Research Paper Volume 16, Issue 7 pp 6118—6134

A novel risk signature based on liquid-liquid phase separation-related genes reveals prognostic and tumour microenvironmental features in clear cell renal cell carcinoma

Qing Lu1, *, , Ping Xi2, *, , Suling Xu2, *, , Zhicheng Zhang3, , Binbin Gong2, , Ji Liu2, , Qiqi Zhu2, , Ting Sun2, , Shaoxing Zhu1, , Ru Chen1, ,

  • 1 Department of Urology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian, P.R. China
  • 2 Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
  • 3 Department of Surgery, Fuzhou First People’s Hospital, Fuzhou 344000, Jiangxi, China
* Equal contribution

Received: September 13, 2023       Accepted: February 7, 2024       Published: March 27, 2024
How to Cite

Copyright: © 2024 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Clear cell renal cell carcinoma(ccRCC) is one of the most common malignancies. However, there are still many barriers to its underlying causes, early diagnostic techniques and therapeutic approaches.

Materials and Methods: The Cancer Genome Atlas (TCGA)- Kidney renal clear cell (KIRC) cohort differentially analysed liquid-liquid phase separation (LLPS)-related genes from the DrLLPS website. Univariate and multivariate Cox regression analyses and LASSO regression analyses were used to construct prognostic models. The E-MTAB-1980 cohort was used for external validation. Then, potential functions, immune infiltration analysis, and mutational landscapes were analysed for the high-risk and low-risk groups. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) experiments as well as single-cell analyses validated the genes key to the model.

Results: We screened 174 LLPS-related genes in ccRCC and constructed a risk signature consisting of five genes (CLIC5, MXD3, NUF2, PABPC1L, PLK1). The high-risk group was found to be associated with worse prognosis in different subgroups. A nomogram constructed by combining age and tumour stage had a strong predictive power for the prognosis of ccRCC patients. In addition, there were differences in pathway enrichment, immune cell infiltration, and mutational landscapes between the two groups. The results of qRT-PCR in renal cancer cell lines and renal cancer tissues were consistent with the biosignature prediction. Three single-cell data of GSE159115, GSE139555, and GSE121636 were analysed for differences in the presence of these five genes in different cells.

Conclusions: We developed a risk signature constructed based on the five LLPS-related genes and can have a high ability to predict the prognosis of ccRCC patients, further providing a strong support for clinical decision-making.


APC: Antigen-presenting cell; AUC: Area Under the Curve; BP: Biological processes; CC: Cell components; ccRCC: Clear cell renal carcinoma; DCA: Decision Curve Analysis; DSS: Disease-Specific Survival; GEO: Gene expression omnibus; GO: Gene Ontology; HK-2: Human Kidney-2; HR: Hazard Ratio; KEGG: Kyoto Encyclopedia of Genes and Genomes; KIRC: Kidney renal clear cell; LLPS: Liquid-liquid phase separation; MF: Molecular function; NBs: Nuclear bodies; NHEJ: Non-homologous end joining; OS: Overall Survival; PPAP: Peroxisome proliferators-activated receptor; qPCR: Quantitative real-time polymerase chain reaction; RCC: Renal cell carcinoma; RNP: Ribonucleoprotein; ROC: Receiver Operating Characteristic Curve; ssGSEA: Single sample gene set enrichment analysis; TCGA: The Cancer Genome Atlas; TNF: Tumor necrosis factor; TPM4: Tropomyosin 4; XCI: X-chromosome inactivation; XIRP2: Xin actin binding repeat containing 2; YBX1: Y-box binding protein 1.