scholarly journals Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

2020 ◽  
Author(s):  
He Li ◽  
Zhao-Yi Liu ◽  
Nayiyuan Wu ◽  
Yong-Chang Chen ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transcription Factors ◽  
Risk Score ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Low Risk ◽  
Consensus Clustering ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Significant Difference

Abstract BackgroundOvarian cancer (OC) is one of the most lethal gynecological cancer globally. Serous ovarian cancer (SOC) is most common pathological type of ovarian cancer. Growing evidence suggests that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, we aimed to develop a transcription factors-related prognostic signature to better predict the survival in patients with SOC.Materials and methodsThe TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. Lasso cox regression and consensus clustering analysis were utilized to construct a novel TFs related signature and cluster model, respectively. Genomic alternative analysis was used to elaborate on the association between the TFs related prognostic signature and genomic aberrations. GSEA analysis was applied to identify the biological functional difference between high risk-score group and low-risk score group.ResultsA 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score, and cluster models were effective at accurately distinguishing the prognosis of SOC. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. Immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups; indicating that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients.ConclusionsIn conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

scholarly journals Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
He Li ◽  
Nayiyuan Wu ◽  
Zhao-Yi Liu ◽  
Yong-Chang Chen ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transcription Factors ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Consensus Clustering ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Significant Difference

AbstractGrowing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score and cluster models were effective at accurately distinguishing the overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. The potential immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups. The results indicated that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients. In conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

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 A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Construction and Validation of a Universal Applicable Prognostic Signature for Gastric Cancer Based on Seven Immune-Related Gene Correlated With Tumor Associated Macrophages

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Critical Role ◽  
External Validation ◽  
Low Risk ◽  
Prognostic Signature ◽  
Tumor Associated Macrophages ◽  
Cox Regression Analysis ◽  
Significant Difference

BackgroundTumor-associated macrophages (TAMs) play a critical role in the progression of malignant tumors, but the detailed mechanism of TAMs in gastric cancer (GC) is still not fully explored.MethodsWe identified differentially expressed immune-related genes (DEIRGs) between GC samples with high and low macrophage infiltration in The Cancer Genome Atlas datasets. A risk score was constructed based on univariate Cox analysis and Lasso penalized Cox regression analysis in the TCGA cohort (n=341). The optimal cutoff determined by the 5-year time-dependent receiver operating characteristic (ROC) curve was considered to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE84437, n=431; GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109) from the Gene Expression Omnibus (GEO) database.ResultsThe signature consisting of 7 genes (FGF1, GRP, AVPR1A, APOD, PDGFRL, CXCR4, and CSF1R) showed good performance in predicting overall survival (OS) in the 5 independent cohorts. The risk score presented an obviously positive correlation with macrophage abundance (cor=0.7, p<0.001). A significant difference was found between the high- and low-risk groups regarding the overall survival of GC patients. The high-risk group exhibited a higher infiltration level of M2 macrophages estimated by the CIBERSORT algorithm. In the five independent cohorts, the risk score was highly positively correlated with the stromal cell score, suggesting that we can also evaluate the infiltration of stromal cells in the tumor microenvironment according to the risk score.ConclusionOur study developed and validated a general applicable prognostic model for GC from the perspective of TAMs, which may help to improve the precise treatment strategy of GC.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiang Fei ◽  
Congli Hu ◽  
Xinyu Wang ◽  
Chaojing Lu ◽  
Hezhong Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Pearson Correlation ◽  
Low Risk ◽  
Clinical Samples ◽  
Prognostic Signature ◽  
Competing Endogenous Rna

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

A stromal-associated gene expression signature predicting for survival in a series of patients with advanced high-grade serous ovarian cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5552-5552
Author(s):  
T. Bonome ◽  
G. Samimi ◽  
M. Randonovich ◽  
J. Brady ◽  
S. Ghosh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cox Regression ◽  
Patient Survival ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Data ◽  
Prognostic Signature ◽  
Qrt Pcr ◽  
Microarray Expression

