Survival Analysis
Recently Published Documents





2021 ◽  
Shiqi Miao ◽  
Jing Song ◽  
Teng Wang ◽  
Qingmao Rao ◽  
Yongyao Tang ◽  

Abstract Background : Metastasis of clear cell renal cell carcinoma (ccRCC) is an important cause of death. The purpose of this study was to study the key gene in the process of tumor metastasis of ccRCC. Methods : Expression profiles of metastatic ccRCC and primary ccRCC were downloaded from the GEO database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. The R limma package was used to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted for overlapping DEGs. Verification of mRNA expression and survival analysis in the GEPIA2 database further identified the key gene. The expression of the key gene in clinical specimens was detected by quantitative real-time PCR (qRT-PCR). Univariate and multivariate cox analysis were performed to determine whether the key gene was independent prognostic factor. Gene Set Enrichment Analysis (GSEA) was used to identify HSD11B2 related signaling pathways. The correlations between the key gene and tumor immune infiltrates were investigated via TISIDB database. Results :A total of 20 DEGs were screened from GSE22541, GSE85258, and GSE105261 datasets. Enrichment analysis indicated that the DEGs were mainly enriched in the extracellular matrix organization, collagen-containing extracellular matrix, extracellular matrix structural constituent, and protein digestion and absorption. Verification of mRN expression and survival analysis identified the key gene HSD11B2. qRT-PCR results showed A that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. Multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor for ccRCC patients. GSEA enrichment results showed that low expression of HSD11B2 could enrich cancer signaling pathways such as Nod-like receptor signaling pathway, cytoplasmic DNA sensing pathway and P53 signaling pathway. Immune analysis showed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. Conclusions : These findings suggest that HSD11B2 may play a key role in the metastasis of ccRCC, and HSD11B2 is correlated with prognosis and tumor immune infiltrates.

2021 ◽  
Vol 2 (4) ◽  
pp. 1-18
Zhaohong Sun ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Kunlun He ◽  
Zhengxing Huang

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.

2021 ◽  
Feng-Wei Guo ◽  
Hong Chen ◽  
Ya-Ling Dong ◽  
Jia-nan Shang ◽  
Li-tao Ruan ◽  

Abstract Objectives: The purpose of this study was to explore the value of the LIMAV measured by ultrasound before CABG in predicting the prognosis of patients after LIMA bypass grafting. Methods: 104 patients who underwent CABG with LIMA as the bridge vessel in the cardiovascular surgery department of our hospital between May 2018 and June 2019 were selected. All patients underwent transthoracic Doppler ultrasonography to measure LIMAV preoperatively. Intraoperatively, MGF and PI of the LIMA bridge were measured using TTFM. The primary endpoint event in this study was cardiac death within 18 months after surgery. Results: The Cox survival analysis showed that the MGF , the LIMAV and LVEF were risk factors for death after CABG . The cut-offs of MGF ,LIMAV and LVEF for the prediction of death after CABG were ≤14 ml/min [AUC: 0.830; Sensitivity:100%; Specificity: 65.6%], ≤60cm/s (AUC: 0.759; Sensitivity:65.5%; Specificity:85.3%) and ≤44%(AUC:0.724; Sensitivity:50%; Specificity: 88.5% )respectively. Compared with the use of MGF, MGF+LIMAV, combination of the MGF+LIMAV+LVEF (AUC:0.929; Sensitivity:100%; Specificity: 81.1%)resulted in a stronger predictive value(MGF vs MGF+LIMAV+LVEF: p=0.02). Conclusion: LIMAV measured by transthoracic ultrasound pre-operation combined with intraoperative MGF and LVEF may have a greater value in predicting patients' risk of cardiac death after CABG.

2021 ◽  
Alejandro Celemín ◽  
Diego A. Estupiñan ◽  
Ricardo Nieto

Abstract Electrical Submersible Pumps reliability and run-life analysis has been extensively studied since its development. Current machine learning algorithms allow to correlate operational conditions to ESP run-life in order to generate predictions for active and new wells. Four machine learning models are compared to a linear proportional hazards model, used as a baseline for comparison purposes. Proper accuracy metrics for survival analysis problems are calculated on run-life predictions vs. actual values over training and validation data subsets. Results demonstrate that the baseline model is able to produce more consistent predictions with a slight reduction in its accuracy, compared to current machine learning models for small datasets. This study demonstrates that the quality of the date and it pre-processing supports the current shift from model-centric to data-centric approach to machine and deep learning problems.

