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.

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Background: Numerous studies on the E2F transcription factors have led to increasing insights that E2Fs could be an important driver of the formation and progression of many human cancers. Little is known about the function of distinct E2Fs in chromophobe renal cell carcinoma (chRCC). Methods: We utilized the UALCAN, GEPIA, Cancer Genome Atlas (TCGA) database, cBioPortal, Metascape, STRING, Cytoscape, GeneMANIA, TIMER, TISIDB, GSCALite, and MEXPRESS databases to investigate the transcription level, genetic alteration, methylation, and biological function of E2Fs in chRCC patients, and its association with the occurrence, progress, prognosis, and immune cell infiltration in patients with chRCC. Results: We found that E2F1/2/4/7/8 were more expressed in chRCC tissues than in normal tissues, while the expression of E2F5/6 was lower in the former than in the latter, and the expression levels of E2F1/2/4/5/6//7/8 were also associated with the histological parameters of chRCC, including T-stage and N-stage. Higher expression of E2F1/2/7/8 was found to be significantly correlated with worse overall survival (OS) in chRCC patients. Cox regression and time-dependent ROC analysis further suggested that E2F1/2 could be the potential independent biomarkers for chRCC prognosis. Besides, a moderate mutation rate of E2Fs (34%) was noticed in chRCC, and the genetic mutations in E2Fs were associated with poor survival of chRCC patients. We noticed that the expression of E2Fs was statistically correlated with the immune cell infiltration in chRCC. Moreover, we also found that the expression of E2F1 was significantly correlated with tumor-infiltrating lymphocytes and immunomodulators, E2F7 expression was associated with MHC molecules, and the expression of E2F1/8 was correlated to their methylation levels. Conclusion:Our results provide novel insights for selecting the prognostic biomarkers for chRCC and suggest that E2F1/2 could act as potential prognostic biomarkers for the survival of chRCC patients. However, more in-depth experiments are required to identify the underlying mechanisms and verify the clinical value of E2F1/2 in the prognosis of chRCC.

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.

Background COL4A family genes are a group of genes related to the extracellular matrix, which have been proven to be associated with various cancers. However, its relationship with kidney renal clear cell carcinoma (KIRC) has not been reported. Methods Hence, we obtained the data of differential expression and survival time of COL4A genes in KIRC from an online open-access database including ONCOMINE, UALCAN, GEPIA, Cancer Genome Atlas (TCGA) database, cBioPortal, Metascape, and STRING. Results We found significant overexpression in COL4A1, COL4A2 while COL4A 3, COL4A4, COL4A5, and COL4A6 has decreased in tumor tissues. Moreover, almost all COL4A family genes obviously correlated with individual cancer stages of KIRC; and higher expression of COL4A1/2/3/BP/4 was found to accompany better overall survival time (OS) while COL4A5 with a lower OS in KIRC patients. We also found that the COL4A genes altered group had longer OS and DFS than unaltered teams.

Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life and causes a global pandemic. The purpose of current research is 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 c, 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 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 symptoms presented a robust positive correlation 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 is 19.8 days in China. The daily Baidu Index value's optimal time lags were the fourth day for cough, third day for fatigue, firth day for sputum production, firth day for shortness of breath, and 0 days for fever. Conclusion: Search terms of COVID-19-related symptoms on the Baidu search engine have significant correlations with confirmed cases. Since the Baidu search engine can reflect the Public's attention to the pandemic and 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.