scholarly journals An Eight-CircRNA Assessment Model for Predicting Biochemical Recurrence in Prostate Cancer

2020 ◽  
Vol 8 ◽  
Author(s):  
Shuo Wang ◽  
Wei Su ◽  
Chuanfan Zhong ◽  
Taowei Yang ◽  
Wenbin Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Biochemical Recurrence ◽  
R Package ◽  
Low Risk ◽  
Assessment Model ◽  
High Morbidity ◽  
Sequencing Data ◽  
Go Analysis ◽  
Score Model

Prostate cancer (PCa) is a high morbidity malignancy in males, and biochemical recurrence (BCR) may appear after the surgery. Our study is designed to build up a risk score model using circular RNA sequencing data for PCa. The dataset is from the GEO database, using a cohort of 144 patients in Canada. We removed the low abundance circRNAs (FPKM < 1) and obtained 546 circRNAs for the next step. BCR-related circRNAs were selected by Logistic regression using the “survival” and “survminer” R package. Least absolute shrinkage and selector operation (LASSO) regression with 10-fold cross-validation and penalty was used to construct a risk score model by “glmnet” R software package. In total, eight circRNAs (including circ_30029, circ_117300, circ_176436, circ_112897, circ_112897, circ_178252, circ_115617, circ_14736, and circ_17720) were involved in our risk score model. Further, we employed differentially expressed mRNAs between high and low risk score groups. The following Gene Ontology (GO) analysis were visualized by Omicshare Online tools. As per the GO analysis results, tumor immune microenvironment related pathways are significantly enriched. “CIBERSORT” and “ESTIMATE” R package were used to detect tumor-infiltrating immune cells and compare the level of microenvironment scores between high and low risk score groups. What’s more, we verified two of eight circRNA’s (circ_14736 and circ_17720) circular characteristics and tested their biological function with qPCR and CCK8 in vitro. circ_14736 and circ_17720 were detected in exosomes of PCa patients’ plasma. This is the first bioinformatics study to establish a prognosis model for prostate cancer using circRNA. These circRNAs were associated with CD8+ T cell activities and may serve as a circRNA-based liquid biopsy panel for disease prognosis.

scholarly journals The Predictive and Therapeutic Role of Complement in Gliomas

2021 ◽  
Author(s):  
Jingwei Zhang ◽  
Shuwang Li ◽  
Fangkun Liu
Keyword(s):  
Tumor Microenvironment ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Low Grade ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Exact Function ◽  
Score Model

Abstract Background The human complement system plays an essential role in innate immunity in the tumor microenvironment. However, the exact function of complement in gliomas progress is still ambivalent and unclear. Methods A total of 194 complement genes were included in our study to build a risk score model based on the CGGA database and was verified by the validation database and our sequencing data. Kaplan-Meier analysis was used to compare survival differences between groups. CIBERSORT and ESTIMATE algorithms were applied to explore immune infiltrates in the tumor microenvironment. The biological processes and functions were identified by GO and KEGG analysis. Results We build a risk score model using univariate and multivariate Cox regression analysis based on the CGGA database and verified in the TCGA database. Patients with gliomas in the low-risk group have a better prognosis and were associated with low grade, 1p19q codeletion, IDH mutant status, MGMT promoter methylation. In addition, the low-risk group is prone to have more infiltration of CD4 naive T cells and monocytes. Patients with gliomas in the low-risk group exhibit temozolomide sensitivity. Moreover, we explored several vital pathways that were associated with complement genes in this study. Conclusion Complement-related gene signature can predict the malignancy and outcome of patients with gliomas and was related to temozolomide sensitivity, which might act as a promising target for gliomas therapy in the future.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

NRG Oncology RTOG 0415: A randomized phase III non-inferiority study comparing two fractionation schedules in patients with low-risk prostate cancer.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 1-1 ◽  
Author(s):  
W. Robert Lee ◽  
James J. Dignam ◽  
Mahul Amin ◽  
Deborah Bruner ◽  
Daniel Low ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Biochemical Recurrence ◽  
Disease Free Survival ◽  
Low Risk ◽  
Free Survival ◽  
Risk Prostate Cancer ◽  
Low Risk Prostate Cancer ◽  
Grade 3 ◽  
Disease Free

