Search

Background: Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved. Methods: A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared. Results: Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00. Conclusions: Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development. [ABSTRACT FROM AUTHOR]

Background: Various clinical similarities are present in ischemic (ICM) and idiopathic dilated cardiomyopathy (IDCM), leading to ambiguity on some occasions. Previous studies have reported that intestinal microbiota appeared dysbiosis in ICM, whether implicating in the IDCM remains unclear. The aim of this study was to assess the alterations in intestinal microbiota and fecal metabolites in ICM and IDCM. Methods: ICM (n = 20), IDCM (n = 22), and healthy controls (HC, n = 20) were enrolled in this study. Stool samples were collected for 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC–MS) analysis. Results: Both ICM and IDCM exhibited reduced alpha diversity and altered microbial community structure compared to HC. At the genus level, nine taxa including Blautia, [Ruminococcus]_torques_group, Christensenellaceae_R-7_group, UCG-002, Corynebacterium, Oceanobacillus, Gracilibacillus, Klebsiella and Citrobacter was specific to ICM, whereas one taxa Alistipes uniquely altered in IDCM. Likewise, these changes were accompanied by significant metabolic differences. Further differential analysis displayed that 18 and 14 specific metabolites uniquely changed in ICM and IDCM, respectively. The heatmap was generated to display the association between genera and metabolites. Receiver operating characteristic curve (ROC) analysis confirmed the predictive value of the distinct microbial-metabolite features in disease status. The results showed that microbial (area under curve, AUC = 0.95) and metabolic signatures (AUC = 0.84) were effective in discriminating ICM from HC. Based on the specific microbial and metabolic features, the patients with IDCM could be separated from HC with an AUC of 0.80 and 0.87, respectively. Furthermore, the gut microbial genus (AUC = 0.88) and metabolite model (AUC = 0.89) were comparable in predicting IDCM from ICM. Especially, the combination of fecal microbial-metabolic features improved the ability to differentiate IDCM from ICM with an AUC of 0.96. Conclusion: Our findings highlighted the alterations of gut microbiota and metabolites in different types of cardiomyopathies, providing insights into the pathophysiological mechanisms of myocardial diseases. Moreover, multi-omics analysis of fecal samples holds promise as a non-invasive tool for distinguishing disease status. [ABSTRACT FROM AUTHOR]

Background: Respiratory variation in the internal jugular vein (IJVV) has not shown promising results in predicting volume responsiveness in ventilated patients with low tidal volume (Vt) in prone position. We aimed to determine whether the baseline respiratory variation in the IJVV value measured by ultrasound might predict fluid responsiveness in patients with adolescent idiopathic scoliosis (AIS) undergoing posterior spinal fusion (PSF) with low Vt. Methods: According to the fluid responsiveness results, the included patients were divided into two groups: those who responded to volume expansion, denoted the responder group, and those who did not respond, denoted the non-responder group. The primary outcome was determination of the value of baseline IJVV in predicting fluid responsiveness (≥15% increases in stroke volume index (SVI) after 7 ml·kg-1 colloid administration) in patients with AIS undergoing PSF during low Vt ventilation. Secondary outcomes were estimation of the diagnostic performance of pulse pressure variation (PPV), stroke volume variation (SVV), and the combination of IJVV and PPV in predicting fluid responsiveness in this surgical setting. The ability of each parameter to predict fluid responsiveness was assessed using a receiver operating characteristic curve. Results: Fifty-six patients were included, 36 (64.29%) of whom were deemed fluid responsive. No significant difference in baseline IJVV was found between responders and non-responders (25.89% vs. 23.66%, p = 0.73), and no correlation was detected between baseline IJVV and the increase in SVI after volume expansion (r = 0.14, p = 0.40). A baseline IJVV greater than 32.00%, SVV greater than 14.30%, PPV greater than 11.00%, and a combination of IJVV and PPV greater than 64.00% had utility in identifying fluid responsiveness, with a sensitivity of 33.33%, 77.78%, 55.56%, and 55.56%, respectively, and a specificity of 80.00%, 50.00%, 65.00%, and 65.00%, respectively. The area under the receiver operating characteristic curve for the baseline values of IJVV, SVV, PPV, and the combination of IJVV and PPV was 0.52 (95% CI, 0.38–0.65, p=0.83), 0.54 (95% CI, 0.40–0.67, p=0.67), 0.58 (95% CI, 0.45–0.71, p=0.31), and 0.57 (95% CI, 0.43–0.71, p=0.37), respectively. Conclusions: Ultrasonic-derived IJVV lacked accuracy in predicting fluid responsiveness in patients with AIS undergoing PSF during low Vt ventilation. In addition, the baseline values of PPV, SVV, and the combination of IJVV and PPV did not predict fluid responsiveness in this surgical setting. Trail registration: This trial was registered at www.chictr.org (ChiCTR2200064947) on 24/10/2022. All data were collected through chart review. [ABSTRACT FROM AUTHOR]

