TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).
METHODS: A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale.
METHODS: ICUs admitting cancer patients.
METHODS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.
METHODS: Integrative study without interventions.
METHODS: Mortality prediction, standardized mortality, discrimination, and calibration.
RESULTS: Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models.
CONCLUSIONS: Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team\'s familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.

UNASSIGNED: Cervical cancer is among the most prevalent malignancies worldwide. This study explores the relationships between angiogenesis-related genes (ARGs) and immune infiltration, and assesses their implications for the prognosis and treatment of cervical cancer. Additionally, it develops a diagnostic model based on angiogenesis-related differentially expressed genes (ARDEGs).
UNASSIGNED: We systematically evaluated 15 ARDEGs using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed using a single-sample gene-set enrichment analysis (ssGSEA) algorithm. We then constructed a diagnostic model for ARDEGs using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and evaluated the diagnostic value of this model and the hub genes in predicting clinical outcomes and immunotherapy responses in cervical cancer.
UNASSIGNED: A set of ARDEGs was identified from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UCSC Xena database. We performed KEGG, GO, and GSEA analyses on these genes, revealing significant involvement in cell proliferation, differentiation, and apoptosis. The ARDEGs diagnostic model, constructed using LASSO regression analysis, showed high predictive accuracy in cervical cancer patients. We developed a reliable nomogram and decision curve analysis to evaluate the clinical utility of the ARDEG diagnostic model. The 15 ARDEGs in the model were associated with clinicopathological features, prognosis, and immune cell infiltration. Notably, ITGA5 expression and the abundance of immune cell infiltration (specifically mast cell activation) were highly correlated.
UNASSIGNED: This study identifies the prognostic characteristics of ARGs in cervical cancer patients, elucidating aspects of the tumor microenvironment. It enhances the predictive accuracy of immunotherapy outcomes and establishes new strategies for immunotherapeutic interventions.

[This corrects the article DOI: 10.3389/fgene.2022.970900.].

UNASSIGNED: This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC).
UNASSIGNED: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy.
UNASSIGNED: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram\'s performance.
UNASSIGNED: The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

UNASSIGNED: This study aimed to investigate the relationship between peripheral blood indices and the efficacy and prognosis of advanced esophageal squamous cell carcinoma (ESCC) patients treated with camrelizumab.
UNASSIGNED: We retrospectively analyzed 64 patients who received camrelizumab for advanced ESCC at the Second People\'s Hospital of Lianyungang City between July 2020 and June 2022. The study included examination of the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic inflammation index (SII), the lymph-to-monocytes ratio (LMR), the absolute lymphocyte count (ALC), and lactate dehydrogenase (LDH). We used multivariate logistic regression analysis to explore the link existing between peripheral blood and the efficacy of treatment. Determination of potential prognostic factors for Progression-free survival (PFS) and Overall survival (OS) using Cox regression analysis. The nomogram model was developed based on the results of the Cox multivariate analysis. Patients were divided into three groups according to the reduction in LDH and LDL levels before treatment, and the Kaplan-Meier survival curves for the three groups were compared and ROC curves for LDH combined with PLR were plotted.
UNASSIGNED: Lower LDH (OR=6.237, 95% CI: 1.625-23.944) were independently associated with disease control rates(DCR). LDH was independently correlated with PFS (HR: 0.227 95% CI: 0.099-0.517). LDH and PLR were independently linked to OS. The C index of the nomogram model is 0.819, indicating good predictive performance. Kaplan-Meier Survival Curve suggested better OS in patients with reduced pretreatment LDH and PLR. The area under the ROC curve showed that the LDH index combined with the PLR index predicts patient survival better than the index alone.
UNASSIGNED: LDH combined with PLR predicted prognosis in patients with ESCC treated with camrelizumab.

UNASSIGNED: To construct an effective prognostic index to predict overall survival (OS) and triplet regimen efficacy for advanced gastric cancer (AGC) patients treated with platinum-based and fluorouracil-based chemotherapy.
UNASSIGNED: Between 2011 and 2021, 679 patients from two randomized phase III trials and one phase II trial were enrolled.
UNASSIGNED: We collected 11 baseline clinicopathological and 14 hematological parameters to establish a prognostic index.
UNASSIGNED: Univariate and multivariate Cox analyses were used to screen prognostic factors, and a prognostic index nomogram was conducted.
UNASSIGNED: Seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic nomogram named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in the low-risk group (median OS, 15.5 versus 8.0 months, p < 0.001). The areas under the curve of the FARS index for 1-, 2-, and 3-year OS were 0.70, 0.72, and 0.77, respectively. A validation and external cohort verified the prognostic value of the FARS index. Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (p = 0.018).
UNASSIGNED: The constructed FARS index to predict the OS of AGC patients and the TRIS index to screen out the dominant population for triplet regimens can be used to aid clinical decision-making and individual risk stratification.
A prognostic index in locally advanced and metastatic gastric cancer To date, no recognized systematic prognostic score has been established for advanced gastric cancer (AGC). Our research aims to construct an effective prognostic index to predict overall survival (OS) for AGC patients to aid clinical decision-making and individual risk stratification. In our research, seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic index named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in low-risk group (median OS, 15.5 months vs. 8.0 months, P < 0.001). Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (P = 0.018).

Background & Aims: Fatty liver disease with metabolic dysfunction (MAFLD) is a new concept proposed to replace the previous concept of Non-Alcoholic Hepatic Steatosis (NAFLD). We developed and internally validated a prognostic model to predict the likelihood of death in a cohort of subjects with MAFLD. Methods: Our work involved two steps: the first was the construction of a bootstrapped multivariable Cox model for mortality risk prognosis and the second was its validation. Results: The study cohort included 1506 subjects, of which 907 were used for internal validation. Discriminant measures for the final model were R2D 0.6845 and Harrell\'s C 0.8422 in the development and R2D 0.6930 and Harrell\'s C 0.8465 in the validation. We used the nine independent prognostic factors selected by the LASSO Cox procedure and fitted by the bootstrap Cox survival model, and observed β were: Gender 0.356 1.42 (p < 0.008), Age 0.146 (p < 0.001), Glycemia 0.004 (p < 0.002), Total Cholesterol -0.0040 (p < 0.009), Gamma Glutamyl Transpeptidase 0.009 (p < 0.001), SBP 0.009 (p < 0.036), DBP -0.016 (p < 0.041), ALP 0.008 (p < 0.071) and Widowhood 0.550 (p < 0.001). Conclusions: We produced and validated a model to estimate the probability of death in subjects with MAFLD. The instruments we used showed satisfactory predictive capabilities.

BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision-making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT.
METHODS: We studied allogenic HSCTs performed on children with malignant disorders at a third-level hospital between 1991 and 2021. Survival was analyzed using the Kaplan-Meier method, log-rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1-year survival after HSCT.
RESULTS: We analyzed 229 HSCTs in 201 patients with a median follow-up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12-1.76, p = .003), oldest period of HSCT (HR 0.46, 95% CI 0.29-0.73, p < .001), and mismatched donor (HR 2.65, 95% CI 1.51-4.65, p = .001). Our prognostic index was associated with 3-year overall survival (OS; p < .001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1-year OS.
CONCLUSIONS: Our index and random forest was effective in predicting 1-year survival. However, further validation in diverse populations is necessary to establish their generalizability.