scholarly journals A Novel Prognostic Risk Score Based on Ferroptosis-related LncRNAs Predicting Ovarian Cancer Patient Survival

2021 ◽  
Author(s):  
Keyu Chen ◽  
Xiaohong Li ◽  
Caixia Qi
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Therapeutic Targets ◽  
Cancer Prognosis ◽  
Cancer Tissue ◽  
Clinical Implementation ◽  
Tumour Immunity ◽  
Cox Regression Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) are thought to be associated with several processes during cancer development and have been shown to be involved in the regulation of ferroptosis. Ovarian cancer is highly malignant tumour with a poor prognosis. The identification biomarkers with prognostic value in ovarian cancer may improve patient outcomes and can help to elucidate potential future therapeutic targets.Results: We report differential expression of 187 ferroptosis-related lncRNAs in normal and ovarian cancer tissue. Using univariate and multivariable Cox regression analysis, we identified four lncRNAs that were strongly associated with prognosis. We constructed a prognostic risk score based on these four lncRNAs which was effectively able to distinguish between low- and high-risk OC patients based on survival time. Univariate and multivariable Cox regression analyses and time-related receiver operating characteristic curve analyses revealed that this risk score represented an independent prognostic factor in patients with ovarian cancer. For clinical implementation, we developed a nomogram based on the prognostic feature and patient age. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the four ferroptosis-related lncRNAs were related to tumour immunity.Conclusions: we identify four novel ferroptosis-related lncRNAs as predictors of ovarian cancer prognosis and potential future therapeutic targets for ovarian cancer.

scholarly journals Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096357
Author(s):  
Xiaoyong Gong ◽  
Bobin Ning
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Prostate ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

scholarly journals Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yutao Wang ◽  
Jiaxing Lin ◽  
Kexin Yan ◽  
Jianfeng Wang
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Test Set ◽  
Cox Regression Analysis ◽  
Significant Difference ◽  
Robust Likelihood

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results. FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC=0.6039) and GSE21032 GPL5188 (140 samples; AUC=0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion. A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

scholarly journals Dys-regulation of Lnc-SNHG1 and miR-216b-5p Correlate With Chemo-resistance and Indicate Poor Prognosis of Serous Epithelial Ovarian Cancer

2020 ◽  
Author(s):  
Mei Li Pei ◽  
Zong Xia Zhao ◽  
Ting Shuang
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Histological Grade ◽  
Disease Free Survival ◽  
Figo Stage ◽  
Tumor Stage ◽  
Nodal Status ◽  
Cancer Tissue ◽  
Chi Square ◽  
Cox Regression Analysis

Abstract Background: Ovarian cancer is the most lethal of gynecological cancers and the 5-year survival rate is still low and chemo-resistance is the main obstacle for the treatment of ovarian cancer. Methods and Results: In this study, the human ovarian cancer tissue microarray was applied, 63 cases of chemo-sensitive serous EOC tissues and 32 cases of chemo-resistant serous EOC tissues were selected. By applying RNA fluorescence in situ hybridization (FISH), we found expression of Lnc-SNHG1 was up-regulated while miR-216b-5p showed low expression in chemo-resistant EOC patients compared with the chemo-sensitive group, both were mainly localized in the cytoplasm of the tissue cells. Chi-square test showed high lnc-SNHG1 level was significantly correlated with tumor stage, histological grade, nodal status, metastasis and chemo-resistance except tumor size. While there was no significant association between miR-216b-5p expression and parameters including tumor stage, histological grade, nodal status, metastasis, except chemo-resistance (P=0.0001). Spearman’s correlation analysis revealed significantly negative correlations between lnc-SNHG1 and miR-216b-5p (r = -0.424, P = 0.0001). Multivariate Cox regression analysis showed the expression of miR-216b-5p (P = 0.012, RR 2.137, 95 % CI 1.109–5.339) and FIGO stage (P = 0.001, RR 3.537, 95 % CI 1.72–7.276) was independent prognostic factors for the overall survival (OS) of serous EOC patients .While the FIGO stage (P = 0.003, RR2.237, 95 % CI 1.323–3.783) was the independent prognostic factor for the disease free survival (DFS) for the serous EOC patients. Kaplan- Meier curves revealed significant association of increased expression of lnc-SNHG1 with less OS and shorter DFS, while patients with low level of miR-216b-5p indicated less OS and DFS. Conclusions: In a word, we claimed over-expression of lnc-SNHG1 and decreased expression of miR-216b-5p were correlated with chemo-resistance of serous EOC patients and indicated less OS and shorter DFS of the patients.