Background: There is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE. Methods: We retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets. Results: A total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866–0.957) and 0.716 (95% CI 0.522–0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions. Conclusion: Radiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery. [ABSTRACT FROM AUTHOR]

Purpose: To explore the relationship between C2 slope with sagittal parameters and clinical function of degenerative cervical kyphosis (DCK). Methods: A retrospective analysis of 127 patients with degenerative cervical spondylosis treated in our spinal deformity center from January 2019 to June 2022. Patients were categorized into two groups and compared based on C2-7 angle (C2-7 ≥ 5° as kyphosis group, C2-7 < 5° as lordosis group). Pearson correlation or Spearman correlation was used to analyze the relationship between C2S and conventional radiological parameters and health -related quality-of-life (HRQOL) outcomes as measured by the EuroQol 5 dimension questionnaire (EQ5D), NRS, and the neck disability index (NDI). The cutoff value of C2S was determined by a receiver operating characteristic (ROC) curve. Results: There were 127 patients who met inclusion criteria (79 men and 48 women). Average 56.00 ± 10.27 years old (range 31–81 years old). C2S of kyphosis group is higher than non-kyphosis group. Aggravating cervical kyphosis increases cSVA positively. For all patients, C2S demonstrated a significant correlation with the O-C2 angle, C2-7 angle, cSVA, and TS-CL (p < 0.05). NRS, NDI and EQ5D-VAS scores revealed a significant correlation with C2S and cSVA (p < 0.01). For the subgroup of patients presenting with DCK, ROC curves demonstrated the cutoff values of C2S as 26.3°, and 30.5°, according to a cSVA of 40 mm, and severe disability expressed by NDI, respectively. Conclusion: On the basis of retaining the consistency of cranio-cervical and cervico-thoracic structure, C2S can better analyze the sagittal alignment of DCK patients than TS-CL and has good practicability in clinical application and HRQOL evaluation. [ABSTRACT FROM AUTHOR]

Background: Pathologic complete response (pCR) following preoperative systemic therapy is associated with improved outcomes after subsequent liver transplant/resection in hepatocellular carcinoma (HCC). However, the relationship between radiographic and histopathological response remains unclear. Methods: We retrospectively examined patients with initially unresectable HCC who received tyrosine kinase inhibitor (TKI) plus anti–programmed death 1 (PD-1) therapy before undergoing liver resection between March 2019 and September 2021 across 7 hospitals in China. Radiographic response was evaluated using mRECIST. A pCR was defined as no viable tumor cells in resected samples. Results: We included 35 eligible patients, of whom 15 (42.9%) achieved pCR after systemic therapy. After a median follow-up of 13.2 months, tumors recurred in 8 non-pCR and 1 pCR patient. Before resection, there were 6 complete responses, 24 partial responses, 4 stable disease cases, and 1 progressive disease case, per mRECIST. Predicting pCR by radiographic response yielded an area under the receiver operating characteristic curve (AUC) of 0.727 (95% CI: 0.558–0.902), with an optimal cutoff value of 80% reduction in the enhanced area in MRI (called major radiographic response), which had a 66.7% sensitivity, 85.0% specificity, and a 77.1% diagnostic accuracy. When radiographic response was combined with α-fetoprotein response, the AUC was 0.926 (95% CI: 0.785–0.999); the optimal cutoff value was 0.446, which had a 91.7% sensitivity, 84.6%, specificity, and an 88.0% diagnostic accuracy. Conclusions: In patients with unresectable HCC receiving combined TKI/anti–PD 1 therapy, major radiographic response alone or combined with α-fetoprotein response may predict pCR. [ABSTRACT FROM AUTHOR]

