bioRxivCancer BiologyPreprint
A multidimensional Pan-cancer analysis of CDKN1A identifies CDKN1A as an Immunological and Prognostic Biomarker
Wenyang ZhangiD, Qinglong Ma, Wenrun Li, Honghui ZhaoiD, Linghui Zhong, Yinan XiaoiD, Yaru Ren, Kaixin YangiD, Yonghong LiiD, Lei ShiiD
Abstract
CDKN1A/p21 is well recognized for its role in cell cycle regulation and genomic stability. However, its functions in the Tumor microenvironment (TME) and tumor immunity are not yet fully understood. Hereby, we explored CDKN1A expression and immunological/prognostic values via various databases and analytical methods including cBioPortal, Kaplan-Meier, UCSCXenaShiny, TIMER, Single-cell RNA sequencing (scRNA-seq) analysis, etc. In addition, we explored different approaches including CCK8, EdU, Colony formation, Drug sensitivity and Annixin-V assay to explore the influence of p21 in proliferative capacity in cancer cells. We found that CDKN1A is lowly expressed in BLCA, BRCA, COAD, KICH, LUAD, LUSC, PRAD, READ and STAD compared to normal samples, whereas it is highly expressed in CHOL, HNSC, KIRC, KIRP and THCA compared to normal cohorts. CDKN1A expression is significantly correlated with overall survival, disease-specific survival, disease-free survival and progression-free interval different cancer types. Additionally, CDKN1A is associated with CD4+ T cell, CD8+ T cell, Neutrophil, Macrophage and Myeloid dendritic cell infiltration in diverse cancer types. Functional experiments reveal that p21 overexpression leads to a significant reduction in proliferative capacity, facilitates cell apoptosis and senescence in multiple cancer cell lines. In contrast, silenced p21 facilitates cell growth and wound closure, prevent cell senescence in different cancer cell lines. In conclusion, our findings suggest that CDKN1A may serve as a valuable prognostic and immunotherapeutic marker in diverse cancer.
- organism: human
- assay: bulk RNA-seq
- research focus: protein folding & dynamics
- research focus: cell signaling & trafficking
- research focus: cancer & tumor microenvironment
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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