kaplan–Meier survival analysis and Cox regression analysis. Results: The levels of p-mTOR were increased in CRC specimens and significantly correlated with poor prognosis in patients with stage II and III CRC. Notably, the p-mTOR level was an independent poor prognostic factor for disease-free survival and overall survival in stage II CRC. Conclusion: Aberrant mTOR activation was significantly associated with the risk of recurrence or death in patients with stage II and III CRC, thus this activated proteins that may serve as a potential biomarker for high-risk CRC.

scholarly journals Prognostic Association between Common Laboratory Tests and Overall Survival in Elderly Men with De Novo Metastatic Castration Sensitive Prostate Cancer: A Population-Based Study in Canada

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2844
Author(s):  
Christopher J. D. Wallis ◽  
Bobby Shayegan ◽  
Scott C. Morgan ◽  
Robert J. Hamilton ◽  
Ilias Cagiannos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
De Novo ◽  
Population Based ◽  
Population Based Study ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Laboratory Markers

De novo cases of metastatic prostate cancer (mCSPC) are associated with poorer prognosis. To assist in clinical decision-making, we aimed to determine the prognostic utility of commonly available laboratory-based markers with overall survival (OS). In a retrospective population-based study, a cohort of 3556 men aged ≥66 years diagnosed with de novo mCSPC between 2014 and 2019 was identified in Ontario (Canada) administrative database. OS was assessed by using the Kaplan–Meier method. Multivariate Cox regression analysis was performed to evaluate the association between laboratory markers and OS adjusting for patient and disease characteristics. Laboratory markers that were assessed include neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin, hemoglobin, serum testosterone and PSA kinetics. Among the 3556 older men with de novo mCSPC, their median age was 77 years (IQR: 71–83). The median survival was 18 months (IQR: 10–31). In multivariate analysis, a statistically significant association with OS was observed with all the markers (NLR, PLR, albumin, hemoglobin, PSA decrease, reaching PSA nadir and a 50% PSA decline), except for testosterone levels. Our findings support the use of markers of systemic inflammation (NLR, PLR and albumin), hemoglobin and PSA metrics as prognostic indicators for OS in de novo mCSPC.

scholarly journals Calumenin contributes to epithelial-mesenchymal transition and predicts poor survival in glioma

2021 ◽  
Vol 12 (1) ◽  
pp. 67-75
Author(s):  
Ying Yang ◽  
Jin Wang ◽  
Shihai Xu ◽  
Fei Shi ◽  
Aijun Shan
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Malignant Gliomas ◽  
Strongly Correlated ◽  
Who Grade ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
R Language ◽  
Biological Adhesion ◽  
Tcga Dataset

Abstract Background Calumenin (CALU) has been reported to be associated with invasiveness and metastasis in some malignancies. However, in glioma, the role of CALU remains unclear. Methods Clinical and transcriptome data of 998 glioma patients, including 301 from CGGA and 697 from TCGA dataset, were included. R language was used to perform statistical analyses. Results CALU expression was significantly upregulated in more malignant gliomas, including higher grade, IDH wildtype, mesenchymal, and classical subtype. Gene Ontology analysis revealed that CALU-correlated genes were mainly enriched in cell/biological adhesion, response to wounding, and extracellular matrix/structure organization, all of which were strongly correlated with the epithelial-mesenchymal transition (EMT) phenotype. GSEA further validated the profound involvement of CALU in EMT. Subsequent GSVA suggested that CALU was particularly correlated with three EMT signaling pathways, including TGFβ, PI3K/AKT, and hypoxia pathway. Furthermore, CALU played synergistically with EMT key markers, including N-cadherin, vimentin, snail, slug, and TWIST1. Survival and Cox regression analysis showed that higher CALU predicted worse survival, and the prognostic value was independent of WHO grade and age. Conclusions CALU was correlated with more malignant phenotypes in glioma. Moreover, CALU seemed to serve as a pro-EMT molecular target and could contribute to predict prognosis independently in glioma.

scholarly journals Upregulation of Serum lncRNA DLEU1 Predicts Progression of Premalignant Endometrial Lesion and Unfavorable Clinical Outcome of Endometrial Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382096558
Author(s):  
Lixia Shan ◽  
Tao Zhao ◽  
Yu Wang
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Hyperplasia ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Critical Role ◽  
Disease Free Survival ◽  
Lymphocytic Leukemia ◽  
Healthy Controls ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective: Long non-coding RNAs (lncRNAs) play a critical role in tumorigenesis. Upregulation of lncRNA deleted in lymphocytic leukemia 1 (DLEU1) has been reported in endometrial cancer (EC) tissues. This prospective study aimed to determine the potential clinical significance of serum lncRNA DLEU1 in EC. Methods: The serum lncRNA DLEU1 level was detected in EC patients, patients with endometrial hyperplasia and healthy controls by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Then its clinical value in EC was further evaluated. Results: Our results demonstrated that serum lncRNA DLEU1 levels were significantly increased in patients with EC, and serum lncRNA DLEU1 showed good performance for discriminating EC patients from patients with endometrial hyperplasia and healthy controls. In addition, EC patients with advanced clinicopathological features had higher circulating lncRNA DLEU1 level than those with favorable clinical characteristics. Moreover, EC patients in the high serum lncRNA DLEU1 group suffered worse overall survival and disease-free survival than those in the low serum lncRNA DLEU1 group. Furthermore, multivariate cox regression analysis displayed that the serum lncRNA DLEU1 served as an independent prognostic factor for EC. Conclusions: Collectively, our study suggests that serum lncRNA DLEU1 is a novel and promising biomarker for prognostic estimation of EC.

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