Lymph node metastasis (LNM) is an important factor affecting the prognosis of patients with gastric adenocarcinoma (STAD), which is the most common malignancy of the human digestive system. Current detection techniques have limited sensitivity and specificity, and there is a lack of effective biomarkers to screen for LNM. Therefore, it is critical to screen for biomarkers that predict LNM in STAD. Gene expression differential analysis (false discovery rate < 0.05, |log2Fold change| ≥1.5) was performed on 102 LNM samples, 224 non-LNM samples, and 29 normal gastric tissue samples from The Cancer Genome Atlas (TCGA) STAD dataset, and 269 LNM-specific genes (DEGs) were obtained. Enrichment analysis showed that LNM-specific genes functioned mainly in cytokine-cytokine receptor interactions, calcium signaling, and other pathways. Ten DEGs significantly associated with overall survival in STAD patients were screened by multivariate Cox regression, and an LNM-based 10-mRNA prognostic signature was established (Logrank P < 0.0001). This 10-mRNA signature was well predicted in both the TCGA training set and the Gene Expression Omnibus validation dataset (GSE84437) and was associated with survival in patients with LNM or advanced-stage STAD. Using Kaplan-Meier survival, receiver operating characteristic curve, C-index analysis, and decision curve analysis, the 10-mRNA signature was found to be a more effective predictor of prognosis in STAD patients than the other two reported models (P < 0.0005). Protein-protein interaction network and gene set enrichment analysis of the 10-mRNA signature revealed that the signature may affect the expression of multiple biological pathways and related genes. Finally, the expression levels of prognostic genes in STAD tissues and cell lines were verified using qRT-PCR, Western blot, and the Human Protein Atlas database. Taken together, the prognostic signature constructed in this study may become an indicator for clinical prognostic assessment of LNM-STAD and provide a new strategy for future targeted therapy. [ABSTRACT FROM AUTHOR]

Background: Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear. Methods: We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(−) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation. Results: A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(−) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77–0.79), the model for end-stage liver disease score (0.70–0.76), and serum alpha-fetoprotein (0.63–0.71) for the prediction of prognosis after liver transplantation. Conclusion: Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation. Clinical trials registration: Not Applicable. [ABSTRACT FROM AUTHOR]

