Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology (preprint)

Background: Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to people's health, which is a high-risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. Methods: We used a bioinformatics and systems biology approach to investigate the relationship between GC and COVID-19. The gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. Results: A total of 209 shared DEGs were identified to explore the linkages between COVID-19 and GC. Functional analyses showed that Immune-related pathway collectively participated in the development and progression of COVID-19 and GC. In addition, there are selected 10 hub genes including CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, and IFIT2. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This study also provides insight into the diseases most associated with mutual DEGs, which may provide new ideas for research on the treatment of COVID-19. Conclusions: This research has the possibility to be contributed to effective therapeutic in COVID-19 and GC.

Exploration of the Link Between COVID-19 and Alcoholic Hepatitis from the Perspective of Bioinformatics and Systems Biology (preprint)

Objective: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been suggested to purpose threats to health of mankind. Alcoholic hepatitis (AH) is a life-threatening acute and chronic liver failure that takes place in sufferers who drink excessively. During the epidemic, AH has an increasing incidence of severe illness and mortality. However, for these two diseases, the intrinsic relationship of molecular pathogenesis, as well as common therapeutic strategies are still poorly understood. Methods: The transcriptome of the COVID-19 and AH has been compared to obtain the altered genes and hub genes were screened out through protein-protein interaction (PPI) network analysis. Via gene ontology (GO), pathway enrichment and transcription regulator analysis, a deeper appreciation of the interplay mechanism between hub genes were established. Results: With 181 common differentially expressed genes (DEGs) of AH and COVID-19 were obtained, 10 hub genes were captured. Follow-up studies located that these 10 genes typically mediated the diseases occurrence by regulating the activities of the immune system. Other results suggest that the common pathways of the two ailments are enriched in regulating the function of immune cells and the release of immune molecules. Conclusion: This study reveals the common pathogenesis of COVID-19 and AH and assist to discover necessary therapeutic targets to combat the ongoing pandemic induced via SARS-CoV-2 infection and acquire promising remedy strategies for the two diseases.

Bioinformatics Approaches to Determine the Effect of SARS-CoV-2 Infection on Patients with Intrahepatic Cholangiocarcinoma (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals throughout the world, which poses a serious threat to human health. COVID-19 is a systemic disease that affects tissues and organs, including the lung and liver. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. The relationship between HCC and COVID-19 has been reported in previous studies, but ICC has been rare. With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC. Transcriptional profiling of COVID-19 and ICC were obtained from the GEO database. A total of 70 common differentially expressed gene (DEGs) of both diseases were identified to investigate shared pathways. Then top-ranked 10 key DEGs (SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1) were identified as hub genes by protein-protein interaction (PPI) network analysis. In addition, transcriptional regulatory networks regulating hub genes were revealed by hub Gene- transcription factor (TF) interaction analysis and hub gene-microRNA (miRNAs) interaction analysis. This study is expected to provide new references for future research and treatment of COVID-19 and ICC.

From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection (preprint)

The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media, motivated by the tight connection between hashtags and news credibility observed in our empirical analyses. We first introduce a new graph that captures all (direct and \textit{indirect}) relationships among hashtags. Then, a language-independent semi-supervised algorithm is developed to predict fake news based on this constructed graph. This study first investigates the indirect relationship among hashtags; the proposed approach can be extended to any homogeneous graph to capture a comprehensive relationship among nodes. Language independence opens the proposed method to multilingual fake news detection. Experiments conducted on two real-world datasets demonstrate the effectiveness of our approach in identifying fake news, especially at an \textit{early} stage of propagation.

Activated neutrophils in the initiation and progression of COVID-19: hyperinflammation and immunothrombosis in COVID-19

Coronavirus disease 2019 (COVID-19) is a pandemic respiratory disease caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). COVID-19 is typically associated with fever and influenza-like symptoms in its early stages. Severe cases progress to acute respiratory distress syndrome/acute lung injury (ARDS/ALI), multiple organ damage, and even death. Until now, there has been a lack of specific and definitive treatment for COVID-19, which further challenges the situation. Previous clinical and laboratory data showed that neutrophils were significantly decreased in patients who died from COVID-19 in the early stages of disease;when patients were admitted to the hospital the number of neutrophils increased dramatically from 7 to 14 days after admission, which is correlated to myocardial and liver injury, thromboembolic complications, and poor prognosis. Autopsy findings revealed abundant neutrophil infiltration in the pulmonary capillaries and exudation into the alveolar cavity. Therefore, we speculate that neutrophils may play an important role in the initiation and progression of COVID-19. In this review, the relationship among the dynamic changes in neutrophils, cytokine storms, and the release of neutrophil extracellular traps (NETs) with the progression of COVID-19 was elucidated in detail. With a better understanding of the pathogenic mechanisms this can lead to improved clinical applications which are identified and discussed in this review.

CHECKED: Chinese COVID-19 Fake News Dataset (preprint)

COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.

ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research (preprint)

First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com.