1 Background: To determine whether the efficacy of a hypofractionated (H) schedule is no worse than a conventional (C) schedule in men with low-risk prostate cancer. Methods: From April 2006 to December 2009, one thousand one hundred fifteen men with low-risk prostate cancer (clinical stage T1-2a, Gleason ≤ 6, PSA < 10) were randomly assigned 1:1 to a conventional (C) schedule (73.8 Gy in 41 fractions over 8.2 weeks) or to a hypofractionated (H) schedule (70 Gy in 28 fractions over 5.6 weeks). The trial was designed to establish with 90% power and alpha = 0.05 that (H) results in 5-year disease-free survival (DFS) that is not lower than (C) by more than 7% (hazard ratio (HR) < 1.52). Secondary endpoints include freedom from biochemical recurrence (FFBR) and overall survival. At the third planned interim analysis (July 2015), the NRG Oncology Data Monitoring Committee recommended that the results of the trial be reported. Results: One thousand one hundred and one protocol eligible men were randomized: 547 to C and 554 to H. Median follow-up is 5.9 years. Baseline characteristics are not different according to treatment arm. At the time of analysis 185 DFS events have been observed; 99 in the C arm and 86 in the H arm. The estimated 7-year disease-free survival is 75.6% (95% CI 70.3, 80.1) in the C arm and 81.8% (77.5, 85.3) in the H arm. The DFS HR (C/H) is 0.85 (0.64, 1.14). Comparison of biochemical recurrence (HR = 0.77, (0.51, 1.17)) and overall survival (HR = 0.95, (0.65, 1.41)) also met protocol non-inferiority criteria. Grade ≥ 3 GI toxicity is 3.0% (C) vs. 4.6% (H), Relative risk (RR) for H vs. C 1.53, (95% CI 0.86, 2.83); grade ≥ 3 GU toxicity is 4.5% (C) vs. 6.4% (H), RR = 1.43 (0.86,2.37). Conclusions: In men with low-risk prostate cancer, 70 Gy in 28 fractions over 5.6 weeks is non-inferior to 73.8 Gy in 41 fractions over 8.2 weeks. Clinical trial information: NCT00331773.

scholarly journals Immunophenotype Rearrangement in Response to Tumor Excision May Be Related to the Risk of Biochemical Recurrence in Prostate Cancer Patients

2021 ◽  
Vol 10 (16) ◽  
pp. 3709
Author(s):  
Paulius Bosas ◽  
Gintaras Zaleskis ◽  
Daiva Dabkevičiene ◽  
Neringa Dobrovolskiene ◽  
Agata Mlynska ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Peripheral Blood ◽  
Biochemical Recurrence ◽  
Risk Group ◽  
Laparoscopic Approach ◽  
Low Risk ◽  
Tumor Excision

Background: Prostate cancer (PCa) is known to exhibit a wide spectrum of aggressiveness and relatively high immunogenicity. The aim of this study was to examine the effect of tumor excision on immunophenotype rearrangements in peripheral blood and to elucidate if it is associated with biochemical recurrence (BCR) in high risk (HR) and low risk (LR) patients. Methods: Radical prostatectomy (RP) was performed on 108 PCa stage pT2–pT3 patients. Preoperative vs. postoperative (one and three months) immunophenotype profile (T- and B-cell subsets, MDSC, NK, and T reg populations) was compared in peripheral blood of LR and HR groups. Results: The BCR-free survival difference was significant between the HR and LR groups. Postoperative PSA decay rate, defined as ePSA, was significantly slower in the HR group and predicted BCR at cut-off level ePSA = −2.0% d−1 (AUC = 0.85 (95% CI, 0.78–0.90). Three months following tumor excision, the LR group exhibited a recovery of natural killer CD3 − CD16+ CD56+ cells, from 232 cells/µL to 317 cells/µL (p < 0.05), which was not detectable in the HR group. Prostatectomy also resulted in an increased CD8+ population in the LR group, mostly due to CD8+ CD69+ compartment (from 186 cells/µL before surgery to 196 cells/µL three months after, p < 001). The CD8+ CD69+ subset increase without total T cell increase was present in the HR group (p < 0.001). Tumor excision resulted in a myeloid-derived suppressor cell (MDSC) number increase from 12.4 cells/µL to 16.2 cells/µL in the HR group, and no change was detectable in LR patients (p = 0.12). An immune signature of postoperative recovery was more likely to occur in patients undergoing laparoscopic radical prostatectomy (LRP). Open RP (ORP) was associated with increased MDSC numbers (p = 0.002), whereas LRP was characterized by an immunity sparing profile, with no change in MDSC subset (p = 0.16). Conclusion: Tumor excision in prostate cancer patients results in two distinct patterns of immunophenotype rearrangement. The low-risk group is highly responsive, revealing postoperative restoration of T cells, NK cells, and CD8+ CD69+ numbers and the absence of suppressor MDSC increase. The high-risk group presented a limited response, accompanied by a suppressor MDSC increase and CD8+ CD69+ increase. The laparoscopic approach, unlike ORP, did not result in an MDSC increase in the postoperative period.

scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P &lt; 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P &lt; 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P&lt; 0.001) and ulceration (P&lt; 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Xitao Wang ◽  
Xiaolin Dou ◽  
Xinxin Ren ◽  
Zhuoxian Rong ◽  
Lunquan Sun ◽  
...  
Keyword(s):  
High Risk ◽  
Single Cell ◽  
Risk Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Assessment Model ◽  
Sequencing Data ◽  
Ductal Cell ◽  
Significant Difference

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p &lt; 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p &lt; 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p &lt; 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p &lt; 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.

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