scholarly journals Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Transcription Factors ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

scholarly journals Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

2021 ◽  
Vol 12 ◽  
Author(s):  
Miaobin Mao ◽  
Hongjian Ling ◽  
Yuping Lin ◽  
Yanling Chen ◽  
Benhua Xu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Differentially Expressed ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

BackgroundPancreatic adenocarcinoma (PAAD) is a highly lethal and aggressive tumor with poor prognoses. The predictive capability of immune-related genes (IRGs) in PAAD has yet to be explored. We aimed to explore prognostic-related immune genes and develop a prediction model for indicating prognosis in PAAD.MethodsThe messenger (m)RNA expression profiles acquired from public databases were comprehensively integrated and differentially expressed genes were identified. Univariate analysis was utilized to identify IRGs that related to overall survival. Whereafter, a multigene signature in the Cancer Genome Atlas cohort was established based on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Moreover, a transcription factors regulatory network was constructed to reveal potential molecular processes in PAAD. PAAD datasets downloaded from the Gene Expression Omnibus database were applied for the validations. Finally, correlation analysis between the prognostic model and immunocyte infiltration was investigated.ResultsTotally, 446 differentially expressed immune-related genes were screened in PAAD tissues and normal tissues, of which 43 IRGs were significantly related to the overall survival of PAAD patients. An immune-based prognostic model was developed, which contained eight IRGs. Univariate and multivariate Cox regression revealed that the risk score model was an independent prognostic indicator in PAAD (HR > 1, P < 0.001). Besides, the sensitivity of the model was evaluated by the receiver operating characteristic curve analysis. Finally, immunocyte infiltration analysis revealed that the eight-gene signature possibly played a pivotal role in the status of the PAAD immune microenvironment.ConclusionA novel prognostic model based on immune genes may serve to characterize the immune microenvironment and provide a basis for PAAD immunotherapy.

scholarly journals Prognostic Biomarkers on a Competitive Endogenous RNA Network Reveals Overall Survival in Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenxing Qin ◽  
Feng Qi ◽  
Jia Li ◽  
Ping Li ◽  
Yuan-Sheng Zang
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Prognostic Model ◽  
Triple Negative ◽  
Cox Regression ◽  
Critical Role ◽  
Differentially Expressed ◽  
Cerna Network ◽  
Competitive Endogenous Rna

The objective of this study was to construct a competitive endogenous RNA (ceRNA) regulatory network using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with triple-negative breast cancer (TNBC) and to construct a prognostic model for predicting overall survival (OS) in patients with TNBC. Differentially expressed lncRNAs, miRNAs, and mRNAs in TNBC patients from the TCGA and Metabric databases were examined. A prognostic model based on prognostic scores (PSs) was established for predicting OS in TNBC patients, and the performance of the model was assessed by a recipient that operated on a distinctive curve. A total of 874 differentially expressed RNAs (DERs) were screened, among which 6 lncRNAs, 295 miRNAs and 573 mRNAs were utilized to construct targeted and coexpression ceRNA regulatory networks. Eight differentially expressed genes (DEGs) associated with survival prognosis, DBX2, MYH7, TARDBP, POU4F1, ABCB11, LHFPL5, TRHDE and TIMP4, were identified by multivariate Cox regression and then used to establish a prognostic model. Our study shows that the ceRNA network has a critical role in maintaining the aggressiveness of TNBC and provides comprehensive molecular-level insight for predicting individual mortality hazards for TNBC patients. Our data suggest that these prognostic mRNAs from the ceRNA network are promising therapeutic targets for clinical intervention.

scholarly journals Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Zhai ◽  
Bin Zhao ◽  
Yuzhen Wang ◽  
Lina Li ◽  
Jingjin Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Prediction Models ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Model Based

Abstract Background Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. Methods The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. Results In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. Conclusions In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

scholarly journals Identification of KIF23 as a prognostic signature for ovarian cancer based on large scale samples and clinical validation

2020 ◽  
Author(s):  
Yuexin Hu ◽  
Mingjun Zheng ◽  
Caixia Wang ◽  
Shuang Wang ◽  
Rui Gou ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Large Scale ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Chemotherapy Resistance ◽  
Survival Prognosis ◽  
Molecular Targeted Drug