5552 Background: Prognostic gene expression signatures have been derived for undissected serous ovarian epithelial tumors, yet the specific contribution of stromal cells to patient survival has not been addressed. The aim of this study is to identify stromal genes impacting patient survival in the context of serous ovarian cancer. Methods: Expression profiling utilizing Affymetrix U133 Plus 2.0 oligonucleotide arrays was completed for 50 microdissected stromal samples derived from high-grade, late-stage serous tumors displaying a broad spectrum of survival endpoints. A semi-supervised dimension reduction method employing multivariate Cox regression and principal components analysis was applied to the expression data to identify genes associated with patient survival and establish a predictive model. qRT-PCR was employed to validate the microarray expression data. Results: Cox regression analysis identified 267 significant genes. The first 6 principal components of these genes, representing >65% of total variance, entered a multivariate Cox model through which the relative hazard of future patients can be predicted. To confirm our finding, the microarray data underwent leave-one-out validation. The patients were equally divided into low- and high-risk groups and non-parametric Kaplan-Meier analysis and log rank test demonstrated the two groups were significantly different in survival (p = 0.0115). Genes associated with cell survival and migration were identified in the prognostic signature. For validation, qRT-PCR data for all 50 specimens was correlated with microarray expression values for a series of select prognostic genes. Conculsions: In this study, we characterized and validated a stromal dervied prognostic signature associated with poor patient survival. Contained in this novel predictor may be stromal targets suitable for the design of new therapeutic interventions, or use as independent diagnostic markers. No significant financial relationships to disclose.

Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jianhui Chen ◽  
Chuan HU ◽  
Reguang Pan ◽  
Xuedan Du ◽  
Haotian Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

scholarly journals Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
M2 Macrophages ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

scholarly journals A Nine-Immune-Related Gene Prognostic Signature for Predicting Outcomes in High-Grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Yahui Jiang ◽  
Tianjiao Lyu ◽  
Tianyu Zhou ◽  
Yiwen Shi ◽  
Weiwei Feng
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
High Grade ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Recently, immune system has been shown to be indispensable for ovarian cancer progression. The key immune-related genes (IRGs) related to the overall survival of ovarian cancer patients should be taken seriously. Here, we screened 9 survival-related IRGs in high-grade serous ovarian cancer (HGSOC) and build a prognostic signature to predict the outcome of HGSOC patients.Methods: We downloaded RNA-sequence profiles from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed genes between normal fallopian tube and HGSOC. Among these genes, IRGs were filtered based on the Immunology Database and Analysis Portal (ImmPort). Using univariate Cox regression, Lasso regression and multivariate Cox regression, we selected 9 survival-related IRGs and established a prognostic signature to compute the risk score. Patients were divided into a low-risk group and a high-risk group, and the immunological feature differences between them were analysed with the ESTIMATE R package, TIMER and GSEA software. Moreover, the prognostic signature was validated by data from Gene Expression Omnibus (GEO) datasets.Results: We obtained 1544 differentially expressed genes in HGSOC compared with normal fallopian tube, among which 99 genes were related to immunology. After univariate Cox regression, Lasso regression and multivariate Cox regression, nine IRGs (HLA-F, PSMC1, PI3, CXCL10, CXCL9, CXCL11, LRP1, STAT1 and OGN) were identified as optimal survival-related IRGs and used to establish a prognostic signature for calculating the risk scores of HGSOC patients. The prognostic signature showed its efficiency in predicting the overall survival of HGSOC patients in TCGA training cohort (p=1.018e-8) and GEO test cohort (p=2.632e-2). Age and risk scores were independent risk factors for overall survival. As the risk scores increased, the proportions of neutrophil, dendritic cells, CD8+ T cells, CD4+ T cells and B cells decreased (p values were 0.026, 1.909e-4, 9.165e-10, 0.003 and 2.658e-4, respectively). In addition, 21 out of 24 HLA-related genes were highly expressed in the low-risk group than in the high-risk group. The above might prompt a stronger immune response in the low-risk group.Conclusions: Our study constructed a nine-IRG-based prognostic signature that could effectively predict the overall survival of HGSOC patients and become a promising therapeutic target for HGSOC treatments.

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