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Zhu Wenbo ◽  
Zhao Qing ◽  
Wang Li ◽  
Zhu Hangju ◽  
Zhang Junying ◽  

Introduction. Distinct from other diseases, as cancer progresses, both the symptoms and treatments evolve, resulting in a complex, time-dependent relationship. Many competing risk factors influence the outcome of cancer. An improved method was used to evaluate the data from 6 non-small-cell lung cancer (NSCLC) clinical trials combined in our center since 2016 to deal with the bias caused by competing risk factors. Material and Methods. Data of 118 lung cancer patients were collected from 2016 to 2020. Fine and Gray’s model for competing risk was used to evaluate survival of different treatment group compares with the classic survival analysis model. Results. Immunotherapy had better progression-free survival than chemotherapy. (HR: 0.62, 95% CI: 0.41-0.95, p = 0.0260 ). However, there were no significant differences in patients who withdrew due to treatment-related adverse events from different groups. ( Z = 0.0508 , p = 0.8217 ). The PD-1/PD-L1 inhibitors in our study did not significantly improve overall survival compared with chemotherapy (HR:0.77, 95% CI:0.48-1.24, p = 0.2812 ), estimated 1-year overall survival rates were 55% and 46%, and 3-year overall survival rates were 17% and 10%, respectively. Conclusion. When the outcome caused by competing risk exists, the corresponding competing risk model method should be adopted to eliminate the bias caused by the classic survival analysis.

2021 ◽  
Vol 157 ◽  
pp. 106861
Joel D. Schwartz ◽  
Ma'ayan Yitshak-Sade ◽  
Antonella Zanobetti ◽  
Qian Di ◽  
Weeberb J. Requia ◽  

2021 ◽  
lin fang li ◽  
Yao liu ◽  
mu hu chen ◽  
ying chun hu

Abstract Objective This study applies the data independent acquisition (DIA) technique combined with bioinformatics to identify differential proteins in sepsis patients and performed ELISA method to validate the candidate protein of clinical value, in an attempt to find new biomarkers for the diagnosis and prognosis of sepsis. Methods Blood samples from sepsis patients (Sepsis group, n = 50) and healthy individuals (NC group, n = 10) were collected from Affiliated Hospital of Southwest Medical University. Mass spectrometry analysis was designed for 22 sepsis samples (randomly selected) and 10 healthy controls by DIA method, and the obtained differential proteins were subjected to GO annotation, meta-analysis and survival analysis to identify the candidate biomarker protein. ELISA was applied to validate the protein expression in original cohorts. ROC curves based on ELISA data were plotted to discuss the diagnostic and prognostic performance of the candidate protein and several clinical indexes, including C-reactive protein (CRP), procalcitonin (PCT) and lactate (Lac). Results DIA data showed that there were 142 differential proteins in the Sepsis group versus the NC group, comprising 36 down-regulated and 106 up-regulated. GO annotation revealed that the differential proteins were significantly enriched in the biological functions involved in immune response, response to stress, inflammatory response, and cell activation. The top 11 proteins with the greatest difference were found according to the p-values in DIA (FUCO2, MGAT1, OAF, AACT, TFRC, CCL14, EXTL2, KLKB1, TETN,CRP,SAA1). Meta-analysis identified significant differential expression of TFRC in the NC versus Sepsis and in the Survival versus Non-survival groups based on GEO database. Survival analysis revealed that the low expression of TFRC indicated a higher survival rate in sepsis patients. ELISA found TFRC concentration in collected clinical samples were significant differential in the NC versus Sepsis and in the Survival versus Nonsurvival groups (p < 0.05). ROC curves gave an AUC of 0.790 for TFRC in distinguishing the normal individuals and sepsis patients, showing good diagnostic performance. Besides, the AUC for TFRC in distinguishing the survivors and deaths was 0.744, indicating good prognostic performance, which was superior to PCT, CRP and Lac. Conclusion This study identified TFRC through DIA, bioinformatics and ELISA analyses, which showed differential expression in sepsis patients as well as good diagnostic and prognostic value. TFRC is expected to be a potential biomarker for sepsis.

2021 ◽  
Vol 20 (5) ◽  
pp. 2980
S. A. Shalnova ◽  
O. M. Drapkina ◽  
A. V. Kontsevaya ◽  
E. B. Yarovaya ◽  
V. A. Kutsenko ◽  