scholarly journals Identification and Validation of Angiogenesis-Related Gene Expression for Predicting Prognosis in Patients With Ovarian Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Yue Wang ◽  
Bao Xuan Li ◽  
Xiang Li
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Cancer Genome ◽  
Risk Scores ◽  
Cox Regression Analysis

Ovarian cancer (OC) is a highly heterogeneous disease with different cellular origins reported; thus, precise prognostic strategies and effective new therapies are urgently needed for patients with OC. A growing number of studies have shown that most malignancies have intensive angiogenesis and rapid growth. Therefore, angiogenesis plays an important role in the development of tumor metastasis. However, the prognostic value of angiogenesis-related genes (ARGs) in OC remains to be further elucidated. In this study, the expression data and corresponding clinical data from patients with OC and normal control samples were downloaded with UCSC XENA. A total of 1,960 differentially expressed ARGs were screened and functionally annotated through Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate Cox regression analysis was performed to identify ARGs associated with prognosis. New ARGs signatures (including ESM1, CXCL13, TPCN2, PTPRD, FOXO1, and ELK3) were constructed for the prediction of overall survival (OS) in OC based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided based on their median risk score. In the The Cancer Genome Atlas (TCGA) training dataset, the survival analysis showed that overall survival was lower in the high-risk group than that in the low-risk group (p &lt; 0.0001). The International Cancer Genome Consortium (ICGC) database was used for validation, and the receiver operating characteristic (ROC) curves showed good performance. Univariate and multivariate Cox analyses were conducted to identify independent predictors of OS. The nomogram, including the risk score, age, stage, grade, and position, can not only show good predictive ability but also can explore the correlation analysis based on ARGs for immunogenicity, immune components, and immune phenotypes with risk score. Risk scores were correlated strongly with the type of immune infiltration. Furthermore, homologous recombination defect (HRD), NtAIscore, LOH score, LSTm score, stemness index (mRNAsi), and stromal cells were significantly correlated with risk score. The present study suggests that the novel signature constructed from six ARGs may serve as effective prognostic biomarkers for OC and contribute to clinical decision making and personalized prognostic monitoring of OC.

scholarly journals Prognostic signature of ovarian cancer based on 14 tumor microenvironment genes

2020 ◽  
Author(s):  
Xiazi Nie ◽  
Lina Song ◽  
Xiaohua Li ◽  
Yirong Wang ◽  
Bo Qu
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Mrna Levels ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Ovarian cancer is one of the lethal gynecological in women. Tumor microenvironment (TME) is emerging as a pivotal biomarker for patients’ therapeutic sensitivity and prognosis. In this study, we proposed to explore the prognostic role of TME-related genes in ovarian cancer. Methods The data of whole genome expression profiles and detailed clinicopathological information of three cohorts of ovarian cancer patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Univariate Cox regression analysis was used to screen TME-related genes with significantly prognostic value based on TCGA cohort. LASSO Cox regression analysis was adapted to the construction of prognostic model. Ovarian cancer cohorts from GEO were used as validation set for verifying the reliability of the prognostic model. Relative infiltrating proportion of 22 immune cells were estimated through CIBERSORT software. Results This study identified a total of 14 TME-related genes that finally incorporated into the prognostic model. The risk score that calculated through the prognostic model was proved as an independent prognostic signature in ovarian cancer. Nomogram that contains TNM stage and risk score could reliably predict the long-term overall survival probability. Additionally, risk score was significantly associated with the relative infiltrating proportion of several immune cells in ovarian cancer and mRNA levels of some immune checkpoint genes. Conclusions This study constructed a prognostic model for ovarian cancer, which was closely associated with the prognosis and immune status. This should provide novel clue for prognosis study in ovarian cancer.

scholarly journals Prognostic Signature of Immune Genes and Immune-Related LncRNAs in Neuroblastoma: A Study Based on GEO and TARGET Datasets

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaodan Zhong ◽  
Ying Tao ◽  
Jian Chang ◽  
Yutong Zhang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Progression Free Survival ◽  
Functional Enrichment ◽  
Th2 Cells ◽  
Prognostic Signature ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Immune Genes

