Clinical and Laboratory Predictors of Severity, Criticality, and Mortality in COVID-19: A Multisystem Disease

Coronavirus disease 2019 (COVID-19) pandemic continues devastating effects on healthcare systems. Such a crisis calls for an urgent need to develop a risk stratification tool. The present chapter aimed to identify laboratory and clinical correlates of adverse outcomes in patients with COVID-19. To this end, we conducted a systematic evaluation of studies that investigated laboratory abnormalities in patients with COVID-19 and compared i. patients with a severe form of disease and patients with a non-severe form of the disease, ii. patients who were in critical condition and patients who were not in critical condition, and iii. patients who survived and patients who died. We included 54 studies in the data synthesis. Compared to patients with a non-severe form of COVID-19, patients who had a severe form of disease revealed higher values for white blood cells (WBC), polymorphonuclear leukocytes (PMN), total bilirubin, alanine aminotransferase (ALT), creatinine, troponin, procalcitonin, lactate dehydrogenase (LDH), and D-dimer. By contrast, platelet count, lymphocyte count, and albumin levels were decreased in patients with a severe form of COVID-19. Also, patients with a severe phenotype of disease were more likely to have diabetes, chronic heart disease, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, hypertension, chronic kidney disease (CKD), and malignancy. Compared to patients who survived, patients who died had higher WBC, PMN, total bilirubin, ALT, procalcitonin, IL-6, creatinine, PT, lymphocyte count, platelet count, and albumin. Also, non-survivors revealed a higher prevalence of diabetes, chronic heart disease, COPD, cerebrovascular disease, and CKD. Meta-analyses identified several laboratory parameters that might help the prediction of severe, critical, and lethal phenotypes of COVID-19. These parameters correlate with the immune system function, inflammation, coagulation, and liver and kidney function.

Identification and validation of a hypoxia-related prognostic and immune microenvironment signature in bladder cancer

Background

Bladder cancer is the leading causes of cancer-associated mortality and seriously affects population health. Hypoxia plays a key role in tumor development and immune escape, which contributes to malignant behaviors.

Methods

In this study, we analyzed the RNA-seq and clinical information of bladder cancer patients from The Cancer Genome Atlas (TCGA) database. To investigate the hypoxia-related prognostic and immune microenvironment in bladder cancer, we constructed a hypoxia-related risk model for overall survival (OS). The RNA-seq and clinical data of bladder cancer patients from the Gene Expression Omnibus (GEO) database were used as validation sets.

Results

The hypoxia-related risk signature was significantly correlated with clinical outcomes and could independently predict OS outcomes. Furthermore, the hypoxia-related risk signature could effectively reflected the levels of immune cell type fractions and the expression of critical immune checkpoint genes were higher in the high-risk group compared to the low-risk group. We also validated the expression levels of the prognostic genes in bladder cancer and paracancerous tissue samples through qRT-PCR analysis.

Conclusion

We established a 7 hypoxia-related gene (HRG) signature that can be used as an independent clinical predictor and provided a potential mechanism in bladder cancer immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01954-4.

Lower body mass index and age are predictive of improved pain and health utility scores following arthroscopic management of femoroacetabular impingement

PURPOSE

To identify patient factors associated with improved pain scores, functional hip scores, health-related quality of life, and re-operation rates after arthroscopic management of femoroacetabular impingement (FAI).

METHODS

Using the comprehensive dataset from the multinational Femoroacetabular Impingement Randomized Controlled Trial (FIRST), a total of 13 prognostic factors that were chosen a priori were identified that would be expected to predict post-surgical outcomes. The primary outcome was pain assessed using a Visual Analogue Scale (VAS) and secondary outcomes included hip function (Hip Outcome Score [HOS] and International Hip Outcome Tool [iHOT-12]), health-related quality of life (Short Form-12 [SF-12] and Euro-Qol 5 Dimensions [EQ-5D]), and re-operation rate. A multivariable linear regression was used to analyse the change questionnaire scores from baseline to 12 months post-surgery including all 13 prognostic factors as independent variables. A total of 27 re-operation events were analysed at 24 months using a multivariable logistic regression including only the treatment group variable.

