PANK1 is a Prognostic Biomarker Associated with Immune Inltration of Clear Cell Renal Carcinoma

Background: PANK1 is expressed in some cancer types, but its role in clear cell renal carcinoma (ccRCC) is unclear. We aimed to demonstrate the relationship between PANK1 and ccRCC based on a cancer genomic atlas (TCGA) database. Methods: The Kruskal-Wallis test, Wilcoxon signed rank test and logistic regression were used to analyze the relationship between the clinical pathological characteristics of ccRCC and the expression of PANK1. The ROC curve was used to describe the prognostic value of PANK1 using area under curve (AUC) scores. Kaplan-Meier method and Cox regression analysis were used to evaluate the factors affecting the prognosis of ccRCC. Gene set enrichment analysis (GSEA) and immuno-inltration analysis were performed to identify a signicantly related function of PANK1. Results: PANK1 expression in renal clear cell carcinoma was different from that in stage N (P=1.3E-03),sex (P=5.1E-07),stage M(P=8.3E-04),residual tumor(P<0.001),T stage(T1 vsT4(P=6.5E-03),T1vsT3(P=6.9E-06)), histological grade (G1vsG4(P=3.6E-0.5),G2vsG4(P=2.1E-10),G3vsG4(P=1.7E-05)),pathologic stage(STAGE 1vs.STAGE4(P=1.4E-05),STAGE1vs.STAGE3(P=7.1E-05)). The ROC curve suggest that PANK1 has signicant diagnostic and prognostic capabilities (AUC =0.898). Low expression of PANK1 predicted poor overall survival (OS) (P<0.001), while that of PANK1 (HR:0.398; 95% CI:0.248-0.639(cid:0) P<0.001) is OS-independent predictor in patients with ccRCC. GSEA and immune inltration analysis showed that the expression of PANK1 is related to extracellular matrix receptor pathway, signaling pathway related to hypertrophic cardiomyopathy, cytokine-cytokine receptor interaction pathway, as well as complement and coagulation cascade pathway. Conclusion: PANK1 expression is signicantly associated with poor survival and immune inltration of ccRCC, which may be a promising prognostic biomarker for ccRCC. pathway enriched in high- and low-PANK1 groups by using GSEA.


Introduction
Renal cell carcinoma is one of the most common cancers and also the most common tumor in the urinary system [1]. Statistics show that 350,000 people are diagnosed with renal cell carcinoma every year [2]. Clear cell carcinoma of the kidney is the most common one, accounting for about 85% [3,4]. The survival rate of early renal cell carcinoma after treatment is 60-70%, while that of advanced renal cell carcinoma has a poor prognosis with a ve-year survival rate less than 10% [5]. CcRCC are aggressive tumors with high metastasis rates [6,7]. At the time of diagnosis, about one-third of the patients have metastasized [8], and another third of the patients may eventually metastasize [9,10]. Patients with metastatic advanced clear cell carcinoma of the kidney are insensitive to both radiotherapy and chemotherapy [11,12], and new targeted drugs are ineffective in patients with a large number of metastatic ccRCC [13,14]. Recent immunotherapy with checkpoints has been shown to be effective for RCCs, but unfortunately only in a small number of people [15][16][17]. In addition, despite the continuous improvement in the treatment of renal cancer, the corresponding mortality rate of renal cancer is still increasing [18]. Therefore, nding new therapeutic targets for renal cancer is of great signi cance for improving the prognosis of patients with advanced renal cancer.
Pantothenate kinase (PanK) is a rate-determining enzyme for the biosynthesis of coenzyme a (CoA) [19]. The mouse pantothenate kinase (Pank1) gene consists of seven introns and eight exons, and is located on chromosome 19 (19C2-3) [19]. Genes encoding pantothenate kinases (the Pank gene family) are also present in the human body and are involved in the metabolism of substances in the human body. PANK1, PANK2, PANK3 and PANK4 are four known isomers of pantothenate kinase [20]. A previous study has shown that the PANK family gene is associated with the prognosis of acute myeloid leukemia and pointed out that high expression of PANK2 may have a good effect on the prognosis of AML, while high expression of PANK4 indicates a poor prognosis [21]. Pantothenate kinase-related neurodegenerative disease is a rare hereditary neurodegenerative disease associated with nucleotide variations in the PANK2 gene encoded by mitochondrial human pantothenate kinase 2(PANK2) protein [22]. Pantothenate kinase-related neurodegenerative diseases are the main symptoms of pan-extrapyramidal dysfunction and non-heme iron accumulation [22]. Although a few studies have suggested that PANK1 may play a key role in the occurrence of cancer [23], there is no literature exploring the correlation between PANK1 and renal clear cell carcinoma.
Therefore, the purpose of this study was to elucidate the expression of PANK1 in ccRCC tissues and its potential therapeutic and prognostic value.
In this study, the RNA-seq data of ccRCC in the Database of Cancer Genome Atlas (TCGA) were used to compare the expression difference of PANK1 between tumor tissues and normal samples, and to explore the correlation between the expression level of PANK1 and the clinical pathological characteristics of ccRCC. Next, we evaluated the prognostic value of PANK1 in ccRCC. In addition, gene set enrichment analysis (GSEA) was performed on the high-expression group and the low-expression group of PANK1 to reveal its potential function. Finally, by analyzing the correlation between the expression of PANK1 and immune in ltration, we comprehensively explored the potential mechanism of PANK1 in regulating the occurrence and development of ccRCC.