Abstract Background: Ovarian cancer is one of the common malignant tumors in gynecology. Although the treatment strategy for ovarian cancer has been greatly improved in recent years, due to the metastasis, recurrence and drug resistance, the 5-year overall survival rate of patients is still less than 47%. However, at present, there is no specific markers for clinical application. The purpose of this study is to verify the expression and clinical significance of KIF23 in ovarian cancer and identify potential targets for the clinical treatment of ovarian cancer. Methods: The expression of KIF23 in ovarian cancer tissues and its relationship between survival prognosis and clinical pathological parameters were analyzed in Oncomine, GEO, and TCGA databases. KIF23 expression was analyzed by Kaplan-Meier plotter database and its relationship with chemo-resistance was studied. The molecular mechanism involved in KIF23 was analyzed from the perspective of gene mutation, copy number variation and other genomics. Finally, immunohistochemistry experiment was used to verify the expression of KIF2, and its relationship between the clinical pathological parameters and prognosis of ovarian cancer patients was analyzed by single factor and multivariate Cox regression models. Results: Bioinformatic and experimental results have demonstrated that KIF23 is highly expressed in ovarian cancer, and its high expression is positively correlated with poor prognosis. Overexpression of KIF23 can cause chemotherapy resistance in ovarian cancer and affect the overall survival of patients. Genomics analysis showed that KIF23 expression was associated with mutations such as FLG2 and TTN, and it was significantly enriched in tumor signaling pathways such as DNA replication and cell cycle. Conclusions: KIF23 can not only be used as a biomarker of poor prognosis in patients with various stages of ovarian cancer, but also be used as a molecular targeted drug and an independent prognostic biomarker for the treatment of ovarian cancer patients.

Propensity score matching analysis of ovarian cancer patients with multiple primary malignant neoplasms using multicenter real-world data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17501-e17501
Author(s):  
Qing-lei Gao ◽  
Xiaofei Jiao ◽  
Ruyuan Li ◽  
Shaoqing Zeng ◽  
Yingjun Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Cancer Patients ◽  
Cox Regression ◽  
Malignant Neoplasms ◽  
Cox Regression Analysis ◽  
Multiple Primary

e17501 Background: Multiple primary malignant neoplasms (MPMNs) in patients with ovarian cancer is rare and has not attracted enough attention. It is unclear how the MPMNs affect the prognosis of ovarian cancer (OC) patients. Methods: This is a multicenter retrospective analysis of 5, 268 ovarian cancer patients from six centers who was diagnosed with ovarian cancer from January 1, 1989 to August 21, 2020. Propensity score matching was used to balance the baseline characteristics between patients with and without MPMNs. Cox regression analysis was utilized to analyze the influence of MPMNs on overall survival (OS). Results: After excluding unqualified medical record, totally 4, 848 patients were analyzed and 240 were concurrent at least one MPMNs other than OC. Ten patients had two MPMNs and one patient had three. The most common concurrent cancer was breast cancer (111/240, 46.25%), followed by endometrial cancer (37/240, 15.42%), and cervical cancer (30/240, 12.50%). Patients with MPMNs were elder than those without MPMNs (52 vs. 51, P = 0.03) when ovarian cancer was diagnosed. And the proportion of early-stage cases was lower in patients with MPMNs (25.8% vs. 27.2%, P < 0.001). Patients with breast cancer had a higher proportion of high-grade serous ovarian cancer (HGSOC) than those without MPMNs. After using the propensity score matching method adjusting age, pathological type, grade, and stage, concurrent MPMNs, including breast cancer, had no effect on OS of ovarian cancer patients. Among 240 patients with MPMNs, patients with breast cancer shared similar age and stage compared with the rest patients, while their proportion of HGSOC was higher than patients with other cancer (68.4% vs. 51.1%, P = 0.028). However, the median OS of those two groups were similar (27.3 m vs.27.1 m, P = 0.744). In addition, 94 patients were diagnosed with breast cancer prior to ovarian cancer, seven diagnosed posteriorly to ovarian cancer, four diagnosed simultaneously, and six had no precise diagnosed dates. There was no remarkable difference in clinical characteristics between the prior and posterior groups, however, the median OS of those seven patients was significantly longer than the prior group (76.0 m vs. 25.4 m, P = 0.002). Conclusions: The MPMNs showed no influence on the overall survival of ovarian cancer patients. The order of diagnosis of ovarian cancer and breast cancer might affect the prognosis.