RESULTS

Of the 154 patients that had VAS scores completed at 12 months, a lower BMI (adjusted mean difference [aMD], 4.48 for a 5-unit decrease in BMI; 95% confidence interval [CI] 0.33-8.63; p = 0.035) was significantly associated with less pain. There was a significant negative association between increasing age and 1-year EQ-5D scores (aMD, - 0.04 for every 10-year increase in age; 95% CI - 0.07 to - 0.006; p = 0.020). The degree of impingement, severity of osteoarthritis, type of procedure, and adjudicated quality of surgery were not significantly associated with improvement across all outcomes at 12 months. Furthermore, there was no significant association between the treatment variable and the incidence of re-operation at 24 months.

CONCLUSION

This study identified that lower BMI and age are predictive of improved pain and health utility scores, respectively, following arthroscopic management of FAI at 12 months post-surgery. These results may be a helpful adjunct in clinical decisions for this patient population when determining candidacy for surgical intervention.

LEVEL OF EVIDENCE

I.

Prognostic value of the early change in neutrophil-to-lymphocyte ratio in metastatic pancreatic adenocarcinoma

In metastatic pancreatic adenocarcinoma, a high neutrophil-to-lymphocyte ratio (NLR) at diagnosis is a marker of poor prognosis. The prognostic role of baseline NLR and NLR change during first-line chemotherapy were determined. We conducted a retrospective study by using data from a single-center prospective cohort and a randomized open-label, multicenter, randomized trial. Two hundred and twelve patients were analyzed. Baseline NLR>5 was an independent marker of poor prognosis for overall survival (HR=2.01, 95% CI 1.33-3.05; P=0.001) and progression-free survival (PFS; HR=1.80, 95% CI 1.23-2.65; P=0.0026). According to NLR dynamics (n=172), patients with NLR≤5 on days 1 and 15 had a significantly better prognosis than those with NLR≤5 on day 1 and NLR>5 on day 15 (HR=2.23, 95% CI 1.18-4.21; P=0.013), NLR >5 on day 1 and NLR ≤5 on day 15 (HR=3.25, 95% CI 1.86-5.68; P<0.001), and NLR>5 on days 1 and 15 (HR=3.37, 95% CI 1.93-5.90; P<0.001). Over time, bad responders (PFS <6 months) had significantly higher mean NLR than good responders (PFS>6 months; group effect: P<0.0001). Seven out of eight patients with baseline NLR>5 had circulating tumor DNA. This study confirmed the independent prognostic value of baseline NLR >5 in metastatic pancreatic cancer. The change in NLR early during chemotherapy was also a prognostic indicator in patients with NLR ≤5.

Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

Background

Growing attention have been paid to the relationship between TP53 and tumor immunophenotype, but there are still lacking enough search on the field of gastric cancer (GC).

Materials and methods

We identified differential expressed immune-related genes (DEIRGs) between the TP53-altered GC samples (n = 183) and without TP53-altered GC samples (n = 192) in The Cancer Genome Atlas and paired them. In the TCGA cohort (n = 350), a risk score was determined through univariate and multivariate cox regression and Lasso regression analysis. Patients were divided into two groups, high-risk and low-risk, based on the median risk score. Four independent cohorts (GSE84437,n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26901, n = 100) from the Gene Expression Omnibus (GEO) database were used to validate the reliability and universal applicability of the model.

Results

The signature contained 11 gene pairs showed good performance in predicting progression-free survival (PFS), disease-free survival (DFS), disease special survival (DSS), and the overall survival (OS) for GC patients in the TCGA cohort. The subgroup analysis showed that the signature was suitable for GC patients with different characteristics. The signature could capable of distinguish GC patients with good prognosis and poor prognosis in all four independent external validation cohorts. The high- and low-risk groups differed significantly in the proportion of several immune cell infiltration, especially for the T cells memory resting, T cells memory activated and follicular helper, and Macrophage M0, which was also related to the prognosis of GC patients.

