Development and validation of a ferroptosis-related model for three digestive tract tumors based on a pan-cancer analysis

Epigenomics ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 1497-1514
Author(s):  
Zhongjing Zhang ◽  
Percy David Papa Akuetteh ◽  
Leilei Lin ◽  
Yiyang Wu ◽  
Yimeng Li ◽  
...  
Keyword(s):  
Digestive Tract ◽  
Cox Regression ◽  
Survival Rates ◽  
Operator Method ◽  
Cox Regression Analysis ◽  
Predictor Model ◽  
Selection Operator ◽  
Development And Validation ◽  
Pan Cancer ◽  
Survival Predictor

Aims: To develop a ferroptosis gene-based survival-predictor model for predicting the prognosis of patients with digestive tract tumors, a pan-caner analysis was performed. Materials & methods: Based on unsupervised clustering and the expression levels of ferroptosis genes, patients with cancer were divided into two clusters. The least absolute shrinkage and selection operator method Cox regression analysis was used to establish the survival-predictor model. Results: Based on the pan-cancer analysis, a 20 gene-based survival-predictor model for predicting survival rates was developed, which was validated in patients with hepatocellular carcinoma. Conclusion: The survival-predictor model accurately predicted the prognosis of patients with digestive tract tumors.

Development and validation of the trans-omics model for pancreatic adenocarcinoma

Epigenomics ◽  
2020 ◽  
Author(s):  
Weiguo Huang ◽  
Wanqing Weng ◽  
Boda Wu ◽  
Tingbo Ye ◽  
Zhuo Lin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Operator Method ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Development And Validation

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method – COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients

scholarly journals Identification and validation of immunotypes for clear cell RCC prognosis

2021 ◽  
Author(s):  
Zhaolin Yang ◽  
Jiale Zhou ◽  
Yizheng Xue ◽  
Yu Zhang ◽  
Kaijun Zhou ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Clear Cell ◽  
Survival Rates ◽  
Prognostic Index ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

scholarly journals Surgical Resection Significantly Promotes the Overall Survival of Patients with Hepatocellular Carcinoma: A Propensity Score Matching Analysis

2021 ◽  
Author(s):  
Pei-Min Hsieh ◽  
Hung-Yu Lin ◽  
Chao-Ming Hung ◽  
Gin-Ho Lo ◽  
I-Cheng Lu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Surgical Resection ◽  
Cox Regression ◽  
Survival Rates ◽  
Hbv Infection ◽  
Cox Regression Analysis

Abstract Background: The benefits of surgical resection (SR) for various Barcelona Clinic Liver Cancer (BCLC) stages of hepatocellular carcinoma (HCC) remain unclear. We investigated the risk factors of overall survival (OS) and survival benefits of SR over nonsurgical treatments in patients with HCC of various BCLC stages.Methods: Overall, 2316 HCC patients were included, and their clinicopathological data and OS were recorded. OS was analyzed by the Kaplan-Meier method and Cox regression analysis. Propensity score matching (PSM) analysis was performed.Results: In total, 66 (2.8%), 865 (37.4%), 575 (24.8%) and 870 (35.0%) patients had BCLC stage 0, A, B, and C disease, respectively. Furthermore, 1302 (56.2%) of all patients, and 37 (56.9%), 472 (54.6%), 313 (54.4%) and 480 (59.3%) of patients with BCLC stage 0, A, B, and C disease, respectively, died. The median follow-up duration time was 20 (range 0-96) months for the total cohort and was subdivided into 52 (8-96), 32 (1-96), 19 (0-84), and 12 (0-79) months for BCLC stages 0, A, B, and C cohorts, respectively. The risk factors for OS were 1) SR and cirrhosis; 2) SR, cirrhosis, and Child-Pugh (C-P) class; 3) SR, hepatitis B virus (HBV) infection, and C-P class; and 4) SR, HBV infection, and C-P class for the BCLC stage 0, A, B, and C cohorts, respectively. Compared to non-SR treatment, SR resulted in significantly higher survival rates in all cohorts. The 5-year OS rates for SR vs non-SR were 44.0% vs 28.7%, 72.2% vs 42.6%, 42.6% vs 36.2, 44.6% vs 23.5%, and 41.4% vs 15.3% (all p-values<0.05) in the total and BCLC stage 0, A, B, and C cohorts, respectively. After PSM, SR resulted in significantly higher survival rates compared to non-SR treatment in various BCLC stages.Conclusion: SR conferred significant survival benefits to patients with HCC of various BCLC stages and should be considered a recommended treatment for select HCC patients, especially patients with BCLC stage B and C disease.

