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Sun P, Wang X, Wang S, Jia X, Feng S, Chen J, Fang Y. Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks. IBRO Neurosci Rep 2024; 17:145-153. [PMID: 39206162 PMCID: PMC11350441 DOI: 10.1016/j.ibneur.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 07/22/2024] [Accepted: 07/30/2024] [Indexed: 09/04/2024] Open
Abstract
Background To construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods. Methods Datasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses. Results The final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571-1.000), sensitivity of 0.818 (95 % CI 0.533-1.000), AUC value of 0.832 (95 % CI 0.642-0.979), and accuracy of 0.792 (95 % CI 0.625-0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways. Conclusion Key genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.
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Affiliation(s)
- Ping Sun
- Qingdao Mental Health Center, Shandong 266034, China
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xiangwen Wang
- Qingdao Mental Health Center, Shandong 266034, China
- School of Mental Health, Research Institute of Mental Health,Jining Medical University, Shandong 272002, China
| | - Shenghai Wang
- Qingdao Mental Health Center, Shandong 266034, China
| | - Xueyu Jia
- Department of Medicine,Qingdao University, Shandong 266000, China
| | - Shunkang Feng
- Qingdao Mental Health Center, Shandong 266034, China
| | - Jun Chen
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China
| | - Yiru Fang
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China
- State Key Laboratory of Neuroscience, Shanghai Institue for Biological Sciences, CAS, Shanghai 200031, China
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Song Y, Yuan M, Wang G. Update value and clinical application of MUC16 (cancer antigen 125). Expert Opin Ther Targets 2023; 27:745-756. [PMID: 37584221 DOI: 10.1080/14728222.2023.2248376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/25/2023] [Accepted: 08/10/2023] [Indexed: 08/17/2023]
Abstract
INTRODUCTION The largest transmembrane mucin, mucin 16 (MUC16), contains abundant glycosylation sites on the molecular surface, allowing it to participate in various molecular pathways. When cells lose polarity and become cancerous, MUC16 is overexpressed, and more of the extracellular region (cancer antigen [CA]125) is released into serum and possibly, promote the development of diseases. Thus, MUC16 plays an indispensable role in clinical research and application. AREAS COVERED This review summarizes the update proposed role of MUC16 in carcinogenesis and metastasis. Most importantly, we prospect its potential value in targeted therapy after screening 1226 articles published within the last 10 years from PubMed. Two reviewers screened each record and each report retrieved independently. We have summarized the progress of MUC16/CA125 in basic research and clinical application, and predicted its possible future development directions. EXPERT OPINION As an important noninvasive co-factor in the diagnosis of gynecological diseases, MUC16 has been used for a long time, especially in the diagnosis and treatment of ovarian cancer. The overexpression of MUC16 plays a very obvious role in regulating inflammatory response, supporting immune suppression, and promoting the proliferation, division, and metastasis of cancer cells. In the next 20 years, there will be a luxuriant clinical application of MUC16 as a target for immune monitoring and immunotherapy.
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Affiliation(s)
- Yaan Song
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Medical Integration and Practice Center, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Ming Yuan
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Guoyun Wang
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
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Christie JR, Daher O, Abdelrazek M, Romine PE, Malthaner RA, Qiabi M, Nayak R, Napel S, Nair VS, Mattonen SA. Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests. J Med Imaging (Bellingham) 2022; 9:066001. [PMID: 36388142 PMCID: PMC9641263 DOI: 10.1117/1.jmi.9.6.066001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
Purpose We developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC). Approach We retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort ( n = 101 ), validated in the testing cohort ( n = 34 ) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan-Meier analysis. Results The model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 ( p < 0.005 ) and 0.60 ( p = 0.008 ), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training ( p < 0.005 ) and the testing ( p = 0.03 ) cohorts. Conclusions Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.
