The most common occurring form of kidney tumor is renal cell carcinoma (RCC), which is responsible for almost 3% of malignant tumors in adults. Every year, more than 350,000 new individuals are affected by RCC, and 140,000 deaths occur (1). In addition, a type of RCC is clear cell renal cell carcinoma (ccRCC), which is responsible for almost 75%–80% of cases (2). The typical treatment for ccRCC is surgery, although it has a significant risk of metastasis, a poor prognosis, and is resistant to traditional chemotherapies and radiotherapies (3). With the rapid advancement of molecular biological tools specifically in the last decade, immune checkpoint inhibitors (ICIs) and targeted therapeutics have been explored as promising treatment strategies (4). Though different types of agents have resulted in enhanced patient outcomes, still complete response results are obtained rarely (5). Therefore, exploring the mechanism of ccRCC development and discovering novel biomarkers remain important areas of research. Previous studies have explored the molecular biological mechanism of ccRCC. However, identification of these genes does not fully explain the pathogenesis of ccRCC, making it necessary to explore novel tumor markers so that the progress and development of the disease can be better understood.

Microarray technology is a powerful tool that facilitates concurrent analysis of the expression of thousands of genes and can provide evidence for the mechanism of tumors. Bioinformatics combines computer technology and molecular biology, and can reveal patterns and interactions to provide insight into mechanisms and identify potential genes and paths of interest (6). The development of microarray technology and bioinformatics analysis allow the detection of gene expression levels, facilitating the search for differentially expressed genes (DEGs) (7) and functional pathways associated with tumorigenesis and development of ccRCC (8, 9). In this study, The Cancer Genome Atlas (TCGA) database was used to mine the DEGs of ccRCC. Subsequently, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, followed by protein–protein interaction (PPI) network construction to identify the selected genes. These genes were then verified in the Oncomine database, and we constructed Kaplan–Meier plots for survival analysis. The goal of this research was to explore the specific genes involved in the growth of ccRCC, and the results provide the research basis for targeted precise treatment and prognosis prediction of this common disease.

As one of the most common malignant tumors, ccRCC is also the most common kidney tumor accounting for roughly 3% of adult cancers (1, 15). Typical symptoms of ccRCC include hematuria, low back pain, and a mass in the upper abdomen or waist, but only few patients present these symptoms, limiting detection of ccRCC before reaching an advanced stage. This cancer is characterized by high incidence, poor prognosis, and insensitivity to radiotherapy and chemotherapy (3). The tumorigenesis and development of ccRCC are complicated processes, with many factors contributing to the development and metastasis of ccRCC, so the details of the specific mechanism remain unclear. Although some significant biomarkers were identified in previous studies, including VHL, PBRM1, and BAP1 (16), the mutation of von Hippel–Lindau (VHL), which is a tumor suppressor gene, has played a significant role in the tumorigenesis of ccRCC (16, 17). VHL is involved in the degradation of hypoxia-inducible factor (HIF) protein, and VHL inactivation leads to abnormal aggregation of HIF protein, leading to an imbalance of HIF-targeted genes that regulate angiogenesis, glycolysis, and apoptosis (16). Recent studies have also found that (Polybromo 1) PBRM1 and (BRCA1-associated protein 1) BAP1 mutations can act in the development of ccRCC (18–20). These are not sufficient to understand the development and prognosis of ccRCC. Therefore, there is a significant need to discover more novel biomarkers, which may facilitate earlier diagnosis, more effective treatment, and improved prognosis of ccRCC. TCGA is a project that was jointly initiated by the American Cancer Center and the American Human Genome Research Center in 2006. TCGA utilizes high-throughput sequencing data to build a comprehensive, multi-dimensional cancer map, to elucidate occurrence, diagnosis, treatment, and pathogenesis of cancer (21). Traditional molecular biology experiments can only study the function of a few genes at the same time. Thus, mining the TCGA database can find patterns in a large amount of biological information.

In our study, bioinformatics technology was used to mine ccRCC genome data downloaded from TCGA. In total, 1,855 DEGs were found in ccRCC, including 1,207 upregulated genes and 648 downregulated genes. GO functional and KEGG pathway enrichment analysis were performed to better understand the relevant biological functions of the DEGs. GO functional enrichment analysis revealed that the 1,855 DEGs were mainly enriched in 19 terms, namely, G-protein-coupled receptor signaling pathway, proteolysis, immune response, cell–cell signaling, acute-phase response, cellular defense response, proteinaceous extracellular matrix, extracellular space, integral component of plasma membrane, calcium ion binding, extracellular region, cell surface, collagen trimer, plasma membrane, integral component of membrane, hormone activity, heparin binding, serine-type endopeptidase activity, and cysteine-type endopeptidase inhibitor activity. KEGG pathway enrichment analysis revealed that the 1,855 DEGs were mainly enriched in 12 pathways, namely, fat digestion and absorption, cytokine–cytokine receptor interaction, complement and coagulation cascades, systemic lupus erythematosus, African trypanosomiasis, PPAR signaling pathway, tyrosine metabolism, protein digestion and absorption, drug metabolism–cytochrome P450, pancreatic secretion, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. Subsequently, the online tool in STRING and Cytoscape were used to construct a PPI network of the identified DEGs, and the ten hub genes with the highest connection degree in the PPI network were selected, namely, C3, CXCR3, CXCL10, CCR5, CCL4, CCL5, INSL5, GNG4, GNB3, and RLN3. To verify the results, these ten hub genes were input to the Oncomine database. Six of these hub genes (C3, CXCR3, CXCL10, CCR5, CCL4, and CCL5) were subjected to meta-analysis, but the data about the other four hub genes (INSL5, GNG4, GNB3, and RLN3) were not found in Oncomine. Additional study will be required to confirm the role of these four genes in ccRCC.