Conclusion

The present work proposed an innovative system for evaluating the prognosis of gastric cancer. Considering its stability and general applicability, which may become a widely used tool in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02846-x.

Development and Validation of a Metabolic-related Prognostic Model for Hepatocellular Carcinoma

BACKGROUND AND AIMS

Growing evidence suggests that metabolic-related genes have a significant impact on the occurrence and development of hepatocellular carcinoma (HCC). However, the prognostic value of metabolic-related genes for HCC has not been fully revealed.

METHODS

mRNA sequencing and clinical data were obtained from The Cancer Genome Atlas and the GTEx Genotype-Tissue Expression comprehensive database. Differentially expressed metabolic-related genes in tumor tissues (n=374) and normal tissues (n=160) were identified by the Wilcoxon test. Time-dependent receiver operating characteristic curve analysis, univariate multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to evaluate the predictive effectiveness and independence of the prognostic model. Two independent cohorts (International Cancer Genome Consortiums and GSE14520) were applied to verify the prognostic model.

RESULTS

Our study included a total of 793 patients with HCC. We constructed a risk score consisting of five metabolic-genes (BDH1, RRM2, CYP2C9, PLA2G7, and TXNRD1). For the overall survival rate, the low-risk group had a considerably higher rate than the high-risk group. Univariate and multivariate Cox regression analyses indicated that the risk score was an independent predictor for the prognosis of HCC.

CONCLUSIONS

We constructed and validated a novel prognostic model, which may provide support for the precise treatment of HCC.

Ventilator-associated pneumonia in the era of COVID-19 pandemic: How common and what is the impact?

We reviewed similarities and differences of ventilator associated pneumonia in Sars-Cov2 infection and with other ARDS. The differences in epidemiology and outcome will be detailed. Possible explanations of differences in pathophysiology of VAP in Sarscov2 infections will be cited and discussed.

Employing hypoxia characterization to predict tumour immune microenvironment, treatment sensitivity and prognosis in hepatocellular carcinoma

The hypoxic microenvironment was recognized as a major driving force of the malignant phenotype in hepatocellular carcinoma (HCC), which contributes to tumour immune microenvironment (TIM) remodeling and tumor progression. Dysregulated hypoxia-related genes (HRGs) result in treatment resistance and poor prognosis by reshaping tumor cellular activities and metabolism. Approaches to identify the relationship between hypoxia and tumor progression provided new sight for improving tumor treatment and prognosis. But, few practical tools, forecasting relationship between hypoxia, TIM, treatment sensitivity and prognosis in HCC were reported. Here, we pooled mRNA transcriptome and clinical pathology data from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and later developed a hypoxia risk model including four HRGs (DCN, DDIT4, PRKCA and NDRG1). The high-risk group displayed poor clinical characteristics, a malignant phenotype with carcinogenesis/proliferation pathways activation (MTORC1 and E2F) and immunosuppressive TIM (decreased immune cell infiltrations and upregulated immunosuppressive cytokines). Meanwhile, activated B cells, effector memory CD8 T cells and EZH2 deregulation were associated with patient’s survival, which might be the core changes of HCC hypoxia. Finally, we validated the ability of the hypoxia risk model to predict treatment sensitivity and found high hypoxia risk patients had poor responses to HCC treatment, including surgical resection, Sorafenib, Transarterial Chemoembolization (TACE) and immunotherapy. In conclusion, based on 4 HRGs, we developed and validated a hypoxia risk model to reflect pathological features, evaluate TIM landscape, predict treatment sensitivity and compounds specific to hypoxia signatures in HCC patients.

Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography as a promising biomarker in patients with advanced non-small cell lung cancer treated with first-line pembrolizumab

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have become the standard of care in the management of advanced non-small cell lung cancer (NSCLC). Nevertheless, only a small proportion of patients benefit from ICIs. The aim of the present study is to assess whether 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography ([18F]FDG-PET/CT)-derived parameters may be used as biomarkers in patients with advanced NSCLC receiving first-line pembrolizumab.

MATERIALS AND METHODS

This is a monocentric retrospective cohort study including patients with advanced NSCLC (stage IV) and Programmed death-ligand 1 (PD-L1) expression ≥50% treated with pembrolizumab. A control group of patients treated with epidermal growth factor receptor (EGFR) inhibitors for EGFR-mutated NSCLC was also enrolled. Only patients with a positive [18F]18F-FDG PET/CT result within 60 days from treatment initiation were included.Total metabolic tumour volume (tMTV) was calculated for each lesion using a dedicated software (PET VCAR; GE Healthcare), which semiautomatically delineates the tumour's contours with a maximum standardised uptake value (SUVmax) threshold of 42% within the lesion. tMTV was obtained summing each lesion's MTV. Potential prognostic parameters for overall survival (OS) were analysed (tMTV, SUVmax, bone/liver metastasis, neutrophil:lymphocyte ratio ≥4, Eastern Cooperative Oncology Group performance status ≥2, lactate dehydrogenase above the upper limit of normal).

RESULTS

Overall, 34 patients treated with first line-pembrolizumab and 40 patients treated with EGFR tyrosine kinase inhibitors were included. In the pembrolizumab group, the median follow-up was 20.3, while the median OS was 4.7 months (95% confidence interval [CI] = 0.3-9.1) for patients with tMTV ≥75 cm³ vs not reached (NR) for patients with tMTV <75 cm³ (95% CI = NR-NR; hazard ratio [HR] = 5.37; 95% CI = 1.72-16.77; p = 0.004). No difference was found in the control group (HR = 1.43; 95% CI = 0.61-3.34; p = 0.411).

CONCLUSION

Our data suggest that tMTV ≥75cm³ can be used as a prognostic biomarker of poor outcomes in patients with PD-L1-high advanced NSCLC treated with first-line pembrolizumab. This information could be useful for the selection of patients who may require the addition of chemotherapy to pembrolizumab.

Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes

Background

Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs).

Methods

The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.

Results

A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration.

Conclusions

We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01915-x.

Overexpressing PTTG family genes predict poor prognosis in kidney renal clear cell carcinoma

Background

Pituitary tumor transforming genes (PTTG1, PTTG2, and PTTG3P) play key roles in the pathogenesis and development of human cancers. The studies show that overexpression of the PTTG genes is associated with tumor progression and migration. However, the function of the PTTG genes in the prognostic value of kidney renal clear cell carcinoma is rarely known by people.

Methods

The expression of PTTG family genes was analyzed by the ONCOMINE, Human Protein Atlas, GEPIA2, and UALCAN database. The relationship between PTTG family genes expression level and clinical indicators including prognostic data in kidney renal clear cell carcinoma was analyzed by GEPIA2, TCGA portal, and UALCAN. cBioPortal database was used to analyze the genetic mutations of differentially expressed PTTG family members. Similar genes of the PTTG family (90 in total) obtained from GEPIA2 and Metascape were used for GO enrichment to explore the interaction among similar genes. The online tools of Metascape and STRING were used for functional and pathway enrichment analysis.

Results

PTTG1, 2, and 3P mRNA and protein expression upregulated in kidney renal clear cell carcinoma kidney renal clear cell carcinoma patients compared with normal tissues. And higher expression level of PTTG family genes was associated with shorter overall survival (OS) and disease-free survival (DFS). Furthermore, overexpression of the PTTG family genes had been found correlated with individual cancer stages and pathological tumor grades. In addition, 18% of mutations in the PTTG family genes were associated with short-term survival in kidney renal clear cell carcinoma patients.

