As of August 19, 2021, more than 209 million people across the world had been infected by COVID-19, with the United States accounting for more than 36 million cases and 618 000 deaths.¹ COVID-19 disproportionately affects racial and ethnic minority groups.² To reduce exposure and mortality rates, it is critical to identify the disparities associated with greater occurrences of COVID-19 among different populations.³

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.

Multi-drug chemoresistance remains one of the most common reasons for chemotherapy failure. The membrane transporter protein ABCG2/BCRP1 has been shown in vitro to effectively reduce the intracellular concentrations of several prominent anticancer chemotherapeutic agents such as mitoxantrone and doxorubicin. Intriguingly, cancer stem cells are known to be characterized by multi-drug chemoresistance. Taking into account that the ABCG2⁺ subset of tumor cells are often enriched with cells with cancer stem-like phenotypes, it has been proposed that ABCG2 activity underlies the ability of cancer cells to regenerate post-chemotherapy. Furthermore, we also review evidence suggesting that tyrosine kinase inhibitors, including imatinib and gefitinib, are both direct and downstream inactivators of ABCG2 and, therefore, serve as candidates to reverse cancer stem cell chemoresistance and potentially target cancer stem cells.

Although 1 in 9 American men will receive a diagnosis of prostate cancer (PC), most men with this diagnosis will not die from it, as most PCs are indolent. However, there is a subset of patients in which the once-indolent PC becomes metastatic and eventually, fatal. In this study, we analyzed microbial compositions of intratumor bacteria in PC to determine the influence of the microbiome on metastatic growth. Using large-scale RNA-sequencing data and corresponding clinical data, we correlated the abundance of microbes to immune pathways and PC risk factors, identifying specific microbes that either significantly deter or contribute to cancer aggressiveness. Interestingly, most of the microbes we found appeared to play anti-tumor roles in PC. Since these anti-tumor microbes were overrepresented in tumor samples, we believe that microbes thrive in the tumor microenvironment, outcompete cancer cells, and directly mitigate tumor growth by recruiting immune cells. These include Listeria monocytogenes, Methylobacterium radiotolerans JCM 2831, Xanthomonas albilineans GPE PC73, and Bradyrhizobium japonicum, which are negatively correlated with Gleason score, Tumor-Node-Metastasis (TNM) stage, prostate-specific antigen (PSA) level, and Androgen Receptor (AR) expression, respectively. We also identified microbes that contribute to tumor growth and are positively correlated with genomic alterations, dysregulated immune-associated (IA) genes, and prostate cancer stem cells (PCSC) genes.

Head and neck squamous cell carcinoma persists as one of the most common and deadly malignancies, with early detection and effective treatment still posing formidable challenges. To expand our currently sparse knowledge of the noncoding alterations involved in the disease and identify potential biomarkers and therapeutic targets, we globally profiled the dysregulation of small nucleolar and long noncoding RNAs in head and neck tumors. Using next-generation RNA-sequencing data from 40 pairs of tumor and matched normal tissues, we found 2808 long noncoding RNA (lncRNA) transcripts significantly differentially expressed by a fold change magnitude ≥2. Meanwhile, RNA-sequencing analysis of 31 tumor-normal pairs yielded 33 significantly dysregulated small nucleolar RNAs (snoRNA). In particular, we identified two dramatically down-regulated lncRNAs and one down-regulated snoRNA whose expression levels correlated significantly with overall patient survival, suggesting their functional significance and clinical relevance in head and neck cancer pathogenesis. We confirmed the dysregulation of these noncoding RNAs in head and neck cancer cell lines derived from different anatomic sites, and determined that ectopic expression of the two lncRNAs inhibited key EMT and stem cell genes and reduced cellular proliferation and migration. As a whole, noncoding RNAs are pervasively dysregulated in head and squamous cell carcinoma. The precise molecular roles of the three transcripts identified warrants further characterization, but our data suggest that they are likely to play substantial roles in head and neck cancer pathogenesis and are significantly associated with patient survival.