Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response
Sun Yat-sen University · Sun Yat-sen University Cancer Center · +3 more institutions
Abstract
Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking.
Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.
Citation impact
- FWCI
- 27.04
- Percentile
- 100%
- References
- 112
Authors
10- ZZZhen ZhangCorresponding
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- ZWZixian Wang
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- YCYan‐Xing Chen
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- HWHao‐Xiang Wu
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- LYLing Yin
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
Topics & keywords
- Cancer stem cell
- Cancer
- Immunotherapy
- Transcriptome
- Gene signature
- Computational biology
- Stem cell
- Oncology
- Good health and well-being
Funding
- NNNational Natural Science Foundation of ChinaAwards: 81772587, 201803040019, 81871985, 82003269, 82073377, 81930065
- CPChina Postdoctoral Science FoundationAward: 2021M693651
- NSNatural Science Foundation of Guangdong ProvinceAwards: 81871985, 2014A030312015, 2019A1515011109, 2021A1515012439
- SAScience and Technology Planning Project of Guangdong ProvinceAwards: 2019B020227002, 81871985