Background: Microarray gene expression profiling has been used in the past by various groups to identify novel markers of clear cell renal cell carcinoma (ccRCC).

Aim: We hypothesized that a meta-analysis of publicly available gene expression datasets of ccRCC may identify a list of the common deregulated genes (DEGs), which should have higher potential to be used as novel tumor markers.

Methods: In the Oncomine platform, genes deregulated in ccRCC relative to the corresponding normal tissue were filtered by a corrected Q value cut-off and concept filters. The identified genes that were common among 5 different microarray datasets were chosen as the candidate markers and their performance to discriminate between cancer and normal tissue was measured by ROC test. Their networks were analyzed by Ingenuity Pathway Analysis. Further Gene Ontology enrichment, KEGG pathways and Transcription Factor Target analyses were performed for the most common DEGs.

Results: We concluded in 93 commonly up-regulated and 76 commonly down-regulated genes in ccRCC vs. normal tissue. The top 10 up-regulated molecules were NDUFA4L2, PLIN2, NNMT, ENO2, AHNAK2, NETO2, CA9, VWF, COL23A1 and EHD2, and the top 10 down-regulated molecules were NPHS2, CALB1, RALYL, KCNJ1, KNG1, SERPINA5, CLDN8, SLC12A3, CA10 and ATP6V0A4. The top canonical pathways included the antigen presentation pathway, the inositol metabolism and the pentose phosphate pathway among others. The common DEGs participate in biological functions such as cancer, inflammatory response, renal and urological disease, reproductive system disease and respiratory disease. The top molecular and cellular functions were cell-to-cell signaling and interaction, cellular function and maintenance, molecular transport, cellular growth and proliferation and carbohydrate metabolism.

Conclusions: This meta-analysis highlights the genes most likely implicated in ccRCC. The genes that exhibit the best discriminatory performance between ccRCC and normal tissue are suggested as predictive markers for the disease.

RESEARCH ARTICLE

Abstract

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RESEARCH ARTICLE

Abstract

Extracted data

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