Conclusions

A single PTTG gene or PTTG family genes as a whole may be a potential prognostic biomarker for kidney renal clear cell carcinoma.

Physiologically increased total bilirubin is associated with reduced risk of first myocardial infarction: A meta-analysis and dose-response analysis

AIM

Bilirubin has potential predictive and prognostic value for myocardial infarction (MI), but the clinical evidence remains controversial. We performed this meta-analysis to systematically quantify the relationships between circulating bilirubin levels and the incidence of MI and post-MI adverse events.

DATA SYNTHESIS

We searched the PubMed, Cochrane Library, Embase, and Web of Science databases for ad-hoc studies, published up to October 17, 2020, recording bilirubin before MI (predictive analyses) or adverse events (prognostic analyses). Relative risks (RR) were pooled by a random-effects model. The dose-response analysis was conducted by restricted cubic splines. In patients without previous MI, increased total bilirubin (TB) reduced the risk of long-term (>3 year) first MI by 22% (95% confidence interval [CI]: 0.69-0.88, n = 4). The dose-response analysis indicated that the RR for first MI decreased by 2.7% per each 2 μmol/L increment of TB (three studies, 95% CI: 1.3%-4.1%, P < 0.001), with a cut-off value of 12.60 μmol/L for RR > 1.00. Elevated bilirubin reduced the incidence of first and recurrent MI by 36% (95% CI: 0.42-0.98, n = 7). However, after suffering MI, higher TB concentrations could not decrease the risk of recurrent MI (RR: 1.02, 95% CI: 0.67-1.55, n = 5) and increased the incidence of short-term (<1 year) post-MI major adverse cardiovascular events, all-cause mortality, and cardiovascular mortality, but not long-term (≥1 year).

CONCLUSION

Higher TB levels within a physiological range reduced the incidence of long-term first MI, with a cut-off value of 12.60 μmol/L.

CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario

CanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis-free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.

Clinical prediction models: diagnosis versus prognosis

Clinical prediction models play an increasingly important role in contemporary clinical care, by informing healthcare professionals, patients and their relatives about outcome risks, with the aim to facilitate (shared) medical decision making and improve health outcomes. Diagnostic prediction models aim to calculate an individual's risk that a disease is already present, whilst prognostic prediction models aim to calculate the risk of particular heath states occurring in the future. This article serves as a primer for diagnostic and prognostic clinical prediction models, by discussing the basic terminology, some of the inherent challenges, and the need for validation of predictive performance and the evaluation of impact of these models in clinical care.

Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

INTRODUCTION

Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC).

METHODS

mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively.

RESULTS

Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan-Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation.

CONCLUSIONS

We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It's assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

Comprehensive analysis of immune-related prognostic genes in the tumour microenvironment of hepatocellular carcinoma

BACKGROUND

The mortality rate of hepatocellular carcinoma (HCC) remains high worldwide despite surgery and chemotherapy. Immunotherapy is a promising treatment for the rapidly expanding HCC spectrum. Therefore, it is necessary to further explore the immune-related characteristics of the tumour microenvironment (TME), which plays a vital role in tumour initiation and progression.

METHODS

In this research, 866 immune-related differentially expressed genes (DEGs) were identified by integrating the DEGs of samples from The Cancer Genome Atlas (TCGA)-HCC dataset and the immune-related genes from databases (InnateDB; ImmPort). Afterwards, 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA).

RESULTS

Seven immune-related prognostic DEGs were identified using the L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model, and the ImmuneRiskScore model was constructed on this basis. The prognostic index of the ImmuneRiskScore model was then validated in the relevant dataset. Patients were divided into high- and low-risk groups according to the ImmuneRiskScore. Differences in the immune cell infiltration of patients with different ImmuneRiskScore values were clarified, and the correlation of immune cell infiltration with immunotherapy biomarkers was further explored.

