A Novel Prognostic Signature Based on Metabolism-Related Genes to Predict Survival and Guide Personalized Treatment for Head and Neck Squamous Carcinoma
Ying LiYouliang WengYuhui PanZongwei HuangXiaochuan ChenWenquan HongTing LinLihua WangWei LiuSufang Qiu
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Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients’ prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group ( p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group ( p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score ( p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.Keywords:
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Background/Aim: Previously, we identified predictors of survival after irradiation of grade II-IV cerebral gliomas. In this supplementary analysis, survival was calculated in a more appropriate way than the original study. Patients and Methods: Ten factors were re-evaluated for survival in patients of the original study including pre-radiotherapy seizures. In the original study, survival was calculated from the end of the last radiotherapy course (primary or re-irradiation). After re-review, this approach was considered inappropriate. Survival should have always been calculated from the first radiotherapy course, as done in this supplementary analysis. Results: On multivariate analysis, WHO-grade II (p=0.006) and upfront resection (p=0.001) were associated with better survival. Unifocal glioma was significant on univariate analysis (p=0.001), where a trend could be identified for age ≤59 years (p=0.057) and seizures (p=0.060). Conclusion: The findings of this supplementary analysis regarding the identification of prognostic factors for survival agree with the results of the original study.
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Abstract This 1:5 case‐control study aimed to identify the risk factors of hospital‐acquired pressure injuries (HAPIs) and to develop a mathematical model of nomogram for the risk prediction of HAPIs. Data for 370 patients with HAPIs and 1971 patients without HAPIs were extracted from the adverse events and the electronic medical systems. They were randomly divided into two sets: training (n = 1951) and validation (n = 390). Significant risk factors were identified by univariate and multivariate analyses in the training set, followed by a nomogram constructed. Age, independent movement, sensory perception and response, moisture, perfusion, use of medical devices, compulsive position, hypoalbuminaemia, an existing pressure injury or scarring from a previous pressure injury, and surgery sufferings were considered significant risk factors and were included to construct a nomogram. In both of the training and validation sets, the areas of 0.90 under the receiver operating characteristic curves showed excellent discrimination of the nomogram; calibration plots demonstrated a good consistency between the observed probability and the nomogram's prediction; decision curve analyses exhibited preferable net benefit along with the threshold probability in the nomogram. The excellent performance of the nomogram makes it a convenient and reliable tool for the risk prediction of HAPIs.
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Objective To explore the prognostic factors of astrocytic tumors as the theoretical reference to the clinical treatment. Methods Data of 89 patients with astrocytic tumors, admitted in Zhujiang Hospital from January to June 2000, were collected and analyzed with regard to patient age, gender, preoperative KPS score, epilepsy before surgery, histologic grade, tumor location and extension, extent of surgery, radiation, date of operation, date of death, physical state in the last follow-up (dead or alive), and cause of death. For the univariate analysis, survival probabilities were estimated based on Kaplan-Meier's survival analysis and Logrank test. Multivariate regression analysis using Cox's proportional-hazards model showed the simultaneous effect of outcome-related variables on survival. Results Univariate analysis demonstrated that patient age, KPS score, epilepsy before surgery, histologic grade and radiation were the significant factors for survival (P0.01). In contrast, multivariate survival analysis showed that patient age, histologic grade and KPS score were independent, statistically significant prognostic factors for patients with astrocytic tumors, whereas preoperative epilepsy and postoperative radiation did not reach the significance level for entry into the stepwise model. And gender, tumor location, tumor extension and extent of sugery had no association with patient survival on both univariate and multivariate analysis. Conclusion Patient age, histologic grade and KPS score are associated strongly with survival, while preoperative epilepsy and postoperative radiation appear to be of limited prognostic value. And there is no correlation among gender, extent of surgery and prognosis. Neither tumor location nor tumor extension is associated with survival.
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Objective: To compare the diagnostic accuracy of various transcutaneous bilirubin (TcB) nomograms for predischarge screening. Methods: The paired total serum bilirubin (TSB) and TcB measurements collected in neonates ≥35 weeks and ≥2000 g birth weight were analyzed. BiliCare™ bilirubinometer was used for TcB measurement. We chose the following nomograms for the study: Bhutani nomogram, Maisel's nomogram, Agarwal nomogram, Thakkar nomogram, American Academy of Pediatrics (AAP) nomogram within 3 mg/dl of phototherapy cutoff, AAP nomogram >70% of phototherapy cutoff and if TcB value is above 13 mg/dl. The diagnostic accuracy of these nomograms for TcB was compared with TSB plotted in the Bhutani nomogram. Results: TcB showed a positive correlation with TSB (Pearson correlation coefficient = 0.783). Bhutani nomogram, Maisel's nomogram and AAP (using within 3 mg/dL cutoff) nomogram showed good sensitivity and low false-negative rate while avoiding blood draws in most neonates. Conclusion: Bhutani nomogram, Maisel's nomogram, and AAP (using within 3 mg/dL of phototherapy cutoff) nomograms have comparable diagnostic accuracy for predischarge bilirubin screening in neonates.
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Abstract Background: The aim of the study was to establish and validate nomograms to predict the mortality risk of patients with COVID-19 using routine clinical indicators. Method: This retrospective study included a development cohort enrolled 2119 hospitalized COVID-19 patients and a validation cohort included 1504 COVID-19 patients. The demographics, clinical manifestations, vital signs and laboratory test results of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct two prognostic nomograms. The models were then tested in an external dataset. Results: Nomogram 1 is a full model included nine factors identified in the multivariate logistic regression and nomogram 2 is built by selecting four factors from nine to perform as a reduced model. Nomogram 1 and nomogram 2 established showed better performance in discrimination and calibration than the MuLBSTA score in training. In validation, Nomogram 1 performed better than nomogram 2 for calibration. Conclusion: Nomograms we established performed better than the MuLBSTA score. We recommend the application of nomogram 1 in general hospital which provide robust prognostic performance but more cumbersome; nomogram 2 in mobile cabin hospitals which depend on less laboratory examinations and more convenient. Both nomograms can help clinicians in identifying patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.
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