scholarly journals Incidence and Prognostic Significance of Liver Metastases for Newly Diagnosed Ovarian Cancer in Relation to Subtypes

2021 ◽  
Author(s):  
Ying Zhu ◽  
Yifang Zhang ◽  
Lingyun Zhai ◽  
Zhigang Zhang ◽  
Jianwei Zhou
Keyword(s):  
Ovarian Cancer ◽  
Liver Metastasis ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Newly Diagnosed ◽  
Histological Types ◽  
Visceral Organs

Abstract Background: Ovarian cancer is a heterogeneous and aggressive malignant tumor, and the liver is one of the most common metastases target visceral organs of ovarian cancer. We aim to analysis the incidence and prognostic relevance of histological subtypes for patients with liver metastases in newly diagnosed ovarian cancer. Methods: In the Surveillance, Epidemiology, and End Results (SEER) database, we identified the ovarian cancer patients from 2010 to 2016. Multivariable logistic regression was used to determine whether histological types were associated with the presence of liver metastases at diagnosis. The Kaplan-Meier method and multivariable Cox regression was performed to identify covariates associated with survival using the histological types.Results: Among 25293 ovarian cancer patients, 1749 cases presented with liver metastases. The incidence proportions were highest among ovarian carcinosarcoma patients (OR=17.76, 95% CI=9.26-34.09), and liver metastasis specificity was the highest in the clear cell type (70.69% of the metastatic subset). The median cancer-specific survival (CSS) for non-metastatic ovarian cancer patients was 77 months, but the ovarian cancer with only liver metastasis was 21 months. The mucinous (5 months; vs nonepithelial subtype, HR=0.26; 95% CI, 0.14-0.49) subtype experienced the shortest median survival among all histologic types.Conclusion: This population-based study provides that liver was one of the most common distant visceral organs for ovarian cancer metastasis, and the incidence proportions of liver metastasis were highest for carcinosarcomas subtype, and the mucinous ovarian cancer with liver metastasis being associated with the poorest survival.

scholarly journals Incidence and Prognostic Significance of Liver Metastases for Newly Diagnosed Ovarian Cancer in Relation to Subtypes

2021 ◽  
Author(s):  
Ying Zhu ◽  
Yifang Zhang ◽  
Lingyun Zhai ◽  
Zhigang Zhang ◽  
Jianwei Zhou
Keyword(s):  
Ovarian Cancer ◽  
Liver Metastasis ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Newly Diagnosed ◽  
Histological Types ◽  
Visceral Organs

Abstract Background: Ovarian cancer is a heterogeneous and aggressive malignant tumor, and the liver is one of the most common metastases target visceral organs of ovarian cancer. We aim to analysis the incidence and prognostic relevance of histological subtypes for patients with liver metastases in newly diagnosed ovarian cancer. Methods: In the Surveillance, Epidemiology, and End Results (SEER) database, we identified the ovarian cancer patients from 2010 to 2016. Multivariable logistic regression was used to determine whether histological types were associated with the presence of liver metastases at diagnosis. The Kaplan-Meier method and multivariable Cox regression was performed to identify covariates associated with survival using the histological types. Results: Among 25293 ovarian cancer patients, 1749 cases presented with liver metastases. The incidence proportions were highest among ovarian carcinosarcoma patients (OR=17.76, 95% CI=9.26-34.09), and liver metastasis specificity was the highest in the clear cell type (70.69% of the metastatic subset). The median cancer-specific survival (CSS) for non-metastatic ovarian cancer patients was 77 months, but the ovarian cancer with only liver metastasis was 21 months. The mucinous (5 months; vs nonepithelial subtype, HR=0.26; 95% CI, 0.14-0.49) subtype experienced the shortest median survival among all histologic types. Conclusion: This population-based study provides that liver was one of the most common distant visceral organs for ovarian cancer metastasis, and the incidence proportions of liver metastasis were highest for carcinosarcomas subtype, and the mucinous ovarian cancer with liver metastasis being associated with the poorest survival.

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<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 Frequency, prognosis and treatment modalities of newly diagnosed small bowel cancer with liver metastases

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaorong Ye ◽  
Lifu Wang ◽  
Yongjun Xing ◽  
Chengjun Song
Keyword(s):  
Small Bowel ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Cox Regression ◽  
Bowel Cancer ◽  
Treatment Modalities ◽  
Diagnostic Model ◽  
Newly Diagnosed ◽  
Small Bowel Cancer ◽  
Good Ability

