scholarly journals Recurrence-Associated Multi-RNA Signature to Predict Disease-Free Survival for Ovarian Cancer Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Yu Zhang ◽  
Qingjian Ye ◽  
Junxian He ◽  
Peigen Chen ◽  
Jing Wan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Noncoding Rna ◽  
Risk Model ◽  
Cox Regression ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Single Type ◽  
Free Survival ◽  
Disease Free

Ovarian cancer (OvCa) is an intractable gynecological malignancy due to the high recurrence rate. Several molecular biomarkers have been previously screened for early identifying patients with a high recurrence risk and poor prognosis. However, all the known studies focused on a single type of RNAs, not integrating various types. This study was to construct a new multi-RNA-based model to predict the recurrence and prognosis for OvCa patients by using the messenger RNA (mRNA, including long noncoding RNA (lncRNA)) and microRNA (miRNA) sequencing data of The Cancer Genome Atlas database. After univariate Cox regression and least absolute shrinkage and selection operator analyses, a multi-RNA-based signature (2 miRNAs: hsa-miR-508, hsa-miR-506; 1 lncRNA: TM4SF1-AS1; 11 mRNAs: MAGI3, SLAMF7, GLI2, PDK1, ARID3A, PLEKHG4B, TNFAIP8L3, C1QTNF3, NDUFAF1, CH25H, TMEM129) was generated and used to establish a risk score model. The high- and low-risk patients classified by the median risk score exhibited significantly different recurrence risks (89% versus 61%, p<0.001) and survival time (the area under the receiver operating characteristic curve (AUC) = 0.901 for 5-year disease-free survival (DFS)). This risk model was independent of other clinical features and superior to pathologic staging for DFS prediction (AUC, 0.906 versus 0.524; C-index, 0.633 versus 0.510). Furthermore, some new interaction axes were revealed to explain the possible functions of these RNAs (competing endogenous RNA: TM4SF1-AS1-miR-186-STEAP2, LINC00536-miR-508-STEAP2, LINC00475-miR-506-TMEM129; coexpression: LINC00598-PLEKHG4B). In conclusion, this multi-RNA-based risk model may be clinically useful to stratify OvCa patients with different recurrence risks and survival outcomes and included RNAs may be potential therapeutic targets.

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

2020 ◽  
Author(s):  
Hao Zhao ◽  
Xuening Zhang ◽  
Zhan Shi ◽  
Songhe Shi
Keyword(s):  
Tumor Microenvironment ◽  
Gleason Score ◽  
Risk Score ◽  
Regression Models ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Data Set ◽  
Free Survival ◽  
Disease Free

Abstract Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

scholarly journals CACNA1C is a prognostic predictor for patients with ovarian cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaohan Chang ◽  
Yunxia Dong
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Signal Pathways ◽  
Regression Analyses ◽  
Free Survival ◽  
Prognostic Predictor

Abstract Background CACNA1C, as a type of voltage-dependent calcium ion transmembrane channel, played regulatory roles in the development and progress of multiple tumors. This study was aimed to analyze the roles of CACNA1C in ovarian cancer (OC) of overall survival (OS) and to explore its relationships with immunity. Methods Single gene mRNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas Database (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Gene set enrichment analysis (GSEA) was used to identify CACNA1C-related signal pathways. Univariate and multivariate Cox regression analyses were applied to evaluate independent prognostic factors. Besides, associations between CACNA1C and immunity were also explored. Results CACNA1C had a lower expression in OC tumor tissues than in normal tissues (P < 0.001), with significant OS (P = 0.013) and a low diagnostic efficiency. We further validated the expression levels of CACNA1C in OC by means of the ICGC dataset (P = 0.01), qRT-PCR results (P < 0.001) and the HPA database. Univariate and multivariate Cox hazard regression analyses indicated that CACNA1C could be an independent risk factor of OS for OC patients (both P < 0.001). Five significant CACNA1C-related signaling pathways were identified by means of GSEA. As for genetic alteration analysis, altered CACNA1C groups were significantly associated with OS (P = 0.0169), progression-free survival (P = 0.0404), disease-free survival (P = 0.0417) and disease-specific survival (P = 9.280e-3), compared with unaltered groups in OC. Besides, CACNA1C was dramatically associated with microsatellite instability (MSI) and immunity. Conclusions Our results shed light on that CACNA1C could be a prognostic predictor of OS in OC and it was closely related to immunity.