scholarly journals A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhentao Liu ◽  
Hao Zhang ◽  
Hongkang Hu ◽  
Zheng Cai ◽  
Chengyin Lu ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Methylation Status ◽  
Survival Rates ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

scholarly journals Identification and Analysis of Three Hub Prognostic Genes Related to Osteosarcoma Metastasis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jianye Tan ◽  
Haofeng Liang ◽  
Bingsheng Yang ◽  
Shuang Zhu ◽  
Guofeng Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Survival Rates ◽  
Prognostic Biomarkers ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Kaplan Meier ◽  
Before And After

Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan–Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

scholarly journals Long-Term Results of Kidney Transplantation in the Elderly: Comparison between Different Donor Settings

2021 ◽  
Vol 10 (22) ◽  
pp. 5308
Author(s):  
Renana Yemini ◽  
Ruth Rahamimov ◽  
Ronen Ghinea ◽  
Eytan Mor
Keyword(s):  
Graft Survival ◽  
Cox Regression ◽  
Survival Rates ◽  
The Elderly ◽  
Long Term Results ◽  
Live Donor ◽  
Allocation Policy ◽  
Cox Regression Analysis ◽  
Factors Associated

With scarce organ supply, a selection of suitable elderly candidates for transplant is needed, as well as auditing the long-term outcomes after transplant. We conducted an observational cohort study among our patient cohort >60 years old with a long follow up. (1). Patients and Methods: We used our database to study the results after transplant for 593 patients >60 years old who underwent a transplant between 2000–2017. The outcome was compared between live donor (LD; n = 257) recipients, an old-to-old (OTO, n = 215) group using an extended criteria donor (ECD) kidney, and a young-to-old (YTO, n = 123) group using a standard-criteria donor. The Kaplan−Meir method was used to calculate the patient and graft survival and Cox regression analysis in order to find risk factors associated with death. (2). Results: The 5- and 10-year patient survival was significantly better in the LD group (92.7% and 66.9%) compared with the OTO group (73.3% and 42.8%) and YTO group (70.9% and 40.6%) (p < 0.0001). The 5- and 10-year graft survival rates were 90.3% and 68.5% (LD), 61.7% and 30.9% (OTO), and 64.1% and 39.9%, respectively (YTO group; p < 0.0001 between the LD and the two DD groups). There was no difference in outcome between patients in their 60’s and their 70’s. Factors associated with mortality included: age (HR-1.060), DM (HR-1.773), IHD (HR-1.510), and LD/DD (HR-2.865). (3). Conclusions: Our 17-years of experience seems to justify the rational of an old-to-old allocation policy in the elderly population. Live-donor transplant should be encouraged whenever possible. Each individual decision of elderly candidates for transplant should be based on the patient’s comorbidity and predicted life expectancy.

scholarly journals Clinical factors affecting prognosis of limb osteosarcoma in China: a multicenter retrospective analysis

2020 ◽  
Vol 48 (8) ◽  
pp. 030006052093085
Author(s):  
Jia Han ◽  
Yiyang Yu ◽  
Sujia Wu ◽  
Zhen Wang ◽  
Weibin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Rate ◽  
Cox Regression ◽  
Survival Rates ◽  
Treatment Method ◽  
Overall Survival Rate ◽  
Clinical Factors ◽  
Standard Chemotherapy ◽  
Factors Affecting ◽  
Cox Regression Analysis

Objective This study was performed to explore the relationship between various clinical factors and the prognosis of limb osteosarcoma. Methods We retrospectively analyzed the clinical data of 336 patients with limb osteosarcoma treated from June 2000 to August 2016 at 7 Chinese cancer centers. Data on the patients’ clinical condition, treatment method, complications, recurrences, metastasis, and prognosis were collected and analyzed. Kaplan–Meier analysis and Cox regression models were used to analyze the data. Results The patients comprised 204 males and 132 females ranging in age from 6 to 74 years (average, 21.1 years). The overall 3- and 5-year survival rates were 65.0% and 55.0%, respectively. The 5-year overall survival rate was 64.0% with standard chemotherapy and 45.6% with non-standard chemotherapy. Cox regression analysis demonstrated that standard chemotherapy, surgery, recurrence, and metastasis were independent factors associated with the prognosis of limb osteosarcoma. Conclusion The survival of patients with limb osteosarcoma can be significantly improved by combining standard chemotherapy and surgery. The overall survival rate can also be improved by adding methotrexate to doxorubicin–cisplatin–ifosfamide triple chemotherapy.

scholarly journals Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer

2020 ◽  
Author(s):  
Mingyi Ju ◽  
Jia Bi ◽  
Qian Wei ◽  
Longyang Jiang ◽  
Qiutong Guan ◽  
...  
Keyword(s):  
Nlrp3 Inflammasome ◽  
Cox Regression ◽  
Human Cancer ◽  
Tumor Infiltrating Lymphocytes ◽  
Predictive Biomarker ◽  
Multiple Cancer ◽  
Cox Regression Analysis ◽  
Systematic Assessment ◽  
Human Cancers ◽  
Pan Cancer

