Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer

Abstract Background: Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV).Material/methods: 379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of snoRNAs. ROC curves and KM plot curves were applied to validate the prediction model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H&E staining and immunohistochemistry. Results: A prognostic model was constructed on the basis of SnoRNAs in OV patients.Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumorsize, advanced stage and with tumor status. KM plot analysis confirmed that patients with high RiskScore had poorer prognosis in subgroup of age, tumor size and stage. 7 of 9 snoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, H&E staining and immunohistochemistry of Ki67, P53 and P16 were confirmed that patients with higher RiskScore are more malignant. Conclusions: In summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.

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A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

BioMed Research International ◽

10.1155/2021/6669570 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiao-Yan Huang ◽

Wen-Tao Qin ◽

Qi-Sheng Su ◽

Cheng-Cheng Qiu ◽

Ruo-Chuan Liu ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Key Genes

Objective. This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). Methods. The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index ( C -index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. Results. A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C -index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. Conclusion. The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

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Role of a Pyroptosis-Related lncRNA Signature in Risk Stratification and Immunotherapy of Ovarian Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.793515 ◽

2022 ◽

Vol 8 ◽

Author(s):

Zeyu Zhang ◽

Zhijie Xu ◽

Yuanliang Yan

Keyword(s):

Ovarian Cancer ◽

Risk Stratification ◽

Survival Data ◽

Risk Model ◽

Cox Regression ◽

Roc Curves ◽

Immune Checkpoints ◽

Random Allocation ◽

Cox Regression Analysis

Background: Pyroptosis is a newly recognized form of cell death. Emerging evidence has suggested the crucial role of long non-coding RNAs (lncRNAs) in the tumorigenesis and progression of ovarian cancer (OC). However, there is still poor understanding of pyroptosis-related lncRNAs in OC.Methods: The TCGA database was accessed for gene expression and clinical data of 377 patients with OC. Two cohorts for training and validation were established by random allocation. Correlation analysis and Cox regression analysis were performed to identify pyroptosis-related lncRNAs and construct a risk model.Results: Six pyroptosis-related lncRNAs were included in the final signature with unfavorable survival data. Subsequent ROC curves showed promising predictive value of patient prognosis. Further multivariate regression analyses confirmed the signature as an independent risk factor in the training (HR: 2.242, 95% CI: 1.598–3.145) and validation (HR: 1.884, 95% CI: 1.204–2.95) cohorts. A signature-based nomogram was also established with a C-index of.684 (95% CI: 0.662–0.705). Involvement of the identified signature in multiple immune-related pathways was revealed by functional analysis. Moreover, the signature was also associated with higher expression of three immune checkpoints (PD-1, B7-H3, and VSIR), suggesting the potential of the signature as an indicator for OC immunotherapies.Conclusion: This study suggests that the identified pyroptosis-related lncRNA signature and signature-based nomogram may serve as methods for risk stratification of OC. The signature is also associated with the tumor immune microenvironment, potentially providing an indicator for patient selection of immunotherapy in OC.

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Comprehensive Analysis of the Tumor Microenvironment and Ferroptosis-Related Genes Predict Prognosis with Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.774400 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiao-xue Li ◽

Li Xiong ◽

Yu Wen ◽

Zi-jian Zhang

Keyword(s):

Ovarian Cancer ◽

Cox Regression ◽

Enrichment Analysis ◽

Roc Curves ◽

Gene Set Enrichment Analysis ◽

Drug Resistant ◽

Cox Regression Analysis ◽

Gene Set ◽

Immune Infiltration ◽

Tcga Dataset

The early diagnosis of ovarian cancer (OC) is critical to improve the prognosis and prevent recurrence of patients. Nevertheless, there is still a lack of factors which can accurately predict it. In this study, we focused on the interaction of immune infiltration and ferroptosis and selected the ESTIMATE algorithm and 15 ferroptosis-related genes (FRGs) to construct a novel E-FRG scoring model for predicting overall survival of OC patients. The gene expression and corresponding clinical characteristics were obtained from the TCGA dataset (n = 375), GSE18520 (n = 53), and GSE32062 (n = 260). A total of 15 FRGs derived from FerrDb with the immune score and stromal score were identified in the prognostic model by using least absolute shrinkage and selection operator (LASSO)–penalized COX regression analysis. The Kaplan–Meier survival analysis and time-dependent ROC curves performed a powerful prognostic ability of the E-FRG model via multi-validation. Gene Set Enrichment Analysis and Gene Set Variation Analysis elucidate multiple potential pathways between the high and low E-FRG score group. Finally, the proteins of different genes in the model were verified in drug-resistant and non–drug-resistant tumor tissues. The results of this research provide new prospects in the role of immune infiltration and ferroptosis as a helpful tool to predict the outcome of OC patients.

