1. Introduction

Renal cell carcinoma (RCC), also known as kidney cancer, is derived from renal tubular epithelial cells and is the most common solid tumor of the kidney, accounting for 3% of adult malignant tumors [1]. It is a heterogeneous group of cancers arising from renal tubular epithelial cells that encompasses 85% of all primary renal neoplasms. Papillary renal cell carcinoma (pRCC) is the second most common histological subtype after clear cell renal cell carcinoma (ccRCC), and 10–15% of RCC histological types are papillary renal cell carcinoma [2]. There are two subtypes of pRCC, type I (basophilic) and type II (acidophilic), and type I has a better prognosis than type II [3]. Most research studies on kidney cancer has focused on ccRCC, and the related studies have shown that compared with ccRCC patients, pRCC patients typically have a lower stage and grade of tumor as well as longer overall survival [4]. The molecular mechanism of pRCC has not been clearly defined. With poor sensitivity to radiotherapy and chemotherapy, surgery is the preferred method for treatment of pRCC, but some patients are prone to metastasis and relapse after surgery. With continued advances in molecular medicine in recent years, the study of the occurrence, development, and metastasis mechanisms of pRCC can help to guide clinical diagnosis and treatment.

The cancer genome atlas (TCGA) project is a joint project of the National Cancer Institute and the National Human Genome Research Institute and aims to apply high-throughput genome analysis technology and to improve the ability to prevent, diagnose, and treat cancer. The cancer genome atlas (TCGA) research network includes analysis of a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels [5]. In this study, TCGA data were used to investigate genes that are deferentially expressed in pRCC. To mine the key genes related to pRCC occurrence and development, we conducted differential gene enrichment (Gene Ontology, GO) analysis and KEGG pathway enrichment analysis, constructed PPI interaction networks, screened hub genes, and performed survival analysis.

2. Materials and Methods

3. Results

3.1. Identification of DEGs

The data for 289 cases of papillary renal cell carcinoma and 32 cases of normal kidney tissue were downloaded from TCGA and used for this study. The data were normalized and logarithmized, probes without corresponding gene annotation information were removed, and repeated probes were removed to finally get the expression profiles of 17,894 genes and 321 samples. Using the edgeR software package, with |logFC|> 2.0 and FDR <0.05 as the screening conditions for differentially expressed genes, a total of 1,992 DEGs were screened for pRCC, including 1,142 upregulated genes and 850 downregulated genes. Using these selected genes, a volcano map (Figure 1) was generated, and the top 50 gene heat maps with the most significant differences were selected (Figure 1(b)).

3.2. GO Term and KEGG Pathway Analyses

In order to better understand the relationships between DEGs and pRCC, we input all DEGs into the online tool DAVID to perform GO analysis. The results revealed that, for GO BP analysis, the DEGs of pRCC were mainly enriched in excretion, epidermis development, ion transmembrane transport, chemical synaptic transmission, chloride transmembrane transport, ion transport, and potassium ion transmembrane transport. For GO CC analysis, DEGs were mainly enriched in integral component of plasma membrane, extracellular region, extracellular space, plasma membrane, apical plasma membrane, anchored component of membrane, proteinaceous extracellular matrix, integral component of membrane, and basolateral plasma membrane. For GO analysis, DEGs were mainly enriched in calcium ion binding, heparin binding, sequence-specific DNA binding, transporter activity, and carbohydrate binding. The GO analysis findings are shown in Figure 2 and Table 1.

Table 1. Gene ontology analysis of DEGs associated with pRCC.

