Background The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.
Cancer stem cells (CSCs) have been shown as a distinct population of cancer cells strongly implicated with resistance to conventional chemotherapy. Metformin, the most widely prescribed drug for diabetes, was reported to target cancer stem cells in various cancers. In this study, we sought to determine the effects of metformin on head and neck squamous cell carcinoma (HNSCC). CSCs and non-stem HNSCC cells were treated with metformin and cisplatin alone, and in combination, and cell proliferation levels were measured through MTS assays. Next, potential targets of metformin were explored through computational small molecule binding analysis. In contrast to the reported effects of metformin on CSCs in other cancers, our data suggests that metformin protects HNSCC CSCs against cisplatin in vitro. Treatment with metformin resulted in a dose-dependent induction of the stem cell genes CD44, BMI-1, OCT-4, and NANOG. On the other hand, we observed that metformin successfully decreased the proliferation of non-stem HNSCC cells. Computational drug–protein interaction analysis revealed mitochondrial complex III to be a likely target of metformin. Based on our results, we present the novel hypothesis that metformin targets complex III to reduce reactive oxygen species (ROS) levels, leading to the differential effects observed on non-stem cancer cells and CSCs.
Immunotherapy has emerged in recent years as arguably the most effective treatment for advanced hepatocellular carcinoma (HCC), but the failure of a large percentage of patients to respond to immunotherapy remains as the ultimate obstacle to successful treatment. Etiology-associated dysregulation of immune-associated (IA) genes may be central to the development of this differential clinical response. We identified immune-associated genes potentially dysregulated by alcohol or viral hepatitis B in HCC and validated alcohol-induced dysregulations in vitro while using large-scale RNA-sequencing data from The Cancer Genome Atlas (TCGA). Thirty-four clinically relevant dysregulated IA genes were identified. We profiled the correlation of all genomic alterations in HCC patients to IA gene expression while using the information theory-based algorithm REVEALER to investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification. We also studied gene expression regulators and identified multiple microRNAs that were implicated in HCC pathogenesis that can potentially regulate these IA genes’ expression. Our study identified potential key pathways, including the IL-7 signaling pathway and TNFRSF4 (OX40)- NF-κB pathway, to target in immunotherapy treatments and presents microRNAs as promising therapeutic targets for dysregulated IA genes because of their extensive regulatory roles in the cancer immune landscape.
Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality in the world, but current treatment options are very limited in efficacy for patients who are diagnosed late, which often occurs because of limitations in current screening methods. Immunotherapy has emerged in recent years as arguably the most effective treatment for advanced HCC, but the failure of a large percentage of patients to respond to immunotherapy remains the ultimate obstacle to successful treatment. A phase I clinical trial with CTLA-4 checkpoint blockade has only achieved a 17.6% partial response rate in patients with advanced HCC, while another clinical trial using the PD-1 checkpoint inhibitor nivolumab has reported 18% partial response. Etiology-associated dysregulation of immune-associated (IA) genes may be able to explain the development of this differential clinical response. Using large-scale RNA-sequencing data from The Cancer Genome Atlas (TCGA), we identified immune-associated genes potentially dysregulated by alcohol or viral hepatitis B in HCC. Thirty-four dysregulated IA genes exhibiting significant correlation of gene expression to survival data and clinical variables were identified. To investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification, we profiled the correlation of all genomic alterations in HCC patients to IA gene expression using the information theory-based algorithm REVEALER. We also studied gene expression regulators and identified multiple microRNAs implicated in HCC pathogenesis that can potentially regulate these IA genes’ expression. Finally, we validated the dysregulation of several genes by alcohol, including the downregulation of NDRG2 and SOCS2, in vitro using HCC cell lines. Our study identified several potentially key pathways, including the IL-7 pathway, TNFRSF4 (OX40)- NF-κB pathway, and IL-10 pathway, that may be targeted in HCC immunotherapy treatments as well as possible sources of dysregulation for pathways known to be implicated in HCC immunotherapy efficacy. Lastly, we present microRNAs as promising therapeutic targets for dysregulated IA genes because of their extensive regulatory roles in the cancer immune landscape. Citation Format: Wei Tse Li, Christine O. Honda, Yuanhao Qu, Thomas K. Honda, Omar A. Saad, Jessica Wang-Rodriguez, Weg M. Ongkeko. Analysis of the hepatocellular carcinoma transcriptome reveals dysregulation in pathways implicated in immunotherapy efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3389.
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