An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of RCC; however, prognostic prediction tools for ccRCC are scant. Developing mRNA or long non-coding RNA (lncRNA)-based risk assessment tools may improve the prognosis in patients with ccRCC. RNA-sequencing and prognostic data from patients with ccRCC were downloaded from The Cancer Genome Atlas and the European Bioinformatics Institute Array database at the National Center for Biotechnology Information. Differentially expressed (DE) RNAs (DERs) and prognostic DERs were screened between less favorable and favorable prognoses using the limma package in R 3.4.1, and analyzed using univariate and multivariate Cox regression analyses, respectively. Risk score models were constructed using optimal combinations of DEmRNAs and DElncRNAs identified using the Least Absolute Shrinkage And Selection Operator Cox regression model of the penalized package. Associations between risk score models and overall survival time were evaluated. Independent prognostic clinical factors were screened using univariate and multivariate Cox regression analyses, and nomogram models were constructed. Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the clusterProfiler package in R3.4.1. A total of 451 DERs were identified, including 404 mRNAs and 47 lncRNAs, between less favorable and favorable prognoses, and 269 DERs, including 233 mRNAs and 36 lncRNAs, were identified as independent prognostic factors. Optimal combinations including 10 DEmRNAs or 10 DElncRNAs were screened using four risk score models based on the status or expression levels of the 10 DEmRNAs or 10 DElncRNAs. The model based on the expression levels of the 10 DEmRNAs had the highest prognostic power. These prognostic DEmRNAs may be involved in biological processes associated with the inflammatory response, complement and coagulation cascades and neuroactive ligand-receptor interaction pathways. The present validated risk assessment tool based on the expression levels of these 10 DEmRNAs may help to identify patients with ccRCC at a high risk of mortality. These 10 DEmRNAs in optimal combinations may serve as prognostic biomarkers and help to elucidate the pathogenesis of ccRCC.

Influence of gene expression on survival of clear cell renal cell carcinoma

“…90 Similarly, 10 differentially expressed genes (AGR3, CSF2, GAL3ST2, IGLL1, PLG, SAA1, SBSN, SOX2, WFDC13, and ZIC2) were included in a recent prognostic risk assessment tool for ccRCC with AUC of 0.99 without a validation set. 91 The results from previous studies are not consistent with one from the current study. Potential reasons for inconsistency can be small sample sizes, different racial/ethnic background, potential environmental factors and more importantly, lack of validation set.…”

“…Recently, seven differentially expressed autophagy‐related genes ( PRKCQ , BID , BAG1 , BIRC5 , ATG16L2 , EIF4EBP1 , and ATG9B ) were included in a recent prognostic survival assessment tool for ccRCC with AUCs of 0.752 and 0.783 for overall and disease free survival, respectively 90 . Similarly, 10 differentially expressed genes ( AGR3 , CSF2 , GAL3ST2 , IGLL1 , PLG , SAA1 , SBSN , SOX2 , WFDC13 , and ZIC2 ) were included in a recent prognostic risk assessment tool for ccRCC with AUC of 0.99 without a validation set 91 . The results from previous studies are not consistent with one from the current study.…”

IGFLR1 as a Novel Prognostic Biomarker in Clear Cell Renal Cell Cancer Correlating With Immune Infiltrates

“…Survival analysis and Cox regression analysis indicated that expression of IGFLR1 was a prognostic factor in ccRCC patients, rather than an independent prognostic factor (Sichani et al, 2010). Besides, although a number of previous studies have constructed prognostic risk models based on clinical factors, gene signature, or taking both into account (Chang et al, 2018;Chen et al, 2019Chen et al, , 2020Xu D. et al, 2020;Xu W.H. et al, 2020), IGFLR1 has not been reported as a factor in model construction.…”

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer

“…Among five lncRNAs in the EpiLncRNASig, several lncRNAs have been well studied in some cancers. For example, LINC00189 has been reported to be associated with several cancers, including ovarian cancer, cervical cancer ( Zhang et al, 2020 ), clear cell renal cell carcinoma ( Xu et al, 2020 ), and urinary bladder cancer ( Zhang et al, 2016 ). By analyzing CACNA1G-AS1 expression levels in 122 pairs of non-small cell lung cancer (NSCLC) and normal tissue samples as well as in NSCLC cell lines, Yu et al (2018) identified CACNA1G-AS1 as an oncogene to promote cell migration, invasion, and epithelial-mesenchymal transition via regulating HNRNPA2B1 .…”