RNA sequencing data and bioinformatics analysis
We used the TCGA database (https://portal.gdc.cancer.gov/) to collect RNA-seq data and clinical information from 539 patients with KIRC and included 72 cases with adjacent tissue matches. All procedures performed in this study were in compliance with the Declaration of Helsinki (revised in 2013).

Gene set enrichment analysis (GSEA)
In this paper, GSEA enrichment analysis were performed according to c2.cp.v7.2.symbols.gmt [Curated]. The gene set comes from the MSIGDB Collections database (https://www.GSEA-MSigDB.org/GSEA/MSigDB/Collections.jsp#c2).The R package cluster pro ler (version 3.6.0) was used to perform GSEA [24,25] between high PANK1 and low PANK1 groups. According to the default statistics, the procedure is repeated 1000 times for each analysis, and is generally considered to satisfy the False discovery rate (FDR) < 0.25 and p. Adjust < 0.05 condition for signi cant enrichment.
Immune in ltration analysis of ssGSEA The immunologic invasion analysis of renal clear cell carcinoma (RCC) was performed using r (version 3.6.3) and its corresponding R package GSVA [26](version 1.34.0) (https://www.bioconductor.org/packages/release/bioc/html/GSVA.html ) SSGSEA algorithm, we quanti ed the level of in ltration of 24 immune cell types according to the Gene expression pro ling available in the literature [27]. In order to explore the correlation between PANK1 and the in ltration level of 24 kinds of immune cells, the P value was determined by Spearman correlation analysis. statistical analysis All statistical analyses were performed using R (version 3.6.3). The Wilcoxon rank sum test, chi-square test, Fisher's exact test and logistic regression were used to analyze the relationship between clinical pathological features and PANK1. The Kaplan-Meier method was used to calculate the survival rate of patients with TCGA. Univariate and multivariate analyses were performed using a Cox proportional risk model to estimate the correlation between clinical and genetic clinical features and overall survival (OS). P-values less than 0.05 were considered statistically signi cant.

PANK1 expression is correlated with ccRCC clinicopathological features
To identify the difference in PANK1 expression, we analyzed PANK1 expression levels in 539 ccRCC tissues and 72 adjacent normal renal tissues and we found low expression of PANK1 in ccRCC tissues (P<0.001, Figure 1A). At the same time, we also analyzed the expression of PANK1 in 72 ccRCC tissues and their matched neighboring tissues. The results showed low expression of PANK1 in ccRCC tissues (P<0.001, Figure   1B). Meanwhile, the expression levels of PANK1 in the normal samples from the GTEx combined TCGA database and the ccRCC samples from the TCGA database were compared. To determine the differential expression of PAK1 in tumor and normal tissues, transcriptional levels of PAK1 in different multiple cancer types and normal tissues were analyzed using TCGA and GTEx databases. This analysis showed higher expression of PANK1 in various types of cancer than in normal tissues ( Figure S1). We downloaded the uni ed and standardized pan-cancer data set: TCGA Target GTEX (Pancan, n = 19131, g = 60499) from the UCSC(https://xenabrowser.net/) database, and further extracted the expression data of PANK1 gene in each sample. Further, we screened the sample sources as follows: Solid Normal, Primary Solid Tumor, Primary  S1). In addition, the receiver operating characteristic (ROC) curve was used to analyze the effectiveness of ccRCC expression levels in distinguishing ccRCC tissues from non-tumor tissues. The area under the curve (AUC) of PANK1 was 0.898, indicating that PANK1 could be an ideal biomarker to differentiate ccRCC from non-neoplastic tissues ( Figure 1C).
Patient characteristics are presented in Table 1, with 539 cases of primary ccRCC with clinical and gene expression data collected from the TCGA database. According to the average relative expression level of PANK1, patients with ccRCC were divided into high-expression group (n=270) and low-expression group Logistic regression was used to analyze the relationship between the clinical pathological characteristics of ccRCC and the expression level of PANK1.

