Automated Author ProfileQin, Yaqin
Qin, Yaqin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Therapeutic studies against human immunodeficiency virus type 1 (HIV-1) infection have become one of the important works in global public health. Differential expression analysis was performed between HIV-positive (HIV+) and HIV-negative (HIV-) patients for GPL6947 and GPL10558 of GSE29429. Coexpression analysis of common genes with the same direction of differential expression identified modules. Module genes were subjected to enrichment analysis, Short Time-series Expression Miner (STEM) analysis, and PPI network analysis. The top 100 most connected genes in the PPI network were screened to construct the LASSO model, and AUC values were calculated to identify the key genes. Methylation modification of key genes were identified by the chAMP package. Differences in immune cell infiltration between HIV + and HIV- patients, as well as between antiretroviral therapy (ART) and HIV + patients, were calculated using ssGSEA. We obtained 3610 common genes, clustered into nine coexpression modules. Module genes were significantly enriched in interferon signalling, helper T-cell immunity, and HIF-1-signalling pathways. We screened out module genes with gradual changes in expression with increasing time from HIV enrolment using STEM software. We identified 12 significant genes through LASSO regression analysis, especially proteasome 20S subunit beta 8 (PSMB8) and interferon alpha inducible protein 27 (IFI27). The expression of PSMB8 and IFI27 were then detected by quantitative real-time PCR. Interestingly, IFI27 was also a persistently dysregulated gene identified by STEM. In addition, 10 of the key genes were identified to be modified by methylation. The significantly infiltrated immune cells in HIV + patients were restored after ART, and IFI27 was significantly associated with immune cells. The above results provided potential target genes for early diagnosis and treatment of HIV + patients. IFI27 may be associated with the progression of HIV infection and may be a powerful target for immunotherapy.
Authors
- Huang, Huijuan ;
- Lv, Jiannan ;
- Huang, Yonglun ;
- Mo, Zhiyi ;
- Xu, Haisheng ;
- Huang, Yiyang ;
- Yang, Linghui ;
- Wu, Zhengqiu ;
- Li, Hongmian ;
- Qin, Yaqin
Therapeutic studies against human immunodeficiency virus type 1 (HIV-1) infection have become one of the important works in global public health. Differential expression analysis was performed between HIV-positive (HIV+) and HIV-negative (HIV-) patients for GPL6947 and GPL10558 of GSE29429. Coexpression analysis of common genes with the same direction of differential expression identified modules. Module genes were subjected to enrichment analysis, Short Time-series Expression Miner (STEM) analysis, and PPI network analysis. The top 100 most connected genes in the PPI network were screened to construct the LASSO model, and AUC values were calculated to identify the key genes. Methylation modification of key genes were identified by the chAMP package. Differences in immune cell infiltration between HIV + and HIV- patients, as well as between antiretroviral therapy (ART) and HIV + patients, were calculated using ssGSEA. We obtained 3610 common genes, clustered into nine coexpression modules. Module genes were significantly enriched in interferon signalling, helper T-cell immunity, and HIF-1-signalling pathways. We screened out module genes with gradual changes in expression with increasing time from HIV enrolment using STEM software. We identified 12 significant genes through LASSO regression analysis, especially proteasome 20S subunit beta 8 (PSMB8) and interferon alpha inducible protein 27 (IFI27). The expression of PSMB8 and IFI27 were then detected by quantitative real-time PCR. Interestingly, IFI27 was also a persistently dysregulated gene identified by STEM. In addition, 10 of the key genes were identified to be modified by methylation. The significantly infiltrated immune cells in HIV + patients were restored after ART, and IFI27 was significantly associated with immune cells. The above results provided potential target genes for early diagnosis and treatment of HIV + patients. IFI27 may be associated with the progression of HIV infection and may be a powerful target for immunotherapy.
Authors
- Huang, Huijuan ;
- Lv, Jiannan ;
- Huang, Yonglun ;
- Mo, Zhiyi ;
- Xu, Haisheng ;
- Huang, Yiyang ;
- Yang, Linghui ;
- Wu, Zhengqiu ;
- Li, Hongmian ;
- Qin, Yaqin