Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020

ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.SettingOne hundred and eighty countries’ patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.DesignUsing the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries’ final scores for each measure.Primary outcomeThe primary outcomes are the COVID-19 cases and fatality grades in 2020.ResultsThe United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries’ preparedness.ConclusionCOVID-19 fatality can be a good proxy for countries’ resources and system’s resilience in managing the pandemic. These findings suggest that countries’ economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.

Characterization of Regional Groundwater System Based on Aquifer Response to Recharge–Discharge Phenomenon and Hierarchical Clustering Analysis

The investigations of groundwater hydrograph reasonably reflect the aquifer response to recharge–discharge phenomenon and its characteristics. A better understanding of aquifer characteristics such as regional aquifer classification, recharge and discharge patterns, aquifer geology and flow patterns are the surface indicators that may be more effective and less costly for interpreting basic regional hydrogeological conditions and assessments. This study deals with the application of Hierarchical Clustering Analysis to understand the groundwater spatio-temporal patterns and to visualize/classify the nature of the aquifer in the regional area of Kaohsiung City, Taiwan. Groundwater level fluctuation patterns and slopes of rising and recession limbs are used to identify the pumping effects and classify aquifers. The results of clustering analysis show that the groundwater observation wells in the study area can be divided into five major characteristics along with the upstream to downstream of Kaoping River. The clusters are consistent with basic lithology distribution and age of sedimentary, which represents the characteristics of groundwater level fluctuation. The identified groundwater hydrographs patterns provide newer insights related to aquifer response to recharge–discharge phenomenon, types of aquifers and their behaviors. The knowledge of water level fluctuations in the observation wells provides a piece of prior information about the abstraction of groundwater. The proposed aquifer classification and pumping effect have great potential for applied use in groundwater management e.g., save drilling cost.

Progression Risk Score Estimation Based on Immunostaining Data in Oral Cancer Using Unsupervised Hierarchical Clustering Analysis: A Retrospective Study in Taiwan

This study aimed to investigate whether the progression risk score (PRS) developed from cytoplasmic immunohistochemistry (IHC) biomarkers is available and applicable for assessing risk and prognosis in oral cancer patients. Participants in this retrospective case-control study were diagnosed between 2012 and 2014 and subsequently underwent surgical intervention. The specimens from surgery were stained by IHC for 16 cytoplasmic target markers. We evaluated the results of IHC staining, clinical and pathological features, progression-free survival (PFS), and overall survival (OS) of 102 oral cancer patients using a novel estimation approach with unsupervised hierarchical clustering analysis. Patients were stratified into high-risk (52) and low-risk (50) groups, according to their PRS; a metric consisting of cytoplasmic PLK1, PhosphoMet, SGK2, and SHC1 expression. Moreover, PRS could be extended for use in the Cox proportional hazard regression model to estimate survival outcomes with associated clinical parameters. Our study findings revealed that the high-risk patients had a significantly increased risk in cancer progression compared with low-risk patients (hazard ratio (HR) = 2.20, 95% confidence interval (CI) = 1.10–2.42, p = 0.026). After considering the influences of demographics, risk behaviors, and tumor characteristics, risk estimation with PRS provided distinct PFS groups for patients with oral cancer (p = 0.017, p = 0.019, and p = 0.020). Our findings support that PRS could serve as an ideal biomarker for clinical use in risk stratification and progression assessment in oral cancer.

The Development and Validation of a Novel 71-Gene Signature for Risk Stratification and Prognosis in Lower Grade Glioma