PANK1 expression associated with poor prognosis in ccRCC patients
The association of PANK1 expression with PFS in ccRCC patients was assessed by Kaplan-Meier analysis and showed a negative association of PANK1 expression with poor OS in ccRCC patients (P<0.001, Figure  3A). In addition, to expand our observation to pan-cancer levels, the relationship between expression of PANK1 and patient survival was further analyzed in a variety of cancer types other than ccRCC. As shown in Figure S2, a signi cant association between PANK1 expression and poor OS was also observed in patients with colon cancer (COAD), renal papillary cell carcinoma (KIRP), brain low-grade gliomas (LGG), mesothelioma (MESO), pancreatic cancer (PAAD), and rectal adenocarcinoma (READ).
Cox univariate and multivariate analysis of prognostic factors in ccRCC Table 3  Correlation signal path of PANK1 based on GSEA The KEGG signaling pathway associated with PANK1 was identi ed using the GSEA method. GSEA showed signi cant differences in MSigDB enrichment (c5) (Padj <0.05, FDR <0.25). According to the screening conditions, we screened four signi cantly related signaling pathways from the KEGG signaling pathway enriched in GSEA, namely, the extracellular matrix receptor pathway, the signaling pathway related to hypertrophic cardiomyopathy, the cytokine-cytokine receptor interaction pathway, and the complement and coagulation cascade pathway ( Correlation between expression of PANK1 and immune in ltration