scholarly journals Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Yufei Yuan ◽  
Fanfan Guo ◽  
Ruoran Wang ◽  
Yidan Zhang ◽  
Guiqin Bai
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cox Regression ◽  
Lung Metastases ◽  
Newly Diagnosed ◽  
Discriminative Ability ◽  
Factors Affecting ◽  
Cox Regression Analysis

Abstract Purpose: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients. Methods: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan–Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P&lt;0.05) to determine the independent prognostic factors of synchronous lung metastases. Results: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases. Conclusion: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.

scholarly journals A robust eleven-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Jieping Yang ◽  
Xiao Xu ◽  
Yutao Wang ◽  
Meng Yu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
High Risk ◽  
B Cell ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cell Memory ◽  
Cox Regression Analysis ◽  
B Cell Memory

Abstract Background: Bladder cancer is the tenth most common cancer in the world, but existing biomarkers and prognostic models are limited.Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used selected genes to construct a prognostic model. Kaplan-Meier analysis, Receiver Operating Characteristic curve, and univariate and multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The model and the 11 genes have excellent performance in predicting overall survival and have been confirmed in 5 cohorts. The model's predictive ability is stronger than other clinical features and has practical significance in clinical application.Through the analysis of the weighted co-expression network, the gene module related to the model was found, and the key genes in this module were mainly enriched in the items related to the tumor microenvironment. When comparing the level of immune cell infiltration in high-risk samples, B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.Conclusion: The model we developed has strong stability and good performance and can stratify the risk of bladder cancer patients, to achieve individualized treatment.

scholarly journals Development and Validation of Nomograms to Predict Lung Metastases in Patients with Ovarian Cancer: A Large Cohort Study

2020 ◽  
Author(s):  
Yufei Yuan ◽  
Fanfan Guo ◽  
Ruoran Wang ◽  
Yidan Zhang ◽  
GuiQin Bai
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Survival Rate ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cox Regression ◽  
Validation Cohort ◽  
Lung Metastases ◽  
Overall Survival Rate ◽  
Newly Diagnosed

Abstract Background Lung metastasis, an independent risk factor affecting the prognosis of patients with ovarian cancer, is associated with poor survival. We tried to develop and validate a nomogram to predict the risk of lung metastases in newly diagnosed patients with ovarian cancer.Methods Patients diagnosed with ovarian cancer from the surveillance, epidemiology and final results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built based on logistic regression. The consistency index (C-index) was used to evaluate the discernment of the lung metastasis nomogram. Calibration plots was drawn to analyze the consistency between the observed probability and predicted probability of lung metastases in patients with ovarian cancer. The Kaplan-Meier method was used to estimate the overall survival rate, and the influencing factors were included in the multivariate Cox regression (P<0.05) to analyze the independent prognostic factors of lung metastases.Results A total of 16,059 eligible patients were randomly divided into training (n = 11242) and validation cohort (n = 4817). AJCC T, N stage, bone metastases, brain metastases and liver metastases were evaluated as predictors of lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was well calibrated and showed good discriminative ability. The C index is 0.761 (0.736-0.787) for the training cohort and 0.757（0.718-0.795）for the validation cohort. The overall survival rate of ovarian cancer patients with lung metastases was reduced. Mixed histological types, chemotherapy and primary site surgery were factors that affect the overall survival of ovarian cancer patients with lung metastases.Conclusion: The clinical prediction model had high accuracy and can be used to predict the lung metastasis risk of newly diagnosed patients with ovarian cancer, which can guide the treatment of patients with lung metastases.

scholarly journals A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Ye Liu ◽  
Zhixiang Qin ◽  
Hai Yang ◽  
Yang Gu ◽  
Kun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Risk Group ◽  
Differentially Expressed ◽  
Data Set ◽  
Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

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