Background: The pathogenesis of pulmonary arterial hypertension (PAH) and associated biomarkers remain to be studied. Copper metabolism is an emerging metabolic research direction in many diseases, but its role in PAH is still unclear. Methods: PAH-related datasets were downloaded from the Gene Expression Omnibus database, and 2067 copper metabolism-related genes (CMGs) were obtained from the GeneCards database. Differential expression analysis and the Venn algorithm were used to acquire the differentially expressed CMGs (DE-CMGs). DE-CMGs were then used for the coexpression network construction to screen candidate key genes associated with PAH. Furthermore, the predictive performance of the model was verified by receiver operating characteristic (ROC) analysis, and genes with area under the curve (AUC) values greater than 0.8 were selected as diagnostic genes. Then support vector machine, least absolute shrinkage and selection operator regression, and Venn diagrams were applied to detect biomarkers. Moreover, gene set enrichment analysis was performed to explore the function of the biomarkers, and immune-related analyses were utilized to study the infiltration of immune cells. The drug-gene interaction database was used to predict potential therapeutic drugs for PAH using the biomarkers. Biomarkers expression in clinical samples was verified by real-time quantitative PCR. Results: Four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) were screened. The ROC analysis showed that the 4 biomarkers performed well (AUCs > 0.7). The high expression groups for the 4 biomarkers were enriched in protein activity-related pathways including protein export, spliceosome and proteasome. Furthermore, 8 immune cell types were significantly different between the two groups, including naive B cells, memory B cells, and resting memory CD4 T cells. Afterward, a gene-drug network was constructed. This network illustrated that STREPTOZOCIN, IBUPROFEN, and CELECOXIB were shared by the PTGER4 and DDIT3. Finally, the results of RT-qPCR in clinical samples further confirmed the results of the public database for the expression of NFKBIA and OSM. Conclusion: In conclusion, four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) with considerable diagnostic values were identified, and a gene-drug network was further constructed. The results of this study may have significant implications for the development of new diagnostic biomarkers and actionable targets to expand treatment options for PAH patients. [ABSTRACT FROM AUTHOR]

Objective: Cervical microbial community in the cervical intraepithelial neoplasia and cervical cancer patients was analysed to study its composition, diversity and signalling pathways by high-throughput 16S rDNA sequencing,and the candidate genes associated with occurrence and progression of cervical intraepithelial neoplasia were screened out and the model was established to predict the evolution of cervical intraepithelial neoplasia malignant transformation from the cervical microbial genes aspect.Methods: Cervical tissues of normal, cervical intraepithelial neoplasia and cervical cancer patients without receiving any treatment were collected. The correlation between candidate genes and cervical intraepithelial neoplasia progression was initially determined by analyzing the microbial flora. Real-time fluorescence quantitative PCR was used to detect the expression of candidate genes in different cervical tissues, ROC curve and logistic regression was used to analyse and predict the risk factors related to the occurrence and progression of cervical intraepithelial neoplasia. Finally, the early warning model of cervical intraepithelial neoplasia occurrence and progression is established.Results: Cervical tissues from normal, cervical intraepithelial neoplasia and cervical cancer patients were collected for microbial community high-throughput 16S rDNA sequencing. The analysis revealed five different pathways related to cervical intraepithelial neoplasia. 10 candidate genes were selected by further bioinformatics analysis and preliminary screening. Real time PCR, ROC curve and Logistic regression analysis showed that human papillomavirus infection, TCT severity, ABCG2, TDG, PCNA were independent risk factors for cervical intraepithelial neoplasia. We used these indicators to establish a random forest model. Seven models were built through different combinations. The model 4 (ABCG2 + PCNA + TDG) was the best early warning model for the occurrence and progression of CIN.Conclusions: A total of 5 differential pathways and 10 candidate genes related to occurrence and progression of cervical intraepithelial neoplasia were found in cervical microbial community. This study firstly identified the genes from cervical microbial community that play an important role in the occurrence and progression of cervical intraepithelial neoplasia. At the same time, the early warning model including ABCG2 + PCNA+TDG genes provided a new idea and target for clinical prediction and blocking the evolution of cervical intraepithelial neoplasia malignant transformation from the aspect of cervical microbiological related genes. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients.Methods: The Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients.Results: Four upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people.Conclusion: The results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC. [ABSTRACT FROM AUTHOR]