CONCLUSION

The ImmuneRiskScore of HCC could be a prognostic marker and can reflect the immune characteristics of the TME. Furthermore, it provides a potential biomarker for predicting the response to immunotherapy in HCC patients.

Bell's palsy workup: Does audiometry add value?

Objective

To investigate if routine audiometry in Bell’s palsy patients has prognostic value.

Methods

Retrospective case review was conducted on all Bell’s palsy patients (n=191) seen at the tertiary otolaryngology specialist outpatient clinic from 2015 to 2017. Correlation of ipsilesional audiometric thresholds with patients’ time-to-recovery and initial clinical severity (measured by House-Brackmann (HB) scoring) were used for the prognostic outcome measure. Audiometry results were analyzed using three contiguous frequency pure-tone average (1kHz, 2kHz, 4kHz). Statistical analysis was done via Stata (v13.1), significance tests were 2-sided at 5% significance level.

Results

There was no significant difference between audiometric thresholds between the ipsilesional ear and the contralateral ear (p=0.87). Time-to-recovery was significantly longer for patients with severe initial presentation as compared to mild and moderate severity (p<0.01). There was no correlation found between the audiometry results and HB score at presentation (p=0.39). There was no correlation found between ipsilesional audiometric thresholds and time-to-recovery (p=0.58).

Conclusion

Our study suggests that routine audiometry has limited prognostic value in Bell’s palsy patients.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

The use of the soluble receptor for advanced glycation-end products (sRAGE) as a potential biomarker of disease risk and adverse outcomes

The soluble receptor for advanced glycation end-products (sRAGE) has been classically considered a sink for pro-inflammatory RAGE ligands and as such has been associated with protection from inflammatory stress and disease. An alternative, though not mutually exclusive view is that high levels of sRAGE in circulation reflect the overstimulation of cell surface RAGE which if persistent, lead to the amplification of pro-inflammatory processes and the exacerbation of pathological states. With these two scenarios in mind this review focuses on the potential role of sRAGE as a prospective biomarker of disease risk and adverse outcomes.

•

The prognostic value of measuring sRAGE levels in blood is subjected to debate.

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Raised sRAGE levels may result from the overstimulation of cell surface RAGE.

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Raised sRAGE may reflect chronic inflammation and multimorbidity rather than a healthy state.

•

sRAGE is a promising biomarker of disease risk and adverse outcomes.

Epigenetic regulation of papillary thyroid carcinoma by long non-coding RNAs

Thyroid cancer is the most common primary endocrine malignancy with papillary thyroid carcinoma (PTC) its most common subtype. The jump in diagnoses over last many years has prompted re-assessment of molecularly targeted therapies and the discovery of novel targets. Long non-coding RNAs (lncRNAs) are increasingly being assessed for their expression in various PTC models. Interestingly, in addition to cell line models, a large proportion of the reported studies have evaluated lncRNA levels in PTC patient samples providing an immediate clinical relevance of their findings. While most lncRNAs either promote or suppress PTC pathogenesis, data on individual lncRNAs is not very clear. As expected, lncRNAs function in PTC through sponging of microRNAs as well as modulation of several signaling pathways. The process of epithelial-mesenchymal transition and the PI3K/Akt and wnt signaling pathways have emerged as the primary targets of lncRNAs in PTC. This comprehensive review discusses all the information that is available on lncRNAs in PTC, ranging from in vitro and in vivo findings to the possible role of lncRNAs as diagnostic and/or prognostic biomarkers.

Identification and validation of a five-gene prognostic signature for hepatocellular carcinoma

BACKGROUND

ARID1A is a commonly mutated tumor suppressor gene found in all human cancer types, but its clinical significance, oncogenic functions, and relevant mechanisms in hepatocellular carcinoma (HCC) are not well understood.

OBJECTIVE

We aimed to improving the prognosis risk classification of HCC from the perspective of ARID1A mutations.

