Background New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. Methods We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. Conclusion The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

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Objective: The COL4A family genes (COL4As) are a set of extracellular matrix-related genes that have been proved a tight relationship among various cancers. However, the functional role of different COL4As (COL4A1/2/3/4/5/6) in clear cell renal cell carcinoma (ccRCC) is unclear. Methods: We obtained the data from online open-access databases including ONCOMINE, UALCAN, GEPIA, Cancer Genome Atlas (TCGA), cBioPortal, METASCAPE, STRING, TIMER, GSCALite, MEXPRESS, and TISIDB to explore the correlation between COL4As expression and genome-wide difference, progression, prognosis, genetic mutation, functional enrichment, tumor immune microenvironment, and methylation in ccRCC patients. Results: The significantly higher COL4A1/2 expression and lower COL4A3/4/5/6 expression were observed in ccRCC tissues than in normal kidney tissues. Transcriptomic levels of COL4A1/2/3/4 were significantly correlated with tumor grade and stage. The higher expression levels of COL4A1/2/3/4 were accompanied by a longer overall survival time (OS); the higher expression levels of COL4A3/4 with lower expression levels of COL4A5 were associated with a longer disease-free time (DFS). Univariate/multivariate regression model analysis showed that COL4A4 could be a potential independent biomarker for ccRCC prognosis. And a high mutation rate (29%) of COL4As was observed in ccRCC patients. However, there were no relationships between mutation rates of COL4As and OS, DFS in ccRCC patients (p>0.05). Besides, we founded that the COL4As expressions were significant associated with the infiltration of the immune cells, tumor-infiltrating lymphocytes, three immunomodulators (immunoinhibitory, immunostimulator, MHC molecule), chemokines, and receptors. Conclusion: The results suggested that the transcript levels of COL4As could act as potential indicators for early disease progression. The expression of COL4A4 could contribute directly to disease prognosis. Besides, COL4A1/2/3/4 widely participated in tumor immunity. However, further studies are needed to confirm their clinical values in the ccRCC patients.

Background Human sarcomas (SARC) are a group of malignant tumors that originated from mesenchymal lineages with more than 60 subtypes. However, potential biomarkers for the diagnosis and prognosis of SARC remain to be investigated. Methods We obtained three GSE raw matrix files (GSE39262, GSE21122, GSE48418) that related to various subtypes of sarcoma from the public GEO database and explored the widely differential expression genes in three obtained GSE files. Then common differential expression genes (CDGEs) were identified. We analyzed the correlation between the expression of the top five interacted genes of CDEGs and genome-wide differences, prognosis, genetic mutation, functional enrichment, immune infiltration, immune checkpoint, and marker genes’ expression of N6-methyladenosine (m 6 A) modification in SARC patients. Besides, a prognostic nomogram was constructed to predict the survival of SARC patients. Results Among the three GSE files, 42 CDGEs were identified, and the top five interacted genes were ASPM, CCNB2, PRC1, AURKA, and SCM2. The expression levels of the five genes were higher in the SARC group than that in the normal group. The transcriptional level of CCNB2, PRC, and SCM2 was correlated to the worse survival of SARC. The constructed nomogram that combined CNB2, PRC1, and SCM2 showed a fairly good incredibility in predicting the survival of SARC (C-index: 0.711). Furthermore, the five genes were widely involved in immune infiltration, immune checkpoint, and m 6 A modification. In addition, we found a minor survival-related mutation rate (9%) of the five identified genes in SARC patients (p < 0.05). Conclusion The results suggested the five identified genes widely participated in the prognosis, immune infiltration, immune checkpoint, and m 6 A modification of SARC patients. This study provided a theoretical basis for the research about the correlation between the level of five identified genes and sarcoma, but the further mechanism needs to be verified by experiments.

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