Background: We have previously reported that ferroptosis has an important role in bladder cancer development. In this study, we aimed to further explore the possible predictive ability of ferroptosis-related long non-coding RNAs (lncRNAs) in bladder cancer and their relation with immune microenvironment and immunotherapy response. Materials and methods: The ferroptosis-related lncRNAs were identified by Pearson's correlation analysis. The predictive lncRNA signature was developed by univariate and multivariate regression analyses. Only the main effects of independent variables in multivariate analysis were included in this signature. The TCGA dataset was defined as the training cohort and GEO was the validation cohort in this study. All samples were grouped into a high- or low-risk group depending on risk signature. The prognostic role of lncRNA signature was explored through survival analysis and receiver operating characteristic curve (ROC) analysis in both TCGA and GEO cohorts. Additionally, the independent prognostic ability of the lncRNA signature was confirmed by multivariate independent analysis. Furthermore, the relationship between lncRNAs and immune microenvironment as well as immunotherapy response in bladder cancers was studied. Results: The Kaplan–Meier curves identified significantly poorer overall survival outcomes for high-risk groups in both TCGA (p < 0.001) and GEO (p < 0.001) cohorts. The area under the curve (AUC) during ROC analysis of 1, 3, and 5 years was 0.781 ± 0.046, 0.784 ± 0.027, and 0.817 ± 0.025, respectively, in the TCGA cohort and 0.665 ± 0.177, 0.719 ± 0.068, and 0.791 ± 0.055, respectively, in the GEO cohort. The multivariate independent analysis in TCGA cohort identified age (p = 0.003), stage (p < 0.001), and signature risk score (p < 0.001) as independent risk factors for overall survival. Furthermore, this study demonstrated a significant difference in infiltration levels of various immune cells between high- and low-risk groups. The high risk group tended to have a lower expression of proteins including PD1 (p < 0.01), PD-L1 (p < 0.01), CTLA-4 (p < 0.05), etc. corresponding to various immune checkpoints. Additionally, the immunotherapy trial confirmed that the high-risk group tended to have a poorer treatment response than the low-risk group (p < 0.001). Conclusions: The ferroptosis-related lncRNAs exhibited a good predictive capacity for overall survival in bladder cancer. Additionally, they could be utilized to reveal tumour-immune microenvironment and immunotherapy responses. [ABSTRACT FROM AUTHOR]

Background: This study explores and analyzes the clinical characteristics and prognostic factors of hepatoblastoma (HB) in children under 6 years old and establishes a new risk-stratification system for individualized therapy.Methods: The clinical data of 382 pediatric patients under 6 years old (231 males and 151 females) who had been diagnosed with HB by pathology between May 2005 and May 2019 were collected. By analyzing the risk factors influencing the survival rate of patients with HB, a new risk-stratification system was established, and it was compared with previous risk-stratification systems by a receiver operating characteristic (ROC) curve.Results: (1) According to a Kaplan-Meier survival analysis, the one-year, three-year, and five-year overall survival (OS) was 93.7, 84.0, and 73.9%, respectively, and the event-free survival (EFS) was 90.5, 79.2, and 67.5%, respectively. (2) The independent risk factors influencing prognosis in pediatric patients with HB were alpha-fetoprotein (AFP) < 100 ng/ml or > 1000 ng/ml (HR = 3.341, P = 0.005); platelet count > 400 × 109/L (pooled hazard ratio [HR] = 2.123, P = 0.026); PRETEXT stage IV (HR = 4.026, P = 0.001); vascular involvement (HR = 2.178, P = 0.019); distant metastasis (HR = 2.634, P = 0.010);and multifocality (HR = 2.215, P = 0.012). (3) A new risk-stratification system was established and divided into three groups: low risk, moderate risk, and high risk. There were statistical differences among the three groups (P = 0.002). Compared with the previous risk-staging systems, there was no significant difference in the survival rate. Although the effect in the guiding therapy was the same, the area under the curve for the ROC curve was 0.835 (95% CI: 0.784-0.885) for the new stratification system.Conclusion: This new risk-stratification system had a better predictive value for the prognosis of pediatric patients with HB than other stratification systems. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC), derived from hepatocytes, is the main histological subtype of primary liver cancer and poses a serious threat to human health due to the high incidence and poor prognosis. This study aimed to establish a multigene prognostic model to predict the prognosis of patients with HCC. Results: Gene expression datasets (GSE121248, GSE40873, GSE62232) were used to identify differentially expressed genes (DEGs) between tumor and adjacent or normal tissues, and then hub genes were screened by protein–protein interaction (PPI) network and Cytoscape software. Seventeen genes among hub genes were significantly associated with prognosis and used to construct a prognostic model through COX hazard regression analysis. The predictive performance of this model was evaluated with TCGA data and was further validated with independent dataset GSE14520. Six genes (CDKN3, ZWINT, KIF20A, NUSAP1, HMMR, DLGAP5) were involved in the prognostic model, which separated HCC patients from TCGA dataset into high- and low-risk groups. Kaplan–Meier (KM) survival analysis and risk score analysis demonstrated that low-risk group represented a survival advantage. Univariate and multivariate regression analysis showed risk score could be an independent prognostic factor. The receiver operating characteristic (ROC) curve showed there was a better predictive power of the risk score than that of other clinical indicators. At last, the results from GSE14520 demonstrated the reliability of this prognostic model in some extent. Conclusion: This prognostic model represented significance for prognosis of HCC, and the risk score according to this model may be a better prognostic factor than other traditional clinical indicators. [ABSTRACT FROM AUTHOR]

