Identification of a Novel Immune-Related Prognostic Biomarker and Small-Molecule Drugs in Clear Cell Renal Cell Carcinoma (ccRCC) by a Merged Microarray-Acquired Dataset and TCGA Database

Abstract

Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal cancer, with a poor prognosis. Our study aimed to identify a biomarker that is significantly associated with ccRCC prognosis and novel immunotherapeutic targets, as well as some novel molecular drugs for ccRCC. Based on the overlap of The Cancer Genome Atlas (TCGA)-Kidney Renal Clear Cell Carcinoma (KIRC) data and the ImmPort database, we obtained 1,292 immune-related genes (IRGs) and constructed a weighed co-expression network based on the IRGs. A total of 39 hub genes were screened out in three modules. CTLA4, which had the highest connectivity degree among the screened genes in a protein–protein interaction network (degree = 24), was selected. Internal validation based on the GEPIA database revealed that patients with a higher expression of CTLA4 had a significantly shorter overall survival time and disease-free survival time. Expression of CTLA4 was also closely correlated with local recurrence, pathologic stage, and immune infiltration level. External validation based on the Oncomine database and merged microarray-acquired dataset validated the mRNA expression level of hub genes. Gene-set enrichment analysis revealed that six KEGG signaling pathways, which were significantly associated with CTLA4, were enriched on immune-related pathways. Further analysis according to the TIMER database demonstrated that CTLA4 expression was positively related to dendritic cells (cor = 0.446, P = 1.32E-23) and negatively associated with tumor purity (cor = −0.267, P = 5.51E-09). Finally, we screened out 293 differentially expressed genes by integrating six datasets from the GEO database. The Connectivity Map (CMap) analysis revealed the strong potential of three small molecule drugs (monensin, quercetin, and fenbufen) for ccRCC treatment. In conclusion, CTLA4 was identified and validated in prognosis of ccRCC. CTLA4 may be a new prognostic biomarker and immunotherapeutic target for ccRCC. Monensin, quercetin, and fenbufen may be novel choices for ccRCC treatment.

Comprehensive Transcriptomic Analysis of Critical RNA Regulation Associated With Metabolism and Prognosis in Clear Cell Renal Carcinoma

“… Xu et al (2019) used available RNA-sequence data from TCGA and Fudan University Shanghai Cancer Center (FUSCC) to reveal that AQP may act as an oncogene and a promising prognostic marker in ccRCC. In addition, Xiao et al (2020) obtained 293 DEGs by integrating six datasets from the GEO database and CMap analysis. However, the role of the metabolism-related gene set in ccRCC remains largely unclear.…”

N6-methyladenosine Methylation Related Immune Biomarkers Correlates With Clinicopathological Characteristics and Prognosis in Clear Cell Renal Cell Carcinoma

“…CTLA4 was found to be elevated in ccRCC and its high expression showed strong correlations with tumor progression and worse outcomes [31]. Xiao et al showed that high level of CTLA4 was associated with a obvious decreased survival time, pathologic stage and local recurrence, as well as negatively related to tumor purity [32]. These results might explain why patients with high risk had poor survival.…”

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

“…The TCGA is well-established as the most comprehensive cancer genomics program, which has produced, evaluated, and made public data related to the genomic sequencing, expression, methylation, and copy number variation of over 11,000 patients with cancer who have been diagnosed with more than 30 distinct forms of cancer (17). Many previous bioinformatics studies have followed the traditional study design of analyzing the TCGA data and followed by the verification of the computationally predicted results using GEO data sets (19)(20)(21)(22)(23)(24). The GEO database is a freely accessible resource for the functional genomics data that contain original data sets from tens of thousands of published microarray or sequencing experiments (25).…”