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.

scholarly journals Overexpression of long noncoding RNA PTPRG-AS1 is associated with poor prognosis in epithelial ovarian cancer

2020 ◽  
Vol 66 (7) ◽  
pp. 948-953
Author(s):  
Xue-Ying Ren ◽  
Wei-Bin Yang ◽  
Yun Tian
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Epithelial Ovarian Cancer ◽  
Noncoding Rna ◽  
Proportional Hazards Model ◽  
Critical Role ◽  
Disease Free Survival ◽  
Cox Proportional Hazards Model ◽  
Free Survival ◽  
Disease Free

SUMMARY OBJECTIVE Long noncoding RNAs (lncRNAs) have been shown to play a critical role in tumor progression. Abnormal expression of LncRNA PTPRG antisense RNA 1 (PTPRG-AS1) has been reported in several tumors. Hence, we aimed to determine the expression and clinical significance of PTPRG-AS1 in epithelial ovarian cancer (EOC) patients. METHODS The expressions of PTPRG-AS1 were assessed in 184 pairs of EOC tumor specimens and adjacent normal tissues. The levels of target lncRNAs and GAPDH were examined using standard SYBR-Green methods. The relationships between the expressions of PTPRG-AS1 and the clinicopathological features were analyzed using the chi-square test. Multivariate analysis using the Cox proportional hazards model was performed to assess the prognostic value of PTPRG-AS1 in EOC patients. RESULTS We confirmed that the expressions of PTPRG-AS1 were distinctly higher in the EOC tissue compared with the adjacent non-tumor specimens (p < 0.01). Higher levels of PTPRG-AS1 in EOC patients were associated with advanced FIGO stage (p = 0.005), grade (p = 0.006), and distant metastasis (p = 0.005). Survival analyses revealed that patients with high expressions of PTPRG-AS1 had a distinctly decreased overall survival (p = 0.0029) and disease-free survival (p = 0.0009) compared with those with low expressions of PTPRG-AS1. Multivariate assays indicated that PTPRG-AS1 expression was an independent prognostic factor for both overall survival and disease-free survival in EOC (Both p < 0.05). CONCLUSIONS Our study suggests that PTPRG-AS1 may serve as a novel prognostic biomarker for EOC patients.

scholarly journals An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

2021 ◽  
Author(s):  
Yanan Shan ◽  
Ran He ◽  
Xiaowei Yang ◽  
Siwen Zang ◽  
Shan Yao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
Risk Score ◽  
Noncoding Rna ◽  
Risk Model ◽  
Cox Regression ◽  
Endocrine System ◽  
The Cancer Genome Atlas ◽  
Close Link ◽  
Cox Regression Analysis

Abstract Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long noncoding RNA (lncRNA) in thyroid cancer and explored its prognostic value. A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb), respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1166 autophagy-associated lncRNAs. Subsequently we conducted univariate Cox regression analysis and multivariate Cox regression analysis, a nine-autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471, AL162231.2) significantly associated with prognosis was identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer. In this study, a nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

Pituitary surgery as alternative to dopamine agonists treatment for microprolactinomas, a cohort study

2021 ◽  
Author(s):  
Bertrand Baussart ◽  
Chiara Villa ◽  
Anne Jouinot ◽  
Marie-Laure Raffin-Sanson ◽  
Luc Foubert ◽  
...  
Keyword(s):  
Dopamine Agonists ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Pituitary Surgery ◽  
Cerebrospinal Fluid Leakage ◽  
Neurosurgical Department ◽  
Free Survival ◽  
Kaplan Meier ◽  
Disease Free

