scholarly journals Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Dan Sun ◽  
Tianren Li
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Group ◽  
Low Risk ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Related Gene ◽  
Training Set ◽  
Kaplan Meier ◽  
Prognosis Model

Abstract Background: High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.Method: The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the Least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.Results: A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant. The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There were differences in some immune cells between the high-risk and low-risk groups as defined by the model. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, many of the immune-related pathways were enriched in the low-risk group, with antigen processing and presentation identified as the most enriched pathways.Conclusion: The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

scholarly journals Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiaxing Lin ◽  
Xiao Xu ◽  
Dan Sun ◽  
Tianren Li
Keyword(s):  
Ovarian Cancer ◽  
Risk Group ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Overall Survival Rate ◽  
Related Gene ◽  
Training Set ◽  
Immune Related Gene ◽  
Kaplan Meier ◽  
Prognosis Model

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.MethodThe study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.ResultsA prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of &lt; 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p &lt; 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p &lt; 0.001). Finally, the immune-related pathways were enriched in the low-risk group.ConclusionThe prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

scholarly journals A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ying Ye ◽  
Qinjin Dai ◽  
Hongbo Qi
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Related Gene

AbstractOvarian cancer (OC) is a highly malignant gynaecological tumour that has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the expression of pyroptosis-related genes in OC and their correlations with prognosis remain unclear. In this study, we identified 31 pyroptosis regulators that were differentially expressed between OC and normal ovarian tissues. Based on these differentially expressed genes (DEGs), all OC cases could be divided into two subtypes. The prognostic value of each pyroptosis-related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 7-gene signature was built and classified all OC patients in the TCGA cohort into a low- or high-risk group. OC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, OC patients from a Gene Expression Omnibus (GEO) cohort were divided into two risk subgroups, and the low-risk group had increased overall survival (OS) time (P = 0.014). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of OC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched and that the immune status was decreased in the high-risk group. In conclusion, pyroptosis-related genes play important roles in tumour immunity and can be used to predict the prognosis of OCs.

scholarly journals Promoter Methylation of the MGRN1 Gene Predicts Prognosis and Response to Chemotherapy of High-Grade Serous Ovarian Cancer Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-fei Li ◽  
Hai-yan Sun ◽  
Tian Hua ◽  
Hai-bo Zhang ◽  
Yun-jie Tian ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Mrna Expression ◽  
Promoter Region ◽  
Upstream Region ◽  
Platinum Resistance ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Kaplan Meier ◽  
Response To Chemotherapy ◽  
Low Expression

Aberrant DNA methylation is considered to play a critical role in the chemoresistance of epithelial ovarian cancer (EOC). In this study, we explored the relationship between hypermethylation of the Mahogunin Ring Finger 1 (MGRN1) gene promoter and primary chemoresistance and clinical outcomes in high-grade serous ovarian cancer (HGSOC) patients. The MALDI-TOF mass spectrometry assays revealed a strong association between hypermethylation of the MGRN1 upstream region and platinum resistance in HGSOC patients. Spearman’s correlation analysis revealed a significantly negative connection between the methylation level of MGRN1 and its expression in HGSOC. In vitro analysis demonstrated that knockdown of MGRN1 reduced the sensitivity of cells to cisplatin and that expression of EGR1 was significantly decreased in SKOV3 cells with low levels of MGRN1 expression. Similarly, EGR1 mRNA expression was lower in platinum-resistant HGSOC patients and was positively correlated with MGRN1 mRNA expression. Kaplan-Meier analyses showed that high methylation of the MGRN1 promoter region and low expression of MGRN1 were associated with worse survival of HGSOC patients. In multivariable models, low MGRN1 expression was an independent factor predicting poor outcome. Furthermore, low expression of EGR1 was also been confirmed to be significantly related to the poor prognosis of HGSOC patients by Kaplan-Meier. The hypermethylation of the MGRN1 promoter region and low expression of MGRN1 were associated with platinum resistance and poor outcomes in HGSOC patients, probably by altering EGR1 expression.

