scholarly journals Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

2021 ◽  
Author(s):  
Huazhen Tang ◽  
Zhenpeng Yang ◽  
Xibo Sun ◽  
Shuai Lu ◽  
Bing Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cancer Patients ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Tumor Stage ◽  
Metabolic Reprogramming ◽  
Smoking History ◽  
Individual Treatment ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.

scholarly journals Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

2020 ◽  
Author(s):  
Yanyun Zhao ◽  
Rong Ma ◽  
Fangxiao Liu ◽  
Liwen Zhang ◽  
Xuemei Lv ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Time Dependent ◽  
Net Benefit ◽  
Data Set ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Mutation Profile

Abstract Background: Emerging studies have shown that a variety of gene mutations occur in development and progression of cancer and highly mutation genes could play oncogenic or tumor suppressive roles in cancer. Therefore, our aim is to explore mutation genes which affect the prognosis of bladder.Methods: Mutation profile was obtained and analyzed from TCGA data set. A mutation-based signature was established by multivariable Cox regression analysis. Kaplan-Meier was performed to assess the prognostic power of signature. Time-dependent ROC was conducted to evaluate predictive accuracy of signature for bladder cancer patients.Results: There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%). In this study, we assessed the prognostic predictive ability of 662 highly mutated genes and identified a mutation signature as an independent indicator for predicting the prognosis of bladder. The time-dependent ROC showed that AUC were 0.893, 0.896, 0.916 and 0.965 at 1, 3, 5 and 10 year, respectively. Stratified analysis and Multivariate Cox analysis showed that this mutation signature was reliable and independent biomarker. Furthermore, the nomogram predictive model can be used to effectively predict clinical prognosis of bladder patients. The decision analysis curve showed patients with risk threshold of 0.03-0.92 potentially yielded clinical net benefit. Finally, we identified several signaling pathways that associated with risk score by GSEA and KEGG analysis including PI3K-Akt signaling pathway and so on.Conclusions: In general, this study provide an optimal mutation signature as potential prognosis biomarker for bladder patients.

scholarly journals Identification and Validation of a Glycolysis Related Metabolic Reprogramming Gene Signature for Predicting Survival in Colon Cancer Patients

2021 ◽  
Author(s):  
Gang Liu ◽  
Xiaowang WU ◽  
Jian Chen
Keyword(s):  
Colon Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognosis Model ◽  
The Relationship

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

The influence of obesity on tumor recurrence in vulvar cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17130-e17130 ◽  
Author(s):  
Rüdiger Klapdor ◽  
Peter Hillemanns ◽  
Linn Lena Woelber ◽  
Julia Kathrin Jueckstock ◽  
Felix Hilpert ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Vulvar Cancer ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Disease Free Survival ◽  
Tumor Grade ◽  
Tumor Stage ◽  
Tumor Diameter ◽  
Cox Regression Analysis

e17130 Background: Obesity is associated with worse patients’ survival in several cancer entities. Vulvar cancer as well as obesity show increasing incidence over the last years. The influence of obesity on prognosis of vulvar cancer patients is not clear. However, knowledge about this may have consequences on prevention, treatment, and follow-up. Methods: This is an analysis of the large AGO-CaRE-1 study. Patients suffering from squamous cell vulvar cancer (UICC stage IB and higher), treated in 29 cancer centers between 1998 and 2008, were categorized in a database, in order to analyze treatment patterns and prognostic factors in a retrospective setting. Results: In total, 849 patients with documented height and weight were divided into two groups depending on their body mass index (BMI, < 30 vs. ≥30 kg/m²). There was no difference in the baseline variables (age, tumor diameter, depth of infiltration, tumor stage, nodal invasion, tumor grade) between both groups (p > 0.05). However, we identified differences regarding ECOG status and preexistent comorbidities (cardiovascular, dementia) towards healthier patients with BMI < 30 kg/m². Treatment variables (R0 resection, chemotherapy, radiotherapy, continuation of adjuvant therapy) did not differ (p > 0.05). Patients with BMI ≥30 kg/m² underwent radical vulvectomy more often (61.1 % vs. 51.8%, p = 0.042). During follow-up, there was a higher recurrence rate in the group having a BMI ≥30 kg/m² (43.4%, vs. 28.3%, p < 0.01) due to an increased rate of local recurrences (33.3% vs. 18.5%, p < 0.01). The rate of groin and distant recurrences was similar between both groups (p > 0.05). Noteworthy, we observed a significantly shorter disease free survival (DSF) of the obese patients in univariate analysis (HR 1.362, 95%CI 1.093-1.696, p = 0.006). Even in multivariate Cox-regression analysis including age, ECOG, tumor stage, type of surgery, nodal invasion, tumor grade, and comorbidities patients with BMI ≥30 kg/m² had a significantly shorter DFS (HR 1.811, 95%CI 1.005-3.262, p = 0.048). Conclusions: In this first large study about the association between obesity and prognosis of vulvar cancer patients, we observed that a BMI ≥30kg/m² was associated with shorter DFS, mainly attributed to a higher risk for local recurrence.