Abstract NLRP3 inflammasome was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. However, a systematic assessment of the NLRP3-inflammasome-related genes across human cancers is lacking, and the predictive role of NLRP3 inflammasome in cancer immunotherapy (CIT) response remains unexplored. Thus, in this study, we performed a pan-cancer analysis of NLRP3-inflammasome-related genes across 24 human cancers. Out of these 24 cancers, 15 cancers had significantly different expression of NLRP3-inflammasome-related genes between normal and tumor samples. Meanwhile, Cox regression analysis showed that the NLRP3 inflammasome score could be served as an independent prognostic factor in skin cutaneous melanoma. Further analysis indicated that NLRP3 inflammasome may influence tumor immunity mainly by mediating tumor-infiltrating lymphocytes and macrophages, and the effect of NLRP3 inflammasome on immunity is diverse across tumor types in tumor microenvironment. We also found that the NLRP3 inflammasome score could be a stronger predictor for immune signatures compared with tumor mutation burden (TMB) and glycolytic activity, which have been reported as immune predictors. Furthermore, analysis of the association between NLRP3 inflammasome and CIT response using six CIT response datasets revealed the predictive value of NLRP3 inflammasome for immunotherapy response of patients in diverse cancers. Our study illustrates the characterization of NLRP3 inflammasome in multiple cancer types and highlights its potential value as a predictive biomarker of CIT response, which can pave the way for further investigation of the prognostic and therapeutic potentials of NLRP3 inflammasome.

scholarly journals Predictors of Survival in a Huntington's Disease Population from Southern Italy

2012 ◽  
Vol 39 (1) ◽  
pp. 48-51 ◽  
Author(s):  
Carlo Rinaldi ◽  
Elena Salvatore ◽  
Ilaria Giordano ◽  
Sara De Matteis ◽  
Tecla Tucci ◽  
...  
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Southern Italy ◽  
Cox Regression ◽  
Age Of Onset ◽  
Survival Rates ◽  
Cox Regression Analysis ◽  
Therapeutic Trials ◽  
Expanded Allele ◽  
History Of

Background:The primary aim of the present study was to determine the survival rates and identify predictors of disease duration in a cohort of Huntington's disease (HD) patients from Southern Italy.Methods:All medical records of HD patients followed between 1977 and 2008 at the Department of Neurological Sciences of Federico II University in Naples were retrospectively reviewed and 135 patients were enrolled in the analysis. At the time of data collection, 41 patients were deceased (19 males and 22 females) with a mean ± SD age at death of 56.6 ± 14.9 years (range 18-83).Results:The median survival time was 20 years (95% CI: 18.3-21.7). Cox regression analysis showed that the number of CAG in the expanded allele (HR 1.09 for 1 point triplet increase, p=0.002) and age of onset (HR 1.05 for 1 point year increase, p=0.022) were independent and significant predictors of lower survival rates.Conclusions:We believe that these findings are important for a better understanding of the natural history of the disease and may be relevant in designing future therapeutic trials.

Pediatric Patients with Adrenal Neuroblastoma: A SEER Analysis, 2004–2013

2020 ◽  
Vol 86 (2) ◽  
pp. 127-133
Author(s):  
Shengxiang Chen ◽  
Wenfeng Tang ◽  
Randong Yang ◽  
Xiaoxiao Hu ◽  
Zhongrong Li
Keyword(s):  
Overall Survival ◽  
Pediatric Patients ◽  
Cox Regression ◽  
Demographic Data ◽  
Survival Rates ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Survival Difference ◽  
Tumor Management ◽  
End Results

Adrenal neuroblastoma (NB) is a relatively common malignancy in children. The Surveillance, Epidemiology, and End Results database was used to present demographic data and a survival analysis with the aim of making tumor management better. The Surveillance, Epidemiology, and End Results database was used to search pediatric patients (age £16 years) with NB from 2004 to 2013. The Kaplan-Meier method was used to calculate the overall survival. And, we used Cox regression analysis to determine hazard ratios for prognostic variables. Independent prognostic factors were selected into the nomogram to predict individual's three-, five-, and seven-year overall survival. The study included a total of 1870 pediatric patients with NB in our cohort. Overall, three-, five-, and seven-year survival rates for adrenal NB were 0.777, 0.701, and 0.665, respectively, whereas the rates for nonadrenal NB were 0.891, 0.859, and 0.832, respectively. The multivariate analysis identified age >1 year, no complete resection (CR)/CR, radiation, and regional/distant metastasis as independent predictors of mortality for adrenal NB. Concordance index of the nomogram was 0.665 (95% confidence interval, 0.627–0.703). Pediatric patients with adrenal NB have significantly worse survival than those with nonadrenal NB. Adrenal NB with age <1 year, treated with surgery, no radiation, and localized tumor leads to a better survival. There was no survival difference for patients to receive CR and no CR.

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