Category	Term	Description	Count	P value
BP	GO:0007588	Excretion	18	4.00E − 05
BP	GO:0008544	Epidermis development	27	2.31E − 04
BP	GO:0034220	Ion transmembrane transport	47	4.69E − 04
BP	GO:0007268	Chemical synaptic transmission	51	7.34E − 04
BP	GO:1902476	Chloride transmembrane transport	26	0.005783318
BP	GO:0006811	Ion transport	31	0.011414198
BP	GO:0071805	Potassium ion transmembrane transport	29	0.034687627
CC	GO:0005887	Integral component of plasma membrane	279	2.76E − 27
CC	GO:0005576	Extracellular region	293	8.03E − 23
CC	GO:0005615	Extracellular space	252	8.22E − 21
CC	GO:0005886	Plasma membrane	543	1.13E − 10
CC	GO:0016324	Apical plasma membrane	69	2.70E − 08
CC	GO:0031225	Anchored component of membrane	37	2.04e − 07
CC	GO:0005578	Proteinaceous extracellular matrix	61	2.45E − 06
CC	GO:0016021	Integral component of membrane	620	3.43E − 05
CC	GO:0016323	Basolateral plasma membrane	44	6.86E − 05
MF	GO:0005509	Calcium ion binding	119	8.07E − 06
MF	GO:0008201	Heparin binding	41	2.33E − 05
MF	GO:0043565	Sequence-specific DNA binding	89	1.82E − 04
MF	GO:0005215	Transporter activity	43	0.002430787
MF	GO:0030246	Carbohydrate binding	40	0.017062891

We next performed KEGG pathway analysis to analyze the pathways at the functional level. The results showed that DEGs were mainly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway, gastric acid secretion, bile secretion, and pancreatic secretion. The KEGG pathways associated with enriched DEGs associated with pRCC are presented in Figure 2(b) and Table 2.

Table 2. KEGG pathway analysis of DEGs associated with pRCC.

Category	Term	Description	Count	P value
KEGG	hsa04080	Neuroactive ligand-receptor interaction	72	4.38E − 11
KEGG	hsa04020	Calcium signaling pathway	46	5.31E − 06
KEGG	hsa04971	Gastric acid secretion	23	0.002127995
KEGG	hsa04976	Bile secretion	21	0.012213919
KEGG	hsa04972	Pancreatic secretion	24	0.046905158

3.4. Survival Analysis of Hub Genes

Expression data for a total of 289 pRCC samples were downloaded from TCGA. The 10 hub genes were grouped by expression levels, and the data were used to conduct survival analyses. Increased expression levels of BDKRB1, NMUR2, PMCH, and SAA1 were associated with a worse survival rate for pRCC patients (Figure 3).

3.5. The Expression of Hub Genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC Tissues and Adjacent Normal Tissues of pRCC Patients

We selected 120 tissue samples (including 60 pRCC tissues and 60 normal adjacent tissues) to analyze the expression of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues by qRT-PCR. The results showed that the expression of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues was significantly higher than that in the normal adjacent tissues (Figures 4(a), 4(c), 4(e), and 4(g)). To further investigate the correlation between hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression and pathological features of pRCC, the above samples were divided into high (above the mean) and low (below the mean) hub genes expression groups. Subsequently, the Chi-square test was used to analyze the relationship between hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression level and pathological characteristics of pRCC patients, and the results showed that the expression level of hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression in pRCC tissues were significantly positively correlated with tumor stage, lymph node metastasis, and histopathological grade of pRCC patients (Figures 4(b), 4(d), 4(f), and 4(h)), while the relationship with gender and age of patients was not statistically significant (Tables 3–6).

Table 3. The relationship between BDKRB1 expression level in pRCC and pathology features of pRCC patients (n = 60).

Characteristics	BDKRB1		Chi-squared test	P value
	Low no. cases	High no. cases
All patients	(n = 23)	(n = 37)
Gender			0.035	0.852
Male	13	20
Female	10	17
Age (years)			0.012	0.914
≤60	9	15
>60	14	22
Tumor stage			7.274	0.007
≤T2	15	11
>T2	8	26
Lymph-node metastasis			5.711	0.017
Negative	16	14
Positive	7	23
Pathology grade			6.332	0.012
Low grade	13	9
High grade	10	28

Table 4. The relationship between NMUR2 expression level in pRCC and pathology features of pRCC patients (n = 60).

Characteristics	NMUR2		Chi-squared test	P value
	Low no. cases	High no. cases
All patients	(n = 25)	(n = 35)
Gender			1.009	0.315
Male	14	15
Female	11	20
Age (years)			0.156	0.693
≤60	12	15
>60	13	20
Tumor stage			6.251	0.012
≤T2	16	11
>T2	9	24
Lymph-node metastasis			7.096	0.008
Negative	18	13
Positive	7	22
Pathology grade			6.898	0.009
Low grade	14	8
High grade	11	27

Table 5. The relationship between PMCH expression level in pRCC and pathology features of pRCC patients (n = 60).