BackgroundThe prognostic value of immune-related genes and lncRNAs in neuroblastoma has not been elucidated, especially in subgroups with different outcomes. This study aimed to explore immune-related prognostic signatures.Materials and MethodsImmune-related prognostic genes and lncRNAs were identified by univariate Cox regression analysis in the training set. The top 20 C-index genes and 17 immune-related lncRNAs were included in prognostic model construction, and random forest and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms were employed to select features. The risk score model was constructed and assessed using the Kaplan-Meier plot and the receiver operating characteristic curve. Functional enrichment analysis of the immune-related lncRNAs was conducted using the STRING database.ResultsIn GSE49710, five immune genes (CDK4, PIK3R1, THRA, MAP2K2, and ULBP2) were included in the risk score five genes (RS5_G) signature, and eleven immune-related lncRNAs (LINC00260, FAM13A1OS, AGPAT4-IT1, DUBR, MIAT, TSC22D1-AS1, DANCR, MIR137HG, ERC2-IT1, LINC01184, LINC00667) were brought into risk score LncRNAs (RS_Lnc) signature. Patients were divided into high/low-risk score groups by the median. Overall survival and event/progression-free survival time were shortened in patients with high scores, both in training and validation cohorts. The same results were found in subgroups. In grouping ability assessment, the area under the curves (AUCs) in distinguishing different groups ranged from 0.737 to 0.94, better in discriminating MYCN status and high risk in training cohort (higher than 0.9). Multivariate Cox analysis demonstrated that RS5_G and RS_Lnc were the independent risk factors for overall and event/progression-free survival (all p-values &lt;0.001). Correlation analysis showed that RS5_G and RS_Lnc were negatively associated with aDC, CD8+ T cells, but positively correlated with Th2 cells. Functional enrichment analyzes demonstrated that immune-related lncRNAs are mainly enriched in cancer-related pathways and immune-related pathways.ConclusionWe identified the immune-related prognostic signature RS5_G and RS_Lnc. The predicting and grouping ability is close to being even better than those reported in other studies, especially in subgroups. This study provided prognostic signatures that may help clinicians to choose optimal treatment strategies and showed a new insight for NB treatment. These results need further biological experiments and clinical validation.

scholarly journals eIF4E Overexpression Is Associated with Poor Prognoses of Ovarian Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jun Zheng ◽  
Xueqing Li ◽  
Chunyan Zhang ◽  
Yiqiang Zhang
Keyword(s):  
Ovarian Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cyclin D1 ◽  
Cox Regression ◽  
Cancer Tissue ◽  
Tissue Samples ◽  
Cox Regression Analysis ◽  
Node Metastasis ◽  
Incidence And Mortality

Aim. Ovarian cancer is a common malignant tumor of the gynecological oncology worldwide, with a high incidence and mortality rate and poor prognosis. Searching for new diagnostic molecular biomarkers for ovarian cancer is extremely significant. Methods. Here, we analyzed the expression rates of eIF4E and cyclin D1 proteins in 123 cases of cancer tissue samples and 38 cases of paracancerous tissue samples and studied the connection between the expression rates of eIF4E and cyclin D1 proteins by immunohistochemistry and statistically correlated with clinicopathological features in ovarian cancer. Results. The results showed that the expression rates of eIF4E and cyclin D1 proteins in ovarian cancer tissues were significantly higher than those in noncancerous epithelial ovarian tissues ( P = 0.001 and P = 0.032 , respectively). Additionally, the results revealed that a higher expression rate of eIF4E ( P = 0.008 ) was found in the advanced stage (stage III/IV), and also patients with cervical lymph node metastasis displayed higher expression of eIF4E ( P < 0.001 ) and cyclin D1 ( P = 0.033 ) than those without lymph node metastasis. Spearman’s rank correlation test showed that there was a significant positive correlation between the eIF4E and cyclin D1 proteins in ovarian cancer. The Kaplan-Meier method showed that patients with lower expression of eIF4E had marginally better survival than those with high expression of eIF4E ( P = 0.012 ). Multivariate Cox regression analysis further identified that positive expression of eIF4E was an independent prognostic factor. Conclusion. In ovarian cancer, eIF4E might be a valuable biomarker to predict poor prognoses and a potential therapeutic target to develop valid treatment strategies.

scholarly journals A prognostic risk model constructed by proteins in predicting prognosis for ovarian cancer

2021 ◽  
Author(s):  
yan rong ◽  
Liangchen Niu ◽  
Li Li
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis ◽  
Survival Risk ◽  
Related Proteins

