A novel stemness-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity of clear cell renal cell carcinoma.

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.

METHOD

Gene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.

RESULTS

SRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.

CONCLUSION

We uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.