Characteristics	PMCH		Chi-squared test	P value
	Low no. cases	High no. cases
All patients	(n = 30)	(n = 30)
Gender			0.067	0.795
Male	17	16
Female	13	14
Age (years)			0.278	0.598
≤60	13	11
>60	17	19
Tumor stage			4.344	0.037
≤T2	17	9
>T2	13	21
Lymph-node metastasis			6.667	0.01
Negative	20	10
Positive	10	20
Pathology grade			7.177	0.007
Low grade	16	6
High grade	14	24

Table 6. The relationship between SAA1 expression level in pRCC and pathology features of pRCC patients (n = 60).

Characteristics	SAA1		Chi-squared test	P value
	Low no. cases	High no. cases
All patients	(n = 31)	(n = 29)
Gender			0.012	0.913
Male	17	15
Female	15	14
Age (years)			0.63	0.427
≤60	15	17
>60	16	12
Tumor stage			8.21	0.004
≤T2	20	8
>T2	11	21
Lymph-node metastasis			4.312	0.038
Negative	19	10
Positive	12	19
Pathology grade			9.121	0.003
Low grade	17	5
High grade	14	24

3.6. Relationship between Hub Genes BDKRB1, NMUR2, PMCH, and SAA1 Expression and Prognosis of Patients with pRCC

The Kaplan–Meier survival analysis was used to study the relationship between hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression and prognosis of patients with pRCC. The results showed that the overall survival rate of patients with high hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression was significantly lower than that of patients with low hub genes BDKRB1, NMUR2, PMCH, and SAA1 expression (Figure 5). Then we conducted the COX proportional risk model analysis. The univariate and multivariate analyses showed that the expression of hub genes BDKRB1, NMUR2, PMCH, and SAA1 were independent risk factor for prognosis in patients with pRCC (Tables 7–10).

Table 7. Univariate and multivariate analysis of overall survival in patients with pRCC (n = 60).

Variable for overall survival	Univariate analysis			Multivariate analysis
	HR	95% CI	P	HR	95% CI	P
Gender			0.108
Male vs. female	0.581	0.3–1.126
Ages (years)			0.134
≤60 vs. >60	1.659	0.855–3.218
Pathology grade			0.403
Low grade vs. high grade	1.331	0.681–2.603
Tumor stage			0.03			0.716
≤T2 vs. >T2	2.11	1.076–4.137		1.616	0.806–3.239
Lymph-node metastasis			0.012			0.111
Negative vs. positive	2.31	1.2–4.448		1.743	0.880–3.450
BDKRB1 expression			0.005			0.065
Low vs. high	2.829	1.366–5.858		2.082	0.957–4.532

• Abbreviations: HR, hazard ratio; CI, confidence interval.

Table 8. Univariate and multivariate analysis of overall survival in patients with pRCC (n = 60).

Variable for overall survival	Univariate analysis			Multivariate analysis
	HR	95% CI	P	HR	95% CI	P
Gender			0.727
Male vs. female	1.118	0.598–2.089
Ages (years)			0.308
≤60 vs. >60	1.386	0.74–2.598
Pathology grade			0.489
Low grade vs. high grade	1.254	0.661–2.38
Tumor stage			0.007			0.086
≤T2 vs. >T2	2.461	1.278–4.739		0.548	0.276–1.089
Lymph-node metastasis			0.012			0.209
Negative vs. positive	2.252	1.198–4.234		0.652	0.335–1.270
NMUR2 expression			0.002			0.021
Low vs. high	2.95	1.488–5.847		0.432	0.212–0.882

• Abbreviations: HR, hazard ratio; CI, confidence interval.

Table 9. Univariate and multivariate analysis of overall survival in patients with pRCC (n = 60).

