scholarly journals Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247827
Author(s):  
Yan Wang ◽  
Jili Wang ◽  
Qiusu Tang ◽  
Guoping Ren
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Signaling Pathway ◽  
Cox Regression ◽  
Cyclin B1 ◽  
Gene Set Enrichment Analysis ◽  
Hub Gene ◽  
Cox Regression Analysis ◽  
Primary Prostate Cancer ◽  
Benign Prostate

Background The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer. Methods Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer. Results 243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient’ disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway. Conclusions UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

scholarly journals Ferroptosis-Related lncRNAs Signature is a Prognostic Factor in Hepatocellular Carcinoma: A Study Based on TCGA Data

2021 ◽  
Author(s):  
Xiangjin Hu ◽  
Sailun Wang ◽  
Jia Guo ◽  
Fang Xiong ◽  
Jun Lv
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Signaling Pathway ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Sphingolipid Metabolism ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer and is the fourth leading cause of cancer-related death worldwide. Ferroptosis is a form of iron-dependent programmed cell death, and is characterized by intracellular accumulation of reactive oxygen species (ROS). Long non-coding RNAs (lncRNAs), as valuable prognostic factors for HCC patients, play a vital role in regulating ferroptosis. Methods RNA-sequencing datasets and ferroptosis-related genes were retrieved from The Cancer Genome Atlas (TCGA) database and the Molecular Signature Database. we performed Pearson correlation analysis between the lncRNAs and ferroptosis-related genes, and subsequently used regression analysis (univariate Cox analysis, multivariate Cox regression analysis, and Lasso regression analysis) to screen the ferroptosis-related lncRNAs with prognostic value in HCC, the prognostic ferroptosis-related lncRNAs signature (FRLS) was finally constructed. In addition, we reevaluated the model in terms of survival, clinical characteristics, and immune microenvironment. Results Univariate Cox regression analysis revealed 34 differently expressed ferroptosis-related lncRNAs related to the prognosis of HCC. Among them, 12 ferroptosis-related lncRNAs (LUCAT1, LINC01224, THUMPD3-AS1, AC116025.2, LINC00942, SNHG10, AC131009.1, POLH-AS1, MKLN1-AS, LINC01138, LNCSRLR, AL031985.3) were regarded as independent prognosis predictors of HCC, and were incorporated into the construction of the prognostic FRLS. Patients were divided into two groups based on the prognostic FRLS. Kaplan–Meier survival plot showed that patients in the high-risk groups exhibited shorter overall survival (OS) than those in low-risk groups (P < 0.001). Compared with clinical data, the area under curve (AUC) values of the risk factors, decision curve analysis (DCA), the AUC values of different years and multivariate Cox regression suggested that the signature had better predictive power. Gene set enrichment analysis (GSEA) revealed the potential pathways of 12 ferroptosis-related lncRNAs, including sphingolipid-metabolism, mTOR signaling pathway, notch signaling pathway, homologous recombination, endocytosis, cell cycle, etc. Immune microenvironment including tumor-infiltrating immune cells, immune-related functions, checkpoint-related genes and N6-methyladenosine (m6A)-related mRNA were also significantly different between the two risk groups. Conclusions This study constructed 12 FRLS for HCC patients to predict survival, which may provide promising targets for the therapy of HCC.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

scholarly journals Expression of LOX Suggests Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Jinfeng Zhu ◽  
Chen Luo ◽  
Jiefeng Zhao ◽  
Xiaojian Zhu ◽  
Kang Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Expression Level ◽  
Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p &lt; 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

Patient experience with service quality: Implications for survival outcomes in prostate cancer.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 39-39
Author(s):  
Christopher G. Lis ◽  
Maurie Markman ◽  
Mark Rodeghier ◽  
Digant Gupta
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Service Quality ◽  
Patient Experience ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Risk Of Mortality ◽  
Patient Reported