Background: Routine preoperative methods to assess airway such as the interincisor distance (IID), Mallampati classification, and upper lip bite test (ULBT) have a certain risk of upper respiratory tract exposure and virus spread. Condyle-tragus maximal distance(C-TMD) can be used to assess the airway, and does not require the patient to expose the upper respiratory tract, but its value in predicting difficult laryngoscopy compared to other indicators (Mallampati classification, IID, and ULBT) remains unknown. The purpose of this study was to observe the value of C-TMD to predict difficult laryngoscopy and the influence on intubation time and intubation attempts, and provide a new idea for preoperative airway assessment during epidemic. Methods: Adult patients undergoing general anesthesia and tracheal intubation were enrolled. IID, Mallampati classification, ULBT, and C-TMD of each patient were evaluated before the initiation of anesthesia. The primary outcome was intubation time. The secondary outcomes were difficult laryngoscopy defined as the Cormack-Lehane Level > grade 2 and the number of intubation attempts. Results: Three hundred four patients were successfully enrolled and completed the study, 39 patients were identified as difficult laryngoscopy. The intubation time was shorter with the C-TMD>1 finger group 46.8 ± 7.3 s, compared with the C-TMD<1 finger group 50.8 ± 8.6 s (p<0.01). First attempt success rate was higher in the C-TMD>1 finger group 98.9% than in the C-TMD<1 finger group 87.1% (P<0.01). The correlation between the C-TMD and Cormack-Lehane Level was 0.317 (Spearman correlation coefficient, P<0.001), and the area under the ROC curve was 0.699 (P<0.01). The C-TMD < 1 finger width was the most consistent with difficult laryngoscopy (κ = 0.485;95%CI:0.286–0.612) and its OR value was 10.09 (95%CI: 4.19–24.28), sensitivity was 0.469 (95%CI: 0.325–0.617), specificity was 0.929 (95%CI: 0.877–0.964), positive predictive value was 0.676 (95%CI: 0.484–0.745), negative predictive value was 0.847 (95%CI: 0.825–0.865). Conclusion: Compared with the IID, Mallampati classification and ULBT, C-TMD has higher value in predicting difficult laryngoscopy and does not require the exposure of upper respiratory tract. Trial registration: The study was registered on October 21, 2019 in the Chinese Clinical Trial Registry (ChiCTR1900026775). [ABSTRACT FROM AUTHOR]