Variable for overall survival	Univariate analysis			Multivariate analysis
	HR	95% CI	P	HR	95% CI	P
Gender			0.16
Male vs. female	0.629	0.329–1.201
Ages (years)			0.107
≤60 vs. >60	1.716	0.89–3.31
Pathology grade			0.49
Low grade vs. high grade	1.259	0.654–2.423
Tumor stage			0.131
≤T2 vs. >T2	0.608	0.318–1.16
Lymph-node metastasis			0.02			0.373
Negative vs. positive	0.468	0.246–0.889		0.732	0.368–1.456
PMCH expression			0			0.001
Low vs. high	0.256	0.13–0.507		0.289	0.139–0.601

• Abbreviations: HR, hazard ratio; CI, confidence interval.

Table 10. Univariate and multivariate analysis of overall survival in patients with pRCC (n = 60).

Variable for overall survival	Univariate analysis			Multivariate analysis
	HR	95% CI	P	HR	95% CI	P
Gender			0.381
Male vs. female	0.75	0.393–1.429
Ages (years)			0.572
≤60 vs. >60	1.202	0.635–2.275
Pathology grade
Low grade vs. high grade	1.335	0.683–2.609	0.399
Tumor stage			0.016			0.209
≤T2 vs. >T2	0.443	0.228–0.861		0.639	0.318–1.284
Lymph-node metastasis			0.036			0.333
Negative vs. positive	0.497	0.259–0.956		0.714	0.361–1.413
SAA1 expression			0			0.004
Low vs. high	0.261	0.132–0.516		0.332	0.158–0.7

• Abbreviations: HR, hazard ratio; CI, confidence interval.

4. Discussion

Most patients with pRCC have no obvious symptoms or signs at the time of diagnosis, but the disease is often found by B-ultrasound or CT examination during a physical examination. Very few patients exhibit the typical triad signs of kidney cancer: hematuria, abdominal mass, and lumbar pain, and the patients that do exhibit these signs typically have advanced disease. The overall prognosis of pRCC is better than that of ccRCC, but pRCC prognosis is significantly worse than that of ccRCC when pRCC invades the renal vein and/or the inferior vena cava [10]. There is currently no specific treatment for pRCC, and surgical treatment is the first choice in clinical practice. The prognosis of advanced patients is poor, a pRCC is insensitive to radiotherapy and chemotherapy. Therefore, the study of the mechanisms of pRCC development and metastasis will help improve clinical diagnosis and treatment.

In this study, bioinformatics technology was used to mine pRCC transcriptomic data downloaded from TCGA. A total of 1,992 DEGs were identified, including 1,142 upregulated genes and 850 downregulated genes. We performed GO and KEGG pathway enrichment analyses to explore interactions between DEGs. The GO analysis revealed that 1,992 DEGs were significantly enriched in 21 terms, including excretion, epidermis development, ion transmembrane transport, chemical synaptic transmission, chloride transmembrane transport, ion transport, potassium ion transmembrane transport, integral component of plasma membrane, extracellular region, extracellular space, plasma membrane, apical plasma membrane, anchored component of membrane, proteinaceous extracellular matrix, integral component of membrane, basolateral plasma membrane, calcium ion binding, heparin binding, sequence-specific DNA binding, transporter activity, and carbohydrate binding. In addition, the KEGG pathway analysis revealed that 1,992 DEGs were significantly enriched in five pathways, including neuroactive ligand-receptor interaction, calcium signaling pathway, gastric acid secretion, bile secretion, and pancreatic secretion. According to the STRING results, we constructed the PPI network. Then hub genes were selected with a high degree of interaction in the PPI network, including BDKRB2, NMUR2, NMU, BDKRB1, LPAR5, KNG1, LPAR3, SAA1, MCHR1, and PMCH. Further analysis of survival related to the expression of these hub genes revealed that BDKRB1, NMUR2, PMCH, and SAA1 are the key genes for the development of pRCC.

