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

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.

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

Frontiers in Medicine ◽

10.3389/fmed.2021.587496 ◽

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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Low expression of circular RNA hsa_circ_0078607 predicts poor prognosis in high-grade serous ovarian cancer

10.21203/rs.3.rs-121237/v1 ◽

2020 ◽

Author(s):

Nan Zhang ◽

Zhiyou Yang ◽

Yue Jin ◽

Shanshan Cheng ◽

Jiani Yang ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Patients ◽

Cancer Progression ◽

Poor Prognosis ◽

Cox Regression ◽

Prognostic Indicator ◽

High Grade ◽

Serous Ovarian Cancer ◽

Diagnosis And Prognosis ◽

Low Expression

Abstract Background Ovarian cancer remains one of the most lethal malignancies in women which is typically diagnosed at a late stage and has no effective screening strategy. It is essential to explore novel biomarkers for the diagnosis and prognosis of ovarian cancer, as well as therapeutic targets. Recent studies have shown that circRNAs participate in ovarian cancer progression by regulating various processes and being able to act as potential biomarkers for ovarian cancer diagnosis and prognosis. In the present study we aimed to explore the prognostic role of circ_0078607 in high-grade serous ovarian cancer. Results The expression of circ_0078607 in 49 high-grade serous ovarian cancer and adjacent non-cancerous tissue samples were detected by quantitative real-time polymerase chain reaction (qRT-PCR). We noticed that circ_0078607 expression was significantly downregulated in ovarian cancer tissues compared with adjacent non-cancerous tissues. Besides, patients with low circ_0078607 expression exhibited parameters associated with poor prognosis, including advanced FIGO stage and higher serum CA125 level. Kaplan-Meier survival curve analysis showed that both progression-free survival and overall survival were significantly shortened in patients with low circ_0078607 expression. Cox regression model analysis showed that low expression of circ_0078607 was an adverse prognostic indicator for high-grade serous ovarian cancer patients. Conclusions Low expression of circ_0078607 might be an adverse prognostic indicator for high-grade serous ovarian cancer patients.

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Comparison of Resected Malignant Tumors of the Right- and Left-Sided Colon—Is There a Difference?

Indian Journal of Surgery ◽

10.1007/s12262-021-03209-y ◽

2021 ◽

Author(s):

Adrienn Biró ◽

László Ternyik ◽

Krisztián Somodi ◽

Anna Dawson ◽

Eszter Csulak ◽

...

Keyword(s):

Colon Cancer ◽

Overall Survival ◽

Cancer Patients ◽

Survival Data ◽

Cox Regression ◽

Malignant Tumors ◽

Rank Test ◽

Cox Regression Analysis ◽

Study Results ◽

The Right

AbstractEmbryological, anatomical, and immunological differences between the right-sided and left-sided colons are well known, but the difference in oncological behavior of colon tumors has only recently become the main subject of studies. Published articles propose that there is a difference not only in symptoms, but also in survival. Our aim was to analyze the clinicopathological and oncological differences among our patients who had been operated for colon cancer in our department. We examined the historical data of our patients who underwent colon resection for malignancy between 1st of January 2016 and 31st of December 2018. Tumor markers, histological results, postoperative complications, and oncological therapies were investigated. The primary outcome was overall survival. We analyzed our patients’ survival data with Kaplan–Meier log-rank test and Cox regression analysis. In our study, 267 patients were enrolled. One hundred thirty-three (49.8%) patients had right-sided colon cancer; 134 (50.2%) patients had left-sided colon cancer. Patients with right-sided colon cancer were significantly more likely to have mucinous adenocarcinoma (p = 0.037). No significant differences were revealed in overall survival between right-sided colon cancer and left-sided colon cancer patients (p = 0.381). Additional subgroup analysis showed that there were no significant differences in overall survival for laterality neither in the metastatic group (p = 0.824) nor in the non-metastatic group (p = 0.345). Based on the conflicting previous study results, our findings repeatedly highlight that the relationship between tumor location in the colon and overall survival is not straightforward.

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Mining of Subtype Markers for the Prognosis of Ovarian Cancer based on Methylation Data

10.21203/rs.3.rs-16749/v1 ◽

2020 ◽

Author(s):

Lili Yin ◽

Ningning Zhang ◽

Qing Yang

Keyword(s):

Ovarian Cancer ◽

Cancer Patients ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtypes ◽

Cox Proportional Hazards Model ◽

Individualized Treatment ◽

The Other ◽

The Cancer Genome Atlas ◽

Significant Difference

Abstract Background: Ovarian cancer is one of three major malignancies involving the female reproductive system, and its morbidity and mortality are ranked number 3 and number 1 among gynecological tumors, respectively. DNA methylation (MET), as one of the main epigenetic modes, is closely related to the occurrence and development of ovarian cancer. To guide individualized treatment and improve the prognosis in ovarian cancer patients, it is of great significance to elucidate effective MET subtype markers.Methods: A total of 571 ovarian cancer MET samples were downloaded from the Cancer Genome Atlas (TCGA), and a COX proportional hazards model was established using the MET spectrum and clinically pathological parameters. Subsequently, the consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses was performed to screen the molecular subtypes, and these CpG loci were subjected to gene function annotation. Finally, CpG MET loci associated with poor prognosis in ovarian cancer patients were further screened by constructing a weighted gene co-expression network analysis (WGCNA).Results: A total of 250 prognosis-related MET loci were obtained by COX regression and 6 molecular subtypes were screened by clustering. There was a remarkable MET difference between most subtypes, of which Cluster 2 had the highest MET level and demonstrated the best prognosis in patients, while Cluster 4 and Cluster 5 had a MET level significantly lower than that of the other subtypes and demonstrated a very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of Stage IV samples in Cluster 4 was evidently greater than that in the other subtypes. Using the co-expression network, 5 CpG loci were eventually obtained: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. The clustering analysis shows that the prognosis in patients with hypomethylation was significantly worse than that in patients with hypermethylation. Conclusions: These MET molecular subtypes can be used not only to evaluate the prognosis in ovarian cancer patients but also to fully distinguish the tumor stage and histological grade in these patients. Prognosis-related CpG loci can be applied as biomarkers for individualized treatment in ovarian cancer patients.