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.

scholarly journals Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Pingfei Tang ◽  
Weiming Qu ◽  
Dajun Wu ◽  
Shihua Chen ◽  
Minji Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Pancreatic Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Dysfunction ◽  
Low Risk ◽  
Excellent Performance ◽  
Kaplan Meier ◽  
Prognostic Prediction

Background. Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC). Methods. Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas–pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. Results. We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan–Meier method ( p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart. Conclusions. We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

Plasmin(ogen) serves as a favorable biomarker for prediction of survival in advanced high-grade serous ovarian cancer

2017 ◽  
Vol 398 (7) ◽  
pp. 765-773 ◽  
Author(s):  
Shuo Zhao ◽  
Julia Dorn ◽  
Rudolf Napieralski ◽  
Axel Walch ◽  
Sandra Diersch ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Multivariable Analysis ◽  
Positive Staining ◽  
Mrna Levels ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract In serous ovarian cancer, the clinical relevance of tumor cell-expressed plasmin(ogen) (PLG) has not yet been evaluated. Due to its proteolytic activity, plasmin supports tumorigenesis, however, angiostatin(-like) fragments, derived from PLG, can also function as potent anti-tumorigenic factors. In the present study, we assessed PLG protein expression in 103 cases of advanced high-grade serous ovarian cancer (FIGO III/IV) by immunohistochemistry (IHC). In 70/103 cases, positive staining of tumor cells was observed. In univariate Cox regression analysis, PLG staining was positively associated with prolonged overall survival (OS) [hazard ratio (HR)=0.59, p=0.026] of the patients. In multivariable analysis, PLG, together with residual tumor mass, remained a statistically significant independent prognostic marker (HR=0.49, p=0.009). In another small patient cohort (n=29), we assessed mRNA expression levels of PLG by quantitative PCR. Here, elevated PLG mRNA levels were also significantly associated with prolonged OS of patients (Kaplan-Meier analysis; p=0.001). This finding was validated by in silico analysis of a microarray data set (n=398) from The Cancer Genome Atlas (Kaplan-Meier analysis; p=0.031). In summary, these data indicate that elevated PLG expression represents a favorable prognostic biomarker in advanced (FIGO III/IV) high-grade serous ovarian cancer.

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 Development a Prognostic Model Integrating lncRNA/ mRNA novel Biomarkers Identified by Bioinformatics Analysis and Experiments in Breast Cancer

2021 ◽  
Author(s):  
Jinrong Wei ◽  
Qianshu Dou ◽  
Futing Ba ◽  
Guo-Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Breast Cancers ◽  
Rt Pcr ◽  
Cerna Network ◽  
Prognosis Model

Abstract Purpose: The purpose of this study is to established a prognosis model based on the expression profiles of lncRNAs and mRNAs for breast cancers.Methods: Single Variable Cox Proportional Risk Regression analysis and difference analysis were applied to screen survival-related and differently expressed lncRNAs and mRNAs between tumor and normal tissues from TCGA data. GO and KEGG analysis were applied for top 30 survival-related genes. LncRNA/mRNA co-expressed network was constructed based on correlation analysis. LASSO analysis and Multivariate Stepwise Cox Regression analysis were applied to establish the prognosis model. RT-PCR experiments were applied to verify the correctness of the analysis results. Relative components of the TME in breast cancers with high and low risk groups were analysed by xCell and Cox proportional risk regression analysis. The ceRNA network was constructed by calculating the Pearson correlation coefficient (PCC) for miRNA-mRNA and miRNA-lncRNA using paired miRNA, mRNA, and lncRNA expression profile data.Results:Venn diagrams showed that there were 60 genes and 54 lncRNAs that were differently expressed and related with survival. Through lncRNA/mRNA co-expressed network construction, 19 lncRNA and 16 mRNA hub genes were gained. The genes were then included in LASSO and multivariate Cox proportional hazard regression analysis, and finally, 3 lncRNAs (LINC01497, LINC02766, LINC02528) and 2 mRNAs (C20orf85, CST1) were selected as prognosis predictive genes. According to the median risk score of the 5 candidates, patients were divided into high-risk group and low-risk group. The results of RT-PCR were consistent with the analysis results. The proportions of Adipocytes, Endothelial cells, HSCs, Fibroblasts were significantly lower in low risk score tissues compared with the high risk score tissues, while the proportions of M1 macrophages, MSCs, Th2 cells were significantly higher. A lncRNA-miRNA-mRNA ceRNA network containing 3 lncRNAs, 2 mRNAs, and 158 miRNAs was finally constructed, preliminarily revealed a proper mechanism of the 5 molecules playing important roles in breast cancer progression and prognosis prediction.Conclusion: We found that LINC01497, LINC02766, LINC02528 and C20orf85, CST1 may serve as a powerful prognostic tool to optimize the prognosis evaluation system of breast cancer.

scholarly journals Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

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