One hub gene, BDKRB1, is a well-established tumor suppressor gene, which is frequently mutated in familial breast and ovarian cancers. The gene product of BDKRB1 functions in a number of cellular pathways that maintain genomic stability, including DNA damage-induced cell cycle checkpoint activation, DNA damage repair, protein ubiquitination, chromatin remodeling, as well as transcriptional regulation and apoptosis. In this study, we found the role of BRCA1 in tumor suppression and DNA damage response, including DNA damage-induced cell cycle checkpoint activation and DNA damage repair. The other hub gene KNG1 (Kininogen-1) is expressed at low level in glioma cells. KNG1 can exert antiangiogenic properties and inhibit the proliferation of endothelial cells [11]. Previous work showed that KNG1 can be used as a serum biomarker for colorectal cancer [12]. Overexpression of the KNG1 inhibited proliferation and induces apoptosis of glioma cells [11]. In this study, KNG1 expression was downregulated in pRCC, which may be associated with the viability and angiogenesis of pRCC, but the analysis revealed no statistical impact of expression of this gene on survival, suggesting further investigation into the relationship between this gene and pRCC is required. Lysophosphatidic acid (LPA) is an extracellular biological lipid that interacts with G protein-coupled LPA receptors (LPAR1 to LPAR6) [13]. The lysophosphatidic acid receptor-3 (LPAR3) mediates viability among malignant cells and aggressiveness among certain tumors [14]. LPAR3 has been characterized as the major promoter of long-term viability in melanoma cells [15]. Other studies found that increased expression of LPAR3 increases malignancy in breast and ovarian cancers in vivo [16, 17]. In this study, LPAR3 was identified as a downregulated gene in pRCC. It was reported with the involvement of LPA5 in the activation of tumor progression in pancreatic cancer cells [13]. Bradykinin (BK) is produced in the inflammatory tissue microenvironment, where it acts in cell proliferation, leukocyte activation, cell migration, and endothelial cell activation [18]. BDKRB1 and BDKRB2 belong to the rhodopsin family of G protein-coupled receptors. The activation of BDKRB1 leads to the activation of macrophages, dendritic cells, and other cells in the tumor microenvironment, which have angiogenic properties and is related to the proliferation of malignant tumors [19]. BDKRB1 contributes to interleukin-8 production and glioblastoma migration [20]. Wang et al. reported that inhibition of BDKRB2, but not the B1 receptor, attenuated bradykinin-mediated invasion and migration in colorectal cancer cells and inhibited ERK1/2 activation and IL-6 production [21]. Thus, the identification of inhibitors against BDKRB1 may be a reasonable strategy to suppress pRCC. Neuromodulin U (NMU) activates the G protein-coupled receptor NMUR2, and NMU signaling interacts with several cancer-related pathways, including the WNT receptor cascade, resulting in increased activation of WNT/planar cell polarity (PCP) effector RAC1, which promotes tumor cell invasion and metastasis [22]. NMUR2 is a receptor that enhances NMU-mediated cell motility and invasion in human pancreas and endometrial cancer cells [23, 24]. Hub genes NMU and NMUR2 have not previously been reported to play roles in pRCC. PMCH encodes the 165 aa prohormone promelanin-concentrating hormone (PMCH), which is proteolytically processed into several peptides, including the oncogenic peptide melanin-concentrating hormone (MCH) [25]. In this study we found that increased expression of PMCH was associated with poor survival in patients with pRCC, suggesting PMCH may be a potential diagnostic biomarker or predictor of prognosis. Human serum amyloid A (SAA) is a high-density lipoprotein (HDL)-related lipoprotein with major roles in the regulation of inflammation and cholesterol transport [26]. Human serum amyloid A (SAA) has been widely regarded as an accurate and sensitive indicator of inflammation, which can be synthesized by the liver and cancer cells [27]. SAA1 regulates cell adhesion and migration and binding to laminin by inducing cytokine expression [28]. A previous study reported a relationship between increased SAA1 concentration and poor prognosis and distant metastasis in ccRCC patients [29].

In conclusion, bioinformatics analysis was used to identify DEGs that may be involved in the development or progression of the pRCC. This study identified several genes that may be involved in the pathology of papillary renal cell carcinoma. BDKRB1, NMUR2, PMCH, and SAA1 may contribute to the occurrence and development of papillary renal cell carcinoma. This identification of specific biological functions that may be involved in the mechanism of pRCC development provides new clues and directions for efforts to develop future treatments for papillary renal cell carcinoma.

Conflicts of Interest

The authors declare that they have no conflicts of interests.

Authors’ Contributions

Le Li and XuKai Liu designed the manuscript. Pan Liu and Ting Sun transcribed the whole article. Yong Wen downloaded the data for analysis. All authors read and approved the final version of the submitted manuscript. Le Li, XuKai Liu, and Yong Wen contributed equally to this work.