Complement C3 is an important component of complement activation. C3 activation results in the production of C3a. C3aR, as the receptor of C3a, is expressed in many immune cells (22). Tumor cell-derived C3a is an important cancer-promoting factor in TME and can enhance immunosuppression to promote tumor cell growth via the C3a-C3aR signaling pathway, providing a potential target for immune checkpoint block (ICB) therapy (23). In the development of breast cancer (24) and lung cancer (23), the C3a-C3aR signaling pathway plays a key role. CXCR3 has seven transmembrane domains. It is expressed on Th1, CTL, NK, and NKT cells, and is regulated by IFN-γ (25). Cancer cells can also express CXCR3, and Monteagudo et al. (24) described that the level at which CXCR3 is expressed in malignant melanoma was related to metastatic potential and the prognosis of patients. CCL5, also known as RANTES, is a member of the C-C chemokine family that is expressed in a wide range of immune cells. Its role in tumor-related formation mechanisms is unclear, because it aids in the elicitation of anti-tumor immune responses, but also can stimulate tumor progression and metastasis (26). CCR5 is a cell membrane protein of the G protein-coupled factor superfamily (GPCR). As a receptor encoding CCL5, it can promote tumorigenesis, matrix formation, and tumor development. CCL5/CCR5 activates nuclear factor-κB through the PI3K/AKT signaling pathway, the effects of mitogen-activated protein kinase and extracellular regulated protein kinases (ERK), leading to activation of αvβ3 integrin and promoting cell migration (27–29). CCL5/CCR5 is a biomarker for poor prognosis of pancreatic cancer (31), prostate cancer (31), lung cancer (29), and ovarian cancer (28). CCL4 belongs to the proinflammatory C-C subfamily and acts in inflammation, immune regulation, and tumor progression. CCL4 is involved in the proliferation and metastasis of various cancers such as breast cancer (31) and squamous cell carcinoma (31). In addition, Fang et al. (32) found that epithelial-to-mesenchymal transition (EMT) induced by CCL4 in RWPE-1 cells can ultimately lead to the development of tumor in prostate. There has been no previous investigation of the roles of C3, CXCR3, CCL4, CCL5, or CCR5 in the development of ccRCC, and these genes should be investigated in future research.

To further analyze the relationship between the level of expression of the ten most-connected hub genes and the corresponding OS rate of ccRCC, survival analysis was performed. Only increased gene expression of CXCL10 was significantly associated with a worse survival rate for ccRCC patients. C-X-C motif chemokine ligand 10 (CXCL10) is also called interferon gamma-inducible protein 10 (IP-10), and is a 10-kDa secreted protein in the CXC subfamily of cytokines. This protein participates in leukocyte trafficking and regulates adaptive immune, inflammatory, hematopoietic, and angiogenic processes (33). CXCL10 is a selective ligand for CXCR3, and the CXCL10/CXCR3 signaling pathway regulates leukocyte trafficking and angiogenesis through paracrine interactions, while signaling of autocrine CXCL10/CXCR3 in tumor cells can promote growth of cancer and metastasis by regulating cell adhesion, invasion, and migration capabilities (34, 35). There are also studies associating the CXCL10/CXCR3 gene expression pathway with enhanced metastatic potential and poor prognosis in patients with melanoma (36) and colon cancer (37). Polimeno et al. (38) discovered that when the results were contrasted with the control group, which was healthy, there was a higher level of CXCL10 in the serum of RCC patients. Another study from Japan found that CXCR3 was associated with metastasis of RCC (39). Thus, CXCR3 could be a new therapeutic target and predictive biomarker for ccRCC.

The goal of this research was to apply bioinformatics analysis for the identification of a key DNA segment that is involved in the ccRCC development. The results of TCGA showed 6 hub genes and 1,855 DEGs. Of the identified hub genes, only CXCL10 expression was linked to a worse outcome in ccRCC patients. However, the function of this gene remains to be characterized experimentally, and further in-depth study is needed to clarify the specific biological functions of CXCL10 in the mechanism of ccRCC. The results presented here should provide new clues and directions for the early diagnosis, prognosis prediction, and treatment of ccRCC.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .

GQ and HW wrote the main manuscript text. GL performed experiments. HY and MW collected data. All authors contributed to the article and approved the submitted version.

The Hunan Natural Science Foundation (#S2021JJQNJJ0018) provided funding for this research.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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