Abstract Background Population-based analysis for the liver metastases of small bowel cancer is currently lacking. This study aimed to analyze the frequency, prognosis and treatment modalities for newly diagnosed small bowel cancer patients with liver metastases. Methods Patients with small bowel cancer diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Binary logistic regression analysis was performed to determine predictors for the presence of liver metastases at diagnosis. Kaplan–Meier method and Cox regression analyses were performed for survival analyses. Results A total of 1461 small bowel cancer patients with liver metastases at initial diagnosis were identified, representing 16.5% of the entire set and 63.9% of the subset with metastatic disease to any distant site. Primary tumor with poorer histological type, larger tumor size, later N staging, more extrahepatic metastatic sites, and tumor on lower part of small intestine had increased propensity of developing liver metastases. The combined diagnostic model exhibited acceptable diagnostic efficiency with AUC value equal to 0.749. Patients with liver metastases had significant poorer survival (P < 0.001) than those without liver metastases. In addition, combination of surgery and chemotherapy (HR = 0.27, P < 0.001) conferred the optimal survival for patients with adenocarcinoma, while the optimal treatment options for NEC and GIST seemed to be surgery alone (HR = 0.24, P < 0.001) and chemotherapy alone (HR = 0.08, P = 0.022), respectively. Conclusions The combined predictor had a good ability to predict the presence of liver metastases. In addition, those patients with different histologic types should be treated with distinct therapeutic strategy for obtaining optimal survival.

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 Nomogram Models for Predicting Risk and Prognosis of Newly Diagnosed Ovarian Cancer Patients with Liver Metastases - A Large Population-Based Real-World Study

2021 ◽  
Vol 12 (24) ◽  
pp. 7255-7265
Author(s):  
Gui-Min Hou ◽  
Chuang Jiang ◽  
Jin-peng Du ◽  
Chang Liu ◽  
Xiang-zheng Chen ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Real World ◽  
Large Population ◽  
Population Based ◽  
Newly Diagnosed

scholarly journals Alternative splicing acts as an independent prognosticator in ovarian carcinoma

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.

scholarly journals Evaluation of Prognostic and Predictive Significance of Circulating MicroRNAs in Ovarian Cancer Patients

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ann Rita Halvorsen ◽  
Gunnar Kristensen ◽  
Andy Embleton ◽  
Cybil Adusei ◽  
Maria Pilar Barretina-Ginesta ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Prognostic Significance ◽  
Response To Treatment ◽  
Rank Test ◽  
Specific Marker ◽  
High Grade ◽  
Standard Chemotherapy ◽  
Validation Set ◽  
Serous Tumors

Ovarian cancer patients are recognized with poor prognosis. This study aimed to identify microRNAs in plasma for predicting response to treatment and outcome. We have investigated microRNAs in plasma from ovarian cancer patients enrolled in a large multicenter study (ICON7), investigating the effect of adding bevacizumab to standard chemotherapy in patients diagnosed with epithelial ovarian cancer. Patients with different histology, grade, and FIGO stages were included (n=207) in this study. Screening of 754 unique microRNAs was performed in the discovery phase (n=91) using TaqMan Low Density Arrays. The results were validated using single assays and RT-qPCR. Low levels of miR-200b, miR-1274A (tRNALys5), and miR-141 were significantly associated with better survival, confirmed with log-rank test in the validation set. The level of miR-1274A (tRNALys5) correlated with outcome was especially pronounced in the high-grade serous tumors. Interestingly, low level of miR-200c was associated with 5-month prolongation of PFS when treated with bevacizumab compared to standard chemotherapy. We found prognostic significance of miR-200b, miR-141, and miR-1274A (tRNALys5) in all histological types, where miR-1274A (tRNALys5) may be a specific marker in high-grade serous tumors. The level of miR-200c may be predictive of effect of treatment with bevacizumab. However, this needs further validation.

Expression and Clinical Significance of Serum Exosomal miR-375 in Patients with Gastric Cancer

2021 ◽  
Vol 7 (5) ◽  
pp. 3896-3904
Author(s):  
Daoting Deng ◽  
Hong Zhang ◽  
Junxi Liu ◽  
Lina Ma ◽  
Xinrui Lei ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cox Regression Analysis ◽  
Healthy Group ◽  
Cancer Group ◽  
Kaplan Meier ◽  
Gastric Cancer Patients ◽  
Gastric Cancer Group

To explore exosomal miR-375 expression in gastric cancer patients and its relationship with patient prognosis. A total of 53 patients diagnosed with gastric cancer in our hospital from May 2014 to May 2016 were included as the gastric cancer group, and 46 healthy women who came to our hospital for physical examination during the same period were enrolled as the healthy group. Exosomal miR-375 expression level was detected using qRT-PCR, and the diagnostic performance and prognostic significance of exosomal miR-375 in gastric cancer were explored. The gastric cancer group showed increased exosomal miR-375 expression than the healthy group (P< 0.05); Kaplan-Meier survival analysis exhibited that serum exosomal miR-375 has an AUC of 0.778, sensitivity of 69.57%, and specificity of 75.47%, whereas Cox regression analysis showed that the miR-375 expression in exosomes was an independent risk factor affecting the prognosis of gastric cancer patients (P< 0.05). Patient with gastric cancer showed upregulated miR-375 expression in serum exosomes. Serum exosomal miR-375 was found to has positive sensitivity and specificity in the diagnosis of gastric cancer, which may be associated with poor prognosis of gastric cancer patients.

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.

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