Aim. As part of a pilot study, to investigate the potential significance of cardiac troponin I (cTnI) in assessing the risk of cardiovascular diseases (CVD) in general population aged 35-64 years of one of the regions from the ESSE-RF study.Material and methods. The study is based on the ESSE-RF observational prospective study using a sample from one Russian region. The analysis included socio-demographic variables, risk factors, history of CVD. The cTnI level was measured from November to December 2021 in serum samples stored at -70° C using high sensitivity chemiluminescent microparticle immunoassay using Architect Stat High Sensitivity Troponin I (Abbott) reagents on an Architect i2000SR immunoassay analyzer (Abbott, Abbot Park IL USA). The endpoints were hard (cardiovascular death and myocardial infarction) and composite endpoints (cardiovascular death, new cases of myocardial infarction, stroke, coronary artery disease and revascularization). The median follow-up was 5,5 years. In total, the analysis included 1120 people aged 35-64 years.Results. Analysis of the associations between Systematic Coronary Risk Evaluation (SCORE) and cTnI showed a significant difference in risk stratification for these two parameters. In women from cTnI-related high-risk category for cardiovascular events (CVE), there were no endpoints at all. In men of moderate and high risk, the proportion of endpoints increases with increasing cTnI-related risk. The survival curves corresponding to first 3 quintiles of cTnI risk distribution did not diverge, and, therefore, the number of CVEs in these groups did not differ. At the same time, the curves corresponding to 4th and 5th quintiles significantly differed from the first 3 quintiles, which indicates a higher CVE risk in subjects from these groups (p<0,001). Considering that there were only 3 endpoints in cTnI-related high-risk group, a survival analysis was performed for low-risk versus moderate-high risk. The curves obtained diverge significantly (p=0.006). Cox proportional hazards models were analyzed to assess the relationship between the cTnI level and endpoints. It was shown that cTnI itself or its logarithm is significantly associated with hard and composite endpoints. The cTnI cut-off point of 12/10 pg/ml (males/females) was associated with hard endpoint, and 6/4 pg/ml — with composite one. It should be noted that the recommended cut-off point of 6/4 pg/ml is close to the upper quartile of cTnI distribution in the European population. For the Russian population, the upper quartile corresponds to cTnI level of 3,5/2,1 pg/ml, which indicates the need to reduce the critical cTnI values in Russia. To assess risk reclassification, Cox models were analyzed using the Net Reclassification Index (NRI), as well as NRIsurvival for survival analysis. For categorical variables, the NRIcategorial was used. Both methods of including cTnI in the model significantly improve the risk classification of severe endpoints in men.Conclusion. The results obtained confirm the need to lower the threshold values for predicting combined endpoints, in particular, in Russian men. cTnl has an independent effect on CVE risk and its addition to SCORE improves the prediction of CVEs among men. However, the data obtained are preliminary and require clarification sing larger sample. At the same time, it is obvious that the determination of cТnI level can play a significant role in cardiovascular risk assessment and be an unfavorable prognosis marker among Russian population.

2021 ◽  
JiaJie Lu ◽  
Rihong Huang ◽  
Haojian Wang ◽  
Yuecheng Peng ◽  
Yongyang Fan ◽  

Abstract BackgroundDespite emerging evidence revealed the remarkable roles of Protein Phosphatase 1 Regulatory Inhibitor Subunit 14A (PPP1R14A) in cancer tumorigenesis and progression, no pan-cancer analysis is available. Our research, for the first time, comprehensively investigated the potential carcinogenic mechanism of PPP1R14A across 33 tumors using bioinformatic techniques. MethodsTCGA datasets and the CPTAC datasets embedded in UALCAN were first applied to study the differential expression of PPP1R14A in various cancer types at the transcription and protein levels, respectively. Besides, we also conducted relevant prognostic survival analysis and used the GEPIA2 database to explore the association between PPP1R14A expression and pathological stages. In addition, cBioPortal and UALCAN databases were employed to analyze the genetic alterations and post-transcriptional phosphorylation of PPP1R14A. Then based on TCGA, we analyzed the relationship between PPP1R14A and immune infiltration, the correlation with tumor mutational burden (TMB), microsatellite instability (MSI) and immune checkpoint molecules (ICMs), and whether it is expected to be a predictive indicator in cancer patients, which was achieved by receiver operating characteristic (ROC) curve. Finally, STRING, GEPIE2 and TIMER2.0 databased were used to explore the potential mechanism of PPP1R14A in cancer and find molecules that have potential close interactions with PPP1R14A.ResultsPPP1R14A is down-expressed in major malignancies and there is a significant correlation between the PPP1R14A expression and the prognosis of patients. Pan-cancer survival analysis indicated that the high expression of PPP1R14A in most cases was associated with poor overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) across patients with various malignant tumors, containing adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA). The results of ROC analysis subsequently exhibited that the molecule has a high reference significance in diagnosing a variety of cancers. Besides, we detected that the frequency of PPP1R14A genetic changes including genetic mutations and copy number alterations (CNAs) in uterine carcinosarcoma reached 16.07%, and these alterations brought misfortune to the survival and prognosis of cancer patients. In addition, the methylation within the promoter region of PPP1R14A DNA was enhanced in a majority of cancers. Downregulated phosphorylation levels of phosphorylation sites including S26, T38, etc. in most cases took place in several tumors, such as breast cancer, colon cancer, etc. PPP1R14A remarkably correlated with the levels of infiltrating cells and immune checkpoint genes. ConclusionsOur research summarized and analyzed the carcinogenic effect of PPP1R14A in different tumors comprehensively and provided a theoretical basis for promising therapeutic and immunotherapy strategies.

Sign in / Sign up

Export Citation Format

Share Document