Objective: Microprolactinomas are currently treated with dopamine agonists. Outcome information on microprolactinoma patients treated by surgery is limited. This study reports the first large series of consecutive non-invasive microprolactinoma patients treated by pituitary surgery and evaluates the efficiency and safety of this treatment. Design: Follow-up of a cohort of consecutive patients treated by surgery. Methods: Between January 2008 and October 2020, 114 adult patients with pure microprolactinomas were operated on in a single tertiary expert neurosurgical department, using an endoscopic endonasal transsphenoidal approach. Eligible patients were presenting a microprolactinoma with no obvious cavernous invasion on MRI. Prolactin was assayed before and after surgery. Disease-free survival was modeled using Kaplan-Meier representation. A cox regression model was used to predict remission. Results: Median follow-up was 18.2 months (range: 2.8 to 155). In this cohort, 14/114 (12%) patients were not cured by surgery, including 10 early surgical failures, and 4 late relapses occurring 37.4 months (33 to 41.8) after surgery. From Kaplan Meier estimates, 1-year and 5-year disease free survival were 90.9% (95% CI, 85.6%-96.4%) and 81% (95% CI,71.2%-92.1%) respectively. The preoperative prolactinemia was the only significant preoperative predictive factor for remission (P<0.05). No severe complication was reported, with no anterior pituitary deficiency after surgery, one diabetes insipidus, and one postoperative cerebrospinal fluid leakage properly treated by muscle plasty. Conclusions: In well selected microprolactinoma patients, pituitary surgery performed by an expert neurosurgical team is a valid first-line alternative treatment to dopamine agonists.

scholarly journals NR5A2 Is One of 12 Transcription Factors Predicting Prognosis in HNSCC and Regulates Cancer Cell Proliferation in a p53-Dependent Manner

2021 ◽  
Vol 11 ◽  
Author(s):  
Kun Zhang ◽  
Ming Xiao ◽  
Xin Jin ◽  
Hongyan Jiang
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Cancer Progression ◽  
Risk Model ◽  
Critical Role ◽  
Disease Free Survival ◽  
Dependent Manner ◽  
Loss Of Function ◽  
Free Survival ◽  
Disease Free

Head and neck squamous cell carcinoma (HNSCC) rank seventh among the most common type of malignant tumor worldwide. Various evidences suggest that transcriptional factors (TFs) play a critical role in modulating cancer progression. However, the prognostic value of TFs in HNSCC remains unclear. Here, we identified a risk model based on a 12-TF signature to predict recurrence-free survival (RFS) in patients with HNSCC. We further analyzed the ability of the 12-TF to predict the disease-free survival time and overall survival time in HNSCC, and found that only NR5A2 down-regulation was strongly associated with shortened overall survival and disease-free survival time in HNSCC. Moreover, we systemically studied the role of NR5A2 in HNSCC and found that NR5A2 regulated HNSCC cell growth in a TP53 status-dependent manner. In p53 proficient cells, NR5A2 knockdown increased the expression of TP53 and activated the p53 pathway to enhance cancer cells proliferation. In contrast, NR5A2 silencing suppressed the growth of HNSCC cells with p53 loss/deletion by inhibiting the glycolysis process. Therefore, our results suggested that NR5A2 may serve as a promising therapeutic target in HNSCC harboring loss-of-function TP53 mutations.

scholarly journals A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5837
Author(s):  
Changwu Wu ◽  
Siming Gong ◽  
Georg Osterhoff ◽  
Nikolas Schopow
Keyword(s):  
Soft Tissue ◽  
Risk Score ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Malignant Tumours ◽  
Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

scholarly journals Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Zhou ◽  
Shasha Hong ◽  
Bingshu Li ◽  
Cheng Liu ◽  
Ming Hu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Figo Stage ◽  
Prognostic Indicator ◽  
Tissue Expression ◽  
The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

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