Background: Patients with critical illness due to infection with the 2019 coronavirus disease (COVID-19) show rapid disease progression to acute respiratory failure. The study aimed to screen the most useful predictive factor for critical illness caused by COVID-19.Methods: The study prospectively involved 61 patients with COVID-19 infection as a derivation cohort, and 54 patients as a validation cohort. The predictive factor for critical illness was selected using LASSO regression analysis. A nomogram based on non-specific laboratory indicators was built to predict the probability of critical illness.Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as an independent risk factor for critical illness in patients with COVID-19 infection. The NLR had an area under receiver operating characteristic of 0.849 (95% confidence interval [CI], 0.707 to 0.991) in the derivation cohort and 0.867 (95% CI 0.747 to 0.944) in the validation cohort, the calibration curves fitted well, and the decision and clinical impact curves showed that the NLR had high standardized net benefit. In addition, the incidence of critical illness was 9.1% (1/11) for patients aged ≥ 50 and having an NLR < 3.13, and 50% (7/14) patients with age ≥ 50 and NLR ≥ 3.13 were predicted to develop critical illness. Based on the risk stratification of NLR according to age, this study has developed a COVID-19 pneumonia management process.Conclusions: We found that NLR is a predictive factor for early-stage prediction of patients infected with COVID-19 who are likely to develop critical illness. Patients aged ≥ 50 and having an NLR ≥ 3.13 are predicted to develop critical illness, and they should thus have rapid access to an intensive care unit if necessary. [ABSTRACT FROM AUTHOR]

Background: As the most common form of lymphoma, diffuse large B-cell lymphoma (DLBCL) is a clinical highly heterogeneous disease with variability in therapeutic outcomes and biological features. It is a challenge to identify of clinically meaningful tools for outcome prediction. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to identify DLBCL prognostic subgroups for CHOP-based treatment.Methods: The expression microarray data and clinical characteristics of 791 DLBCL patients from two Gene Expression Omnibus (GEO) databases were used to establish and validate this model. By using univariate Cox regression, eight clinical or genetic signatures were analyzed. The elastic net-regulated Cox regression analysis was used to select the best prognosis related factors into the predictive model. To estimate the prognostic capability of the model, Kaplan-Meier curve and the area under receiver operating characteristic (ROC) curve (AUC) were performed.Results: A predictive model comprising 4 clinical factors and 2 pharmacogenomic gene signatures was established after 1000 times cross validation in the training dataset. The AUC of the comprehensive risk model was 0.78, whereas AUC value was lower for the clinical only model (0.68) or the gene only model (0.67). Compared with low-risk patients, the overall survival (OS) of DLBCL patients with high-risk scores was significantly decreased (HR = 4.55, 95% CI 3.14-6.59, log-rank p value = 1.06 × 10-15). The signature also enables to predict prognosis within different molecular subtypes of DLBCL. The reliability of the integrated model was confirmed by independent validation dataset (HR = 3.47, 95% CI 2.42-4.97, log rank p value = 1.53 × 10-11).Conclusions: This integrated model has a better predictive capability to ascertain the prognosis of DLBCL patients prior to CHOP-like treatment, which may improve the clinical management of DLBCL patients and provide theoretical basis for individualized treatment. [ABSTRACT FROM AUTHOR]

Background: Circulating extracelluar vesicles (EVs) in epithelial ovarian cancer (EOC) patients emanate from multiple cells. These EVs are emerging as a new type of biomarker as they can be obtained by non-invasive approaches. The aim of this study was to investigate circulating EVs from EOC patients and healthy women to evaluate their biological function and potential as diagnostic biomarkers.Methods: A quantitative proteomic analysis (iTRAQ) was applied and performed on 10 EOC patients with advanced stage (stage III-IV) and 10 controls. Twenty EOC patients and 20 controls were applied for validation. The candidate proteins were further validated in another 40-paired cohort to investigate their biomarker potential. Coagulation cascades activation was accessed by determining Factor X activity.Results: Compared with controls, 200 proteins were upregulated and 208 proteins were downregulated in the EOC group. The most significantly involved pathway is complement and coagulation cascades. ApoE multiplexed with EpCAM, plg, serpinC1 and C1q provide optimal diagnostic information for EOC with AUC = 0.913 (95% confidence interval (CI) =0.848-0.957, p < 0.0001). Level of activated Factor X was significantly higher in EOC group than control (5.35 ± 0.14 vs. 3.69 ± 0.29, p < 0.0001).Conclusions: Our study supports the concept of circulating EVs as a tool for non-invasive diagnosis of ovarian cancer. EVs also play pivotal roles in coagulation process, implying the inherent mechanism of generation of thrombus which often occurred in ovarian cancer patients at late stages. [ABSTRACT FROM AUTHOR]