scholarly journals A Growth Differentiation Factor 15-Based Risk Score Model to Predict Mortality in Hemodialysis Patients

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 286
Author(s):  
Jia-Feng Chang ◽  
Po-Cheng Chen ◽  
Chih-Yu Hsieh ◽  
Jian-Chiun Liou
Keyword(s):  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Maintenance Hemodialysis ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Growth Differentiation Factor 15 ◽  
Differentiation Factor ◽  
Score Model

Background: The risk of cardiovascular (CV) and fatal events remains extremely high in patients with maintenance hemodialysis (MHD), and the growth differentiation factor 15 (GDF15) has emerged as a valid risk stratification biomarker. We aimed to develop a GDF15-based risk score as a death prediction model for MHD patients. Methods: Age, biomarker levels, and clinical parameters were evaluated at study entry. One hundred and seventy patients with complete information were finally included for data analysis. We performed the Cox regression analysis of various prognostic factors for mortality. Then, age, GDF15, and robust clinical predictors were included as a risk score model to assess the predictive accuracy for all-cause and CV death in the receiver operating characteristic (ROC) curve analysis. Results: Age, GDF15, and albumin were significantly associated with higher all-cause and CV mortality risk that were combined as a risk score model. The highest tertile of GDF-15 (>1707.1 pg/mL) was associated with all-cause mortality (adjusted hazard ratios (aHRs): 3.06 (95% confidence interval (CI): 1.20–7.82), p < 0.05) and CV mortality (aHRs: 3.11 (95% CI: 1.02–9.50), p < 0.05). The ROC analysis of GDF-15 tertiles for all-cause and CV mortality showed 0.68 (95% CI = 0.59 to 0.77) and 0.68 (95% CI = 0.58 to 0.79), respectively. By contrast, the GDF15-based prediction model for all-cause and CV mortality showed 0.75 (95% CI: 0.67–0.82) and 0.72 (95% CI: 0.63–0.81), respectively. Conclusion: Age, GDF15, and hypoalbuminemia predict all-cause and CV death in MHD patients, yet a combination scoring system provides more robust predictive powers. An elevated GDF15-based risk score warns clinicians to determine an appropriate intervention in advance. In light of this, the GDF15-based death prediction model could be developed in the artificial intelligence-based precision medicine.

scholarly journals Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382097167
Author(s):  
Tianqi Luo ◽  
Yufei Du ◽  
Jinling Duan ◽  
Chengcai Liang ◽  
Guoming Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Groups ◽  
Tumor Stage ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis ( p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival ( p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient. In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.

scholarly journals Effects of marital status on overall and cancer-specific survival in laryngeal cancer patients: a population-based study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhao Ding ◽  
Deshun Yu ◽  
Hefeng Li ◽  
Yueming Ding
Keyword(s):  
Marital Status ◽  
Cancer Patients ◽  
Laryngeal Cancer ◽  
Cox Regression ◽  
Group Analysis ◽  
Population Based ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
Prognostic Effect ◽  
The Difference

AbstractMarital status has long been recognized as an important prognostic factor for many cancers, however its’ prognostic effect for patients with laryngeal cancer has not been fully examined. We retrospectively analyzed 8834 laryngeal cancer patients in the Surveillance Epidemiology and End Results database from 2004 to 2010. Patients were divided into four groups: married, widowed, single, and divorced/separated. The difference in overall survival (OS) and cancer-specific survival (CSS) of the various marital subgroups were calculated using the Kaplan–Meier curve. Multivariate Cox regression analysis screened for independent prognostic factors. Propensity score matching (PSM) was also conducted to minimize selection bias. We included 8834 eligible patients (4817 married, 894 widowed, 1732 single and 1391 divorced/separated) with laryngeal cancer. The 5-year OS and CSS of married, widowed, single, and separated/divorced patients were examined. Univariate and multivariate analyses found marital status to be an independent predictor of survival. Subgroup survival analysis showed that the OS and CSS rates in widowed patients were always the lowest in the various American Joint Committee on Cancer stages, irrespective of sex. Widowed patients demonstrated worse OS and CSS in the 1:1 matched group analysis. Among patients with laryngeal cancer, widowed patients represented the highest-risk group, with the lowest OS and CSS.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Ouyang ◽  
Kaide Xia ◽  
Xue Yang ◽  
Shichao Zhang ◽  
Li Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Alternative Splicing ◽  
Ovarian Carcinoma ◽  
Cancer Patients ◽  
Cox Regression ◽  
Excision Repair ◽  
Splicing Factors ◽  
Prognostic Signature ◽  
Cox Regression Analysis

AbstractAlternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

scholarly journals Trends of incidence and prognosis of primary adenocarcinoma of the bladder

2021 ◽  
Vol 13 ◽  
pp. 175628722110180
Author(s):  
Haowen Lu ◽  
Weidong Zhu ◽  
Weipu Mao ◽  
Feng Zu ◽  
Yali Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Marital Status ◽  
Cox Regression ◽  
Tnm Staging ◽  
Primary Site ◽  
Primary Adenocarcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis. Methods: Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated. Results: The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms. Conclusion: The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