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Affiliation(s)
- Jaryd R. Christie
- Western University, Department of Medical Biophysics, London, Ontario, Canada
- London Regional Cancer Program, Baines Imaging Research Laboratory, London, Ontario, Canada
| | - Omar Daher
- Western University, Department of Medical Imaging, London, Ontario, Canada
| | - Mohamed Abdelrazek
- Western University, Department of Medical Imaging, London, Ontario, Canada
| | - Perrin E. Romine
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, Washington, United States
- University of Washington School of Medicine, Division of Medical Oncology, Seattle, Washington, United States
| | - Richard A. Malthaner
- Western University, Division of Thoracic Surgery, Department of Surgery, London, Ontario, Canada
| | - Mehdi Qiabi
- Western University, Division of Thoracic Surgery, Department of Surgery, London, Ontario, Canada
| | - Rahul Nayak
- Western University, Division of Thoracic Surgery, Department of Surgery, London, Ontario, Canada
| | - Sandy Napel
- Stanford University, Department of Radiology, Stanford, California, United States
| | - Viswam S. Nair
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, Washington, United States
- University of Washington School of Medicine, Division of Pulmonary and Critical Care Medicine, Seattle, Washington, United States
| | - Sarah A. Mattonen
- Western University, Department of Medical Biophysics, London, Ontario, Canada
- London Regional Cancer Program, Baines Imaging Research Laboratory, London, Ontario, Canada
- Western University, Department of Oncology, London, Ontario, Canada
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He T, Li J, Wang P, Zhang Z. Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma. Comput Struct Biotechnol J 2022; 20:2352-2359. [PMID: 35615023 PMCID: PMC9123088 DOI: 10.1016/j.csbj.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/24/2022] Open
Abstract
Background The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD). Methods Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms. Results Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset. Conclusions The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.
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Zhang Z, Huang L, Li J, Wang P. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system. BMC Bioinformatics 2022; 23:124. [PMID: 35395711 PMCID: PMC8991575 DOI: 10.1186/s12859-022-04657-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/11/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms. Methods The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model. Results Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis. Conclusions The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04657-3.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
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Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model. Chin Med J (Engl) 2021; 134:1701-1708. [PMID: 34133353 PMCID: PMC8318661 DOI: 10.1097/cm9.0000000000001539] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background: The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm. Methods: The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models. Results: Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients. Conclusions: The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.
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He T, Huang L, Li J, Wang P, Zhang Z. Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System. Front Med (Lausanne) 2021; 8:587496. [PMID: 34109184 PMCID: PMC8180546 DOI: 10.3389/fmed.2021.587496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
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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Affiliation(s)
- Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
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Garcia-Montemayor V, Martin-Malo A, Barbieri C, Bellocchio F, Soriano S, Pendon-Ruiz de Mier V, Molina IR, Aljama P, Rodriguez M. Predicting mortality in hemodialysis patients using machine learning analysis. Clin Kidney J 2020; 14:1388-1395. [PMID: 34221370 PMCID: PMC8247746 DOI: 10.1093/ckj/sfaa126] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 06/10/2020] [Indexed: 12/18/2022] Open
Abstract
Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.
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Affiliation(s)
| | - Alejandro Martin-Malo
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain.,RETICs-REDinREN (National Institute of Health Carlos III), Madrid, Spain
| | - Carlo Barbieri
- Fresenius Medical Care Italia, Vaiano Cremasco, Cremona, Italy
| | | | - Sagrario Soriano
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain
| | - Victoria Pendon-Ruiz de Mier
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain
| | - Ignacio R Molina
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain
| | - Pedro Aljama
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain
| | - Mariano Rodriguez
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain.,RETICs-REDinREN (National Institute of Health Carlos III), Madrid, Spain
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Zhang Y, Huang J, Zou Q, Che J, Yang K, Fan Q, Qian D, Wu J, Bao E, Song L, Zhang F. Methylated PTGER4 is better than CA125, CEA, Cyfra211 and NSE as a therapeutic response assessment marker in stage IV lung cancer. Oncol Lett 2020; 19:3229-3238. [PMID: 32256818 PMCID: PMC7074558 DOI: 10.3892/ol.2020.11434] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 01/30/2020] [Indexed: 12/11/2022] Open
Abstract
Real-time assessment of therapeutic response in patients with advanced lung cancer presents a major challenge throughout the treatment process. Currently, computed tomography imaging is often used; however, it is radiation-based and hysteretic and is not suitable for repeated use as a real-time assessment. Blood biomarkers represent a novel solution for assessing therapeutic response in patients with advanced lung cancer. In the present study, the efficacy of a methylation marker [methylated prostaglandin E receptor 4 (mPTGER4)] and four protein markers [carcinoma antigen 125 (CA125), carcinoembryonic antigen (CEA), cytokeratin 19-fragments (cyfra21-1) and neuron-specific enolase (NSE)] were simultaneously evaluated to determine their potential in facilitating therapeutic response monitoring as well as their prognostic values in patients with stage IV lung cancer. The results indicated that, following treatment, the blood levels of methylated PTGER4 and NSE had significantly decreased, and mPRGER4, CA125, CEA and NSE exhibited a significant decrease in percentage level. Since mPTGER4 exhibited a higher rate of positive detection prior to therapy, and a greater response of sensitivity to therapy compared to the protein markers, it may represent an improved marker for the monitoring of therapeutic response. The efficacy of the markers in predicting the overall survival (OS) rate of patients with stage IV lung cancer was also assessed. Results from the follow-up of patients (up to 891 days) revealed that the blood levels of mPTGER4, CA125 and NSE before treatment were able to predict overall survival (OS) rate. Additionally, the percentage change in expression levels of CA125, CEA and NSE was also able to predict the OS rate. In conclusion, the present results indicate that mPTGER4 represents an improved biomarker for monitoring therapeutic efficacy compared with CA125, CEA, Cyfra21-1 and NSE. In predicting the long-term survival of patients with stage IV lung cancer; however, the pre-treatment levels of mPTGER4, CA125 and NSE and the percentage changes of CA125, CEA and NSE may be used as the markers.
