MATERIAL AND METHODS: Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.
RESULTS: In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.
CONCLUSION: The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.
METHODS: Prospectively collected data from the AARC database were analyzed.
RESULTS: Of the 1249 AH patients, (aged 43.8 ± 10.6 years, 96.9% male, AARC score 9.2 ± 1.9), 38.8% died on a 90 day follow-up. Of these, 150 (12.0%) had mild-moderate AH (MAH), 65 (5.2%) had SAH and 1034 (82.8%) had ACLF. Two hundred and eleven (16.9%) patients received CS, of which 101 (47.87%) were steroid responders by day 7 of Lille's model, which was associated with improved survival [Hazard ratio (HR) 0.15, 95% CI 0.12-0.19]. AARC-ACLF grade 3 [OR 0.28, 0.14-0.55] was an independent predictor of steroid non-response and mortality [HR 3.29, 2.63-4.11]. Complications increased with degree of liver failure [AARC grade III vs. II vs I], bacterial infections [48.6% vs. 37% vs. 34.7%; p
METHODS: All patients with traumatic brain injury (mild, moderate, and severe) who were admitted to Queen Elizabeth Hospital from November 1, 2017, to January 31, 2019, were prospectively analyzed through a data collection sheet. The discriminatory power of the models was assessed as area under the receiver operating characteristic curve and calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test and Cox calibration regression analysis.
RESULTS: We analyzed 281 patients with significant TBI treated in a single neurosurgical center in Malaysia over a 2-year period. The overall observed 14-day mortality was 9.6%, a 6-month unfavorable outcome of 23.5%, and a 6-month mortality of 13.2%. Overall, both the CRASH and IMPACT models showed good discrimination with AUCs ranging from 0.88 to 0.94 and both models calibrating satisfactorily H-L GoF P>0.05 and calibration slopes >1.0 although IMPACT seemed to be slightly more superior compared to the CRASH model.
CONCLUSIONS: The CRASH and IMPACT prognostic models displayed satisfactory overall performance in our cohort of TBI patients, but further investigations on factors contributing to TBI outcomes and continuous updating on both models remain crucial.
METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.
CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.
RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.
CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.
OBJECTIVE: This review examined the survival rate and prognostic factors of patients with Pca in Southeast Asia (SEA).
METHODS: We conducted a systematic search of three databases (PubMed, Scopus, Web of Science) and a manual search until April 1, 2022. The selected papers were evaluated using the Newcastle-Ottawa Quality Assessment Form for Cohort Studies. The review protocol was registered with PROSPERO (CRD42022326521). Pooled prevalence rates were calculated using the programme R version 4.2.1. Heterogeneity was assessed using the I2 statistic and p-value. A narrative approach was used to describe prognostic factors. Studies were selected and finalised based on the review question. The quality of the included studies was assessed.
RESULTS: A total of 11 studies were included in this review. The 1-, 3-, 5- and 10-year survival rates of SEA Pca cases were 80.8%, 51.9%, 66.1% (range 32.1-100) and 78% (range 55.9-100), respectively. Prognostic factors for Pca were discussed in terms of sociodemographic, disease-related and treatment-related aspects. The predictors of significantly lower survival were age more than 75 years, cancer detected during transurethral resection of the prostate, Gleason score more or equal to eight, high-risk group, metastases and no adjuvant radiotherapy. A meta-analysis on the pooled HR of prostate cancer could not be performed due to the heterogeneity of prognostic factors. The pooled prevalence of localised and metastatic prostate cancer in SEA countries was 39% 95% CI [20-62] and 40% 95% CI [28-53], respectively.
CONCLUSION: The survival rate in SEA countries can be determined by prognostic factors, which can be divided into sociodemographic, disease-related and treatment-related factors. Therefore, further studies are needed to improve the understanding and treatment of Pca in the region SEA.