Tumor Stage After Neoadjuvant Chemotherapy Determines Survival After Surgery for Adenocarcinoma of the Esophagus and Esophagogastric Junction

2014 ◽  
Vol 32 (27) ◽  
pp. 2983-2990 ◽  
Author(s):  
Andrew R. Davies ◽  
James A. Gossage ◽  
Janine Zylstra ◽  
Fredrik Mattsson ◽  
Jesper Lagergren ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Surgical Resection ◽  
Esophagogastric Junction ◽  
Cox Regression ◽  
Tumor Regression ◽  
Clinical Stage ◽  
Tumor Stage ◽  
Response To Treatment ◽  
Cox Regression Analysis ◽  
Systemic Recurrence

Purpose Neoadjuvant chemotherapy is established in the management of most resectable esophageal and esophagogastric junction adenocarcinomas. However, assessing the downstaging effects of chemotherapy and predicting response to treatment remain challenging, and the relative importance of tumor stage before and after chemotherapy is debatable. Methods We analyzed consecutive resections for esophageal or esophagogastric junction adenocarcinomas performed at two high-volume cancer centers in London between 2000 and 2010. After standard investigations and multidisciplinary team consensus, all patients were allocated a clinical tumor stage before treatment, which was compared with pathologic stage after surgical resection. Survival analysis was conducted using Kaplan-Meier analysis and Cox regression analysis. Results Among 584 included patients, 400 patients (68%) received neoadjuvant chemotherapy. Patients with downstaged tumors after neoadjuvant chemotherapy experienced improved survival compared with patients without response (P < .001), and such downstaging (hazard ratio, 0.43; 95% CI, 0.31 to 0.59) was the strongest independent predictor of survival after adjusting for patient age, tumor grade, clinical tumor stage, lymphovascular invasion, resection margin status, and surgical resection type. Patients downstaged by chemotherapy, compared with patients with no response, experienced lower rates of local recurrence (6% v 13%, respectively; P = .030) and systemic recurrence (19% v 29%, respectively; P = .027) and improved Mandard tumor regression scores (P < .001). Survival was strongly dictated by stage after neoadjuvant chemotherapy, rather than clinical stage at presentation. Conclusion The stage of esophageal or esophagogastric junction adenocarcinoma after neoadjuvant chemotherapy determines prognosis rather than the clinical stage before neoadjuvant chemotherapy, indicating the importance of focusing on postchemotherapy staging to more accurately predict outcome and eligibility for surgery. Patients who are downstaged by neoadjuvant chemotherapy benefit from reduced rates of local and systemic recurrence.

scholarly journals A nomogram based on pretreatment levels of serum bilirubin and total bile acid levels predicts survival in colorectal cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yinghao Cao ◽  
Shenghe Deng ◽  
Lizhao Yan ◽  
Junnan Gu ◽  
Jia Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Factors ◽  
Bile Acid ◽  
Bile Acids ◽  
Cancer Patients ◽  
Serum Bilirubin ◽  
Cox Regression ◽  
Total Bile Acid ◽  
Cox Regression Analysis ◽  
Colorectal Cancer Patients

Abstract Background Serum bilirubin and total bile acid (TBA) levels have been reported to be strongly associated with the risk and prognosis of certain cancers. Here, we aimed to investigate the effects of pretreatment levels of serum bilirubin and bile acids on the prognosis of patients with colorectal cancer (CRC). Methods A retrospective cohort of 1474 patients with CRC who underwent surgical resection between January 2015 and December 2017 was included in the study. Survival analysis was used to evaluate the predictive value of pretreatment levels of bilirubin and bile acids. X-Tile software was used to identify optimal cut-off values for total bilirubin (TBIL), direct bilirubin (DBIL) and TBA in terms of overall survival (OS) and disease-free survival (DFS). Results DBIL, TBIL, and TBA were validated as significant prognostic factors by univariate Cox regression analysis for both 3-year OS and DFS. Multivariate Cox regression analyses confirmed that high DBIL, TBIL and TBA levels were independent prognostic factors for both OS (HR: 0.435, 95% CI: 0.299–0.637, P < 0.001; HR: 0.436, 95% CI: 0.329–0.578, P < 0.001; HR: 0.206, 95% CI: 0.124–0.341, P < 0.001, respectively) and DFS (HR: 0.583, 95% CI: 0.391–0.871, P = 0.008; HR:0.437,95% CI: 0.292–0.655, P <0.001; HR: 0.634, 95% CI: 0.465–0.865, P = 0.004, respectively). In addition, nomograms for OS and DFS were established according to all significant factors, and the c-indexes were 0.819 (95% CI: 0.806–0.832) and 0.835 (95% CI: 0.822–0.849), respectively. Conclusions TBIL, DBIL and TBA levels are independent prognostic factors in colorectal cancer patients. The nomograms based on OS and DFS can be used as a practical model for evaluating the prognosis of CRC patients.

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