Background: Lower-grade gliomas (LGG) are a diverse group of primary brain tumors with relatively poor overall survival in young adults. In this study, we aimed to establish novel method that are effectively predictive of prognosis of LGG patients. Methods: We detected and validated prognosis-associated genes using gene expression and c`Clinical data of LGG patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. We then established a novel prognostic 71-gene score and 17-gene nomograms and analyzed their relationship with overall survival (OS) and relapse-free survival (RFS) in LGG patients. We also performed Gene Set Enrichment Analysis to investigate the altered signalling pathways associated with the 71-gene score phenotype and hierarchical clustering analysis of 71 genes to detect subgroups of LGG patients with distinct clinical characteristics. Results: We identified 1489 genes significantly correlated with patients’ prognosis in LGG. The 71-gene score was predictive of favourable OS and RFS in LGG patients independently of clinicopathological characteristics. The wnt signalling pathway, glutathione metabolism, primary immunodeficiency, galactose metabolism were the potential pathways involved in the prognostication of the 71-gene score. Hierarchical clustering analysis of the 71 genes revealed three subgroups of LGG patients in the TCGA dataset. The cluster2 LGG tumours were associated with higher grade, more frequent radiation therapy, poorer OS and RFS than cluster1 and cluster3 tumours. The 71-gene nomogram incorporating the survival‐related clinical factors showed good prediction accuracies for overall survival, 3-year and 5‐year survival (area under curve [AUC] = 0.79, 0.67 and 0.75 respectively). Conclusions: The 71-gene nomogram may turn out to be a useful and robust method to remarkably ameliorate the prognostic prediction in LGG.

CTNNB1-Mutant Aldosterone-Producing Adenomas With Somatic Mutations of GNA11/GNAQ Have Distinct Phenotype and Genotype

Background: We report (this meeting) somatic mutation of GNA11/Q in CTNNB1-mutant APAs. The recurrent co-driver mutation causes reversible hypertension in puberty, pregnancy, or menopause. We have investigated the molecular mechanism of this association. Methods: Gene expression profiles in 3 double mutant APAs were studied by unsupervised hierarchical clustering analysis and enrichment analysis of 362 differentially expressed genes and validated by qPCR, IFC and IHC in 10 double mutant APAs or transfected primary adrenal cells. Multiple region biopsies were performed in 7 adrenals adjacent to double-mutant APAs and 4 APAs with KCNJ5 or CACNA1D mutations. The findings of APA mutations in adjacent adrenals were replicated in each case by ddPCR ± NGS. Results: Unsupervised hierarchical clustering analysis showed clustering of the double-mutant APAs, and a high proportion of genes were many-fold upregulated compared to other APAs. LHCGR, TMEM132E, DKK1, C9orf84, FAP, GNRHR and MPP3 are among the genes with high expression. A small number of genes are down-regulated in the double-mutant APAs, including CYP11B1. qPCR confirmed an average of ~10 to 1000-fold higher expression of the hallmark genes in double-mutants. Enrichment analysis showed significant enrichment of features or terms concerned with cell junction and cell adhesion (P<10–8). IFC confirmed LHCGR intensity was 31–144 fold higher in primary adrenal cells with GNA11-p.Gln209Pro transfection and high CTNNB1 intensity. LHCGR intensity was qualitatively and quantitatively associated with immunofluorescence for CTNNB1. IHC of double-mutant APAs showed absent CYP11B1 but strong staining of CYP11B2. qPCR confirmed a lower CYP11B1/CYP11B2 ratio and a higher LHCGR expression (P<10–3, both). IHC confirmed LHCGR positivity in double-mutant APAs but distribution varied both within and between cells. Adjacent ZG was hyperplastic, with absence of both CYP11B1 and CYP11B2 staining, but weak/moderate staining for LHCGR. The same GNA11 ± CTNNB1 somatic mutations were detected in multiple regions of the adjacent adrenals to 3 double mutant APAs. qPCR of hallmark APA genes differed from the APAs. High concordance between ddPCR, NGS and Sanger sequencing of the findings of APA mutations in adjacent adrenals when analysed in the same sample. No mutations were found in 4 adrenals adjacent to APAs with KCNJ5 or CACNA1D mutations, nor in other 4 adrenals adjacent to double-mutant APAs. Conclusions: Patients harboring APAs with somatic mutations in both GNA11/GNAQ Q209 and CTNNB1 have distinct phenotype in both the APAs and their adjacent adrenals. Same GNA11 ± CTNNB1 somatic mutations were found in the adjacent adrenals to double mutant APAs. We infer that a double-hit within related pathways is more likely than a single-hit to cause large increases in expression of LHCGR, and of other genes which may influence clinical presentation.