Abstract BackgroundsOvarian cancer is the most lethal malignant tumor in gynecological cancers worldwide. Approximately 70% of patients have a poor prognosis, who experienced progression or recurrence within 5 years. The aim of this study attempts is to screen out the potential prognosis-related proteins and establish a prognostic risk model for predicting the prognostic risk for patients with ovarian cancer.MethodData were obtained from the Cancer Proteome Atlas (TCPA) and the Cancer Genome Atlas (TCGA). The proteins significantly related to survival risk in ovarian cancer patients were screened out by Kaplan-Meier test and COX regression analysis. A prognostic risk model was constructed based on the optimal proteins selected by multivariate Cox analysis. The prognostic risk model was validated in different clinical characteristics. The sankyl diagram was used to visualize the relationship between the prognosis-related proteins and their co-expression proteins.ResultsA prognostic risk model consisting of seven proteins that significantly related to prognosis was established. Patients with high risk score were associated with poor survival and relative protein expression. In the multivariate cox regress analysis, only age and the risk score were the independence prognosis factors. The AUC for the risk score was 0.721 in ROC curve for patients under 70 years old. Pearson’s correlation analysis showed that 25 co-expression proteins correlated with the prognosis-related proteins.ConclusionOur study demonstrated that a novel prognostic risk model constructed by proteins could predict prognosis for patients with ovarian cancer.

Stromal cell-associated expression of kallikrein-related peptidase 6 (KLK6) indicates poor prognosis of ovarian cancer patients

2012 ◽  
Vol 393 (5) ◽  
pp. 391-401 ◽  
Author(s):  
Lina Seiz ◽  
Julia Dorn ◽  
Matthias Kotzsch ◽  
Axel Walch ◽  
Nicolai I. Grebenchtchikov ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Protein Expression ◽  
Cancer Patients ◽  
Stromal Cell ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Progression Free Survival ◽  
Residual Tumor ◽  
Cancer Tissue ◽  
Cox Regression Analysis

Abstract Several members of the human kallikrein-related peptidase family, including KLK6, are up-regulated in ovarian cancer. High KLK6 mRNA or protein expression, measured by quantitative polymerase chain reaction and enzyme-linked immunoassay, respectively, was previously found to be associated with a shortened overall and progression-free survival (OS and PFS, respectively). In the present study, we aimed at analyzing KLK6 protein expression in ovarian cancer tissue by immunohistochemistry. Using a newly developed monospecific polyclonal antibody, KLK6 immunoexpression was initially evaluated in normal tissues. We observed strong staining in the brain and moderate staining in the kidney, liver, and ovary, whereas the pancreas and the skeletal muscle were unreactive, which is in line with previously published results. Next, both tumor cell- and stromal cell-associated KLK6 immunoexpression were analyzed in tumor tissue specimens of 118 ovarian cancer patients. In multivariate Cox regression analysis, only stromal cell-associated expression, besides the established clinical parameters FIGO stage and residual tumor mass, was found to be statistically significant for OS and PFS [high vs. low KLK6 expression; hazard ratio (HR), 1.92; p=0.017; HR, 1.80; p=0.042, respectively]. These results indicate that KLK6 expressed by stromal cells may considerably contribute to the aggressiveness of ovarian cancer.

scholarly journals Identification of 5-methylcytosine-related signature for predicting prognosis in ovarian cancer

2021 ◽  
Vol 54 (1) ◽  
Author(s):  
Lei Wang ◽  
Song Gao
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Epigenetic Modification ◽  
Critical Role ◽  
Clinicopathological Features ◽  
Cancer Prognosis ◽  
Risk Marker ◽  
Internal Validation ◽  
Cox Regression Analysis ◽  
Regulatory Factors

Abstract Background Ovarian cancer is one of the most common malignancies often resulting in a poor prognosis. 5-methylcytosine (m5C) is a common epigenetic modification with roles in eukaryotes. However, the expression and function of m5C regulatory factors in ovarian cancer remained unclear. Results Two molecular subtypes with different prognostic and clinicopathological features were identified based on m5C regulatory factors. Meanwhile, functional annotation showed that in the two subtypes, 452 differentially expressed genes were significantly related to the malignant progression of ovarian cancer. Subsequently, four m5C genes were screened to construct a risk marker predictive of overall survival and indicative of clinicopathological features of ovarian cancer, also the robustness of the risk marker was verified in external dataset and internal validation set. multifactorial cox regression analysis and nomogram demonstrated that risk score was an independent prognostic factor for ovarian cancer prognosis. Conclusion In conclusion, our results revealed that m5C-related genes play a critical role in tumor progression in ovarian cancer. Further detection of m5C methylation could provide a novel targeted therapy for treating ovarian cancer.

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