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

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e17501 ◽

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.

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

Bioscience Reports ◽

10.1042/bsr20203089 ◽

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.

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

10.21203/rs.3.rs-46013/v1 ◽

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.

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Mining of Subtype Markers for the Prognosis of Ovarian Cancer based on Methylation Data

10.21203/rs.2.23311/v2 ◽

2020 ◽

Author(s):

Lili Yin ◽

Ningning Zhang ◽

Qing Yang

Keyword(s):

Ovarian Cancer ◽

Cancer Patients ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtypes ◽

Cox Proportional Hazards Model ◽

Individualized Treatment ◽

The Other ◽

The Cancer Genome Atlas ◽

Significant Difference

Abstract Aims: Ovarian cancer is one of three major malignancies involving the female reproductive system, and its morbidity and mortality are ranked number 3 and number 1 among gynecological tumors, respectively. DNA methylation (MET), as one of the main epigenetic modes, is closely related to the occurrence and development of ovarian cancer. To guide individualized treatment and improve the prognosis in ovarian cancer patients, it is of great significance to elucidate effective MET subtype markers. Methods: A total of 571 ovarian cancer MET samples were downloaded from the Cancer Genome Atlas (TCGA), and a COX proportional hazards model was established using the MET spectrum and clinically pathological parameters. Subsequently, the consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses was performed to screen the molecular subtypes, and these CpG loci were subjected to gene function annotation. Finally, CpG MET loci associated with poor prognosis in ovarian cancer patients were further screened by constructing a weighted gene co-expression network analysis (WGCNA). Results: A total of 250 prognosis-related MET loci were obtained by COX regression and 6 molecular subtypes were screened by clustering. There was a remarkable MET difference between most subtypes, of which Cluster 2 had the highest MET level and demonstrated the best prognosis in patients, while Cluster 4 and Cluster 5 had a MET level significantly lower than that of the other subtypes and demonstrated a very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of Stage IV samples in Cluster 4 was evidently greater than that in the other subtypes. Using the co-expression network, 5 CpG loci were eventually obtained: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. The clustering analysis shows that the prognosis in patients with hypomethylation was significantly worse than that in patients with hypermethylation. Conclusions: These MET molecular subtypes can be used not only to evaluate the prognosis in ovarian cancer patients but also to fully distinguish the tumor stage and histological grade in these patients. Prognosis-related CpG loci can be applied as biomarkers for individualized treatment in ovarian cancer patients.

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Mining of Subtype Markers for the Prognosis of Ovarian Cancer based on Methylation Data

10.21203/rs.2.23311/v1 ◽

2020 ◽

Author(s):

Lili Yin ◽

Ningning Zhang ◽

qing yang

Keyword(s):

Ovarian Cancer ◽

Cancer Patients ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtypes ◽

Cox Proportional Hazards Model ◽

Individualized Treatment ◽

The Other ◽

The Cancer Genome Atlas ◽

Significant Difference

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Clinical value evaluation of microRNA-324-3p and other available biomarkers in patients with HBV-infection-related hepatocellular carcinoma

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab108 ◽

2021 ◽

Author(s):

Li Zhao ◽

Qian Yang ◽

Jianbo Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Hepatitis B ◽

Diagnostic Performance ◽

Cox Regression ◽

Hbv Infection ◽

Healthy Controls ◽

Survival Prognosis ◽

Diagnosis And Prognosis ◽

Hcc Cells

Abstract Background Patients with hepatitis B virus (HBV) infection are at high risk of hepatocellular carcinoma (HCC). This study aimed to evaluate the expression of microRNA-324-3p (miR-324-3p) in HBV-related HCC, and explore the clinical significance of serum miR-324-3p and other available biomarkers in the diagnosis and prognosis of HBV-related HCC. Methods Expression of miR-324-3p in HBV-infection-related cells and patients was estimated using quantitative real-time PCR. The receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of serum miR-324-3p, AFP and PIVKA-II in the differentiation of HBV-related HCC from healthy controls and chronic hepatitis B (CHB). The relationship between serum miR-324-3p and patients’ clinical features was assessed using Chi-square test, and the value of miR-324-3p to predict overall survival prognosis was evaluated using Kaplan-Meier methods and Cox regression assay in patients with HBV-related HCC. Results HBV-related HCC cells had significantly increased miR-324-3p compared with normal and HBV-unrelated HCC cells, and serum miR-324-3p in HCC patients with HBV infection was also higher than that in healthy controls and CHB. Serum miR-324-3p had relatively high diagnostic accuracy for the screening of HCC case with HBV infection, and the combination of miR-324-3p, AFP and PIVKA-II showed the improved diagnostic performance. Additionally, high serum miR-324-2p in HBV-related HCC patients was associated with cirrhosis, tumor size, clinical stage and poor overall survival prognosis. Conclusion Serum increased miR-324-3p may be involved in the progression of HBV-related hepatitis to HCC, and may serve as a candidate biomarker for the diagnosis and prognosis of HBV-related HCC.

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