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Affiliation(s)
- Yunxia Zhang
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Jianfeng Huang
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Qinzhou Zou
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Jun Che
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Kaihua Yang
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Qiang Fan
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Danqi Qian
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Jia Wu
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Erwen Bao
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
| | - Lele Song
- Department of Radiotherapy, The Eighth Medical Center of The Chinese People's Liberation Army General Hospital, Haidian, Beijing 100091, P.R. China.,HaploX Biotechnology, Co., Ltd., Shenzhen, Guangdong 518057, P.R. China
| | - Fuzheng Zhang
- Department of Radiotherapy, The Affiliated Hospital of Jiangnan University, Binhu, Wuxi, Jiangsu 214062, P.R. China
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Zhang Z, Li J, He T, Ding J. Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms. Front Oncol 2020; 10:330. [PMID: 32296631 PMCID: PMC7137378 DOI: 10.3389/fonc.2020.00330] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022] Open
Abstract
An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jianqiang Ding
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
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Majmudar K, Golemi I, Tafur AJ, Toro JD, Visonà A, Falgá C, Sahuquillo JC, Lorente MA, Tufano A, Weinberg I, Di Micco P, Monreal M. Outcomes after venous thromboembolism in patients with gastric cancer: Analysis of the RIETE Registry. Vasc Med 2020; 25:210-217. [PMID: 32000631 DOI: 10.1177/1358863x19893432] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Gastric cancer is the fifth most common malignancy worldwide. Venous thromboembolism is an independent predictor of death among patients with gastric cancer. We aimed to describe the factors associated with mortality, thrombosis recurrence, and bleeding complications in patients with gastric cancer who develop venous thromboembolism. We included 612 patients with gastric cancer and venous thromboembolism in the Registro Informatizado de la Enfermedad TromboEmbólica (RIETE) registry from 2001 to 2018. We used Cox proportional hazard ratios and a Fine-Gray model to define factors associated with outcomes. The overall mortality at 6 months was 44.4%. Factors associated with increased 6-month mortality included immobility (HR 1.8, 95% CI 1.3-2.4; p < 0.001), anemia (HR 1.4, 95% CI 1.1-1.8; p < 0.02), and leukocytosis (HR 1.8, 95% CI 1.4-2.3; p < 0.001). Recurrent thrombosis occurred in 6.5% of patients and major bleeding complications in 8.5% of the cohort. Male sex was the main factor associated with thrombosis recurrence (HR 2.1, 95% CI 1.1-4.0; p < 0.02) and hemoglobin below 10 g/dL (HR 1.6, 95% CI 1.05-2.50; p = 0.03) the main factor associated with bleeding. In conclusion, patients with gastric cancer who develop venous thrombosis have a very high likelihood of death. Low hemoglobin in this population is associated with poor outcomes.
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Affiliation(s)
- Kaushal Majmudar
- Department of Medicine, Division of Internal Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | - Iva Golemi
- Department of Medicine, Division of Internal Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | - Alfonso J Tafur
- Department of Medicine, Division of Vascular Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jorge Del Toro
- Department of Internal Medicine, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Adriana Visonà
- Department of Vascular Medicine, Ospedale Castelfranco Veneto, Castelfranco Veneto, Italy
| | - Conxita Falgá
- Department of Internal Medicine, Hospital de Mataró, Barcelona, Spain
| | | | | | - Antonella Tufano
- Department of Clinical Medicine and Surgery, Regional Reference Centre for Coagulation Disorders, Federico II University Hospital, Naples, Italy
| | - Ido Weinberg
- Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pierpaolo Di Micco
- Department of Medicine, UOC Medicina, Fatebenefratelli Hospital of Napoli, Italy
| | - Manuel Monreal
- Department of Internal Medicine, Hospital Germans Trias i Pujol, Universidad Autónoma de Barcelona, Barcelona, Spain
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