Background: Numerous studies have reported that abnormally HOXA cluster antisense RNA 2 (HOXA-AS2) expression plays a critical role in various cancers. Thus, we performed this meta-analysis to comprehensively evaluate the prognostic value of HOXA-AS2 in human cancers.Methods: Databases, including PubMed, Web of Science, Embase, China National Knowledge Infrastructure, and Wanfang Data, were searched to retrieve articles on HOXA-AS2 and the prognosis of cancer patients, which were then screened. The association between HOXA-AS2 and overall survival (OS) and the clinicopathological characteristics of patients with cancers were assessed using hazard ratios (HRs) and odds ratios (ORs) combined with 95% confidence intervals (CIs). A subgroup analysis and the Begg test were used to assess the risk of bias of the included studies. Data from The Cancer Genome Atlas (TCGA) were analyzed to verify the results, and the potential regulation mechanism of HOXA-AS2 in cancers was revealed by an immune analysis.Results: A total of 17 articles, comprising 1,176 patients, were included in this meta-analysis. The results showed that high HOXA-AS2 expression was associated with worse OS, advanced tumor node metastasis (TNM) stage, larger tumor size, lymph node metastasis, and distant metastasis in cancer patients but was not related to age, sex, or poor histological grade. The results of the analysis of TCGA data further supported our findings. Additionally, the immune analysis revealed that the expression of HOXA-AS2 was associated with immune cell infiltration and various immune checkpoints.Conclusions: In summary, our results suggest that the high expression of HOXA-AS2 is associated with poor prognosis and the clinicopathological characteristics of cancer patients; thus, it could serve as a prognosis biomarker and therapeutic target for various cancers. However, the small sample size of this study and the inclusion of participants of a single race might have affected the generalizability of our findings.Thus, large-sample, multicenter studies need to be conducted to further evaluate the prognostic role of HOXA-AS2.

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients.

BackgroundDeoxythymidylate kinase (DTYMK) is a rate-limiting enzyme in pyrimidine metabolism. Crucially, it is overexpressed in a variety of tumors and is associated with poor prognosis in liver cancer. The role of DTYMK in lung adenocarcinoma remains poorly understood, particularly with respect to immune infiltration. MethodsThe present study aimed to compare the expression of DTYMK between normal tissues, lung adenocarcinoma, and other cancer types using data obtained via The Cancer Genome Atlas, Tumor Immune Estimation Resource 2.0, Gene Expression Database of Normal and Tumor Tissues 2, and Gene Expression Profiling Interactive Analysis (GEPIA). The prognostic value of DTYMK in patients with lung adenocarcinoma was evaluated using GEPIA and Kaplan–Meier plotter. STRING and GeneMANIA were employed to assess protein and gene interaction with DTYMK. R software and TISIDB were used to analyze the correlation between DTYMK expression and immune infiltration. Finally, we validated DTYMK protein expression in lung adenocarcinoma using the UALCAN and HPA databases. ResultsDTYMK expression was significantly increased in various cancer and lung adenocarcinoma tissues (p < 0.05), and was associated with poor prognosis (p < 0.05) in patients with lung adenocarcinoma. In addition, DTYMK was negatively associated with the vast majority of immune cells in lung adenocarcinoma.ConclusionsIncreased expression of DTYMK is associated with poor prognosis in lung adenocarcinoma patients. DTYMK may serve as a potential prognostic biomarker in lung adenocarcinoma. In addition, DTYMK may influence the progression and prognosis of lung adenocarcinoma by influencing the immune microenvironment of tumors.

BackgroundPrognostic models in intensive care unit (ICU) were extensively applied in high-income countries, but were limited in lower income countries for lack of resource. A simple prognostic model for easy use in ICU was more attractive. The modified early warning score (MEWS), age, body mass index (BMI), red cell distribution width(RDW), neutrophil to lymphocyte ratio (NLR), lactate (lac) and osmolarity were easily accessible and objective indexes in resource limited settings to predict mortality of patients in ICU. However, using these indexes alone to predict mortality has the disadvantage of insufficient diagnostic efficiency. The usefulness of a combination of these indexes to predict 28-day mortality of critical illness was unclear.MethodsThis was a retrospective cohort study of patients admitted to ICU in MIMIC-III database. A total of 44103 patients older than 14 years were randomly assigned to a development (29402 patients) or validation cohort (14701 patients). Captured variables included demographic characteristics, clinical assessments, laboratory data, acute physiology and Chronic Health Evaluation (APACHE)-II, and MEWS. After identifying independent risk factors of 28-day mortality, we developed a 28-day mortality prediction model with multivariate logistic regression in development cohort. We compared the difference among Broaln-MEWS, APACHE-II, MEWS, RDW, NLR, lac and osmolarity by using area under the receiver operating characteristic curve (AUROC) to calculate discriminative ability, Hosmer-Lemeshow C-Statistic (H/L C-statistic) to assess calibration and Brier score to evaluate accuracy. Finally, the model was validated in the validation cohort and nomogram graph was offered to fast calculate predicted mortality risk.ResultsThe 28-day mortality of critical illness was 22.85% in the development cohort. Patients with increased age, MEWS, NLR, RDW, lac, osmolarity and decreased BMI at baseline exhibited higher risks of 28-day mortality. The mortality prediction model (Broaln-MEWS) that incorporated age, BMI, MEWS, RDW, NLR, lac and osmolarity had an AUROC of 0.741 for the developing data, which was higher than that of MEWS (0.669), NLR (0.634), RDW (0.591), lac (0.580), or osmolarity (0.592) alone(p < 0.000) and slightly lower than APACHE-II (0.747) (p = 0.060). The H/L C-statistic of Broaln-MEWS were equal to 9.641(p = 0.291) and Brier score was 0.103, respectively. Additionally, Broaln-MEWS model demonstrated good discriminative power in the validation group by AUROC that was 0.744 (p < 0.000)ConclusionThe Broaln-MEWS model had higher predictive value for 28-day prognosis in critically illness, which was used easily in resource limited settings.