Background: To reveal roles of reactive oxygen species (ROS) status in chemotherapy resistance and to develop a ROS scoring system for prognosis prediction in ovarian cancer.Methods: We tested the sensitizing effects of ROS elevating drugs to cisplatin (cDDP) in ovarian cancer both in vitro and in vivo. A ROS scoring system was developed using The Cancer Genome Atlas (TCGA) database of ovarian cancer. The associations between ROS scores and overall survival (OS) were analyzed in TCGA, Tothill dataset, and our in-house dataset (TJ dataset).Results: ROS-inducing drugs increased cisplatin-induced ovarian cancer cell injury in vitro and in vivo. ROS scoring system was established using 25 ROS-related genes. Patients were divided into low (scores 0-12) and high (scores 13-25) score groups. Improved patient survival was associated with higher scores (TCGA dataset hazard ratio (HR) = 0.43, P < 0.001; Tothill dataset HR = 0.65, P = 0.022; TJ dataset HR = 0.40, P = 0.003). The score was also significantly associated with OS in multiple datasets (TCGA dataset r2 = 0.574, P = 0.032; Thothill dataset r2 = 0.266, P = 0.049; TJ dataset r2 = 0.632, P = 0.001) and with cisplatin sensitivity in ovarian cancer cell lines (r2 = 0.799, P = 0.016) when used as a continuous variable. The scoring system showed better prognostic performance than other clinical factors by receiver operating characteristic (ROC) curves (TCGA dataset area under the curve (AUC) = 0.71 v.s. 0.65, Tothill dataset AUC = 0.73 v.s. 0.67, TJ dataset AUC = 0.74 v.s. 0.66).Conclusions: ROS status is associated with chemotherapy resistance. ROS score system might be a prognostic biomarker in predicting the survival benefit from ovarian cancer patients. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation.Methods: We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis.Results: Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves.Conclusion: The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. [ABSTRACT FROM AUTHOR]

Background: In the new Sepsis-3 definition, sepsis is defined as "life-threatening organ dysfunction due to a dysregulated host response to infection." We tested the predictive validity of the systematic inflammatory response syndrome (SIRS) criteria in patients in the Sepsis-3 cohort.Methods: Among 1243 electronic health records from 1 January to 31 December 2015 at Sichuan University West China Hospital, we identified patients with sepsis and septic shock according to the Sepsis-3 definition and divided them into 2 subsets: SIRS-positive and SIRS-negative. We compared their characteristics and outcomes as well as the predictive validity of the SIRS criteria for in-hospital mortality.Results: Of the 1243 patients, 631 were enrolled. Among these, 538 (85.3%) patients had SIRS-positive sepsis or septic shock, 168 (31.2%) of whom died, and 93 (14.7%) had SIRS-negative sepsis or septic shock, 20 (21.5%) of whom died (p = 0.06). Over a 1-year period, these groups had similar characteristics and changes in mortality. Among patients of the Sepsis-3 cohort admitted to the intensive care unit, the predictive validity for in-hospital mortality was lower for the SIRS criteria (area under the receiver operating characteristic curve [AUROC], 0.53; 95% confidence interval [95% CI], 0.49-0.57) than for the sequential (sepsis-related) organ failure assessment (SOFA) criteria (AUROC, 0.70; 95% CI, 0.66-0.74; p ≤ 0.01 for both). The SIRS score had poor predictive validity for the risk of in-hospital mortality.Conclusions: In this cohort study of the new Sepsis-3 definition, we found that the SIRS criteria are weaker than the SOFA criteria with respect to their predictive efficacy for in-hospital death. [ABSTRACT FROM AUTHOR]