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Pre-operative Circulating Plasma Gelsolin Predicts Residual Disease and Detects Early Stage Ovarian Cancer

Scientific Reports ◽

10.1038/s41598-019-50436-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Meshach Asare-Werehene ◽

Laudine Communal ◽

Euridice Carmona ◽

Tien Le ◽

Diane Provencher ◽

...

Keyword(s):

Ovarian Cancer ◽

Sensitivity And Specificity ◽

Cox Regression ◽

Residual Disease ◽

Early Stage ◽

Progression Free Survival ◽

Actin Binding ◽

Clinicopathological Parameters ◽

Cox Regression Analysis ◽

Stage 1

Abstract Ovarian cancer (OVCA) patients with suboptimal residual disease (RD) and advanced stages have poor survival. pGSN is an actin binding protein which protects OVCA cells from cisplatin-induced death. There is an urgent need to discover reliable biomarkers to optimize individualized treatment recommendations. 99 plasma samples with pre-determined CA125 were collected from OVCA patients and pGSN assayed using sandwich-based ELISA. Associations between CA125, pGSN and clinicopathological parameters were examined using Fisher’s exact test, T test and Kruskal Wallis Test. Univariate and multivariate Cox proportional hazard models were used to statistically analyze clinical outcomes. At 64 µg/ml, pGSN had sensitivity and specificity of 60% and 60% respectively, for the prediction of RD where as that of CA125 at 576.5 U/mL was 43.5% and 56.5% respectively. Patients with stage 1 tumor had increased levels of pre-operative pGSN compared to those with tumor stage >1 and healthy subjects (P = 0.005). At the value of 81 µg/mL, pGSN had a sensitivity and specificity of 75% and 78.4%, respectively for the detection of early stage OVCA. At the value of 0.133, the Indicator of Stage 1 OVCA (ISO1) provided a sensitivity of 100% at a specificity of 67% (AUC, 0.89; P < 0.001). In the multivariate Cox regression analysis, pGSN (HR, 2.00; CI, 0.99–4.05; P = 0.05) was an independent significant predictor of progression free survival (PFS) but not CA125 (HR, 0.68; CI, 0.41–1.13; P = 0.13). Pre-operative circulating pGSN is a favorable and independent biomarker for early disease detection, RD prediction and patients’ prognosis.

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Prognostic signature of ovarian cancer based on 14 tumor microenvironment genes

10.21203/rs.3.rs-56329/v1 ◽

2020 ◽

Author(s):

Xiazi Nie ◽

Lina Song ◽

Xiaohua Li ◽

Yirong Wang ◽

Bo Qu

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Risk Score ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Mrna Levels ◽

Prognostic Signature ◽

Cox Regression Analysis

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

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Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

10.21203/rs.3.rs-147085/v1 ◽

2021 ◽

Author(s):

Li Wang ◽

Jialin Qu ◽

Man Jiang ◽

Na Zhou ◽

Zhixuan Ren ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Integrated Analysis ◽

Consensus Clustering ◽

Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

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Alternative splicing acts as an independent prognosticator in ovarian carcinoma

Scientific Reports ◽

10.1038/s41598-021-89778-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yan Ouyang ◽

Kaide Xia ◽

Xue Yang ◽

Shichao Zhang ◽

Li Wang ◽

...

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Alternative Splicing ◽

Ovarian Carcinoma ◽

Cancer Patients ◽

Cox Regression ◽

Excision Repair ◽

Splicing Factors ◽

Prognostic Signature ◽

Cox Regression Analysis

AbstractAlternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

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Trends of incidence and prognosis of primary adenocarcinoma of the bladder

Therapeutic Advances in Urology ◽

10.1177/17562872211018006 ◽

2021 ◽

Vol 13 ◽

pp. 175628722110180

Author(s):

Haowen Lu ◽

Weidong Zhu ◽

Weipu Mao ◽

Feng Zu ◽

Yali Wang ◽

...