Discussion
In this study, we investigated the expression of PANK1 in ccRCC and its correlation with the diagnosis and prognosis of ccRCC. According to our results, PANK1 is an important gene related to substance metabolism.
Some studies have shown that insulin resistance caused by high-fat diet is an important factor leading to obesity, type 2 diabetes and cancer, and the PANK1 gene participates in the pathogenic process of many diseases caused by insulin resistance [28]. Besides, studies have shown that for leptin-de cient mice, knocking out the PANK1 gene can reduce the incidence of hyperglycemia and hyperinsulinemia, and improve the systemic material metabolism [29]. It has been reported that the PANK series genes, including PANK1, are dysfunctional in several cancer types and play an important role in the occurrence and development of cancer. For example, high PANK2 expression may have a favorable effect on the prognosis of patients with acute myeloid leukemia, while high PANK4 expression indicates a poor prognosis of patients with acute myeloid leukemia [21]. All these studies indicate that the genes of the PANK family may play different roles in a variety of cancer types. This study showed a decrease in PANK1 levels in ccRCC tissues, which was associated with a poor prognosis for our patient. In addition to ccRCC, survival analysis showed that PANK1 can also be used as a prognostic indicator for colorectal cancer, pancreatic cancer, mesothelioma, glioblastoma, and renal papillary cell carcinoma [21,28,29].
A major focus of this work will be to predict the potential mechanisms of PANK1 in regulating the development of ccRCC. PANK1 was found to be involved in the extracellular matrix receptor interaction through GSEA experiments. Many articles have suggested that ECM is related to the occurrence and development of various tumors. For example, studies have suggested that most differential genes related to breast cancer are related to extracellular matrix [30]. Changes in the density and composition of the extracellular matrix (ECM) play an important role in tumors; The stiffness and degradation of the extracellular matrix contribute to the growth and progression of tumors [31]. Previous studies have shown that the accumulation and remodeling of extracellular matrix (ECM) is considered to be the key to brotic diseases such as uterine broids [32]. Anti-brosis therapy can normalize the tumor microenvironment [33]. Other studies have shown that ECM is related to such diseases as gastric cancer [34], colorectal cancer [35], tongue cancer[36], and pancreatic cancer [37]. The evidence provided in the above literature suggests that PANK1 may play a role in a variety of tumors. This is also basically consistent with the above-mentioned relationship between PANK1 and the prognosis of multiple types of tumors. The formation of tumor microenvironment is related to cytokines. In the process of cancer cell formation, cancer cells release various cytokines to the surrounding, and recruit and reprogram many other types of cells to establish a tumor microenvironment[38]. We also found in this GSEA analysis that PANK1 was related to cytokines and the interaction between cytokines. The above evidences suggested that PANK1 might be related to the construction of tumor microenvironment. The complement system is an ancient and critical effector mechanism of the innate immune system, consisting of the central components of the entire cascade (C1-C9), regulators and inhibitors, proteases and newly assembled enzymes, receptors for a variety of activation products and complement components, and their products [39]. Complement can be activated within seconds of infection or stimulation. Complement activation can produce allergic peptide, cell detoxi cation compounds, and antibacterial compound. These generated molecules in turn activate pro-in ammatory mediators and recruit effector cells, thereby providing an immediate barrier against invading microorganisms or modi ed self-cells, including tumor cells [40,41]. In addition to supplementing the cascade system, the coagulation and brinolysis system is also an enzyme-dependent cascade system present in the blood. Coagulation and brinolysis system are the main vascular injuries that play a role when bleeding. Weakened clotting and brinolysis systems can cause uncontrolled bleeding. Excessive coagulation and brinolysis can lead to the occurrence of thrombotic diseases. In this study, we found that PANK1 was related to the complement system and coagulation reaction, suggesting that PANK1 might play a role in some immune diseases and hematological system diseases. This idea needs further experimental veri cation.
Another important aspect of this study is to investigate the relationship between the expression of PANK1 and different levels of immune in ltration in ccRCC. From ssGSEA analysis, we can nd that PANK1 has a negative correlation with Th2,TFH and other immune cells that can promote tumor progression, indicating that PANK1 has the effect of inhibiting tumor, that is, patients with high expression of PANK1 have a better prognosis, which is consistent with the better prognosis of patients with renal clear cell carcinoma with high PANK1 in our study. In addition, PANK1 has a positive correlation with immune cells such as Th17 that inhibit tumor progression, which is also consistent with the above trend. However, there is also a negative correlation between PANK1 and CD8+T cells, Treg cells and other immunosuppressive immune cells, but there is no other evidence that PANK1 can aggravate the tumor.
To the best of our knowledge, this is the rst effort to explore the relationship between PANK1 and ccRCC, although with some limitations. First, the current research is mainly based on bioinformatics analysis, which can be further strengthened through experimental research. Second, the number of healthy subjects used as controls differs greatly from the number of cancer patients. Last but not least, retrospective studies still have their own limitations, especially inconsistent interventions and lack of some information. Therefore, further studies are needed to further validate our ndings.

Conclusion
In conclusion, we observed an increase in PANK1 expression in ccRCC associated with a better clinical prognosis in patients with ccRCC. PANK1 may participate in the development of ccRCC by affecting the extracellular matrix, cytokine-related interactions, and immune components such as complement. This study partially reveals the role of PANK1 in ccRCC and provides potential biomarkers for the diagnosis and treatment of renal clear cell carcinoma.   Tables   Table 1 Correlation between PANK1 expression and clinicopathological characteristics in renal clear cell cancer in TCGA.  Table 3 Associations with overall survival and clinicopathological characteristics in TCGA patients using Cox regression.  Table 4 KEGG pathway enriched in high-and low-PANK1 groups by using GSEA.  Table 5 The correlation between PANK1 expression and 24 immune cells was detected by Spearman correlation method.  Figure S1 is not available with this version.   PANK1 expression and prognosis in patients with ccRCC. Kaplan-Meier curve was drawn using the R package survminer to evaluate the prognostic value of PANK1 in OS of ccRCC patients. PANK1 expression value was divided into high and low expression group according to median value. (A) All patients with clear cell renal carcinoma in TCGA were selected for the study. (B) All patients with clear cell carcinoma of the kidney in TCGA who were younger than or equal to 60 years of age were selected for the study. (C) All patients with clear cell carcinoma of the kidney in TCGA who were older than 60 years of age were selected for the study.  Forest plot for multivariate COX regression of clinicopathologic features and overall survival in patients with TCGA renal clear cell carcinoma.  Enrichment plot from the GSEA. The data set was on the left signi cantly enriched in red area (PANK1 high expression group). NES, normalized NS; Padj, adjust P value; FDR, false discovery rate.