MATERIALS AND METHODS

We examined the interaction between ARID1A mutations and the overall survival via Kaplan-Meier survival analysis. We used gene set enrichment analysis (GSEA) to elucidate the influence of ARID1A mutations on signaling pathways. A prognostic model was constructed using LASSO and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was used to estimate the performance and accuracy of the model.

RESULTS

HCC patients with ARID1A mutations presented poor prognosis. By GSEA, we showed that genes upregulated by reactive oxygen species (ROS) and regulated by MYC were positively correlated with ARID1A mutations. A prognostic signature consisting of 5 genes (SRXN1, LDHA, TFDP1, PPM1G, and EIF2S1) was constructed in our research. The signature showed good performance in predicting overall survival (OS) for HCC patients by internal and external validation.

CONCLUSION

Our research proposed a novel and robust approach for the prognostic risk classification of HCC patients, and this approach may provide new insights to improve the treatment strategy of HCC.

Comparison of the Prognostic Ability of the HCT-CI, the Modified EBMT, and the EBMT-ADT Pre-transplant Risk Scores for Acute Leukemia

BACKGROUND

Allogeneic hematopoietic cell transplantation (HCT) outcomes may be predicted by published risk scores; however, the ideal system has not been identified for acute leukemias.

PATIENTS AND METHODS

We retrospectively examined the Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI), modified European Group for Blood and Marrow Transplantation (mEBMT), EBMT-Alternating Decision Tree (ADT), and others on 231 patients with acute leukemia.

RESULTS

Acute myeloid leukemia was diagnosed in 200 patients, and acute lymphocytic leukemia was diagnosed in 31 patients. For HCT-CI, patients were grouped as 0 to 1, 2 to 3, and > 3. For mEBMT, patients were grouped as 0 to 2, 3, and > 3. For EBMT-ADT, the 100-day mortality was calculated and grouped as ≤ 4.1%, 4.1% to 11.5%, and > 11.5%. Higher HCI-CI demonstrated inferior overall survival (P = .04; c-statistic, 0.57), whereas mEBMT and EBMT-ADT did not stratify well. A new weighted score was developed that assigned 1 point for age ≥ 60 years, acute lymphocytic leukemia diagnosis, mismatch unrelated or haploidentical donor, cardiovascular comorbidity, and pre-transplant diabetes, whereas arrhythmia received 2 points. The new weighted score assigned 0 points to 88 (38%), 1 to 2 points to 121 (52%) and ≥ 3 points to 22 (10%) patients, and demonstrated improved prognostic capability compared with the other scores (P = .0001; c-statistic, 0.61).

CONCLUSIONS

The HCT-CI stratifies patients with leukemia for overall survival but is inferior to our single-center score, which is influenced by cardiac comorbidity and arrhythmia. Differences in pre-transplant risk scores may be related to different transplant practices.

Inhibition of lncRNA KCNQ1OT1 Improves Apoptosis and Chemotherapy Drug Response in Small Cell Lung Cancer by TGF-β1 Mediated EMT

Purpose

Drug resistance is one of the main causes of chemotherapy failure in patients with small cell lung cancer (SCLC), and extensive biological studies into chemotherapy drug resistance are required.

Materials and Methods

In this study, we performed lncRNA microarray, in vitro functional assays, in vivo models and cDNA microarray to evaluate the impact of lncRNA in SCLC chemoresistance.

Results

The results showed that KCNQ1OT1 expression was upregulated in SCLC tissues and was a poor prognostic factor for patients with SCLC. Knockdown of KCNQ1OT1 inhibited cell proliferation, migration, chemoresistance and promoted apoptosis of SCLC cells. Mechanistic investigation showed that KCNQ1OT1 can activate transforming growth factor-β1 mediated epithelial-to-mesenchymal transition in SCLC cells.

Conclusion

Taken together, our study revealed the role of KCNQ1OT1 in the progression and chemoresistance of SCLC, and suggested KCNQ1OT1 as a potential diagnostic and prognostic biomarker in SCLC clinical management.