Expression quantitative trait locus (eQTL) analysis, which links variations in gene expression to genotypes, is essential to understanding gene regulation and to interpreting disease-associated loci. Currently identified eQTLs are mainly in samples of blood and other normal tissues. However, no database comprehensively provides eQTLs in large number of cancer samples. Using the genotype and expression data of 9196 tumor samples in 33 cancer types from The Cancer Genome Atlas (TCGA), we identified 5 606 570 eQTL-gene pairs in the cis-eQTL analysis and 231 210 eQTL-gene pairs in the trans-eQTL analysis. We further performed survival analysis and identified 22 212 eQTLs associated with patient overall survival. Furthermore, we linked the eQTLs to genome-wide association studies (GWAS) data and identified 337 131 eQTLs that overlap with existing GWAS loci. We developed PancanQTL, a user-friendly database (http://bioinfo.life.hust.edu.cn/PancanQTL/), to store cis-eQTLs, trans-eQTLs, survival-associated eQTLs and GWAS-related eQTLs to enable searching, browsing and downloading. PancanQTL could help the research community understand the effects of inherited variants in tumorigenesis and development.

Long non-coding RNAs (lncRNAs) play key roles in various cellular contexts and diseases by diverse mechanisms. With the rapid growth of identified lncRNAs and disease-associated single nucleotide polymorphisms (SNPs), there is a great demand to study SNPs in lncRNAs. Aiming to provide a useful resource about lncRNA SNPs, we systematically identified SNPs in lncRNAs and analyzed their potential impacts on lncRNA structure and function. In total, we identified 495 729 and 777 095 SNPs in more than 30 000 lncRNA transcripts in human and mouse, respectively. A large number of SNPs were predicted with the potential to impact on the miRNA–lncRNA interaction. The experimental evidence and conservation of miRNA–lncRNA interaction, as well as miRNA expressions from TCGA were also integrated to prioritize the miRNA–lncRNA interactions and SNPs on the binding sites. Furthermore, by mapping SNPs to GWAS results, we found that 142 human lncRNA SNPs are GWAS tagSNPs and 197 827 lncRNA SNPs are in the GWAS linkage disequilibrium regions. All these data for human and mouse lncRNAs were imported into lncRNASNP database (http://bioinfo.life.hust.edu.cn/lncRNASNP/), which includes two sub-databases lncRNASNP-human and lncRNASNP-mouse. The lncRNASNP database has a user-friendly interface for searching and browsing through the SNP, lncRNA and miRNA sections.

In December 2019, an outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS‐Cov‐2) infection occurred in Wuhan, and rapidly spread to worldwide, which has attracted many people's concerns about the patients. However, studies on the infection status of medical personnel is still lacking. A total of 54 cases of SARS‐Cov‐2 infected medical staff from Tongji Hospital between 7 January and 11 February 2020 were analyzed in this retrospective study. Clinical and epidemiological characteristics were compared between different groups by statistical method. From 7 January to 11 February 2020, 54 medical staff of Tongji Hospital were hospitalized due to coronavirus disease 2019 (COVID‐19). Most of them were from other clinical departments (72.2%) rather than emergency department (3.7%) or medical technology departments (18.5%). Among the 54 patients with COVID‐19, the distribution of age had a significant difference between non‐severe type and severe/critical cases (median age: 47 years vs 38 years; P = .0015). However, there was no statistical difference in terms of gender distribution and the first symptoms between theses two groups. Furthermore, we observed that the lesion regions in SARS‐Cov‐2 infected lungs with severe‐/critical‐type of medical staff were more likely to exhibit lesions in the right upper lobe (31.7% vs 0%; P = .028) and right lung (61% vs 18.2%; P = .012). Based on our findings with medical staff infection data, we suggest training for all hospital staff to prevent infection and preparation of sufficient protection and disinfection materials.

Previous epidemiological studies on the relation between dietary legume consumption and risk of colorectal cancer (CRC) remain controversial. We conducted a meta-analysis based on prospective cohort studies to investigate the association between dietary legume consumption and risk of CRC. Fourteen cohort studies were finally included, containing a total of 1903459 participants and 12261 cases who contributed 11628960 person-years. We found that higher legume consumption was associated with a decreased risk of CRC (RR, relative risk = 0.91; 95% CI, confidence interval = 0.84–0.98). Subgroup analyses suggested that higher legume consumption was inversely associated with CRC risk in Asian (RR = 0.82; 95% CI = 0.74–0.91) and soybean intake was associated with a decreased risk of CRC (RR = 0.85; 95% CI = 0.73–0.99). Findings from our meta-analysis supported an association between higher intake of legume and a reduced risk of CRC. Further studies controlled for appropriate confounders are warranted to validate the associations.

Alternative splicing (AS) is a widespread process that increases structural transcript variation and proteome diversity. Aberrant splicing patterns are frequently observed in cancer initiation, progress, prognosis and therapy. Increasing evidence has demonstrated that AS events could undergo modulation by genetic variants. The identification of splicing quantitative trait loci (sQTLs), genetic variants that affect AS events, might represent an important step toward fully understanding the contribution of genetic variants in disease development. However, no database has yet been developed to systematically analyze sQTLs across multiple cancer types. Using genotype data from The Cancer Genome Atlas and corresponding AS values calculated by TCGASpliceSeq, we developed a computational pipeline to identify sQTLs from 9 026 tumor samples in 33 cancer types. We totally identified 4 599 598 sQTLs across all cancer types. We further performed survival analyses and identified 17 072 sQTLs associated with patient overall survival times. Furthermore, using genome-wide association study (GWAS) catalog data, we identified 1 180 132 sQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerSplicingQTL (http://www.cancersplicingqtl-hust.com/) for users to conveniently browse, search and download data of interest. This database provides an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms in human cancer.