39 Background: Prostate cancer is the second leading cause of cancer death among U.S. men. While self-reported quality of life has been shown to be prognostic of survival, there has been limited exploration of whether a patient’s assessment of the overall quality-of-care received might influence survival in prostate cancer. We evaluated the relationship between patient-reported experience with service quality and overall survival in prostate cancer. Methods: 832 returning prostate cancer patients treated at Cancer Treatment Centers of America between July 2007 and December 2010. Overall patient experience (“considering everything, how satisfied are you with your overall experience?”) was measured on a 7-point Likert scale ranging from “completely dissatisfied” to “completely satisfied”. It was dichotomized into 2 categories: top box response (7) versus all others (1-6). Cox regression was used to evaluate the association between patient experience and survival. Results: 560 patients were newly diagnosed while 272 had been previously treated. Majority of patients (n=570, 68.5%) had stage II disease at diagnosis. The mean age was 63.6 years. By the time of this analysis, 93 (11.2%) patients had expired. 710 (85.3%) patients were “completely satisfied” with the service quality they received while 122 (14.7%) patients were not. Median overall survival was 47.9 months. On univariate Cox regression analysis, “completely satisfied” patients had a significantly lower risk of mortality compared to those not “completely satisfied” (HR=0.48; 95% CI: 0.30-0.78; p=0.003). On multivariate Cox regression analysis controlling for stage at diagnosis, treatment history and age, “completely satisfied” patients demonstrated significantly lower mortality (HR=0.50; 95% CI: 0.29-0.87; p=0.01) compared to those not “completely satisfied”. Conclusions: Patient experience with service quality was an independent predictor of survival in prostate cancer. Based on this provocative observation, it is reasonable to suggest that further exploration of a possible meaningful relationship between patient perceptions of the care they have received and outcome in prostate cancer is indicated.

scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P &lt; 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

scholarly journals Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

2020 ◽  
Author(s):  
Xiangkun Wu ◽  
Wenjie Li ◽  
Daojun Lv ◽  
Yongda Liu ◽  
Di Gu
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Cox Regression Analysis ◽  
Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

Chromogranine A and neuron-specific enolase as the main predictors of survival for hormone-refractory prostate cancer (HRPC) patients

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15594-15594
Author(s):  
A. Banu ◽  
E. Banu ◽  
D. Dionysopoulos ◽  
J. Medioni ◽  
F. Scotte ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Gleason Score ◽  
Cox Regression ◽  
Neuron Specific Enolase ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Metastatic Sites ◽  
Ecog Ps ◽  
The Impact

15594 Background: Clinical studies suggested that the extent of neuro-endocrine differentiation in prostate cancer increases with tumor progression and the development of androgen refractory status. Chromogranine (CgA) and neuron-specific enolase (NSE) are currently explored as surrogate markers. Methods: Eligible chemonaive HRPC patients (pts) were required to have an ECOG performance status (PS) ≤ 2. Before chemotherapy initiation, we quantified NSE, CgA and PSA in the venous blood using commercial kits. We evaluated the impact of baseline NSE, CgA and PSA on overall survival (OS) using multivariate Cox regression analysis, stratified by chemotherapy regimen. Secondary, we studied the correlation between NSE, CgA, PSA and other important variables as age, Gleason score, hemoglobin, number of metastatic sites and ECOG PS. Results: Data of 39 consecutive HRPC pts treated between December 01–06 in a single French center were analyzed. Chemotherapy was docetaxel-based in 92% of pts. Median age was 71 years (range 51–86) and 79% of pts had bone metastases. Elevated NSE, CgA and PSA were observed in 6, 9 and 30% of pts and median levels were 10.8, 67 and 23.3 ng/mL, respectively. Gleason 8–10 was present in 49% of pts. Significant correlations were observed between NSE and the number of metastatic sites and between CgA and age, hemoglobin and ECOG PS. The baseline PSA was only correlated with Gleason score. Median OS for the entire cohort was 24.4 months (95% CI, 18.8–29.9). Two-year OS was 15% and only 19% of patients are dead. Univariate Cox regression analysis showed only a significant relationship between OS and baseline NSE: hazard ratio= 1.09 (95% CI, 1.03–1.16), P=0.006. No other known prognostic factors are related to outcome. A multivariate model including baseline NSE, CgA, ECOG PS and Gleason score showed a 15% rise of the risk of death related to NSE (borderline P value). Conclusions: NSE was the most powerful predictor of survival for HRPC pts. Our results emphasize the theory that cells secreting NSE are chemoresistant, with a negative impact on OS. No significant financial relationships to disclose.

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