Keyword(s):

Prediction Model ◽

Marital Status ◽

Cox Regression ◽

Tnm Staging ◽

Primary Site ◽

Primary Adenocarcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Cox Regression Analysis ◽

Ajcc Stage

Background: Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis. Methods: Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated. Results: The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms. Conclusion: The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

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Clinical relevance of kallikrein-related peptidase 6 (KLK6) and 8 (KLK8) mRNA expression in advanced serous ovarian cancer

Biological Chemistry ◽

10.1515/hsz-2016-0177 ◽

2016 ◽

Vol 397 (12) ◽

pp. 1265-1276 ◽

Cited By ~ 14

Author(s):

Nancy Ahmed ◽

Julia Dorn ◽

Rudolf Napieralski ◽

Enken Drecoll ◽

Matthias Kotzsch ◽

...

Keyword(s):

Ovarian Cancer ◽

Mrna Expression ◽

Cox Regression ◽

Multivariable Analysis ◽

Progression Free Survival ◽

Predictive Marker ◽

Residual Tumor ◽

Mrna Levels ◽

Serous Ovarian Cancer ◽

Cox Regression Analysis

Abstract Most members of the kallikrein-related peptidase family have been demonstrated to be dysregulated in ovarian cancer and modulate tumor growth, migration, invasion, and resistance to chemotherapy. In the present study, we assessed the mRNA expression levels of KLK6 and KLK8 by quantitative PCR in 100 patients with advanced serous ovarian cancer FIGO stage III/IV. A pronounced correlation between KLK6 and KLK8 mRNA expression (rs = 0.636, p < 0.001) was observed, indicating coordinate expression of both peptidases. No significant associations of clinical parameters with KLK6, KLK8, and a combined score KLK6+KLK8 were found. In univariate Cox regression analysis, elevated mRNA levels of KLK6 were significantly linked with shortened overall survival (OS) (hazard ratio [HR] = 2.07, p = 0.007). While KLK8 values were not associated with patients’ outcome, high KLK6+KLK8 values were significantly associated with shorter progression-free survival (HR = 1.82, p = 0.047) and showed a trend towards significance in the case of OS (HR = 1.82, p = 0.053). Strikingly, in multivariable analysis, elevated KLK6 mRNA values, apart from residual tumor mass, remained an independent predictive marker for poor OS (HR = 2.33, p = 0.005). As KLK6 mRNA and protein levels correlate, KLK6 may represent an attractive therapeutic target for potent and specific inhibitors of its enzymatic activity.

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High expression of fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer

BMC Cancer ◽

10.1186/s12885-020-07541-6 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Min Li ◽

Xue Cheng ◽

Rong Rong ◽

Yan Gao ◽

Xiuwu Tang ◽

...

Keyword(s):

Ovarian Cancer ◽

Cox Regression ◽

Paraffin Section ◽

Progression Free Survival ◽

High Grade ◽

Serous Ovarian Cancer ◽

Systematic Analysis ◽

Cox Regression Analysis ◽

Genes Expression ◽

High Level

Abstract Background High-grade serous ovarian cancer (HGSOC) is a fatal form of ovarian cancer. Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC. Methods TCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and progression-free survival (PFS), were calculated by Cox regression analysis for hazard rates (HR). Histopathological investigation of the identified genes was carried out in 151 Chinese HGSOC patients to validate gene expression in different stages of HGSOC. Results Of all 57,331 genes that were analyzed, FAP was identified as the only novel gene that significantly contributed to both OS and PFS of HGSOC. In addition, FAP had a consistent expression profile between carcinoma-paracarcinoma and early-advanced stages of HGSOC. Immunological tests in paraffin section also confirmed that up-regulation of FAP was present in advanced stage HGSOC patients. Prediction of FAP network association suggested that FN1 could be a potential downstream gene which further influenced HGSOC survival. Conclusions High-level expression of FAP was associated with poor prognosis of HGSOC via FN1 pathway.

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